Citations

See also Google Scholar Citations and Citations by hand (old).

Cited publications: 52  ·  Citations: 6614  ·  h-index: 31
Generated 2026-03-10 09:23 · Source: OpenAlex, Semantic Scholar and CrossRef APIs with help from Claude.

Ehweiner A, Duch C, Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116.

  1. Latorre-Estivalis J., Ares M., Farina W. (2026): Experience-dependent modulation of Fox transcription factors in the stingless bee Tetragonisca fiebrigi. https://doi.org/10.21203/rs.3.rs-8872933/v1
  2. Lebovich L., Alisch T., Redhead E., Parker M., Loewenstein Y., Couzin I., et al. (2024): Spatiotemporal dynamics of locomotor decisions in Drosophila melanogaster. https://doi.org/10.1101/2024.09.04.611038

Brembs B, Huneman P, Schönbrodt F, Nilsonne G, Susi T, Siems R, Perakakis P, Trachana V, Ma L, Rodriguez-Cuadrado S. (2023): Replacing academic journals. Royal Society Open Science 10(7).

  1. Garancini D. (2026): Should New Regulations be Imposed on Academic Publishing?. Bulletin of Science, Technology & Society 46(1):17-25. https://doi.org/10.1177/02704676261422613
  2. Turba R., Thoré E., Bertram M., Bridg H., Sabet S., Gamboa M., et al. (2026): Global North-South science inequalities due to language and funding barriers. Peer Community Journal 6. https://doi.org/10.24072/pcjournal.677
  3. Unknown authors (2025): Rethinking Research. Rethinking Clinical Research. https://doi.org/10.1017/9781009391733.006
  4. Skulmowski A., Engel-Hermann P. (2025): The ethics of erroneous AI-generated scientific figures. Ethics and Information Technology 27(2). https://doi.org/10.1007/s10676-025-09835-4
  5. Elliott B., Burtson K., McMahon M., Chandel A. (2025): The Researchers’ Journal of Internal Medicine: A New Way to Publish Research. Researchers’ Journal of Internal Medicine. https://doi.org/10.63495/1648394
  6. Jiao C., Darch P. (2025): Peer review of data papers: Does it achieve expectations for facilitating data sharing and reuse?. Journal of Information Science. https://doi.org/10.1177/01655515251379048
  7. Haiech J. (2025): L’inconduite scientifique : la tentation de la fausse monnaie académique. médecine/sciences 41(2):111-112. https://doi.org/10.1051/medsci/2024154
  8. Teixeira da Silva J. (2025): Manuscript management systems require sensible management: The case of authors from different geographic regions. Science Editor and Publisher 10(1):61-69. https://doi.org/10.24069/sep-25-37
  9. Fahnestock J. (2025): The Controversy behind the Controversies: Scientific Discourse in the Twenty-First Century. Rhetoric Society Quarterly 55(3):223-243. https://doi.org/10.1080/02773945.2025.2484162
  10. Guedes J. (2025): Navigating the Madness of Academic Publishing. Qeios 7(2). https://doi.org/10.32388/h7yd78.3
  11. Karen B. Schmaling, Robert M. Kaplan (2025): Confronting the Crises in Peer Review and Academic Publishing. Rethinking Clinical Research. https://doi.org/10.1017/9781009391733.014
  12. Knibbe M., de Rijcke S., Penders B. (2025): Care for the soul of science: Equity and virtue in reform and reformation. Cultures of Science 8(1):12-23. https://doi.org/10.1177/20966083251329632
  13. Fido M., Hoesli E., Barazzone E., Zenobi R., Slack E. (2025): LC-Inspector: a simple open-source viewer for targeted hyphenated mass spectrometry analysis. https://doi.org/10.1101/2025.04.28.650946
  14. Fido M., Hoesli E., Barazzone E., Zenobi R., Slack E. (2025): LCMSpector: A simple open-source viewer for targeted hyphenated mass spectrometry analysis. PLOS Computational Biology 21(12):e1013095. https://doi.org/10.1371/journal.pcbi.1013095
  15. ACRL Research Planning and Review Committee (2024): 2024 Top Trends in Academic Libraries: A Review of the Trends and Issues. College & Research Libraries News 85(6). https://doi.org/10.5860/crln.85.6.231
  16. Kenner A., Dréano C., Invernizzi N., Kaşdoğan D., Khandekar A., Okune A., et al. (2024): Caring for Scholarship in Transition. Engaging Science, Technology, and Society 9(3). https://doi.org/10.17351/ests2023.2623
  17. Ropponen A., Rugulies R., Burdorf A. (2024): Towards the year 2049: The next 25 years of occupational health and safety research. Scandinavian Journal of Work, Environment & Health 50(8):581-587. https://doi.org/10.5271/sjweh.4200
  18. Widding A. (2024): Beyond Transformative Agreements: Ways Forward for Universities. European Review 32(S1):S28-S38. https://doi.org/10.1017/s1062798724000036
  19. Bucur C. (2024): Linkflows: Towards Genuine Semantic Publishing in Science. https://doi.org/10.5463/thesis.592
  20. Mills D. (2024): One index, two publishers and the global research economy. Oxford Review of Education 51(4):545-560. https://doi.org/10.1080/03054985.2024.2348448
  21. Giglia E. (2024): Open Science e valutazione: non solo una questione di “alternative”. Quaderni di Sociologia 95(LXVIII):125-153. https://doi.org/10.4000/13k0j
  22. Tommasi F., Beltràn M., Maria Meneghini A., de Cordova F. (2024): On the Social Utility of Reading and Academic Writing in Work and Organizational Psychology. Le travail humain No 87(4):299-315. https://doi.org/10.3917/e.th.874.0299
  23. Tommasi F., Beltràn M., Maria Meneghini A., de Cordova F. (2024): On the Social Utility of Reading and Academic Writing in Work and Organizational Psychology. Le travail humain nº 87(4):299-315. https://doi.org/10.3917/th.874.0299
  24. Houghton F. (2024): Gandy & ‘Books under threat’: A response. Area 57(3). https://doi.org/10.1111/area.12975
  25. Giller K., James E., Ardley J., Unkovich M. (2024): Science losing its way: examples from the realm of microbial N2-fixation in cereals and other non-legumes. Plant and Soil 511(1-2):1-24. https://doi.org/10.1007/s11104-024-07001-1
  26. Engwall L. (2024): Academic Publishing in Modern Society. European Review 32(S1):S7-S27. https://doi.org/10.1017/s1062798724000061
  27. Kuular M., Podkorytova N. (2024): Scientific Library in a Common Space of Scientific Knowledge. Proceedings of SPSTL SB RAS. https://doi.org/10.20913/2618-7515-2024-3-20-28
  28. Berlin O., Neufend M., Kindling M., Fischer G. (2024): Open Access: Eine Auswertung der Ausgangslage. Open-Access-Bericht Berlin. https://doi.org/10.21428/986c5d43.718bbcf0
  29. Parra Saiani P. (2024): Sulle spalle di giganti di sabbia. Quaderni di Sociologia 95(LXVIII):155-182. https://doi.org/10.4000/13k0k
  30. Siems R. (2024): Subprime Impact Crisis. Bibliotheken, Politik und digitale Souveränität. Bibliothek Forschung und Praxis 48(2):311-321. https://doi.org/10.1515/bfp-2024-0008
  31. Krueger S., Frank R. (2024): Are We Practicing What We Preach? Towards Greater Transborder Inclusivity in Information Science Systematic Reviews. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-57867-0_6
  32. Widmark W. (2024): How can we get beyond the Transformative Agreements: a Swedish perspective. Revista Española de Documentación Científica 47(4):e402. https://doi.org/10.3989/redc.2024.4.1646
  33. Fassin Y., Teixeira da Silva J. (2024): Fractures in the academic publishing business model: a stakeholder perspective. Science and Public Policy 52(2):326-328. https://doi.org/10.1093/scipol/scae080
  34. Brembs B., Lenardic A., Murray-Rust P., Chan L., Irawan D. (2023): Mastodon over Mammon: towards publicly owned scholarly knowledge. Royal Society Open Science 10(7). https://doi.org/10.1098/rsos.230207
  35. Lindsay D. (2023): A Plea to Psychology Professional Societies that Publish Journals: Assess Computational Reproducibility. Meta-Psychology 7. https://doi.org/10.15626/mp.2023.4020
  36. O’Boyle E., Götz M., Zivic D. (2023): Increasing the practical relevance of management research: In honor of Timothy T. Baldwin. Business Horizons 67(2):161-171. https://doi.org/10.1016/j.bushor.2023.12.004
  37. Schönbrodt F., Aspaas P. (2023): Responsible Research Assessment. Open Science Talk. https://doi.org/10.7557/19.7344

Brembs B, Lenardic A, Murray-Rust P, Chan L, Irawan DE. (2023): Mastodon over Mammon: towards publicly owned scholarly knowledge. Royal Society Open Science 10(7).

  1. Skulmowski A., Engel-Hermann P. (2025): The ethics of erroneous AI-generated scientific figures. Ethics and Information Technology 27(2). https://doi.org/10.1007/s10676-025-09835-4
  2. Peña-Fernández S., Larrondo-Ureta A., Morales-i-Gras J. (2025): Straddling Two Platforms: From Twitter to Mastodon, an Analysis of the Evolution of an Unfinished Social Media Migration. Social Sciences 14(7):402. https://doi.org/10.3390/socsci14070402
  3. Peters G., Crutzen R. (2024): Knowing What We’re Talking About. Meta-Psychology 8. https://doi.org/10.15626/mp.2022.3638
  4. Cusick J., George E., Greenway E., Watve M., Graham K., Raby C. (2024): Is it time to get over the X? Assessing the global impact and future of social media conferences in animal behaviour. Animal Behaviour 213:33-50. https://doi.org/10.1016/j.anbehav.2024.04.001
  5. Mullen L. (2024): Open Access, Scholarly Communication, and Open Science in Psychology: An Overview for Researchers. Sage Open 14(1_suppl). https://doi.org/10.1177/21582440231205390
  6. Lázaro-Rodríguez P. (2024): Análisis de la investigación sobre el Fediverso: Mastodon, Lemmy, Pleroma y otras de sus plataformas. Infonomy 2(2). https://doi.org/10.3145/infonomy.24.017
  7. Tamari R., Ospina P., Oriel S., Finck W. (2024): Sensemaking Networks: Transforming Social Media into a Sensemaking Layer for Science. OSF Preprints. https://doi.org/10.31222/osf.io/wcsfa
  8. Yu W., Chen J., Deng S. (2024): Open Science Under Debate: Disentangling the Interest on Twitter and Scholarly Research. Sage Open 14(3). https://doi.org/10.1177/21582440241271300
  9. Batist Z., Roe J. (2024): Open Archaeology, Open Source? Collaborative practices in an emerging community of archaeological software engineers. Internet Archaeology. https://doi.org/10.11141/ia.67.13
  10. Brembs B., Huneman P., Schönbrodt F., Nilsonne G., Susi T., Siems R., et al. (2023): Replacing academic journals. Royal Society Open Science 10(7). https://doi.org/10.1098/rsos.230206
  11. O’Boyle E., Götz M., Zivic D. (2023): Increasing the practical relevance of management research: In honor of Timothy T. Baldwin. Business Horizons 67(2):161-171. https://doi.org/10.1016/j.bushor.2023.12.004
  12. Batist Z., Roe J. (2023): Open-archaeo: A Resource for Documenting Archaeological Software Development Practices. Journal of Open Archaeology Data 11. https://doi.org/10.5334/joad.111
  13. Peters G., Crutzen R. (2022): Knowing What We’re Talking About: Facilitating Decentralized, Unequivocal Publication of and Reference to Psychological Construct Definitions and Instructions. https://doi.org/10.31234/osf.io/8tpcv

Brembs B, Lenardic A, Chan L. (2023): Mastodon: a move to publicly owned scholarly knowledge. Nature 614(7949):624.

  1. Michán-Aguirre L., Romero-Pérez M. (2024): Inmediatez en salud: la tecnología RSS. Investigación en Educación Médica 13(49):120-128. https://doi.org/10.22201/fm.20075057e.2024.49.23577
  2. Lázaro-Rodríguez P. (2024): Análisis de la investigación sobre el Fediverso: Mastodon, Lemmy, Pleroma y otras de sus plataformas. Infonomy 2(2). https://doi.org/10.3145/infonomy.24.017
  3. Timpka T. (2024): Time for Medicine and Public Health to Leave Platform X. JMIR Medical Education 10:e53810-e53810. https://doi.org/10.2196/53810
  4. Schukow C., Punjabi L., Gardner J. (2023): #PathMastodon: An Up-In-Coming Platform for Pathology Education Among Pathologists, Trainees, and Medical Students. Advances in Anatomic Pathology 31(1):52-57. https://doi.org/10.1097/pap.0000000000000405
  5. Sauvat L., Bleibtreu A., Peiffer-Smadja N. (2023): Les réseaux sociaux pour l’infectiologue. Médecine et Maladies Infectieuses Formation 2(3):130-138. https://doi.org/10.1016/j.mmifmc.2023.06.007

Damrau C, Colomb J, Brembs B. (2021): Sensitivity to expression levels underlies differential dominance of a putative null allele of the Drosophila tßh gene in behavioral phenotypes. PLoS Biol. 19(5):e3001228.

  1. Patil Y., Joshi R. (2025): From Signals to Sustenance: The Role of Biogenic Amines in Insect Feeding Behavior. Journal of Insect Behavior 38(2). https://doi.org/10.1007/s10905-025-09879-w
  2. Colomb J., Winter Y. (2021): Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle. Frontiers in Neuroscience 15. https://doi.org/10.3389/fnins.2021.742652
  3. Colomb J., Winter Y. (2021): Creating detailed metadata for an R Shiny analysis of circadian behavior sequence data. https://doi.org/10.1101/2021.07.16.452645

Grossmann A, Brembs B. (2021): Current market rates for scholarly publishing services. F1000Res. 10:20.

  1. Garancini D. (2026): Should New Regulations be Imposed on Academic Publishing?. Bulletin of Science, Technology & Society 46(1):17-25. https://doi.org/10.1177/02704676261422613
  2. Teixeira da Silva J. (2026): Paying peer reviewers: benefits, risks, and challenges. Naunyn-Schmiedeberg’s Archives of Pharmacology. https://doi.org/10.1007/s00210-025-04969-0
  3. Butler L., Boisgontier M. (2026): Rethinking where and how we publish in health sciences. European Rehabilitation Journal 6(1):1-10. https://doi.org/10.52057/erj.v6i1.81
  4. Ervens B., Carslaw K., Koop T., Pöschl U. (2025): Review of interactive open-access publishing with community-based open peer review for improved scientific discourse and quality assurance. Atmospheric Chemistry and Physics 25(20):13903-13952. https://doi.org/10.5194/acp-25-13903-2025
  5. Sabel B., Larhammar D. (2025): Reformation of science publishing: the Stockholm Declaration. Royal Society Open Science 12(11). https://doi.org/10.1098/rsos.251805
  6. Hosur B., Tripathi M., Vyas S., Shaikh S., Ahuja C. (2025): Historiography of Scientific Publishing across Cultures and Disciplines. Indian Journal of Radiology and Imaging 35(S 01):S2-S8. https://doi.org/10.1055/s-0044-1800865
  7. Heller B., Robinson C. (2025): Integrating Open Science Principles into Quasi-Experimental Social Science Research. Social Sciences 14(8):499. https://doi.org/10.3390/socsci14080499
  8. Dias C. (2025): Perfil dos portais de periódicos científicos das instituições de ensino superior públicas brasileiras. Transinformação 37. https://doi.org/10.1590/23180889202537e2513844
  9. Peppas G., Papachristopoulos L., Tsakonas G. (2025): Same Coin, Different Value: A Multi-Year Comparative Analysis of Financial Performance of Open Access and Legacy Publishers. Publications 13(4):46. https://doi.org/10.3390/publications13040046
  10. Mondal H., Dash A., Mondal S., Behera J. (2025): Strengths and Weaknesses of Top Indian Medical Colleges across Key Domains: Analysis of National Institutional Ranking Framework 2024 Rankings. Indian Journal of Vascular and Endovascular Surgery 12(1):19-23. https://doi.org/10.4103/ijves.ijves_90_24
  11. Guedes J. (2025): Navigating the Madness of Academic Publishing. Qeios. https://doi.org/10.32388/h7yd78.2
  12. Bos J., McCurley K. (2025): Lowering the Cost of Diamond Open Access Journals. arXiv. https://doi.org/10.48550/arXiv.2504.10424
  13. J Madegowda M. (2025): High Costs, Long Waits, and Ethical Dilemmas: A Review of Challenges in Academic Publishing. Journal of Scientometric Research 14(2):413-423. https://doi.org/10.5530/jscires.20250015
  14. Dumith S. (2025): Pague ou Pereça. Revista Brasileira de Atividade Física & Saúde 30:1-5. https://doi.org/10.12820/rbafs.30e0394
  15. Macdonald S. (2025): The Scandal of Academic Publishing. Publishing Research Quarterly 41(3-4):328-356. https://doi.org/10.1007/s12109-025-10042-8
  16. Onuoha S. (2025): The health research–public awareness gap: why scientific progress is failing to reach communities. Journal of Global Health Economics and Policy 5. https://doi.org/10.7189/001c.154124
  17. Schultz T., Borchardt R., Dawson D. (2025): Barriers and benefits of transitioning to an equitable open access model: interviews with LIS journal editors. Insights the UKSG journal 38. https://doi.org/10.1629/uksg.677
  18. Vicente V. (2025): Los conflictos de intereses en las publicaciones médicas. FMC – Formación Médica Continuada en Atención Primaria 32(4):153-154. https://doi.org/10.1016/j.fmc.2025.01.001
  19. Глиснер Р., Суд А. (2025): Специальные выпуски и их роль в научной коммуникации в условиях трансформации издательской деятельности. Научный редактор и издатель 9(2):152-167. https://doi.org/10.24069/sep-24-21
  20. Maedche A., Elshan E., Höhle H., Lehrer C., Recker J., Sunyaev A., et al. (2024): Open Science. Business & Information Systems Engineering 66(4):517-532. https://doi.org/10.1007/s12599-024-00858-7
  21. Oladokun B., Sambo A., Bassey M., Enakrire R. (2024): The Open Access Effect: Transforming Collection Development Using Open Repositories. International Journal of Librarianship 9(4):36-51. https://doi.org/10.23974/ijol.2024.vol9.4.395
  22. Mais C. (2024): Publish (in English) or Perish: Greek Academia and the Imposition of English Language. The Journal of Electronic Publishing 27(1). https://doi.org/10.3998/jep.5329
  23. Teixeira da Silva J. (2024): The Conceptual ‘APC Ring’: Is There a Risk of APC-Driven Guest Authorship, and Is a Change in the Culture of the APC Needed?. Journal of Scholarly Publishing 55(3):404-425. https://doi.org/10.3138/jsp-2023-0060
  24. Levchenko M., Parkin M., McEntyre J., Harrison M. (2024): Enabling preprint discovery, evaluation, and analysis with Europe PMC. PLOS ONE 19(9):e0303005. https://doi.org/10.1371/journal.pone.0303005
  25. Bardiau M., Dony C. (2024): Measuring back: bibliodiversity and the Journal Impact Factor™ brand, a case study of IF-journals included in the 2021 Journal Citations Report™. Insights the UKSG journal 37. https://doi.org/10.1629/uksg.633
  26. L. Seghier M. (2024): Paying reviewers and regulating the number of papers may help fix the peer-review process. F1000Research 13:439. https://doi.org/10.12688/f1000research.148985.4
  27. Godden-Rasul N., Serisier T. (2024): Publishing, Precarious Labour Relations and Sexual Violence in Academia. Feminist Legal Studies 32(3):253-258. https://doi.org/10.1007/s10691-024-09561-0
  28. Cockett R., Purves S., Koch F., Morrison M. (2024): Continuous Tools for Scientific Publishing. Proceedings of the Python in Science Conference. https://doi.org/10.25080/nkvc9349
  29. Gleasner R., Sood A. (2024): Special issues: The roles of special issues in scholarly communication in a changing publishing landscape. Learned Publishing 38(1). https://doi.org/10.1002/leap.1635
  30. Oliveira T., Nada C., Magalhães A. (2024): Navigating an Academic Career in Marketized Universities: Mapping the International Literature. Review of Educational Research 95(2):255-292. https://doi.org/10.3102/00346543231226336
  31. Chen X. (2024): Interactions of Publication Volume, Journal Impact, and Article Processing Charges: Comparative Study of China and Global Practices in Nature Portfolio. Publications 12(4):46. https://doi.org/10.3390/publications12040046
  32. Ahmed A., Al-Khatib A., Boum Y., Debat H., Gurmendi Dunkelberg A., Hinchliffe L., et al. (2023): The future of academic publishing. Nature Human Behaviour 7(7):1021-1026. https://doi.org/10.1038/s41562-023-01637-2
  33. Klepac B., Branch S., McVey L., Mowle A., Riley T., Craike M. (2023): Scoping review of practice-focused resources to support the implementation of place-based approaches. Health Promotion Journal of Australia 35(3):596-608. https://doi.org/10.1002/hpja.809
  34. C. Miller, R. Rice (2023): Toward a Potential Solution of the Crisis in Scholarly Publishing: An Academic Research Community Alliance Model. https://www.semanticscholar.org/paper/9aa06ad6b50b0ded274c9c4b382646ae415bbc9f
  35. Fernandez -Blanco D., Lacassin R., Gouiza M., Perez-Diaz L., Magee C., McCarthy D., et al. (2023): Tektonika: The Community-Led Diamond Open-Access Journal for Tectonics and Structural Geology. Tektonika 1(2). https://doi.org/10.55575/tektonika2023.1.1.56
  36. Kendall G., Teixeira da Silva J. (2023): Risks of abuse of large language models, like ChatGPT, in scientific publishing: Authorship, predatory publishing, and paper mills. Learned Publishing 37(1):55-62. https://doi.org/10.1002/leap.1578
  37. Saloojee H., Pettifor J. (2023): Maximizing Access and Minimizing Barriers to Research in Low- and Middle-Income Countries: Open Access and Health Equity. Calcified Tissue International 114(2):83-85. https://doi.org/10.1007/s00223-023-01151-7
  38. Teixeira da Silva J. (2023): The risk of abuse of environmental sustainable developmental goals (SDGs) by academia and publishers for cheap reputational gains. Mitigation and Adaptation Strategies for Global Change 28(5). https://doi.org/10.1007/s11027-023-10061-w
  39. Bos J., McCurley K. (2023): LaTeX, metadata, and publishing workflows. arXiv. https://doi.org/10.48550/arXiv.2301.08277
  40. Bos J., McCurley K. (2023): Metadata in journal publishing. TUGboat 44(1):71-76. https://doi.org/10.47397/tb/44-1/tb136bos-metadata
  41. Chang K. (2023): Writing and Reading Today: The History of the Humanities Tomorrow. History of Humanities 8(2):217-227. https://doi.org/10.1086/726365
  42. Butler L., Matthias L., Simard M., Mongeon P., Haustein S. (2023): The oligopoly’s shift to open access: How the big five academic publishers profit from article processing charges. Quantitative Science Studies 4(4):778-799. https://doi.org/10.1162/qss_a_00272
  43. Ruiz-Corbella M., Arteaga-Martínez B., López-Gómez E., Galán A. (2023): Luces y Sombras del Proceso de Acreditación a Catedrático de Universidad: El Caso de las Áreas de Educación (2018-2022). REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación 21(4):65-85. https://doi.org/10.15366/reice2023.21.4.004
  44. Kashif Al-Ghita M., Cobey K., Moher D., Leeflang M., Ebrahimzadeh S., Lam E., et al. (2023): Cross-Sectional Evaluation of Open Science Practices at Imaging Journals: A Meta-Research Study. Canadian Association of Radiologists Journal 75(2):330-343. https://doi.org/10.1177/08465371231211290
  45. Dufour Q., Pontille D., Torny D. (2023): Supporting diamond open access journals. Interest and feasibility of direct funding mechanisms. bioRxiv. https://doi.org/10.1101/2023.05.03.539231
  46. Dolan S., Banks L., Yu W. (2023): Why should early-career scientists publish in society journals. mBio 15(1). https://doi.org/10.1128/mbio.01994-23
  47. Warr W. (2023): From the Era of Print to the Reality of Electronic Publishing. Chemistry International 45(4):2-5. https://doi.org/10.1515/ci-2023-0401
  48. Ghasemi A., Mirmiran P., Kashfi K., Bahadoran Z. (2022): Scientific Publishing in Biomedicine: A Brief History of Scientific Journals. International Journal of Endocrinology and Metabolism 21(1). https://doi.org/10.5812/ijem-131812
  49. Rousi A., Laakso M. (2022): Overlay journals: A study of the current landscape. Journal of Librarianship and Information Science 56(1):15-28. https://doi.org/10.1177/09610006221125208
  50. Pallares C., Vélez Cuartas G., Uribe-Tirado A., Restrepo D., Ochoa J., Suárez M. (2022): Situación del acceso abierto y los pagos por APC en Colombia. Un modelo de análisis aplicable a Latinoamérica. Revista Española de Documentación Científica 45(4):e342. https://doi.org/10.3989/redc.2022.4.1931
  51. Rowe C., Agius M., Convers J., Funning G., Galasso C., Hicks S., et al. (2022): The launch of Seismica: a seismic shift in publishing. Seismica 1(1). https://doi.org/10.26443/seismica.v1i1.255
  52. Mascarenhas F., Lazzarotti Filho A., Vianna L. (2022): A ciência e a RBCE em mais um ano de pandemia. Revista Brasileira de Ciências do Esporte 44. https://doi.org/10.1590/rbce.44.ed4401
  53. Racimo F., Galtier N., De Herde V., Bonn N., Phillips B., Guillemaud T., et al. (2022): Ethical Publishing: How Do We Get There?. Philosophy, Theory, and Practice in Biology 14(0). https://doi.org/10.3998/ptpbio.3363
  54. Eguiluz I., Sy A., Brage E., González-Agüero M. (2022): Rapid qualitative health research from the Global South: Reflections and learnings from Argentina, Brazil, Chile, and Mexico during the COVID-19 pandemic. Frontiers in Sociology 7. https://doi.org/10.3389/fsoc.2022.983303
  55. Teixeira da Silva J., Yamada Y. (2022): Accelerated Peer Review and Paper Processing Models in Academic Publishing. Publishing Research Quarterly 38(3):599-611. https://doi.org/10.1007/s12109-022-09891-4
  56. Koley M., Namdeo S., Suchiradipta B., Afifi N. (2022): Digital platform for open and equitable sharing of scholarly knowledge in India. Journal of Librarianship and Information Science 55(2):403-413. https://doi.org/10.1177/09610006221083678
  57. Koley M., Lala K. (2022): Are journal archiving and embargo policies impeding the success of India’s open access policy?. Learned Publishing 35(2):175-186. https://doi.org/10.1002/leap.1441
  58. Kim S., Park K. (2022): Changes in article share and growth by publisher and access type in Journal Citation Reports 2016, 2018, and 2020. Science Editing 9(1):30-36. https://doi.org/10.6087/kcse.260
  59. Göttker S. (2022): Open Access: Koste es, was es wolle?. Bibliotheksdienst 56(5):295-315. https://doi.org/10.1515/bd-2022-0046
  60. Morretta V., Vurchio D., Carrazza S. (2022): The socio-economic value of scientific publications: The case of Earth Observation satellites. Technological Forecasting and Social Change 180:121730. https://doi.org/10.1016/j.techfore.2022.121730
  61. Brembs B., Huneman P., Schönbrodt F., Nilsonne G., Susi T., Siems R., et al. (2021): Replacing academic journals. Royal Society Open Science 10(7). https://doi.org/10.1098/rsos.230206
  62. Triggle C., MacDonald R., Triggle D., Grierson D. (2021): Requiem for impact factors and high publication charges. Accountability in Research 29(3):133-164. https://doi.org/10.1080/08989621.2021.1909481
  63. Lehe L., Levinson D. (2021): The Economics of Findings. Findings. https://doi.org/10.32866/001C.19105
  64. Ryzhkova M., Spitsin V., Skrylnikova N. (2021): Development of the it sector in Russia: drivers and stimulation methods. Vestnik Universiteta. https://doi.org/10.26425/1816-4277-2021-10-83-93
  65. Alexander Maedche, Edona Elshan, Hartmut, Christiane Lehrer, Jan Recker, A. Sunyaev, et al. (): DISCUSSION Open Science Towards Greater Transparency and Openness in Science. https://www.semanticscholar.org/paper/0bee3dfa81000a6cecff43ec1253bef836a7bc80
  66. Joppe W. Bos, K. McCurley (): LaTeX, metadata, and publishing workflows (updated April 3. https://www.semanticscholar.org/paper/76ccabf6e1afa52018e45f21c695bbe633bffb32

Steymans I, Pujol-Lereis LM, Brembs B, Gorostiza EA. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. PLoS One 16(8):e0256560.

  1. Jones S., Gil-Martí B., Sacristán-Horcajada E., Edison A., Butler E., Miandashti N., et al. (2025): A memory transcriptome time course reveals essential long-term memory transcription factors. Nature Communications 16(1). https://doi.org/10.1038/s41467-025-64379-x
  2. Triphan T., Ferreira C., Huetteroth W. (2025): Play-like behavior exhibited by the vinegar fly Drosophila melanogaster. Current Biology 35(5):1145-1155.e2. https://doi.org/10.1016/j.cub.2025.01.025
  3. Triphan T., Huetteroth W. (2023): Seeking voluntary passive movement in flies is play-like behavior. https://doi.org/10.1101/2023.08.03.551880
  4. de Bivort B., Buchanan S., Skutt-Kakaria K., Gajda E., Ayroles J., O’Leary C., et al. (2022): Precise Quantification of Behavioral Individuality From 80 Million Decisions Across 183,000 Flies. Frontiers in Behavioral Neuroscience 16. https://doi.org/10.3389/fnbeh.2022.836626
  5. de Bivort B., Buchanan S., Skutt-Kakaria K., Gajda E., O’Leary C., Reimers P., et al. (2021): Precise quantification of behavioral individuality from 80 million decisions across 183,000 flies. https://doi.org/10.1101/2021.12.15.472856

Brembs B. (2021): The brain as a dynamically active organ. Biochemical and Biophysical Research Communications 564:55–69.

  1. Hila A. (2026): An Enactivist Approach to Human-Computer Interaction: Bridging the Gap Between Human Agency and Affordances. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-032-12657-3_3
  2. Liao C., Chang N., Liu Y., Lo C. (2025): Flexible Steering and Conflict Resolution: Pro-Goal/Anti-Goal Gating in Drosophila Lateral Accessory Lobes. https://doi.org/10.1101/2025.10.19.683286
  3. Keijzer F. (2025): Full Naturalism: The Objectivity of Subjective Points of View. Biological Theory. https://doi.org/10.1007/s13752-025-00493-9
  4. Potter H., Mitchell K. (2025): Beyond Mechanism—Extending Our Concepts of Causation in Neuroscience. European Journal of Neuroscience 61(5). https://doi.org/10.1111/ejn.70064
  5. Jones I., Grice J., Abramson C. (2025): Living Control Systems: Exploring a Teleonomic Account of Behavior in Apis mellifera. Insects 16(8):848. https://doi.org/10.3390/insects16080848
  6. Cheng K. (2025): Random-rate processing in navigation in bacteria, archaea, and desert ants. Psihologijske teme 34(1):79-96. https://doi.org/10.31820/pt.34.1.4
  7. Westphal K. (2025): Kant’s Cognitive Architecture & Bickhard’s Interactivism. Phenomenology and the Cognitive Sciences. https://doi.org/10.1007/s11097-025-10070-x
  8. Weber S., Bühler J., Bolton T., Aybek S. (2025): Altered brain network dynamics in motor functional neurological disorders: the role of the right temporo-parietal junction. Translational Psychiatry 15(1). https://doi.org/10.1038/s41398-025-03385-5
  9. Deeti S., Cheng K. (2025): Desert ants (Melophorus bagoti) oscillate and scan more in navigation when the visual scene changes. Animal Cognition 28(1). https://doi.org/10.1007/s10071-025-01936-3
  10. Deeti S., Cheng K. (2025): Ants oscillate and scan more in navigation when the visual scene changes. https://doi.org/10.1101/2025.01.13.632872
  11. Schneider A., Weber S., Wyss A., Loukas S., Aybek S. (2024): BOLD signal variability as potential new biomarker of functional neurological disorders. NeuroImage: Clinical 43:103625. https://doi.org/10.1016/j.nicl.2024.103625
  12. Han R., Tan Y., Lo C. (2024): Attractiveness versus stickiness: Behavioural preferences of Drosophila melanogaster with competing visual stimuli. Journal of Insect Physiology 159:104716. https://doi.org/10.1016/j.jinsphys.2024.104716
  13. Weber S., Bühler J., Loukas S., Bolton T., Vanini G., Bruckmaier R., et al. (2024): Transient resting-state salience-limbic co-activation patterns in functional neurological disorders. NeuroImage: Clinical 41:103583. https://doi.org/10.1016/j.nicl.2024.103583
  14. Weber S., Bühler J., Bolton T., Aybek S. (2024): Altered brain network dynamics in motor functional neurological disorders: The role of the right temporo-parietal junction. https://doi.org/10.21203/rs.3.rs-4294300/v1
  15. Felin T., Holweg M. (2024): Theory Is All You Need: AI, Human Cognition, and Causal Reasoning. Strategy Science 9(4):346-371. https://doi.org/10.1287/stsc.2024.0189
  16. Felin T., Holweg M. (2024): Theory Is All You Need: AI, Human Cognition, and Decision Making. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4737265
  17. Zhong G., Kroo L., Prakash M. (2023): Thermotaxis in an apolar, non-neuronal animal. Journal of The Royal Society Interface 20(206). https://doi.org/10.1098/rsif.2023.0279
  18. Najenson J. (2023): Encoding without perceiving: Can memories be implanted?. Philosophical Psychology 38(4):1847-1874. https://doi.org/10.1080/09515089.2023.2295927
  19. Potter H., Mitchell K. (2022): Naturalising Agent Causation. Entropy 24(4):472. https://doi.org/10.3390/e24040472
  20. Potter H., Mitchell K. (2022): Naturalising Agent Causation. https://doi.org/10.31234/osf.io/27qba
  21. Clement L., Schwarz S., Wystrach A. (2022): An intrinsic oscillator underlies visual navigation in ants. Current Biology 33(3):411-422.e5. https://doi.org/10.1016/j.cub.2022.11.059
  22. Clement L., Schwarz S., Wystrach A. (2022): An intrinsic oscillator underlies visual navigation in ants. https://doi.org/10.1101/2022.04.22.489150
  23. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. PLOS ONE 16(8):e0256560. https://doi.org/10.1371/journal.pone.0256560
  24. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. https://doi.org/10.1101/2021.01.15.426803

Palazzo O, Rass M, Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. Open Biol. 10(12):200295.

  1. Menti G., Bruzzone M., Zerbinati S., Zordan M., Visentin P., Drago A., et al. (2026): Repellent olfactory cues influence the optomotor response modulation in Drosophila melanogaster. https://doi.org/10.64898/2026.02.02.702750
  2. Ladd C., Simpson J. (2025): Behavior choices amongst grooming, feeding and courting in Drosophila show contextual flexibility, not an absolute hierarchy of needs. Journal of Experimental Biology 228(23). https://doi.org/10.1242/jeb.250826
  3. Ladd C., Simpson J. (2025): Behavior choices amongst grooming, feeding, and courting in Drosophila show contextual flexibility, not an absolute hierarchy of needs. https://doi.org/10.1101/2025.05.09.653186
  4. Barrera Grijalba C., Ordoñez J., Montenegro J., Wollesen T. (2025): Insights into adhesive and neuronal cell populations of the chaetognath Spadella cephaloptera using a single-nuclei transcriptomic atlas and genomic resources. https://doi.org/10.1101/2025.01.31.635879
  5. Tsai F., Lin C., Su Y., Yu J., Kuo D. (2025): Evolutionary History of Bilaterian FoxP Genes: Complex Ancestral Functions and Evolutionary Changes Spanning 2R-WGD in the Vertebrate Lineage. Molecular Biology and Evolution 42(4). https://doi.org/10.1093/molbev/msaf072
  6. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  7. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.1
  8. Lai C., Hsieh M., Yeh C., Lin T., Chou D., Wang H., et al. (2024): CtBP1 is essential for epigenetic silencing of μ-opioid receptor genes in the dorsal root ganglion in spinal nerve ligation-induced neuropathic pain. Neurotherapeutics 22(1):e00493. https://doi.org/10.1016/j.neurot.2024.e00493
  9. Lebovich L., Alisch T., Redhead E., Parker M., Loewenstein Y., Couzin I., et al. (2024): Spatiotemporal dynamics of locomotor decisions in Drosophila melanogaster. https://doi.org/10.1101/2024.09.04.611038
  10. Ehweiner A., Duch C., Brembs B. (2022): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant selflearning in Drosophila. https://doi.org/10.1101/2022.12.16.520755
  11. Damrau C., Colomb J., Brembs B. (2021): Sensitivity to expression levels underlies differential dominance of a putative null allele of the Drosophila tβh gene in behavioral phenotypes. PLOS Biology 19(5):e3001228. https://doi.org/10.1371/journal.pbio.3001228
  12. Damrau C., Colomb J., Brembs B. (2018): Differential dominance of an allele of the Drosophila tßh gene challenges standard genetic techniques. https://doi.org/10.1101/504332

Wolf R, Heisenberg M, Brembs B, Waddell S, Mishra A, Kehrer A, Simenson A. (2020): Memory, anticipation, action – working with Troy D. Zars. Journal of Neurogenetics 34(1):9–20.

No citations found.

Brembs B. (2019): Reliable novelty: New should not trump true. PLOS Biology 17(2):e3000117.

  1. Hartmann H., Gürsoy Ç., Lischke A., Mueckstein M., Sperl M., Vogel S., et al. (2025): ARIADNE: A Scientific Navigator to Find Your Way Through the Resource Labyrinth of Psychological Sciences. Advances in Methods and Practices in Psychological Science 8(1). https://doi.org/10.1177/25152459241297674
  2. Pollo P., Martinig A., Mizuno A., Morrison K., Pottier P., Ricolfi L., et al. (2025): Harnessing meta-analyses’ insights in ecology and evolution research. Royal Society Open Science 12(10). https://doi.org/10.1098/rsos.250759
  3. Aik Kah T. (2025): Bayesian Hypothesis Generation: A Probabilistic Framework for Evaluating Novel Hypotheses Before Data Collection. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5394925
  4. Kah T. (2025): Bayesian hypothesis generation: a probabilistic framework for evaluating novel hypotheses before data collection. Medical Hypotheses 206:111841. https://doi.org/10.1016/j.mehy.2025.111841
  5. Wongvorachan T. (2025): Rethinking Academic Publishing: A Call for Inclusive, Transparent, and Sustainable Reforms. Preprints.org. https://doi.org/10.20944/preprints202505.1897.v1
  6. Derksen M., Meirmans S., Brenninkmeijer J., Pols J., de Boer A., van Eyghen H., et al. (2024): Replication studies in the Netherlands: Lessons learned and recommendations for funders, publishers and editors, and universities. Accountability in Research 32(7):1285-1303. https://doi.org/10.1080/08989621.2024.2383349
  7. Derksen M., Meirmans S., Brenninkmeijer J., Pols J., de Boer A., Van Eyghen H., et al. (2024): Replication studies in the Netherlands: Lessons learned and recommendations for funders, publishers and editors, and universities. https://doi.org/10.31219/osf.io/bj8xz
  8. Pollo P., Lagisz M., Yang Y., Culina A., Nakagawa S. (2024): Synthesis of sexual selection: a systematic map of meta‐analyses with bibliometric analysis. Biological Reviews 99(6):2134-2175. https://doi.org/10.1111/brv.13117
  9. Höller Y., Urbschat M., Bathke A. (2024): Sustainable scientific publishing: a pilot survey on stakeholder motivations and opinions. Discover Sustainability 5(1). https://doi.org/10.1007/s43621-023-00175-1
  10. Brakista A. (2023): Open access. https://doi.org/10.31219/osf.io/dyjg3
  11. Brembs B., Huneman P., Schönbrodt F., Nilsonne G., Susi T., Siems R., et al. (2023): Replacing academic journals. Royal Society Open Science 10(7). https://doi.org/10.1098/rsos.230206
  12. Brembs B., Lenardic A., Murray-Rust P., Chan L., Irawan D. (2023): Mastodon over Mammon: towards publicly owned scholarly knowledge. Royal Society Open Science 10(7). https://doi.org/10.1098/rsos.230207
  13. Frick C., Heller L. (2023): Ausflug in eine ferne nahe Welt: Forschungsalltag 2040. Bibliothek Forschung und Praxis 47(1):52-57. https://doi.org/10.1515/bfp-2022-0059
  14. Hartmann H., Gürsoy Ç., Lischke A., Mueckstein M., Sperl M., Vogel S., et al. (2023): ARIADNE – a scientific navigator to find your way through the resource labyrinth of psychological sciences. https://doi.org/10.31219/osf.io/jfh3t
  15. Jensen J., Krakow M., Christy K., Ratcliff C., Pokharel M., Lillie H. (2023): Validating cross‐modal measures for comparative research: Message veracity, novelty, and memorability. Psychology & Marketing 40(12):2686-2710. https://doi.org/10.1002/mar.21910
  16. Pollo P., Lagisz M., Yang Y., Culina A., Nakagawa S. (2023): Synthesis of sexual selection: a systematic map of meta-analyses with bibliometric analysis. https://doi.org/10.32942/x29s3g
  17. Caballero C., Fajardo E. (2022): Reflection article | Scientific publications: Knowledge A market or a common good?. Global Rheumatology. https://doi.org/10.46856/grp.26.et144
  18. Caballero C., Fajardo E. (2022): Artigo de reflexão | Publicações científicas: Conhecimento como mercado ou como bem comum?. Global Rheumatology. https://doi.org/10.46856/grp.26.ept144
  19. Caballero C., Fajardo E. (2022): Artículo de reflexión | Publicaciones científicas: ¿El conocimiento como un mercado o como un bien común. Global Rheumatology. https://doi.org/10.46856/grp.26.e144
  20. Roche D., Raby G., Norin T., Ern R., Scheuffele H., Skeeles M., et al. (2022): Paths towards greater consensus building in experimental biology. Journal of Experimental Biology 225(Suppl_1). https://doi.org/10.1242/jeb.243559
  21. Knudson D. (2022): What Kinesiology Research is Most Visible to the Academic World?. Quest 74(3):285-298. https://doi.org/10.1080/00336297.2022.2092880
  22. Teixeira da Silva J. (2022): A Synthesis of the Formats for Correcting Erroneous and Fraudulent Academic Literature, and Associated Challenges. Journal for General Philosophy of Science 53(4):583-599. https://doi.org/10.1007/s10838-022-09607-4
  23. Aly M., Colunga E., Crockett M., Goldrick M., Gomez P., Kung F., et al. (2022): Changing the Culture of Peer Review for a More Inclusive and Equitable Psychological Science. https://doi.org/10.31234/osf.io/435xz
  24. Gardner P., Paterson J., McGimpsey S., Ashari-Ghomi F., Umu S., Pawlik A., et al. (2022): Sustained software development, not number of citations or journal choice, is indicative of accurate bioinformatic software. Genome Biology 23(1). https://doi.org/10.1186/s13059-022-02625-x
  25. Bekkers R. (2022): Ten Meta Science Insights to Deal With the Credibility Crisis in the Social Sciences. https://doi.org/10.31235/osf.io/rm4p8
  26. Triki Z., Bshary R. (2022): A proposal to enhance data quality and FAIRness. Ethology 128(9):647-651. https://doi.org/10.1111/eth.13320
  27. Grossmann A., Brembs B. (2021): Current market rates for scholarly publishing services. F1000Research 10:20. https://doi.org/10.12688/f1000research.27468.1
  28. Grossmann A., Brembs B. (2021): Current market rates for scholarly publishing services. F1000Research 10:20. https://doi.org/10.12688/f1000research.27468.2
  29. Marshall B. (2021): Make like a glass frog: In support of increased transparency in herpetology. Herpetological Journal. https://doi.org/10.33256/31.1.3545
  30. Triggle C., MacDonald R., Triggle D., Grierson D. (2021): Requiem for impact factors and high publication charges. Accountability in Research 29(3):133-164. https://doi.org/10.1080/08989621.2021.1909481
  31. Morris D., MacGillivray E., Pither E. (2021): Self-promotion and the need to be first in science. FACETS 6:1881-1891. https://doi.org/10.1139/facets-2021-0100
  32. Lazar M. (2021): Novel biomedical research must not be a work of fiction. Journal of Clinical Investigation 131(18). https://doi.org/10.1172/jci150827
  33. Marshall B., Strine C. (2020): Make like a glass frog: In support of increased transparency in herpetology. https://doi.org/10.31219/osf.io/74frd
  34. Casarotto P., Brembs B. (2020): A platform for reproducibility. Journal for Reproducibility in Neuroscience 1:303. https://doi.org/10.31885/jrn.1.2020.303
  35. Knudson D., Liu T., Schmidt D., Van Mullem H. (2019): Mentoring Tenure-Track Faculty in Kinesiology. Kinesiology Review 8(4):312-317. https://doi.org/10.1123/kr.2019-0041
  36. Stojmenova Duh E., Duh A., Droftina U., Kos T., Duh U., Simonič Korošak T., et al. (2019): Publish-and-Flourish: Using Blockchain Platform to Enable Cooperative Scholarly Communication. Publications 7(2):33. https://doi.org/10.3390/publications7020033
  37. Tennan J., Crane H., Crick T., Davila J., Enkhbayar A., Havemann J., et al. (2019): Ten hot topics around scholarly publishing. Bibliosphere. https://doi.org/10.20913/1815-3186-2019-3-3-25
  38. Tennant J., Crane H., Crick T., Davila J., Enkhbayar A., Havemann J., et al. (2019): Ten Hot Topics around Scholarly Publishing. Publications 7(2):34. https://doi.org/10.3390/publications7020034
  39. Tennant J., Crane H., Crick T., Davila J., Enkhbayar A., Havemann J., et al. (2019): Ten myths around open scholarly publishing. https://doi.org/10.7287/peerj.preprints.27580v1
  40. Gardner P., Paterson J., McGimpsey S., Ashari-Ghomi F., Umu S., Pawlik A., et al. (2016): Sustained software development, not number of citations or journal choice, is indicative of accurate bioinformatic software. https://doi.org/10.1101/092205

Werkhoven Z, Rohrsen C, Qin C, Brembs B, de Bivort B. (2019): MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology. PLoS One 14(11):e0224243.

  1. Hong C., Huang S., Chan C. (2026): Linking physiology to behavioral individuality in Drosophila melanogaster: Methods and Mechanisms. Physiology & Behavior 308:115267. https://doi.org/10.1016/j.physbeh.2026.115267
  2. Shi M., Ge W., Li C., Liu B., Deng X., Liu C., et al. (2026): Versatile CRISPR‐Cas Tools for Gene Regulation in Zebrafish via an Enhanced Q Binary System. Advanced Science. https://doi.org/10.1002/advs.202511485
  3. Maloney R., Ye A., Saint-Pre S., Alisch T., Zimmerman D., Pittoors N., et al. (2026): Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds. https://doi.org/10.7554/elife.103585.2
  4. Bates A., Phelps J., Kim M., Yang H., Matsliah A., Ajabi Z., et al. (2025): Distributed control circuits across a brain-and-cord connectome. https://doi.org/10.1101/2025.07.31.667571
  5. Lenhart B., Bergland A. (2025): The inversion In(2L)t impacts complex, environmentally sensitive behaviors in Drosophila melanogaster. https://doi.org/10.1101/2025.09.17.676861
  6. de Bivort B. (2025): The Developmental Origins of Behavioral Individuality. Annual Review of Cell and Developmental Biology 41(1):331-352. https://doi.org/10.1146/annurev-cellbio-101323-025423
  7. Zimmerman D., de Bivort B., Samuel A. (2025): The larval Drosophila mushroom body balances lateralized sensing and interhemispheric integration. https://doi.org/10.1101/2025.03.02.641007
  8. Macartney E., Burke S., Pottier P., Hamoudi Z., Hart C., Ahmed R., et al. (2025): Sex‐Specific Effects of Social Environment on Behaviour and Their Correlations in Drosophila melanogaster. Ecology and Evolution 15(4). https://doi.org/10.1002/ece3.71261
  9. Liu F., Lin W., McMillan L., Yang C. (2025): Fire ants exhibit self-medication but lack preventive behavioral immunity against a viral pathogen. Journal of Invertebrate Pathology 211:108339. https://doi.org/10.1016/j.jip.2025.108339
  10. Abou Daya F., Mandigo T., Ober L., Patel D., Maher M., Math S., et al. (2025): Identifying links between cardiovascular disease and insomnia by modeling genes from a pleiotropic locus. Disease Models & Mechanisms 18(5). https://doi.org/10.1242/dmm.052139
  11. Iwasaki K., Neuhauser C., Stokes C., Rayshubskiy A. (2025): The fruit fly, Drosophila melanogaster , as a microrobotics platform. Proceedings of the National Academy of Sciences 122(15). https://doi.org/10.1073/pnas.2426180122
  12. Ulloa L., Roy D., Minichiello A., Bechthold F., de Bivort B., Elya C. (2025): Evidence that Entomophthora muscae controls the timing of host death via its own circadian clock. https://doi.org/10.1101/2025.06.18.660419
  13. Lall S. (2025): Why and how does personality emerge? Studying the evolution of individuality using thousands of fruit flies. https://doi.org/10.64628/aai.chmmjxs93
  14. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2025): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.2
  15. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2025): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.3
  16. Abou Daya F., Mandigo T., Patel D., Math S., Ober L., Maher M., et al. (2025): Drosophila Modeling Identifies Increased Sleep as a Link Between Insomnia and Cardiovascular Disease. https://doi.org/10.1101/2025.04.07.647668
  17. Beckerson W., Werner S., Goebbels M., Ramalho J., Swafford A., Soroka I., et al. (2025): The First of Us: Ophiocordyceps use a novel scramblase-binding peptide to manipulate zombie ants. https://doi.org/10.1101/2025.09.09.674826
  18. Sofela F., Lopez Valencia M., Jongens T., Sehgal A. (2024): Effects of Nf1 on sleep behavior are mediated through starvation caused by deficits in SARM1 dependent NAD+ metabolism. https://doi.org/10.1101/2024.09.14.612058
  19. Dayton J., Owens A. (2024): iLAM : Imaging Locomotor Activity Monitor for circadian phenotyping of large‐bodied flying insects. Methods in Ecology and Evolution 15(10):1814-1821. https://doi.org/10.1111/2041-210x.14403
  20. Wu J., Xi J., Zhu Z., Zhao W., Peng Q., Liu C., et al. (2024): An automated microfluidic platform for toxicity testing based on Caenorhabditis elegans. https://doi.org/10.1101/2024.08.27.610021
  21. Iwasaki K., Neuhauser C., Stokes C., Rayshubskiy A. (2024): The fruit fly, Drosophila melanogaster , as a micro-robotics platform. https://doi.org/10.1101/2024.05.24.595748
  22. Lebovich L., Alisch T., Redhead E., Parker M., Loewenstein Y., Couzin I., et al. (2024): Spatiotemporal dynamics of locomotor decisions in Drosophila melanogaster. https://doi.org/10.1101/2024.09.04.611038
  23. Maloney R., Ye A., Saint-Pre S., Alisch T., Zimmerman D., Pittoors N., et al. (2024): Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds. https://doi.org/10.7554/elife.103585
  24. Maloney R., Ye A., Saint-Pre S., Alisch T., Zimmerman D., Pittoors N., et al. (2024): Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds. https://doi.org/10.1101/2024.09.05.611301
  25. Maloney R., Ye A., Saint-Pre S., Alisch T., Zimmerman D., Pittoors N., et al. (2024): Drift in Individual Behavioral Phenotype as a Strategy for Unpredictable Worlds. https://doi.org/10.7554/elife.103585.1
  26. Gill S., Mandigo T., Elmali A., Leger B., Yang B., Tran S., et al. (2024): A conserved role for ALG10/ALG10B and the N -glycosylation pathway in the sleep-epilepsy axis. https://doi.org/10.1101/2024.12.11.24318624
  27. Sen S. (2024): eLife Assessment: Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1.sa3
  28. Thomas F Mathejczyk, Cara Knief, Muhammad A Haidar, Florian Freitag, Tydings McClary, Mathias F Wernet, et al. (2024): Reviewer #3 (Public Review): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1.sa0
  29. Thomas F Mathejczyk, Cara Knief, Muhammad A Haidar, Florian Freitag, Tydings McClary, Mathias F Wernet, et al. (2024): Reviewer #1 (Public Review): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1.sa2
  30. Thomas F Mathejczyk, Cara Knief, Muhammad A Haidar, Florian Freitag, Tydings McClary, Mathias F Wernet, et al. (2024): Reviewer #2 (Public Review): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1.sa1
  31. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2024): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171
  32. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2024): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1
  33. Craciun V., Luca M., Lefter R. (2024): Software for Laboratory Test: FARM-Framework for Activity Real-Time Monitoring. IFMBE Proceedings. https://doi.org/10.1007/978-3-031-62502-2_59
  34. Liu Y., Li W., Liu X., Li Z., Yue J. (2024): Deep learning in multiple animal tracking: A survey. Computers and Electronics in Agriculture 224:109161. https://doi.org/10.1016/j.compag.2024.109161
  35. Elya C., Lavrentovich D., Lee E., Pasadyn C., Duval J., Basak M., et al. (2023): Neural mechanisms of parasite-induced summiting behavior in ‘zombie’ Drosophila. eLife 12. https://doi.org/10.7554/elife.85410
  36. Dayton J., Owens A. (2023): iLAM: imaging Locomotor Activity Monitor for circadian phenotyping of large-bodied flying insects. https://doi.org/10.1101/2023.11.20.567947
  37. Corley R., Dawson W., Bishop T. (2023): A simple method to account for thermal boundary layers during the estimation of CTmax in small ectotherms. Journal of Thermal Biology 116:103673. https://doi.org/10.1016/j.jtherbio.2023.103673
  38. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2023): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.1101/2023.11.26.568741
  39. Beckerson W., Krider C., Mohammad U., de Bekker C. (2023): 28 minutes later: investigating the role of aflatrem-like compounds in Ophiocordyceps parasite manipulation of zombie ants. Animal Behaviour 203:225-240. https://doi.org/10.1016/j.anbehav.2023.06.011
  40. Huda A., Omelchenko A., Vaden T., Castaneda A., Ni L. (2022): Responses of differentDrosophilaspecies to temperature changes. Journal of Experimental Biology 225(11). https://doi.org/10.1242/jeb.243708
  41. de Bivort B., Buchanan S., Skutt-Kakaria K., Gajda E., Ayroles J., O’Leary C., et al. (2022): Precise Quantification of Behavioral Individuality From 80 Million Decisions Across 183,000 Flies. Frontiers in Behavioral Neuroscience 16. https://doi.org/10.3389/fnbeh.2022.836626
  42. Elya C., Lavrentovich D., Lee E., Pasadyn C., Duval J., Basak M., et al. (2022): Neural mechanisms of parasite-induced summiting behavior in “zombie” Drosophila. https://doi.org/10.1101/2022.12.01.518723
  43. Tao L., Bhandawat V. (2022): Mechanisms of Variability Underlying Odor-Guided Locomotion. Frontiers in Behavioral Neuroscience 16. https://doi.org/10.3389/fnbeh.2022.871884
  44. Garg V., André S., Giraldo D., Heyer L., Göpfert M., Dosch R., et al. (2022): A Markerless Pose Estimator Applicable to Limbless Animals. Frontiers in Behavioral Neuroscience 16. https://doi.org/10.3389/fnbeh.2022.819146
  45. Beckerson W., Krider C., Mohammad U., de Bekker C. (2022): 28 Minutes Later: Investigating the role of aflatrem-like compounds in Ophiocordyceps parasite manipulation of zombie ants. https://doi.org/10.1101/2022.09.08.507134
  46. Omelchenko A., Huda A., Castaneda A., Vaden T., Ni L. (2021): Using TrackMate to Analyze Drosophila Larval and Adult Locomotion. https://doi.org/10.1101/2021.09.28.462241
  47. Lesar A., Tahir J., Wolk J., Gershow M. (2021): Switch-like and persistent memory formation in individual Drosophila larvae. eLife 10. https://doi.org/10.7554/elife.70317
  48. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. https://doi.org/10.1101/2021.10.04.463010
  49. Larsen L., Neerup M., Hallam J. (2021): Online computational ethology based on modern IT infrastructure. Ecological Informatics 63:101290. https://doi.org/10.1016/j.ecoinf.2021.101290
  50. Jourjine N., Hoekstra H. (2021): Expanding evolutionary neuroscience: insights from comparing variation in behavior. Neuron 109(7):1084-1099. https://doi.org/10.1016/j.neuron.2021.02.002
  51. Panadeiro V., Rodriguez A., Henry J., Wlodkowic D., Andersson M. (2021): A review of 28 free animal-tracking software applications: current features and limitations. Lab Animal 50(9):246-254. https://doi.org/10.1038/s41684-021-00811-1
  52. Werkhoven Z., Bravin A., Skutt-Kakaria K., Reimers P., Pallares L., Ayroles J., et al. (2021): The structure of behavioral variation within a genotype. eLife 10. https://doi.org/10.7554/elife.64988
  53. Singh A., Pietrasik M., Natha G., Ghouaiel N., Brizel K., Ray N. (2020): Animal Detection in Man-made Environments. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv45572.2020.9093504
  54. Versace E., Caffini M., Werkhoven Z., de Bivort B. (2020): Individual, but not population asymmetries, are modulated by social environment and genotype in Drosophila melanogaster. Scientific Reports 10(1). https://doi.org/10.1038/s41598-020-61410-7
  55. Akhund-Zade J., Yoon D., Bangerter A., Polizos N., Campbell M., Soloshenko A., et al. (2020): Wild flies hedge their thermal preference bets in response to seasonal fluctuations. https://doi.org/10.1101/2020.09.16.300731
  56. Akhund-Zade J., Yoon D., Bangerter A., Polizos N., Campbell M., Soloshenko A., et al. (2020): Wild flies hedge their thermal preference bets in response to seasonal fluctuations. Zenodo. https://doi.org/10.5281/zenodo.4026736
  57. Scheiner R., Frantzmann F., Jäger M., Mitesser O., Helfrich-Förster C., Pauls D. (2020): A Novel Thermal-Visual Place Learning Paradigm for Honeybees (Apis mellifera). Frontiers in Behavioral Neuroscience 14. https://doi.org/10.3389/fnbeh.2020.00056
  58. Xu C., Theisen E., Maloney R., Peng J., Santiago I., Yapp C., et al. (2019): Control of Synaptic Specificity by Establishing a Relative Preference for Synaptic Partners. Neuron 103(5):865-877.e7. https://doi.org/10.1016/j.neuron.2019.06.006
  59. Tadres D., Louis M. (2019): PiVR: an affordable and versatile closed-loop platform to study unrestrained sensorimotor behavior. https://doi.org/10.1101/2019.12.20.885442
  60. Graving J., Chae D., Naik H., Li L., Koger B., Costelloe B., et al. (2019): DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 8. https://doi.org/10.7554/elife.47994
  61. Liu G., Nath T., Linneweber G., Claeys A., Guo Z., Li J., et al. (2018): A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila. PLOS Computational Biology 14(8):e1006410. https://doi.org/10.1371/journal.pcbi.1006410

Tennant J, Beamer JE, Bosman J, Brembs B, et al. (2019): Foundations for Open Scholarship Strategy Development. Center for Open Science.

  1. Frattini J., Montgomery L., Fucci D., Unterkalmsteiner M., Mendez D., Fischbach J. (2024): Requirements quality research artifacts: Recovery, analysis, and management guideline. Journal of Systems and Software 216:112120. https://doi.org/10.1016/j.jss.2024.112120
  2. Frank M., Grüning D., Pronizius E., Korbmacher M., Elsherif M., Bergmann L., et al. (2024): Mapping students’ Open Science attitudes and preferences: Country and education level differences – PCI RR Stage 1 Report. https://doi.org/10.31219/osf.io/7gbvp
  3. Arthur P., Hearn L. (2024): Open Scholarship in the Humanities. Bloomsbury Publishing Plc eBooks. https://doi.org/10.5040/9781350232303
  4. Kullmann S., Weimer V. (2024): Teaching as part of open scholarship: developing a scientometric framework for Open Educational Resources. Scientometrics 129(10):6065-6087. https://doi.org/10.1007/s11192-024-05007-1
  5. Žero A., Mastilović A. (2023): ISBN, ISSN & DOI MEĐUNARODNI IDENTIFIKATORI U SLUŽBI OTVORENE NAUKE / INTERNATIONAL IDENTIFIERS ISBN, ISSN & DOI IN THE SERVICE OF OPEN SCIENCE. Pregled: časopis za društvena pitanja / Periodical for social issues 64(2):69-85. https://doi.org/10.48052/19865244.2023.2.69
  6. Winter C. (2023): Open Scholarship Policy in Focus. Open Scholarship Press Curated Volumes: Policy. https://doi.org/10.21428/47bc126e.5abba88b
  7. De-Filippo D., Lascurain-Sánchez M., Sánchez F. (2023): Mapping open science at Spanish universities. Analysis of higher education systems. El Profesional de la información. https://doi.org/10.3145/epi.2023.jul.06
  8. Zarghani M., Nemati-Anaraki L., Sedghi S., Noroozi Chakoli A., Rowhani-Farid A. (2023): The Application of Open Science Potentials in Research Processes: A Comprehensive Literature Review. Libri 73(2):167-186. https://doi.org/10.1515/libri-2022-0007
  9. Weimer V., Heck T., van Leeuwen T., Rittberger M. (2023): The quantification of open scholarship—a mapping review. Quantitative Science Studies 4(3):650-670. https://doi.org/10.1162/qss_a_00266
  10. Сокольчик В. (2023): Открытая наука как новая парадигма научных исследований: проблемы и перспективы (на примере биомедицинских исследований). Труды БГТУ Серия 6. https://doi.org/10.52065/2520-6885-2023-269-1-30
  11. Arbuckle A., Siemens R., Bath J., Crompton C., Estill L., Niemann T., et al. (2022): An Open Social Scholarship Path for the Humanities. The Journal of Electronic Publishing 25(2). https://doi.org/10.3998/jep.1973
  12. Hosseini M., Senabre Hidalgo E., Horbach S., Güttinger S., Penders B. (2022): Messing with Merton: The intersection between open science practices and Mertonian values. Accountability in Research 31(5):428-455. https://doi.org/10.1080/08989621.2022.2141625
  13. Class B., de Bruyne M., Wuillemin C., Donzé D., Claivaz J. (2021): Towards Open Science for the Qualitative Researcher: From a Positivist to an Open Interpretation. International Journal of Qualitative Methods 20. https://doi.org/10.1177/16094069211034641
  14. Méndez E. (2021): Open Science por defecto. La nueva normalidad para la investigación. Arbor 197(799):a587. https://doi.org/10.3989/arbor.2021.799002
  15. Bezuidenhout L., Havemann J. (2021): The varying openness of digital open science tools. F1000Research 9:1292. https://doi.org/10.12688/f1000research.26615.2
  16. Borges M., Casado E. (2021): Sob a lente da Ciência Aberta: Olhares de Portugal, Espanha e Brasil. Imprensa da Universidade de Coimbra eBooks. https://doi.org/10.14195/978-989-26-2022-0
  17. Longley Arthur P., Hearn L. (2021): Toward Open Research: A Narrative Review of the Challenges and Opportunities for Open Humanities. Journal of Communication. https://doi.org/10.1093/joc/jqab028
  18. Arthur P., Hearn L. (2021): Reshaping How Universities Can Evaluate the Research Impact of Open Humanities for Societal Benefit. The Journal of Electronic Publishing 24(1). https://doi.org/10.3998/jep.788
  19. Mendez D., Graziotin D., Wagner S., Seibold H. (2020): Open Science in Software Engineering. Contemporary Empirical Methods in Software Engineering. https://doi.org/10.1007/978-3-030-32489-6_17
  20. Tennant J., Agarwal R., Baždarić K., Brassard D., Crick T., Dunleavy D., et al. (2020): A tale of two ‘opens’: intersections between Free and Open Source Software and Open Scholarship. https://doi.org/10.31235/osf.io/2kxq8
  21. Tennant J., Bielczyk N., Tzovaras B., Masuzzo P., Steiner T. (2020): Introducing Massively Open Online Papers (MOOPs). KULA: Knowledge Creation, Dissemination, and Preservation Studies 4:1. https://doi.org/10.5334/kula.63
  22. Tennant J. (2020): A value proposition for Open Science. https://doi.org/10.31235/osf.io/k9qhv
  23. Tennant J., Francuzik W., Dunleavy D., Fecher B., Gonzalez-Marquez M., Steiner T. (2020): Open Scholarship as a mechanism for the United Nations Sustainable Development Goals. https://doi.org/10.31235/osf.io/8yk62
  24. Tennant J., Chung N., Steiner T. (2020): Major socio-cultural barriers to widespread adoption of open scholarship. https://doi.org/10.31235/osf.io/bth73
  25. Bezuidenhout L., Havemann J. (2020): The varying openness of digital open science tools. F1000Research 9:1292. https://doi.org/10.12688/f1000research.26615.1
  26. Geange S., von Oppen J., Strydom T., Boakye M., Gauthier T., Gya R., et al. (2020): Next‐generation field courses: Integrating Open Science and online learning. Ecology and Evolution 11(8):3577-3587. https://doi.org/10.1002/ece3.7009
  27. Asubiaro T. (2020): Digital Archiving by Nigerian and Foreign Authors in a Low Resource Context: A Content Analysis of Publications on Natural Language Processing of Nigerian Languages. Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l’ACSI. https://doi.org/10.29173/cais1175
  28. Mostafa M. (2019): Modelling and Analysing Behaviours and Emotions via Complex User Interactions. arXiv. https://doi.org/10.48550/arxiv.1902.07683
  29. Leible S., Schlager S., Schubotz M., Gipp B. (2019): A Review on Blockchain Technology and Blockchain Projects Fostering Open Science. Frontiers in Blockchain 2. https://doi.org/10.3389/fbloc.2019.00016
  30. Steiner T. (2019): What do we talk about when we talk about “Open”? On Education, Science, Research, and Open Scholarship. https://doi.org/10.59350/dxdqq-e4n18
  31. Steiner T. (2019): What do we talk about when we talk about “Open”? On Education, Science, Research, and Open Scholarship. https://doi.org/10.59350/zddf5-kda18

Brembs B. (2018): Prestigious science journals struggle to reach even average reliability. Front. Hum. Neurosci. 12:37.

  1. Nosek B., Allison D., Jamieson K., McNutt M., Nielsen A., Wolf S. (2026): A framework for assessing the trustworthiness of scientific research findings. Proceedings of the National Academy of Sciences 123(6). https://doi.org/10.1073/pnas.2536736123
  2. Knudson D. (2026): Bibliometrics of Measurement in Physical Education and Exercise Science Over the Last Twenty-Five Years. Measurement in Physical Education and Exercise Science. https://doi.org/10.1080/1091367x.2026.2621420
  3. Lendvai G., Kohus Z. (2026): With greater volume comes greater prestige? – an analysis of social sciences journals’ publication patterns between 2004 and 2024. https://doi.org/10.21203/rs.3.rs-8745328/v1
  4. Anikin A. (2025): Can I trust this paper?. Psychonomic Bulletin & Review 32(6):2633-2647. https://doi.org/10.3758/s13423-025-02740-3
  5. Redman B. (2025): Wissenschaftliche Bewertung: Peer-Review, Bibliometrie und Forschungsimpact-Bewertung. Die Forschungsintegrität wiederherstellen. https://doi.org/10.1007/978-3-031-92469-9_8
  6. Paiva B., Campolino L., Silva A., Catib G., Paiva C. (2025): Global trends in oncology shared decision-making and advance care planning: bibliometric study. BMJ Supportive & Palliative Care. https://doi.org/10.1136/spcare-2025-005735
  7. Mossi-Martínez C., Gandia-Ferrero M., Parra-Hernández M., García-Villar C., Martí-Bonmatí L. (2025): Analysing the level of evidence of publications in the journal Radiología. Radiología (English Edition) 67(5):101576. https://doi.org/10.1016/j.rxeng.2025.101576
  8. Marcum C. (2025): Drinking from the firehose? Write more and Publish Less (Version 2). https://doi.org/10.54900/vr8ax-nz653
  9. Marcum C. (2025): Drinking from the firehose? Write more and Publish Less (Version 2). https://doi.org/10.54900/tvby7-zsx87
  10. Knudson D. (2025): Properties of Kinesiology-Related Journals According to Cabells Journalytics Not Apparent in Other Journal Metrics. Quest. https://doi.org/10.1080/00336297.2025.2603938
  11. Bulletti F., Guido M., Coccia M., Palagiano A., Giacomucci E., Bulletti C. (2025): Reforming medical career progression: a call for merit-based systems. Frontiers in Medicine 12. https://doi.org/10.3389/fmed.2025.1643399
  12. Teixeira da Silva J., Nazarovets S. (2025): The publish or perish, publish and perish, publish then perish, and now retract and perish cultures in academia. Naunyn-Schmiedeberg’s Archives of Pharmacology 399(3):3115-3131. https://doi.org/10.1007/s00210-025-04651-5
  13. Guedes J. (2025): Navigating the Madness of Academic Publishing. Qeios 7(2). https://doi.org/10.32388/h7yd78.3
  14. Qi J., Woolway M., Kyropoulou M., Kanellopoulos P., Steele D. (2025): Telematics in Insurance: Challenges and Limitations. IEEE Access 13:147449-147466. https://doi.org/10.1109/access.2025.3600095
  15. Xia J. (2025): Temporal shifts in retraction reasons. Journal of Documentation 82(1):96-110. https://doi.org/10.1108/jd-07-2025-0185
  16. Knöchelmann M., Schendzielorz C. (2025): Writing in the Sciences: Scientists, Scientific Writers, and the Division of Writing Labour. Minerva. https://doi.org/10.1007/s11024-025-09606-x
  17. Sabol M., Winton B. (2025): Enhancing research quality: a comprehensive review of citation practices in information systems. VINE Journal of Information and Knowledge Management Systems 56(1):91-105. https://doi.org/10.1108/vjikms-03-2024-0085
  18. Hochstenbach P., Van de Sompel H., Verborgh R. (2025): The Claims Network: Collecting Research, Education, Impact, and Leadership Claims on the Decentralized Web. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-032-05409-8_11
  19. Neveu R., Neveu A. (2025): Reputation shortcoming in academic publishing. PLOS One 20(4):e0322012. https://doi.org/10.1371/journal.pone.0322012
  20. Arabi S., Ni C., Hutchins B. (2025): Most researchers would receive more recognition if assessed by article-level metrics than by journal-level metrics. PLOS Biology 23(12):e3003532. https://doi.org/10.1371/journal.pbio.3003532
  21. Abdelwahab S., Farasani A., Moshi J., Alshahrani S., Hassan W. (2025): Germany in pharmacology publishing: 192 articles with 1367 citations in Naunyn–Schmiedeberg’s Archives vs. 16,775 articles with 274,225 citations elsewhere. Naunyn-Schmiedeberg’s Archives of Pharmacology 398(11):16251-16257. https://doi.org/10.1007/s00210-025-04224-6
  22. Wongvorachan T. (2025): Rethinking Academic Publishing: A Call for Inclusive, Transparent, and Sustainable Reforms. Preprints.org. https://doi.org/10.20944/preprints202505.1897.v1
  23. MENG W. (2025): Independent scholars in a collaborative wave: visual modelling of a systematic critique of the sharp decline in the number of single authors in management SCI journals. https://doi.org/10.31235/osf.io/zsd3t_v1
  24. Aktaş Çimen Z., Güdekli D., Kutlu D. (2025): A BIBLIOMETRIC ANALYSIS OF THE FIELDS OF INTERNATIONAL TRADE AND WOMEN ENTREPRENEURSHIP. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 26(4):25-52. https://doi.org/10.53443/anadoluibfd.1645572
  25. Barnett A., Allen L., Aldcroft A., Lash T., McCreanor V. (2024): Examining uncertainty in journal peer reviewers’ recommendations: a cross-sectional study. Royal Society Open Science 11(9). https://doi.org/10.1098/rsos.240612
  26. Barnett A., McCreanor V., Allen L., Lash T., Aldcroft A. (2024): Uncertainty in peer review. https://doi.org/10.31219/osf.io/fm6st
  27. Barnett A., White N. (2024): Something is off-base with this title: P esteems, statical significance and more slapdash stats. Significance 21(1):11-13. https://doi.org/10.1093/jrssig/qmae007
  28. Gärtner A., Leising D., Schönbrodt F. (2024): Towards responsible research assessment: How to reward research quality. PLOS Biology 22(2):e3002553. https://doi.org/10.1371/journal.pbio.3002553
  29. Allard A., Clavien C. (2024): Teaching epistemic integrity to promote reliable scientific communication. Frontiers in Psychology 15. https://doi.org/10.3389/fpsyg.2024.1308304
  30. Marshall B., Duthie A. (2024): A Habitat Selection Multiverse Reveals Largely Consistent Results Despite a Multitude of Analysis Options. https://doi.org/10.1101/2024.06.19.599733
  31. Mossi-Martínez C., Gandia-Ferrero M., Parra-Hernández M., García-Villar C., Martí-Bonmatí L. (2024): Análisis del nivel de evidencia de las publicaciones en la revista Radiología. Radiología 67(5):101576. https://doi.org/10.1016/j.rx.2024.02.007
  32. Van Lissa C., Clapper E., Kuiper R. (2024): A tutorial on aggregating evidence from conceptual replication studies using the product Bayes factor. Research Synthesis Methods 15(6):1231-1243. https://doi.org/10.1002/jrsm.1765
  33. Marcum C. (2024): Drinking from the Firehose? Write More and Publish Less. https://doi.org/10.54900/r8zwg-62003
  34. Knudson D. (2024): Are There Meaningful Prestige Metrics of Kinesiology-Related Journals?. Measurement in Physical Education and Exercise Science 28(4):316-337. https://doi.org/10.1080/1091367x.2024.2341849
  35. Knudson D. (2024): Scopus Citation Metrics for Top and Bottom Quintile Kinesiology-Related Journals. International Journal of Kinesiology in Higher Education 8(4):343-354. https://doi.org/10.1080/24711616.2024.2416184
  36. Giglia E. (2024): Open Science e valutazione: non solo una questione di “alternative”. Quaderni di Sociologia 95(LXVIII):125-153. https://doi.org/10.4000/13k0j
  37. Etzel F., Seyffert-Müller A., Schönbrodt F., Kreuzer L., Gärtner A., Knischewski P., et al. (2024): Inter-Rater Reliability in Assessing the Methodological Quality of Research Papers in Psychology. https://doi.org/10.31234/osf.io/4w7rb
  38. Cantone G., Tomaselli V. (2024): Characterisation and calibration of multiversal methods. Advances in Data Analysis and Classification 19(4):989-1021. https://doi.org/10.1007/s11634-024-00610-9
  39. Cantone G., Tomaselli V. (2024): Characterisation and Calibration of Multiversal Models. OSF Preprints. https://doi.org/10.31222/osf.io/pa98g
  40. Andersen J., Horbach S., Ross-Hellauer T. (2024): Through the secret gate: a study of member-contributed submissions in PNAS. Scientometrics 129(9):5673-5687. https://doi.org/10.1007/s11192-024-05115-y
  41. Querejeta M., Laguens A., Romanazzi M. (2024): Expresiones artísticas de niñas, niños y adolescentes durante el aislamiento por COVID-19. Books2bits. https://doi.org/10.51438/b2bquelaro2024
  42. Knöchelmann M. (2024): Science in Formation: The Indexed Scientist and the Inaccessibility of Scientific Information. https://doi.org/10.31235/osf.io/xwpq6
  43. Bardiau M., Dony C. (2024): Measuring back: bibliodiversity and the Journal Impact Factor™ brand, a case study of IF-journals included in the 2021 Journal Citations Report™. Insights the UKSG journal 37. https://doi.org/10.1629/uksg.633
  44. Ordak M. (2024): Poor statistical reporting: do we have a reason for concern? A narrative review and recommendations. Current Opinion in Allergy & Clinical Immunology 24(4):237-242. https://doi.org/10.1097/aci.0000000000000965
  45. Syed M. (2024): Three Persistent Myths about Open Science. Journal of Trial and Error 4(2). https://doi.org/10.36850/mr11
  46. Nilsson R., Jansson A., Wurzbacher C., Anslan S., Belford P., Corcoll N., et al. (2024): 20 years of bibliometric data illustrates a lack of concordance between journal impact factor and fungal species discovery in systematic mycology. MycoKeys 110:273-285. https://doi.org/10.3897/mycokeys.110.136048
  47. Siems R. (2024): Subprime Impact Crisis. Bibliotheken, Politik und digitale Souveränität. Bibliothek Forschung und Praxis 48(2):311-321. https://doi.org/10.1515/bfp-2024-0008
  48. Guerrero S., Rodríguez-Gutiérrez J., Eslava-Mocha P. (2024): Criterios de categorización de revistas colombianas Publindex. Análisis comparado con los sistemas de información: Dialnet, Latindex, Redalyc y SciELO. Entramado 21(1). https://doi.org/10.18041/1900-3803/entramado.1.11270
  49. Anoop T., Rahman Z. (2024): Online Impulse Buying: A Systematic Review of 25 Years of Research Using Meta Regression. Journal of Consumer Behaviour 24(1):363-391. https://doi.org/10.1002/cb.2418
  50. Habiba U., Ahmed S. (2024): Understanding and Mitigating the Menace of Predatory Journals: Perspectives of University Teachers in Bangladesh. Journal of Academic Ethics 23(2):305-328. https://doi.org/10.1007/s10805-024-09538-3
  51. Höller Y., Urbschat M., Bathke A. (2024): Sustainable scientific publishing: a pilot survey on stakeholder motivations and opinions. Discover Sustainability 5(1). https://doi.org/10.1007/s43621-023-00175-1
  52. Kalmar E., Elzer T., Nastase N., Bolhuis T., Germain N., Rietveld M., et al. (2024): Trust in open publishing practices. F1000Research 13:851. https://doi.org/10.12688/f1000research.152168.1
  53. Unknown authors (2023): Praise Page. Distrust. https://doi.org/10.1093/oso/9780192868459.002.0001
  54. Unknown authors (2023): Copyright Page. Distrust. https://doi.org/10.1093/oso/9780192868459.002.0004
  55. Stoimenov A. (2023): Reliability or liability in the contemporary mathematics publishing process? An ethical and technological case study. Cogent Social Sciences 9(2). https://doi.org/10.1080/23311886.2023.2244259
  56. Gärtner A., Leising D., Schönbrodt F. (2023): Empfehlungen zur Bewertung wissenschaftlicher Leistungen bei Berufungsverfahren in der Psychologie. Psychologische Rundschau 74(3):166-174. https://doi.org/10.1026/0033-3042/a000630
  57. Gärtner A., Leising D., Schönbrodt F. (2023): Empfehlungen zur Bewertung wissenschaftlicher Leistungen bei Berufungsverfahren in der Psychologie. https://doi.org/10.31234/osf.io/3yjz7
  58. Alaedini A., Heinricher M., Wormser G. (2023): Bloated Claims in Biomedical Research Publications: Implications for Science and Society. The American Journal of Medicine 136(9):841-843. https://doi.org/10.1016/j.amjmed.2023.04.010
  59. Caliskan A., Dangwal S., Dandekar T. (2023): Metadata integrity in bioinformatics: Bridging the gap between data and knowledge. Computational and Structural Biotechnology Journal 21:4895-4913. https://doi.org/10.1016/j.csbj.2023.10.006
  60. Redman B. (2023): Science Evaluation: Peer Review, Bibliometrics, and Research Impact Assessment. Reconstructing Research Integrity. https://doi.org/10.1007/978-3-031-27111-3_8
  61. Brembs B., Huneman P., Schönbrodt F., Nilsonne G., Susi T., Siems R., et al. (2023): Replacing academic journals. Royal Society Open Science 10(7). https://doi.org/10.1098/rsos.230206
  62. Van Lissa C., Kuiper R., Clapper E. (2023): Aggregating evidence from conceptual replication studies using the product Bayes factor. https://doi.org/10.31234/osf.io/nvqpw
  63. Matyukira C., Mhangara P. (2023): Advancement in the Application of Geospatial Technology in Archaeology and Cultural Heritage in South Africa: A Scientometric Review. Remote Sensing 15(19):4781. https://doi.org/10.3390/rs15194781
  64. Knudson D., Cardinal B., McCullagh P. (2023): Synthesis of Publication Metrics in Kinesiology-Related Journals: Proxies for Rigor, Usage, and Prestige. Quest 76(1):93-112. https://doi.org/10.1080/00336297.2023.2237150
  65. Rodrigues E. (2023): A Necessária e Difícil Reforma da Avaliação da Investigação. Políticas de Ciência e da Língua, Publicação Científica e Rankings Académicos. https://doi.org/10.21814/uminho.ed.66.9
  66. F. Antolini, F. G. Truglia, S. Cesarini (2023): ASA 2022 Data-Driven Decision Making. Proceedings e report. https://doi.org/10.36253/979-12-215-0106-3
  67. Smith G. (2023): Distrust. https://doi.org/10.1093/oso/9780192868459.001.0001
  68. Smith G. (2023): The Paranormal Is Normal. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0002
  69. Smith G. (2023): A Post-Fact World. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0005
  70. Smith G. (2023): Looking for Needles in Haystacks. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0009
  71. Smith G. (2023): Squeezing Blood from Rocks. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0006
  72. Smith G. (2023): Beat the Market. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0010
  73. Smith G. (2023): Provocative, but Wrong. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0008
  74. Smith G. (2023): Irreproducible Research. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0014
  75. Smith G. (2023): Too Much Data. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0011
  76. Smith G. (2023): Restoring the Luster of Science. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0016
  77. Smith G. (2023): Flying Saucers and Space Tourists. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0003
  78. Smith G. (2023): Overpromising and Underdelivering. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0012
  79. Smith G. (2023): Elite Conspiracies. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0004
  80. Smith G. (2023): Most Medicines Disappoint. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0007
  81. Smith G. (2023): Introduction. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0001
  82. Smith G. (2023): The Replication Crisis. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0015
  83. Smith G. (2023): Artificial Unintelligence. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0013
  84. Cantone G. (2023): The multiversal methodology as a remedy of the replication crisis. OSF Preprints. https://doi.org/10.31222/osf.io/kuhmz
  85. Cantone G., Tomaselli V. (2023): Misinformation and disinformation in statistical methodology for social sciences: causes, consequences and remedies. Proceedings e report. https://doi.org/10.36253/979-12-215-0106-3.10
  86. Pölönen J., Guns R., Engels T. (2023): Journal metrics as predictors of Research Excellence Framework 2021 results: Comparison of impact factor quartiles and Finnish expert-ratings. 27th International Conference on Science, Technology and Innovation Indicators (STI 2023). https://doi.org/10.55835/643e529c0b149e8673ee2d95
  87. Zollman K., García J., Handfield T. (2023): Academic Journals, Incentives, and the Quality of Peer Review: A Model. Philosophy of Science 91(1):186-203. https://doi.org/10.1017/psa.2023.81
  88. Xu L., Ding K., Lin Y., Zhang C. (2023): Does citation polarity help evaluate the quality of academic papers?. Scientometrics 128(7):4065-4087. https://doi.org/10.1007/s11192-023-04734-1
  89. Martins M., Pires H. (2023): Políticas de Ciência e da Língua, Publicação Científica e Rankings Académicos. UMinho Editora/CECS eBooks. https://doi.org/10.21814/uminho.ed.66
  90. Uddin S., Khan A., Lu H. (2023): Impact of COVID-19 on Journal Impact Factor. Journal of Informetrics 17(4):101458. https://doi.org/10.1016/j.joi.2023.101458
  91. Chu S., Tadayonnejad R., Corlier J., Wilson A., Citrenbaum C., Leuchter A. (2023): Rumination symptoms in treatment-resistant major depressive disorder, and outcomes of repetitive Transcranial Magnetic Stimulation (rTMS) treatment. Translational Psychiatry 13(1). https://doi.org/10.1038/s41398-023-02566-4
  92. Dawson S. (2023): Measuring Impact in Drug Repurposing. Measuring Impact in Drug Repurposing. https://doi.org/10.58647/rexpo.23020
  93. Bauwens T., Reike D., Calisto-Friant M. (2023): Science for sale? Why academic marketization is a problem and what sustainability research can do about it. Environmental Innovation and Societal Transitions 48:100749. https://doi.org/10.1016/j.eist.2023.100749
  94. Dienes Z. (2023): The credibility crisis and democratic governance: how to reform university governance to be compatible with the nature of science. Royal Society Open Science 10(1). https://doi.org/10.1098/rsos.220808
  95. Aarts A. (2022): Making the Most of Tenure in Two Acts: An Additional Way to Help Change Incentives in Psychological Science?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4176916
  96. Aarts A. (2022): The Natural Selection of Bad Psychological Scientists. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4176925
  97. Delios A., Clemente E., Wu T., Tan H., Wang Y., Gordon M., et al. (2022): Examining the generalizability of research findings from archival data. Proceedings of the National Academy of Sciences 119(30). https://doi.org/10.1073/pnas.2120377119
  98. Ghasemi A., Mirmiran P., Kashfi K., Bahadoran Z. (2022): Scientific Publishing in Biomedicine: A Brief History of Scientific Journals. International Journal of Endocrinology and Metabolism 21(1). https://doi.org/10.5812/ijem-131812
  99. Brand C. (2022): An outdated publishing system threatens both research integrity and the retention of rigorous early career researchers. OSF Preprints. https://doi.org/10.31222/osf.io/nr3vt
  100. Dunleavy D. (2022): Progressive and degenerative journals: on the growth and appraisal of knowledge in scholarly publishing. European Journal for Philosophy of Science 12(4). https://doi.org/10.1007/s13194-022-00492-8
  101. Dunleavy D. (2022): Progressive and Degenerative Journals: On the Growth and Appraisal of Knowledge in Scholarly Publishing. OSF Preprints. https://doi.org/10.31222/osf.io/yskhj
  102. Racimo F., Galtier N., De Herde V., Bonn N., Phillips B., Guillemaud T., et al. (2022): Ethical Publishing: How Do We Get There?. Philosophy, Theory, and Practice in Biology 14(0). https://doi.org/10.3998/ptpbio.3363
  103. Grisi G., Barreto Segundo J., Freire C., Matias D., Cruz M., Laporte L., et al. (2022): Evidence on the role of journal editors in the COVID19 infodemic: metascientific study analyzing COVID19 publication rates and patterns. https://doi.org/10.1101/2022.01.23.22269716
  104. Seppänen J., Värri H., Ylönen I. (2022): Co-citation Percentile Rank and JYUcite: a new network-standardized output-level citation influence metric and its implementation using Dimensions API. Scientometrics 127(6):3523-3541. https://doi.org/10.1007/s11192-022-04393-8
  105. Yeh J., Shulruf B., Lee H., Huang P., Kuo W., Hwang T., et al. (2022): Faculty appointment and promotion in Taiwan’s medical schools, a systematic analysis. BMC Medical Education 22(1). https://doi.org/10.1186/s12909-022-03435-2
  106. Tennant J., Breznau N. (2022): Legacy of Jon Tennant, “Open science is just good science”. https://doi.org/10.31235/osf.io/hfns2
  107. Alperin J., Schimanski L., La M., Niles M., McKiernan E. (2022): The Value of Data and Other Non-traditional Scholarly Outputs in Academic Review, Promotion, and Tenure in Canada and the United States. The Open Handbook of Linguistic Data Management. https://doi.org/10.7551/mitpress/12200.003.0017
  108. Knöchelmann M., Schendzielorz C. (2022): Writing in the Sciences: Scientists as Writers, Scientific Writing, and the Persuasive Story. https://doi.org/10.31235/osf.io/fmcsp
  109. Gordon M., Bishop M., Chen Y., Dreber A., Goldfedder B., Holzmeister F., et al. (2022): Forecasting the publication and citation outcomes of COVID-19 preprints. Royal Society Open Science 9(9). https://doi.org/10.1098/rsos.220440
  110. Loizides M., Alvarado P., Moreau P., Assyov B., Halasů V., Stadler M., et al. (2022): Has taxonomic vandalism gone too far? A case study, the rise of the pay-to-publish model and the pitfalls of Morchella systematics. Mycological Progress 21(1):7-38. https://doi.org/10.1007/s11557-021-01755-z
  111. Dougherty M., Horne Z. (2022): Citation counts and journal impact factors do not capture some indicators of research quality in the behavioural and brain sciences. Royal Society Open Science 9(8). https://doi.org/10.1098/rsos.220334
  112. Hosseini M., Senabre Hidalgo E., Horbach S., Güttinger S., Penders B. (2022): Messing with Merton: The intersection between open science practices and Mertonian values. Accountability in Research 31(5):428-455. https://doi.org/10.1080/08989621.2022.2141625
  113. Syed M. (2022): Three Myths about Open Science That Just Won’t Die. https://doi.org/10.31234/osf.io/w8xs2
  114. Bekkers R. (2022): Better Academic Research Writing: A Practical Guide. https://doi.org/10.31219/osf.io/4umea
  115. Bekkers R. (2022): Ten Meta Science Insights to Deal With the Credibility Crisis in the Social Sciences. https://doi.org/10.31235/osf.io/rm4p8
  116. Sharifi S., Mahmoud N., Voke E., Landry M., Mahmoudi M. (2022): Importance of Standardizing Analytical Characterization Methodology for Improved Reliability of the Nanomedicine Literature. Nano-Micro Letters 14(1). https://doi.org/10.1007/s40820-022-00922-5
  117. Sharifi S., Reuel N., Kallmyer N., Sun E., Landry M., Mahmoudi M. (2022): The Issue of Reliability and Repeatability of Analytical Measurement in Industrial and Academic Nanomedicine. ACS Nano 17(1):4-11. https://doi.org/10.1021/acsnano.2c09249
  118. Ke S. (2022): Liberate academic research from paywalls. https://doi.org/10.31235/osf.io/ey8c5
  119. Afonso Vieira V., Wolter J., Falcão Araujo C., Saraiva Frio R. (2022): What makes the corporate social responsibility impact on Customer–Company identification stronger? A meta-analysis. International Journal of Research in Marketing 40(2):475-492. https://doi.org/10.1016/j.ijresmar.2022.09.002
  120. Dienes Z. (2022): The credibility crisis and democratic governance: How to reform university governance to be compatible with the nature of science. https://doi.org/10.31234/osf.io/8wmna
  121. Azarpazhooh A., Cardoso E., Sgro A., Elbarbary M., Laghapour Lighvan N., Badewy R., et al. (2021): A Scoping Review of 4 Decades of Outcomes in Nonsurgical Root Canal Treatment, Nonsurgical Retreatment, and Apexification Studies—Part 1: Process and General Results. Journal of Endodontics 48(1):15-28. https://doi.org/10.1016/j.joen.2021.09.018
  122. Armani A., Lee J. (2021): Evaluating the impact of ideation and actualization of multidisciplinary research. Communications Physics 4(1). https://doi.org/10.1038/s42005-021-00714-0
  123. Marshall B. (2021): Make like a glass frog: In support of increased transparency in herpetology. Herpetological Journal. https://doi.org/10.33256/31.1.3545
  124. Myers B., Kahn K. (2021): Practical publication metrics for academics. Clinical and Translational Science 14(5):1705-1712. https://doi.org/10.1111/cts.13067
  125. Triggle C., MacDonald R., Triggle D., Grierson D. (2021): Requiem for impact factors and high publication charges. Accountability in Research 29(3):133-164. https://doi.org/10.1080/08989621.2021.1909481
  126. Rowbottom D. (2021): Peer Review May Not Be Such a Bad Idea: Response to Heesen and Bright. The British Journal for the Philosophy of Science 73(4):927-940. https://doi.org/10.1086/714787
  127. Morales E., McKiernan E., Niles M., Schimanski L., Alperin J. (2021): How faculty define quality, prestige, and impact of academic journals. PLOS ONE 16(10):e0257340. https://doi.org/10.1371/journal.pone.0257340
  128. Morales E., McKiernan E., Niles M., Schimanski L., Alperin J. (2021): How faculty define quality, prestige, and impact in research. https://doi.org/10.1101/2021.04.14.439880
  129. Lantsoght E., Abambres M., Ribeiro T., Sousa A. (2021): Use and misuse of the journal impact factor for evaluating researchers. Bitácora Académica 8(1). https://doi.org/10.18272/ba.v8i1.3324
  130. Ehrhart F., Evelo C. (2021): Ten simple rules to make your publication look better. PLOS Computational Biology 17(5):e1008938. https://doi.org/10.1371/journal.pcbi.1008938
  131. Desmond H. (2021): Incentivizing Replication Is Insufficient to Safeguard Default Trust. Philosophy of Science 88(5):906-917. https://doi.org/10.1086/715657
  132. Abeysooriya M., Soria M., Kasu M., Ziemann M. (2021): Gene name errors: Lessons not learned. PLOS Computational Biology 17(7):e1008984. https://doi.org/10.1371/journal.pcbi.1008984
  133. Marcel Knöchelmann (2021): The Democratisation Myth. Science & Technology Studies 34(2):65-89. https://doi.org/10.23987/sts.94964
  134. Knöchelmann M. (2021): Systemimmanenz und Transformation: Die Bibliothek der Zukunft als lokale Verwalterin?. Bibliothek Forschung und Praxis 45(1):151-162. https://doi.org/10.1515/bfp-2020-0101
  135. Pagliaro M. (2021): Did You Ask for Citations? An Insight into Preprint Citations en route to Open Science. Publications 9(3):26. https://doi.org/10.3390/publications9030026
  136. Pagliaro M. (2021): Did you Ask for Citations? An Insight into Preprint Citations en route to Open Science. Preprints.org. https://doi.org/10.20944/preprints202104.0448.v1
  137. Orhan M. (2021): Dynamic interactionism between research fraud and research culture: a commentary to Harvey’s analysis. Quality in Higher Education 27(1):134-146. https://doi.org/10.1080/13538322.2021.1857900
  138. Spyrison N., Lee B., Besançon L. (2021): “Is IEEE VIS *that* good?” On key factors in the initial assessment of manuscript and venue quality. https://doi.org/10.31219/osf.io/65wm7
  139. Pourret O., Hedding D., Ibarra D., Irawan D., Liu H., Tennant J. (2021): International disparities in open access practices in the Earth Sciences. European Science Editing 47. https://doi.org/10.3897/ese.2021.e63663
  140. Palavalli-Nettimi R. (2021): Toward a Sustainable Model of Scientific Publishing. Journal of Science Policy & Governance 18(01). https://doi.org/10.38126/jspg180111
  141. Gosselin R. (2021): Insufficient transparency of statistical reporting in preclinical research: a scoping review. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-83006-5
  142. Lodi S., Godoy B., Ortega J., Bini L. (2021): Quality of meta‐analyses in freshwater ecology: A systematic review. Freshwater Biology 66(5):803-814. https://doi.org/10.1111/fwb.13695
  143. Pavlov Y., Adamian N., Appelhoff S., Arvaneh M., Benwell C., Beste C., et al. (2021): #EEGManyLabs: Investigating the replicability of influential EEG experiments. Cortex 144:213-229. https://doi.org/10.1016/j.cortex.2021.03.013
  144. Unknown authors (2020): Publikationsberatung an Universitäten. transcript Verlag eBooks. https://doi.org/10.1515/9783839450727
  145. Marshall B., Strine C. (2020): Make like a glass frog: In support of increased transparency in herpetology. https://doi.org/10.31219/osf.io/74frd
  146. Smedsrød B., Longva L. (2020): The costly prestige ranking of scholarly journals. Ravnetrykk. https://doi.org/10.7557/15.5507
  147. Almeida C., Grácio M. (2020): Fator de Impacto e a decisão de publicação de um artigo. Páginas a&b : Arquivos & Bibliotecas. https://doi.org/10.21747/21836671/pag13a12
  148. Almeida C., Gracio M. (2020): Aspectos metodológicos e de utilização do fator de impacto. BIBLOS – Revista do Instituto de Ciências Humanas e da Informação 34(1):127-144. https://doi.org/10.14295/biblos.v34i1.9658
  149. Rodrigues E. (2020): A pandemia e a emergência da Ciência Aberta. A Universidade do Minho em tempos de pandemia. https://doi.org/10.21814/uminho.ed.24.12
  150. Büttner F., Toomey E., McClean S., Roe M., Delahunt E. (2020): Are questionable research practices facilitating new discoveries in sport and exercise medicine? The proportion of supported hypotheses is implausibly high. British Journal of Sports Medicine 54(22):1365-1371. https://doi.org/10.1136/bjsports-2019-101863
  151. Bordignon F. (2020): Self-correction of science: a comparative study of negative citations and post-publication peer review. Scientometrics 124(2):1225-1239. https://doi.org/10.1007/s11192-020-03536-z
  152. Da Costa G., Alves C., Luizeti B. (2020): Os Princípios de Hong Kong e sua importância para o ecossistema científico atual. Journal of Evidence-Based Healthcare 2(2):159-166. https://doi.org/10.17267/2675-021xevidence.v2i2.3247
  153. Leng G., Leng R. (2020): The Matter of Facts. The MIT Press eBooks. https://doi.org/10.7551/mitpress/12228.001.0001
  154. Major G., Avval T., Moeini B., Pinto G., Shah D., Jain V., et al. (2020): Assessment of the frequency and nature of erroneous x-ray photoelectron spectroscopy analyses in the scientific literature. Journal of Vacuum Science & Technology A 38(6). https://doi.org/10.1116/6.0000685
  155. Seppänen J., Värri H., Ylönen I. (2020): Co-Citation Percentile Rank and JYUcite: a new network-standardized output-level citation influence metric and its implementation using Dimensions API. https://doi.org/10.1101/2020.09.23.310052
  156. Tennant J., Agarwal R., Baždarić K., Brassard D., Crick T., Dunleavy D., et al. (2020): A tale of two ‘opens’: intersections between Free and Open Source Software and Open Scholarship. https://doi.org/10.31235/osf.io/2kxq8
  157. Tennant J., Wien C. (2020): Fixing the crisis state of scientific evaluation. https://doi.org/10.31235/osf.io/f4zk9
  158. Tennant J. (2020): How open science is fighting against private, proprietary publishing platforms. https://doi.org/10.31235/osf.io/wq4x8
  159. Karin Lackner, Lisa Schilhan, Christian Kaier (2020): Publikationsberatung an Universitäten. transcript Verlag eBooks. https://doi.org/10.14361/9783839450727
  160. Correia L., Barreto Segundo J. (2020): An immunization program against the COVID-19 infodemic. Journal of Evidence-Based Healthcare 2(1):7-9. https://doi.org/10.17267/2675-021xevidence.v2i1.3124
  161. Martins M., Rodrigues E. (2020): A Universidade do Minho em tempos de pandemia: Tomo II: Re(Ações). A Universidade do Minho em tempos de pandemia. https://doi.org/10.21814/uminho.ed.24
  162. Knöchelmann M. (2020): The Democratisation Myth: Open Access and the Solidification of Epistemic Injustices. https://doi.org/10.31235/osf.io/hw7at
  163. Orhan M. (2020): Pardon my French: On superfluous journal rankings, incentives, and impacts on industrial-organizational psychology publication practices in French business schools. Industrial and Organizational Psychology 13(3):295-306. https://doi.org/10.1017/iop.2020.59
  164. Braganza O. (2020): A simple model suggesting economically rational sample-size choice drives irreproducibility. PLOS ONE 15(3):e0229615. https://doi.org/10.1371/journal.pone.0229615
  165. Pourret O., Hedding D., Irawan D., Liu H., Tennant J. (2020): International disparities in open access practices of the Earth Sciences community. https://doi.org/10.31223/osf.io/cpxmy
  166. Pourret O., Hedding D., Ibarra D., Irawan D., Liu H., Tennant J. (2020): International disparities in open access practices in the Earth Sciences. https://doi.org/10.31223/x5hw2s
  167. Hanel P. (2020): Conducting High Impact Research With Limited Financial Resources (While Working from Home). Meta-Psychology 4. https://doi.org/10.15626/mp.2020.2560
  168. Casarotto P., Brembs B. (2020): A platform for reproducibility. Journal for Reproducibility in Neuroscience 1:303. https://doi.org/10.31885/jrn.1.2020.303
  169. Herbert R. (2020): Accept Me, Accept Me Not: What Do Journal Acceptance Rates Really Mean? [ICSR Perspectives]. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3526365
  170. Gray R. (2020): Sorry, we’re open: Golden open-access and inequality in non-human biological sciences. Scientometrics 124(2):1663-1675. https://doi.org/10.1007/s11192-020-03540-3
  171. Gray R. (2020): Sorry, we’re open: Golden Open Access and inequality in the natural sciences. https://doi.org/10.1101/2020.03.12.988493
  172. Wyatt T. (2020): Reproducible research into human chemical communication by cues and pheromones: learning from psychology’s renaissance. Philosophical Transactions of the Royal Society B: Biological Sciences 375(1800):20190262. https://doi.org/10.1098/rstb.2019.0262
  173. Sjöberg Y., Siewert M., Rudy A., Paquette M., Bouchard F., Malenfant‐Lepage J., et al. (2020): Hot trends and impact in permafrost science. Permafrost and Periglacial Processes 31(4):461-471. https://doi.org/10.1002/ppp.2047
  174. Pavlov Y., Adamian N., Appelhoff S., Arvaneh M., Benwell C., Beste C., et al. (2020): #EEGManyLabs: Investigating the Replicability of Influential EEG Experiments. https://doi.org/10.31234/osf.io/528nr
  175. Bahadoran Z., Mirmiran P., Kashfi K., Ghasemi A. (2020): Scientific Publishing in Biomedicine: How to Choose a Journal?. International Journal of Endocrinology and Metabolism 19(1). https://doi.org/10.5812/ijem.108417
  176. Unknown authors (2019): References. Casting Light on the Dark Side of Brain Imaging. https://doi.org/10.1016/b978-0-12-816179-1.00042-6
  177. Griffiths A., Modinou I., Heslop C., Brand C., Weatherill A., Baker K., et al. (2019): AccessLab: Workshops to broaden access to scientific research. PLOS Biology 17(5):e3000258. https://doi.org/10.1371/journal.pbio.3000258
  178. Berkowitz A. (2019): Playing the genome card. Journal of Neurogenetics 34(1):189-197. https://doi.org/10.1080/01677063.2019.1706093
  179. Balaji B., Dhanamjaya M. (2019): Preprints in Scholarly Communication: Re-Imagining Metrics and Infrastructures. Publications 7(1):6. https://doi.org/10.3390/publications7010006
  180. Brembs B. (2019): Reliable novelty: New should not trump true. PLOS Biology 17(2):e3000117. https://doi.org/10.1371/journal.pbio.3000117
  181. Zeiss C., Shin D., Vander Wyk B., Beck A., Zatz N., Sneiderman C., et al. (2019): Menagerie: A text-mining tool to support animal-human translation in neurodegeneration research. PLOS ONE 14(12):e0226176. https://doi.org/10.1371/journal.pone.0226176
  182. Hartgerink C. (2019): Verified, Shared, Modular, and Provenance Based Research Communication with the Dat Protocol. Publications 7(2):40. https://doi.org/10.3390/publications7020040
  183. Hartgerink C. (2019): Contributions towards understanding and building sustainable science. https://doi.org/10.31237/osf.io/4wtpc
  184. Parker D. (2019): Psychoneural reduction: a perspective from neural circuits. Biology & Philosophy 34(4). https://doi.org/10.1007/s10539-019-9697-8
  185. Dennis Tourish (2019): Notes. Management Studies in Crisis. https://doi.org/10.1017/9781108616669.012
  186. Hadley D., Campbell K., Gabriel M., Silva E. (2019): Open-source 3D printed air-jet for generating monodispersed alginate microhydrogels. https://doi.org/10.1101/804849
  187. McKiernan E., Schimanski L., Muñoz Nieves C., Matthias L., Niles M., Alperin J. (2019): Use of the Journal Impact Factor in academic review, promotion, and tenure evaluations. eLife 8. https://doi.org/10.7554/elife.47338
  188. Stagge J., Rosenberg D., Abdallah A., Akbar H., Attallah N., James R. (2019): Assessing data availability and research reproducibility in hydrology and water resources. Scientific Data 6(1). https://doi.org/10.1038/sdata.2019.30
  189. Tennan J., Crane H., Crick T., Davila J., Enkhbayar A., Havemann J., et al. (2019): Ten hot topics around scholarly publishing. Bibliosphere. https://doi.org/10.20913/1815-3186-2019-3-3-25
  190. Tennant J., Crane H., Crick T., Davila J., Enkhbayar A., Havemann J., et al. (2019): Ten Hot Topics around Scholarly Publishing. Publications 7(2):34. https://doi.org/10.3390/publications7020034
  191. Tennant J., Crane H., Crick T., Davila J., Enkhbayar A., Havemann J., et al. (2019): Ten myths around open scholarly publishing. https://doi.org/10.7287/peerj.preprints.27580v1
  192. Estácio L., Andrade W., Kern V., Cunha C. (2019): O produtivismo acadêmico na vida dos discentes de pós-graduação. Em Questão. https://doi.org/10.19132/1808-5245251.133-158
  193. Wass M., Ray L., Michaelis M. (2019): Understanding of researcher behavior is required to improve data reliability. GigaScience 8(5). https://doi.org/10.1093/gigascience/giz017
  194. Dougherty M., Horne Z. (2019): Citation counts and journal impact factors do not capture some indicators research quality in the behavioral and brain sciences. https://doi.org/10.31234/osf.io/9g5wk
  195. Abambres M., Ribeiro T., Sousa A., Lantsoght E. (2019): Research Counts, Not the Journal. https://doi.org/10.31235/osf.io/497xr
  196. Abambres M., Lantsoght E. (2019): Research Counts, Not the Journal. https://doi.org/10.32388/853674
  197. Oliver Braganza (2019): Economically rational sample-size choice and irreproducibility. arXiv (Cornell University).
  198. Heesen R., Bright L. (2019): Is Peer Review a Good Idea?. The British Journal for the Philosophy of Science 72(3):635-663. https://doi.org/10.1093/bjps/axz029
  199. Panda S. (2019): The peer review process: Yesterday, today and tomorrow. Indian Journal of Dermatology, Venereology and Leprology 85(3):239. https://doi.org/10.4103/ijdvl.ijdvl_296_19
  200. Leible S., Schlager S., Schubotz M., Gipp B. (2019): A Review on Blockchain Technology and Blockchain Projects Fostering Open Science. Frontiers in Blockchain 2. https://doi.org/10.3389/fbloc.2019.00016
  201. Paulus F., Cruz N., Krach S. (2018): The Impact Factor Fallacy. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.01487
  202. Teixeira da Silva J., Tsigaris P. (2018): Academics must list all publications on their CV. KOME 6(1):94-99. https://doi.org/10.17646/kome.2018.16
  203. Tennant J. (2018): The state of the art in peer review. FEMS Microbiology Letters 365(19). https://doi.org/10.1093/femsle/fny204
  204. Schimanski L., Alperin J. (2018): The evaluation of scholarship in academic promotion and tenure processes: Past, present, and future. F1000Research 7:1605. https://doi.org/10.12688/f1000research.16493.1
  205. Wass M., Ray L., Michaelis M. (2018): Researcher Conduct Determines Data Reliability. Preprints.org. https://doi.org/10.20944/preprints201804.0068.v1
  206. Abambres M., Ribeiro T., Sousa A., Lantsoght E. (2018): Research Counts, Not the Journal. https://doi.org/10.31219/osf.io/4z39a
  207. Carl N., Kirkegaard E., Dalliard M., Frost P., Kura K., Meisenberg G., et al. (2018): Editorial: A Response to Criticisms of the OpenPsych Journals. Open Differential Psychology. https://doi.org/10.26775/odp.2018.11.02
  208. Bertholf R., Ghezzi P. (2018): Retraction. Laboratory Medicine 49(4):297-297. https://doi.org/10.1093/labmed/lmy060
  209. Roope Oskari Kaaronen (2018): Exploration and Exploitation in Scientific Inquiry: Towards a Society of Explorers. PhilSci-Archive (University of Pittsburgh).
  210. Sauer S., Sülzenbrück S. (2018): Die Arbeitsweise der Forschung zu Zeiten von Digitalisierung und Reproduzierbarkeitskrise: Neue Methoden, alte Probleme. FOM-Edition. https://doi.org/10.1007/978-3-658-23397-6_11

Damrau C, Toshima N, Tanimura T, Brembs B, Colomb J. (2018): Octopamine and tyramine contribute separately to the counter-regulatory response to sugar deficit in Drosophila. Front. Syst. Neurosci. 11:100.

  1. Xu G., Fu L., Wu L., Lu J., Xu M., Qian R., et al. (2025): A tyramine receptor gene LsTAR2 is involved in reproduction and feeding in the small brown planthopper Laodelphax striatellus. Pesticide Biochemistry and Physiology 209:106335. https://doi.org/10.1016/j.pestbp.2025.106335
  2. Nunez K., Sherer L., Walley A., Salamon S., Chan V., Talay M., et al. (2025): Hunger Recruits a Parallel Circuit Encoding Alcohol Reward. https://doi.org/10.1101/2025.10.14.682140
  3. Gong W., Lubawy J., Marciniak P., Smagghe G., Słocińska M., Liu D., et al. (2025): Transcriptome and Neuroendocrinome Responses to Environmental Stress in the Model and Pest Insect Spodoptera frugiperda. International Journal of Molecular Sciences 26(2):691. https://doi.org/10.3390/ijms26020691
  4. Volonté Y., Heredia F., Zanini R., Menezes J., Perez M., Gualdino M., et al. (2025): Relaxin signaling is critical for virgin female reproductive physiology in Drosophila. https://doi.org/10.1101/2025.04.03.647069
  5. Berger M., Fraatz M., Auweiler K., Dorn K., Khadrawe T., Scholz H. (2024): Octopamine integrates the status of internal energy supply into the formation of food-related memories. https://doi.org/10.7554/elife.88247.2
  6. Berger M., Fraatz M., Auweiler K., Dorn K., El Khadrawe T., Scholz H. (2024): Octopamine integrates the status of internal energy supply into the formation of food-related memories. eLife 12. https://doi.org/10.7554/elife.88247.3
  7. Ramya R., Venkatesh C., Shyamala B. (2024): olf413 an octopamine biogenesis pathway gene is required for axon growth and pathfinding during embryonic nervous system development in Drosophila melanogaster. BMC Research Notes 17(1). https://doi.org/10.1186/s13104-024-06700-3
  8. Rosikon K., Bone M., Lawal H. (2023): Regulation and modulation of biogenic amine neurotransmission in Drosophila and Caenorhabditis elegans. Frontiers in Physiology 14. https://doi.org/10.3389/fphys.2023.970405
  9. Berger M., Fraatz M., Auweiler K., Dorn K., El Khadrawe T., Scholz H. (2023): Octopamine integrates the status of internal energy supply into the formation of food-related memories. eLife 12. https://doi.org/10.7554/elife.88247
  10. Berger M., Fraatz M., Auweiler K., Dorn K., Khadrawe T., Scholz H. (2023): Octopamine integrates the status of internal energy supply into the formation of food-related memories. https://doi.org/10.1101/2023.06.01.543187
  11. Berger M., Auweiler K., Tegtmeier M., Dorn K., El Khadrawe T., Scholz H. (2023): Octopamine integrates the status of internal energy supply into the formation of food-related memories. https://doi.org/10.7554/elife.88247.1
  12. Ramya R., Shyamala B. (2023): olf413 Gene Controls Taste Recognition, Preference and Feeding Activity in Drosophila melanogaster. Annual Research & Review in Biology. https://doi.org/10.9734/arrb/2023/v38i530585
  13. Zheng W., Ma H., Liu Z., Zhou Y., Zhu H., Liu J., et al. (2023): Knockout of tyramine receptor 1 results in a decrease of oviposition, mating, and sex pheromone biosynthesis in female Plutella xylostella. Pest Management Science 79(10):3903-3912. https://doi.org/10.1002/ps.7571
  14. Gáliková M., Klepsatel P. (2022): Endocrine control of glycogen and triacylglycerol breakdown in the fly model. Seminars in Cell & Developmental Biology 138:104-116. https://doi.org/10.1016/j.semcdb.2022.03.034
  15. Liessem S., Held M., Bisen R., Haberkern H., Lacin H., Bockemühl T., et al. (2022): Behavioral state-dependent modulation of insulin-producing cells in Drosophila. Current Biology 33(3):449-463.e5. https://doi.org/10.1016/j.cub.2022.12.005
  16. Göttler C., Amador G., van de Kamp T., Zuber M., Böhler L., Siegwart R., et al. (2021): Fluid mechanics and rheology of the jumping spider body fluid. Soft Matter 17(22):5532-5539. https://doi.org/10.1039/d1sm00338k
  17. Damrau C., Colomb J., Brembs B. (2021): Sensitivity to expression levels underlies differential dominance of a putative null allele of the Drosophila tβh gene in behavioral phenotypes. PLOS Biology 19(5):e3001228. https://doi.org/10.1371/journal.pbio.3001228
  18. Finetti L., Roeder T., Calò G., Bernacchia G. (2021): The Insect Type 1 Tyramine Receptors: From Structure to Behavior. Insects 12(4):315. https://doi.org/10.3390/insects12040315
  19. Thamm M., Wagler K., Brockmann A., Scheiner R. (2021): Tyramine 1 Receptor Distribution in the Brain of Corbiculate Bees Points to a Conserved Function. Brain, Behavior and Evolution 96(1):13-25. https://doi.org/10.1159/000517014
  20. White M., Chen D., Wolfner M. (2021): She’s got nerve: roles of octopamine in insect female reproduction. Journal of Neurogenetics 35(3):132-153. https://doi.org/10.1080/01677063.2020.1868457
  21. MEGHASHREE R., NAGARAJ K. (2021): Characterization of the immune induced antimicrobial peptide in Drosophila melanogaster and Drosophila ananassae. European Journal of Entomology 118:355-363. https://doi.org/10.14411/eje.2021.037
  22. Shahraki A., Yu Y., Gul Z., Liang C., Birgul Iyison N. (2020): Whole genome sequencing of Thaumetopoea pityocampa revealed putative pesticide targets. Genomics 112(6):4203-4207. https://doi.org/10.1016/j.ygeno.2020.07.017
  23. Lubawy J., Urbański A., Colinet H., Pflüger H., Marciniak P. (2020): Role of the Insect Neuroendocrine System in the Response to Cold Stress. Frontiers in Physiology 11. https://doi.org/10.3389/fphys.2020.00376
  24. Finetti L., Tiedemann L., Zhang X., Civolani S., Bernacchia G., Roeder T. (2020): Monoterpenes alter TAR1-driven physiology in Drosophila species. Journal of Experimental Biology. https://doi.org/10.1242/jeb.232116
  25. Finetti L., Tiedemann L., Zhang X., Civolani S., Bernacchia G., Roeder T. (2020): Monoterpenes alter TAR1-driven physiology in Drosophila species. https://doi.org/10.1101/2020.06.26.173732
  26. RAZA M., SU S. (2020): DIFFERENTIAL ROLES FOR DOPAMINE D1-LIKE AND D2-LIKE RECEPTORS IN LEARNING AND BEHAVIOR OF HONEYBEE AND OTHER INSECTS. Applied Ecology and Environmental Research 18(1):1317-1327. https://doi.org/10.15666/aeer/1801_13171327
  27. Roeder T. (2020): The control of metabolic traits by octopamine and tyramine in invertebrates. Journal of Experimental Biology 223(7). https://doi.org/10.1242/jeb.194282
  28. Blenau W., Wilms J., Balfanz S., Baumann A. (2020): AmOctα2R: Functional Characterization of a Honeybee Octopamine Receptor Inhibiting Adenylyl Cyclase Activity. International Journal of Molecular Sciences 21(24):9334. https://doi.org/10.3390/ijms21249334
  29. Schatton A., Agoro J., Mardink J., Leboulle G., Scharff C. (2018): Identification of the neurotransmitter profile of AmFoxP expressing neurons in the honeybee brain using double-label in situ hybridization. BMC Neuroscience 19(1). https://doi.org/10.1186/s12868-018-0469-1
  30. Damrau C., Colomb J., Brembs B. (2018): Differential dominance of an allele of the Drosophila tßh gene challenges standard genetic techniques. https://doi.org/10.1101/504332
  31. Pauls D., Blechschmidt C., Frantzmann F., el Jundi B., Selcho M. (2018): A comprehensive anatomical map of the peripheral octopaminergic/tyraminergic system of Drosophila melanogaster. Scientific Reports 8(1). https://doi.org/10.1038/s41598-018-33686-3

Benjamin DJ, Berger JO, Johannesson M, Nosek BA, Wagenmakers E-J, Brembs B, et al. (2017): Redefine statistical significance. Nature Human Behaviour 2:6–10.

  1. Teymoori A., Trappes R. (2026): The Recurrence of Fundamental Questions: A Historical and Philosophical Analysis of Major Disciplinary Crises in Psychology. Review of General Psychology. https://doi.org/10.1177/10892680261421875
  2. Joffe A., Khaira G. (2026): Acute Necrotizing Encephalopathy in Children: Meta-Analysis of Observational Studies on the Efficacy of Steroid Treatment. Pediatric Neurology 178:41-48. https://doi.org/10.1016/j.pediatrneurol.2026.02.006
  3. Kenwood B., Zhang C., Chambers D., Zhu W., Blount B., Wang L. (2026): Benzene exposure biomarkers are associated with recently smoking tobacco and pumping gasoline in the U.S. population aged 12 and over: NHANES 2017–March 2020. Environmental Research 294:123841. https://doi.org/10.1016/j.envres.2026.123841
  4. Hyatt C., Fulton T., Guelfo A., Lathan E., Turner J., Turner M., et al. (2026): Structure and correlates of the Moral Injury Exposure and Symptom Scale for Civilians (MIESS-C) in a community sample with high trauma exposure. European Journal of Psychotraumatology 17(1). https://doi.org/10.1080/20008066.2026.2617839
  5. Ngo-Hoang D., Yen D., Duyen D. (2026): Farmers’ Acceptance of Aquaponics in Vietnam’s Coastal Mekong Delta: An Extended Technology Acceptance Model under Climate Stress, Post-COVID Livelihood Risk, and Industry 4.0 Readiness. https://doi.org/10.21203/rs.3.rs-8547771/v1
  6. Habibzadeh F. (2026): Uncertain use of a fixed p value significance threshold in randomized clinical trials. Trials 27(1). https://doi.org/10.1186/s13063-026-09438-4
  7. Frau F., Pizzo Junior E., Succa V., Stagi S., Moro F., Sguaizer F., et al. (2026): Specific Bioelectrical Vector Reference Values for Italian Adults: A Multicentre Study. Journal of Functional Morphology and Kinesiology 11(1):81. https://doi.org/10.3390/jfmk11010081
  8. Hunde F., Benti A., Kapula T. (2026): Impact of population pressure on forest resources depletion in Yayo coffee forest Biosphere Reserve, Southwest Ethiopia. PLOS One 21(1):e0324407. https://doi.org/10.1371/journal.pone.0324407
  9. Perelli G., Micheletto M., Concas S., Puglisi G., Luca Marcialis G. (2026): Robust deepfake detection in compressed videos with scalable network strategies. Expert Systems with Applications 317:131761. https://doi.org/10.1016/j.eswa.2026.131761
  10. Lamb J., Nagarkatti K., Diniz M., Cabeen R., Estrada M., Crawford K., et al. (2026): Methods for randomized, blinded, controlled evaluation of putative disease interventions in multilaboratory, preclinical assessment networks. Lab Animal 55(3):74-82. https://doi.org/10.1038/s41684-026-01683-z
  11. Zvonkovic J., MT Robertson C., Ghasemi E., Dinu I., Joffe A. (2026): Executive Functioning in Kindergarten-Aged Children After Complex Cardiac Surgery in Early Infancy. CJC Pediatric and Congenital Heart Disease. https://doi.org/10.1016/j.cjcpc.2026.01.002
  12. Zeng J., Peng A., Huang F., Wu C. (2026): Fostering engagement in EFL listening: how teacher autonomy support mediates the impact of academic emotions. Frontiers in Psychology 17. https://doi.org/10.3389/fpsyg.2026.1746522
  13. Zhang J., Zhou M., Liu A., Ye R., Wang Y. (2026): Determining optimal Barthel Index cutoff scores for predicting Longshi Scale grades across age groups in stroke patients. Frontiers in Aging 7. https://doi.org/10.3389/fragi.2026.1701910
  14. Choi J., Lee K., Chavalarias D., Shin J., Ioannidis J. (2026): Evolution of Reporting P-values Across the Biomedical Literature, 1990-2025: an Updated Meta-Research Study. https://doi.org/10.64898/2026.01.14.26344149
  15. Slim K., Dziri C., Occean B. (2026): Goodbye P< 0.05. P-value is simply one item among many to gauge scientific evidence. Journal of Visceral Surgery. https://doi.org/10.1016/j.jviscsurg.2026.02.005
  16. Mammeri K., Legendre G., Journal F., Fernandez N., Ruppen-Maret H., Combey J., et al. (2026): Age and gender-related neurophysiological changes in sleep and wake states during childhood. Developmental Cognitive Neuroscience 78:101681. https://doi.org/10.1016/j.dcn.2026.101681
  17. Russo L., Lentini N., Soru L., Pastorino R., Boccia S., Ioannidis J. (2026): Randomized controlled trials claiming “personalized”, “individualized” and “precision” interventions: characteristics, transparency and bias. https://doi.org/10.64898/2026.02.09.26345904
  18. Phillips M., Sandoval-Powers M., Briar R., Scaffo M., Zhou S., Burke M. (2026): Strength of selection potentiates distinct adaptive responses in an evolution experiment with outcrossing yeast. G3: Genes, Genomes, Genetics 16(3). https://doi.org/10.1093/g3journal/jkag009
  19. Spitzer M. (2026): The emerging submission crisis in behavioral science. Trends in Neuroscience and Education 42:100276. https://doi.org/10.1016/j.tine.2026.100276
  20. Wall M., Demetriou L., Giribaldi B., Roseman L., Ertl N., Erritzoe D., et al. (2026): Thresholding and Perfusion Considerations in Interpreting Reduced Brain Responsiveness With Escitalopram: Response to Knudsen et al. American Journal of Psychiatry 183(1):80-81. https://doi.org/10.1176/appi.ajp.20251190
  21. Karapınar M., Uğurlu Z., Gür Hatip F., Başkurt F., Şahin M., Doğru A. (2026): Diagnostic accuracy of SARC-F and SARC-CalF as sarcopenia screening instruments in rheumatoid arthritis according to European Working Group on Sarcopenia in Older People. Physiotherapy Theory and Practice. https://doi.org/10.1080/09593985.2026.2627992
  22. Krämer M., Asselmann E., Harzer C., Denissen J., Bleidorn W. (2026): Psychological traits predict who uses self-help products but usage is not associated with two-year personality change. Scientific Reports 16(1). https://doi.org/10.1038/s41598-026-39468-6
  23. Kazi M. (2026): The Fragility of Significance: Why P-values and the Fragility Index Are Not Enough. Indian Journal of Surgical Oncology. https://doi.org/10.1007/s13193-026-02531-9
  24. Albers N., Antel J., Föcker M., Libuda L., Bühlmeier J., Hirtz R., et al. (2026): Children and adolescents with psychiatric disorders have high relative leptin levels upon adjustment for sex, BMI, and pubertal status. European Child & Adolescent Psychiatry. https://doi.org/10.1007/s00787-025-02921-4
  25. Arning N., Fryer H., Wilson D. (2026): Identifying direct risk factors in UK Biobank via simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink. Proceedings of the National Academy of Sciences 123(1). https://doi.org/10.1073/pnas.2514138122
  26. Alshahrani N. (2026): Statistical reproducibility of correlation tests: Pearson, Spearman, and Kendall. AIMS Mathematics 11(1):957-976. https://doi.org/10.3934/math.2026042
  27. Nai R., Sulis E., Vernero F., Vinai M. (2026): User Experience, Student Behaviour, and Learning Effectiveness: A Process Mining Approach in Microlearning. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2026.2616404
  28. Carrelero-Camp S., Dalmau-Pastor M., Oliva-Garballo V., Simón-de Blas C., Vergés-Sala C., de Planell-Mas E. (2026): Reliability and Agreement of a Dual-Method Radiographic Standard vs. Clinical Goniometry for Shank–Forefoot Alignment: A GRRAS-Compliant Study. Diagnostics 16(5):703. https://doi.org/10.3390/diagnostics16050703
  29. Mays S., Stark S. (2026): The use of Fisher’s exact test in contingency table analysis in palaeopathology. International Journal of Paleopathology 52:135-139. https://doi.org/10.1016/j.ijpp.2026.01.005
  30. Huber S., Rajh-Weber H., Cloude E., Boehlke E., Edlinger M., Kiili K., et al. (2026): Cognitive, Affective, and Motivational Effects of Gamified Learning in a Research-Guided Teaching Context. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-032-11043-5_3
  31. Chandrashekar S., Viganola D., Dreber A., Johannesson M., Pfeiffer T., Siegel A., et al. (2026): Using prediction markets and forecasting surveys to predict 28 replication outcomes of classic articles in social psychology and judgement and decision making. Royal Society Open Science 13(1). https://doi.org/10.1098/rsos.250377
  32. Adhikari S., Dhakal S. (2026): Landslide susceptibility assessment and slope stability analysis of cut slopes along Dumre-Paudhi Bazar road section, western Nepal. Geosystems and Geoenvironment 5(2):100499. https://doi.org/10.1016/j.geogeo.2026.100499
  33. Johari T., Chicco D. (2026): A modular transcriptomic signature paired with machine learning reveals core immune pathways in sepsis diagnosis. Discover Computing 29(1). https://doi.org/10.1007/s10791-026-09974-2
  34. Costa T., Manuello J., Cauda F., Crocetta A., Liloia D. (2026): Extracting Weight of Evidence from p-Value via Bayesian Approach to Activation Likelihood Estimation Meta-Analysis. Brain Sciences 16(1):87. https://doi.org/10.3390/brainsci16010087
  35. Garai U., Pal A., Ghosh K., Salunke D., Garain U. (2026): Benchmarking deep learning models for predicting anticancer drug potency (IC50) with insights for medicinal chemists. Communications Chemistry 9(1). https://doi.org/10.1038/s42004-026-01916-9
  36. Ten V. (2026): The Role of 100% Oxygen in the Resuscitation and Neurologic Recovery of Neonates Born with Perinatal Depression and Arrested Circulation. The Journal of Pediatrics 293:115034. https://doi.org/10.1016/j.jpeds.2026.115034
  37. Nguimdo V., Abwe E., Ketchen M., Mfossa D., Abwe A., Betobe N., et al. (2026): Immigrant-driven hunting and wildlife decline in an Afrotropical rainforest landscape. https://doi.org/10.21203/rs.3.rs-6700922/v1
  38. Huang Z., Qiu G., Yang B., Shao Y., Lin S., Zhou H., et al. (2026): Healthful Plant-Based Diets and Cognitive Function in Older Adults: Mediation by Nutritional Status and Modification by Urban–Suburban Location and Gender in a Shanghai Community-Based Study. Nutrients 18(2):316. https://doi.org/10.3390/nu18020316
  39. Hazak A., Liuhanen J., Kantojärvi K., Sulkava S., Jääskeläinen T., Salomaa V., et al. (2025): Schizophrenia genetic risk and labour market outcomes in the Finnish general population: Are schizophrenia-related traits penalised or rewarded?. Comprehensive Psychiatry 140:152600. https://doi.org/10.1016/j.comppsych.2025.152600
  40. Bouyamourn A. (2025): Collusive and adversarial replication. Research & Politics 12(1). https://doi.org/10.1177/20531680241282828
  41. Kalwij A. (2025): The effect of past successes on the probability of a current success: evidence from a natural experiment. Journal of the Royal Statistical Society Series A: Statistics in Society. https://doi.org/10.1093/jrsssa/qnaf065
  42. Kalwij A. (2025): Home advantage for tournament victory: empirical evidence from FIFA World Cups and continental championships. Journal of Quantitative Analysis in Sports. https://doi.org/10.1515/jqas-2024-0056
  43. Meule A., Dieffenbacher A., Kolar D., Voderholzer U. (2025): Weight Suppression, Binge Eating, and Purging as Predictors of Weight Gain During Inpatient Treatment in Persons With Bulimia Nervosa. European Eating Disorders Review 33(5):941-949. https://doi.org/10.1002/erv.3197
  44. Meule A., Schuchardt P., Kolar D. (2025): Psychometric properties and correlates of the German version of the Fear of Food Questionnaire. https://doi.org/10.31234/osf.io/36ymd_v1
  45. Meule A., Schuchardt P., Kolar D. (2025): Relationships between emetophobia symptomatology, fear of food, and body mass index. https://doi.org/10.31234/osf.io/zwue6_v1
  46. Meule A., Ertl S., Forbush K., Mindrup L., Ehrenthal J., Kolar D. (2025): Psychometric properties of the German version of the Eating Pathology Symptoms Inventory. Journal of Eating Disorders 13(1). https://doi.org/10.1186/s40337-025-01253-7
  47. Meule A., Kroll D., Bönsch M., Schneeberger T., Jarosch I., Gloeckl R., et al. (2025): Mental and physical health in persons receiving inpatient pulmonary rehabilitation treatment for post-COVID condition. PLOS One 20(8):e0330938. https://doi.org/10.1371/journal.pone.0330938
  48. Meule A., Schuchardt P., Kolar D. (2025): Psychometric properties and correlates of the German version of the Fear of Food Questionnaire. Current Psychology 45(1). https://doi.org/10.1007/s12144-025-08707-w
  49. Treves A., Khorozyan I. (2025): Robust inference and errors in studies of wildlife control. https://doi.org/10.21203/rs.3.rs-3478813/v2
  50. Faure-Carvallo A., Nieto-Fernández S., Calderon C., Gustems J. (2025): Relationship between procrastination, time management, personality, and psychological distress in higher education. Journal of Further and Higher Education 49(4):417-429. https://doi.org/10.1080/0309877x.2025.2459852
  51. Xing A., Xing X., Murad M., Lin L. (2025): Evaluating the properties of the fragility index of meta-analyses. BMC Medical Research Methodology 25(1). https://doi.org/10.1186/s12874-025-02648-5
  52. Bultez A., Herrmann J. (2025): Value added to marketing research diagnoses by add-ons to $${\varvec{p}}$$-values. Journal of Marketing Analytics 13(2):445-466. https://doi.org/10.1057/s41270-024-00351-w
  53. Monteleone A., Carfagno M., Meule A., Naab S., Cascino G., Voderholzer U., et al. (2025): Effects of Childhood Emotional Abuse on Treatment Outcome in Adolescent Inpatients With Anorexia Nervosa. International Journal of Eating Disorders 58(9):1769-1776. https://doi.org/10.1002/eat.24484
  54. Sherry A., Msaouel P., Miller A., Lin T., Abi Jaoude J., Kouzy R., et al. (2025): Reproducibility of statistically significant phase III oncology trials: An In Silico meta-epidemiological analysis. European Journal of Cancer 226:115596. https://doi.org/10.1016/j.ejca.2025.115596
  55. Sherry A., Liu Y., Msaouel P., Lin T., Koong A., Lin C., et al. (2025): Survival-Inferred Fragility of Statistical Significance in Phase III Oncology Trials. https://doi.org/10.1101/2025.01.11.25320398
  56. Sherry A., Liu Y., Msaouel P., Lin T., Koong A., Lin C., et al. (2025): Survival-inferred fragility of statistical significance in phase III oncology trials. npj Precision Oncology 9(1). https://doi.org/10.1038/s41698-025-01024-2
  57. Mutua A., de Fréin R. (2025): Quantum-Enhanced Battery Anomaly Detection in Smart Transportation Systems. Applied Sciences 15(17):9452. https://doi.org/10.3390/app15179452
  58. Pérez-Fargallo A., Gacitúa-Ferrada P., Marín-Restrepo L., Bienvenido-Huertas D. (2025): Relationship between the architectural and constructive characteristics of dwellings and the adaptive actions of their occupants. Energy Efficiency 18(4). https://doi.org/10.1007/s12053-025-10312-6
  59. Tlessov A., Courtney M., Leckie G. (2025): Stratified support: shadow education and maths achievement in East Asia’s six top-performing jurisdictions. British Journal of Sociology of Education. https://doi.org/10.1080/01425692.2025.2596164
  60. Doggett A., Belisario K., McDonald A., De Jesus J., Vandehei E., Gillard J., et al. (2025): Changes in cannabis attitudes and perceptions in the five years following recreational legalization in Canada: Findings from an observational cohort study of community adults. International Journal of Drug Policy 140:104782. https://doi.org/10.1016/j.drugpo.2025.104782
  61. Mehrez A., Machery E. (2025): Does God know our future sins?. Religious Studies 61(S1):S115-S134. https://doi.org/10.1017/s0034412525000071
  62. Cardini A. (2025): Measurement error and effect size in geometric morphometrics: assessing the impact of 2D landmark digitization error in interspecific comparisons of Procrustes shape data. Zoomorphology 144(2). https://doi.org/10.1007/s00435-025-00717-3
  63. Cardini A. (2025): “Visiting scientist effect”? Exploring the impact of time‐lags in the digitization of 2D landmark data. The Anatomical Record 308(12):3230-3258. https://doi.org/10.1002/ar.25649
  64. Simkus A., Coolen-Maturi T., Coolen F., Bendtsen C. (2025): Statistical Perspectives on Reproducibility: Definitions and Challenges. Journal of Statistical Theory and Practice 19(3). https://doi.org/10.1007/s42519-025-00459-x
  65. Stang A., Schäfer H., Idrissi-Yaghir A., Friedrich C., Fox M. (2025): Statistical inference and effect measures in abstracts of major HIV and AIDS journals, 1987–2022: A systematic review. Global Epidemiology 10:100213. https://doi.org/10.1016/j.gloepi.2025.100213
  66. Delios A., Hu T., Yu S., Zhou N., Ahsan F., Bahl M., et al. (2025): The insights from the crowd: Drawing inferences from many approaches to key empirical questions in international business. Journal of International Business Studies 56(9):1102-1124. https://doi.org/10.1057/s41267-025-00808-9
  67. Miles A., Samim Y., Khan S. (2025): Testing the Psychological Distinctiveness of Proscriptive and Prescriptive Moral Norms. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5500177
  68. Gvirtz A., Montecchi M., Selby A., Götz F. (2025): Human Values Across the Lifespan: Age-Graded Differences at Three Hierarchical Levels and What We Can Learn From Them. Personality and Social Psychology Bulletin. https://doi.org/10.1177/01461672241312570
  69. Alonso A. (2025): Step-by-step learning. Expert Systems with Applications 284:127939. https://doi.org/10.1016/j.eswa.2025.127939
  70. Dreber A. (2025): Predicting replication outcomes and generalizability of results. European Review of Agricultural Economics 52(4):644-661. https://doi.org/10.1093/erae/jbaf035
  71. Dreber A., Johannesson M., Nave G., L. Apicella C., Geniole S., Imai T., et al. (2025): Investigating the effects of single-dose intranasal testosterone on economic preferences in a large randomized trial of men. Proceedings of the National Academy of Sciences 122(39). https://doi.org/10.1073/pnas.2508519122
  72. Dreber A., Johanneson M., Nave G., Apicella C., Geniole S., Imai T., et al. (2025): No Evidence of Effects of Testosterone on Economic Preferences: Results From a Large (N =1,000) Double-Blind Randomized Controlled Study. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5115145
  73. van Meijeren A., Langeloo D., van Jonbergen H., Meijer M. (2025): Femoral components are positioned in greater external rotation using functional alignment in robot‐assisted total knee arthroplasty compared to mechanical alignment. Journal of Experimental Orthopaedics 12(3). https://doi.org/10.1002/jeo2.70362
  74. Joffe A., Ryan L., Lequier L., Robertson C. (2025): Over 30 Years of Neonatal Respiratory Extracorporeal Membrane Oxygenation From a Regional Program. ASAIO Journal 72(2):165-172. https://doi.org/10.1097/mat.0000000000002531
  75. Spanos A. (2025): Revisiting the Replication Crisis and the Untrustworthiness of Empirical Evidence. Stats 8(2):41. https://doi.org/10.3390/stats8020041
  76. Holzknecht A., Huber J., Kirchler M., Neugebauer T. (2025): Speculating in zero-value assets: The greater fool game experiment. European Economic Review 180:105180. https://doi.org/10.1016/j.euroecorev.2025.105180
  77. Duport A., Diotalevi G., Morel P., Le Blanc F., Léonard G., Devanne H. (2025): Pain-Induced Changes in Corticospinal Excitability Reflect Adaptive Motor Synergy Reorganization. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5361674
  78. Okeke A. (2025): Navigating institutional pressures: assessing sustainability and supply chain management practices in the oil and gas industry of a developing economy. International Journal of Energy Sector Management 19(6):1489-1513. https://doi.org/10.1108/ijesm-09-2024-0022
  79. Parish A., Tolis G., Ioannidis J. (2025): Across 73 meta-analyses mortality improvements are uncommon with newer interventions in adult cardiac surgery. Journal of Clinical Epidemiology 182:111764. https://doi.org/10.1016/j.jclinepi.2025.111764
  80. Loudová Stralczynská B., Chroustová K., Krčmářová T., Chytrý V., Bílek M. (2025): Gramotnost žáků v oblasti ochrany zdraví a bezpečnosti: Jak se rozvíjí schopnost žáků 1. stupně základní školy identifikovat a vysvětlit rizika?. Pedagogická orientace 34(1-2). https://doi.org/10.5817/pedor2024-1-2-135
  81. Loudová Stralczynská B., Chroustová K., Bílek M., Chytrý V., Krčmářová T., Krátká J. (2025): Injury Prevention in Pre-Primary and Primary Education: An Analysis of Teachers´ Perspectives and Expertise. Revija za elementarno izobraževanje 18(1):1. https://doi.org/10.18690/rei.3788
  82. Lovett B. (2025): Universal vs. Targeted Interventions. Open Science and Socially Responsive Science. https://doi.org/10.1007/978-3-032-06569-8_3
  83. Helm B., Wetherill L. (2025): Research methods in genetic counseling: Statistical approaches and resources. Journal of Genetic Counseling 34(3). https://doi.org/10.1002/jgc4.70042
  84. Avcı B., Çalık P. (2025): Dual-acting single-engineered hybrid-architectured promoters enhance and convert expressions into multi-carbon source-regulated systems in Komagataella phaffii. Enzyme and Microbial Technology 191:110713. https://doi.org/10.1016/j.enzmictec.2025.110713
  85. Yao B., Long Z., Lin X., Chen G., Li X., Ye Z., et al. (2025): The potential value of the use of berberine in depression: a systematic review and meta-analysis of preclinical studies. Frontiers in Pharmacology 16. https://doi.org/10.3389/fphar.2025.1664784
  86. Liu B. (2025): Enhanced speech sound perception through rhythmic motor priming in noisy conditions for adults who stutter. Clinical Linguistics & Phonetics. https://doi.org/10.1080/02699206.2025.2594472
  87. Heizmann B. (2025): Good enough? A comparison of different harmonization procedures and their substantive consequences using the example of life satisfaction. Quality & Quantity 59(S2):1369-1392. https://doi.org/10.1007/s11135-025-02060-7
  88. Eroğlu B., Yiğit T. (2025): Nonparametric seasonal cointegration tests. Communications in Statistics – Simulation and Computation. https://doi.org/10.1080/03610918.2025.2598425
  89. Ducate C., Bostrom S., Proctor K., Niemeyer R. (2025): The theory crisis in criminology: Causes, consequences, and solutions. Theoretical Criminology. https://doi.org/10.1177/13624806251372169
  90. Riccio-Rengifo C., Cascianelli S., Ceddia G., Masseroli M. (2025): Inferring Breast Cancer Subtype Associations Using an Original Omics Integration Based on Non-negative Matrix Tri-Factorization. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-90714-2_19
  91. Piguet C., Celen Z., Meuleman B., Schilliger Z., Magnus Smith M., Mendola E., et al. (2025): Impact of a Mindfulness‐Based Intervention on Symptoms and Emotion Regulation Strategies in Young Adolescents From the General Population: A Randomized Controlled Trial. Depression and Anxiety 2025(1). https://doi.org/10.1155/da/2679049
  92. Martínez de Ibarreta C., Martín-García D., Arroyo-Barrigüete J. (2025): The balcony peer effect in urban political expression: A comparative two-case study from a Spanish context. Social Sciences & Humanities Open 12:102056. https://doi.org/10.1016/j.ssaho.2025.102056
  93. Sarabia C., Salado I., Fernández‐Gil A., vonHoldt B., Hofreiter M., Vilà C., et al. (2025): Potential Adaptive Introgression From Dogs in Iberian Grey Wolves (Canis lupus). Molecular Ecology 34(12). https://doi.org/10.1111/mec.17639
  94. Burns C., Fracasso A., Rousselet G. (2025): Bias in data-driven replicability analysis of univariate brain-wide association studies. Scientific Reports 15(1). https://doi.org/10.1038/s41598-025-89257-w
  95. Shen C., Zhou S., Wang Q., Bowman S., Chung C., Li C. (2025): Maxillary molar distalization with clear aligner therapy and infrazygomatic temporary skeletal anchorage devices. American Journal of Orthodontics and Dentofacial Orthopedics 169(2):140-154. https://doi.org/10.1016/j.ajodo.2025.09.011
  96. Franck C. (2025): Quantitative models of discounting. Handbook of Operant Behavioral Economics. https://doi.org/10.1016/b978-0-323-95745-8.00004-3
  97. Crouch D. (2025): The false evidence rate: An approach to frequentist error rate control conditioning on the observed P value. Proceedings of the National Academy of Sciences 122(2). https://doi.org/10.1073/pnas.2415706122
  98. Oakley D., Mortazavi M., Rivera D., Samsam L., Seitz T., Streeter L. (2025): Assessing Brain Neurophysiology in COVID-19 Patients With Prolonged Cognitive Fatigue: A Comparison With Persistent Post-concussion Symptoms. Cureus. https://doi.org/10.7759/cureus.88160
  99. Millimet D., Whitacre T. (2025): Partisan mortality cycles. Journal of Population Economics 38(4). https://doi.org/10.1007/s00148-025-01133-z
  100. Oakley D., joffe D., Palermo F., Spada M., Yathiraj S. (2025): The P200 ERP Response in Mild Cognitive Impairment and the Aging Population. Clinical EEG and Neuroscience 56(6):549-555. https://doi.org/10.1177/15500594241310533
  101. Gonzalez-Jimenez D., Capozza F., Dirkmaat T., van de Veer E., van Druten A., Baillon A. (2025): Falling and failing (to learn): Evidence from a nation-wide cybersecurity field experiment with SMEs. Journal of Economic Behavior & Organization 230:106868. https://doi.org/10.1016/j.jebo.2024.106868
  102. Bickel D. (2025): A small-sample Bayesian information criterion that does not overstate the evidence, with an application to calibrating p-values from likelihood-ratio tests. Statistical Papers 66(3). https://doi.org/10.1007/s00362-025-01682-1
  103. Bickel D. (2025): Bayesian Model Checking by Betting: A Game-Theoretic Alternative to Bayesian p -values and Classical Bayes Factors. The American Statistician 79(4):508-519. https://doi.org/10.1080/00031305.2025.2507764
  104. Chicco D., Sichenze A., Jurman G. (2025): A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics. BioData Mining 18(1). https://doi.org/10.1186/s13040-025-00465-6
  105. Chicco D., Coelho V. (2025): A teaching proposal for a short course on biomedical data science. PLOS Computational Biology 21(4):e1012946. https://doi.org/10.1371/journal.pcbi.1012946
  106. Mayo D. (2025): Severe Testing: Error Statistics versus Bayes Factor Tests. The British Journal for the Philosophy of Science. https://doi.org/10.1086/736950
  107. Su D., Liao J., Peng K. (2025): The kinship estrangement effect of occupational diversity on trust. Current Psychology 44(3):1598-1612. https://doi.org/10.1007/s12144-024-07059-1
  108. Zhu D., Lin X., Liu S. (2025): Spatio-temporal variation in the potential ammonia oxidation rates and microbial communities of mangrove wetlands with different sediment textures in South China. Marine Pollution Bulletin 220:118463. https://doi.org/10.1016/j.marpolbul.2025.118463
  109. Knipe D., de Ossorno Garcia S., Salhi L., Afzal N., Sammut S., Mainstone-Cotton L., et al. (2025): Digital mental health service engagement changes during Covid-19 in children and young people across the UK: Presenting concerns, service activity, and access by gender, ethnicity, and deprivation. PLOS ONE 20(2):e0316468. https://doi.org/10.1371/journal.pone.0316468
  110. KORT E., DELMAS P., JUBIN J., OULEVEY BACHMANN A., ORTOLEVA BUCHER C. (2025): Facteurs protecteurs de la qualité de vie d’infirmières françaises durant la pandémie de COVID-19 : un devis descriptif-corrélationnel. Recherche et Avancées en Sciences Infirmières. https://doi.org/10.25965/reasci.574
  111. Brennan E., Lampinen L., Paek H., Wang X., Romano H., Bal V. (2025): Deconstructing Information About Autism Diagnosis in Adults on TikTok: A Cross-Sectional, Descriptive Content Analysis. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-025-07123-0
  112. Knight E., Nave G., Apicella C., Dreber A., Geniole S., Johannesson M., et al. (2025): <p>Does Testosterone Affect Cognitive Reflection? Evidence from a &nbsp;Double-Blind, Randomized Controlled Study of 1,000 Participants</p>. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5238324
  113. Hulland E., Charpignon M., Hayek G., Desai A., Majumder M. (2025): On the importance of communication, advocacy, and information accuracy in destigmatizing mpox disease. Scientific Reports 15(1). https://doi.org/10.1038/s41598-025-06864-3
  114. Lu F., Li L., Wang J., Chen X., Yang H., Li X., et al. (2025): Distinct effects of global signal regression on brain activity during propofol and sevoflurane anesthesia. Frontiers in Neuroscience 19. https://doi.org/10.3389/fnins.2025.1576535
  115. Lu F., Wang J., Qin X., Chen X., Liu T., Li X., et al. (2025): Differential engagement of thalamic nuclei orchestrates consciousness states across anesthesia, sleep, and disorders of consciousness. Communications Biology 8(1). https://doi.org/10.1038/s42003-025-09205-2
  116. Habibzadeh F. (2025): On the effect of flexible adjustment of the p value significance threshold on the reproducibility of randomized clinical trials. PLOS One 20(6):e0325920. https://doi.org/10.1371/journal.pone.0325920
  117. Habibzadeh F. (2025): The P Value: What It Is and What It Is Not. Journal of Korean Medical Science 40(44). https://doi.org/10.3346/jkms.2025.40.e321
  118. Holzmeister F., Johannesson M. (2025): Skills vs. Luck: Decomposing Deviations from Expected Performance in European Football Leagues. Journal of Sports Economics 26(8):953-975. https://doi.org/10.1177/15270025251374620
  119. Bertolini F., Bovo S., Bolner M., Schiavo G., Ribani A., Zambonelli P., et al. (2025): Identification of biomarkers for feed efficiency and growth rate by exploring the plasma metabolome of divergent heavy pigs. animal 20(1):101725. https://doi.org/10.1016/j.animal.2025.101725
  120. Frau F., Pizzo Junior E., Cabras S., Massidda M., Marini E. (2025): Non-Linear Association Between Phase Angle and Body Fat in a Sample of US Adults. Biology 14(11):1621. https://doi.org/10.3390/biology14111621
  121. Retez G., Soofi M., Ghoddousi A., Oeser J., Grancea A., Kuemmerle T. (2025): Space use of a diverse megafauna community in a rewilding area in the southwestern Carpathians. Biological Conservation 302:110977. https://doi.org/10.1016/j.biocon.2025.110977
  122. Schweizer G., Köppel M. (2025): In search of the lost interaction: A theoretical and methodological framework for researching interactions. Europe’s Journal of Psychology 21(3):249-262. https://doi.org/10.5964/ejop.14957
  123. Pietroluongo G., Podestà M., Belluscio D., Berio E., Canonico C., Casalone C., et al. (2025): Assessing fishery interaction on cetaceans stranded along the Italian coastline between 1986 and 2023. PLOS One 20(9):e0330441. https://doi.org/10.1371/journal.pone.0330441
  124. SEYA H., KOIKE A., IWASAKI K. (2025): STATISTICAL INFERENCE IN INFRASTRUCTURE PLANNING AND MANAGEMENT: CURRENT PRACTICES AND FUTURE DIRECTIONS. Japanese Journal of JSCE 81(12):n/a. https://doi.org/10.2208/jscejj.25-00035
  125. Elomaa H., Tarkiainen V., Äijälä V., Sirniö P., Ahtiainen M., Sirkiä O., et al. (2025): Associations of mucinous differentiation and mucin expression with immune cell infiltration and prognosis in colorectal adenocarcinoma. British Journal of Cancer 132(7):660-669. https://doi.org/10.1038/s41416-025-02960-3
  126. Rajh-Weber H., Huber S., Arendasy M. (2025): A practice-oriented guide to statistical inference in linear modeling for non-normal or heteroskedastic error distributions. Behavior Research Methods 57(12). https://doi.org/10.3758/s13428-025-02801-4
  127. Lyu H., Fan N., Wen H., Zhang X., Mao H., Bian Q., et al. (2025): Interplay between BMI, neutrophil, triglyceride and uric acid: a case-control study and bidirectional multivariate mendelian randomization analysis. Nutrition & Metabolism 22(1). https://doi.org/10.1186/s12986-025-00896-2
  128. Cerqueira H., Santana S., Cerqueira C. (2025): Diagnóstico precoce do transtorno do espectro autista leve e sua relação com a crença de autoeficácia. Jornal Brasileiro de Psiquiatria 74. https://doi.org/10.1590/0047-2085-2025-0002
  129. Oyama H., Hamada T., Nevo D., Nakai Y., Nakai Y., Petrov M., et al. (2025): Relationship of Intrapancreatic Fat Deposition With Pancreatic Cancer Differs According to Carcinoma Types. Gastroenterology 169(4):718-721.e5. https://doi.org/10.1053/j.gastro.2025.04.032
  130. Kim H., Park H. (2025): Does Mutual Funds’ Window-Dressing Status Affect Investee Firms’ Earnings Management?. European Accounting Review 35(1):205-235. https://doi.org/10.1080/09638180.2025.2497313
  131. Krause I., Hirsch J., Vorwerk H., Stuck B., Neff A. (2025): Occurrence of Jaw Osteonecrosis and Frequency of Prophylactic Tooth Extractions Prior to Head and Neck Radiotherapy: A Retrospective Study of 497 Irradiated Patients. Journal of Clinical Medicine 14(5):1661. https://doi.org/10.3390/jcm14051661
  132. McDonough I., Lin C., Kraemer K., Black S., Thomas K., Dean L., et al. (2025): Relationship Between Perceived and Objective Financial Abilities Among Older Adults: Results From the Advanced Cognitive Training for Independent and Vital Elderly Cohort. The Gerontologist 65(7). https://doi.org/10.1093/geront/gnaf125
  133. Aramendía I., Cuitiño J., Raigemborn M., Bouza P., Vrdoljak J., Ghiglione M. (2025): Unroofing and provenance of the Miocene Austral–Magallanes foreland basin, Argentina. Journal of Sedimentary Research 95(4):760-776. https://doi.org/10.2110/jsr.2024.005
  134. Patwardhan I., Mitchell A., Chmelka M., Barnes B., Tyler P. (2025): Parent and Youth Executive Function Outcomes Following Participation in the Online Boys Town Common Sense Parenting Program: A Pilot Study Based on Parental Ratings. Child and Adolescent Social Work Journal. https://doi.org/10.1007/s10560-025-01046-6
  135. Nishi I., Yoshitomi T., Nakano F., Uemura H., Kawakami T. (2025): Identification of an aryl hydrocarbon receptor agonistic disperse dye in commercially available textile products by effect-directed analysis. Chemosphere 375:144247. https://doi.org/10.1016/j.chemosphere.2025.144247
  136. Kim J., Cho A., Bien T., Yoon H., Park J., Byun S. (2025): Advancement of the Pressure Variation Model for Improved State Estimation in Underwater Vehicles. Journal of Ocean Engineering and Technology 39(2):212-225. https://doi.org/10.26748/ksoe.2025.012
  137. Johnels J., Kuja‐Halkola R., Larsson H., Chang Z., Brikell I., Lundström S. (2025): Does the family situation impact academic achievement differently in students with versus without neurodevelopmental disorders?. British Journal of Educational Psychology. https://doi.org/10.1111/bjep.70050
  138. Granados Samayoa J., Moore C., Ruisch B., Ladanyi J., Fazio R. (2025): Is there anything good about conspiracy beliefs? Belief in COVID-19 conspiracy theories is associated with benefits to well-being. PLOS ONE 20(3):e0319896. https://doi.org/10.1371/journal.pone.0319896
  139. Mazoit J. (2025): Statistics in biology: a survey of the three major multidisciplinary journals. https://doi.org/10.1101/2025.02.04.636422
  140. Fahnestock J. (2025): The Controversy behind the Controversies: Scientific Discourse in the Twenty-First Century. Rhetoric Society Quarterly 55(3):223-243. https://doi.org/10.1080/02773945.2025.2484162
  141. Murphy J., Caldwell A., Mesquida C., Ladell A., Encarnación-Martínez A., Tual A., et al. (2025): Estimating the Replicability of Sports and Exercise Science Research. Sports Medicine 55(10):2659-2679. https://doi.org/10.1007/s40279-025-02201-w
  142. Talbot J., Gatti D., Boccalari M., Marchetti M., Mitaritonna D., Convertino G., et al. (2025): Exploring Inhibitory Control Processes in Highly Superior Autobiographical Memory (HSAM): A Single Case Study. Journal of Cognition 8(1). https://doi.org/10.5334/joc.421
  143. Song J., Li N., Yang Y., Chen B., Hu J., Tian Y., et al. (2025): Cell-free hemoglobin released from hemolysis induces programmed cell death through iron overload and oxidative stress in grass carp (Ctenopharyngodon idella). Fish & Shellfish Immunology 157:110106. https://doi.org/10.1016/j.fsi.2024.110106
  144. Gu J., Yu X., Qi D., Hou R., Zhang L., Lan G., et al. (2025): Giant panda seasonal adaptations in feeding strategies and blood physiology. Frontiers in Veterinary Science 12. https://doi.org/10.3389/fvets.2025.1703367
  145. Cao J., Xu L., Yiu M., Qin J., Tang B. (2025): GPH: An Efficient and Effective Perfect Hashing Scheme for GPU Architectures. Proceedings of the ACM on Management of Data 3(3):1-26. https://doi.org/10.1145/3725406
  146. Kelly J., Belisario K., MacKillop J. (2025): Prevalence, predictors, correlates, and dynamic changes in the NIAAA‐defined “recovery” definition. Alcohol, Clinical and Experimental Research 49(11):2579-2591. https://doi.org/10.1111/acer.70172
  147. DelosReyes J., Padilla M. (2025): Obtaining a Bayesian Estimate of Coefficient Alpha Using a Posterior Normal Distribution. Educational and Psychological Measurement 85(4):829-852. https://doi.org/10.1177/00131644241311877
  148. Ioannidis J. (2025): Features and signals in precocious citation impact: A meta-research study. PLOS One 20(8):e0328531. https://doi.org/10.1371/journal.pone.0328531
  149. Henssler J., Reis-Pardal J., Koppel L., Ioannidis J. (2025): Trustworthiness and Transparency Features Were Less Frequent in Randomized Trials Presenting Large Effects in Abstracts. https://doi.org/10.1101/2025.10.20.25338369
  150. Park J., Sung B., Myung J., Lee S., Yon D., Shin J., et al. (2025): Adherence to Animal Research: Reporting of In Vivo Experiments Guidelines in Stem Cell Studies in Animal Models of Parkinson’s Disease: A Systematic Review. Yonsei Medical Journal 66(11):743. https://doi.org/10.3349/ymj.2024.0409
  151. Mulder J., van Aert R. (2025): Bayes factor hypothesis testing in meta-analyses: Practical advantages and methodological considerations. Research Synthesis Methods. https://doi.org/10.1017/rsm.2025.10060
  152. Mulder J., Pfadt J., Wagenmakers E. (2025): A tutorial on Bayesian hypothesis testing of correlation coefficients using the BFpack-module in JASP. Behavior Research Methods 57(11). https://doi.org/10.3758/s13428-025-02846-5
  153. Leota J., Presby D., Le F., Czeisler M., Mascaro L., Capodilupo E., et al. (2025): Dose-response relationship between evening exercise and sleep. Nature Communications 16(1). https://doi.org/10.1038/s41467-025-58271-x
  154. Vrdoljak J., Soto I., Carreira V., Padró J. (2025): Environmental stress differentially affects phenotypic modularity and fluctuating asymmetry in generalist and specialist cactophilic Drosophila. Journal of Evolutionary Biology 38(3):404-416. https://doi.org/10.1093/jeb/voaf006
  155. Jerke J., Velicu A., Winter F., Rauhut H. (2025): Publication bias in the social sciences since 1959: Application of a regression discontinuity framework. PLOS ONE 20(2):e0305666. https://doi.org/10.1371/journal.pone.0305666
  156. Krauspe J., Ebersbach M., Ludwig A., Scharf F. (2025): Do worked examples boost the spacing effect on lasting learning?. Learning and Instruction 97:102103. https://doi.org/10.1016/j.learninstruc.2025.102103
  157. Sánchez‐García J., Lima M., Marques S., Gil‐Lacruz A., Gil‐Lacruz M. (2025): National Perceptions of Over‐70s’ Status as a Moderator in the Link Between Volunteering and Subjective Well‐Being Among Older Adults in 29 European Countries. Journal of Applied Social Psychology 55(6):413-428. https://doi.org/10.1111/jasp.13099
  158. Marewski J., Hoffrage U. (2025): Heuristics: how simple models of the mind can serve as tools for transparent scientific justification. Mind & Society 24(2):947-998. https://doi.org/10.1007/s11299-025-00354-9
  159. Hirsch J., Krause I., Tangmanee C., Vorwerk H., Stuck B., Pitak-Arnnop P., et al. (2025): Surgical predictors of osteoradionecrosis in irradiated head and neck cancer patients: Jaw resection increases risk whereas neck dissection does not. Journal of Stomatology Oral and Maxillofacial Surgery 127(1):102538. https://doi.org/10.1016/j.jormas.2025.102538
  160. Nagarkatti K., Diniz M., Cabeen R., Estrada M., Crawford K., Rogatko A., et al. (2025): Methods for randomized, blinded, controlled evaluation of putative disease interventions in multi-laboratory, preclinical assessment networks. https://doi.org/10.21203/rs.3.rs-3054771/v1
  161. Lohse K., Kliethermes S. (2025): Approaching Significance: Statistical Guidance for Authors and Reviewers. Journal of Neurologic Physical Therapy 49(4):240-247. https://doi.org/10.1097/npt.0000000000000526
  162. Yeung K., Gatton M., Wraith D. (2025): The Interplay between Gambling Activity Groups, Problem Gambling Symptoms, and Mental Wellbeing from a Public Health Perspective. Journal of Gambling Studies. https://doi.org/10.1007/s10899-025-10461-4
  163. Robledo K., Rieger I., Finlayson S., Tarnow-Mordi W., Martin A. (2025): Balancing precision and affordability in assessing infant development in large-scale mortality trials: secondary analysis of a randomised controlled trial. Archives of Disease in Childhood – Fetal and Neonatal Edition 110(4):409-414. https://doi.org/10.1136/archdischild-2024-327762
  164. Munch L., Varga S., Latham A. (2025): Privacy: An experimental approach. The Philosophical Quarterly. https://doi.org/10.1093/pq/pqaf068
  165. Elkin L., Pettigrew R. (2025): Opinion Pooling. Cambridge University Press eBooks. https://doi.org/10.1017/9781009315203
  166. Hafner L., Sturm G., Lumpp S., Drton M., List M. (2025): Single-cell differential expression analysis between conditions within nested settings. Briefings in Bioinformatics 26(4). https://doi.org/10.1093/bib/bbaf397
  167. Reyes L., Colón H., Rosario A. (2025): Willingness to Use Fentanyl Test Strips among Street Drug Users in Puerto Rico: A Cross- Sectional Study. https://doi.org/10.21203/rs.3.rs-6856261/v1
  168. Liu L., Xue Y., Liu W., Cui J., Lv H., Chang J. (2025): Systematic evidence grading evaluates multisystemic associations and risks of vitiligo. Nature Communications 16(1). https://doi.org/10.1038/s41467-025-64653-y
  169. Koppel L., Andersson D., Johannesson M., Strømland E., Tinghög G. (2025): Comprehension in economic games. Journal of Economic Behavior & Organization 234:107039. https://doi.org/10.1016/j.jebo.2025.107039
  170. Darvin L., Lotspeich S., Hindman L., Pegoraro A. (2025): ‘Who’ Makes a ‘Good’ Leader? Examining the Influence of Leader Gender with Perceptions of Leader Competency and Employee Outcomes. Gender Issues 42(3). https://doi.org/10.1007/s12147-025-09371-x
  171. Lampinen L., Ederer J., Bal V. (2025): Associations Between the SRS-2 and the ASEBA Adult Self Report: Implications for Interpretation of the SRS-2 in Autistic Adults. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-025-06988-5
  172. Gan L., Yang Y., Zhao B., Yu K., Guo K., Fang F., et al. (2025): Dietary carbohydrate intake and risk of type 2 diabetes: a 16-year prospective cohort study. Science China Life Sciences 68(4):1149-1157. https://doi.org/10.1007/s11427-024-2804-0
  173. Russo L., Siena L., Farina S., Pastorino R., Boccia S., Ioannidis J. (2025): High-impact trials with genetic and -omics information focus on cancer mutations, are industry-funded, and less transparent. Journal of Clinical Epidemiology 180:111676. https://doi.org/10.1016/j.jclinepi.2025.111676
  174. Kcomt L., Evans-Polce R., Engstrom C., Dean F., Hoak S., Scott C., et al. (2025): Associations Between Having a Spouse/Partner with Alcohol Problems and One’s Own Risk of Mental Health and Substance Use Disorders by Sexual Orientation. Substance Use & Misuse 60(14):2214-2224. https://doi.org/10.1080/10826084.2025.2537142
  175. Almeida L., Vohra S., Johnson J., Khademioureh S., Dinu I., Robertson C., et al. (2025): Health-Related Quality of Life in Children 4-5 Years After Open Heart Surgery in Early Infancy. CJC Pediatric and Congenital Heart Disease 4(3):140-149. https://doi.org/10.1016/j.cjcpc.2025.01.001
  176. Kastrati L., Farina S., Valz Gris A., Raeisi-Dehkordi H., Llanaj E., Quezada-Pinedo H., et al. (2025): Evaluation of reported claims of sex-based differences in treatment effects across meta-analyses: a meta-research study. BMJ Evidence-Based Medicine. https://doi.org/10.1136/bmjebm-2024-113359
  177. Tideman L., Moser F., Migas L., Spathies J., Djambazova K., Marshall C., et al. (2025): Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis. https://doi.org/10.1101/2025.10.27.684518
  178. Bogaards M., Wierenga R. (2025): Aspectual cognate constructions in Afrikaans and Dutch. IMPACT: Studies in Language, Culture and Society. https://doi.org/10.1075/impact.55.09bog
  179. Varlet M., Nozaradan S., Keller P. (2025): Dynamic neural processing of self-other synchronization error in interpersonal coordination. iScience 28(12):114081. https://doi.org/10.1016/j.isci.2025.114081
  180. Hülsemann M., Löffler C., Schubert A. (2025): Task-specific theta enhancement and domain-general alpha/beta suppression as oscillatory signatures of individual differences in cognitive flexibility. https://doi.org/10.1101/2025.09.26.678741
  181. Arriaza M., Aramendi J., Clarke R., Maté-González M., Yravedra J., de la Peña P., et al. (2025): Is the StW 53 cranium (Sterkfontein, South Africa) the earliest evidence of tool-assisted hominin modification? New data from a neotaphonomic experiment and the virtual reconstruction of its linear marks. Journal of Archaeological Science 183:106389. https://doi.org/10.1016/j.jas.2025.106389
  182. Chugunova M., Harhoff D., Hölzle K., Kaschub V., Malagimani S., Morgalla U., et al. (2025): Who uses AI in research, and for what? Large-scale survey evidence from Germany. Research Policy 55(2):105381. https://doi.org/10.1016/j.respol.2025.105381
  183. Chugunova M., Harhoff D., Hölzle K., Kaschub V., Malagimani S., Morgalla U., et al. (2025): Who Uses AI in Research, and for What? Large-scale Survey Evidence from Germany. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5259847
  184. Bers M., Alrawashdeh G., Nievera M., Nadler E. (2025): Exploring teacher perspectives on integrating character education into K-3 computer science curriculum. Journal of Moral Education. https://doi.org/10.1080/03057240.2025.2569883
  185. Fordellone M., Schiattarella P., Nicolao G., Signoriello S., Chiodini P. (2025): Decision Rules in Frequentist and Bayesian Hypothesis Testing: P-Value and Bayes Factor. International Journal of Public Health 70. https://doi.org/10.3389/ijph.2025.1608258
  186. Poncet M., Batziou P., Chakravarthi R. (2025): Neural representations of visual categories are dynamically tailored to the discrimination required by the task. Cerebral Cortex 35(8). https://doi.org/10.1093/cercor/bhaf212
  187. Calle M., Abad F., Juan M. (2025): Augmented Reality for Therapeutic Education in Patients with Diabetes: Short- and Mid-Term Learning Benefits. Sensors 25(4):1017. https://doi.org/10.3390/s25041017
  188. Heino M., Ilmarinen V., Leikas S., Lipsanen J., Lönnqvist J., Olkkonen M., et al. (2025): Mitä toistettavuuskriisistä pitäisi ajatella? Kymmenen suomalaistutkijaa sähköpostihaastattelussa. Psykologia 54(6):440-459. https://doi.org/10.62443/psykologia.v54i6.143375
  189. Calle Velez M., Luzuriaga Once C., Carpio Mosquera C. (2025): Autoestima y violencia de género: Cuestionando supuestos lineales. Revista Científica de Salud y Desarrollo Humano 6(2):1966-1986. https://doi.org/10.61368/r.s.d.h.v6i2.729
  190. Wierzbicka M., Świątek D., Porębski A., Markowski J., Ciuba K., Makuszewska M., et al. (2025): Functional dependence predicts adverse outcomes among geriatric otolaryngology patients better than more complex risk scales: a multivariate analysis of hospitalization risks on elderly group. Frontiers in Medicine 12. https://doi.org/10.3389/fmed.2025.1690442
  191. Shen M., Jing Y., Liu Q., Li C., Xu N. (2025): Visualization analysis for emotional characteristics of autism spectrum disorder from cinemetrics perspective. Frontiers in Public Health 13. https://doi.org/10.3389/fpubh.2025.1608608
  192. Garber M., Samokhvalov A., Chorny Y., LaBelle O., Rush B., Costello J., et al. (2025): Diagnostic validity of drinking behaviour for identifying alcohol use disorder: Findings from a representative sample of community adults and an inpatient clinical sample. Addiction 120(7):1431-1440. https://doi.org/10.1111/add.70037
  193. Fischer M., Gollwitzer M. (2025): Personality effects on two types of whistleblowing decisions. Social Psychological Bulletin 20. https://doi.org/10.32872/spb.12243
  194. Gernsbacher M., Seyl C., Cox A. (2025): Is Psychopathology a Less Stigmatizing Course Name Than Abnormal Psychology?. Teaching of Psychology 52(4):411-419. https://doi.org/10.1177/00986283251328321
  195. Nakaya M., Kamagata K., Takabayashi K., Andica C., Uchida W., Hagiwara A., et al. (2025): Magnetic resonance imaging indices for early Alzheimer’s disease detection: Brain clearance markers. Journal of Cerebral Blood Flow & Metabolism 45(8):1558-1568. https://doi.org/10.1177/0271678×251321305
  196. HaGani N., Clare P., Merom D., Smith B., Ding D. (2025): Loneliness and all cause mortality in Australian women aged 45 years and older: causal inference analysis of longitudinal data. BMJ Medicine 4(1):e001004. https://doi.org/10.1136/bmjmed-2024-001004
  197. Tran N., Tran T., Nguyen T. (2025): Fragility of Evidence for the Efficacy of Anti-Fracture Medications. The Journal of Clinical Endocrinology & Metabolism 111(1):e70-e82. https://doi.org/10.1210/clinem/dgaf332
  198. Pangborn N., Froude A., Barkovich L., Brassard S., Balodis I. (2025): Acute stress response dysfunction in problem gambling (PG) and relationships with features of addiction. Psychoneuroendocrinology 180:107563. https://doi.org/10.1016/j.psyneuen.2025.107563
  199. Sobetska O. (2025): Irrationality in humans and creativity in AI. Frontiers in Artificial Intelligence 8. https://doi.org/10.3389/frai.2025.1579704
  200. Sirkiä O., Karjalainen H., Elomaa H., Väyrynen S., Tuomisto A., Sirniö P., et al. (2025): Multimarker Assessment of B-Cell and Plasma Cell Subsets and Their Prognostic Role in the Colorectal Cancer Microenvironment. Clinical Cancer Research 31(12):2466-2477. https://doi.org/10.1158/1078-0432.ccr-24-4083
  201. von Hippel P., Schuetze B. (2025): How Not to Fool Ourselves About Heterogeneity of Treatment Effects. Advances in Methods and Practices in Psychological Science 8(2). https://doi.org/10.1177/25152459241304347
  202. Janusz P., Panzera F., Bergamo P., Perron V., Fäh D. (2025): Mapping site amplification with the dense recording of ambient vibration for the city of Lucerne (Switzerland): comparison between two approaches. Bulletin of Earthquake Engineering 23(4):1431-1462. https://doi.org/10.1007/s10518-024-02091-9
  203. Sorokin P., Mironenko I. (2025): The Replicability Crisis and Human Agency in the Neo-Structured World. Integrative Psychological and Behavioral Science 59(1). https://doi.org/10.1007/s12124-024-09887-z
  204. Ye P., Li Z., Yang Z., Chen P., Zhang Z., Li N., et al. (2025): Periodic watermarking for copyright protection of large language models in cloud computing security. Computer Standards & Interfaces 94:103983. https://doi.org/10.1016/j.csi.2025.103983
  205. Ebert P., Miller D., Comerford D., Diggins M. (2025): End user and forecaster interpretations of the European Avalanche Danger Scale: A study of avalanche probability judgments in Scotland. Risk Analysis 45(8):2285-2299. https://doi.org/10.1111/risa.70016
  206. Quatto P., Ripamonti E., Marasini D. (2025): Characterization of a credibility index. Journal of Biopharmaceutical Statistics. https://doi.org/10.1080/10543406.2025.2456170
  207. Wu Q., Hu H. (2025): Geometric and statistical curvatures of the skew- t distributions and their application. Communications in Statistics – Theory and Methods 54(20):6428-6442. https://doi.org/10.1080/03610926.2025.2457494
  208. Zhang Q., Liu H., Tu C. (2025): Risk-informed Bayesian point null hypothesis testing. Communications in Statistics – Theory and Methods 55(7):2292-2315. https://doi.org/10.1080/03610926.2025.2545588
  209. Liu R., Colazo M., Baes C., Miglior F., Stothard P., Plastow G., et al. (2025): Identification of single nucleotide polymorphisms associated with serum acute phase proteins during the transition period in dairy cows. Journal of Dairy Science 109(2):1715-1726. https://doi.org/10.3168/jds.2025-27357
  210. Pathak R., Chaurasia R., Sapra B., Gaikwad P., Bargir U., Madkaikar M., et al. (2025): Retarded DSB repair kinetics suggestive of augmented radiation sensitivity and genetic instability in Wiskott-Aldrich syndrome patients. https://doi.org/10.21203/rs.3.rs-5719467/v1
  211. Ventura R. (2025): Measuring the quality of experimental research. Synthese 205(2). https://doi.org/10.1007/s11229-024-04905-4
  212. Klement R., Matzat J. (2025): Subjective Experiences and Blood Parameter Changes in Individuals From Germany Following a Self-Conceived “Carnivore Diet”: An Explorative Study. Cureus. https://doi.org/10.7759/cureus.82521
  213. Klement R., Matzat J. (2025): Subjective Experiences and Blood Parameter Changes of Individuals from Germany Following a Self-Prescribed ‘Carnivore Diet’. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5127873
  214. Bekkers R. (2025): A Modular Approach to Research Quality. https://doi.org/10.59350/x5h3c-54450
  215. Bekkers R. (2025): A Modular Approach to Research Quality. https://doi.org/10.59350/yaj6t-tcx28
  216. Meslé R. (2025): L’erreur alpha : mieux comprendre un concept central pour interpréter les essais cliniques en ostéopathie [Éditorial]. La Revue de l’Ostéopathie. https://doi.org/10.65071/rev.osteo2025.01.31.1
  217. Morcet-Delattre R., Duvergé L., Bourbonne V., Le Scodan R., Lapierre A., Pointreau Y., et al. (2025): Local control assessment following SABR for lung metastases of colorectal cancer: A multicenter retrospective analysis. Radiotherapy and Oncology 209:110992. https://doi.org/10.1016/j.radonc.2025.110992
  218. Shields R. (2025): Teaching the teacher? How content of Australian initial teacher education programs relates to professional satisfaction across the career. The Australian Educational Researcher 52(5):3731-3759. https://doi.org/10.1007/s13384-025-00874-w
  219. Lindsay R. (2025): The Null Hypothesis Statistical Testing Paradigm Undermines Knowledge Acquisition in Management Accounting Research: It Needs to Be Abandoned. Advances in Management Accounting. https://doi.org/10.1108/s1474-787120250000037001
  220. Luiz R. (2025): Inferência estatística versus inferência causal. Revista Brasileira de Saúde Ocupacional 50. https://doi.org/10.1590/2317-6369/00124pt2025v50edisfl1
  221. van Haasteren R. (2025): Calculation of p -values for quadratic statistics in pulsar timing arrays. Physical Review D 112(10). https://doi.org/10.1103/b3y4-yfr1
  222. Fujihira R., Watanabe H., Taga G. (2025): Individual differences in how infants change behaviours from spontaneous to instrumental. Communications Psychology 3(1). https://doi.org/10.1038/s44271-025-00333-3
  223. Chand S., Chiu N., Chou Y., Alian A., Shelley K., Wu H. (2025): Comparison of feature-based indices derived from photoplethysmogram recorded from different body locations during lower body negative pressure. https://doi.org/10.1101/2025.05.02.25326908
  224. Chand S., Chiu N., Chou Y., Alian A., Shelley K., Wu H. (2025): Comparison of feature-based indices derived from photoplethysmogram recorded from different body locations during lower body negative pressure. Physiological Measurement 46(8):085002. https://doi.org/10.1088/1361-6579/adf489
  225. Ueno S., Takeuchi O. (2025): Frequentist vs. Bayesian methods: Choosing appropriate statistical methods in second language research. Research Methods in Applied Linguistics 4(3):100256. https://doi.org/10.1016/j.rmal.2025.100256
  226. de Vries S., Baliatsas C., Verheij R., Dückers M. (2025): Domestic gardens and morbidity: Associations between private green space and diagnosed health conditions in the Netherlands. Environment International 199:109450. https://doi.org/10.1016/j.envint.2025.109450
  227. Baron S., Latham A., Varga S. (2025): Explainable AI and stakes in medicine: A user study. Artificial Intelligence 340:104282. https://doi.org/10.1016/j.artint.2025.104282
  228. Pawel S., Held L. (2025): Closed-Form Power and Sample Size Calculations for Bayes Factors. The American Statistician 79(3):330-344. https://doi.org/10.1080/00031305.2025.2467919
  229. Vejandia¹ S., Sadeesh² A., Srinivasasainagendra¹ V., Appah¹ M., Tiwari¹ H. (2025): Beyond 5×10⁻⁸: MAF-Specific Significance Thresholds for Genome-Wide Association Studies in three major populations. https://doi.org/10.21203/rs.3.rs-6234338/v1
  230. Vejandla S., Sadeesh A., Srinivasasainagendra V., Appah M., Tiwari H. (2025): Calibrating genome wide significance by minor allele frequency across three major populations. Scientific Reports 15(1). https://doi.org/10.1038/s41598-025-19644-w
  231. Greenland S. (2025): Statistical Methods: Basic Concepts, Interpretations, and Cautions. Handbook of Epidemiology. https://doi.org/10.1007/978-1-4614-6625-3_54-1
  232. Ugai S., Liu L., Kosumi K., Kawamura H., Hamada T., Mima K., et al. (2025): Long-term yogurt intake and colorectal cancer incidence subclassified by Bifidobacterium abundance in tumor. Gut Microbes 17(1). https://doi.org/10.1080/19490976.2025.2452237
  233. Semenyna S., Vasey P., Honey P. (2025): Relationships Among Sex, Sexual Orientation, Dark Triad Traits, Sociosexuality, and Sexual Excitation/Inhibition. Archives of Sexual Behavior 54(3):1261-1270. https://doi.org/10.1007/s10508-025-03092-8
  234. Leopold S. (2025): Editor’s Spotlight/Take 5: What Would Be the Effect of Lowering the Threshold of Statistical Significance From 0.05 to 0.005 in Foot and Ankle Randomized Controlled Trials?. Clinical Orthopaedics & Related Research 484(1):3-8. https://doi.org/10.1097/corr.0000000000003779
  235. Hennemann S., Weirich A., Meule A., Bräscher A., Witthöft M. (2025): German version of the specific phobia of vomiting inventory (SPOVI): psychometric properties and correlates in a clinical and non-clinical sample. BMC Psychiatry 25(1). https://doi.org/10.1186/s12888-025-06744-0
  236. Bender S., Green D., Hadaya J., Haridas S., Chan C., Challita R., et al. (2025): Closed-loop electrical block of vagus nerve scales from rodent to porcine cardiac models. Journal of Neural Engineering 22(3):036022. https://doi.org/10.1088/1741-2552/add8be
  237. Sacco S., Chen K., Jin J., Tang B., Wang F., Aseltine R. (2025): Identifying patients at risk of suicide using data from health information exchanges. BMC Public Health 25(1). https://doi.org/10.1186/s12889-025-22752-x
  238. Mohoric S., Alobaidi R., McGraw T., Joffe A. (2025): The determinants of fluid accumulation in critically ill children: a prospective single-center cohort study. Pediatric Nephrology 40(11):3555-3562. https://doi.org/10.1007/s00467-025-06875-2
  239. Patel S., Green A. (2025): Death by p-value: the overreliance on p-values in critical care research. Critical Care 29(1). https://doi.org/10.1186/s13054-025-05307-9
  240. Shokri S., Hemami M., Ahmadi M., Pourmanafi S., Bhagwat T., Kamp J., et al. (2025): Ecological drivers of change in waterbird communities of Iranian wetlands. Journal for Nature Conservation 89:127150. https://doi.org/10.1016/j.jnc.2025.127150
  241. Natsume S., Roach N., Miyazaki M. (2025): Concomitant motor responses facilitate the acquisition of multiple prior distributions in human coincidence timing. Proceedings of the Royal Society B: Biological Sciences 292(2039). https://doi.org/10.1098/rspb.2024.2438
  242. Genc S., Ball G., Chamberland M., Raven E., Tax C., Ward I., et al. (2025): MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression. Nature Communications 16(1). https://doi.org/10.1038/s41467-025-58604-w
  243. Kalénine S., Ott L., Casalis S. (2025): Improving lexico-semantic integration with gesture-enriched pictures: A word-learning study using the Picture-Word Interference paradigm. Memory & Cognition 53(7):2111-2125. https://doi.org/10.3758/s13421-025-01701-4
  244. Varga S., Latham A. (2025): Is “Dysfunction” a Value-Neutral Concept?. Philosophical Studies 182(8):2313-2333. https://doi.org/10.1007/s11098-025-02359-z
  245. Varga S., Latham A., Machery E. (2025): Concepts of health and disease: insights from experimental philosophy of medicine. Synthese 206(5). https://doi.org/10.1007/s11229-025-05280-4
  246. Varga S., Latham A., Machery E. (2025): The Wicked and the Ill. Philosophical Psychology. https://doi.org/10.1080/09515089.2025.2515236
  247. Juul S., Riberholt C., Olsen M., Milan J., Hafliðadóttir S., Svanholm J., et al. (2025): Trial Sequential Analysis for dichotomous outcomes – a practical guide for systematic review protocols. BMC Medical Research Methodology 25(1). https://doi.org/10.1186/s12874-025-02716-w
  248. Pethick S., Wass M., Michaelis M. (2025): Is There a Reproducibility Crisis? On the Need for Evidence-based Approaches. International Studies in the Philosophy of Science 38(4):287-303. https://doi.org/10.1080/02698595.2025.2538937
  249. Schüürhuis S., Konietschke F., Brunner E. (2025): A New Approach to the Nonparametric Behrens–Fisher Problem With Compatible Confidence Intervals. Biometrical Journal 67(6). https://doi.org/10.1002/bimj.70096
  250. Peters S., Meyer J., Stefan M. (2025): Perception of Sustainable Investing&nbsp;<div> <p>A Survey Experiment among the General Population and Financial Advisors</p></div>. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5503040
  251. Duchêne S., Nguyen-Huu A., Dubois D., Willinger M. (2025): Valuing Environmental Externalities under Risk: Evidence from a Lab-in-the-Field Experiment. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5722730
  252. Matson T., Bobb J., Oliver M., Berger D., Jack H., Steel T., et al. (2025): Alcohol consumption reported on routine healthcare screenings is associated with all‐cause mortality in primary care patients: A retrospective cohort study. Alcohol, Clinical and Experimental Research 49(12):2875-2886. https://doi.org/10.1111/acer.70192
  253. Pirlot T., Mihailovic T., Gimenez P., Millet G., Brocherie F., Fruchart E., et al. (2025): Psychological, Sleep, and Heart Rate Variability Responses During Early- and Middle-Term Acclimatization of “Living High-Training Low and High”. High Altitude Medicine & Biology 26(3):291-300. https://doi.org/10.1089/ham.2024.0118
  254. Heston T. (2025): The p–fr–nb triplet: a unified framework for statistical fragility and robustness across clinical study designs. https://doi.org/10.1101/2025.11.20.25340684
  255. MacGillavry T., Pelliccioni F., Jønsson K., Nagombi E., Field D., Fusani L. (2025): Did complex song and dance coevolve with brain size in the birds-of-paradise (Aves: Paradisaeidae)?. Ornithology 142(3). https://doi.org/10.1093/ornithology/ukaf009
  256. Müller T., Schweim D. (2025): From Theory to Practice: Exploring the Chi-Squared Goodness-of-Fit Test for Poisson Distributions with Car Parking Data. Journal of Statistical Theory and Applications 24(4):1033-1055. https://doi.org/10.1007/s44199-025-00136-9
  257. Loose T., Collet O., Nuyt A., Goulet-Pelletier J., Worrell F., Côté S., et al. (2025): Long-Term Educational Outcomes of Individuals Born Preterm. JAMA Network Open 8(10):e2534918. https://doi.org/10.1001/jamanetworkopen.2025.34918
  258. Lee T. (2025): Separating Biological Variance from Noise by Applying Expectation–Maximization Algorithm to Modified General Linear Model. Journal of Computational Biology 32(12):1121-1130. https://doi.org/10.1177/15578666251370766
  259. Zhou T., Short N., Jain N., Patel K., Jabbour E., Kantarjian H., et al. (2025): Age-related prognoses of genetic subtypes in B-cell acute lymphoblastic leukemia/lymphoma (B-ALL): insights from a decade of national data. Leukemia 39(7):1769-1772. https://doi.org/10.1038/s41375-025-02588-5
  260. Yu T., Mao Y., Qiu J., Zhang Y., Liu J. (2025): Positive memory bias among grateful people: Examining gratitude as emotion, mood, and affective trait. Personality and Individual Differences 247:113403. https://doi.org/10.1016/j.paid.2025.113403
  261. Badrick T., El-Khoury J., Theodorsson E. (2025): Laboratory reference intervals – history and modern approaches for improved utility. Scandinavian Journal of Clinical and Laboratory Investigation 85(4):229-241. https://doi.org/10.1080/00365513.2025.2512995
  262. Hamada T., Oyama H., Nevo D., Tange S., Takaoka S., Kawaguchi Y., et al. (2025): Risk factors for pancreatic cancer in individuals with intraductal papillary mucinous neoplasms and no high-risk stigmata during up to 5 years of surveillance: a prospective longitudinal cohort study. Gut 74(6):971-982. https://doi.org/10.1136/gutjnl-2024-333259
  263. Hamada T., Ugai T., Gurjao C., Ugai S., Zhang X., Haruki K., et al. (2025): Smoking habit and long-term colorectal cancer incidence by exome-wide mutational and neoantigen loads: evidence based on the prospective cohort incident-tumour biobank method. BMJ Oncology 4(1):e000787. https://doi.org/10.1136/bmjonc-2025-000787
  264. Hamada T., Michihata N., Saito T., Kimura Y., Iwashita T., Matsui H., et al. (2025): Real-world impact of implementing lumen-apposing metal stents for pancreatic fluid collections: a nationwide Japanese study. Gut 75(3):521-529. https://doi.org/10.1136/gutjnl-2025-335067
  265. Samarasinghe U., Tennakoon W., Sajeewani K. (2025): Generation Z’s Response to YouTube Non-Skippable Ads: Exploring Determinants of Purchase Intention in Sri Lanka. Journal of Digital Marketing and Communication 5(2):144-161. https://doi.org/10.53623/jdmc.v5i2.799
  266. Milano V., Vrdoljak J., Buono M., Gaetán M., Grandi M. (2025): Key insights from 3D periotic morphology in odontocete taxonomy. Biological Journal of the Linnean Society 144(3). https://doi.org/10.1093/biolinnean/blaf013
  267. Nguimdo V., Abwe E., Morgan B., Ketchen M., Mfossa D., Abwe A., et al. (2025): Long‐Term Monitoring of Hunting Signs Reveals Complex Spatiotemporal Patterns of Hunting Activities in an Unprotected African Rainforest. Diversity and Distributions 31(1). https://doi.org/10.1111/ddi.13951
  268. Rowlatt V., Malatzky C., Wraith D. (2025): Disentangling harms and benefits: how gambling benefits influence the relationship between risky gambling engagement and harm. Journal of Public Health. https://doi.org/10.1007/s10389-025-02638-3
  269. Tapiainen V., Sirniö P., Karjalainen H., Äijälä V., Kastinen M., Pohjanen V., et al. (2025): Impact of stromal maturity and proportion on prognosis and immune landscape in colorectal cancer. Annals of Medicine 58(1). https://doi.org/10.1080/07853890.2025.2606512
  270. Suresh V., Bansal B., Panchawagh S. (2025): Replicative significance index (RSI): A simulation-based metric for statistical inference and reproducibility. https://doi.org/10.21203/rs.3.rs-7476592/v1
  271. Jiang W. (2025): The influence of the educational platform Rain Classroom on emotional intelligence, creativity, academic independence, and concentration of Chinese students: An empirical study. Revista de Psicodidáctica (English ed.) 30(2):500170. https://doi.org/10.1016/j.psicoe.2025.500170
  272. Jiang W. (2025): La influencia de la plataforma educativa Rain Classroom en la inteligencia emocional, la creatividad, la independencia académica y la concentración del alumnado chino: un estudio empírico. Revista de Psicodidáctica 30(2):500170. https://doi.org/10.1016/j.psicod.2025.500170
  273. Jiang W., Pang X., Ha P., Li C., Chang G., Zhang Y., et al. (2025): Fibromodulin selectively accelerates myofibroblast apoptosis in cutaneous wounds by enhancing interleukin 1β signaling. Nature Communications 16(1). https://doi.org/10.1038/s41467-025-58906-z
  274. Xing X., Xing A., Natarajan K., Chu H., Lin L., Tong J. (2025): An alternative method for assessing the fragility of survival analysis results: a proof-of-concept study based on the log-rank test. American Journal of Epidemiology. https://doi.org/10.1093/aje/kwaf229
  275. Cheng X., Song C., Ouyang F., Ma T., He L., Fang F., et al. (2025): Systolic blood pressure variability: risk of cardiovascular events, chronic kidney disease, dementia, and death. European Heart Journal 46(27):2673-2687. https://doi.org/10.1093/eurheartj/ehaf256
  276. Wang Y., Hsu M., Wang M., Xiao H. (2025): MEDILEGACY: A gamified platform for enhancing medical terminology learning through engagement and retention. Health Education Journal 85(2):150-164. https://doi.org/10.1177/00178969251389388
  277. Miao Y., Zhao B., Yang Y., Yu K., Liao L., Weinstein S., et al. (2025): Healthy lifestyle partly mediates the association between self-rated health and risk of overall and cause-specific mortality. BMC Medicine 23(1). https://doi.org/10.1186/s12916-025-04399-y
  278. Peng Y., Zhan Y., Zhang Q. (2025): Dynamic Brain Network Biomarkers for Depression Prediction: A Multi-Cohort Analysis of Global Neuroimaging Databases. Psychology Research and Behavior Management Volume 18:2469-2494. https://doi.org/10.2147/prbm.s552134
  279. Takashima Y., Dias Costa A., Akimoto N., Ugai T., Bell P., Väyrynen J., et al. (2025): T-cell Subset Features and Distributions Evolve across the Colorectal Precancer–Cancer Spectrum. Cancer Immunology Research 14(1):46-59. https://doi.org/10.1158/2326-6066.cir-25-0481
  280. Yang Y., Johanneson M., Fossen F., Neyse L., Holzmeister F. (2025): Heterogeneity in gender differences in self-reported political preferences, trust, and well-being across 39 European countries. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5232078
  281. Yang Y., Johannesson M., Fossen F., Neyse L., Holzmeister F. (2025): Heterogeneity in gender differences in self-reported political preferences, trust, and well-being across 39 European countries. Scientific Reports 16(1). https://doi.org/10.1038/s41598-025-33362-3
  282. Yao Y., Yang M., Liu Z., Dong K., Gu X., Wang C. (2025): Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection. Proceedings of the ACM on Web Conference 2025. https://doi.org/10.1145/3696410.3714767
  283. Shimozono Y., Shinya Y., Matsuda S. (2025): What Would Be the Effect of Lowering the Threshold of Statistical Significance From 0.05 to 0.005 in Foot and Ankle Randomized Controlled Trials?. Clinical Orthopaedics & Related Research 484(1):9-19. https://doi.org/10.1097/corr.0000000000003689
  284. Guo Y., Su H., Li J., Xu P., Tian B., Xu P. (2025): Hydrophobic and microwave-responsive modified asphalt for enhanced anti-icing and de-icing performance. Construction and Building Materials 484:141859. https://doi.org/10.1016/j.conbuildmat.2025.141859
  285. Valdman‐Grinshpoun Y., Zaga A., Cohen A., Schonmann Y., Czarnowicki T. (2025): Comorbidities and Healthcare Utilization in 4197 Patients With Prurigo Nodularis in Israel: A Cross‐Sectional Population‐Based Analysis. International Journal of Dermatology. https://doi.org/10.1111/ijd.70107
  286. Ebrahimi Monfared Z., Mirkarimi S., Kangarani H., Soofi M. (2025): Emotions and perceptions predict local communities’ attitudes toward the conservation of large carnivores. Conservation Science and Practice 7(12). https://doi.org/10.1111/csp2.70176
  287. Monfared Z., Mirkarimi S., Kangarani H., Soofi M. (2025): Fear and belief predict perceived carnivore abundance in Golestan National Park, Iran. Wildlife Biology 2026(1). https://doi.org/10.1002/wlb3.01530
  288. Zhu Z., Wen J., Duanmu X., Yuan W., Zheng Q., Guo T., et al. (2025): Identifying brain degeneration patterns in early-stage Parkinson’s disease: a multimodal MRI study. npj Parkinson’s Disease 11(1). https://doi.org/10.1038/s41531-025-00975-4
  289. González-Velasco O., Simon M., Yilmaz R., Parlato R., Weishaupt J., Imbusch C., et al. (2025): Identifying similar populations across independent single cell studies without data integration. NAR Genomics and Bioinformatics 7(2). https://doi.org/10.1093/nargab/lqaf042
  290. Said M., Hamed N., Haridy D., Moustafa S., Al-Ghtanyi S., Metwally D., et al. (2025): Determinants of knowledge, attitudes, and practices regarding campus sustainability: A field study at Cairo University. Scientific African 31:e03126. https://doi.org/10.1016/j.sciaf.2025.e03126
  291. Unknown authors (2024): . Journal of Trial and Error 4(2). https://doi.org/10.36850/i4.2
  292. Hoffmann A., Crevecoeur F. (2024): Dissociable Effects of Urgency and Evidence Accumulation during Reaching Revealed by Dynamic Multisensory Integration. eneuro 11(12):ENEURO.0262-24.2024. https://doi.org/10.1523/eneuro.0262-24.2024
  293. Granger A. (2024): Individual and Structural Contributors to Implicit and Explicit Anti-Muslim Bias in the United States. https://doi.org/10.15760/etd.3713
  294. Younas A. (2024): Beyond ‘statistical significance’: A nontechnical primer of Bayesian statistics and Bayes factors for health researchers. Journal of Evaluation in Clinical Practice 30(7):1218-1226. https://doi.org/10.1111/jep.14032
  295. Gikandi A., Hallet J., Koerkamp B., Clark C., Lillemoe K., Narayan R., et al. (2024): Distinguishing Clinical From Statistical Significances in Contemporary Comparative Effectiveness Research. Annals of Surgery 279(6):907-912. https://doi.org/10.1097/sla.0000000000006250
  296. MENKVELD A., DREBER A., HOLZMEISTER F., HUBER J., JOHANNESSON M., KIRCHLER M., et al. (2024): Nonstandard Errors. The Journal of Finance 79(3):2339-2390. https://doi.org/10.1111/jofi.13337
  297. Pons-Escoda A., Garcia-Ruiz A., Naval-Baudin P., Martinez-Zalacain I., Castell J., Camins A., et al. (2024): Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles. European Radiology 34(8):5320-5330. https://doi.org/10.1007/s00330-024-10611-z
  298. Pons-Escoda A., Naval-Baudin P., Viveros M., Flores-Casaperalta S., Martinez-Zalacaín I., Plans G., et al. (2024): DSC-PWI presurgical differentiation of grade 4 astrocytoma and glioblastoma in young adults: rCBV percentile analysis across enhancing and non-enhancing regions. Neuroradiology 66(8):1267-1277. https://doi.org/10.1007/s00234-024-03385-0
  299. Vásquez A., Escarela Pérez G., Núñez-Antonio G., Márquez Urbina J. (2024): La replicabilidad en la ciencia y el papel transformador de la metodología estadística de knockoffs. SAHUARUS. REVISTA ELECTRÓNICA DE MATEMÁTICAS. ISSN: 2448-5365 8(1):1-22. https://doi.org/10.36788/sah.v8i1.148
  300. Nakhostin-Ansari A., Tackett S. (2024): Regional Distribution of Foreign-Born Medical Graduates in US Primary Care Specialty Residencies from 2010 to 2022. Journal of General Internal Medicine 40(2):347-353. https://doi.org/10.1007/s11606-024-09151-5
  301. Cardini A. (2024): Allometry and phylogenetic divergence: Correspondence or incongruence?. The Anatomical Record 308(3):868-891. https://doi.org/10.1002/ar.25544
  302. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  303. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.1
  304. King A., Thompson J., Hart S., Nossaman B. (2024): Videolaryngoscopy during Urgent Cesarean Delivery: Association with Neonatal Intensive Care Unit Admission. Southern Medical Journal 117(8):494-497. https://doi.org/10.14423/smj.0000000000001722
  305. Chen A. (2024): Do t-Statistic Hurdles Need to Be Raised?. Management Science 71(7):5830-5848. https://doi.org/10.1287/mnsc.2023.03083
  306. Dreber A., Johannesson M., Yang Y. (2024): Selective reporting of placebo tests in top economics journals. Economic Inquiry 62(3):921-932. https://doi.org/10.1111/ecin.13217
  307. Dreber A., Johanneson M., Naurin E., Öhberg P. (2024): Daughters and Political Preferences of Politicians: A Preregistered Prospective Study on Swedish Politicians. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4953642
  308. Brenoe A., Eyibak Z., Heursen L., Ranehill E., Weber R. (2024): Gender Identity and Economic Decision Making. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4889851
  309. Arslan A., Zenker F. (2024): Cohen’s convention, the seriousness of errors, and the body of knowledge in behavioral science. Synthese 204(6). https://doi.org/10.1007/s11229-024-04753-2
  310. Spanos A. (2024): How the Post-Data Severity Converts Testing Results into Evidence for or against Pertinent Inferential Claims. Entropy 26(1):95. https://doi.org/10.3390/e26010095
  311. Holzknecht A., Huber J., Kirchler M., Neugebauer T. (2024): Speculating in Zero-Value Assets: The Greater Fool Game Experiment. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5056850
  312. Parish A., Tolis G., Ioannidis J. (2024): Are there mortality improvements with newer interventions in adult cardiac surgery? Evidence from 73 meta-analyses. https://doi.org/10.1101/2024.10.31.24316530
  313. Fitzpatrick B., Gorman D., Trombatore C. (2024): Impact of redefining statistical significance on P-hacking and false positive rates: An agent-based model. PLOS ONE 19(5):e0303262. https://doi.org/10.1371/journal.pone.0303262
  314. Sundermann B., Pfleiderer B., McLeod A., Mathys C. (2024): Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain. Clinical Neuroradiology 34(3):531-539. https://doi.org/10.1007/s00062-024-01422-2
  315. Clarke B., Alley L., Ghai S., Flake J., Rohrer J., Simmons J., et al. (2024): Looking our limitations in the eye: A call for more thorough and honest reporting of study limitations. Social and Personality Psychology Compass 18(7). https://doi.org/10.1111/spc3.12979
  316. Zhao B., Gan L., Graubard B., Männistö S., Fang F., Weinstein S., et al. (2024): Plant and Animal Fat Intake and Overall and Cardiovascular Disease Mortality. JAMA Internal Medicine 184(10):1234. https://doi.org/10.1001/jamainternmed.2024.3799
  317. Nguyen B., Clare P., Mielke G., Brown W., Ding D. (2024): Physical activity across midlife and health-related quality of life in Australian women: A target trial emulation using a longitudinal cohort. PLOS Medicine 21(5):e1004384. https://doi.org/10.1371/journal.pmed.1004384
  318. Kline B. (2024): Classicalp-values and the Bayesian posterior probability that the hypothesis is approximately true. Journal of Econometrics 240(1):105677. https://doi.org/10.1016/j.jeconom.2024.105677
  319. Porter B., Machery E. (2024): AI-generated poetry is indistinguishable from human-written poetry and is rated more favorably. Scientific Reports 14(1). https://doi.org/10.1038/s41598-024-76900-1
  320. Porter B., Barr K., Bencherifa A., Buckwalter W., Deguchi Y., Fabiano E., et al. (2024): A puzzle about knowledge ascriptions. Noûs 59(2):392-408. https://doi.org/10.1111/nous.12515
  321. Belleï-Rodriguez C., Colloca L., Lorrain D., Marchand S., Léonard G. (2024): Nocebo Effect on Pain Perception and Attention with Children With and Without Attention Deficit And/Or Hyperactivity Disorder. Journal of Developmental & Behavioral Pediatrics 45(6):e537-e544. https://doi.org/10.1097/dbp.0000000000001314
  322. Van Lissa C., Clapper E., Kuiper R. (2024): A tutorial on aggregating evidence from conceptual replication studies using the product Bayes factor. Research Synthesis Methods 15(6):1231-1243. https://doi.org/10.1002/jrsm.1765
  323. O’Connell C., Dalton P., Hutmacher D. (2024): Why bioprinting in regenerative medicine should adopt a rational technology readiness assessment. Trends in Biotechnology 42(10):1218-1229. https://doi.org/10.1016/j.tibtech.2024.03.006
  324. Feng C., Wang L., Yang S., Wu X., Fan Y., Yan H., et al. (2024): A New Pilot Hole Preparation System for Percutaneous Pedicle Screw Placement. Spine 50(2):115-121. https://doi.org/10.1097/brs.0000000000005184
  325. Crandall C., Giner-Sorolla R., Biernat M. (2024): Ethical Issues in Psychological Science. Handbook of Research Methods in Social and Personality Psychology. https://doi.org/10.1017/9781009170123.003
  326. Huber C., Holzmeister F., Johannesson M., König-Kersting C., Dreber A., Huber J., et al. (2024): Do experimental asset market results replicate? High-powered preregistered replications of 17 claims. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5048949
  327. Pérignon C., Akmansoy O., Hurlin C., Dreber A., Holzmeister F., Huber J., et al. (2024): Computational Reproducibility in Finance: Evidence from 1,000 Tests. The Review of Financial Studies 37(11):3558-3593. https://doi.org/10.1093/rfs/hhae029
  328. Holmberg C. (2024): Toward a Better Understanding of Statistical Significance and p Values in Nursing. Nursing Forum 2024(1). https://doi.org/10.1155/2024/7263781
  329. Holmberg C. (2024): Why we need to discuss statistical significance and p-values (again): Addressing the underlying issue of different probability interpretations and actionable recommendations. Nordic Journal of Nursing Research 44. https://doi.org/10.1177/20571585241253177
  330. Schmerwitz C., Kopp B. (2024): The future of neuropsychology is digital, theory-driven, and Bayesian: a paradigmatic study of cognitive flexibility. Frontiers in Psychology 15. https://doi.org/10.3389/fpsyg.2024.1437192
  331. Blanchet C., Ramisch A., Tjallingii R., Ionita M., Laruelle L., Bagge M., et al. (2024): Climatic pacing of extreme Nile floods during the North African Humid Period. Nature Geoscience 17(7):638-644. https://doi.org/10.1038/s41561-024-01471-9
  332. Tang D., Boker S., Tong X. (2024): Are the Signs of Factor Loadings Arbitrary in Confirmatory Factor Analysis? Problems and Solutions. https://doi.org/10.31219/osf.io/b7h6d
  333. Spaeth D., Reffert S., Hunt E., Kaminski A., Quirrenbach A. (2024): Non-radial oscillations mimicking a brown dwarf orbiting the cluster giant NGC 4349 No. 127. Astronomy & Astrophysics 689:A91. https://doi.org/10.1051/0004-6361/202450163
  334. Jugend D., Fiorini P., Fournier P., Latan H., Chiappetta Jabbour C., Scaliza J. (2024): Industry 4.0 technologies for the adoption of the circular economy: An analysis of institutional pressures and the effects on firm performance. Journal of Environmental Management 370:122260. https://doi.org/10.1016/j.jenvman.2024.122260
  335. Krpan D. (2024): Beyond a Dream: The Practical Foundations of Disconnected Psychology. Meta-Psychology 8. https://doi.org/10.15626/mp.2020.2740
  336. Bickel D. (2024): Bayesian and frequentist inference derived from the maximum entropy principle with applications to propagating uncertainty about statistical methods. Statistical Papers 65(8):5389-5407. https://doi.org/10.1007/s00362-024-01597-3
  337. Bickel D. (2024): The Propagation and Reduction of Uncertainty Left Unquantified by Confidence Intervals, p-Values, Neural Network Predictions, Posterior Distributions, and Other Statistical Results. Journal of Verification, Validation and Uncertainty Quantification 9(3). https://doi.org/10.1115/1.4066380
  338. Black D., Ioannidis J., Phei Wee C., Kirkpatrick M. (2024): Sex differences in cigarette smoking following a mindfulness-based cessation randomized controlled trial. Addictive Behaviors 160:108177. https://doi.org/10.1016/j.addbeh.2024.108177
  339. Chicco D., Karaiskou A., De Vos M. (2024): Ten quick tips for electrocardiogram (ECG) signal processing. PeerJ Computer Science 10:e2295. https://doi.org/10.7717/peerj-cs.2295
  340. Mayo D. (2024): Error statistics, Bayes-factor Tests and the Fallacy of Non-exhaustive Alternatives. https://doi.org/10.31219/osf.io/tmgqd
  341. Pelt D., Habets P., Vinkers C., Ligthart L., van Beijsterveldt C., Pool R., et al. (2024): Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort. Nature Mental Health 2(10):1217-1230. https://doi.org/10.1038/s44220-024-00294-2
  342. Holmes D., Hart A., Powell D., Stoute B., Chodorow N., Davids M., et al. (2024): In Pursuit of Racial Equality in American Psychoanalysis: Findings and Recommendations from the Holmes Commission. Journal of the American Psychoanalytic Association 72(3):407-552. https://doi.org/10.1177/00030651241253623
  343. Poznyak E., Rochat L., Badoud D., Meuleman B., Debbané M. (2024): Unpacking mentalizing: The roles of age and executive functioning in self-other appraisal and perspective taking. Quarterly Journal of Experimental Psychology 78(8):1707-1720. https://doi.org/10.1177/17470218241311415
  344. Gsottbauer E., Kirchler M., König-Kersting C. (2024): Financial professionals and climate experts have diverging perspectives on climate action. Communications Earth & Environment 5(1). https://doi.org/10.1038/s43247-024-01331-9
  345. Marini E., Stagi S., Cabras S., Comandini O., Ssensamba J., Fewtrell M., et al. (2024): Associations of bioelectrical impedance and anthropometric variables among populations and within the full spectrum of malnutrition. Nutrition 127:112550. https://doi.org/10.1016/j.nut.2024.112550
  346. Fanti E., Di Sarno M., Di Pierro R. (2024): When the Others Are Dangerous: Paranoid Presentations in Subclinical Forms of Personality Disorders. Journal of Personality Disorders 38(6):573-598. https://doi.org/10.1521/pedi.2024.38.6.573
  347. Buchanan E. (2024): Statistical Power: How Not to Miss What’s Right in Front of You. The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences. https://doi.org/10.1017/9781009000796.012
  348. Melilli E., Veronese P. (2024): Confidence distributions and hypothesis testing. Statistical Papers 65(6):3789-3820. https://doi.org/10.1007/s00362-024-01542-4
  349. Zisler E., Meule A., Koch S., Schennach R., Voderholzer U. (2024): Duration of daily life activities in persons with and without obsessive–compulsive disorder. Journal of Psychiatric Research 173:6-13. https://doi.org/10.1016/j.jpsychires.2024.02.052
  350. Zisler E., Meule A., Koch S., Voderholzer U. (2024): Willingness to experience unpleasant thoughts, emotions, and bodily sensations at admission does not predict treatment outcome in inpatients with obsessive–compulsive disorder. Discover Mental Health 4(1). https://doi.org/10.1007/s44192-024-00073-6
  351. Fan F. (2024): Self-interest or ethic: Consumer segmentation for purchasing agricultural products online based on attitude in China. Journal of Infrastructure Policy and Development 8(7):5425. https://doi.org/10.24294/jipd.v8i7.5425
  352. Habibzadeh F. (2024): On the use of receiver operating characteristic curve analysis to determine the most appropriate p value significance threshold. Journal of Translational Medicine 22(1). https://doi.org/10.1186/s12967-023-04827-8
  353. Habibzadeh F. (2024): Reinterpretation of the results of randomized clinical trials. PLOS ONE 19(6):e0305575. https://doi.org/10.1371/journal.pone.0305575
  354. Echenique F., He K. (2024): Screening p -hackers: Dissemination noise as bait. Proceedings of the National Academy of Sciences 121(21). https://doi.org/10.1073/pnas.2400787121
  355. Holzmeister F., Johannesson M., Camerer C., Chen Y., Ho T., Hoogeveen S., et al. (2024): Examining the replicability of online experiments selected by a decision market. Nature Human Behaviour 9(2):316-330. https://doi.org/10.1038/s41562-024-02062-9
  356. Pargent F., Koch T., Kleine A., Lermer E., Gaube S. (2024): A Tutorial on Tailored Simulation-Based Sample-Size Planning for Experimental Designs With Generalized Linear Mixed Models. Advances in Methods and Practices in Psychological Science 7(4). https://doi.org/10.1177/25152459241287132
  357. Cova F., Abatista A. (2024): Estimating the Reproducibility of Experimental Philosophy. https://doi.org/10.31234/osf.io/hr4zs
  358. Bertolino F., Manca M., Musio M., Racugno W., Ventura L. (2024): A new Bayesian discrepancy measure. Statistical Methods & Applications 33(2):381-405. https://doi.org/10.1007/s10260-024-00745-1
  359. Houde F., Butler R., St-Onge E., Martel M., Thivierge V., Descoteaux M., et al. (2024): Anatomical measurements and field modeling to assess transcranial magnetic stimulation motor and non-motor effects. Neurophysiologie Clinique 54(6):103011. https://doi.org/10.1016/j.neucli.2024.103011
  360. Dudbridge F. (2024): Empirical Bayes factors for common hypothesis tests. PLOS ONE 19(2):e0297874. https://doi.org/10.1371/journal.pone.0297874
  361. Emmert-Streib F. (2024): Trends in null hypothesis significance testing: Still going strong. Heliyon 10(21):e40133. https://doi.org/10.1016/j.heliyon.2024.e40133
  362. Fossen F., Neyse L., Schröder C. (2024): Does Cognitive Reflection Relate to Preferences and Socioeconomic Outcomes?. Journal of Political Economy Microeconomics 3(2):303-343. https://doi.org/10.1086/732653
  363. Bartoš F. (2024): Untrustworthy Evidence in Dishonesty Research. Meta-Psychology 8. https://doi.org/10.15626/mp.2023.3987
  364. Vivó G., Alonso A. (2024): Prediction of Intraday Electricity Supply Curves. Applied Sciences 14(22):10663. https://doi.org/10.3390/app142210663
  365. Cerono G., Chicco D. (2024): Ensemble machine learning reveals key features for diabetes duration from electronic health records. PeerJ Computer Science 10:e1896. https://doi.org/10.7717/peerj-cs.1896
  366. Shi G., Zhang J., Liu J., Xu J., Chen Y., Wang Y. (2024): Spatial Analysis of Lung Cancer Patients and Associated Influencing Factors from the Perspective of Urban Sustainable Development: A Case Study of Jiangsu Province, China. Sustainability 16(22):9898. https://doi.org/10.3390/su16229898
  367. Wilhere G. (2024): The US Endangered Species Act and acceptable risk. Biological Conservation 297:110749. https://doi.org/10.1016/j.biocon.2024.110749
  368. Gradl-Dietsch G., Peters T., Meule A., Hebebrand J., Voderholzer U. (2024): Body Mass Index Distribution in Female Child, Adolescent and Adult Inpatients with Anorexia Nervosa—A Retrospective Chart Review. Nutrients 16(11):1732. https://doi.org/10.3390/nu16111732
  369. Bernardino G. (2024): Problematic probabilities: Reassessing the p-value in public health research. Population Medicine 6(April):1-3. https://doi.org/10.18332/popmed/185251
  370. Pianowski G., Miguel F., Carvalho L. (2024): Screening personality disorders in a Brazilian community sample. Current Psychology 43(46):35299-35307. https://doi.org/10.1007/s12144-024-07022-0
  371. Nakhlé G., Tardif J., Dubé M., Dubois A., LeLorier J. (2024): Efficacy of Colchicine in Coronary Disease: Bayesian Analysis and Null-Hypothesis Testing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4781598
  372. Cisotto G., Chicco D. (2024): Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing. PeerJ Computer Science 10:e2256. https://doi.org/10.7717/peerj-cs.2256
  373. Chernov G. (2024): The Alternative Factors Leading to Replication Crisis: Prediction and Evaluation. Evaluation Review 49(1):147-164. https://doi.org/10.1177/0193841×241229106
  374. Sultanova G., Shora N. (2024): Comparing the Impact of Non-Cognitive Skills in STEM and Non-STEM Contexts in Kazakh Secondary Education. Education Sciences 14(10):1109. https://doi.org/10.3390/educsci14101109
  375. Fahimi H., Soofi M., Ahmadi N., Qashqaei A., Heidari H., Bungum H., et al. (2024): Distribution, behavior and diet of the Asiatic black bear in human modified landscapes. Basic and Applied Ecology 80:23-30. https://doi.org/10.1016/j.baae.2024.07.003
  376. Yoon H., Lee Y., Park E., Lee S. (2024): A small sauropod trackway from the Upper Cretaceous Jindong Formation (Cenomanian), Goseong County, South Korea. Cretaceous Research 166:106022. https://doi.org/10.1016/j.cretres.2024.106022
  377. Zhuang H., Acuna D. (2024): Incorporating costs and benefits to the evaluation of uncertain research results: Applications to cancer research funding. Quantitative Science Studies 5(4):1047-1069. https://doi.org/10.1162/qss_a_00332
  378. Elomaa H., Härkönen J., Väyrynen S., Ahtiainen M., Ogino S., Nowak J., et al. (2024): Quantitative Multiplexed Analysis of Indoleamine 2,3-Dioxygenase (IDO) and Arginase-1 (ARG1) Expression and Myeloid Cell Infiltration in Colorectal Cancer. Modern Pathology 37(4):100450. https://doi.org/10.1016/j.modpat.2024.100450
  379. Si H., Fu C., Yu F., Li Z. (2024): Feathered Icons: Drivers of Global Attention on Bird Species. https://doi.org/10.1101/2024.12.12.628088
  380. Bao H., Huang Y., Sun Y., Chen Y., Luo Y., Yan L., et al. (2024): Prevalence of anemia of varying severity, geographic variations, and association with metabolic factors among women of reproductive age in China: a nationwide, population-based study. Frontiers of Medicine 18(5):850-861. https://doi.org/10.1007/s11684-024-1070-x
  381. Makse H., Zava M. (2024): Social Media Influencers and Politics. Understanding Complex Systems. https://doi.org/10.1007/978-3-031-78058-5_2
  382. Institute for Monetary and Financial Research H. (2024): Non-Standard Errors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4752597
  383. Khorozyan I. (2024): Conservation implications of sex-specific daily movements of leopards: A global perspective. Biological Conservation 302:110928. https://doi.org/10.1016/j.biocon.2024.110928
  384. Fitzgerald J., Stroet P., Weißmüller K., van Witteloostuijn A. (2024): Is There a Foreign Language Effect on Workplace Bribery Susceptibility? Evidence from a Randomized Controlled Vignette Experiment. Journal of Business Ethics 197(1):73-97. https://doi.org/10.1007/s10551-024-05731-x
  385. Penberthy J., Claro H., Kalelioglu T., Centeno C., Ladoni A., Ragone E., et al. (2024): Impact of Meditation Versus Exercise on Psychological Characteristics, Paranormal Experiences, and Beliefs: Randomized Trial. Journal of Scientific Exploration 38(1):28-40. https://doi.org/10.31275/20242849
  386. Davis J., Meares T., Arnesen E. (2024): Improving Programming in Juvenile Detention: The Impact of Project Safe Neighborhoods Youth Outreach Forums. Journal of Quantitative Criminology. https://doi.org/10.1007/s10940-024-09584-5
  387. Cooper J., Campbell Q., Conner T. (2024): Healthier but not happier? The lifestyle habits of health influencer followers. Cyberpsychology: Journal of Psychosocial Research on Cyberspace 18(2). https://doi.org/10.5817/cp2024-2-4
  388. Koehler J., Dong M., Bierlich A., Fischer S., Späth J., Plank I., et al. (2024): Machine learning classification of autism spectrum disorder based on reciprocity in naturalistic social interactions. Translational Psychiatry 14(1). https://doi.org/10.1038/s41398-024-02802-5
  389. Edlund J., Sdougkou K., Papazian S., Wu W., Martin J., Harlid S. (2024): Chemical exposomics in biobanked plasma samples and associations with breast cancer risk factors. Journal of Exposure Science & Environmental Epidemiology 35(4):567-577. https://doi.org/10.1038/s41370-024-00736-0
  390. Wang J., Lobo J., Shutters S., Strumsky D. (2024): Fueling a net-zero future: The influence of government-funded research on climate change mitigation inventions. Environmental Innovation and Societal Transitions 51:100836. https://doi.org/10.1016/j.eist.2024.100836
  391. List J. (2024): Optimally generate policy-based evidence before scaling. Nature 626(7999):491-499. https://doi.org/10.1038/s41586-023-06972-y
  392. Jon A. Krosnick, Tobias H. Stark, Amanda L. Scott (2024): Challenges of Research on Implicit Bias. The Cambridge Handbook of Implicit Bias and Racism. https://doi.org/10.1017/9781108885492.017
  393. Tendeiro J., Kiers H., Hoekstra R., Wong T., Morey R. (2024): Diagnosing the Misuse of the Bayes Factor in Applied Research. Advances in Methods and Practices in Psychological Science 7(1). https://doi.org/10.1177/25152459231213371
  394. Stern J., Krämer M., Schumacher A., MacDonald G., Richter D. (2024): Differences Between Lifelong Singles and Ever-Partnered Individuals in Big Five Personality Traits and Life Satisfaction. Psychological Science 35(12):1364-1381. https://doi.org/10.1177/09567976241286865
  395. Stern J., Krämer M., Schumacher A., MacDonald G., Richter D. (2024): Differences between lifelong singles and ever-partnered individuals in Big Five personality traits and life satisfaction. https://doi.org/10.31234/osf.io/pvuzb
  396. Sytsma J. (2024): Quantitative Vignette Studies: t-Tests—Case Studies on Judgments About Unfelt Pains. Springer Graduate Texts in Philosophy. https://doi.org/10.1007/978-3-031-58049-9_3
  397. Landes J., Corsi E., Baldi P. (2024): Knowledge Representation, Scientific Argumentation and Non-monotonic Logic. Logic, Argumentation & Reasoning. https://doi.org/10.1007/978-3-031-77892-6_8
  398. Villar-Gouy K., Salmon C., Salvatori R., Kellner M., Krauss M., Rocha T., et al. (2024): Brain morphometry and estimation of aging brain in subjects with congenital untreated isolated GH deficiency. Journal of Endocrinological Investigation 47(11):2797-2807. https://doi.org/10.1007/s40618-024-02372-9
  399. Yan K., Fong S., Li T., Song Q. (2024): Multimodal Machine Learning for Prognosis and Survival Prediction in Renal Cell Carcinoma Patients: A Two-Stage Framework with Model Fusion and Interpretability Analysis. Applied Sciences 14(13):5686. https://doi.org/10.3390/app14135686
  400. Seale K., Teschendorff A., Reiner A., Voisin S., Eynon N. (2024): A comprehensive map of the aging blood methylome in humans. Genome Biology 25(1). https://doi.org/10.1186/s13059-024-03381-w
  401. Lehnertz K. (2024): Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems. Chaos: An Interdisciplinary Journal of Nonlinear Science 34(7). https://doi.org/10.1063/5.0214733
  402. Al Ali L., Meijers W., Beldhuis I., Groot H., Lipsic E., van Veldhuisen D., et al. (2024): Association of fibrotic markers with diastolic function after STEMI. Scientific Reports 14(1). https://doi.org/10.1038/s41598-024-69926-y
  403. Jussim L., Careem A., Goldberg Z., Honeycutt N., Stevens S. (2024): IAT Scores, Racial Gaps, and Scientific Gaps. The Cambridge Handbook of Implicit Bias and Racism. https://doi.org/10.1017/9781108885492.021
  404. Rose L., Carter N., Lynam D., Miller J., Oltmanns T. (2024): Validity, Stability, and Change in Psychopathic Traits in Older Adults: A Registered Report. Clinical Psychological Science 13(3):664-679. https://doi.org/10.1177/21677026241298273
  405. Sannwald L., Moskopp D., Moskopp M. (2024): The Extension of Traumatic Subdural Hematoma into the Interhemispheric Fissure Is Associated with Coagulation Disorders: A Retrospective Study. Journal of Neurological Surgery Part A: Central European Neurosurgery 86(02):148-155. https://doi.org/10.1055/s-0043-1777859
  406. Hafner L., Sturm G., List M. (2024): Single-cell differential expression analysis between conditions within nested settings. https://doi.org/10.1101/2024.08.01.606200
  407. Held L., Pawel S., Micheloud C. (2024): The assessment of replicability using the sum of p -values. Royal Society Open Science 11(8). https://doi.org/10.1098/rsos.240149
  408. Gandhi L., Manning B., Duckworth A. (2024): Effect Size Magnification: No Variable Is as Important as the One You’re Thinking About—While You’re Thinking About It. Current Directions in Psychological Science 33(6):347-354. https://doi.org/10.1177/09637214241268222
  409. Courtenay L. (2024): An open letter to evolutionary and human sciences; Statistics has moved on and so should we. A proposal for more transparent research, and some notes on p < 0.003. Quaternary Environments and Humans 2(6):100041. https://doi.org/10.1016/j.qeh.2024.100041
  410. Russo L., Siena L., Farina S., Pastorino R., Boccia S., Ioannidis J. (2024): Genetic and other omics-based information in the most-cited recent clinical trials. https://doi.org/10.1101/2024.10.21.24315878
  411. Liekefett L., Sebben S., Becker J. (2024): The Effect of Brooding About Societal Problems on Conspiracy Beliefs: A Registered Report. Collabra: Psychology 10(1). https://doi.org/10.1525/collabra.92995
  412. Rudolph L., Thurner P. (2024): Are ideological and partisan affinities determining voters’ support of arms deliveries? Insights from a large-scale survey experiment in France and Germany. European Political Science Review 17(1):1-21. https://doi.org/10.1017/s1755773924000109
  413. Kastrati L., Farina S., Gris A., Raeisi-Dehkordi H., Llanaj E., Quezada-Pinedo H., et al. (2024): Evaluation of reported claims of sex-based differences in treatment effects across meta-analyses: A meta-research study. https://doi.org/10.1101/2024.07.04.24309572
  414. Bornmann L., Marewski J. (2024): Opium in science and society: numbers and other quantifications. Scientometrics 129(9):5313-5346. https://doi.org/10.1007/s11192-024-05104-1
  415. Mercado-Diaz L., Veeranki Y., Marmolejo-Ramos F., Posada-Quintero H. (2024): EDA-Graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection. IEEE Journal of Biomedical and Health Informatics 28(8):4599-4612. https://doi.org/10.1109/jbhi.2024.3405491
  416. Mercado-Diaz L., Veeranki Y., Large E., Posada-Quintero H. (2024): Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy. Sensors 24(24):8130. https://doi.org/10.3390/s24248130
  417. Koks-Leensen M., Menko A., Raaijmakers F., Fransen-Kuppens G., Bevelander K. (2024): An Accessible Web-Based Survey to Monitor the Mental Health of People With Mild Intellectual Disability or Low Literacy Skills During the COVID-19 Pandemic: Comparative Data Analysis. JMIR Public Health and Surveillance 10:e44827. https://doi.org/10.2196/44827
  418. Takenaka M., Gonoi W., Sato T., Saito T., Hanaoka S., Hamada T., et al. (2024): Artificial intelligence–based skeletal muscle estimates and outcomes of EUS-guided treatment of pancreatic fluid collections. iGIE 3(3):382-392.e8. https://doi.org/10.1016/j.igie.2024.06.006
  419. Venumuddula M., Kirchner K., Chen A., Rood R., Gronewold A. (2024): Combining Satellite, Teleconnection, and In Situ Data to Improve Understanding of Multi‐Decadal Coastal Ice Cover Dynamics on Earth’s Largest Freshwater Lake. Earth and Space Science 11(12). https://doi.org/10.1029/2024ea003845
  420. Aschenwald M., Holzknecht A., Kirchler M., Razen M. (2024): Covariates of behavioral consistency among adolescents. Journal of Behavioral and Experimental Finance 44:100986. https://doi.org/10.1016/j.jbef.2024.100986
  421. Leonti M., Cabras S., Castellanos Nueda M., Casu L. (2024): Food drugs as drivers of therapeutic knowledge and the role of chemosensory qualities. Journal of Ethnopharmacology 328:118012. https://doi.org/10.1016/j.jep.2024.118012
  422. Thurow M., Welz T., Knop E., Friede T., Pauly M. (2024): Robust confidence intervals for meta-regression with interaction effects. Computational Statistics. https://doi.org/10.1007/s00180-024-01530-0
  423. Soto M., Schimmack U. (2024): Credibility of results in emotion science: a Z -curve analysis of results in the journals Cognition & Emotion and Emotion. Cognition and Emotion 39(8):1803-1819. https://doi.org/10.1080/02699931.2024.2443016
  424. Rubin M. (2024): Type I Error Rates are Not Usually Inflated. Journal of Trial and Error 4(2). https://doi.org/10.36850/4d35-44bd
  425. Birkenbach M., Egloff B. (2024): Effects of matching climate change appeals to personal values. Scientific Reports 14(1). https://doi.org/10.1038/s41598-024-56631-z
  426. Götz M., Sarma A., O’Boyle E. (2024): The multiverse of universes: A tutorial to plan, execute and interpret multiverses analyses using the R package multiverse. International Journal of Psychology 59(6):1003-1014. https://doi.org/10.1002/ijop.13229
  427. Moskopp M., Moskopp D., Sannwald L. (2024): Impact of early follow-up CT in the conservative management of traumatic brain injury on surgical decision making: A retrospective, single-center analysis with special respect to coagulopathy. European Journal of Trauma and Emergency Surgery 50(6):3015-3026. https://doi.org/10.1007/s00068-024-02449-3
  428. Samad M., Sutherland M., Ganier D., Broussard D., Koveleskie J., Nossaman V., et al. (2024): Perioperative efficiency of sugammadex following minimally invasive gastric sleeve surgery: A superiority trial. Journal of Perioperative Practice. https://doi.org/10.1177/17504589241267859
  429. Liebscher M., Dell’Orco A., Doll-Lee J., Buerger K., Dechent P., Ewers M., et al. (2024): Short communication: Lifetime musical activity and resting-state functional connectivity in cognitive networks. PLOS ONE 19(5):e0299939. https://doi.org/10.1371/journal.pone.0299939
  430. Mollura M., Chicco D., Paglialonga A., Barbieri R. (2024): Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records. PLOS Digital Health 3(3):e0000459. https://doi.org/10.1371/journal.pdig.0000459
  431. Karakus M., Tlessov A., Hajar A., Courtney M. (2024): Illuminating the shadows: the role of private supplementary tutoring on student math performance in PISA 2022. Large-scale Assessments in Education 12(1). https://doi.org/10.1186/s40536-024-00228-5
  432. Chai M., Vining A., Koveleskie J., Sumrall W., Nossaman B. (2024): Risk of Instrumental Delivery in Maternal Obesity: Estimates With Measures of Effect Size. Ochsner Journal 24(3):192-197. https://doi.org/10.31486/toj.24.0041
  433. DeDonno M., Objio B., Crowder A. (2024): Observable, but Not Unobservable Health Numbers are Associated With Self-Reported Health: NHANES 2017-2020. American Journal of Lifestyle Medicine. https://doi.org/10.1177/15598276241291451
  434. Eskenazi M., Semenyna S., Ferguson C. (2024): Pipelines and Master Bedrooms: How Harmful is Harmful Language?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4960240
  435. Kent M., Parkinson T., Schiavon S. (2024): Indoor environmental quality in WELL-certified and LEED-certified buildings. Scientific Reports 14(1). https://doi.org/10.1038/s41598-024-65768-w
  436. Park M., Park Y., Hung H. (2024): Distribution of a p -Value When the Alternative Hypothesis is True for Binary Outcomes. Statistics in Biopharmaceutical Research 17(4):567-575. https://doi.org/10.1080/19466315.2024.2441850
  437. Garber M., Belisario K., Levitt E., McCabe R., Kelly J., MacKillop J. (2024): Psychometric validation of the Diagnostic Assessment Research Tool: Alcohol use disorder module. Alcohol and Alcoholism 60(1). https://doi.org/10.1093/alcalc/agae088
  438. Milad N., Belisario K., MacKillop J., Hirota J. (2024): Dried Cannabis Use, Tobacco Smoking, and COVID-19 Infection: Findings from a Longitudinal Observational Cohort Study. Cannabis. https://doi.org/10.26828/cannabis/2024/000248
  439. Bahman N., Naser N., Khan E., Mahmood T. (2024): Environmental science, policy, and industry nexus: Integrating Frameworks for better transport sustainability. Global Transitions 7:29-40. https://doi.org/10.1016/j.glt.2024.12.001
  440. Arning N., Fryer H., Wilson D. (2024): Identifying direct risk factors in UK Biobank with simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink. https://doi.org/10.1101/2024.01.01.24300687
  441. Vizioli N. (2024): La llamada crisis teórica de la psicología y las alternativas propuestas para sortearla. Prometeica – Revista de Filosofía y Ciencias 30:161-175. https://doi.org/10.34024/prometeica.2024.30.16057
  442. Sekulovski N., Marsman M., Wagenmakers E. (2024): A Good check on the Bayes factor. Behavior Research Methods 56(8):8552-8566. https://doi.org/10.3758/s13428-024-02491-4
  443. Sekulovski N., Marsman M., Wagenmakers E. (2024): A Good Check on the Bayes Factor. https://doi.org/10.31234/osf.io/59gj8
  444. Mi N., He Q., Liu Y., Li Y., Li Y., Wu Y., et al. (2024): Metabolic health and genetic predisposition in inflammatory bowel disease: Insights from a prospective cohort study. European Journal of Internal Medicine 128:119-126. https://doi.org/10.1016/j.ejim.2024.06.020
  445. Adeleke O., Adebayo S., Aworinde H., Adeleke O., Adeniyi A., Aroba O. (2024): Machine learning evaluation of a hypertension screening program in a university workforce over five years. Scientific Reports 14(1). https://doi.org/10.1038/s41598-024-74360-1
  446. Goldsmith P., Abel R. (2024): Embodied Cultural Capital, Social Class, Race and Ethnicity, and Sports Performance in Girls Soccer. Sociology of Sport Journal 41(3):244-254. https://doi.org/10.1123/ssj.2023-0070
  447. Janusz P., Panzera F., Bergamo P., Perron V., Fäh D. (2024): Mapping site amplification with the dense recording of ambient vibration for the city of Lucerne (Switzerland) – comparison between two approaches. https://doi.org/10.21203/rs.3.rs-3912894/v1
  448. Filip P., Vojtíšek L., Jičínská A., Valenta Z., Horák O., Hrunka M., et al. (2024): Wide-spread brain alterations early after the onset of Crohn’s disease in children in remission—a pilot study. Frontiers in Neuroscience 18. https://doi.org/10.3389/fnins.2024.1491770
  449. Chejor P., Atee M., Cain P., Whiting D., Morris T., Porock D. (2024): Pain prevalence, intensity, and association with neuropsychiatric symptoms of dementia in immigrant and non-immigrant aged care residents in Australia. Scientific Reports 14(1). https://doi.org/10.1038/s41598-024-68110-6
  450. Fabricant P. (2024): Statistical Power and Power Calculations. Practical Clinical Research Design and Application. https://doi.org/10.1007/978-3-031-58380-3_4
  451. Grünwald P., de Heide R., Koolen W. (2024): Safe testing. Journal of the Royal Statistical Society Series B: Statistical Methodology 86(5):1091-1128. https://doi.org/10.1093/jrsssb/qkae011
  452. Jacobsson P., Hopwood C., Krueger R., Söderpalm B., Nilsson T. (2024): Conceptualizing adult ADHD with the DSM alternative model of personality disorder. Personality and Mental Health 18(4):369-386. https://doi.org/10.1002/pmh.1632
  453. Sterner P., Friemelt B., Goretzko D., Kraus E., Bühner M., Pargent F. (2024): Das Konfidenz- / Signifikanzniveau impliziert ein bestimmtes Kostenverhältnis zwischen Fehler 1. Art und Fehler 2. Art. Diagnostica 70(3):126-138. https://doi.org/10.1026/0012-1924/a000329
  454. WANG Q., Takatori C., CHEN Z. (2024): Urban Dynamics and Their Implication on Greenness Trends: A 25-Year Retrospective Study of the Changing Face of Tokyo Metropolitan Area. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4820036
  455. Wang Q., Takatori C., Chen Z. (2024): Urban dynamics and their implication on greenness trends: A 25-year retrospective study of the changing face of tokyo metropolitan area. Environmental and Sustainability Indicators 23:100427. https://doi.org/10.1016/j.indic.2024.100427
  456. Holm Hansen R., von Essen M., Reith Mahler M., Cobanovic S., Sellebjerg F. (2024): Sustained effects on immune cell subsets and autoreactivity in multiple sclerosis patients treated with oral cladribine. Frontiers in Immunology 15. https://doi.org/10.3389/fimmu.2024.1327672
  457. Dananjaya R., Sutrisno S., Apriliani T. (2024): PENGARUH VARIABEL BEBAS DALAM ANALISIS KAPASITAS DUKUNG DAN PENURUNAN FONDASI TIANG MENGGUNAKAN CORRELATION BASED FEATURE SELECTION (CFS). Matriks Teknik Sipil 11(3):237. https://doi.org/10.20961/mateksi.v11i3.65116
  458. De Rosa R., Bernagozzi M., Georgoulas A., Romagnuolo L., Frosina E., Senatore A. (2024): An Open-Source algorithm for automatic geometrical optimization of extruded liquid cold plates for enhanced thermal management in railway electronics. Applied Thermal Engineering 260:124873. https://doi.org/10.1016/j.applthermaleng.2024.124873
  459. Sedrati R., Bouchachi D., Attallah R. (2024): Correlation analysis of the long-term interplay of cosmic rays, solar activity, and solar irradiance. Physica Scripta 99(11):115032. https://doi.org/10.1088/1402-4896/ad8848
  460. Boylu R., Erguvan M., Amini S. (2024): CO2 regeneration in a packed bed reactor using zeolite 13X under microwave conditions. Energy Conversion and Management 323:119265. https://doi.org/10.1016/j.enconman.2024.119265
  461. Prometeica R. (2024): Número completo 30. Prometeica – Revista de Filosofía y Ciencias 30:01-371. https://doi.org/10.34024/prometeica.2024.30.18843
  462. Leigh R., Corrigan A., Murphy R., Taylor-Pickard J., Moran C., Walsh F. (2024): Yeast mannan rich fraction positively influences microbiome uniformity, productivity associated taxa, and lay performance. Animal Microbiome 6(1). https://doi.org/10.1186/s42523-024-00295-7
  463. Giner-Sorolla R., Montoya A., Reifman A., Carpenter T., Lewis N., Aberson C., et al. (2024): Power to Detect What? Considerations for Planning and Evaluating Sample Size. Personality and Social Psychology Review 28(3):276-301. https://doi.org/10.1177/10888683241228328
  464. Mayrhofer R., Büchner I., Hevesi J. (2024): The quantitative paradigm and the nature of the human mind. The replication crisis as an epistemological crisis of quantitative psychology in view of the ontic nature of the psyche. Frontiers in Psychology 15. https://doi.org/10.3389/fpsyg.2024.1390233
  465. Kohavi R., Chen N. (2024): False Positives in A/B Tests. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3637528.3671631
  466. Bellomo R., Zavalis E., Ioannidis J. (2024): Assessment of transparency indicators in space medicine. PLOS ONE 19(4):e0300701. https://doi.org/10.1371/journal.pone.0300701
  467. de Vries S., Baliatsas C., Verheij R., Dückers M. (2024): Domestic Gardens and Morbidity: Associations between Private Green Space and Diagnosed Health Conditions in the Netherlands. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4878928
  468. Shende S., Rathored J., Barole N. (2024): Exploring Multifactorial Relationships: Assessing the Correlation Between Cardiovascular Health Indicators and Metabolic Markers. Cureus. https://doi.org/10.7759/cureus.59934
  469. Campbell S., Moore D. (2024): Overprecision in the Survey of Professional Forecasters. Collabra: Psychology 10(1). https://doi.org/10.1525/collabra.92953
  470. Schiavone S., Vazire S., Rhemtulla M. (2024): Researchers’ Perceptions of the State of Social and Personality Psychology. https://doi.org/10.31234/osf.io/ja4u2
  471. Schreiber S., Hewitt D., Seymour B., Yoshida W. (2024): Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks. Wellcome Open Research 9:423. https://doi.org/10.12688/wellcomeopenres.22288.1
  472. Ugai S., Yao Q., Takashima Y., Zhong Y., Matsuda K., Kawamura H., et al. (2024): Clinicopathological, molecular, and prognostic features of colorectal carcinomas with KRAS c.34G>T (p.G12C) mutation. Cancer Science 115(10):3455-3465. https://doi.org/10.1111/cas.16262
  473. Semenyna S., Vasey P., Honey P. (2024): Sex and Sexual Orientation Differences in Dark Triad Traits, Sexual Excitation/Inhibition, and Sociosexuality. Archives of Sexual Behavior 54(8):2907-2919. https://doi.org/10.1007/s10508-024-02895-5
  474. Bachler S., Holzknecht A., Huber J., Kirchler M. (2024): From Individual Choices to the 4-Eyes-Principle: The Big Robber Game Revisited Among Financial Professionals and Students. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4784316
  475. Schuetz S., Kuai L., Lacity M., Steelman Z. (2024): A qualitative systematic review of trust in technology. Journal of Information Technology 40(1):55-76. https://doi.org/10.1177/02683962241254392
  476. Genc S., Schiavi S., Chamberland M., Tax C., Raven E., Daducci A., et al. (2024): Developmental differences in canonical cortical networks: Insights from microstructure-informed tractography. Network Neuroscience 8(3):946-964. https://doi.org/10.1162/netn_a_00378
  477. Genc S., Ball G., Chamberland M., Raven E., Tax C., Ward I., et al. (2024): MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression. https://doi.org/10.1101/2024.07.30.605934
  478. Genc S., Ball G., Chamberland M., Raven E., Tax C., Ward I., et al. (2024): MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression. https://doi.org/10.21203/rs.3.rs-4883534/v1
  479. Briel S., Feuser N., Moldenhauer E., Kabisch J., Neubauer P., Junne S. (2024): Digital holographic microscopy is suitable for lipid accumulation analysis in single cells of Yarrowia lipolytica. Journal of Biotechnology 397:32-43. https://doi.org/10.1016/j.jbiotec.2024.11.011
  480. Varga S., Latham A. (2024): Is health the absence of disease?. Inquiry. https://doi.org/10.1080/0020174x.2024.2361694
  481. Lippert S., Dreber A., Johannesson M., Tierney W., Cyrus-Lai W., Uhlmann E., et al. (2024): Can large language models help predict results from a complex behavioural science study?. Royal Society Open Science 11(9). https://doi.org/10.1098/rsos.240682
  482. Bruns S., Deressa T., Stanley T., Doucouliagos C., Ioannidis J. (2024): Estimating the extent of selective reporting: An application to economics. Research Synthesis Methods 15(4):590-602. https://doi.org/10.1002/jrsm.1711
  483. Shimizu T., Miguchi H. (2024): Effects of Environmental Changes Caused by Small Group Selective Logging on the Carabid Beetle Assemblages in a Secondary Forest of Beech. Journal of the Japanese Forest Society 106(9):271-278. https://doi.org/10.4005/jjfs.106.271
  484. Ho T., Hsu S., Jin L., Kim D., Kim J., Leong C. (2024): How to induce honesty: results from a large-scale experiment. Policy and Society 44(2):218-228. https://doi.org/10.1093/polsoc/puae030
  485. Chondrogiannis T., Grossniklaus M. (2024): Highway systems: How good are they, really?. GeoInformatica 29(3):403-433. https://doi.org/10.1007/s10707-024-00533-9
  486. Sextl-Plötz T., Steinhoff M., Baumeister H., Cuijpers P., Ebert D., Zarski A. (2024): A systematic review of predictors and moderators of treatment outcomes in internet- and mobile-based interventions for depression. Internet Interventions 37:100760. https://doi.org/10.1016/j.invent.2024.100760
  487. Buser T., Ahlskog R., Johannesson M., Koellinger P., Oskarsson S. (2024): The causal effect of genetic variants linked to cognitive and non-cognitive skills on education and labor market outcomes. Labour Economics 90:102544. https://doi.org/10.1016/j.labeco.2024.102544
  488. Buser T. (2024): Adversarial Economic Preferences Predict Right-Wing Voting. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4688020
  489. Lee T. (2024): Separating biological variance from noise by applying EM algorithm to modified General Linear Model. https://doi.org/10.1101/2024.09.29.615661
  490. Jones T., Hedrick T., Chase A. (2024): Heterogeneity, Bayesian thinking, and phenotyping in critical care: A primer. American Journal of Health-System Pharmacy 81(18):812-832. https://doi.org/10.1093/ajhp/zxae139
  491. Yu T., Mao Y., Qiu J., Zhang Y., Liu J. (2024): Positive Memory Bias Among Grateful People: Examining Gratitude as Emotion, Mood, And Affective Trait. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5074673
  492. Spampatti T., Brosch T., Trutnevyte E., Hahnel U. (2024): A trust inoculation to protect public support of governmentally mandated actions to mitigate climate change. Journal of Experimental Social Psychology 115:104656. https://doi.org/10.1016/j.jesp.2024.104656
  493. Spampatti T., Hahnel U., Brosch T. (2024): Conservatives are less accurate than liberals at recognizing false climate statements, and disinformation makes conservatives less discerning: Evidence from 12 countries. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-160
  494. Edwards T., Dawes C., Willoughby E., McGue M., Lee J. (2024): More than g: Verbal and performance IQ as predictors of socio-political attitudes. Intelligence 108:101876. https://doi.org/10.1016/j.intell.2024.101876
  495. Ishikura T., Sato W., Takamatsu J., Yuguchi A., Cho S., Ding M., et al. (2024): Delivery of pleasant stroke touch via robot in older adults. Frontiers in Psychology 14. https://doi.org/10.3389/fpsyg.2023.1292178
  496. Ugai T., Väyrynen J., Ugai S., Zhong R., Haruki K., Lau M., et al. (2024): Long-term marine ω-3 polyunsaturated fatty acids intake in relation to incidence of colorectal cancer subclassified by macrophage infiltrates. The Innovation Medicine 2(3):100082. https://doi.org/10.59717/j.xinn-med.2024.100082
  497. Hamada T., Oyama H., Tange S., Hakuta R., Ishigaki K., Kanai S., et al. (2024): The Revised Kyoto Criteria and Risk of Malignancy Among Patients With Intraductal Papillary Mucinous Neoplasms. Clinical Gastroenterology and Hepatology 22(12):2413-2423.e18. https://doi.org/10.1016/j.cgh.2024.05.043
  498. Hamada T., Masuda A., Michihata N., Saito T., Tsujimae M., Takenaka M., et al. (2024): Comorbidity burden and outcomes of endoscopic ultrasound‐guided treatment of pancreatic fluid collections: Multicenter study with nationwide data‐based validation. Digestive Endoscopy 37(4):413-425. https://doi.org/10.1111/den.14924
  499. Rao V., Bye J., Varma S. (2024): The psychological reality of the learned “p < .05” boundary. Cognitive Research: Principles and Implications 9(1). https://doi.org/10.1186/s41235-024-00553-x
  500. Bonnici V., Chicco D. (2024): Seven quick tips for gene-focused computational pangenomic analysis. BioData Mining 17(1). https://doi.org/10.1186/s13040-024-00380-2
  501. Vovk V., Wang R. (2024): True and false discoveries with independent and sequential e‐values. Canadian Journal of Statistics 52(4). https://doi.org/10.1002/cjs.11833
  502. Chopik W., Götschi K., Carrillo A., Weidmann R., Potter J. (2024): Changes in Need for Uniqueness From 2000 Until 2020. Collabra: Psychology 10(1). https://doi.org/10.1525/collabra.121937
  503. CHANG X., GAO H., LI W. (2024): Discontinuous Distribution of Test Statistics Around Significance Thresholds in Empirical Accounting Studies. Journal of Accounting Research 63(1):165-206. https://doi.org/10.1111/1475-679x.12579
  504. Zhou X., Shen X., Johnson J., Spakowicz D., Agnello M., Zhou W., et al. (2024): Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. Cell Host & Microbe 32(4):506-526.e9. https://doi.org/10.1016/j.chom.2024.02.012
  505. Zhou X., Shen X., Johnson J., Spakowicz D., Agnello M., Zhou W., et al. (2024): Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease. https://doi.org/10.1101/2024.02.01.577565
  506. Teo Y., Shringarpure S., Jeong H., Prasannan N., Brecht B., Silberhorn C., et al. (2024): Evidence-Based Certification of Quantum Dimensions. Physical Review Letters 133(5). https://doi.org/10.1103/physrevlett.133.050204
  507. Zha Y., Chen C., Jiao Q., Zeng X., Cui X., Ning K. (2024): Comprehensive profiling of antibiotic resistance genes in diverse environments and novel function discovery. The Innovation Life 2(1):100054. https://doi.org/10.59717/j.xinn-life.2024.100054
  508. Liao Z., Scaltritti M., Xu Z., Dinh T., Chen J., Ghaderi A. (2024): A Bibliometric Analysis of Scientific Publications on Eating Disorder Prevention in the Past Three Decades. Nutrients 16(8):1111. https://doi.org/10.3390/nu16081111
  509. Dienes Z. (2024): The Inner Workings of Registered Reports. The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences. https://doi.org/10.1017/9781009000796.015
  510. Rádai Z., Váradi A., Takács P., Nagy N., Schmitt N., Prépost E., et al. (2024): An overlooked phenomenon: complex interactions of potential error sources on the quality of bacterial de novo genome assemblies. BMC Genomics 25(1). https://doi.org/10.1186/s12864-023-09910-4
  511. Zoltán Rádai, Alex Váradi, Péter Takács, Nikoletta Andrea Nagy, Nicholas D. Schmitt, Eszter Prépost, et al. (2024): Additional file 1 of An overlooked phenomenon: complex interactions of potential error sources on the quality of bacterial de novo genome assemblies. Figshare. https://doi.org/10.6084/m9.figshare.24971382
  512. González-Velasco Ó., Simon M., Yilmaz R., Parlato R., Weishaupt J., Imbusch C., et al. (2024): Identifying similar populations across independent single cell studies without data integration. https://doi.org/10.1101/2024.09.27.615367
  513. Chumakov E., Ashenbrenner Y., Gvozdetskii A., Limankin O., Petrova N. (2024): Individual Burden of Illness Index in Bipolar Disorder Remission: A Cross-Sectional Study. Consortium Psychiatricum 5(2):17-30. https://doi.org/10.17816/cp15471
  514. Corrigan A., Leigh R., Walsh F., Murphy R. (2023): Microbial community diversity and structure in the cecum of laying hens with and without mannan-rich fraction supplementation. Journal of Applied Poultry Research 32(2):100342. https://doi.org/10.1016/j.japr.2023.100342
  515. Haslam A., Olivier T., Prasad V. (2023): Design, power, and alpha levels in randomized phase II oncology trials. ESMO Open 8(1):100779. https://doi.org/10.1016/j.esmoop.2022.100779
  516. Hoffmann A., Crevecoeur F. (2023): Dissociable effects of urgency and evidence accumulation during reaching revealed by dynamic multisensory integration. https://doi.org/10.1101/2023.12.15.571806
  517. Saleh A., Chin G., Othman M., Mohamad F., Chen C. (2023): Immersive Visualization of Python Coding Using Virtual Reality. International Journal on Advanced Science, Engineering and Information Technology 13(1):336-347. https://doi.org/10.18517/ijaseit.13.1.16028
  518. Rodríguez A., Sansó B. (2023): An interview with Luis Raúl Pericchi. International Statistical Review 91(1):1-17. https://doi.org/10.1111/insr.12537
  519. Smiley A., Glazier J., Shoda Y. (2023): Null regions: a unified conceptual framework for statistical inference. Royal Society Open Science 10(11). https://doi.org/10.1098/rsos.221328
  520. Purohit A., Bergram K., Barclay L., Bezençon V., Holzer A. (2023): Starving the Newsfeed for Social Media Detox: Effects of Strict and Self-regulated Facebook Newsfeed Diets. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581187
  521. Kalwij A., De Jaegher K. (2023): The age-performance relationship for a cognitive-intensive task: Empirical evidence from chess grandmasters. Sports Economics Review 2:100010. https://doi.org/10.1016/j.serev.2023.100010
  522. Kalwij A. (2023): Risk preferences, preventive behaviour, and the probability of a loss: Empirical evidence from the COVID-19 pandemic. Social Science & Medicine 334:116169. https://doi.org/10.1016/j.socscimed.2023.116169
  523. Meule A., Hesse S., Brähler E., Hilbert A. (2023): Hedonic Overeating–Questionnaire: Exploring interactive effects between wanting, liking, and dyscontrol on body mass index. Appetite 187:106592. https://doi.org/10.1016/j.appet.2023.106592
  524. Meule A., Hesse S., Brähler E., Hilbert A. (2023): Hedonic Overeating–Questionnaire: exploring interactive effects between wanting, liking, and dyscontrol on body mass index. https://doi.org/10.31234/osf.io/r26kc
  525. Treves A., Khorozyan I. (2023): Robust inference and errors in studies of wildlife control. https://doi.org/10.21203/rs.3.rs-3478813/v1
  526. Dias A., Jácomo R., Nery L., Naves L. (2023): Effect size and inferential statistical techniques coupled with machine learning for assessing the association between prolactin concentration and metabolic homeostasis. Clinica Chimica Acta 552:117688. https://doi.org/10.1016/j.cca.2023.117688
  527. González-Roz A., Belisario K., Secades-Villa R., Muñiz J., MacKillop J. (2023): Behavioral economic analysis of legal and illegal cannabis demand in Spanish young adults with hazardous and non-hazardous cannabis use. Addictive Behaviors 149:107878. https://doi.org/10.1016/j.addbeh.2023.107878
  528. Huertas Herrera A., Toro-Manríquez M., Lorenzo C., Lencinas M., Martínez Pastur G. (2023): Perspectives on socio-ecological studies in the Northern and Southern Hemispheres. Humanities and Social Sciences Communications 10(1). https://doi.org/10.1057/s41599-023-01545-w
  529. Monteleone A., Cascino G., Meule A., Barone E., Voderholzer U., Kolar D. (2023): Pathways between childhood maltreatment and life satisfaction in adolescents with eating disorders: A network analysis. European Eating Disorders Review 31(5):724-733. https://doi.org/10.1002/erv.3000
  530. Libman A., Popova O. (2023): Children of Communism: Former Party Membership and the Demand for Redistribution. Eastern European Economics 61(3):199-237. https://doi.org/10.1080/00128775.2023.2195833
  531. Alim-Marvasti A., Jawad M., Ogbonnaya C., Naghieh A. (2023): Workforce diversity in specialist physicians: Implications of findings for religious affiliation in Anaesthesia & Intensive Care. PLOS ONE 18(8):e0288516. https://doi.org/10.1371/journal.pone.0288516
  532. Gupta A., Bosco F. (2023): Tempest in a teacup: An analysis of p-Hacking in organizational research. PLOS ONE 18(2):e0281938. https://doi.org/10.1371/journal.pone.0281938
  533. Quiroga Gutierrez A., Lindegger D., Taji Heravi A., Stojanov T., Sykora M., Elayan S., et al. (2023): Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action. International Journal of Environmental Research and Public Health 20(2):1473. https://doi.org/10.3390/ijerph20021473
  534. Stefan A., Schönbrodt F. (2023): Big little lies: a compendium and simulation of p -hacking strategies. Royal Society Open Science 10(2). https://doi.org/10.1098/rsos.220346
  535. Kelberga (Kelberg) A., Martinsone B. (2023): Motivation of sex workers who provide camming services to engage in sex with their real-life and virtual partners. Frontiers in Psychology 14. https://doi.org/10.3389/fpsyg.2023.1173902
  536. Seewald A., Rief W. (2023): Therapist’s warmth and competence increased positive outcome expectations and alliance in an analogue experiment. Psychotherapy Research 34(5):663-678. https://doi.org/10.1080/10503307.2023.2241630
  537. Closas A., Arriola E., Amarilla M., Jovanovich E. (2023): Relaciones causales entre aspectos de educación virtual y percepción del aprendizaje ad-quirido en contexto de pandemia. Cuaderno de Pedagogía Universitaria 20(39):97-110. https://doi.org/10.29197/cpu.v20i39.486
  538. Alhamdan A., Murphy M., Pickering H., Crewther S. (2023): The Contribution of Visual and Auditory Working Memory and Non-Verbal IQ to Motor Multisensory Processing in Elementary School Children. Brain Sciences 13(2):270. https://doi.org/10.3390/brainsci13020270
  539. Alhamdan A., Murphy M., Crewther S. (2023): Visual Motor Reaction Times Predict Receptive and Expressive Language Development in Early School-Age Children. Brain Sciences 13(6):965. https://doi.org/10.3390/brainsci13060965
  540. Spanos A. (2023): Revisiting the Large n (Sample Size) Problem: How to Avert Spurious Significance Results. Stats 6(4):1323-1338. https://doi.org/10.3390/stats6040081
  541. Spanos A. (2023): Why the Medical Diagnostic Screening Perspective Misrepresents Frequentist Testing and Misdiagnoses the Replication Crisis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4508545
  542. Lechat B., Nguyen D., Reynolds A., Loffler K., Escourrou P., McEvoy R., et al. (2023): Single-Night Diagnosis of Sleep Apnea Contributes to Inconsistent Cardiovascular Outcome Findings. CHEST 164(1):231-240. https://doi.org/10.1016/j.chest.2023.01.027
  543. Guttman-Kenney B., Adams P., Hunt S., Laibson D., Stewart N., Leary J. (2023): The Semblance of Success in Nudging Consumers to Pay Down Credit Card Debt. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4601712
  544. Cornwell B., Qu T. (2023): “I Love You to Death”: Social Networks and the Widowhood Effect on Mortality. Journal of Health and Social Behavior 65(2):273-291. https://doi.org/10.1177/00221465231175685
  545. Dreyfuss B., Heffetz O., Hoffman G., Ishai G., Kshirsagar A. (2023): Additive vs. subtractive earning in shared human-robot work environments. Journal of Economic Behavior & Organization 217:692-704. https://doi.org/10.1016/j.jebo.2023.11.024
  546. Dreyfuss B., Heffetz O., Hoffman G., Ishai G., Kshirsagar A. (2023): Additive vs. Subtractive Earning in Shared Human-Robot Work Environments. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4642203
  547. Kennedy B., Most S., Grootswagers T., Bowden V. (2023): Memory benefits when actively, rather than passively, viewing images. Attention, Perception, & Psychophysics 86(1):1-8. https://doi.org/10.3758/s13414-023-02814-1
  548. Willy B., Beyene A., Amare D. (2023): The Determinants of Beef Cattle Market Participation on Beef Cattle Producers’ Welfare: A Case Study of West Shewa Zone, Oromia Region, Ethiopia. Advances in Agriculture 2023:1-13. https://doi.org/10.1155/2023/8822032
  549. O’Connor C. (2023): Modelling Scientific Communities. Cambridge University Press eBooks. https://doi.org/10.1017/9781009359535
  550. de Benito Moreno C. (2023): From (semi-)oppositional to non-oppositional middles: the case of Spanish reír(se). STUF – Language Typology and Universals 76(2):121-164. https://doi.org/10.1515/stuf-2023-2006
  551. Burns C., Fracasso A., Rousselet G. (2023): Bias in data-driven estimates of the replicability of univariate brain-wide association studies. https://doi.org/10.1101/2023.09.21.558661
  552. Andica C., Kamagata K., Uchida W., Saito Y., Takabayashi K., Hagiwara A., et al. (2023): Fiber‐Specific White Matter Alterations in Parkinson’s Disease Patients with GBA Gene Mutations. Movement Disorders 38(11):2019-2030. https://doi.org/10.1002/mds.29578
  553. Huber C., Dreber A., Huber J., Johannesson M., Kirchler M., Weitzel U., et al. (2023): Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs. Proceedings of the National Academy of Sciences 120(23). https://doi.org/10.1073/pnas.2215572120
  554. Brydges C., Che X., Lipkin W., Fiehn O. (2023): Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts. Metabolites 13(9):984. https://doi.org/10.3390/metabo13090984
  555. Evans C., Cipolli W., Draper Z., Binfet J. (2023): Repurposing a Peer-Reviewed Publication to Engage Students in Statistics: An Illustration of Study Design, Data Collection, and Analysis. Journal of Statistics and Data Science Education 31(3):236-247. https://doi.org/10.1080/26939169.2023.2238018
  556. Freijo C., Herraiz J., Arias-Valcayo F., Ibáñez P., Moreno G., Villa-Abaunza A., et al. (2023): Robustness of Single- and Dual-Energy Deep-Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays. Algorithms 16(12):565. https://doi.org/10.3390/a16120565
  557. Davis-Stober C., Dana J., Kellen D., McMullin S., Bonifay W. (2023): Better Accuracy for Better Science . . . Through Random Conclusions. Perspectives on Psychological Science 19(1):223-243. https://doi.org/10.1177/17456916231182097
  558. Gallego-Fabrega C., Temprano-Sagrera G., Cárcel-Márquez J., Muiño E., Cullell N., Lledós M., et al. (2023): A multitrait genetic study of hemostatic factors and hemorrhagic transformation after stroke treatment. Journal of Thrombosis and Haemostasis 22(4):936-950. https://doi.org/10.1016/j.jtha.2023.11.027
  559. Das D., Das T. (2023): The “P”-Value: The Primary Alphabet of Research Revisited. International Journal of Preventive Medicine 14(1). https://doi.org/10.4103/ijpvm.ijpvm_200_22
  560. Münch D., Bortoleto E., Lima R. (2023): Analysis of the effect of a banded structure on jaspilite abrasiveness. Wear 530-531:204960. https://doi.org/10.1016/j.wear.2023.204960
  561. Vélez Ramos D., Pericchi Guerra L., Pérez Hernández M. (2023): From p-Values to Posterior Probabilities of Null Hypotheses. Entropy 25(4):618. https://doi.org/10.3390/e25040618
  562. Lammers D., Richman J., Holcomb J., Jansen J. (2023): Use of Bayesian Statistics to Reanalyze Data From the Pragmatic Randomized Optimal Platelet and Plasma Ratios Trial. JAMA Network Open 6(2):e230421. https://doi.org/10.1001/jamanetworkopen.2023.0421
  563. Lammers D., McClellan J. (2023): Modern Statistical Methods for the Surgeon Scientist. Surgical Clinics of North America 103(2):259-269. https://doi.org/10.1016/j.suc.2022.12.001
  564. Bickel D. (2023): The p-value interpreted as the posterior probability of explaining the data: Applications to multiple testing and to restricted parameter spaces. Sankhya A 86(1):464-493. https://doi.org/10.1007/s13171-023-00328-4
  565. Bickel D. (2023): Fiducialize statistical significance: transformingp-values into conservative posterior probabilities and Bayes factors. Statistics 57(4):941-959. https://doi.org/10.1080/02331888.2023.2232912
  566. Chicco D., Shiradkar R. (2023): Ten quick tips for computational analysis of medical images. PLOS Computational Biology 19(1):e1010778. https://doi.org/10.1371/journal.pcbi.1010778
  567. Chicco D., Haupt R., Garaventa A., Uva P., Luksch R., Cangelosi D. (2023): Computational intelligence analysis of high-risk neuroblastoma patient health records reveals time to maximum response as one of the most relevant factors for outcome prediction. European Journal of Cancer 193:113291. https://doi.org/10.1016/j.ejca.2023.113291
  568. Gorman D. (2023): Commentary: Effectiveness of a hybrid digital substance abuse prevention approach combining e-learning and in-person class sessions. Frontiers in Digital Health 5. https://doi.org/10.3389/fdgth.2023.1158414
  569. Kagan D., Hollings J., Batabyal A., Lukowiak K. (2023): Five-minute exposure to a novel appetitive food substance is sufficient time for a microRNA-dependent long-term memory to form. Journal of Comparative Physiology A 210(1):83-90. https://doi.org/10.1007/s00359-023-01650-w
  570. Büsch D., Loffing F. (2023): Interpretation of empirical results in intervention studies: a commentary and kick-off for discussion. German Journal of Exercise and Sport Research 54(4):615-620. https://doi.org/10.1007/s12662-023-00915-5
  571. van Ravenzwaaij D., Bakker M., Heesen R., Romero F., van Dongen N., Crüwell S., et al. (2023): Perspectives on scientific error. Royal Society Open Science 10(7). https://doi.org/10.1098/rsos.230448
  572. Van den Bergh D., Vandermeulen N., Lesterhuis M., De Maeyer S., Van Steendam E., Rijlaarsdam G., et al. (2023): How Prior Information from National Assessments can be used when Designing Experimental Studies without a Control Group. Journal of Writing Research 14(vol. 14 issue 3):447-469. https://doi.org/10.17239/jowr-2023.14.03.05
  573. Hermer E., Irwin A., Roche D., Dakin R. (2023): How prior and p-value heuristics are used when interpreting data. https://doi.org/10.1101/2023.09.03.556128
  574. Surucu-Balci E., Iris Ç., Balci G. (2023): Digital information in maritime supply chains with blockchain and cloud platforms: Supply chain capabilities, barriers, and research opportunities. Technological Forecasting and Social Change 198:122978. https://doi.org/10.1016/j.techfore.2023.122978
  575. Levitt E., Belisario K., Gillard J., DeJesus J., Gohari M., Leatherdale S., et al. (2023): High-resolution examination of changes in drinking during the COVID-19 pandemic: Nine-wave findings from a longitudinal observational cohort study of community adults. Journal of Psychiatric Research 168:249-255. https://doi.org/10.1016/j.jpsychires.2023.10.027
  576. Zavalis E., Contopoulos-Ioannidis D., Ioannidis J. (2023): Transparency in Infectious Disease Research: Meta-research Survey of Specialty Journals. The Journal of Infectious Diseases 228(3):227-234. https://doi.org/10.1093/infdis/jiad130
  577. KARA E. (2023): Tekrarlanabilirlik Krizi ve Geçerlilik Krizi Kıskacındaki Psikoloji ve Sosyal Bilimlerde Krizden Çıkış İçin Öne Çıkan İki Trend: “Yeni İstatistik” ve “Bayesyen İstatistik”. Anadolu Journal of Educational Sciences International 13(2):599-624. https://doi.org/10.18039/ajesi.1240655
  578. Steiner E., Young S. (2023): Sex Differences in Attention and Attitude Toward Infant and Sexual Images. Archives of Sexual Behavior 52(8):3291-3299. https://doi.org/10.1007/s10508-023-02676-6
  579. Wagenmakers E., Sarafoglou A., Aczel B. (2023): Facing the Unknown Unknowns of Data Analysis. Current Directions in Psychological Science 32(5):362-368. https://doi.org/10.1177/09637214231168565
  580. McClure E., Hamilton L., Schauer G., Matson T., Lapham G. (2023): Cannabis and nicotine co-use among primary care patients in a state with legal cannabis access. Addictive Behaviors 140:107621. https://doi.org/10.1016/j.addbeh.2023.107621
  581. Zisler E., Meule A., Koch S., Voderholzer U. (2023): Willingness to experience unpleasant thoughts, emotions, and bodily sensations at admission does not predict treatment outcome in inpatients with obsessive–compulsive disorder. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-2405445/v1
  582. CHEN F., LI Y., WANG X., WU G., LI P., GENG J., et al. (2023): Experimental study of density gradient-driven micro-instabilities and the confinement degradation during H-mode in EAST. Plasma Science and Technology 25(8):085102. https://doi.org/10.1088/2058-6272/acc4aa
  583. Li F., Lin Q., Zhao X., Hu Z. (2023): Description length guided nonlinear unified Granger causality analysis. Network Neuroscience 7(3):1109-1128. https://doi.org/10.1162/netn_a_00316
  584. Blondiaux F., Lebrun L., Hanseeuw B., Crevecoeur F. (2023): Impairments of saccadic and reaching adaptation in essential tremor are linked to movement execution. Journal of Neurophysiology 130(5):1092-1102. https://doi.org/10.1152/jn.00165.2023
  585. Blondiaux F., Lebrun L., Hanseeuw B., Crevecoeur F. (2023): Impairments of motor adaptation in Essential Tremor are linked to movement execution. https://doi.org/10.1101/2023.04.21.537795
  586. Pargent F., Koch T., Kleine A., Lermer E., Gaube S. (2023): A Tutorial on Tailored Simulation-Based Sample-Size Planning for Experimental Designs with Generalized Linear Mixed Models. https://doi.org/10.31234/osf.io/rpjem
  587. Fossen F., Neyse L. (2023): Entrepreneurship, Management, and Cognitive Reflection: A Preregistered Replication Study With Extensions. Entrepreneurship Theory and Practice 48(4):1082-1109. https://doi.org/10.1177/10422587231211005
  588. Fossen F., Neyse L., Schroeder C. (2023): Does Cognitive Reflection Relate to Preferences and Socio-Economic Outcomes?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4599840
  589. Bartoš F., Wagenmakers E. (2023): A general approximation to nested Bayes factors with informed priors. Stat 12(1). https://doi.org/10.1002/sta4.600
  590. Bartoš F., Maier M., Stanley T., Wagenmakers E. (2023): Robust Bayesian Meta-Regression — Model-Averaged Moderation Analysis in the Presence of Publication Bias. https://doi.org/10.31234/osf.io/98xb5
  591. Gunnarsdottir F., Bendahl P., Johansson A., Benfeitas R., Rydén L., Bergenfelz C., et al. (2023): Serum immuno-oncology markers carry independent prognostic information in patients with newly diagnosed metastatic breast cancer, from a prospective observational study. Breast Cancer Research 25(1). https://doi.org/10.1186/s13058-023-01631-6
  592. Cerono G., Melaiu O., Chicco D. (2023): Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme. Journal of Healthcare Informatics Research 8(1):1-18. https://doi.org/10.1007/s41666-023-00138-1
  593. Di Poi G., Dukes D., Meuleman B., Banta Lavenex P., Lavenex P., Papon A., et al. (2023): Anxiety in families of individuals with neurodevelopmental conditions in the early months of the COVID-19 pandemic in Switzerland. Frontiers in Education 8. https://doi.org/10.3389/feduc.2023.951970
  594. Chernov G. (2023): How We Identify Cause-Effect Relationships Given Evidence: An Exploratory Study. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4657687
  595. Giguère G., Bourassa C., Brouillette-Alarie S. (2023): Effect of the differential item functioning (DIF) of LS/CMI items with convicted men and women. Journal of Experimental Criminology 20(3):761-785. https://doi.org/10.1007/s11292-023-09559-9
  596. Elomaa H., Ahtiainen M., Väyrynen S., Ogino S., Nowak J., Lau M., et al. (2023): Spatially resolved multimarker evaluation of CD274 (PD-L1)/PDCD1 (PD-1) immune checkpoint expression and macrophage polarisation in colorectal cancer. British Journal of Cancer 128(11):2104-2115. https://doi.org/10.1038/s41416-023-02238-6
  597. Huang H., Wlazlo P., Sahu A., Walker A., Goulart A., Davis K., et al. (2023): Validating an Emulation-Based Cybersecurity Model With a Physical Testbed. IEEE Transactions on Dependable and Secure Computing 21(4):2997-3011. https://doi.org/10.1109/tdsc.2023.3321176
  598. Wu H., Harezlak J. (2023): Application of de-shape synchrosqueezing to estimate gait cadence from a single-sensor accelerometer placed in different body locations. Physiological Measurement 44(5):055009. https://doi.org/10.1088/1361-6579/accefe
  599. Douglas H. (2023): The importance of values for science. Interdisciplinary Science Reviews 48(2):251-263. https://doi.org/10.1080/03080188.2023.2191559
  600. Raspe H., Zielonka V., Hofer G. (2023): Klinische Forschung im Dienst der Heilkunde: Kontexte, Praxis, Methodik und Theorie des „klinischen Beweises“ von Paul Martini. Beitrag 3: Kausalanalytisch relevante Elemente des klinischen Beweises und epistemologische Kommentare. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen 182-183:106-113. https://doi.org/10.1016/j.zefq.2023.07.005
  601. Latan H., Lopes de Sousa Jabbour A., Sarkis J., Chiappetta Jabbour C., Ali M. (2023): The nexus of supply chain performance and blockchain technology in the digitalization era: Insights from a fast-growing economy. Journal of Business Research 172:114398. https://doi.org/10.1016/j.jbusres.2023.114398
  602. Santos H., Mestechkin R., Vogt-Yerem R., Rosica D., Heck P., Heydari P., et al. (2023): To respond or not to respond: The effect of forced and prefer not to answer options in demographic questionnaires. https://doi.org/10.31234/osf.io/ga6b2
  603. Dobewall H., Keltikangas-Järvinen L., Marttila S., Mishra P., Saarinen A., Cloninger C., et al. (2023): The relationship of trait-like compassion with epigenetic aging: The population-based prospective Young Finns Study. Frontiers in Psychiatry 14. https://doi.org/10.3389/fpsyt.2023.1018797
  604. Ando H., Ahmed W., Okabe S., Kitajima M. (2023): Tracking the effects of the COVID-19 pandemic on viral gastroenteritis through wastewater-based retrospective analyses. Science of The Total Environment 905:166557. https://doi.org/10.1016/j.scitotenv.2023.166557
  605. Khorozyan I., Heurich M. (2023): Patterns of predation by the Eurasian lynx Lynx lynx throughout its range: ecological and conservation implications. Mammal Review 53(3):177-188. https://doi.org/10.1111/mam.12317
  606. Ciubotariu I., Bosch G. (2023): Teaching students to R3eason, not merely to solve problem sets: The role of philosophy and visual data communication in accessible data science education. PLOS Computational Biology 19(6):e1011160. https://doi.org/10.1371/journal.pcbi.1011160
  607. Jaljuli I., Kafkafi N., Giladi E., Golani I., Gozes I., Chesler E., et al. (2023): A multi-lab experimental assessment reveals that replicability can be improved by using empirical estimates of genotype-by-lab interaction. PLOS Biology 21(5):e3002082. https://doi.org/10.1371/journal.pbio.3002082
  608. Böschen I. (2023): Changes in methodological study characteristics in psychology between 2010-2021. PLOS ONE 18(5):e0283353. https://doi.org/10.1371/journal.pone.0283353
  609. Bargiotas I., Kalogeratos A., Vayatis N. (2023): A Framework for Paired-Sample Hypothesis Testing for High-Dimensional Data. 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). https://doi.org/10.1109/ictai59109.2023.00011
  610. Ng J., Chong J., Ng H. (2023): The way I see the world, the way I envy others: a person-centered investigation of worldviews and the malicious and benign forms of envy among adolescents and adults. Humanities and Social Sciences Communications 10(1). https://doi.org/10.1057/s41599-023-02409-z
  611. Kim J., Shamsuddin A. (2023): Stock market anomalies: An extreme bounds analysis. International Review of Financial Analysis 90:102841. https://doi.org/10.1016/j.irfa.2023.102841
  612. Polidori J., Paulson H., Gronewold A. (2023): Assessing trends in urban municipal water use across the Great Lakes Basin. Journal of Great Lakes Research 50(1):102243. https://doi.org/10.1016/j.jglr.2023.102243
  613. Greenberg J., Sands D., Cattani G., Porac J. (2023): Rating systems and increased heterogeneity in firm performance: Evidence from the New York City Restaurant Industry, 1994–2013. Strategic Management Journal 45(1):36-65. https://doi.org/10.1002/smj.3545
  614. Silver J., Shi L. (2023): Punishing Protesters on the “Other Side”: Partisan Bias in Public Support for Repressive and Punitive Responses to Protest Violence. Socius: Sociological Research for a Dynamic World 9. https://doi.org/10.1177/23780231231182908
  615. Zemla J., Sloman S., Bechlivanidis C., Lagnado D. (2023): Not so simple! Causal mechanisms increase preference for complex explanations. Cognition 239:105551. https://doi.org/10.1016/j.cognition.2023.105551
  616. Ma J., Kalincik T., Sharmin S., Malpas C. (2023): The <i>p</i> Value is a Poor Measure of Strength of Evidence in Multiple Sclerosis Research. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4498419
  617. Wulff J., Taylor L. (2023): How and why alpha should depend on sample size: A Bayesian-frequentist compromise for significance testing. Strategic Organization 22(3):550-581. https://doi.org/10.1177/14761270231214429
  618. Wulff J., Taylor L. (2023): How and why alpha should depend on sample size: A Bayesian-frequentist compromise for significance testing. https://doi.org/10.31234/osf.io/3cbh7
  619. Fu J., Hsiao C. (2023): The Data Mechanisms of Diagnosis and Intelligence. Symmetry 15(2):278. https://doi.org/10.3390/sym15020278
  620. Sun J., Xie Y. (2023): Reply to Y.-F. Zhao et al. Journal of Clinical Oncology 41(16):3076-3077. https://doi.org/10.1200/jco.23.00332
  621. Stevenson J. (2023): Data Analysis Frameworks for Investigating Behavioural Differences. Developmental Psychopathology. https://doi.org/10.1007/978-3-031-45787-6_2
  622. Li J., Huang T., Zhang M., Tong X., Chen J., Zhang Z., et al. (2023): Metagenomic sequencing reveals swine lung microbial communities and metagenome-assembled genomes associated with lung lesions—a pilot study. International Microbiology 26(4):893-906. https://doi.org/10.1007/s10123-023-00345-1
  623. Sinsuebchuea J., Paenkaew P., Wutthiin M., Nantanaranon T., Laeman K., Kittichotirat W., et al. (2023): Characterization of the Gut Microbiota in Urban Thai Individuals Reveals Enterotype-Specific Signature. Microorganisms 11(1):136. https://doi.org/10.3390/microorganisms11010136
  624. van Hugten J., Coreynen W., Vanderstraeten J., van Witteloostuijn A. (2023): The Dunning-Kruger effect and entrepreneurial self-efficacy: How tenure and search distance jointly direct entrepreneurial self-efficacy. Journal of Business Research 161:113810. https://doi.org/10.1016/j.jbusres.2023.113810
  625. Peloquin J., Santare M., Elliott D. (2023): Volume Loss and Recovery in Bovine Knee Meniscus Loaded in Circumferential Tension. Journal of Biomechanical Engineering 145(7). https://doi.org/10.1115/1.4062142
  626. DelosReyes J., Padilla M. (2023): Bootstrap Correlation Confidence Interval Estimation: The Positive Impact of a Symmetric Distribution. The Journal of Experimental Education 92(3):559-579. https://doi.org/10.1080/00220973.2023.2196659
  627. Ioannidis J., Contopoulos-Ioannidis D. (2023): Prepandemic cross-reactive humoral immunity to SARS-CoV-2 in Africa: Systematic review and meta-analysis. International Journal of Infectious Diseases 134:160-167. https://doi.org/10.1016/j.ijid.2023.06.009
  628. Birch J. (2023): Medical AI, inductive risk and the communication of uncertainty: the case of disorders of consciousness. Journal of Medical Ethics. https://doi.org/10.1136/jme-2023-109424
  629. Kang J., Kim D., Rhee J., Seo J., Park I., Kim J., et al. (2023): Baf155 regulates skeletal muscle metabolism via HIF-1a signaling. PLOS Biology 21(7):e3002192. https://doi.org/10.1371/journal.pbio.3002192
  630. Mulder J., Friel N., Leifeld P. (2023): Bayesian testing of scientific expectations under exponential random graph models. Social Networks 78:40-53. https://doi.org/10.1016/j.socnet.2023.11.004
  631. Kuipers J., de Winter J., Mulder M. (2023): From fear to forecast: The role of simulators, accompanied driving, age, gender, and information-processing style in driver training and beyond. Transportation Research Part F: Traffic Psychology and Behaviour 99:389-407. https://doi.org/10.1016/j.trf.2023.10.003
  632. Fink J., Palan S., Theissen E. (2023): Earnings Autocorrelation and the Post-Earnings-Announcement Drift: Experimental Evidence. Journal of Financial and Quantitative Analysis 59(6):2799-2837. https://doi.org/10.1017/s0022109023000881
  633. Arroyo-Barriguete J., Bada C., Lazcano L., Márquez J., Ortiz-Lozano J., Rua-Vieites A. (2023): Is it possible to redress noninstructional biases in student evaluation of teaching surveys? Quantitative analysis in accounting and finance courses. Studies in Educational Evaluation 77:101263. https://doi.org/10.1016/j.stueduc.2023.101263
  634. Reiter J., Knorr A., Lengersdorff L., Pfundmair M. (2023): Same same but different? – How much of a difference does it make how we operationalize attitudes towards political violence in radicalization studies?. https://doi.org/10.31234/osf.io/k83b4
  635. Rose J., Kirchler M., Palan S. (2023): Status and reputation nudging. Journal of Behavioral and Experimental Economics 105:102031. https://doi.org/10.1016/j.socec.2023.102031
  636. Yanaoka K., Nishida K., Endo T. (2023): Asymmetric impacts of ingroup behaviors on delay of gratification in preschoolers. Cognitive Development 68:101381. https://doi.org/10.1016/j.cogdev.2023.101381
  637. Hartman K., Chen I., van der Harst P., Moura A., Jahnke M., Pilot M., et al. (2023): Kinship study reveals stable non-kin-based associations in a medium-sized delphinid. Behavioral Ecology and Sociobiology 77(12). https://doi.org/10.1007/s00265-023-03411-w
  638. Flösch K., Flaisch T., Imhof M., Schupp H. (2023): Alpha/beta oscillations reveal cognitive and affective brain states associated with role taking in a dyadic cooperative game. Cerebral Cortex 34(1). https://doi.org/10.1093/cercor/bhad487
  639. Miller K., Weiss C. (2023): Disentangling Population Level Differences in Juvenile Migration Phenology for Three Species of Salmon on the Yukon River. Journal of Marine Science and Engineering 11(3):589. https://doi.org/10.3390/jmse11030589
  640. Chisholm K., Schirmbeck F., Pinkham A., Sasson N., Simons C., de Haan L., et al. (2023): A Cross-sectional Conceptual Replication and Longitudinal Evaluation of the PANSS-Autism-Severity-Score Measure Suggests it Does Not Capture Autistic Traits in Individuals With Psychosis. Schizophrenia Bulletin 51(1):186-197. https://doi.org/10.1093/schbul/sbad161
  641. Kosumi K., Baba Y., Yamamura K., Nomoto D., Okadome K., Yagi T., et al. (2023): Intratumour Fusobacterium nucleatum and immune response to oesophageal cancer. British Journal of Cancer 128(6):1155-1165. https://doi.org/10.1038/s41416-022-02112-x
  642. Bourne K., Curtis A., Chipman J., Chen C., Borsuk M. (2023): Patterns of Co-contamination in Freshwater and Marine Fish of the Northeastern USA. Environmental Modeling & Assessment 28(6):1127-1137. https://doi.org/10.1007/s10666-023-09912-2
  643. Seale K., Teschendorff A., Reiner A., Voisin S., Eynon N. (2023): A comprehensive map of the ageing blood methylome. https://doi.org/10.1101/2023.12.20.572666
  644. Rostgaard K. (2023): Simple nested Bayesian hypothesis testing for meta-analysis, Cox, Poisson and logistic regression models. Scientific Reports 13(1). https://doi.org/10.1038/s41598-023-31838-8
  645. Türkarslan K., Çınarbaş D., Perogamvros L. (2023): The Roles of Intrusive Visual Imagery and Verbal Thoughts in Pre-Sleep Arousal of Patients with Insomnia Disorder: A Path Model. Cognitive Therapy and Research 49(1):193-205. https://doi.org/10.1007/s10608-023-10442-0
  646. Hall L., Dawel A., Greenwood L., Monaghan C., Berryman K., Jack B. (2023): Estimating statistical power for ERP studies using the auditory N1, Tb, and P2 components. Psychophysiology 60(11). https://doi.org/10.1111/psyp.14363
  647. Heatlie L., Petterson L., Vasey P. (2023): Heterosexual Men’s Visual Attention to Nude Images Depicting Cisgender Males, Cisgender Females, and Gynandromorphs. Archives of Sexual Behavior 54(8):2893-2905. https://doi.org/10.1007/s10508-023-02657-9
  648. Heatlie L., Petterson L., Vasey P. (2023): Heterosexual men’s pupillary responses to stimuli depicting cisgender males, cisgender females, and gynandromorphs. Biological Psychology 178:108518. https://doi.org/10.1016/j.biopsycho.2023.108518
  649. Botzet L., Shea A., Vitzthum V., Druet A., Sheesley M., Gerlach T. (2023): The Link Between Age and Partner Preferences in a Large, International Sample of Single Women. Human Nature 34(4):539-568. https://doi.org/10.1007/s12110-023-09460-4
  650. Nauha L., Farrahi V., Jurvelin H., Jämsä T., Niemelä M., Kangas M., et al. (2023): Comparison and agreement between device-estimated and self-reported sleep periods in adults. Annals of Medicine 55(1). https://doi.org/10.1080/07853890.2023.2191001
  651. Pettitt L., Biswas R., Bhowmik J. (2023): Women’s Attitudes Toward Intimate Partner Violence in Low- and Middle-Income Countries of Southern Asia. American Journal of Health Promotion 38(1):12-18. https://doi.org/10.1177/08901171231198451
  652. Heursen L., Friess S., Chugunova M. (2023): Reputational Concerns and Advice-Seeking at Work. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4538216
  653. Lin L., Li C., Chen S., Boucher N., Chung C. (2023): Transverse growth of the mandibular body in untreated children: a longitudinal CBCT study. Clinical Oral Investigations 27(5):2097-2107. https://doi.org/10.1007/s00784-023-05019-w
  654. Siena L., Papamanolis L., Siebert M., Bellomo R., Ioannidis J. (2023): Industry Involvement and Transparency in the Most Cited Clinical Trials, 2019-2022. JAMA Network Open 6(11):e2343425. https://doi.org/10.1001/jamanetworkopen.2023.43425
  655. Neyse L., Fossen F., Johannesson M., Dreber A. (2023): Cognitive reflection and 2D:4D: Evidence from a large population sample. Journal of Economic Behavior & Organization 209:288-307. https://doi.org/10.1016/j.jebo.2023.03.020
  656. Bright L., Heesen R. (2023): To Be Scientific Is To Be Communist. Social Epistemology 37(3):249-258. https://doi.org/10.1080/02691728.2022.2156308
  657. Wang L., Saeedi B., Mahdi Z., Krasinskas A., Robinson B. (2023): Analysis of KRAS Mutations in Gastrointestinal Tract Adenocarcinomas Reveals Site-Specific Mutational Signatures. Modern Pathology 36(2):100014. https://doi.org/10.1016/j.modpat.2022.100014
  658. Favela L., Machery E. (2023): Investigating the concept of representation in the neural and psychological sciences. Frontiers in Psychology 14. https://doi.org/10.3389/fpsyg.2023.1165622
  659. Strickland L., Boag R., Heathcote A., Bowden V., Loft S. (2023): Automated decision aids: When are they advisors and when do they take control of human decision making?. Journal of Experimental Psychology: Applied 29(4):849-868. https://doi.org/10.1037/xap0000463
  660. Diaz L., Veeranki Y., Marmolejo-Ramos F., Posada-Quintero H. (2023): EDA-graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection. https://doi.org/10.36227/techrxiv.24311716.v1
  661. Mercado Diaz L., Rao Veeranki Y., Marmolejo-Ramos F., Posada-Quintero H. (2023): EDA-graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection. https://doi.org/10.36227/techrxiv.24311716
  662. Sulzbach Denardin M., Bumiller-Bini Hoch V., Salviano-Silva A., Lobo-Alves S., Adelman Cipolla G., Malheiros D., et al. (2023): Genetic Association and Differential RNA Expression of Histone (De)Acetylation-Related Genes in Pemphigus Foliaceus—A Possible Epigenetic Effect in the Autoimmune Response. Life 14(1):60. https://doi.org/10.3390/life14010060
  663. Radzvilas M., Peden W., De Pretis F. (2023): Making decisions with evidential probability and objective Bayesian calibration inductive logics. International Journal of Approximate Reasoning 162:109030. https://doi.org/10.1016/j.ijar.2023.109030
  664. Furlan M., Mariano E. (2023): Effects of democracy, social inequality and economic growth on climate justice: An analysis with structural equation modelling. Natural Resources Forum 48(2):435-467. https://doi.org/10.1111/1477-8947.12320
  665. Garcia M., Coz-Yataco A., Al-Jaghbeer M. (2023): Pulmonary arterial hypertension trials put to the test: Using the fragility index to assess trials robustness. Heart & Lung 61:147-152. https://doi.org/10.1016/j.hrtlng.2023.05.019
  666. Bogomolov M., Heller R. (2023): Replicability Across Multiple Studies. Statistical Science 38(4). https://doi.org/10.1214/23-sts892
  667. Brandt M., Vallabha S. (2023): Intraindividual Changes in Political Identity Strength (But Not Direction) Are Associated With Political Animosity in the United States and the Netherlands. Personality and Social Psychology Bulletin 51(5):828-844. https://doi.org/10.1177/01461672231203471
  668. Sarstedt M., Adler S. (2023): An advanced method to streamline p-hacking. Journal of Business Research 163:113942. https://doi.org/10.1016/j.jbusres.2023.113942
  669. Sarstedt M., Adler S. (2023): An advanced method to streamline p-hacking. https://doi.org/10.31234/osf.io/5ynfw
  670. Mas-Machuca M., Akhmedova A., Marimon F. (2023): The social mission works: internalizing the mission to achieve organizational performance in social enterprises. Review of Managerial Science 18(4):965-989. https://doi.org/10.1007/s11846-023-00627-y
  671. Eisend M., Kuß A. (2023): Hypothesen und Modelle beim Theorie-Test. Grundlagen empirischer Forschung. https://doi.org/10.1007/978-3-658-42690-3_7
  672. Lakomý M. (2023): Impact of family structure on the quality of life of older adults, its stability, and gender differences in the European context. Acta Sociologica 67(3):330-351. https://doi.org/10.1177/00016993231210660
  673. Courtney M., Karakus M., Sharplin E., Hernández-Torrano D., Helmer J., Jumakulov Z. (2023): The role of teacher selection criteria and preparation on teacher self-efficacy, satisfaction, and commitment: an analysis of Kazakhstani TALIS data. Teacher Development 27(3):394-414. https://doi.org/10.1080/13664530.2023.2176354
  674. Fritz M. (2023): Implementation and Assessment of a Multipurpose Appraisal-Driven Emotion Awareness Tool Based on Self-Report. https://doi.org/10.31237/osf.io/myezb
  675. Korbmacher M., Azevedo F., Pennington C., Hartmann H., Pownall M., Schmidt K., et al. (2023): The replication crisis has led to positive structural, procedural, and community changes. Communications Psychology 1(1). https://doi.org/10.1038/s44271-023-00003-2
  676. Linde M., van Ravenzwaaij D. (2023): baymedr: an R package and web application for the calculation of Bayes factors for superiority, equivalence, and non-inferiority designs. BMC Medical Research Methodology 23(1). https://doi.org/10.1186/s12874-023-02097-y
  677. Zhao M., Lau M., Haruki K., Väyrynen J., Gurjao C., Väyrynen S., et al. (2023): Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data. npj Precision Oncology 7(1). https://doi.org/10.1038/s41698-023-00406-8
  678. Yang M., Wang L., Xu L., Ke M., Sun L. (2023): Health Behaviours among Travellers Regarding Risk Compensation Following COVID‐19 Vaccination in Taizhou, China. Canadian Journal of Infectious Diseases and Medical Microbiology 2023(1). https://doi.org/10.1155/2023/1329291
  679. Pittelkow M. (2023): Assessing evidence and uncertainty. https://doi.org/10.33612/diss.762945985
  680. Dufner M., Wieg F., Kraft L., Grapsas S., Hagemeyer B. (2023): Motive-Specific Affective Contingencies and Their Relevance for Personality and Motivated Behavior. European Journal of Personality 38(2):225-240. https://doi.org/10.1177/08902070231156842
  681. Kent M., Huynh N., Mishra A., Tartarini F., Lipczynska A., Li J., et al. (2023): Energy savings and thermal comfort in a zero energy office building with fans in Singapore. Building and Environment 243:110674. https://doi.org/10.1016/j.buildenv.2023.110674
  682. Schaerer M., du Plessis C., Nguyen M., van Aert R., Tiokhin L., Lakens D., et al. (2023): On the trajectory of discrimination: A meta-analysis and forecasting survey capturing 44 years of field experiments on gender and hiring decisions. Organizational Behavior and Human Decision Processes 179:104280. https://doi.org/10.1016/j.obhdp.2023.104280
  683. Białek M., Misiak M., Dziekan M. (2023): The vicious cycle that stalls statistical revolution. Nature Human Behaviour 7(2):161-163. https://doi.org/10.1038/s41562-022-01515-3
  684. Wedel M., Gal D. (2023): Beyond statistical significance: Five principles for the new era of data analysis and reporting. Journal of Consumer Psychology 34(1):177-186. https://doi.org/10.1002/jcpy.1379
  685. Parsons M., Barneche D., Speed C., McCauley R., Day R., Dang C., et al. (2023): A large-scale experiment finds no consistent evidence of change in mortality or commercial productivity in silverlip pearl oysters (Pinctada maxima) exposed to a seismic source survey. Marine Pollution Bulletin 199:115480. https://doi.org/10.1016/j.marpolbul.2023.115480
  686. Mortazavi M., Lucini F., Joffe D., Oakley D. (2023): Electrophysiological trajectories of concussion recovery: From acute to prolonged stages in late teenagers. Journal of Pediatric Rehabilitation Medicine 16(2):287-299. https://doi.org/10.3233/prm-210114
  687. Scarfe M., Belisario K., Gillard J., De Jesus J., Frey B., Van Ameringen M., et al. (2023): Periodicity and severity of changes in depression and anxiety during the COVID-19 pandemic: Ten-wave longitudinal findings from an observational cohort study of community adults. Psychiatry Research 326:115267. https://doi.org/10.1016/j.psychres.2023.115267
  688. Scarfe M., Belisario K., Gillard J., DeJesus J., Frey B., Van Ameringen M., et al. (2023): Changes in posttraumatic stress disorder symptom severity during the COVID-19 pandemic: Ten-wave findings from a longitudinal observational cohort study of community adults. Psychiatry Research 329:115496. https://doi.org/10.1016/j.psychres.2023.115496
  689. Sokolowski M., Hawes Z., Leibovich-Raveh T., Ansari D. (2023): Number symbols are processed more automatically than nonsymbolic numerical magnitudes: Findings from a Symbolic-Nonsymbolic Stroop task. https://doi.org/10.32920/24093939.v1
  690. Sokolowski M., Hawes Z., Leibovich-Raveh T., Ansari D. (2023): Number symbols are processed more automatically than nonsymbolic numerical magnitudes: Findings from a Symbolic-Nonsymbolic Stroop task. https://doi.org/10.32920/24093939
  691. Al Kakoun N. (2023): Priming and Mining the Civil Engineering Mindset: How Personal Values and Perfectionism Shape Societal Engagement and Consideration in Design. https://doi.org/10.23889/suthesis.64671
  692. Al Kakoun N., Boy F., Xavier P. (2023): Don’t let perfect be the enemy of good: how perfectionism influences human-centred designing engagement and communal design production in civil engineering. Research in Engineering Design 35(2):171-189. https://doi.org/10.1007/s00163-023-00428-0
  693. Henriksen N., Galvano A., Fischer M. (2023): Sound change in Western Andalusian Spanish: Investigation into the actuation and propagation of post-aspiration. Journal of Phonetics 98:101238. https://doi.org/10.1016/j.wocn.2023.101238
  694. Henriksen N., García-Amaya L., Fischer M., Czapla J., Dakki N., Galvano A., et al. (2023): Perceptions of regional origin and social attributes of phonetic variants used in Iberian Spanish. Journal of Linguistic Geography 11(2):119-143. https://doi.org/10.1017/jlg.2023.6
  695. Coy N., Bendixen A., Grimm S., Roeber U., Schröger E. (2023): Deviants violating higher-order auditory regularities can become predictive and facilitate behaviour. Attention, Perception, & Psychophysics 85(8):2731-2750. https://doi.org/10.3758/s13414-023-02763-9
  696. EYNON N., Seale K., Teschendorff A., Reiner A., Voisin S. (2023): A comprehensive map of the ageing blood methylome. https://doi.org/10.21203/rs.3.rs-3755475/v1
  697. Mohammed N., Guerbyenne H. (2023): Bayesian inference in a multiple contaminated autoregressive model with trend. Journal of Statistical Computation and Simulation 93(12):2067-2109. https://doi.org/10.1080/00949655.2023.2172171
  698. Treichel N., Dukes D., Meuleman B., Van Herwegen J., Samson A. (2023): “Not in the mood”: The fear of being laughed at is better predicted by humor temperament traits than diagnosis in neurodevelopmental conditions. Research in Developmental Disabilities 137:104513. https://doi.org/10.1016/j.ridd.2023.104513
  699. Treichel N. (2023): Humor in neurodevelopmental conditions. https://doi.org/10.51363/unifr.lth.2024.027
  700. Laccourreye O., Lisan Q., Vincent C., Righini C., Leboulanger N., Franco-Vidal V., et al. (2023): Les clefs d’une publication réussie dans les Eur Ann Otorhinolaryngol Head Neck Dis : analyse STROBE de la relecture des articles scientifiques soumis en 2020–2021. Annales françaises d’Oto-rhino-laryngologie et de Pathologie Cervico-faciale 140(1):21-26. https://doi.org/10.1016/j.aforl.2022.04.002
  701. Montero O., Hedeland M., Balgoma D. (2023): Trials and tribulations of statistical significance in biochemistry and omics. Trends in Biochemical Sciences 48(6):503-512. https://doi.org/10.1016/j.tibs.2023.01.009
  702. Chén O., Bodelet J., Saraiva R., Phan H., Di J., Nagels G., et al. (2023): The roles, challenges, and merits of the p value. Patterns 4(12):100878. https://doi.org/10.1016/j.patter.2023.100878
  703. Supplisson O., Sofonea M. (2023): Pièges et mésusages en analyse de données. Anesthésie & Réanimation 9(5-6):440-450. https://doi.org/10.1016/j.anrea.2023.08.002
  704. Wallisch P., Mackey W., Karlovich M., Heeger D. (2023): The visible gorilla: Unexpected fast—not physically salient—Objects are noticeable. Proceedings of the National Academy of Sciences 120(22). https://doi.org/10.1073/pnas.2214930120
  705. Lyden P., Diniz M., Bosetti F., Lamb J., Nagarkatti K., Rogatko A., et al. (2023): A multi-laboratory preclinical trial in rodents to assess treatment candidates for acute ischemic stroke. Science Translational Medicine 15(714). https://doi.org/10.1126/scitranslmed.adg8656
  706. Taylor P., Reynolds R., Calhoun V., Gonzalez-Castillo J., Handwerker D., Bandettini P., et al. (2023): Highlight results, don’t hide them: Enhance interpretation, reduce biases and improve reproducibility. NeuroImage 274:120138. https://doi.org/10.1016/j.neuroimage.2023.120138
  707. von Hippel P., Schuetze B. (2023): How Not to Fool Ourselves About Heterogeneity of Treatment Effects. https://doi.org/10.31234/osf.io/zg8hv
  708. Chejor P., Atee M., Cain P., Whiting D., Morris T., Porock D. (2023): Comparing clinico-demographics and neuropsychiatric symptoms for immigrant and non-immigrant aged care residents living with dementia: a retrospective cross-sectional study from an Australian dementia-specific support service. BMC Geriatrics 23(1). https://doi.org/10.1186/s12877-023-04447-3
  709. Edelsbrunner P., Thurn C. (2023): Improving the utility of non-significant results for educational research: A review and recommendations. Educational Research Review 42:100590. https://doi.org/10.1016/j.edurev.2023.100590
  710. Ejbye-Ernst P., Moeller K., Liebst L., Thomas J., Sexton M., Lindegaard M. (2023): “It’s illegal to buy drugs from street dealers”—a video-based pre-post study of a behavioral intervention to displace dealers from an Amsterdam open-air drug market. Journal of Experimental Criminology 21(2):559-576. https://doi.org/10.1007/s11292-023-09602-9
  711. Waters P., Rucker B., Love M., Vassar M. (2023): Lowering the statistical significance threshold of randomized controlled trials in three major general anesthesiology journals. Canadian Journal of Anesthesia/Journal canadien d’anesthésie 70(9):1441-1448. https://doi.org/10.1007/s12630-023-02529-9
  712. Razavi P., Hodges S., Condon D., Wagner D., Srivastava S. (2023): Social Consequences of Anger Expression: The Role of Target of Harm and Expresser’s Gender. https://doi.org/10.31234/osf.io/aysfx
  713. Fang Q., Maeda K., Kuncarayakti H., Nagao T. (2023): An aspherical distribution for the explosive burning ash of core-collapse supernovae. Nature Astronomy 8(1):111-118. https://doi.org/10.1038/s41550-023-02120-8
  714. Peng Q., Wang W., Yang X., Wang Y., Chen J. (2023): Research on Affective Interaction in Mini Public Transport Based on IPA-FMEA. Sustainability 15(9):7033. https://doi.org/10.3390/su15097033
  715. Holm Hansen R., von Essen M., Mahler M., Cobanovic S., Binko T., Sellebjerg F. (2023): Cladribine Effects on T and B Cells and T Cell Reactivity in Multiple Sclerosis. Annals of Neurology 94(3):518-530. https://doi.org/10.1002/ana.26684
  716. de Pinho R., Pequeno P., Alfaia S., Barbosa R., Lincoln N. (2023): Soil fertility in indigenous swidden fields and fallows in northern Amazonia, Brazil. Soil Use and Management 39(4):1517-1531. https://doi.org/10.1111/sum.12886
  717. Ventura R. (2023): Reliability models in cultural phylogenetics. Biology & Philosophy 38(3). https://doi.org/10.1007/s10539-023-09900-6
  718. De Rosa R., Romagnuolo L., Frosina E., Senatore A. (2023): DOE optimization of a liquid cold plate used in a thermal management system for power converters in the railway sector through 3D CFD analysis. Journal of Physics: Conference Series 2648(1):012097. https://doi.org/10.1088/1742-6596/2648/1/012097
  719. Klement R., Joos F., Reuss-Borst M., Kämmerer U. (2023): Measurement of body composition by DXA, BIA, Leg-to-leg BIA and near-infrared spectroscopy in breast cancer patients – comparison of the four methods. Clinical Nutrition ESPEN 54:443-452. https://doi.org/10.1016/j.clnesp.2023.02.013
  720. Vogt-Yerem R., Mestechkin R., Chabris C., Meyer M. (2023): Objecting to consensual experiments even while approving of nonconsensual imposition of the policies they contain. https://doi.org/10.31234/osf.io/8r9p7
  721. Green R., Fischerová D., Testa A., Franchi D., Frühauf F., Lindqvist P., et al. (2023): Sonographic, Demographic, and Clinical Characteristics of Pre- and Postmenopausal Women with Endometrial Cancer; Results from a Post Hoc Analysis of the IETA4 (International Endometrial Tumor Analysis) Multicenter Cohort. Diagnostics 14(1):1. https://doi.org/10.3390/diagnostics14010001
  722. Anderson R., Peña A., Johnson B., Nowlin R., Hudson T., Vassar M. (2023): What’s in a P-Value? A Fragility Analysis of RCTs in the AUA Guidelines for Benign Prostatic Hyperplasia. Urology 176:127-136. https://doi.org/10.1016/j.urology.2022.12.063
  723. Waziry R., Ryan C., Corcoran D., Huffman K., Kobor M., Kothari M., et al. (2023): Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial. Nature Aging. https://doi.org/10.1038/s43587-022-00357-y
  724. Larisch R., Vitay J., Hamker F. (2023): Detecting Anomalies in System Logs With a Compact Convolutional Transformer. IEEE Access 11:113464-113479. https://doi.org/10.1109/access.2023.3323252
  725. Nennstiel R., Brosy Z. (2023): Less Student Dropout, More Frequent Change of Study Subjects: Evidence from Swiss Administrative Data, 1975–2018. European Education 55(1):44-59. https://doi.org/10.1080/10564934.2023.2222725
  726. Balkin R. (2023): Addressing Limitations of p in Counseling Research through Reporting of Bayes Factor Bound and Effect Size Precision. Counseling Outcome Research and Evaluation 14(2):167-173. https://doi.org/10.1080/21501378.2023.2226386
  727. Mentz R., Anstrom K., Eisenstein E., Sapp S., Greene S., Morgan S., et al. (2023): Effect of Torsemide vs Furosemide After Discharge on All-Cause Mortality in Patients Hospitalized With Heart Failure. JAMA 329(3):214. https://doi.org/10.1001/jama.2022.23924
  728. Giner-Sorolla R., Montoya A., Aberson C., Carpenter T., Lewis N., Bostyn D., et al. (2023): Power to Detect What? Considerations for Planning and Evaluating Sample Size. https://doi.org/10.31234/osf.io/rv3kw
  729. Bellomo R., Zavalis E., Ioannidis J. (2023): Assessment of transparency indicators in Space Medicine. https://doi.org/10.1101/2023.12.01.23299278
  730. Ding R., Zou X., Qin Y., Gong L., Chen H., Ma X., et al. (2023): xQTLbiolinks: a comprehensive and scalable tool for integrative analysis of molecular QTLs. Briefings in Bioinformatics 25(1). https://doi.org/10.1093/bib/bbad440
  731. Lu S., Rutegård M., Ahmed M., Häggström C., Gylfe Å., Harlid S., et al. (2023): Prediagnostic Prescription Antibiotics Use and Survival in Patients with Colorectal Cancer: A Swedish National Register-Based Study. Cancer Epidemiology, Biomarkers & Prevention 32(10):1391-1401. https://doi.org/10.1158/1055-9965.epi-23-0340
  732. Rasti S. (2023): Multiverse Meta-Analysis: Proposing an Exploratory Framework. https://doi.org/10.31237/osf.io/racx9
  733. Lawrence S., Puhl R., Watson R., Schwartz M., Lessard L., Foster G. (2023): Family‐based weight stigma and psychosocial health: A multinational comparison. Obesity 31(6):1666-1677. https://doi.org/10.1002/oby.23748
  734. Philibotte S., Spivack S., Spilka N., Passman I., Wallisch P. (2023): The Whole is Not Different From its Parts. Music Perception 40(3):220-236. https://doi.org/10.1525/mp.2023.40.3.220
  735. Voisin S., Seale K., Jacques M., Landen S., Harvey N., Haupt L., et al. (2023): Exercise is associated with younger methylome and transcriptome profiles in human skeletal muscle. Aging Cell 23(1). https://doi.org/10.1111/acel.13859
  736. Clifford S., Flynn D., Nyhan B., Rhee K. (2023): Decider in Chief? Why and How the Public Exaggerates the Power of the Presidency. Political Research Quarterly 77(2):469-484. https://doi.org/10.1177/10659129231218676
  737. Semenyna S., Gómez Jiménez F., VanderLaan D., Vasey P. (2023): Male androphilia, fraternal birth order, and female fecundity in Samoa: A 10-y retrospective. Proceedings of the National Academy of Sciences 120(50). https://doi.org/10.1073/pnas.2313284120
  738. Coşkun S., Kazan H. (2023): Bayesian analysis of the relationship between process improvement practices and procurement maturity. Computers & Industrial Engineering 181:109297. https://doi.org/10.1016/j.cie.2023.109297
  739. Landen S., Jacques M., Hiam D., Alvarez-Romero J., Schittenhelm R., Shah A., et al. (2023): Sex differences in muscle protein expression and DNA methylation in response to exercise training. Biology of Sex Differences 14(1). https://doi.org/10.1186/s13293-023-00539-2
  740. Shanie Landen, Macsue Jacques, Danielle Hiam, Javier Alvarez‐Romero, Ralf B. Schittenhelm, Anup D. Shah, et al. (2023): Additional file 2 of Sex differences in muscle protein expression and DNA methylation in response to exercise training. Figshare. https://doi.org/10.6084/m9.figshare.24091589
  741. Wang S., Ting C., Chen C., Liu C., Lin N., Loong C., et al. (2023): Arterial blood pressure waveform in liver transplant surgery possesses variability of morphology reflecting recipients’ acuity and predicting short term outcomes. Journal of Clinical Monitoring and Computing 37(6):1521-1531. https://doi.org/10.1007/s10877-023-01047-9
  742. Zhang S., Hur J., Song R., Wang P., Cao Y., Wu K., et al. (2023): Adherence to the World Cancer Research Fund/American Institute for Cancer Research cancer prevention recommendations throughout the life course and risk of colorectal cancer precursors. British Journal of Cancer 128(12):2243-2252. https://doi.org/10.1038/s41416-023-02255-5
  743. Genc S., Raven E., Drakesmith M., Blakemore S., Jones D. (2023): Novel insights into axon diameter and myelin content in late childhood and adolescence. Cerebral Cortex 33(10):6435-6448. https://doi.org/10.1093/cercor/bhac515
  744. Genc S., Schiavi S., Chamberland M., Tax C., Raven E., Daducci A., et al. (2023): Developmental differences in canonical cortical networks: insights from microstructure-informed tractography. https://doi.org/10.1101/2023.10.30.564863
  745. Rinne S. (2023): Estimating the merit-order effect using coarsened exact matching: Reconciling theory with the empirical results to improve policy implications. Energy Policy 185:113931. https://doi.org/10.1016/j.enpol.2023.113931
  746. Mammola S., Adamo M., Antić D., Calevo J., Cancellario T., Cardoso P., et al. (2023): Drivers of species knowledge across the tree of life. eLife 12. https://doi.org/10.7554/elife.88251.3
  747. Mammola S., Adamo M., Antić D., Calevo J., Cancellario T., Cardoso P., et al. (2023): Drivers of species knowledge across the tree of life. eLife 12. https://doi.org/10.7554/elife.88251
  748. Mammola S., Adamo M., Antić D., Calevo J., Cancellario T., Cardoso P., et al. (2023): Drivers of species knowledge across the Tree of Life. https://doi.org/10.7554/elife.88251.2
  749. Mammola S., Adamo M., Antić D., Calevo J., Cancellario T., Cardoso P., et al. (2023): Drivers of species knowledge across the Tree of Life. https://doi.org/10.7554/elife.88251.1
  750. Mammola S., Adamo M., Antić D., Calevo J., Cancellario T., Cardoso P., et al. (2023): Drivers of species knowledge across the Tree of Life. https://doi.org/10.1101/2023.03.27.534304
  751. Adler S., Röseler L., Schöniger M. (2023): A toolbox to evaluate the trustworthiness of published findings. Journal of Business Research 167:114189. https://doi.org/10.1016/j.jbusres.2023.114189
  752. Adler S., Röseler L., Kasper-Schöniger M. (2023): A Toolbox to Evaluate the Trustworthiness of Published Findings. https://doi.org/10.31219/osf.io/s5mzp
  753. Scharf T., Kirkland C., Barham M., Yakymchuk C., Puzyrev V. (2023): Does Zircon Shape Retain Petrogenetic Information?. Geochemistry, Geophysics, Geosystems 24(10). https://doi.org/10.1029/2023gc011018
  754. Xu T., Li X., Wang D., Zhang Y., Zhang Q., Yan J., et al. (2023): Hand grip strength should be normalized by weight not height for eliminating the influence of individual differences: Findings from a cross-sectional study of 1,511 healthy undergraduates. Frontiers in Nutrition 9. https://doi.org/10.3389/fnut.2022.1063939
  755. Enam T., McDonough I. (2023): Integrating multiple cues in metamemory: using the illusory effect of font size and level of processing to inform FOK judgments. Metacognition and Learning 19(1):169-188. https://doi.org/10.1007/s11409-023-09367-6
  756. Kirketeig T., Söreskog E., Jacobson T., Karlsten R., Zethraeus N., Borgström F. (2023): Real-world outcomes in spinal cord stimulation: predictors of reported effect and explantation using a comprehensive registry-based approach. PAIN Reports 8(6):e1107. https://doi.org/10.1097/pr9.0000000000001107
  757. Rants’o T., Koekemoer L., van Zyl R. (2023): The insecticidal activity of essential oil constituents against pyrethroid-resistant Anopheles funestus (Diptera: Culicidae). Parasitology International 95:102749. https://doi.org/10.1016/j.parint.2023.102749
  758. Bulajic T., Amu N., Ren J., Berner C., Fomina D., Shah S., et al. (2023): Validating Instruments to Screen for Psychopathy in a Non-Institutionalized Population. https://doi.org/10.31234/osf.io/2eyxf
  759. Vilgis T. (2023): Conclusion—Or: What Remains?. Nutrition Biophysics. https://doi.org/10.1007/978-3-662-67597-7_7
  760. Leung T., Ho J., Wang X., Lam W., Pang H. (2023): The impact of lowering the study design significance threshold to 0.005 on sample size in randomized cancer clinical trials. Journal of Clinical and Translational Science 8(1). https://doi.org/10.1017/cts.2023.699
  761. Ballard T., Evans N., Fisher G., Sewell D. (2023): Using mixture modeling to examine differences in perceptual decision-making as a function of the time and method of participant recruitment. Behavior Research Methods 56(3):2194-2212. https://doi.org/10.3758/s13428-023-02142-0
  762. Spampatti T., Brosch T., Trutnevyte E., Hahnel U. (2023): A Preregistered Field Study of the Trust Inoculation Against a Negative Event Involving Geothermal Energy Systems. Collabra: Psychology 9(1). https://doi.org/10.1525/collabra.98755
  763. Nweze T., Ezenwa M., Ajaelu C., Okoye C. (2023): Childhood mental health difficulties mediate the long‐term association between early‐life adversity at age 3 and poorer cognitive functioning at ages 11 and 14. Journal of Child Psychology and Psychiatry 64(6):952-965. https://doi.org/10.1111/jcpp.13757
  764. Ugai T., Akimoto N., Haruki K., Harrison T., Cao Y., Qu C., et al. (2023): Prognostic role of detailed colorectal location and tumor molecular features: analyses of 13,101 colorectal cancer patients including 2994 early-onset cases. Journal of Gastroenterology 58(3):229-245. https://doi.org/10.1007/s00535-023-01955-2
  765. Ugai T., Shimizu T., Kawamura H., Ugai S., Takashima Y., Usui G., et al. (2023): Inverse relationship between Fusobacterium nucleatum amount and tumor CD274 (PD‐L1) expression in colorectal carcinoma. Clinical & Translational Immunology 12(8). https://doi.org/10.1002/cti2.1453
  766. Kalincik T., Roos I., Sharmin S., Malpas C. (2023): Methodological considerations for observational studies of treatment effectiveness in neurology: a clinician’s guide. Journal of Neurology, Neurosurgery & Psychiatry. https://doi.org/10.1136/jnnp-2022-330038
  767. Heggedal T., Helland L., Moen E. (2023): SEQUENTIAL PRICE SETTING: THEORY AND EVIDENCE FROM A LAB EXPERIMENT. International Economic Review 65(2):693-727. https://doi.org/10.1111/iere.12680
  768. Ollerenshaw T. (2023): Affective polarization and the destabilization of core political values. Political Science Research and Methods 13(1):212-220. https://doi.org/10.1017/psrm.2023.34
  769. Hamada T., Michihata N., Saito T., Iwashita T., Shiomi H., Takenaka M., et al. (2023): Inverse association of hospital volume with in-hospital mortality rate of patients receiving EUS-guided interventions for pancreatic fluid collections. Gastrointestinal Endoscopy 98(4):597-606.e2. https://doi.org/10.1016/j.gie.2023.04.2091
  770. Wong T., Mulder J. (2023): On the Credibility of Statistical Inference Within the Neyman-Pearson Hypothesis Testing Framework. https://doi.org/10.31234/osf.io/kh3mu
  771. Radhoe T., Agelink van Rentergem J., Torenvliet C., Groenman A., van der Putten W., Geurts H. (2023): Finding Similarities in Differences Between Autistic Adults: Two Replicated Subgroups. Journal of Autism and Developmental Disorders 54(9):3449-3466. https://doi.org/10.1007/s10803-023-06042-2
  772. Lane T., Carroll M., Borg B., McCaffrey T., Smith C., Gao C., et al. (2023): Long‐term effects of extreme smoke exposure on COVID ‐19: A cohort study. Respirology 29(1):56-62. https://doi.org/10.1111/resp.14591
  773. Lane T., Carroll M., Borg B., McCaffrey T., Smith C., Gao C., et al. (2023): Long-term effects of extreme smoke exposure on COVID-19: A cohort study. https://doi.org/10.1101/2023.04.12.23288500
  774. Peters U. (2023): Do current evidential standards in the science of consciousness help or hinder the discovery of signs of consciousness?. https://doi.org/10.31234/osf.io/v2gc4
  775. Macko V., Felber P., Bergram K., Holzer A. (2023): Using Educational Robotics to Support Active Learning Experiences and Foster Computational Thinking Skills among Non-STEM University Students. 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) 30:1-8. https://doi.org/10.1109/tale56641.2023.10398343
  776. Johnson V., Pramanik S., Shudde R. (2023): Bayes factor functions for reporting outcomes of hypothesis tests. Proceedings of the National Academy of Sciences 120(8). https://doi.org/10.1073/pnas.2217331120
  777. Satterthwaite W. (2023): The reproducibility crisis meets stock assessment science: Sources of inadvertent bias in the stock assessment prioritization and review process. Fisheries Research 266:106763. https://doi.org/10.1016/j.fishres.2023.106763
  778. Chopik W., Oh J., Weidmann R., Weaver J., Balzarini R., Zoppolat G., et al. (2023): The Perks of Pet Ownership? The Effects of Pet Ownership on Well-Being During the COVID-19 Pandemic. Personality and Social Psychology Bulletin 51(6):928-948. https://doi.org/10.1177/01461672231203417
  779. Choi W. (2023): Problems and alternatives of testing significance using null hypothesis and P-value in food research. Food Science and Biotechnology 32(11):1479-1487. https://doi.org/10.1007/s10068-023-01348-4
  780. Meng X. (2023): A Not-so-radical Rejoinder: Habituate Systems Thinking and Data (Science) Confession for Quality Enhancement. The New England Journal of Statistics in Data Science. https://doi.org/10.51387/22-nejsds6rej
  781. Li X., Joh H., Hur J., Song M., Zhang X., Cao Y., et al. (2023): Fructose consumption from different food sources and cardiometabolic biomarkers: cross-sectional associations in US men and women. The American Journal of Clinical Nutrition 117(3):490-498. https://doi.org/10.1016/j.ajcnut.2023.01.006
  782. Zhang X., Meng Z., Beusch C., Gharibi H., Cheng Q., Lyu H., et al. (2023): Ultralight Ultrafast Enzymes**. Angewandte Chemie International Edition 63(3). https://doi.org/10.1002/anie.202316488
  783. Zhang X., Meng Z., Beusch C., Gharibi H., Cheng Q., Lyu H., et al. (2023): Ultralight Ultrafast Enzymes**. Angewandte Chemie 136(3). https://doi.org/10.1002/ange.202316488
  784. Wan Y., Tobias D., Dennis K., Guasch-Ferré M., Sun Q., Rimm E., et al. (2023): Association between changes in carbohydrate intake and long term weight changes: prospective cohort study. BMJ 382:e073939. https://doi.org/10.1136/bmj-2022-073939
  785. Masugi Y., Takamatsu M., Tanaka M., Hara K., Inoue Y., Hamada T., et al. (2023): Post‐operative mortality and recurrence patterns in pancreatic cancer according to KRAS mutation and CDKN2A, p53, and SMAD4 expression. The Journal of Pathology: Clinical Research 9(5):339-353. https://doi.org/10.1002/cjp2.323
  786. Mizuno Y., Ohishi Y., Ishikawa N. (2023): A simple metric that correlates with public Wi‐Fi throughput. Electronics Letters 59(8). https://doi.org/10.1049/ell2.12795
  787. Ji Y., Temprano-Sagrera G., Holle L., Bebo A., Brody J., Le N., et al. (2023): Antithrombin, Protein C, and Protein S: Genome and Transcriptome-Wide Association Studies Identify 7 Novel Loci Regulating Plasma Levels. Arteriosclerosis, Thrombosis, and Vascular Biology 43(7). https://doi.org/10.1161/atvbaha.122.318213
  788. Aruka Y., Chakrabarti B., Mizuno T., Sato H. (2023): Editorial: Interdisciplinary approaches towards the evolution of socio-economic systems under selective trend pressures. Frontiers in Physics 11. https://doi.org/10.3389/fphy.2023.1236585
  789. Liu Z., Al Amer F., Xiao M., Xu C., Furuya-Kanamori L., Hong H., et al. (2023): The normality assumption on between-study random effects was questionable in a considerable number of Cochrane meta-analyses. BMC Medicine 21(1). https://doi.org/10.1186/s12916-023-02823-9
  790. Kekecs Z., Palfi B., Szaszi B., Szecsi P., Zrubka M., Kovacs M., et al. (2023): Raising the value of research studies in psychological science by increasing the credibility of research reports: the transparent Psi project. Royal Society Open Science 10(2). https://doi.org/10.1098/rsos.191375
  791. Unknown authors (2022): Decision letter for “That’s a Lot to Process! Pitfalls of Popular Path Models”. https://doi.org/10.1177/25152459221095827/v2/decision1
  792. Unknown authors (2022): Decision letter for “That’s a Lot to Process! Pitfalls of Popular Path Models”. https://doi.org/10.1177/25152459221095827/v3/decision1
  793. Unknown authors (2022): Dedication. Knowing Science. https://doi.org/10.1093/oso/9780199606658.002.0004
  794. Unknown authors (2022): Preface. Knowing Science. https://doi.org/10.1093/oso/9780199606658.002.0005
  795. Unknown authors (2022): Copyright Page. Knowing Science. https://doi.org/10.1093/oso/9780199606658.002.0003
  796. Grjibovski A., Gvozdeckii A. (2022): Interpretation of and alternatives to p-values in biomedical sciences. Ekologiya cheloveka (Human Ecology) 29(3):67-76. https://doi.org/10.17816/humeco97249
  797. Syverson A., Li C., Zheng Z., Proskurnin E., Chung C., Zou M. (2022): Maxillary sinus dimensions in skeletal class II population with different vertical skeletal patterns. Clinical Oral Investigations 26(7):5045-5060. https://doi.org/10.1007/s00784-022-04476-z
  798. Almaatouq A., Griffiths T., Suchow J., Whiting M., Evans J., Watts D. (2022): Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences. Behavioral and Brain Sciences 47. https://doi.org/10.1017/s0140525x22002874
  799. Brodeur A., Cook N., Hartley J., Heyes A. (2022): Do Pre-Registration and Pre-Analysis Plans Reduce p-Hacking and Publication Bias?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4180594
  800. Brodeur A., Cook N., Hartley J., Heyes A. (2022): Do Pre-Registration and Pre-Analysis Plans Reduce P-Hacking and Publication Bias?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4188287
  801. Meule A., Kolar D., Voderholzer U. (2022): Weight suppression and body mass index at admission interactively predict weight trajectories during inpatient treatment of anorexia nervosa. Journal of Psychosomatic Research 158:110924. https://doi.org/10.1016/j.jpsychores.2022.110924
  802. Xing A., Lin L. (2022): Empirical assessment of fragility index based on a large database of clinical studies in the Cochrane Library. Journal of Evaluation in Clinical Practice 29(2):359-370. https://doi.org/10.1111/jep.13787
  803. Nikolaidis A., Chen A., He X., Shinohara R., Vogelstein J., Milham M., et al. (2022): Suboptimal phenotypic reliability impedes reproducible human neuroscience. https://doi.org/10.1101/2022.07.22.501193
  804. de Cheveigné A. (2022): Preregistration: the good, the bad, and the confusing. https://doi.org/10.31234/osf.io/bcd9t
  805. Aarts A. (2022): Psychological Science Replicates Just Fine, Thanks. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4176922
  806. Bird A. (2022): Knowing Science. https://doi.org/10.1093/oso/9780199606658.001.0001
  807. Bird A. (2022): The Aim of Science. Knowing Science. https://doi.org/10.1093/oso/9780199606658.003.0002
  808. Bird A. (2022): Abductive Knowledge. Knowing Science. https://doi.org/10.1093/oso/9780199606658.003.0007
  809. Bird A. (2022): Introduction: Science and the Pursuit of Knowledge. Knowing Science. https://doi.org/10.1093/oso/9780199606658.003.0001
  810. Bird A. (2022): Evidence. Knowing Science. https://doi.org/10.1093/oso/9780199606658.003.0005
  811. Bird A. (2022): Science as Social Knowing. Knowing Science. https://doi.org/10.1093/oso/9780199606658.003.0004
  812. Bird A. (2022): Scientific Progress. Knowing Science. https://doi.org/10.1093/oso/9780199606658.003.0003
  813. Bird A. (2022): Metascientific Knowledge?. Knowing Science. https://doi.org/10.1093/oso/9780199606658.003.0009
  814. Bird A. (2022): Probability and Plausibility. Knowing Science. https://doi.org/10.1093/oso/9780199606658.003.0008
  815. Bird A. (2022): Observation. Knowing Science. https://doi.org/10.1093/oso/9780199606658.003.0006
  816. Kirchner‐Häusler A., Schönbrodt F., Uskul A., Vignoles V., Rodríguez‐Bailón R., Castillo V., et al. (2022): Proximal and distal honor fit and subjective well‐being in the Mediterranean region. Journal of Personality 92(1):38-54. https://doi.org/10.1111/jopy.12803
  817. Kirchner-Häusler A., Schönbrodt F., Uskul A., Vignoles V., Rodriguez-Bailon R., Castillo V., et al. (2022): Proximal and Distal Honor Fit and Subjective Well-Being in the Mediterranean Region. https://doi.org/10.31234/osf.io/ra8qe
  818. Daboul A., Krüger M., Ivanovska T., Obst A., Ewert R., Stubbe B., et al. (2022): Do brachycephaly and nose size predict the severity of obstructive sleep apnea (OSA)? A sample‐based geometric morphometric analysis of craniofacial variation in relation to OSA syndrome and the role of confounding factors. Journal of Sleep Research 32(3). https://doi.org/10.1111/jsr.13801
  819. Xu A., Ortiz-Babilonia C., Gupta A., Rogers D., Aiyer A., Vulcano E. (2022): The Statistical Fragility of Platelet-Rich Plasma as Treatment for Chronic Noninsertional Achilles Tendinopathy: A Systematic Review and Meta-analysis. Foot & Ankle Orthopaedics 7(3). https://doi.org/10.1177/24730114221119758
  820. Andrea C. (2022): As fast as a hare: Did intraspecific morphological change bring the Hallands Väderö Island population of Lepus timidus close to interspecific differences in less than 150 years?. Zoology 152:126014. https://doi.org/10.1016/j.zool.2022.126014
  821. Ehweiner A., Duch C., Brembs B. (2022): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant selflearning in Drosophila. https://doi.org/10.1101/2022.12.16.520755
  822. Stefan A., Schönbrodt F. (2022): Big Little Lies: A Compendium and Simulation of p-Hacking Strategies. https://doi.org/10.31234/osf.io/xy2dk
  823. Bruno A., Blue N. (2022): Challenges in Interpreting Obstetrics and Gynecology Literature. Clinical Obstetrics & Gynecology 65(2):225-235. https://doi.org/10.1097/grf.0000000000000707
  824. Dreber A., Heikensten E., Säve-Söderbergh J. (2022): Why do women ask for less?. Labour Economics 78:102204. https://doi.org/10.1016/j.labeco.2022.102204
  825. Temp A., Ly A., van Doorn J., Wagenmakers E., Tang Y., Lutz M., et al. (2022): A Bayesian perspective on Biogen’s aducanumab trial. Alzheimer’s & Dementia 18(11):2341-2351. https://doi.org/10.1002/alz.12615
  826. Temp A., Naumann M., Hermann A., Glaß H. (2022): Applied Bayesian Approaches for Research in Motor Neuron Disease. Frontiers in Neurology 13. https://doi.org/10.3389/fneur.2022.796777
  827. Seewald A., Rief W. (2022): How to Change Negative Outcome Expectations in Psychotherapy? The Role of the Therapist’s Warmth and Competence. Clinical Psychological Science 11(1):149-163. https://doi.org/10.1177/21677026221094331
  828. van Meijeren A., Ties D., de Koning M., van Dijk R., van Blokland I., Lizana Veloz P., et al. (2022): Association of epicardial adipose tissue with different stages of coronary artery disease: A cross-sectional UK Biobank cardiovascular magnetic resonance imaging substudy. IJC Heart & Vasculature 40:101006. https://doi.org/10.1016/j.ijcha.2022.101006
  829. De Comite A., Crevecoeur F., Lefèvre P. (2022): Continuous Tracking of Task Parameters Tunes Reaching Control Online. eneuro 9(4):ENEURO.0055-22.2022. https://doi.org/10.1523/eneuro.0055-22.2022
  830. Dörnemann A., Boenisch N., Schommer L., Winkelhorst L., Wingen T. (2022): How do Good and Bad News Impact Mood During the Covid-19 Pandemic? The Role of Similarity. Journal of European Psychology Students 13(1):107-116. https://doi.org/10.5334/jeps.566
  831. Rodríguez-Hernández A., Sevcik C. (2022): Hidden chaos factors inducing random walks which reduce hospital operative efficiency. PLOS ONE 17(1):e0262815. https://doi.org/10.1371/journal.pone.0262815
  832. Sadri A. (2022): Machine Learning Can Solve the Reproducibility Crisis by Supplanting Reductionist Statistics. OSF Preprints. https://doi.org/10.31222/osf.io/yxba5
  833. Hyytinen A., Tuimala J., Hammar M. (2022): Enhancing the adoption of digital public services: Evidence from a large-scale field experiment. Government Information Quarterly 39(3):101687. https://doi.org/10.1016/j.giq.2022.101687
  834. Spanos A. (2022): Frequentist Model-based Statistical Induction and the Replication Crisis. Journal of Quantitative Economics 20(S1):133-159. https://doi.org/10.1007/s40953-022-00312-z
  835. Gupta A., Ortiz-Babilonia C., Xu A., Rogers D., Vulcano E., Aiyer A. (2022): The Statistical Fragility of Platelet-Rich Plasma as Treatment for Plantar Fasciitis: A Systematic Review and Simulated Fragility Analysis. Foot & Ankle Orthopaedics 7(4). https://doi.org/10.1177/24730114221144049
  836. Gupta A., Mo K., Movsik J., al Farii H. (2022): Statistical Fragility of Ketamine Infusion During Scoliosis Surgery to Reduce Opioid Tolerance and Postoperative Pain. World Neurosurgery 164:135-142. https://doi.org/10.1016/j.wneu.2022.04.121
  837. Reddy A., Scott J., Joshua Stephens B., Patel A., Checketts J., Stotler W., et al. (2022): Evaluation of Proposed Protocol Changing Statistical Significance From 0.05 to 0.005 in Foot and Ankle Randomized Controlled Trials. The Journal of Foot and Ankle Surgery 61(5):925-926. https://doi.org/10.1053/j.jfas.2022.03.005
  838. Duport A., Pelletier R., Martel M., Léonard G. (2022): The influence of kinesiophobia and pain catastrophizing on pain-induced corticomotor modulation in healthy participants: A cross sectional study. Neurophysiologie Clinique 52(5):375-383. https://doi.org/10.1016/j.neucli.2022.08.001
  839. Pabst A., Bollen Z., Masson N., Billaux P., de Timary P., Maurage P. (2022): An eye-tracking study of biased attentional processing of emotional faces in severe alcohol use disorder. Journal of Affective Disorders 323:778-787. https://doi.org/10.1016/j.jad.2022.12.027
  840. Luthra A., Gao R., Remers S., Carducci P., Sue J. (2022): Evidence-Informed Approach to De-Prescribing of Atypical Antipsychotics (AAP) in the Management of Behavioral Expressions (BE) in Advanced Neurocognitive Disorders (NCD): Results of a Retrospective Study. Geriatrics 7(1):14. https://doi.org/10.3390/geriatrics7010014
  841. Parish A., Alina Cristea I., Schuit E., Ioannidis J. (2022): 2,109 randomized oncology trials map continuous, meager improvements in progression-free and overall survival over 50 years. Journal of Clinical Epidemiology 150:106-115. https://doi.org/10.1016/j.jclinepi.2022.06.013
  842. Stähli B., Foster Witassek F., Roffi M., Eberli F., Rickli H., Erne P., et al. (2022): Trends in treatment and outcomes of patients with diabetes and acute myocardial infarction: Insights from the nationwide AMIS plus registry. International Journal of Cardiology 368:10-16. https://doi.org/10.1016/j.ijcard.2022.08.032
  843. Langenberg B., Janczyk M., Koob V., Kliegl R., Mayer A. (2022): A tutorial on using the paired t test for power calculations in repeated measures ANOVA with interactions. Behavior Research Methods 55(5):2467-2484. https://doi.org/10.3758/s13428-022-01902-8
  844. Leichtmann B., Nitsch V., Mara M. (2022): Crisis Ahead? Why Human-Robot Interaction User Studies May Have Replicability Problems and Directions for Improvement. Frontiers in Robotics and AI 9. https://doi.org/10.3389/frobt.2022.838116
  845. Clarke B., Schiavone S., Vazire S. (2022): What limitations are reported in short articles in social and personality psychology?. https://doi.org/10.31234/osf.io/n4eq7
  846. Finley B., Kalwij A., Kapteyn A. (2022): Born to be wild: Second-to-fourth digit length ratio and risk preferences. Economics & Human Biology 47:101178. https://doi.org/10.1016/j.ehb.2022.101178
  847. Hobbs C., Vozarova P., Sabharwal A., Shah P., Button K. (2022): Is depression associated with reduced optimistic belief updating?. Royal Society Open Science 9(2). https://doi.org/10.1098/rsos.190814
  848. Tate C. (2022): The Importance of Type III and Type IV Epistemic Errors for Improving Empirical Science. Research Integrity. https://doi.org/10.1093/oso/9780190938550.003.0012
  849. Lee C., Ahsan H., Chae H., Esnard D., Broussard D., Hart S., et al. (2022): Perioperative Efficiency of Sugammadex Following Laparoscopic Cholecystectomy in Clinical Practice. Ochsner Journal 22(4):292-298. https://doi.org/10.31486/toj.22.0064
  850. Christian P. Robert (2022): 50 shades of Bayesian testing of hypotheses. Handbook of Statistics. https://doi.org/10.1016/bs.host.2022.06.003
  851. Semken C., Rossell D. (2022): Specification Analysis for Technology Use and Teenager Well-Being: Statistical Validity and a Bayesian Proposal. Journal of the Royal Statistical Society Series C: Applied Statistics 71(5):1330-1355. https://doi.org/10.1111/rssc.12578
  852. Pérignon C., Akmansoy O., Hurlin C., Dreber A., Holzmeister F., Huber J., et al. (2022): Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4064172
  853. Brydges C., Che X., Lipkin W., Fiehn O. (2022): Bayesian statistics improves biological interpretability of metabolomics data from human cohorts. https://doi.org/10.1101/2022.05.17.492312
  854. Franck C., Madigan M., Lazar N. (2022): How to write about alternatives to classical hypothesis testing outside of the statistical literature: Approximate Bayesian model selection applied to a biomechanics study. Stat 11(1). https://doi.org/10.1002/sta4.508
  855. Gosling C., Cortese S., Konofal E., Lecendreux M., Faraone S. (2022): Association of Parent-Rated Sleep Disturbances With Attention-Deficit/Hyperactivity Disorder Symptoms: 9-Year Follow-up of a Population-Based Cohort Study. Journal of the American Academy of Child & Adolescent Psychiatry 62(2):244-252. https://doi.org/10.1016/j.jaac.2022.05.013
  856. Vélez D., Pérez M., Pericchi L. (2022): Increasing the replicability for linear models via adaptive significance levels. TEST 31(3):771-789. https://doi.org/10.1007/s11749-022-00803-4
  857. Berner D., Amrhein V. (2022): Why and how we should join the shift from significance testing to estimation. Journal of Evolutionary Biology 35(6):777-787. https://doi.org/10.1111/jeb.14009
  858. Berner D., Amrhein V. (2022): Why and How We Should Join the Shift From Significance Testing to Estimation. Preprints.org. https://doi.org/10.20944/preprints202112.0235.v2
  859. Berrar D. (2022): Using p-values for the comparison of classifiers: pitfalls and alternatives. Data Mining and Knowledge Discovery 36(3):1102-1139. https://doi.org/10.1007/s10618-022-00828-1
  860. Fatori D., Suen P., Bacchi P., Afonso L., Klein I., Cavendish B., et al. (2022): Trajectories of common mental disorders symptoms before and during the COVID-19 pandemic: findings from the ELSA-Brasil COVID-19 Mental Health Cohort. Social Psychiatry and Psychiatric Epidemiology 57(12):2445-2455. https://doi.org/10.1007/s00127-022-02365-0
  861. Dunleavy D. (2022): Are We Meeting Best Practice Standards?: A Longitudinal Analysis of Mental Health Practices Within the Florida Child Welfare System with Implications for Child Well-being. Journal of Trial and Error 2(1):81-88. https://doi.org/10.36850/rga3
  862. Schad D., Nicenboim B., Bürkner P., Betancourt M., Vasishth S. (2022): Workflow techniques for the robust use of bayes factors. Psychological Methods 28(6):1404-1426. https://doi.org/10.1037/met0000472
  863. Schad D., Vasishth S. (2022): The posterior probability of a null hypothesis given a statistically significant result. The Quantitative Methods for Psychology 18(2):130-99. https://doi.org/10.20982/tqmp.18.2.p011
  864. Leising D., Thielmann I., Glöckner A., Gärtner A., Schönbrodt F. (2022): Ten steps toward a better personality science – how quality may be rewarded more in research evaluation. Personality Science 3. https://doi.org/10.5964/ps.6029
  865. Sidebotham D. (2022): Fooled by Significance Testing: An Analysis of the LOVIT Vitamin C Trial. The Journal of ExtraCorporeal Technology 54(4):324-329. https://doi.org/10.1051/ject/202254324
  866. David Sidebotham (2022): Fooled by Significance Testing: An Analysis of the LOVIT Vitamin C Trial. PubMed. https://doi.org/10.1182/ject-2200030
  867. Chicco D., Agapito G. (2022): Nine quick tips for pathway enrichment analysis. PLOS Computational Biology 18(8):e1010348. https://doi.org/10.1371/journal.pcbi.1010348
  868. Chicco D., Alameer A., Rahmati S., Jurman G. (2022): Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning. BioData Mining 15(1). https://doi.org/10.1186/s13040-022-00312-y
  869. Chicco D., Jurman G. (2022): A brief survey of tools for genomic regions enrichment analysis. Frontiers in Bioinformatics 2. https://doi.org/10.3389/fbinf.2022.968327
  870. Chicco D., Jurman G. (2022): The ABC recommendations for validation of supervised machine learning results in biomedical sciences. Frontiers in Big Data 5. https://doi.org/10.3389/fdata.2022.979465
  871. Chakraborty D., Guinat C., Müller N., Briand F., Andraud M., Scoizec A., et al. (2022): Phylodynamic analysis of the highly pathogenic avian influenza H5N8 epidemic in France, 2016–2017. Transboundary and Emerging Diseases 69(5). https://doi.org/10.1111/tbed.14490
  872. Mayo D., Hand D. (2022): Statistical significance and its critics: practicing damaging science, or damaging scientific practice?. Synthese 200(3). https://doi.org/10.1007/s11229-022-03692-0
  873. Dzadey D., Biswas R., Bhowmik J. (2022): Investigating factors affecting HIV/AIDS knowledge among women in low and middle-income countries in Asia. Journal of Health Psychology 28(11):1085-1098. https://doi.org/10.1177/13591053221127531
  874. Spathis D., Perez-Pozuelo I., Gonzales T., Wu Y., Brage S., Wareham N., et al. (2022): Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments. npj Digital Medicine 5(1). https://doi.org/10.1038/s41746-022-00719-1
  875. Lee D., Tabung F., Giovannucci E. (2022): Association of animal and plant protein intakes with biomarkers of insulin and insulin-like growth factor axis. Clinical Nutrition 41(6):1272-1280. https://doi.org/10.1016/j.clnu.2022.04.003
  876. Mo D., Zou X., Wong W. (2022): Neural stylist: Towards online styling service. Expert Systems with Applications 203:117333. https://doi.org/10.1016/j.eswa.2022.117333
  877. Parslow E., Rose J. (2022): Stress and risk — Preferences versus noise. Judgment and Decision Making 17(4):883-936. https://doi.org/10.1017/s1930297500008974
  878. Vail E., Avidan M. (2022): Trials with ‘non-significant’ results are not insignificant trials: a common significance threshold distorts reporting and interpretation of trial results. British Journal of Anaesthesia 129(5):643-646. https://doi.org/10.1016/j.bja.2022.06.023
  879. de Oliveira Santana Amaral E. (2022): Uso atual de análises de tamanho de efeito ou intervalo de confiança em pesquisa clínica e biomédica. https://doi.org/10.47749/t/unicamp.2022.1242658
  880. Zavalis E., Ioannidis J. (2022): A meta-epidemiological assessment of transparency indicators of infectious disease models. PLOS ONE 17(10):e0275380. https://doi.org/10.1371/journal.pone.0275380
  881. Zavalis E., Ioannidis J. (2022): A meta-epidemiological assessment of transparency indicators of infectious disease models. https://doi.org/10.1101/2022.04.11.22273744
  882. Zavalis E., Contopoulos-Ioannidis D., Ioannidis J. (2022): Transparency in infectious disease research: a meta-research survey of specialty journals. https://doi.org/10.1101/2022.11.11.22282231
  883. Wagenmakers E., Ly A. (2022): History and nature of the Jeffreys–Lindley paradox. Archive for History of Exact Sciences 77(1):25-72. https://doi.org/10.1007/s00407-022-00298-3
  884. Wagenmakers E., Sarafoglou A., Aczel B. (2022): Facing the Unknown Unknowns of Data Analysis. https://doi.org/10.31234/osf.io/mjw2c
  885. Gandelman E., Miller S., Back S. (2022): Imaginal exposure processing during Concurrent Treatment of PTSD and Substance Use Disorders using Prolonged Exposure (COPE) therapy: Examination of linguistic markers of cohesiveness. Journal of Traumatic Stress 35(2):682-693. https://doi.org/10.1002/jts.22786
  886. Koster E., Marchetti I., Grahek I. (2022): Focusing Inward: A Timely Yet Daunting Challenge for Clinical Psychological Science. Psychological Inquiry 33(4):273-275. https://doi.org/10.1080/1047840x.2022.2149183
  887. Arza E., Ceberio J., Irurozki E., Pérez A. (2022): Comparing Two Samples Through Stochastic Dominance: A Graphical Approach. Journal of Computational and Graphical Statistics 32(2):551-566. https://doi.org/10.1080/10618600.2022.2084405
  888. Holzmeister F., Huber J., Kirchler M., Schwaiger R. (2022): Nudging debtors to pay their debt: Two randomized controlled trials. Journal of Economic Behavior & Organization 198:535-551. https://doi.org/10.1016/j.jebo.2022.04.006
  889. Wang F., Ugai T., Haruki K., Wan Y., Akimoto N., Arima K., et al. (2022): Healthy and unhealthy plant‐based diets in relation to the incidence of colorectal cancer overall and by molecular subtypes. Clinical and Translational Medicine 12(8). https://doi.org/10.1002/ctm2.893
  890. Hartig F., Barraquand F. (2022): The evidence contained in the P-value is context dependent. Trends in Ecology & Evolution 37(7):569-570. https://doi.org/10.1016/j.tree.2022.02.011
  891. Panzera F., Bergamo P., Perron V., Fäh D. (2022): On the correlation between earthquake coda horizontal-to-vertical spectral ratios and amplification functions at the KiK-net network. Frontiers in Earth Science 10. https://doi.org/10.3389/feart.2022.993078
  892. Ortega F., Mora-Gonzalez J., Cadenas-Sanchez C., Esteban-Cornejo I., Migueles J., Solis-Urra P., et al. (2022): Effects of an Exercise Program on Brain Health Outcomes for Children With Overweight or Obesity. JAMA Network Open 5(8):e2227893. https://doi.org/10.1001/jamanetworkopen.2022.27893
  893. Ortega F., Mora-Gonzalez J., Cadenas-Sanchez C., Esteban-Cornejo I., Migueles J., Solis-Urra P., et al. (2022): Effects of exercise on brain health outcomes in children with overweight/obesity: the ActiveBrains randomized controlled trial. https://doi.org/10.1101/2022.03.17.22272506
  894. Corotto F. (2022): Philosophical objections. Wise Use of Null Hypothesis Tests. https://doi.org/10.1016/b978-0-323-95284-2.00011-2
  895. Bartoš F., Aust F., Haaf J. (2022): Informed Bayesian survival analysis. BMC Medical Research Methodology 22(1). https://doi.org/10.1186/s12874-022-01676-9
  896. Bartoš F., Maier M. (2022): Power or Alpha? The Better Way of Decreasing the False Discovery Rate. Meta-Psychology 6. https://doi.org/10.15626/mp.2020.2460
  897. Majumdar G., Yazin F., Banerjee A., Roy D. (2022): Emotion dynamics as hierarchical Bayesian inference in time. Cerebral Cortex 33(7):3750-3772. https://doi.org/10.1093/cercor/bhac305
  898. Mertens G., Krypotos A. (2022): Preregistration of studies with existing data. https://doi.org/10.31234/osf.io/65z3b
  899. Mertens G., Krypotos A. (2022): Preregistration of Studies with Existing Data. Integrity of Scientific Research. https://doi.org/10.1007/978-3-030-99680-2_36
  900. Wallace G., Barrett K., Henry K., Prince M., Conner B. (2022): Examining underlying structures of cognitive emotion regulation strategies using exploratory structural equation modeling. Quality & Quantity 57(5):4171-4192. https://doi.org/10.1007/s11135-022-01531-5
  901. Temprano‐Sagrera G., Sitlani C., Bone W., Martin‐Bornez M., Voight B., Morrison A., et al. (2022): Multi‐phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations. Journal of Thrombosis and Haemostasis 20(6):1331-1349. https://doi.org/10.1111/jth.15698
  902. Albert G., Richardson G., Arnocky S., Bird B., Fisher M., Hlay J., et al. (2022): A Psychometric Evaluation of the Intrasexual Competition Scale. Archives of Sexual Behavior 51(6):2741-2758. https://doi.org/10.1007/s10508-021-02167-6
  903. Li G., So M., Tam K. (2022): Identifying the Big Shots—A Quantile-Matching Way in the Big Data Context. ACM Transactions on Management Information Systems 13(2):1-30. https://doi.org/10.1145/3490395
  904. Moraes G., Soares V., Chiminazzo J. (2022): Temporal analysis of goals scored in futsal: a comparison of two models. Human Movement 23(4):63-69. https://doi.org/10.5114/hm.2022.108319
  905. Coqueret G. (2022): Forking paths in empirical studies. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3999379
  906. Fréchette G., Sarnoff K., Yariv L. (2022): Experimental Economics: Past and Future. Annual Review of Economics 14(1):777-794. https://doi.org/10.1146/annurev-economics-081621-124424
  907. Sokolowski H., Hawes Z., Leibovich-Raveh T., Ansari D. (2022): Number symbols are processed more automatically than nonsymbolic numerical magnitudes: Findings from a Symbolic-Nonsymbolic Stroop task. Acta Psychologica 228:103644. https://doi.org/10.1016/j.actpsy.2022.103644
  908. Solomon H., Kim B., Rajagopalan A., Funk M. (2022): “Doctor” Badge Promotes Accurate Role Identification and Reduces Gender-Based Aggressions in Female Resident Physicians. Academic Psychiatry 46(5):611-615. https://doi.org/10.1007/s40596-022-01641-0
  909. Elomaa H., Ahtiainen M., Väyrynen S., Ogino S., Nowak J., Friman M., et al. (2022): Prognostic significance of spatial and density analysis of T lymphocytes in colorectal cancer. British Journal of Cancer 127(3):514-523. https://doi.org/10.1038/s41416-022-01822-6
  910. Walach H., Ofner M., Ruof V., Herbig M., Klement R. (2022): Why do people consent to receiving SARS-CoV-2 vaccinations? A representative survey in Germany. BMJ Open 12(8):e060555. https://doi.org/10.1136/bmjopen-2021-060555
  911. Walach H., Ofner M., Ruof V., Herbig M., Klement R. (2022): Why do people consent to receiving SARS-CoV2 vaccinations? A Representative Survey in Germany. https://doi.org/10.21203/rs.3.rs-1216502/v1
  912. Pashler H., Harris C. (2022): What Can We Do About Our (Untrustworthy) Literature?. Research Integrity. https://doi.org/10.1093/oso/9780190938550.003.0003
  913. McDonough I., Cody S., Harrell E., Garrett S., Popp T. (2022): Cognitive differences across ethnoracial category, socioeconomic status across the Alzheimer’s disease spectrum: Can an ability discrepancy score level the playing field?. Memory & Cognition 51(3):543-560. https://doi.org/10.3758/s13421-022-01304-3
  914. Kardum I., Hudek-Knezevic J., Marijanović K., Shackelford T. (2022): Predicting Mate Poaching Experiences from Personality Traits Using a Dyadic Analysis. The Journal of Sex Research 60(3):384-398. https://doi.org/10.1080/00224499.2022.2092586
  915. Khorozyan I., Heurich M. (2022): Large-Scale Sheep Losses to Wolves (Canis lupus) in Germany Are Related to the Expansion of the Wolf Population but Not to Increasing Wolf Numbers. Frontiers in Ecology and Evolution 10. https://doi.org/10.3389/fevo.2022.778917
  916. Khorozyan I. (2022): Importance of non-journal literature in providing evidence for predator conservation. Perspectives in Ecology and Conservation 20(4):346-351. https://doi.org/10.1016/j.pecon.2022.08.003
  917. Berro I., Varela J., Gutiérrez L. (2022): An image‐based methodology to evaluate oat panicle architecture. Crop Science 63(2):648-661. https://doi.org/10.1002/csc2.20884
  918. Lee I., Skuse D., Constable P., Marmolejo-Ramos F., Olsen L., Thompson D. (2022): The electroretinogram b-wave amplitude: a differential physiological measure for Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder. Journal of Neurodevelopmental Disorders 14(1). https://doi.org/10.1186/s11689-022-09440-2
  919. Fagogeni I., Metlerska J., Falgowski T., Górski M., Lipski M., Nowicka A. (2022): Effectiveness of Teeth Whitening after Regenerative Endodontics Procedures: An In Vitro Study. Journal of Clinical Medicine 11(23):7016. https://doi.org/10.3390/jcm11237016
  920. Schauer J. (2022): On the Accuracy of Replication Failure Rates. Multivariate Behavioral Research 58(3):598-615. https://doi.org/10.1080/00273171.2022.2066500
  921. Scholl J., Trier H., Rushworth M., Kolling N. (2022): The effect of apathy and compulsivity on planning and stopping in sequential decision-making. PLOS Biology 20(3):e3001566. https://doi.org/10.1371/journal.pbio.3001566
  922. Thompson J., Dreber A., Gaunt T., Gordon M., Holzmeister F., Huber J., et al. (2022): Using prediction markets to estimate ratings of academic research quality in a mock Research Excellence Framework exercise. OSF Preprints. https://doi.org/10.31222/osf.io/gsc8f
  923. Gómez-Ramírez J., Fernández-Blázquez M., González-Rosa J. (2022): A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain. Life 12(10):1586. https://doi.org/10.3390/life12101586
  924. Moreno J., Puertas L. (2022): Estudio de validez de criterio de un instrumento para medir estilos conductuales en población ecuatoriana. CienciAmérica 11(1):123. https://doi.org/10.33210/ca.v11i1.385
  925. Bartlett J., Charles S. (2022): Power to the People: A Beginner’s Tutorial to Power Analysis using jamovi. Meta-Psychology 6. https://doi.org/10.15626/mp.2021.3078
  926. Moody J., Keister L., Ramos M. (2022): Reproducibility in the Social Sciences. Annual Review of Sociology 48(1):65-85. https://doi.org/10.1146/annurev-soc-090221-035954
  927. Bagrow J., Ahn Y. (2022): Network cards: concise, readable summaries of network data. Applied Network Science 7(1). https://doi.org/10.1007/s41109-022-00514-7
  928. Vojáček J. (2022): Jaká je skutečná chybovost hladiny významnosti p &lt; 0,05 a proč nepoužívat označení statisticky významný/nevýznamný rozdíl. Intervenční a akutní kardiologie 21(2):64-70. https://doi.org/10.36290/kar.2022.017
  929. Granados Samayoa J., Moore C., Ruisch B., Boggs S., Ladanyi J., Fazio R. (2022): A gateway conspiracy? Belief in COVID-19 conspiracy theories prospectively predicts greater conspiracist ideation. PLOS ONE 17(10):e0275502. https://doi.org/10.1371/journal.pone.0275502
  930. Samayoa J., Moore C., Ruisch B., Boggs S., Ladanyi J., Fazio R. (2022): A gateway conspiracy? Belief in COVID-19 conspiracy theories prospectively predicts greater conspiracist ideation. https://doi.org/10.31234/osf.io/q4gct
  931. Boone J., Davids A., Joffe D., Arese Lucini F., Oakley D., Oakley M., et al. (2022): In-Clinic Measurements of Vascular Risk and Brain Activity. Journal of Ageing and Longevity 2(3):240-251. https://doi.org/10.3390/jal2030020
  932. Zhang J., Tao S. (2022): Vocal Characteristics Influence Women’s Perceptions of Infidelity and Relationship Investment in China. Evolutionary Psychology 20(3). https://doi.org/10.1177/14747049221108883
  933. Park J., Woolley J., Mendes W. (2022): The effects of intranasal oxytocin on black participants’ responses to outgroup acceptance and rejection. Frontiers in Psychology 13. https://doi.org/10.3389/fpsyg.2022.916305
  934. Pinto da Costa J., Cabral M. (2022): Statistical Methods with Applications in Data Mining: A Review of the Most Recent Works. Mathematics 10(6):993. https://doi.org/10.3390/math10060993
  935. Pick J., Lemon H., Thomson C., Hadfield J. (2022): Decomposing phenotypic skew and its effects on the predicted response to strong selection. Nature Ecology & Evolution 6(6):774-785. https://doi.org/10.1038/s41559-022-01694-2
  936. Stricker J., Barthels F., Müller R., Pietrowsky R. (2022): An exploratory study of associations between the ICD-11 personality disorder model and eating pathology. Journal of Eating Disorders 10(1). https://doi.org/10.1186/s40337-022-00658-y
  937. Carey J., Nyhan B., Phillips J., Reifler J. (2022): Partisanship Unmasked? The Role of Politics and Social Norms in COVID-19 Mask-Wearing Behavior. Journal of Experimental Political Science 10(3):377-390. https://doi.org/10.1017/xps.2022.20
  938. Ioannidis J., Contopoulos-Ioannidis D. (2022): Pre-pandemic cross-reactive humoral immunity to SARS-CoV-2 in Africa: systematic review and meta-analysis. https://doi.org/10.1101/2022.10.07.22280814
  939. Pek J., Hoisington-Shaw K., Wegener D. (2022): Avoiding Questionable Research Practices Surrounding Statistical Power Analysis. Avoiding Questionable Research Practices in Applied Psychology. https://doi.org/10.1007/978-3-031-04968-2_11
  940. Vandenbruaene J., Ceuster M., Annaert J. (2022): Efficient Spread Betting Markets: A Literature Review. Journal of Sports Economics 23(7):907-949. https://doi.org/10.1177/15270025211071042
  941. Willmann J., Vlaskou Badra E., Adilovic S., Ahmadsei M., Christ S., van Timmeren J., et al. (2022): Evaluation of the prognostic value of the ESTRO EORTC classification of oligometastatic disease in patients treated with stereotactic body radiotherapy: A retrospective single center study. Radiotherapy and Oncology 168:256-264. https://doi.org/10.1016/j.radonc.2022.01.019
  942. Jubin J., Delmas P., Gilles I., Oulevey Bachmann A., Ortoleva Bucher C. (2022): Protective Factors and Coping Styles Associated with Quality of Life during the COVID-19 Pandemic: A Comparison of Hospital or Care Institution and Private Practice Nurses. International Journal of Environmental Research and Public Health 19(12):7112. https://doi.org/10.3390/ijerph19127112
  943. Tendeiro J., Kiers H., Hoekstra R., Wong T., Morey R. (2022): Diagnosing the Misuse of the Bayes factor in Applied Research. https://doi.org/10.31234/osf.io/du3fc
  944. Mulder J. (2022): Bayesian Testing of Linear Versus Nonlinear Effects Using Gaussian Process Priors. The American Statistician 77(1):1-11. https://doi.org/10.1080/00031305.2022.2028675
  945. Bak-Coleman J., Mann R., Bergstrom C., Gross K., West J. (2022): Revisiting the replication crisis without false positives. https://doi.org/10.31235/osf.io/rkyf7
  946. Wortzel J., Maeng D., Francis A., Oldham M. (2022): Evaluating the Effectiveness of an Educational Module for the Bush-Francis Catatonia Rating Scale. Academic Psychiatry 46(2):185-193. https://doi.org/10.1007/s40596-021-01582-0
  947. Sarmiento J., Ocampo C. (2022): Enfoques Frecuentista y Bayesiano en el Estudio del Plagio Académico. Una Propuesta Innovadora en Investigación Educativa. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación 21(1):139-158. https://doi.org/10.15366/reice2023.21.1.007
  948. Arroyo-Barrigüete J., Obregón A., Ortiz-Lozano J., Rua-Vieites A. (2022): Spain is not different: teaching quantitative courses can also be hazardous to one’s career (at least in undergraduate courses). PeerJ 10:e13456. https://doi.org/10.7717/peerj.13456
  949. Rohrer, Julia M., Huenermund, Paul, Arslan, Ruben C., Elson, Malte (2022): Author response for “That’s a Lot to Process! Pitfalls of Popular Path Models”. https://doi.org/10.1177/25152459221095827/v2/response1
  950. Rohrer, Julia M., Huenermund, Paul, Arslan, Ruben C., Elson, Malte (2022): Author response for “That’s a Lot to Process! Pitfalls of Popular Path Models”. https://doi.org/10.1177/25152459221095827/v3/response1
  951. Kishikawa J., Ugai T., Fujiyoshi K., Chen Y., Haruki K., Liu L., et al. (2022): Smoking and colorectal cancer survival in relation to tumor LINE-1 methylation levels: a prospective cohort study. Epigenetics Communications 2(1). https://doi.org/10.1186/s43682-022-00012-y
  952. Orr J., Li C., Shah S., Backstrand M., Chung C., Boucher N. (2022): Mandibular transverse dentoalveolar and skeletal changes associated with lip bumper and rapid maxillary expander: A cone-beam computed tomography study. American Journal of Orthodontics and Dentofacial Orthopedics 163(3):407-425. https://doi.org/10.1016/j.ajodo.2021.12.026
  953. Cowgill K., Erosheva E., Elder A., Miljacic L., Buskin S., Duchin J. (2022): Anti-SARS-CoV-2 seroprevalence in King County, WA—Cross-sectional survey, August 2020. PLOS ONE 17(8):e0272783. https://doi.org/10.1371/journal.pone.0272783
  954. Lohse K. (2022): No Estimation without Inference. Communications in Kinesiology 1(4). https://doi.org/10.51224/cik.2022.49
  955. Rice K., Krakauer C. (2022): Three-Decision Methods: A Sensible Formulation of Significance Tests—and Much Else. Annual Review of Statistics and Its Application 10(1):525-546. https://doi.org/10.1146/annurev-statistics-033021-111159
  956. Zahrai K., Veer E., Ballantine P., de Vries H., Prayag G. (2022): Either you control social media or social media controls you: Understanding the impact of self‐control on excessive social media use from the dual‐system perspective. Journal of Consumer Affairs 56(2):806-848. https://doi.org/10.1111/joca.12449
  957. Türkarslan K., Çınarbaş D., Perogamvros L. (2022): The Roles of Intrusive Visual Imagery and Verbal Thoughts in Pre-sleep Arousal of Patients with Insomnia Disorder: A Path Model. https://doi.org/10.31234/osf.io/uwkb9
  958. Liebst L., Ejbye-Ernst P., de Bruin M., Thomas J., Lindegaard M. (2022): No evidence that mask-wearing in public places elicits risk compensation behavior during the COVID-19 pandemic. Scientific Reports 12(1). https://doi.org/10.1038/s41598-022-05270-3
  959. Lilleholt L., Zettler I. (2022): A Closer Look on the Relation Between Nostalgia and Risk-Taking. Personality and Social Psychology Bulletin 49(4):600-611. https://doi.org/10.1177/01461672221074113
  960. Mas-Cuesta L., Baltruschat S., Cándido A., Catena A. (2022): Relationships between Personality Traits and Brain Gray Matter Are Different in Risky and Non-risky Drivers. Behavioural Neurology 2022:1-15. https://doi.org/10.1155/2022/1775777
  961. Nossaman L., Nossaman B. (2022): Hawthorne Effect: More Than Just Telephones. Ochsner Journal 22(4):286-289. https://doi.org/10.31486/toj.22.5031
  962. Jussim L., Stevens S., Anglin S. (2022): Questionable Interpretive Practices. Research Integrity. https://doi.org/10.1093/oso/9780190938550.003.0009
  963. Coenen L., Smits T. (2022): Strong-Form Frequentist Testing In Communication Science: Principles, Opportunities, And Challenges. Communication Methods and Measures 16(4):237-265. https://doi.org/10.1080/19312458.2022.2086690
  964. Held L., Matthews R. (2022): Carl Liebermeister and the emergence of modern medical statistics, Part 1: his remarkable work in historical context. Journal of the Royal Society of Medicine 115(2):69-72. https://doi.org/10.1177/01410768221077205
  965. Neyse L., Fossen F., Johanneson M., Dreber A. (2022): Cognitive Reflection and 2D:4D: Evidence from a Large Population Sample. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4063697
  966. Lin L., Xing A., Chu H., Murad M., Xu C., Baer B., et al. (2022): Assessing the robustness of results from clinical trials and meta-analyses with the fragility index. American Journal of Obstetrics and Gynecology 228(3):276-282. https://doi.org/10.1016/j.ajog.2022.08.053
  967. Lin L., Chu H. (2022): Assessing and visualizing fragility of clinical results with binary outcomes in R using the fragility package. PLOS ONE 17(6):e0268754. https://doi.org/10.1371/journal.pone.0268754
  968. Ladelsky L., Lee T. (2022): Effect of risky decision-making and job satisfaction on turnover intention and turnover behavior among information technology employees. International Journal of Organizational Analysis 31(7):3553-3581. https://doi.org/10.1108/ijoa-10-2022-3465
  969. Zhao L., Jin L., Petrick J., Zeng H., Wang F., Tang L., et al. (2022): Specific botanical groups of fruit and vegetable consumption and liver cancer and chronic liver disease mortality: a prospective cohort study. The American Journal of Clinical Nutrition 117(2):278-285. https://doi.org/10.1016/j.ajcnut.2022.12.004
  970. Iwasaki M., Kanda J., Tanaka H., Shindo T., Sato T., Doki N., et al. (2022): Impact of HLA Epitope Matching on Outcomes After Unrelated Bone Marrow Transplantation. Frontiers in Immunology 13. https://doi.org/10.3389/fimmu.2022.811733
  971. Atanaw M., Chekol Y., Wolde A., Amogne F. (2022): Monitoring of dairy farm management practice and production performances using Structural Equation Modeling in Amhara region, Ethiopia. https://doi.org/10.21203/rs.3.rs-1324991/v1
  972. Morkunas M. (2022): Measuring the Cohesion of Informal Economy in Agriculture in New European Union Member States. Economies 10(11):285. https://doi.org/10.3390/economies10110285
  973. Radzvilas M., De Pretis F., Peden W., Tortoli D., Osimani B. (2022): Incentives for Research Effort: An Evolutionary Model of Publication Markets with Double-Blind and Open Review. Computational Economics 61(4):1433-1476. https://doi.org/10.1007/s10614-022-10250-w
  974. Godard M., Wamain Y., Ott L., Delepoulle S., Kalénine S. (2022): How Competition between Action Representations Affects Object Perception during Development. Journal of Cognition and Development 23(3):360-384. https://doi.org/10.1080/15248372.2022.2025808
  975. Furlan M., Mariano E. (2022): Measuring the effects of climate techs and social inequality on climate performance using a SEM-DEA approach. Journal of Environmental Planning and Management 67(3):632-661. https://doi.org/10.1080/09640568.2022.2130037
  976. Resende M., Alves R. (2022): Statistical significance, selection accuracy, and experimental precision in plant breeding. Crop Breeding and Applied Biotechnology 22(3). https://doi.org/10.1590/1984-70332022v22n3a31
  977. Matabuena M., Karas M., Riazati S., Caplan N., Hayes P. (2022): Estimating Knee Movement Patterns of Recreational Runners Across Training Sessions Using Multilevel Functional Regression Models. The American Statistician 77(2):169-181. https://doi.org/10.1080/00031305.2022.2105950
  978. Nadal M., Skov M. (2022): No sound evidence supports the notion that we can “read” art. Physics of Life Reviews 44:110-112. https://doi.org/10.1016/j.plrev.2022.12.017
  979. van de Weijer M., de Vries L., Pelt D., Ligthart L., Willemsen G., Boomsma D., et al. (2022): Self-rated health when population health is challenged by the COVID-19 pandemic; a longitudinal study. Social Science & Medicine 306:115156. https://doi.org/10.1016/j.socscimed.2022.115156
  980. Wenzel M., Rowland Z., Mey L., Kurth K., Tüscher O., Kubiak T. (2022): Variability in negative affect is an important feature of neuroticism above mean negative affect once measurement issues are accounted for. European Journal of Personality 37(3):338-351. https://doi.org/10.1177/08902070221089139
  981. Colley M., Hummler C., Rukzio E. (2022): Effects of mode distinction, user visibility, and vehicle appearance on mode confusion when interacting with highly automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour 89:303-316. https://doi.org/10.1016/j.trf.2022.06.020
  982. Jusup M., Holme P., Kanazawa K., Takayasu M., Romić I., Wang Z., et al. (2022): Social physics. Physics Reports 948:1-148. https://doi.org/10.1016/j.physrep.2021.10.005
  983. Müssig M., Pfeiler T., Egloff B. (2022): Why They Eat What They Eat: Comparing 18 Eating Motives Among Omnivores and Veg*ns. Frontiers in Nutrition 9. https://doi.org/10.3389/fnut.2022.780614
  984. Dufwenberg M., Johansson-Stenman O., Kirchler M., Lindner F., Schwaiger R. (2022): Mean markets or kind commerce?. Journal of Public Economics 209:104648. https://doi.org/10.1016/j.jpubeco.2022.104648
  985. Holst M., Faust A., Strech D. (2022): Do German university medical centres promote robust and transparent research? A cross-sectional study of institutional policies. Health Research Policy and Systems 20(1). https://doi.org/10.1186/s12961-022-00841-2
  986. Wilkinson-Stokes M., Betson J., Sawyer S. (2022): Adverse events from nitrate administration during right ventricular myocardial infarction: a systematic review and meta-analysis. Emergency Medicine Journal 40(2):108-113. https://doi.org/10.1136/emermed-2021-212294
  987. Bennett M., Goodall E. (2022): The Reproducibility Crisis and Autism Spectrum Research. Addressing Underserved Populations in Autism Spectrum Research. https://doi.org/10.1108/978-1-80382-463-520221011
  988. Courtney M., Karakus M., Ersozlu Z., Nurumov K. (2022): The influence of ICT use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s PISA surveys. Large-scale Assessments in Education 10(1). https://doi.org/10.1186/s40536-022-00128-6
  989. Courtney M., Rakhymbayeva Z., Shilibekova A., Ziyedenova D., Soltangazina S., Muratkyzy A., et al. (2022): Kazakh, Russian, and Uyghur child language literacy: The role of the updated curriculum on longitudinal growth trajectories in Kazakhstan. Studies in Educational Evaluation 75:101189. https://doi.org/10.1016/j.stueduc.2022.101189
  990. West M., Rorie M. (2022): Data Simulations as a Means of Improving Compliance Measurement. Measuring Compliance. https://doi.org/10.1017/9781108770941.016
  991. Maura Burke, Sean Devine, Valentine Delrue, Sarahanne Field, Stefan Plinio, Casarotto Copyedited, et al. (2022): . Journal of Trial and Error 2. https://doi.org/10.36850/i2.1
  992. Karakus M., Courtney M., Aydin H. (2022): Understanding the academic achievement of the first- and second-generation immigrant students: a multi-level analysis of PISA 2018 data. Educational Assessment, Evaluation and Accountability 35(2):233-278. https://doi.org/10.1007/s11092-022-09395-x
  993. Ghislain M., Gerard O., Emeric T., Adolphe M. (2022): Improvement of environmental characteristics of natural monoesters for use as insulating liquid in power transformers. Environmental Technology & Innovation 27:102784. https://doi.org/10.1016/j.eti.2022.102784
  994. Al Rahwanji M., Abouras H., Shammout M., Altalla R., Al Sakaan R., Alhalabi N., et al. (2022): The optimal period for oocyte retrieval after the administration of recombinant human chorionic gonadotropin in in vitro fertilization. BMC Pregnancy and Childbirth 22(1). https://doi.org/10.1186/s12884-022-04412-9
  995. Krämer M., van Scheppingen M., Chopik W., Richter D. (2022): The transition to grandparenthood: No consistent evidence for change in the Big Five personality traits and life satisfaction. European Journal of Personality 37(5):560-586. https://doi.org/10.1177/08902070221118443
  996. Krämer M., van Scheppingen M., Chopik W., Richter D. (2022): The Transition to Grandparenthood: No Consistent Evidence for Change in the Big Five Personality Traits and Life Satisfaction. https://doi.org/10.31234/osf.io/87emk
  997. Gordon M., Bishop M., Chen Y., Dreber A., Goldfedder B., Holzmeister F., et al. (2022): Forecasting the publication and citation outcomes of COVID-19 preprints. Royal Society Open Science 9(9). https://doi.org/10.1098/rsos.220440
  998. Kent M., Schiavon S. (2022): Predicting Window View Preferences Using the Environmental Information Criteria. LEUKOS 19(2):190-209. https://doi.org/10.1080/15502724.2022.2077753
  999. Fay M., Proschan M., Brittain E., Tiwari R. (2022): Interpreting p-Values and Confidence Intervals Using Well-Calibrated Null Preference Priors. Statistical Science 37(4). https://doi.org/10.1214/21-sts833
  1000. Nuijten M. (2022): Assessing and Improving Robustness of Psychological Research Findings in Four Steps. Avoiding Questionable Research Practices in Applied Psychology. https://doi.org/10.1007/978-3-031-04968-2_17
  1001. Gerken M. (2022): Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.001.0001
  1002. Gerken M. (2022): List of Principles. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.005.0001
  1003. Gerken M. (2022): Public Scientific Testimony II. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.003.0007
  1004. Gerken M. (2022): List of Figures. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.002.0008
  1005. Gerken M. (2022): The Nature of Testimony. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.003.0003
  1006. Gerken M. (2022): Coda. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.003.0009
  1007. Gerken M. (2022): Public Scientific Testimony I. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.003.0006
  1008. Gerken M. (2022): Intra-Scientific Testimony. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.003.0005
  1009. Gerken M. (2022): Scientific Justification as the Basis of Scientific Testimony. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.003.0004
  1010. Gerken M. (2022): Preface. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.002.0006
  1011. Gerken M. (2022): Introduction. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.003.0001
  1012. Gerken M. (2022): Copyright Page. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.002.0003
  1013. Gerken M. (2022): Testimony and the Scientific Enterprise. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.003.0002
  1014. Gerken M. (2022): The Significance of Scentific Testimony. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.003.0008
  1015. Gerken M. (2022): Dedication. Scientific Testimony. https://doi.org/10.1093/oso/9780198857273.002.0004
  1016. van den Bemd M., Schalk B., Bischoff E., Cuypers M., Leusink G. (2022): Chronic diseases and comorbidities in adults with and without intellectual disabilities: comparative cross-sectional study in Dutch general practice. Family Practice 39(6):1056-1062. https://doi.org/10.1093/fampra/cmac042
  1017. Szreder M. (2022): Opportunities and illusions of using large samples in statistical inference. Wiadomości Statystyczne. The Polish Statistician 67(8):1-16. https://doi.org/10.5604/01.3001.0015.9704
  1018. Mojtaba Kafi, Maryam Ansari‐Lari (2022): “A statistically non-significant difference”: Do we have to change the rules or our way of thinking?. PubMed. https://doi.org/10.22099/ijvr.2022.44044.6470
  1019. Atee M., Hoti K., Chivers P., Hughes J. (2022): Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool. Frontiers in Pain Research 3. https://doi.org/10.3389/fpain.2022.827551
  1020. Uthso N., Akter N. (2022): Determinants of life satisfaction among women of reproductive age (15–49 years) in Bangladesh: A cross-sectional analysis. PLOS ONE 17(10):e0276563. https://doi.org/10.1371/journal.pone.0276563
  1021. Elpers N., Jensen G., Holmes K. (2022): Does grammatical gender affect object concepts? Registered replication of Phillips and Boroditsky (2003). Journal of Memory and Language 127:104357. https://doi.org/10.1016/j.jml.2022.104357
  1022. Akimoto N., Väyrynen J., Zhao M., Ugai T., Fujiyoshi K., Borowsky J., et al. (2022): Desmoplastic Reaction, Immune Cell Response, and Prognosis in Colorectal Cancer. Frontiers in Immunology 13. https://doi.org/10.3389/fimmu.2022.840198
  1023. Fackler N., Karasavvidis T., Ehlers C., Callan K., Lai W., Parisien R., et al. (2022): The Statistical Fragility of Operative vs Nonoperative Management for Achilles Tendon Rupture: A Systematic Review of Comparative Studies. Foot & Ankle International 43(10):1331-1339. https://doi.org/10.1177/10711007221108078
  1024. Malhotra N., Zigerell L. (2022): Publication Bias in the Social Sciences. Research Integrity. https://doi.org/10.1093/oso/9780190938550.003.0013
  1025. Quang-Loc N. (2022): Replication crisis. https://doi.org/10.31219/osf.io/4wd5x
  1026. Fox N., Honeycutt N., Jussim L. (2022): Better Understanding the Population Size and Stigmatization of Psychologists Using Questionable Research Practices. Meta-Psychology 6. https://doi.org/10.15626/mp.2020.2601
  1027. Byrd N. (2022): Great Minds do not Think Alike: Philosophers’ Views Predicted by Reflection, Education, Personality, and Other Demographic Differences. Review of Philosophy and Psychology 14(2):647-684. https://doi.org/10.1007/s13164-022-00628-y
  1028. Byrd N., Thompson M. (2022): Testing for implicit bias: Values, psychometrics, and science communication. WIREs Cognitive Science 13(5). https://doi.org/10.1002/wcs.1612
  1029. Byrd N. (2022): Great Minds Do Not Think Alike: Philosophers’ Views Predicted by Reflection, Education, Personality, And Other Demographic Differences. https://doi.org/10.31234/osf.io/xd83m
  1030. Byrd N., Thompson M. (2022): Testing for Implicit Bias: Values, Psychometrics, and Science Communication. https://doi.org/10.31234/osf.io/y5nm9
  1031. Marchi N., Pizzarotti B., Solelhac G., Berger M., Haba‐Rubio J., Preisig M., et al. (2022): Abnormal brain iron accumulation in obstructive sleep apnea: A quantitative MRI study in the HypnoLaus cohort. Journal of Sleep Research 31(6). https://doi.org/10.1111/jsr.13698
  1032. Geeraert N., Ward C., Hanel P. (2022): Returning home: The role of expectations in re‐entry adaptation. Applied Psychology: Health and Well-Being 14(3):949-966. https://doi.org/10.1111/aphw.12361
  1033. Coy N., Bendixen A., Grimm S., Roeber U., Schröger E. (2022): Is the Oddball Just an Odd-One-Out? The Predictive Value of Rule-Violating Events. Auditory Perception & Cognition 5(3-4):169-191. https://doi.org/10.1080/25742442.2022.2094657
  1034. Pocuca N., London-Nadeau K., Geoffroy M., Chadi N., Séguin J., Parent S., et al. (2022): Changes in emerging adults’ alcohol and cannabis use from before to during the COVID-19 pandemic: Evidence from a prospective birth cohort. Psychology of Addictive Behaviors 36(7):786-797. https://doi.org/10.1037/adb0000826
  1035. Serdarevic N., Pompeo M. (2022): Is Information Enough? The Case of Republicans and Climate Change. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4089165
  1036. Laccourreye O., Lisan Q., Vincent C., Righini C., Leboulanger N., Franco-Vidal V., et al. (2022): Keys for successful publication in Eur Ann Otorhinolaryngol Head Neck Dis: A STROBE analysis of peer reviews of articles submitted in 2020–2021. European Annals of Otorhinolaryngology, Head and Neck Diseases 140(1):19-24. https://doi.org/10.1016/j.anorl.2022.05.001
  1037. Martin O., Teste F. (2022): A call for changing data analysis practices: from philosophy and comprehensive reporting to modeling approaches and back. Plant and Soil 476(1-2):743-753. https://doi.org/10.1007/s11104-022-05329-0
  1038. Gureje O., Oladeji B., Kola L., Bello T., Ayinde O., Faregh N., et al. (2022): Effect of intervention delivered by frontline maternal care providers to improve outcome and parenting skills among adolescents with perinatal depression in Nigeria (the RAPiD study): A cluster randomized controlled trial. Journal of Affective Disorders 312:169-176. https://doi.org/10.1016/j.jad.2022.06.032
  1039. Elbæk C., Lystbæk M., Mitkidis P. (2022): On the psychology of bonuses: The effects of loss aversion and Yerkes-Dodson law on performance in cognitively and mechanically demanding tasks. Journal of Behavioral and Experimental Economics 98:101870. https://doi.org/10.1016/j.socec.2022.101870
  1040. Constable P., Marmolejo-Ramos F., Gauthier M., Lee I., Skuse D., Thompson D. (2022): Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder. Frontiers in Neuroscience 16. https://doi.org/10.3389/fnins.2022.890461
  1041. Taylor P., Reynolds R., Calhoun V., Gonzalez-Castillo J., Handwerker D., Bandettini P., et al. (2022): Highlight Results, Don’t Hide Them: Enhance interpretation, reduce biases and improve reproducibility. https://doi.org/10.1101/2022.10.26.513929
  1042. Adams P., Guttman‐Kenney B., Hayes L., Hunt S., Laibson D., Stewart N. (2022): Do Nudges Reduce Borrowing and Consumer Confusion in the Credit Card Market?. Economica 89(S1). https://doi.org/10.1111/ecca.12427
  1043. Sterner P., Friemelt B., Goretzko D., Kraus E., Bühner M., Pargent F. (2022): The confidence/significance level implies a certain cost ratio between type I error and type II error: For a stronger focus on decision theory in psychological assessment – Das Konfidenz-/Signifikanzniveau impliziert ein bestimmtes Kostenverhältnis zwischen Fehler 1. Art und Fehler 2. Art: Für ein stärkeres Einbeziehen der Entscheidungstheorie in die psychologische Einzelfalldiagnostik. https://doi.org/10.31234/osf.io/rsqvt
  1044. Quatto P., Ripamonti E., Marasini D. (2022): Beyond p < .05: a critical review of new Bayesian proposals for assessing the p-value. Journal of Biopharmaceutical Statistics 32(2):308-329. https://doi.org/10.1080/10543406.2021.2009497
  1045. Razavi P., Shaban-Azad H., Srivastava S. (2022): Gheirat as a complex emotional reaction to relational boundary violations: A mixed-methods investigation. Journal of Personality and Social Psychology 124(1):179-214. https://doi.org/10.1037/pspp0000424
  1046. Ergen P., Koçoğlu M., Nural M., Kuşkucu M., Aydin Ö., İnal F., et al. (2022): Carbapenem-resistant Klebsiella pneumoniae outbreak in a COVID-19 intensive care unit; a case-control study. Journal of Chemotherapy 34(8):517-523. https://doi.org/10.1080/1120009x.2022.2064698
  1047. Holm Hansen R., Talbot J., Højsgaard Chow H., Bredahl Hansen M., Buhelt S., Herich S., et al. (2022): Increased Intrathecal Activity of Follicular Helper T Cells in Patients With Relapsing-Remitting Multiple Sclerosis. Neurology Neuroimmunology & Neuroinflammation 9(5). https://doi.org/10.1212/nxi.0000000000200009
  1048. Holm Hansen R., Højsgaard Chow H., Talbot J., Buhelt S., Nickelsen Hellem M., Nielsen J., et al. (2022): Peripheral helper T cells in the pathogenesis of multiple sclerosis. Multiple Sclerosis Journal 28(9):1340-1350. https://doi.org/10.1177/13524585211067696
  1049. Biswas R., Arusha A., Ananna N., Kabir E., Bhowmik J. (2022): Carer involvement with children and child‐friendly book ownership in Bangladesh. Children & Society 37(2):326-342. https://doi.org/10.1111/chso.12594
  1050. Angarita Cáceres R. (2022): Diferencias de género en estudios experimentales de distribución de recursos con participación de niños. El Ágora USB 22(1):486-508. https://doi.org/10.21500/16578031.4738
  1051. Klement R., Sweeney R. (2022): Impact of a ketogenic diet intervention during radiotherapy on body composition: V. Final results of the KETOCOMP study for head and neck cancer patients. Strahlentherapie und Onkologie 198(11):981-993. https://doi.org/10.1007/s00066-022-01941-2
  1052. Klement R., Walach H. (2022): Is the Network of World Economic Forum Young Global Leaders Associated With COVID-19 Non-Pharmaceutical Intervention Severity?. Cureus. https://doi.org/10.7759/cureus.29990
  1053. Klement R. (2022): Bioelectrical phase angle is no adequate biomarker of inflammatory status. International Journal of Obesity 46(11):2063-2063. https://doi.org/10.1038/s41366-022-01181-5
  1054. Ellis R. (2022): Questionable Research Practices, Low Statistical Power, and Other Obstacles to Replicability: Why Preclinical Neuroscience Research Would Benefit from Registered Reports. eneuro 9(4):ENEURO.0017-22.2022. https://doi.org/10.1523/eneuro.0017-22.2022
  1055. Berk R. (2022): Causal Inference Challenges with Interrupted Time Series Designs: An Evaluation of an Assault Weapons Ban in California. Observational Studies 8(1):1-27. https://doi.org/10.1353/obs.2022.0000
  1056. Wolff R., Struck O., Osiander C., Senghaas M., Stephan G. (2022): Justice perceptions of occupational training subsidies: findings from a factorial survey. Journal for Labour Market Research 56(1). https://doi.org/10.1186/s12651-022-00311-w
  1057. Kelter R. (2022): The evidence interval and the Bayesian evidence value: On a unified theory for Bayesian hypothesis testing and interval estimation. British Journal of Mathematical and Statistical Psychology 75(3):550-592. https://doi.org/10.1111/bmsp.12267
  1058. Kelter R. (2022): bayesanova: An R package for Bayesian Inference in the Analysis of Variance via Markov Chain Monte Carlo in Gaussian Mixture Models. The R Journal 14(1):54-78. https://doi.org/10.32614/rj-2022-009
  1059. Leigh R., McKenna C., McWade R., Lynch B., Walsh F. (2022): Comparative genomics and pangenomics of vancomycin-resistant and susceptible Enterococcus faecium from Irish hospitals. Journal of Medical Microbiology 71(10). https://doi.org/10.1099/jmm.0.001590
  1060. MacCoun R. (2022): P-hacking. Research Integrity. https://doi.org/10.1093/oso/9780190938550.003.0011
  1061. Merl R., Stöckl T., Palan S. (2022): Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions. Journal of Banking & Finance 154:106490. https://doi.org/10.1016/j.jbankfin.2022.106490
  1062. Kohavi R., Deng A., Vermeer L. (2022): A/B Testing Intuition Busters. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3534678.3539160
  1063. Walley R., Brayshaw N. (2022): From innovative thinking to pharmaceutical industry implementation: Some success stories. Pharmaceutical Statistics 21(4):712-719. https://doi.org/10.1002/pst.2222
  1064. Di Pierro R., Amelio S., Macca M., Madeddu F., Di Sarno M. (2022): What If I Feel Rejected? Borderline Personality, Pathological Narcissism, and Social Rejection in Daily Life. Journal of Personality Disorders 36(5):559-582. https://doi.org/10.1521/pedi.2022.36.5.559
  1065. Bower R., Hager J., Cherniakov C., Gupta S., Cipolli W. (2022): A Case for Nonparametrics. The American Statistician 77(2):212-219. https://doi.org/10.1080/00031305.2022.2141858
  1066. Schroeder S. (2022): An Ethical Framework for Presenting Scientific Results to Policy-Makers. Kennedy Institute of Ethics Journal 32(1):33-67. https://doi.org/10.1353/ken.2022.0002
  1067. Kang S., Lee B., Song C., Eeom K., Jang B., Kim I., et al. (2022): Clinical implementation of PerFRACTION™ for pre-treatment patient-specific quality assurance. Journal of the Korean Physical Society 80(6):516-525. https://doi.org/10.1007/s40042-022-00440-y
  1068. Garofalo S., Giovagnoli S., Orsoni M., Starita F., Benassi M. (2022): Interaction effect: Are you doing the right thing?. PLOS ONE 17(7):e0271668. https://doi.org/10.1371/journal.pone.0271668
  1069. Nostedt S., Joffe A. (2022): Critical Care Randomized Trials Demonstrate Power Failure: A Low Positive Predictive Value of Findings in the Critical Care Research Field. Journal of Intensive Care Medicine 37(8):1082-1093. https://doi.org/10.1177/08850666221077203
  1070. Voisin S., Seale K., Jacques M., Landen S., Harvey N., Haupt L., et al. (2022): Exercise is associated with younger methylome and transcriptome profiles in human skeletal muscle. https://doi.org/10.1101/2022.12.27.522062
  1071. Gamage S., Biswas R., Bhowmik J. (2022): Health awareness and skilled birth attendance: An assessment of sustainable development goal 3.1 in south and south-east Asia. Midwifery 115:103480. https://doi.org/10.1016/j.midw.2022.103480
  1072. Semenyna S., Gómez Jiménez F., Vasey P. (2022): Intra- and Intersexual Mate Competition in Two Cultures. Human Nature 33(2):145-171. https://doi.org/10.1007/s12110-022-09424-0
  1073. Semenyna S., Rule N., Vasey P. (2022): Fertility Status Does Not Facilitate Women’s Judgment of Male Sexual Orientation. Archives of Sexual Behavior 51(7):3351-3360. https://doi.org/10.1007/s10508-022-02356-x
  1074. Grant S., Wendt K., Leadbeater B., Supplee L., Mayo-Wilson E., Gardner F., et al. (2022): Transparent, Open, and Reproducible Prevention Science. Prevention Science 23(5):701-722. https://doi.org/10.1007/s11121-022-01336-w
  1075. AbdusSalam S., Agocs F., Allanach B., Athron P., Balázs C., Bagnaschi E., et al. (2022): Simple and statistically sound recommendations for analysing physical theories. Reports on Progress in Physics 85(5):052201. https://doi.org/10.1088/1361-6633/ac60ac
  1076. Khan S., Rasheed D., Muhammad G. (2022): A SEM-based approach towards the Utilization of Technology and its Relationship to the performance of private business education institutions. Propel Journal of Academic Research 2(2):1-22. https://doi.org/10.55464/pjar.v2i2.37
  1077. Fujihara S., Tabuchi T. (2022): The impact of COVID-19 on the psychological distress of youths in Japan: A latent growth curve analysis. Journal of Affective Disorders 305:19-27. https://doi.org/10.1016/j.jad.2022.02.055
  1078. Schwab S., Janiaud P., Dayan M., Amrhein V., Panczak R., Palagi P., et al. (2022): Ten simple rules for good research practice. PLOS Computational Biology 18(6):e1010139. https://doi.org/10.1371/journal.pcbi.1010139
  1079. Medić S., Anastassopoulou C., Lozanov-Crvenković Z., Vuković V., Dragnić N., Petrović V., et al. (2022): Risk and severity of SARS-CoV-2 reinfections during 2020–2022 in Vojvodina, Serbia: A population-level observational study. The Lancet Regional Health – Europe 20:100453. https://doi.org/10.1016/j.lanepe.2022.100453
  1080. Medić S., Anastassopoulou C., Lozanov-Crvenković Z., Vuković V., Dragnić N., Petrović V., et al. (2022): Risk and severity of SARS-CoV-2 reinfections during 2020-2022 in Vojvodina, Serbia: a population-level study. https://doi.org/10.1101/2022.04.08.22273571
  1081. Medić S., Anastassopoulou C., Lozanov-Crvenković Z., Dragnić N., Petrović V., Ristić M., et al. (2022): Incidence, risk and severity of SARS-CoV-2 reinfections in children and adolescents: a population-level study between March 2020 and July 2022. https://doi.org/10.1101/2022.10.09.22280690
  1082. Harlid S., Van Guelpen B., Qu C., Gylling B., Aglago E., Amitay E., et al. (2022): Diabetes mellitus in relation to colorectal tumor molecular subtypes: A pooled analysis of more than 9000 cases. International Journal of Cancer 151(3):348-360. https://doi.org/10.1002/ijc.34015
  1083. Chabert S., Mallinger R., Sénéchal C., Fougeroux A., Geist O., Guillemard V., et al. (2022): Importance of maternal resources in pollen limitation studies with pollinator gradients: A case study with sunflower. Agriculture, Ecosystems & Environment 330:107887. https://doi.org/10.1016/j.agee.2022.107887
  1084. Muff S., Nilsen E., O’Hara R., Nater C. (2022): Response to ‘Why P values are not measures of evidence’ by D. Lakens. Trends in Ecology & Evolution 37(4):291-292. https://doi.org/10.1016/j.tree.2022.01.001
  1085. Muff S., Nilsen E., Nater C., O’Hara R. (2022): Joint reply to ‘Rewriting results in the language of compatibility’ by V. Amrhein and S. Greenland, and to ‘The evidence contained in the P-value is context dependent’ by F. Hartig and F. Barraquand. Trends in Ecology & Evolution 37(7):571-572. https://doi.org/10.1016/j.tree.2022.03.007
  1086. Mammola S., Viel N., Amiar D., Mani A., Hervé C., Heard S., et al. (2022): Taxonomic practice, creativity and fashion: what’s in a spider name?. Zoological Journal of the Linnean Society 198(2):494-508. https://doi.org/10.1093/zoolinnean/zlac097
  1087. Mammola S., Viel N., Amiar D., Mani A., Hervé C., Heard S., et al. (2022): Taxonomic practice, creativity, and fashion: What’s in a spider name?. https://doi.org/10.1101/2022.02.06.479275
  1088. Clift S., Grebosz-Haring K., Thun-Hohenstein L., Schuchter-Wiegand A., Bathke A. (2022): The need for robust critique of arts and health research: An examination of the Goldbeck and Ellerkamp (2012) randomised controlled trial of music therapy for anxiety in children, and its treatment in four systematic reviews. Approaches: An Interdisciplinary Journal of Music Therapy 16(2):371. https://doi.org/10.56883/aijmt.2024.64
  1089. Igarashi T., Okuda S., Sasahara K. (2022): Development of the Japanese Version of the Linguistic Inquiry and Word Count Dictionary 2015. Frontiers in Psychology 13. https://doi.org/10.3389/fpsyg.2022.841534
  1090. Suzuki T., Masugi Y., Inoue Y., Hamada T., Tanaka M., Takamatsu M., et al. (2022): KRAS variant allele frequency, but not mutation positivity, associates with survival of patients with pancreatic cancer. Cancer Science 113(9):3097-3109. https://doi.org/10.1111/cas.15398
  1091. Luster T., Gunderson Z., Sun S., Loder R. (2022): Slipped Capital Femoral Epiphysis, Food Deserts, Poverty, and Urban/Rural Residence: Is There a Link?. Journal of Pediatric Orthopaedics 43(3):e230-e235. https://doi.org/10.1097/bpo.0000000000002315
  1092. Oliveira T. (2022): Aggressive policing and undermined legitimacy: assessing the impact of police stops at gunpoint on perceptions of police in São Paulo, Brazil. Journal of Experimental Criminology 20(1):83-121. https://doi.org/10.1007/s11292-022-09527-9
  1093. Vilgis T. (2022): Fazit – oder: Was bleibt?. Biophysik der Ernährung. https://doi.org/10.1007/978-3-662-65108-7_7
  1094. Bertelsen T., Hoffart A., Rekdal S., Zahl-Olsen R. (2022): Bayes factor benefits for clinical psychology: review of child and adolescent evidence base. F1000Research 11:171. https://doi.org/10.12688/f1000research.76842.2
  1095. Dudek T., Brenøe A., Feld J., Rohrer J. (2022): No Evidence That Siblings’ Gender Affects Personality Across Nine Countries. Psychological Science 33(9):1574-1587. https://doi.org/10.1177/09567976221094630
  1096. Schilling T., Brenoe A., Feld J., Rohrer J. (2022): No Evidence that Siblings’ Gender Affects Personality Across Nine Countries. https://doi.org/10.31234/osf.io/vmqsk
  1097. Dudek T., Brenoe A., Feld J., Rohrer J. (2022): No Evidence that Siblings’ Gender Affects Personality Across Nine Countries. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4054790
  1098. Dudek T., Brenoe A., Feld J., Rohrer J. (2022): No Evidence that Siblings’ Gender Affects Personality Across Nine Countries. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4055210
  1099. Dudek T., Brenøe A., Feld J., Rohrer J. (2022): No Evidence that Siblings’ Gender Affects Personality Across Nine Countries. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4114696
  1100. Melman T., Tapus A., Jublot M., Mouton X., Abbink D., de Winter J. (2022): Do sport modes cause behavioral adaptation?. Transportation Research Part F: Traffic Psychology and Behaviour 90:58-69. https://doi.org/10.1016/j.trf.2022.07.017
  1101. Hardwicke T., Salholz-Hillel M., Malički M., Szűcs D., Bendixen T., Ioannidis J. (2022): Statistical Guidance to Authors at Top-Ranked Journals across Scientific Disciplines. The American Statistician 77(3):239-247. https://doi.org/10.1080/00031305.2022.2143897
  1102. Hardwicke T., Salholz-Hillel M., Malički M., Szűcs D., Bendixen T., Ioannidis J. (2022): Statistical guidance to authors at top-ranked journals across scientific disciplines. OSF Preprints. https://doi.org/10.31222/osf.io/q6ajt
  1103. Ugai T., Haruki K., Harrison T., Cao Y., Qu C., Chan A., et al. (2022): Molecular Characteristics of Early-Onset Colorectal Cancer According to Detailed Anatomical Locations: Comparison With Later-Onset Cases. American Journal of Gastroenterology 118(4):712-726. https://doi.org/10.14309/ajg.0000000000002171
  1104. Ugai T., Liu L., Tabung F., Hamada T., Langworthy B., Akimoto N., et al. (2022): Prognostic role of inflammatory diets in colorectal cancer overall and in strata of tumor‐infiltrating lymphocyte levels. Clinical and Translational Medicine 12(11). https://doi.org/10.1002/ctm2.1114
  1105. Heggedal T., Helland L., Våge Knutsen M. (2022): The power of outside options in the presence of obstinate types. Games and Economic Behavior 136:454-468. https://doi.org/10.1016/j.geb.2022.10.011
  1106. Dirnagl U. (2022): Prolog: Translationale neurologische Forschung – From Bench to Bedside. Diagnostik und Therapie Neurologischer Erkrankungen. https://doi.org/10.1016/b978-3-437-21884-2.00032-5
  1107. Hoch V., Kohler A., Augusto D., Lobo-Alves S., Malheiros D., Cipolla G., et al. (2022): Genetic Associations and Differential mRNA Expression Levels of Host Genes Suggest a Viral Trigger for Endemic Pemphigus Foliaceus. Viruses 14(5):879. https://doi.org/10.3390/v14050879
  1108. Tomaselli V., Cantone G., Miracula V. (2022): Multiversal Methods in Observational Studies: The Case of COVID-19. Springer Proceedings in Mathematics & Statistics. https://doi.org/10.1007/978-3-031-16609-9_22
  1109. Forgetta V., Jiang L., Vulpescu N., Hogan M., Chen S., Morris J., et al. (2022): An effector index to predict target genes at GWAS loci. Human Genetics 141(8):1431-1447. https://doi.org/10.1007/s00439-022-02434-z
  1110. Otte W., Vinkers C., Habets P., van IJzendoorn D., Tijdink J. (2022): Analysis of 567,758 randomized controlled trials published over 30 years reveals trends in phrases used to discuss results that do not reach statistical significance. PLOS Biology 20(2):e3001562. https://doi.org/10.1371/journal.pbio.3001562
  1111. Deng X., Liang X., Zhan X., Rosenfeld J., Olson J., Yan G., et al. (2022): A novel and effective item‐source complex trial protocol: Discrimination of guilty from both knowledgeable and unknowledgeable innocent subjects. Psychophysiology 59(8). https://doi.org/10.1111/psyp.14033
  1112. Meng X. (2022): Double Your Variance, Dirtify Your Bayes, Devour Your Pufferfish, and Draw your Kidstrogram. The New England Journal of Statistics in Data Science. https://doi.org/10.51387/22-nejsds6
  1113. Xu X., Zhang Y., Ha P., Chen Y., Li C., Yen E., et al. (2022): A novel injectable fibromodulin‐releasing granular hydrogel for tendon healing and functional recovery. Bioengineering & Translational Medicine 8(1). https://doi.org/10.1002/btm2.10355
  1114. Rachamin Y., Jäger L., Meier R., Grischott T., Senn O., Burgstaller J., et al. (2022): Prescription Rates, Polypharmacy and Prescriber Variability in Swiss General Practice—A Cross-Sectional Database Study. Frontiers in Pharmacology 13. https://doi.org/10.3389/fphar.2022.832994
  1115. Masugi Y., Takamatsu M., Tanaka M., Hara K., Inoue Y., Hamada T., et al. (2022): KRAS, CDKN2A, TP53, And SMAD4 Alterations in Relation to Postoperative Survival and Recurrence Patterns Among Patients with Pancreatic Cancer. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4197932
  1116. Ji Y., Temprano-Sagrera G., Holle L., Bebo A., Brody J., Le N., et al. (2022): Antithrombin, protein C and protein S: Genome and transcriptome wide association studies identify 7 novel loci regulating plasma levels. https://doi.org/10.1101/2022.11.01.22281689
  1117. Chuang Z., Martin J., Shapiro J., Nguyen D., Neocleous P., Jones P. (2022): Minimum false-positive risk of primary outcomes and impact of reducing nominal P-value threshold from 0.05 to 0.005 in anaesthesiology randomised clinical trials: a cross-sectional study. British Journal of Anaesthesia 130(4):412-420. https://doi.org/10.1016/j.bja.2022.11.001
  1118. Hou Z., Wang D. (2022): New Observations on Zipf’s Law in Passwords. IEEE Transactions on Information Forensics and Security 18:517-532. https://doi.org/10.1109/tifs.2022.3176185
  1119. Hu Z., Li F., Cheng M., Lin Q. (2022): Investigating Large-Scale Network with Unified Granger Causality Analysis. Computational and Mathematical Methods in Medicine 2022:1-15. https://doi.org/10.1155/2022/6962359
  1120. Šauerová Markéta Š., Irena S. (2022): ACCURACY OF PUPILS´ SELF-ASSESSMENT. EduPort 6(2):13-25. https://doi.org/10.21062/edp.2022.009
  1121. Unknown authors (2021): Peer Review #1 of “Local ancestry prediction with PyLAE (v0.1)”. https://doi.org/10.7287/peerj.12502v0.1/reviews/1
  1122. Unknown authors (2021): Peer Review #2 of “Local ancestry prediction with PyLAE (v0.1)”. https://doi.org/10.7287/peerj.12502v0.1/reviews/2
  1123. Unknown authors (2021): Peer Review #2 of “Local ancestry prediction with PyLAE (v0.2)”. https://doi.org/10.7287/peerj.12502v0.2/reviews/2
  1124. Unknown authors (2021): . Lviv University Herald. Series: Psychological sciences. https://doi.org/10.30970/ps.2021.9
  1125. Unknown authors (2021): Decision letter for “That’s a Lot to Process! Pitfalls of Popular Path Models”. https://doi.org/10.1177/25152459221095827/v1/decision1
  1126. Unknown authors (2021): Review for “That’s a Lot to Process! Pitfalls of Popular Path Models”. https://doi.org/10.1177/25152459221095827/v1/review2
  1127. Chalfin A., Hansen B., Lerner J., Parker L. (2021): Reducing Crime Through Environmental Design: Evidence from a Randomized Experiment of Street Lighting in New York City. Journal of Quantitative Criminology 38(1):127-157. https://doi.org/10.1007/s10940-020-09490-6
  1128. Ouedraogo A., Danquah A., Tignegre J., Poda L., Batieno J., Asante I., et al. (2021): Determination of inheritance of aphid resistance in cowpea genotypes and identification of single sequence repeat markers linked to resistance genes. Legume Science 4(2). https://doi.org/10.1002/leg3.127
  1129. Praetzellis A. (2021): Archaeology of San Francisco Jews: Themes for the Study of Jewish Domestic Life. International Journal of Historical Archaeology 25(4):1024-1064. https://doi.org/10.1007/s10761-021-00589-5
  1130. Bultez A., Derbaix C., Herrmann J. (2021): Statistically significant? Let us recognize that estimates of tested effects are uncertain. Recherche et Applications en Marketing (English Edition) 37(1):82-105. https://doi.org/10.1177/20515707211040743
  1131. Bultez A., Derbaix C., Herrmann J. (2021): « Statistiquement significatif » ? Respectons l’incertitude de l’effet testé. Recherche et Applications en Marketing (French Edition) 37(1):87-112. https://doi.org/10.1177/07673701211010844
  1132. Menkveld A., Dreber A., Holzmeister F., Huber J., Johanneson M., Kirchler M., et al. (2021): Non-Standard Errors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3961574
  1133. Menkveld A. (2021): Non-Standard Errors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3979358
  1134. Reito A. (2021): Past, Present and Future With p-Values, Confidence Intervals and Effect Sizes. The Journal of Foot and Ankle Surgery 60(3):642-643. https://doi.org/10.1053/j.jfas.2020.04.017
  1135. Jackson A. (2021): Are knowledge ascriptions sensitive to social context?. Synthese 199(3-4):8579-8610. https://doi.org/10.1007/s11229-021-03176-7
  1136. Murphy A., Jerolmack C., Smith D. (2021): Ethnography, Data Transparency, and the Information Age. Annual Review of Sociology 47(1):41-61. https://doi.org/10.1146/annurev-soc-090320-124805
  1137. Montoya A. (2021): Review for “That’s a Lot to Process! Pitfalls of Popular Path Models”. https://doi.org/10.1177/25152459221095827/v1/review3
  1138. Abdol A., Wicherts J. (2021): Science Abstract Model Simulation Framework. https://doi.org/10.31234/osf.io/zy29t
  1139. Berry‐Blunt A., Holtzman N., Donnellan M., Mehl M. (2021): The story of “I” tracking: Psychological implications of self‐referential language use. Social and Personality Psychology Compass 15(12). https://doi.org/10.1111/spc3.12647
  1140. Berry-Blunt A., Holtzman N., Donnellan B., Mehl M. (2021): The Story of “I” Tracking: Psychological Implications of Self-Referential Language Use. https://doi.org/10.31234/osf.io/5gyfe
  1141. Álvarez-Muelas A., Gómez-Berrocal C., Sierra J. (2021): Study of Sexual Satisfaction in Different Typologies of Adherence to the Sexual Double Standard. Frontiers in Psychology 11. https://doi.org/10.3389/fpsyg.2020.609571
  1142. Lieberoth A., Lin S., Stöckli S., Han H., Kowal M., Gelpi R., et al. (2021): Stress and worry in the 2020 coronavirus pandemic: relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey. Royal Society Open Science 8(2). https://doi.org/10.1098/rsos.200589
  1143. Lieberoth A., Lin S., Stoeckli S., Han H., Kowal M., Chrona S., et al. (2021): Stress and worry in the 2020 coronavirus pandemic: Relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey. https://doi.org/10.31234/osf.io/f7ghw
  1144. Clements A., Kinman G. (2021): Job demands, organizational justice, and emotional exhaustion in prison officers. Criminal Justice Studies 34(4):441-458. https://doi.org/10.1080/1478601x.2021.1999114
  1145. Fowlie A. (2021): Comment on “Reproducibility and Replication of Experimental Particle Physics Results”. Harvard Data Science Review. https://doi.org/10.1162/99608f92.b9bfc518
  1146. Fowlie A. (2021): Neyman–Pearson lemma for Bayes factors. Communications in Statistics – Theory and Methods 52(15):5379-5386. https://doi.org/10.1080/03610926.2021.2007265
  1147. Kemp A., Fisher Z. (2021): Application of Single-Case Research Designs in Undergraduate Student Reports: An Example From Wellbeing Science. Teaching of Psychology 50(1):86-92. https://doi.org/10.1177/00986283211029929
  1148. Bruno A., Shea A., Einerson B., Metz T., Allshouse A., Scott J., et al. (2021): Impact of the p-Value Threshold on Interpretation of Trial Outcomes in Obstetrics and Gynecology. American Journal of Perinatology 38(12):1223-1230. https://doi.org/10.1055/s-0041-1731345
  1149. Akhmedova A., Mas-Machuca M., Magomedova N. (2021): Nexus between strategic fit and social mission accomplishment in social enterprises: Does organizational form matter?. Journal of Cleaner Production 330:129891. https://doi.org/10.1016/j.jclepro.2021.129891
  1150. Temp A., Lutz M., Trepel D., Tang Y., Wagenmakers E., Khachaturian A., et al. (2021): How Bayesian statistics may help answer some of the controversial questions in clinical research on Alzheimer’s disease. Alzheimer’s & Dementia 17(6):917-919. https://doi.org/10.1002/alz.12374
  1151. Bonkhoff A., Grefkes C. (2021): Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain 145(2):457-475. https://doi.org/10.1093/brain/awab439
  1152. Bishara A., Li J., Conley C. (2021): Informal versus formal judgment of statistical models: The case of normality assumptions. Psychonomic Bulletin & Review 28(4):1164-1182. https://doi.org/10.3758/s13423-021-01879-z
  1153. Turner A., Parmar N., Jovanovski A., Hearne G. (2021): Assessing Group-Based Changes in High-Performance Sport. Part 1: Null Hypothesis Significance Testing and the Utility of p Values. Strength & Conditioning Journal 43(3):112-116. https://doi.org/10.1519/ssc.0000000000000625
  1154. Ives A., Zhu L., Wang F., Zhu J., Morrow C., Radeloff V. (2021): Statistical inference for trends in spatiotemporal data. Remote Sensing of Environment 266:112678. https://doi.org/10.1016/j.rse.2021.112678
  1155. De Comite A., Crevecoeur F., Lefèvre P. (2021): Online modification of goal-directed control in human reaching movements. Journal of Neurophysiology 125(5):1883-1898. https://doi.org/10.1152/jn.00536.2020
  1156. De Comite A., Crevecoeur F., Lefèvre P. (2021): Reward-dependent selection of feedback gains impacts rapid motor decisions. https://doi.org/10.1101/2021.07.25.453678
  1157. Dörnemann A., Boenisch N., Schommer L., Winkelhorst L., Wingen T. (2021): How do Good and Bad News Impact Mood During the Covid-19 Pandemic? The Role of Similarity. https://doi.org/10.31219/osf.io/sy2kd
  1158. Afiaz A., Biswas R. (2021): Awareness on menstrual hygiene management in Bangladesh and the possibilities of media interventions: using a nationwide cross-sectional survey. BMJ Open 11(4):e042134. https://doi.org/10.1136/bmjopen-2020-042134
  1159. Afiaz A., Masud M., Mansur M. (2021): Impact of child’s cognitive and social-emotional difficulties on child abuse: Does mother’s justification of intimate partner violence also play a role?. Child Abuse & Neglect 117:105028. https://doi.org/10.1016/j.chiabu.2021.105028
  1160. Coker B., Rudin C., King G. (2021): A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. Management Science 67(10):6174-6197. https://doi.org/10.1287/mnsc.2020.3818
  1161. Baer B., Gaudino M., Fremes S., Charlson M., Wells M. (2021): The fragility index can be used for sample size calculations in clinical trials. Journal of Clinical Epidemiology 139:199-209. https://doi.org/10.1016/j.jclinepi.2021.08.010
  1162. Baer B., Gaudino M., Fremes S., Charlson M., Wells M. (2021): Reassembling the fragility index: a demonstration of statistical reasoning. Journal of Clinical Epidemiology 142:317-318. https://doi.org/10.1016/j.jclinepi.2021.10.010
  1163. Fritz B., King C., Mickle A., Wildes T., Budelier T., Oberhaus J., et al. (2021): Effect of electroencephalogram-guided anaesthesia administration on 1-yr mortality: follow-up of a randomised clinical trial. British Journal of Anaesthesia 127(3):386-395. https://doi.org/10.1016/j.bja.2021.04.036
  1164. Hughes B., Costello C., Pearman J., Razavi P., Bedford-Petersen C., Ludwig R., et al. (2021): The Big Five Across Socioeconomic Status: Measurement Invariance, Relationships, and Age Trends. https://doi.org/10.31234/osf.io/wkhfx
  1165. Weiss B., Nygart V., Pommerencke L., Carhart-Harris R., Erritzoe D. (2021): Examining Psychedelic-Induced Changes in Social Functioning and Connectedness in a Naturalistic Online Sample Using the Five-Factor Model of Personality. Frontiers in Psychology 12. https://doi.org/10.3389/fpsyg.2021.749788
  1166. Weiss B., Jahn A., Hyatt C., Owens M., Carter N., Sweet L., et al. (2021): Investigating the neural substrates of Antagonistic Externalizing and social-cognitive Theory of Mind: an fMRI examination of functional activity and synchrony. Personality Neuroscience 4. https://doi.org/10.1017/pen.2020.12
  1167. Kline B. (2021): Bayes Factors Based on p-Values and Sets of Priors With Restricted Strength. The American Statistician 76(3):203-213. https://doi.org/10.1080/00031305.2021.1877815
  1168. Murphy B., Lilienfeld S. (2021): C. S. Peirce’s Forgotten but Enduring Relevance to Psychological Science. The American Journal of Psychology 134(3):347-361. https://doi.org/10.5406/amerjpsyc.134.3.0347
  1169. Nosek B., Hardwicke T., Moshontz H., Allard A., Corker K., Dreber A., et al. (2021): Replicability, Robustness, and Reproducibility in Psychological Science. Annual Review of Psychology 73(1):719-748. https://doi.org/10.1146/annurev-psych-020821-114157
  1170. Nosek B., Hardwicke T., Moshontz H., Allard A., Corker K., Dreber A., et al. (2021): Replicability, Robustness, and Reproducibility in Psychological Science. https://doi.org/10.31234/osf.io/ksfvq
  1171. Verschuere B., De Schryver M., van den Bergh D., Wagenmakers E., Meijer E. (2021): Are dishonest politicians more likely to be reelected? A Bayesian view. Proceedings of the National Academy of Sciences 118(6). https://doi.org/10.1073/pnas.2022718118
  1172. Stern C., Axt J. (2021): Were Americans’ Political Attitudes Linked to Objective Threats From COVID-19? An Examination of Data From Project Implicit During Initial Months of the Pandemic. Personality and Social Psychology Bulletin 48(12):1682-1700. https://doi.org/10.1177/01461672211052121
  1173. Shen C., Ferro E., Xu H., Kramer D., Patell R., Kazi D. (2021): Underperformance of Contemporary Phase III Oncology Trials and Strategies for Improvement. Journal of the National Comprehensive Cancer Network 19(9):1072-1078. https://doi.org/10.6004/jnccn.2020.7690
  1174. Jørgensen C., Larsson A., Forsare C., Aaltonen K., Jansson S., Bradshaw R., et al. (2021): PAM50 Intrinsic Subtype Profiles in Primary and Metastatic Breast Cancer Show a Significant Shift toward More Aggressive Subtypes with Prognostic Implications. Cancers 13(7):1592. https://doi.org/10.3390/cancers13071592
  1175. Li C., Teixeira H., Tanna N., Zheng Z., Chen S., Zou M., et al. (2021): The Reliability of Two- and Three-Dimensional Cephalometric Measurements: A CBCT Study. Diagnostics 11(12):2292. https://doi.org/10.3390/diagnostics11122292
  1176. Limbachia C., Morrow K., Khibovska A., Meyer C., Padmala S., Pessoa L. (2021): Controllability over stressor decreases responses in key threat-related brain areas. Communications Biology 4(1). https://doi.org/10.1038/s42003-020-01537-5
  1177. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. https://doi.org/10.1101/2021.10.04.463010
  1178. Andrews C., Vries M. (2021): The experimental method in Public Administration: lessons from replication in Psychology. Revista de Administração Pública 55(5):1017-1033. https://doi.org/10.1590/0034-761220200746
  1179. Damrau C., Colomb J., Brembs B. (2021): Sensitivity to expression levels underlies differential dominance of a putative null allele of the Drosophila tβh gene in behavioral phenotypes. PLOS Biology 19(5):e3001228. https://doi.org/10.1371/journal.pbio.3001228
  1180. Huber C., Huber J., Kirchler M. (2021): Market shocks and professionals’ investment behavior – Evidence from the COVID-19 crash. Journal of Banking & Finance 133:106247. https://doi.org/10.1016/j.jbankfin.2021.106247
  1181. Huber C., Huber J., Kirchler M. (2021): Volatility shocks and investment behavior. OSF Preprints (OSF Preprints). https://doi.org/10.31219/osf.io/jr4eb
  1182. Huber C., Huber J., Kirchler M. (2021): Volatility shocks and investment behavior. Journal of Economic Behavior & Organization 194:56-70. https://doi.org/10.1016/j.jebo.2021.12.007
  1183. Griesbach C., Groll A., Bergherr E. (2021): Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques. PLOS ONE 16(7):e0254178. https://doi.org/10.1371/journal.pone.0254178
  1184. Keating C., Fraser D., Sowden S., Cook J. (2021): Differences Between Autistic and Non-Autistic Adults in the Recognition of Anger from Facial Motion Remain after Controlling for Alexithymia. Journal of Autism and Developmental Disorders 52(4):1855-1871. https://doi.org/10.1007/s10803-021-05083-9
  1185. Hyatt C., Crowe M., West S., Vize C., Carter N., Chester D., et al. (2021): An empirically based power primer for laboratory aggression research. Aggressive Behavior 48(3):279-289. https://doi.org/10.1002/ab.21996
  1186. Wijaya D., DS J., Barus S., Pasaribu B., Sirbu L., Dharma A. (2021): Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover. International Journal of Artificial Intelligence Research 5(1). https://doi.org/10.29099/ijair.v4i2.169
  1187. Schley D., Ferecatu A., Chan H., Gunadi M. (2021): How Categorization Shapes the Probability Weighting Function. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3959751
  1188. Aguilar-Velázquez D. (2021): Critical Neural Networks Minimize Metabolic Cost. Physics 3(1):42-58. https://doi.org/10.3390/physics3010005
  1189. Berner D., Amrhein V. (2021): Why and How We Should Join the Shift From Significance Testing to Estimation. Preprints.org. https://doi.org/10.20944/preprints202112.0235.v1
  1190. Encinas D., Fuentes Diestra A. (2021): La geografía política de las Elecciones Presidenciales de 2021 en Perú. Revista Elecciones 20(22):231-282. https://doi.org/10.53557/elecciones.2021.v20n22.07
  1191. Dunleavy D., Lacasse J. (2021): The Use and Misuse of Classical Statistics: A Primer for Social Workers. Research on Social Work Practice 31(5):438-453. https://doi.org/10.1177/10497315211008247
  1192. Sikavi D., Nguyen L., Haruki K., Ugai T., Ma W., Wang D., et al. (2021): The Sulfur Microbial Diet and Risk of Colorectal Cancer by Molecular Subtypes and Intratumoral Microbial Species in Adult Men. Clinical and Translational Gastroenterology 12(8):e00338. https://doi.org/10.14309/ctg.0000000000000338
  1193. Meshi D., Freestone D., Özdem-Mertens C. (2021): Problematic social media use is associated with the evaluation of both risk and ambiguity during decision making. Journal of Behavioral Addictions 10(3):779-787. https://doi.org/10.1556/2006.2021.00047
  1194. Krpan D. (2021): (When) should psychology be a science?. Journal for the Theory of Social Behaviour 52(1):183-198. https://doi.org/10.1111/jtsb.12316
  1195. Yeo D., Srivathsan A., Puniamoorthy J., Maosheng F., Grootaert P., Chan L., et al. (2021): Mangroves are an overlooked hotspot of insect diversity despite low plant diversity. BMC Biology 19(1). https://doi.org/10.1186/s12915-021-01088-z
  1196. Bilén D., Dreber A., Johannesson M. (2021): Are women more generous than men? A meta-analysis. Journal of the Economic Science Association 7(1):1-18. https://doi.org/10.1007/s40881-021-00105-9
  1197. Johnstone D. (2021): Accounting research and the significance test crisis. Critical Perspectives on Accounting 89:102296. https://doi.org/10.1016/j.cpa.2021.102296
  1198. Lacko D., Prošek T. (2021): Podpora nulové hypotézy a její miskoncepce v psychologii: Teoretické představení testování ekvivalence. TESTFÓRUM 9(14):65-86. https://doi.org/10.5817/tf2021-14-13648
  1199. Mitre‐Becerril D., Chalfin A. (2021): Testing public policy at the frontier: The effect of the $15 minimum wage on public safety in Seattle. Criminology & Public Policy 20(2):291-328. https://doi.org/10.1111/1745-9133.12539
  1200. Priilaid D., Hall D. (2021): Price preferences reveal asymmetric price effect—A preliminary study. Journal of Sensory Studies 36(4). https://doi.org/10.1111/joss.12665
  1201. Bickel D. (2021): Coherent checking and updating of Bayesian models without specifying the model space: A decision-theoretic semantics for possibility theory. International Journal of Approximate Reasoning 142:81-93. https://doi.org/10.1016/j.ijar.2021.11.006
  1202. Bickel D. (2021): Interval estimation, point estimation, and null hypothesis significance testing calibrated by an estimated posterior probability of the null hypothesis. Communications in Statistics – Theory and Methods 52(3):763-787. https://doi.org/10.1080/03610926.2021.1921805
  1203. Kirk D., Rovira M. (2021): An audit experiment to investigate the “war on cops”: a research note. Journal of Experimental Criminology 18(3):569-580. https://doi.org/10.1007/s11292-021-09458-x
  1204. Sidebotham D. (2021): Understanding significance testing. Anaesthesia 76(12):1659-1664. https://doi.org/10.1111/anae.15591
  1205. Chicco D., Jurman G. (2021): An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis. IEEE Access 9:24485-24498. https://doi.org/10.1109/access.2021.3057196
  1206. Chicco D., Lovejoy C., Oneto L. (2021): A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease. IEEE Access 9:165132-165144. https://doi.org/10.1109/access.2021.3133700
  1207. Chakraborty D., Guinat C., Müller N., Briand F., Andraud M., Scoizec A., et al. (2021): Phylodynamic assessment of control measures for highly pathogenic avian influenza epidemics in France. https://doi.org/10.1101/2021.06.23.449570
  1208. Singh D., Bradley S., Zucker K. (2021): A Follow-Up Study of Boys With Gender Identity Disorder. Frontiers in Psychiatry 12. https://doi.org/10.3389/fpsyt.2021.632784
  1209. Urbig D., Bönte W., Schmutzler J., Curcio A., Andonova V. (2021): Diverging associations of dimensions of competitiveness with gender and personality. Personality and Individual Differences 176:110775. https://doi.org/10.1016/j.paid.2021.110775
  1210. Tchoualeu D., Harvey B., Nyaku M., Opare J., Traicoff D., Bonsu G., et al. (2021): Evaluation of the Impact of Immunization Second Year of Life Training Interventions on Health Care Workers in Ghana. Global Health: Science and Practice 9(3):498-507. https://doi.org/10.9745/ghsp-d-21-00091
  1211. Viganola D., Buckles G., Chen Y., Diego-Rosell P., Johannesson M., Nosek B., et al. (2021): Using prediction markets to predict the outcomes in the Defense Advanced Research Projects Agency’s next-generation social science programme. Royal Society Open Science 8(7):181308. https://doi.org/10.1098/rsos.181308
  1212. Vogel D., Xu C. (2021): Everything hacked? What is the evidential value of the experimental public administration literature?. Journal of Behavioral Public Administration 4(2). https://doi.org/10.30636/jbpa.42.239
  1213. van Ravenzwaaij D., Etz A. (2021): Simulation Studies as a Tool to Understand Bayes Factors. Advances in Methods and Practices in Psychological Science 4(1). https://doi.org/10.1177/2515245920972624
  1214. Hang D., He X., Kværner A., Chan A., Wu K., Ogino S., et al. (2021): Plasma sex hormones and risk of conventional and serrated precursors of colorectal cancer in postmenopausal women. BMC Medicine 19(1). https://doi.org/10.1186/s12916-020-01895-1
  1215. Gilan D., Müssig M., Hahad O., Kunzler A., Samstag S., Röthke N., et al. (2021): Protective and Risk Factors for Mental Distress and Its Impact on Health-Protective Behaviors during the SARS-CoV-2 Pandemic between March 2020 and March 2021 in Germany. International Journal of Environmental Research and Public Health 18(17):9167. https://doi.org/10.3390/ijerph18179167
  1216. Strømland E. (2021): Making our “meta-hypotheses” clear: heterogeneity and the role of direct replications in science. European Journal for Philosophy of Science 11(2). https://doi.org/10.1007/s13194-021-00348-7
  1217. Tenney E., Costa E., Allard A., Vazire S. (2021): Open science and reform practices in organizational behavior research over time (2011 to 2019). Organizational Behavior and Human Decision Processes 162:218-223. https://doi.org/10.1016/j.obhdp.2020.10.015
  1218. Bugliarello E., Cotterell R., Okazaki N., Elliott D. (2021): Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs. Transactions of the Association for Computational Linguistics 9:978-994. https://doi.org/10.1162/tacl_a_00408
  1219. Swanson E. (2021): When Is Science Significant? Understanding the p Value. Plastic & Reconstructive Surgery 147(6):1080e-1081e. https://doi.org/10.1097/prs.0000000000007962
  1220. Hepper E., Ellett L., Kerley D., Kingston J. (2021): Are they out to get me? Individual differences in nonclinical paranoia as a function of narcissism and defensive self‐protection. Journal of Personality 90(5):727-747. https://doi.org/10.1111/jopy.12693
  1221. Wagenmakers E., Sarafoglou A., Aarts S., Albers C., Algermissen J., Bahník Š., et al. (2021): Seven steps toward more transparency in statistical practice. Nature Human Behaviour 5(11):1473-1480. https://doi.org/10.1038/s41562-021-01211-8
  1222. Wagenmakers E. (2021): Defiant Denial is Self-Defeating. Psychological Inquiry 32(1):12-16. https://doi.org/10.1080/1047840x.2021.1889314
  1223. van Zwet E., Cator E. (2021): The significance filter, the winner’s curse and the need to shrink. Statistica Neerlandica 75(4):437-452. https://doi.org/10.1111/stan.12241
  1224. van Zwet E., Cator E. (2021): The significance filter, the winner’s curse and the need to shrink. Statistica Neerlandica 75(4):437-452. https://doi.org/10.1111/stan.12241
  1225. Sjoberg E., Ramos S., López-Tolsa G., Johansen E., Pellón R. (2021): The irrelevancy of the inter-trial interval in delay-discounting experiments on an animal model of ADHD. Behavioural Brain Research 408:113236. https://doi.org/10.1016/j.bbr.2021.113236
  1226. Gyberg F., Svensson Y., Wängqvist M., Syed M. (2021): Discrimination and its relation to psychosocial well‐being among diverse youth in Sweden. New Directions for Child and Adolescent Development 2021(176):163-181. https://doi.org/10.1002/cad.20399
  1227. Echenique F., He K. (2021): Screening $p$-Hackers: Dissemination Noise as Bait. arXiv. https://doi.org/10.48550/arxiv.2103.09164
  1228. Holzmeister F., Huber J., Kirchler M., Schwaiger R. (2021): Nudging Debtors to Pay Their Debt: Two Randomized Controlled Trials. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3888370
  1229. Felix Thoemmes (2021): Review for “That’s a Lot to Process! Pitfalls of Popular Path Models”. https://doi.org/10.1177/25152459221095827/v1/review1
  1230. Squazzoni F., Bravo G., Grimaldo F., García-Costa D., Farjam M., Mehmani B. (2021): Gender gap in journal submissions and peer review during the first wave of the COVID-19 pandemic. A study on 2329 Elsevier journals. PLOS ONE 16(10):e0257919. https://doi.org/10.1371/journal.pone.0257919
  1231. Cicconardi F., Lewis J., Martin S., Reed R., Danko C., Montgomery S. (2021): Chromosome Fusion Affects Genetic Diversity and Evolutionary Turnover of Functional Loci but Consistently Depends on Chromosome Size. Molecular Biology and Evolution 38(10):4449-4462. https://doi.org/10.1093/molbev/msab185
  1232. Panzera F., Bergamo P., Fäh D. (2021): Canonical Correlation Analysis Based on Site-Response Proxies to Predict Site-Specific Amplification Functions in Switzerland. Bulletin of the Seismological Society of America 111(4):1905-1920. https://doi.org/10.1785/0120200326
  1233. Fossen F., Neyse L., Johannesson M., Dreber A. (2021): 2D:4D and Self-Employment: A Preregistered Replication Study in a Large General Population Sample. Entrepreneurship Theory and Practice 46(1):21-43. https://doi.org/10.1177/1042258720985478
  1234. Rosenkranz G. (2021): Replicability of studies following a dual‐criterion design. Statistics in Medicine 40(18):4068-4076. https://doi.org/10.1002/sim.9014
  1235. Torbahn G., Sulz I., Großhauser F., Hiesmayr M., Kiesswetter E., Schindler K., et al. (2021): Predictors of incident malnutrition—a nutritionDay analysis in 11,923 nursing home residents. European Journal of Clinical Nutrition 76(3):382-388. https://doi.org/10.1038/s41430-021-00964-9
  1236. Cohen-Blankshtain G., Sulitzeanu-Kenan R. (2021): Foregone and predicted futures: challenges of opportunity cost neglect and impact bias for public participation in policymaking. Journal of European Public Policy 28(5):677-697. https://doi.org/10.1080/13501763.2021.1912152
  1237. Majumdar G., Yazin F., Banerjee A., Roy D. (2021): Emotion Dynamics as Hierarchical Bayesian Inference in Time. https://doi.org/10.1101/2021.11.30.470667
  1238. Li G., Zhang Q., Lin Q., Gao W. (2021): A Three-Level Radial Basis Function Method for Expensive Optimization. IEEE Transactions on Cybernetics 52(7):5720-5731. https://doi.org/10.1109/tcyb.2021.3061420
  1239. Richardson G., McGee N., Copping L. (2021): Advancing the Psychometric Study of Human Life History Indicators. Human Nature 32(2):363-386. https://doi.org/10.1007/s12110-021-09398-5
  1240. Agapito G., Cannataro M. (2021): Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data. BMC Bioinformatics 22(S13). https://doi.org/10.1186/s12859-021-04297-z
  1241. Teah G., Conner T. (2021): Psychological and Demographic Predictors of Vaping and Vaping Susceptibility in Young Adults. Frontiers in Psychology 12. https://doi.org/10.3389/fpsyg.2021.659206
  1242. Baltussen G., Swinkels L., Van Vliet P. (2021): Global factor premiums. Journal of Financial Economics 142(3):1128-1154. https://doi.org/10.1016/j.jfineco.2021.06.030
  1243. Imbens G. (2021): Statistical Significance,p-Values, and the Reporting of Uncertainty. Journal of Economic Perspectives 35(3):157-174. https://doi.org/10.1257/jep.35.3.157
  1244. Gurkan G., Benjamini Y., Braun H. (2021): Defensible inferences from a nested sequence of logistic regressions: a guide for the perplexed. Large-scale Assessments in Education 9(1). https://doi.org/10.1186/s40536-021-00111-7
  1245. Li G., Walter S., Thabane L. (2021): Shifting the focus away from binary thinking of statistical significance and towards education for key stakeholders: revisiting the debate on whether it’s time to de-emphasize or get rid of statistical significance. Journal of Clinical Epidemiology 137:104-112. https://doi.org/10.1016/j.jclinepi.2021.03.033
  1246. Moshontz H., Ebersole C., Weston S., Klein R. (2021): A guide for many authors: Writing manuscripts in large collaborations. Social and Personality Psychology Compass 15(4). https://doi.org/10.1111/spc3.12590
  1247. Raspe H., Lill C. (2021): Regionale Deprivation und Merkmale der Krankheit und ihrer Versorgung bei chronisch-entzündlichen Darmerkrankungen. Das Gesundheitswesen 85(03):149-157. https://doi.org/10.1055/a-1530-5529
  1248. Søndergaard H., Airas L., Christensen J., Nielsen B., Börnsen L., Oturai A., et al. (2021): Pregnancy-Induced Changes in microRNA Expression in Multiple Sclerosis. Frontiers in Immunology 11. https://doi.org/10.3389/fimmu.2020.552101
  1249. Lopez H., Devos T., Somo A. (2021): State-level cultural tightness–looseness accounts for implicit associations between American and White identities. Current Research in Ecological and Social Psychology 3:100033. https://doi.org/10.1016/j.cresp.2021.100033
  1250. Cheng H., Chang T., Su W., Tsai H., Wang J. (2021): Narrative review of the influence of diabetes mellitus and hyperglycemia on colorectal cancer risk and oncological outcomes. Translational Oncology 14(7):101089. https://doi.org/10.1016/j.tranon.2021.101089
  1251. Bae H., Kim S., Kim C. (2021): Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks. Frontiers in Systems Neuroscience 14. https://doi.org/10.3389/fnsys.2020.615129
  1252. Cleasby I., Morrissey B., Bolton M., Owen E., Wilson L., Wischnewski S., et al. (2021): What is our power to detect device effects in animal tracking studies?. Methods in Ecology and Evolution 12(7):1174-1185. https://doi.org/10.1111/2041-210x.13598
  1253. Cieśliński I., Gierczuk D., Sadowski J. (2021): Identification of success factors in elite wrestlers—An exploratory study. PLOS ONE 16(3):e0247565. https://doi.org/10.1371/journal.pone.0247565
  1254. Khorozyan I. (2021): Defining practical and robust study designs for interventions targeted at terrestrial mammalian predators. Conservation Biology 36(2). https://doi.org/10.1111/cobi.13805
  1255. Khorozyan I. (2021): Dealing with false positive risk as an indicator of misperceived effectiveness of conservation interventions. PLOS ONE 16(8):e0255784. https://doi.org/10.1371/journal.pone.0255784
  1256. Jaljuli I., Kafkafi N., Giladi E., Golani I., Gozes I., Chesler E., et al. (2021): Improving replicability using interaction with laboratories: a multi-lab experimental assessment. https://doi.org/10.1101/2021.12.05.471264
  1257. Böschen I. (2021): Evaluation of JATSdecoder as an automated text extraction tool for statistical results in scientific reports. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-98782-3
  1258. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. PLOS ONE 16(8):e0256560. https://doi.org/10.1371/journal.pone.0256560
  1259. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. https://doi.org/10.1101/2021.01.15.426803
  1260. Grahek I., Schaller M., Tackett J. (2021): Anatomy of a Psychological Theory: Integrating Construct-Validation and Computational-Modeling Methods to Advance Theorizing. Perspectives on Psychological Science 16(4):803-815. https://doi.org/10.1177/1745691620966794
  1261. Grahek I., Schaller M., Tackett J. (2021): Anatomy of a Psychological Theory: Integrating Construct Validation and Computational Modeling Methods to Advance Theorizing. https://doi.org/10.31234/osf.io/kx5hj
  1262. Christensen J., Orquin J., Perkovic S., Lagerkvist C. (2021): Inflated false-positive risk in common regression analyses: A combinatorial analysis of model sets. https://doi.org/10.31234/osf.io/cj3xq
  1263. Fisher J., Hamilton K. (2021): Integrating Media Selection and Media Effects Using Decision Theory. Journal of Media Psychology 33(4):215-225. https://doi.org/10.1027/1864-1105/a000315
  1264. Fisher J., Hamilton K. (2021): Integrating Media Selection and Media Effects Using Decision Theory. https://doi.org/10.33767/osf.io/pseza
  1265. Bhowmik J., Biswas R., Hossain S. (2021): Child Marriage and Adolescent Motherhood: A Nationwide Vulnerability for Women in Bangladesh. International Journal of Environmental Research and Public Health 18(8):4030. https://doi.org/10.3390/ijerph18084030
  1266. Smith J., Muhling B., Sweeney J., Tommasi D., Pozo Buil M., Fiechter J., et al. (2021): The potential impact of a shifting Pacific sardine distribution on U.S. West Coast landings. Fisheries Oceanography 30(4):437-454. https://doi.org/10.1111/fog.12529
  1267. Bartlett J., Charles S. (2021): Power to the People: A Beginner’s Tutorial to Power Analysis using jamovi. https://doi.org/10.31234/osf.io/bh8m9
  1268. Brophy J. (2021): Key Issues in the Statistical Interpretation of Randomized Clinical Trials. Canadian Journal of Cardiology 37(9):1312-1321. https://doi.org/10.1016/j.cjca.2020.12.014
  1269. Catford J., Wilson J., Pyšek P., Hulme P., Duncan R. (2021): Addressing context dependence in ecology. Trends in Ecology & Evolution 37(2):158-170. https://doi.org/10.1016/j.tree.2021.09.007
  1270. Turna J., Belisario K., Balodis I., Van Ameringen M., Busse J., MacKillop J. (2021): Cannabis use and misuse in the year following recreational cannabis legalization in Canada: A longitudinal observational cohort study of community adults in Ontario. Drug and Alcohol Dependence 225:108781. https://doi.org/10.1016/j.drugalcdep.2021.108781
  1271. Brinkerink J. (2021): When Shooting for the Stars Becomes Aiming for Asterisks: P-Hacking in Family Business Research. Entrepreneurship Theory and Practice 47(2):304-343. https://doi.org/10.1177/10422587211050354
  1272. Miller J., Ulrich R. (2021): Optimizing Research Output: How Can Psychological Research Methods Be Improved?. Annual Review of Psychology 73(1):691-718. https://doi.org/10.1146/annurev-psych-020821-094927
  1273. Borowsky J., Haruki K., Lau M., Dias Costa A., Väyrynen J., Ugai T., et al. (2021): Association of Fusobacterium nucleatum with Specific T-cell Subsets in the Colorectal Carcinoma Microenvironment. Clinical Cancer Research 27(10):2816-2826. https://doi.org/10.1158/1078-0432.ccr-20-4009
  1274. Logg J., Dorison C. (2021): Pre-registration: Weighing costs and benefits for researchers. Organizational Behavior and Human Decision Processes 167:18-27. https://doi.org/10.1016/j.obhdp.2021.05.006
  1275. Freese J., Schnell S., Schäfer A., Klement R., Krüger S., Lückemann L., et al. (2021): How to dismantle modern stressors: does a short trip to simulated Paleolithic conditions in the wild reduce cortisol levels?. F1000Research 10:238. https://doi.org/10.12688/f1000research.50793.1
  1276. Wu J., Nivargi R., Lanka S., Menon A., Modukuri S., Nakshatri N., et al. (2021): Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models. arXiv. https://doi.org/10.48550/arxiv.2104.04580
  1277. Everett J., Colombatto C., Awad E., Boggio P., Bos B., Brady W., et al. (2021): Moral dilemmas and trust in leaders during a global health crisis. Nature Human Behaviour 5(8):1074-1088. https://doi.org/10.1038/s41562-021-01156-y
  1278. Zhang J., Zheng L. (2021): Masculine Voices Predict Attachment Style and Relationship Communication Patterns in Romantic Relationships. Journal of Sex & Marital Therapy. https://doi.org/10.1080/0092623x.2020.1869125
  1279. Kruschke J. (2021): Bayesian Analysis Reporting Guidelines. Nature Human Behaviour 5(10):1282-1291. https://doi.org/10.1038/s41562-021-01177-7
  1280. Doris J. (2021): Character Trouble. https://doi.org/10.1093/oso/9780198719601.001.0001
  1281. Doris J. (2021): Preface. Character Trouble. https://doi.org/10.1093/oso/9780198719601.002.0007
  1282. Doris J. (2021): Epigraph. Character Trouble. https://doi.org/10.1093/oso/9780198719601.002.0005
  1283. Doris J. (2021): Copyright Page. Character Trouble. https://doi.org/10.1093/oso/9780198719601.002.0003
  1284. Doris J. (2021): Dedication. Character Trouble. https://doi.org/10.1093/oso/9780198719601.002.0004
  1285. Schnog J., Samson M., Gans R., Duits A. (2021): An urgent call to raise the bar in oncology. British Journal of Cancer 125(11):1477-1485. https://doi.org/10.1038/s41416-021-01495-7
  1286. Svensson J., Schain M., Knudsen G., Ogden R., Plavén-Sigray P. (2021): Early stopping in clinical PET studies: How to reduce expense and exposure. Journal of Cerebral Blood Flow & Metabolism 41(11):2805-2819. https://doi.org/10.1177/0271678×211017796
  1287. de Winter J., Dodou D. (2021): Pitfalls of Statistical Methods in Traffic Psychology. International Encyclopedia of Transportation. https://doi.org/10.1016/b978-0-08-102671-7.10665-7
  1288. Mulder J., Williams D., Gu X., Tomarken A., Böing-Messing F., Olsson-Collentine A., et al. (2021): BFpack: Flexible Bayes Factor Testing of Scientific Theories in R. Journal of Statistical Software 100(18). https://doi.org/10.18637/jss.v100.i18
  1289. Habiger J., Liang Y. (2021): Publication Policies for Replicable Research and the Community-Wide False Discovery Rate. The American Statistician 76(2):131-141. https://doi.org/10.1080/00031305.2021.1999857
  1290. Ighalo J., Igwegbe C., Adeniyi A., Abdulkareem S. (2021): Artificial Neural Network Modeling of the Water Absorption Behavior of Plantain Peel and Bamboo Fibers Reinforced Polystyrene Composites. Journal of Macromolecular Science, Part B 60(7):472-484. https://doi.org/10.1080/00222348.2020.1866282
  1291. Wortzel J., Turner B., Weeks B., Fragassi C., Ramos V., Truong T., et al. (2021): Trends in US pediatric mental health clinical trials: An analysis of ClinicalTrials.gov from 2007–2018. PLOS ONE 16(4):e0248898. https://doi.org/10.1371/journal.pone.0248898
  1292. José Storópoli (2021): Estatística Bayesiana com R e Stan.
  1293. José Storópoli (2021): Estatística Bayesiana com R e Stan: O que é Estatística Bayesiana?.
  1294. José Storópoli, Leonardo Vils (2021): Estatística com R: p-Valores, Hipótese Nula e Pressupostos.
  1295. Väyrynen J., Haruki K., Lau M., Väyrynen S., Ugai T., Akimoto N., et al. (2021): Spatial Organization and Prognostic Significance of NK and NKT-like Cells via Multimarker Analysis of the Colorectal Cancer Microenvironment. Cancer Immunology Research 10(2):215-227. https://doi.org/10.1158/2326-6066.cir-21-0772
  1296. Väyrynen J., Haruki K., Väyrynen S., Lau M., Dias Costa A., Borowsky J., et al. (2021): Prognostic significance of myeloid immune cells and their spatial distribution in the colorectal cancer microenvironment. Journal for ImmunoTherapy of Cancer 9(4):e002297. https://doi.org/10.1136/jitc-2020-002297
  1297. Decroix J., Ott L., Morgado N., Kalénine S. (2021): Can the early visual processing of others’ actions be related to social power and dominance?. Psychological Research 86(6):1858-1870. https://doi.org/10.1007/s00426-021-01617-z
  1298. Bountress K., Vladimirov V., McMichael G., Taylor Z., Hardiman G., Chung D., et al. (2021): Gene Expression Differences Between Young Adults Based on Trauma History and Post-traumatic Stress Disorder. Frontiers in Psychiatry 12. https://doi.org/10.3389/fpsyt.2021.581093
  1299. Salmela‐Aro K., Upadyaya K., Vinni‐Laakso J., Hietajärvi L. (2021): Adolescents’ Longitudinal School Engagement and Burnout Before and During COVID‐19—The Role of Socio‐Emotional Skills. Journal of Research on Adolescence 31(3):796-807. https://doi.org/10.1111/jora.12654
  1300. Silaj K., Schwartz S., Castel A., McDonough I. (2021): Is the Future Bright or Bleak? Assessing Past and Future Outlooks Across the Adult Lifespan. Gerontology and Geriatric Medicine 7. https://doi.org/10.1177/23337214211046080
  1301. Klauenberg K., Müller C., Elster C. (2021): Hypothesis-based Acceptance Sampling for Modules F and F1 of the European Measuring Instruments Directive. Statistics and Public Policy 8(1):9-17. https://doi.org/10.1080/2330443x.2021.1900762
  1302. Barclay K., Hällsten M. (2021): Does the impact of parental death vary by parental socioeconomic status? A study of children’s educational and occupational attainment. Journal of Marriage and Family 84(1):141-164. https://doi.org/10.1111/jomf.12786
  1303. Noguchi K., Konietschke F., Marmolejo-Ramos F., Pauly M. (2021): Permutation tests are robust and powerful at 0.5% and 5% significance levels. Behavior Research Methods 53(6):2712-2724. https://doi.org/10.3758/s13428-021-01595-5
  1304. Barrafrem K., Västfjäll D., Tinghög G. (2021): The arithmetic of outcome editing in financial and social domains. Journal of Economic Psychology 86:102408. https://doi.org/10.1016/j.joep.2021.102408
  1305. Mima K., Kosumi K., Miyanari N., Tajiri T., Kanemitsu K., Takematsu T., et al. (2021): Impairment of Activities of Daily Living is an Independent Risk Factor for Recurrence and Mortality Following Curative Resection of Stage I–III Colorectal Cancer. Journal of Gastrointestinal Surgery 25(10):2628-2636. https://doi.org/10.1007/s11605-021-04990-7
  1306. Mima K., Miyanari N., Kosumi K., Tajiri T., Kanemitsu K., Takematsu T., et al. (2021): The efficacy of adjuvant chemotherapy for resected high-risk stage II and stage III colorectal cancer in frail patients. International Journal of Clinical Oncology 26(5):903-912. https://doi.org/10.1007/s10147-021-01876-1
  1307. Duvergé L., Bondiau P., Claude L., Supiot S., Vaugier L., Thillays F., et al. (2021): Discontinuous stereotactic body radiotherapy schedule increases overall survival in early-stage non-small cell lung cancer. Lung Cancer 157:100-108. https://doi.org/10.1016/j.lungcan.2021.05.016
  1308. Petterson L., Vasey P. (2021): Canadian undergraduate men’s visual attention to cisgender women, cisgender men, and feminine trans individuals. Scientific Reports 11(1). https://doi.org/10.1038/s41598-020-79870-2
  1309. Liebst L., Ejbye‐Ernst P., Bruin M., Thomas J., Lindegaard M. (2021): Face‐touching behaviour as a possible correlate of mask‐wearing: A video observational study of public place incidents during the COVID‐19 pandemic. Transboundary and Emerging Diseases 69(3):1319-1325. https://doi.org/10.1111/tbed.14094
  1310. Liebst L., Ejbye-Ernst P., de Bruin M., Thomas J., Lindegaard M. (2021): Mask-wearing and social distancing: Evidence from a video-observational and natural-experimental study of public space behavior during the COVID-19 pandemic. https://doi.org/10.31234/osf.io/ep8jg
  1311. Israel L., Paukner P., Schiestel L., Diepold K., Schönbrodt F. (2021): The OpenLAV video database for affect induction: Analyzing the uniformity of video stimuli effects. https://doi.org/10.31234/osf.io/vhmbq
  1312. Alteio L., Séneca J., Canarini A., Angel R., Jansa J., Guseva K., et al. (2021): A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biology and Biochemistry 160:108357. https://doi.org/10.1016/j.soilbio.2021.108357
  1313. Alteio L., Séneca J., Canarini A., Angel R., Guseva K., Jansa J., et al. (2021): A critical perspective on interpreting amplicon sequencing data in soil ecological research. https://doi.org/10.22541/au.161919535.51886448/v1
  1314. Backhausen L., Herting M., Tamnes C., Vetter N. (2021): Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders. Neuropsychology Review 32(2):400-418. https://doi.org/10.1007/s11065-021-09496-2
  1315. Lessard L., Puhl R., Himmelstein M., Pearl R., Foster G. (2021): Eating and Exercise‐Related Correlates of Weight Stigma: A Multinational Investigation. Obesity 29(6):966-970. https://doi.org/10.1002/oby.23168
  1316. Villani L., Pastorino R., Ricciardi W., Ioannidis J., Boccia S. (2021): Inverse correlates of COVID-19 mortality across European countries during the first versus subsequent waves. BMJ Global Health 6(8):e006422. https://doi.org/10.1136/bmjgh-2021-006422
  1317. Held L., Matthews R., Ott M., Pawel S. (2021): Reverse‐Bayes methods for evidence assessment and research synthesis. Research Synthesis Methods 13(3):295-314. https://doi.org/10.1002/jrsm.1538
  1318. Held L., Matthews R., Ott M., Pawel S. (2021): Reverse-Bayes methods for evidence assessment and research synthesis. arXiv. https://doi.org/10.48550/arxiv.2102.13443
  1319. Hanin L. (2021): Cavalier Use of Inferential Statistics Is a Major Source of False and Irreproducible Scientific Findings. Mathematics 9(6):603. https://doi.org/10.3390/math9060603
  1320. Neyse L., Johannesson M., Dreber A. (2021): 2D:4D does not predict economic preferences: Evidence from a large, representative sample. Journal of Economic Behavior & Organization 185:390-401. https://doi.org/10.1016/j.jebo.2021.02.029
  1321. Simon L., Keshav V., Baharozian C., Masli S., Lee H. (2021): Thrombospondin 1 polymorphism associated with decreased expression and increased risk of pterygium. Graefe’s Archive for Clinical and Experimental Ophthalmology 259(8):2301-2307. https://doi.org/10.1007/s00417-021-05121-3
  1322. Andersen L., Bailey R., Bhatia U., Burch-Brown J., Bright L., Brooke J., et al. (2021): What’s the Point of Authors?. The British Journal for the Philosophy of Science 75(2):487-517. https://doi.org/10.1086/715539
  1323. Tzavella L., Lawrence N., Button K., Hart E., Holmes N., Houghton K., et al. (2021): Effects of go/no-go training on food-related action tendencies, liking and choice. Royal Society Open Science 8(8):210666. https://doi.org/10.1098/rsos.210666
  1324. Al-Muzian L., Almuzian M., Mohammed H., Ulhaq A., Keightley A. (2021): Are developmentally missing teeth a predictive risk marker of malignant diseases in non-syndromic individuals? A systematic review. Journal of Orthodontics 48(3):221-230. https://doi.org/10.1177/1465312520984166
  1325. Amendola L. (2021): Golden Hat of Schifferstadt. Journal of Skyscape Archaeology 7(1). https://doi.org/10.1558/jsa.18113
  1326. Salcedo L., LeBlanc B., Martin S., Nossaman B. (2021): Preoperative Administration of Hycet Elixir Reduces Hospital Length of Stay After Pediatric Outpatient Adeno/Tonsillectomy. Ochsner Journal 21(3):240-244. https://doi.org/10.31486/toj.20.0101
  1327. Hensel L., Witte M., Caria A., Fetzer T., Fiorin S., Götz F., et al. (2021): Global Behaviors, Perceptions, and the Emergence of Social Norms at the Onset of the COVID-19 Pandemic. Journal of Economic Behavior & Organization 193:473-496. https://doi.org/10.1016/j.jebo.2021.11.015
  1328. Strickland L., Heathcote A., Bowden V., Boag R., Wilson M., Khan S., et al. (2021): Inhibitory Cognitive Control Allows Automated Advice to Improve Accuracy While Minimizing Misuse. Psychological Science 32(11):1768-1781. https://doi.org/10.1177/09567976211012676
  1329. Villarroel del Pino L. (2021): Impacto sobre el tamaño de las muestras en estudios nacionales si cambiara el nivel de significación estadística de &#945;= 0,05 a &#945; = 0,005. Revista médica de Chile 149(1):45-51. https://doi.org/10.4067/s0034-98872021000100045
  1330. Said M., Yeung M., van de Vegte Y., Benjamins J., Dullaart R., Ruotsalainen S., et al. (2021): Genome-Wide Association Study and Identification of a Protective Missense Variant on Lipoprotein(a) Concentration. Arteriosclerosis, Thrombosis, and Vascular Biology 41(5):1792-1800. https://doi.org/10.1161/atvbaha.120.315300
  1331. Alister M., Vickers-Jones R., Sewell D., Ballard T. (2021): How Do We Choose Our Giants? Perceptions of Replicability in Psychological Science. Advances in Methods and Practices in Psychological Science 4(2). https://doi.org/10.1177/25152459211018199
  1332. Aguert M. (2021): Paraverbal Expression of Verbal Irony: Vocal Cues Matter and Facial Cues Even More. Journal of Nonverbal Behavior 46(1):45-70. https://doi.org/10.1007/s10919-021-00385-z
  1333. Matabuena M., Riazati S., Caplan N., Hayes P. (2021): Are Multilevel functional models the next step in sports biomechanics and wearable technology? A case study of Knee Biomechanics patterns in typical training sessions of recreational runners. arXiv. https://doi.org/10.48550/arxiv.2103.15704
  1334. van de Weijer M., Pelt D., van Beijsterveldt C., Willemsen G., Bartels M. (2021): Genetic factors explain a significant part of associations between adolescent well-being and the social environment. European Child & Adolescent Psychiatry 31(10):1611-1622. https://doi.org/10.1007/s00787-021-01798-3
  1335. Lackner M., Sonnabend H. (2021): Coping with advantageous inequity—Field evidence from professional penalty kicking. Journal of Behavioral and Experimental Economics 91:101678. https://doi.org/10.1016/j.socec.2021.101678
  1336. Rubin M. (2021): When to adjust alpha during multiple testing: A consideration of disjunction, conjunction, and individual testing. OSF Preprints. https://doi.org/10.31222/osf.io/tj6pm
  1337. Rubin M. (2021): When to adjust alpha during multiple testing: a consideration of disjunction, conjunction, and individual testing. Synthese 199(3-4):10969-11000. https://doi.org/10.1007/s11229-021-03276-4
  1338. Rubin M. (2021): When to adjust alpha during multiple testing: A consideration of disjunction, conjunction, and individual testing. https://doi.org/10.31234/osf.io/qc9kf
  1339. Serra-Garcia M., Gneezy U. (2021): Nonreplicable publications are cited more than replicable ones. Science Advances 7(21). https://doi.org/10.1126/sciadv.abd1705
  1340. Eisend M., Kuß A. (2021): Hypothesen und Modelle beim Theorietest. Grundlagen empirischer Forschung. https://doi.org/10.1007/978-3-658-32890-0_7
  1341. Rydén M., Englund M., Ali N. (2021): ProteoMill: efficient network-based functional analysis portal for proteomics data. Bioinformatics 37(20):3491-3493. https://doi.org/10.1093/bioinformatics/btab373
  1342. Henderson M., Brayson D., Halsey L. (2021): The cardio‐respiratory effects of passive heating and the human thermoneutral zone. Physiological Reports 9(16). https://doi.org/10.14814/phy2.14973
  1343. Frias-Navarro D., Pascual-Soler M., Perezgonzalez J., Monterde-i-Bort H., Pascual-Llobell J. (2021): Spanish Scientists’ Opinion about Science and Researcher Behavior. The Spanish Journal of Psychology 24. https://doi.org/10.1017/sjp.2020.59
  1344. Schlögl M., Käch I., Beeler P., Pape H., Neuhaus V. (2021): Trauma patients with hypokalemia have an increased risk of morbidity and mortality. Surgery in Practice and Science 7:100041. https://doi.org/10.1016/j.sipas.2021.100041
  1345. McGue M., Anderson E., Willoughby E., Giannelis A., Iacono W., Lee J. (2021): Not by g alone: The benefits of a college education among individuals with low levels of general cognitive ability. https://doi.org/10.31234/osf.io/uj84p
  1346. Ullrich M., S. Strong D. (2021): EXPLORING STUDENTS’ INTERPRETATIONS OF SUCCESS: A RESEARCH INSTRUMENT. Proceedings of the Canadian Engineering Education Association (CEEA). https://doi.org/10.24908/pceea.vi0.14931
  1347. Linde M., Tendeiro J., Selker R., Wagenmakers E., van Ravenzwaaij D. (2021): Decisions about equivalence: A comparison of TOST, HDI-ROPE, and the Bayes factor. Psychological Methods 28(3):740-755. https://doi.org/10.1037/met0000402
  1348. Awal M., Masud M., Hossain M., Bulbul A., Mahmud S., Bairagi A. (2021): A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data. IEEE Access 9:10263-10281. https://doi.org/10.1109/access.2021.3050852
  1349. Awal M., Hossain M., Debjit K., Ahmed N., Nath R., Habib G., et al. (2021): An Early Detection of Asthma Using BOMLA Detector. IEEE Access 9:58403-58420. https://doi.org/10.1109/access.2021.3073086
  1350. Minhas M., Murphy C., Balodis I., Samokhvalov A., MacKillop J. (2021): Food addiction in a large community sample of Canadian adults: prevalence and relationship with obesity, body composition, quality of life and impulsivity. Addiction 116(10):2870-2879. https://doi.org/10.1111/add.15446
  1351. McDonald M., James R., Roberto D. (2021): True Crime Consumption as Defensive Vigilance: Psychological Mechanisms of a Rape Avoidance System. Archives of Sexual Behavior 50(5):2085-2108. https://doi.org/10.1007/s10508-021-01990-1
  1352. Belshan M., Holbrook A., George J., Durant H., Callahan M., Jaquet S., et al. (2021): Discovery of candidate HIV-1 latency biomarkers using an OMICs approach. Virology 558:86-95. https://doi.org/10.1016/j.virol.2021.03.003
  1353. Constant M., Trofa D., Saltzman B., Ahmad C., Li X., Parisien R. (2021): The Fragility of Statistical Significance in Patellofemoral Instability Research: A Systematic Review. The American Journal of Sports Medicine 50(13):3714-3718. https://doi.org/10.1177/03635465211039202
  1354. Gordon M., Viganola D., Dreber A., Johannesson M., Pfeiffer T. (2021): Predicting replicability—Analysis of survey and prediction market data from large-scale forecasting projects. PLOS ONE 16(4):e0248780. https://doi.org/10.1371/journal.pone.0248780
  1355. Gordon M., Pfeiffer T. (2021): Can scientists change their minds?. Nature Human Behaviour 5(12):1598-1599. https://doi.org/10.1038/s41562-021-01201-w
  1356. Karlovich M., Wallisch P. (2021): Scintillating Starbursts: Concentric Star Polygons Induce Illusory Ray Patterns. i-Perception 12(3). https://doi.org/10.1177/20416695211018720
  1357. Kent M., Jakubiec J. (2021): An examination of range effects when evaluating discomfort due to glare in Singaporean buildings. Lighting Research & Technology 54(6):514-528. https://doi.org/10.1177/14771535211047220
  1358. Lombardo M., Busuoli E., Schreibman L., Stahmer A., Pramparo T., Landi I., et al. (2021): Pre-treatment clinical and gene expression patterns predict developmental change in early intervention in autism. Molecular Psychiatry 26(12):7641-7651. https://doi.org/10.1038/s41380-021-01239-2
  1359. Kaschak M., Madden J. (2021): Embodiment in the Lab: Theory, Measurement, and Reproducibility. Handbook of Embodied Psychology. https://doi.org/10.1007/978-3-030-78471-3_27
  1360. Schiffinger M. (2021): A world of p(ain) (Forschungsmethodik). Austrian Management Review. https://doi.org/10.5771/9783957104014-112
  1361. Beets M., von Klinggraeff L., Burkart S., Jones A., Ioannidis J., Weaver R., et al. (2021): Impact of risk of generalizability biases in adult obesity interventions: A meta‐epidemiological review and meta‐analysis. Obesity Reviews 23(2). https://doi.org/10.1111/obr.13369
  1362. Białek M., Domurat A., Paruzel-Czachura M., Muda R. (2021): Limits of the foreign language effect: intertemporal choice. Thinking & Reasoning 28(1):97-124. https://doi.org/10.1080/13546783.2021.1934899
  1363. Scandola M., Romano D. (2021): Bayesian multilevel single case models using ‘Stan’. A new tool to study single cases in neuropsychology. Neuropsychologia 156:107834. https://doi.org/10.1016/j.neuropsychologia.2021.107834
  1364. Nuijten M. (2021): Assessing and improving robustness of psychological research findings in four steps. https://doi.org/10.31234/osf.io/a4bu2
  1365. Zhang M., Liu J., Zhang H., Verrelli D., Wang Q., Hu L., et al. (2021): CTA-Based Non-invasive Estimation of Pressure Gradients Across a CoA: a Validation Against Cardiac Catheterisation. Journal of Cardiovascular Translational Research 14(5):873-882. https://doi.org/10.1007/s12265-020-10092-7
  1366. Truong M., Berger M., Haba-Rubio J., Siclari F., Marques-Vidal P., Heinzer R. (2021): Impact of smoking on sleep macro– and microstructure. Sleep Medicine 84:86-92. https://doi.org/10.1016/j.sleep.2021.05.024
  1367. Nguyen M. (2021): Bayesian analysis in social sciences. https://doi.org/10.31219/osf.io/zwdg2
  1368. Mansur M., Afiaz A., Hossain M. (2021): Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method. PLOS ONE 16(8):e0256729. https://doi.org/10.1371/journal.pone.0256729
  1369. Almesned M., Prins F., Lipšic E., Connelly M., Garcia E., Dullaart R., et al. (2021): Temporal Course of Plasma Trimethylamine N-Oxide (TMAO) Levels in ST-Elevation Myocardial Infarction. Journal of Clinical Medicine 10(23):5677. https://doi.org/10.3390/jcm10235677
  1370. Pompeo M., Serdarevic N. (2021): Is information enough? The case of Republicans and climate change. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3981552
  1371. Naeem M., Yu J., Aamir M., Khan S., Adeleye O., Khan Z. (2021): Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak. PeerJ Computer Science 7:e746. https://doi.org/10.7717/peerj-cs.746
  1372. Ruest M., Léonard G., Thomas A., Desrosiers J., Guay M. (2021): Algo’s Integrated Knowledge Translation Process in Homecare Services: A Cross-Sectional Correlational Study for Identifying its Level of Utilization and its Associated Characteristics. Canadian Journal of Occupational Therapy 89(1):13-25. https://doi.org/10.1177/00084174211064495
  1373. Akimoto N., Zhao M., Ugai T., Zhong R., Lau M., Fujiyoshi K., et al. (2021): Tumor Long Interspersed Nucleotide Element-1 (LINE-1) Hypomethylation in Relation to Age of Colorectal Cancer Diagnosis and Prognosis. Cancers 13(9):2016. https://doi.org/10.3390/cancers13092016
  1374. Stevens N., Hagar L. (2021): Comparative Probability Metrics: Using Posterior Probabilities to Account for Practical Equivalence in A/B tests. The American Statistician 76(3):224-237. https://doi.org/10.1080/00031305.2021.2000495
  1375. Berselli N., Filippini T., Adani G., Vinceti M. (2021): Dismissing the use of P-values and statistical significance testing in scientific research: new methodological perspectives in toxicology and risk assessment. Toxicological Risk Assessment and Multi-System Health Impacts from Exposure. https://doi.org/10.1016/b978-0-323-85215-9.00002-7
  1376. Hanson N., Lavallee M., Thiele R. (2021): Apophenia and anesthesia: how we sometimes change our practice prematurely. Canadian Journal of Anesthesia/Journal canadien d’anesthésie 68(8):1185-1196. https://doi.org/10.1007/s12630-021-02005-2
  1377. Huyen N., Ho M., Huyen D. (2021): 研究领域执着的执着. https://doi.org/10.31219/osf.io/b7mqt
  1378. David Bowman N., Lin J., Wu C. (2021): A Chinese-Language Validation of the Video Game Demand Scale (VGDS-C). Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411764.3445348
  1379. Barban N., De Cao E., Francesconi M. (2021): Gene-Environment Effects on Female Fertility. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3938650
  1380. Nelson N., Chung J., Ichikawa K., Malik M. (2021): Psychology Exceptionalism and the Multiple Discovery of the Replication Crisis. Review of General Psychology 26(2):184-198. https://doi.org/10.1177/10892680211046508
  1381. Bhagwat N., Barry A., Dickie E., Brown S., Devenyi G., Hatano K., et al. (2021): Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses. GigaScience 10(1). https://doi.org/10.1093/gigascience/giaa155
  1382. Moshkov N., Smetanin A., Tatarinova T. (2021): Local ancestry prediction with PyLAE. PeerJ 9:e12502. https://doi.org/10.7717/peerj.12502
  1383. Nikita Moshkov, Aleksandr Smetanin, Tatiana Tatarinova, Alexander Smetanin, D Alexander, J Novembre, et al. (2021): Peer Review #1 of “Local ancestry prediction with PyLAE (v0.2)”. https://doi.org/10.7287/peerj.12502v0.2/reviews/1
  1384. Rachev N., Han H., Lacko D., Gelpí R., Yamada Y., Lieberoth A. (2021): Replicating the Disease framing problem during the 2020 COVID-19 pandemic: A study of stress, worry, trust, and choice under risk. PLOS ONE 16(9):e0257151. https://doi.org/10.1371/journal.pone.0257151
  1385. van Dongen N. (2021): Doing Science: Methods and Philosophy. https://doi.org/10.31234/osf.io/yegvs
  1386. Krepel N. (2021): Two steps forward, one step back. https://doi.org/10.26481/dis.20210618nk
  1387. Laccourreye O., Fakhry N., Franco-Vidal V., Jankowski R., Karkas A., Leboulanger N., et al. (2021): Les statistiques des articles scientifiques publiés dans les European Annals of Otorhinolaryngology Head & Neck Diseases. Annales françaises d’Oto-rhino-laryngologie et de Pathologie Cervico-faciale 138(2):98-102. https://doi.org/10.1016/j.aforl.2020.05.013
  1388. Laccourreye O., Jankowski R., Lisan Q. (2021): Maîtriser les statistiques descriptives utilisées en otorhinolaryngologie. Annales françaises d’Oto-rhino-laryngologie et de Pathologie Cervico-faciale 138(5):390-393. https://doi.org/10.1016/j.aforl.2020.09.006
  1389. Gulevich O., Krivoshchekov V., Sorokina A., Samekin A. (2021): Are Benevolent Attitudes More Closely Related to Attitudes toward Homosexuals than Hostile Ones? Cases of Belarus, Kazakhstan, and Russia. Journal of Homosexuality 69(5):796-820. https://doi.org/10.1080/00918369.2020.1855030
  1390. Gutiérrez-Hernández O., García L. (2021): Multiplicity Eludes Peer Review: The Case of COVID-19 Research. International Journal of Environmental Research and Public Health 18(17):9304. https://doi.org/10.3390/ijerph18179304
  1391. Baghele O. (2021): A Comprehensive Update on Crown-Lengthening Procedures with New Concepts and Inputs. Journal of the International Clinical Dental Research Organization 13(1):17-27. https://doi.org/10.4103/jicdro.jicdro_62_20
  1392. Kane P., Kimmelman J. (2021): Is preclinical research in cancer biology reproducible enough?. eLife 10. https://doi.org/10.7554/elife.67527
  1393. Sonon P., Collares C., Ferreira M., Almeida R., Sadissou I., Cordeiro M., et al. (2021): Peripheral spectrum neurological disorder after arbovirus infection is associated with HLA-F variants among Northeastern Brazilians. Infection, Genetics and Evolution 92:104855. https://doi.org/10.1016/j.meegid.2021.104855
  1394. Bazilinskyy P., Kooijman L., Dodou D., de Winter J. (2021): How should external human-machine interfaces behave? Examining the effects of colour, position, message, activation distance, vehicle yielding, and visual distraction among 1,434 participants. Applied Ergonomics 95:103450. https://doi.org/10.1016/j.apergo.2021.103450
  1395. Janiaud P., Agarwal A., Tzoulaki I., Theodoratou E., Tsilidis K., Evangelou E., et al. (2021): Validity of observational evidence on putative risk and protective factors: appraisal of 3744 meta-analyses on 57 topics. BMC Medicine 19(1). https://doi.org/10.1186/s12916-021-02020-6
  1396. Soukup P., Trahorsch P., Chytrý V. (2021): Míry věcné významnosti s intervaly spolehlivosti a ukázky jejich využití v pedagogické praxi. Studia paedagogica 26(3):131. https://doi.org/10.5817/sp2021-3-6
  1397. Thakur P., Jha V. (2021): Potential effects of lowering the threshold of statistical significance in the field of chronic rhinosinusitis – A meta-research on published randomized controlled trials over last decade. Brazilian Journal of Otorhinolaryngology 88:S83-S89. https://doi.org/10.1016/j.bjorl.2021.11.004
  1398. Zheng Q., Guo Y., Wang Z., Andrasik F., Kuang Z., Li J., et al. (2021): Exploring Weibo users’ attitudes toward lesbians and gays in Mainland China: A natural language processing and machine learning approach. Computers in Human Behavior 127:107021. https://doi.org/10.1016/j.chb.2021.107021
  1399. Hoàng V., La V., Nguyen M., Ho M. (2021): Bản hòa tấu dữ liệu xã hội. https://doi.org/10.31219/osf.io/9jreq
  1400. Bausell R. (2021): Questionable Research Practices (QRPs) and Their Devastating Scientific Effects. The Problem with Science. https://doi.org/10.1093/oso/9780197536537.003.0004
  1401. Bausell R. (2021): False-Positive Results and a Nontechnical Overview of Their Modeling. The Problem with Science. https://doi.org/10.1093/oso/9780197536537.003.0003
  1402. Valentine K., Buchanan E., Cunningham A., Hopke T., Wikowsky A., Wilson H. (2021): Have psychologists increased reporting of outliers in response to the reproducibility crisis?. Social and Personality Psychology Compass 15(5). https://doi.org/10.1111/spc3.12591
  1403. Klement R., Koebrunner P., Meyer D., Kanzler S., Sweeney R. (2021): Impact of a ketogenic diet intervention during radiotherapy on body composition: IV. Final results of the KETOCOMP study for rectal cancer patients. Clinical Nutrition 40(7):4674-4684. https://doi.org/10.1016/j.clnu.2021.05.015
  1404. Catalano R., Karasek D., Bruckner T., Casey J., Saxton K., Ncube C., et al. (2021): African American Unemployment and the Disparity in Periviable Births. Journal of Racial and Ethnic Health Disparities 9(3):840-848. https://doi.org/10.1007/s40615-021-01022-7
  1405. Bajpai R., Chaturvedi H. (2021): Toward a More Nuanced Interpretation of Statistical Significance in Biomedical Research. Asian Journal of Oncology 07:049-051. https://doi.org/10.1055/s-0041-1727066
  1406. Loder R., Kacena M., Ogbemudia B., Ngwe H., Aasar A., Ninad N., et al. (2021): Bibliometric Analysis of the English Musculoskeletal Literature over the Last 30 Years. The Scientific World Journal 2021:1-29. https://doi.org/10.1155/2021/5548481
  1407. Puhl R., Lessard L., Himmelstein M., Foster G. (2021): The roles of experienced and internalized weight stigma in healthcare experiences: Perspectives of adults engaged in weight management across six countries. PLOS ONE 16(6):e0251566. https://doi.org/10.1371/journal.pone.0251566
  1408. Puhl R., Lessard L., Pearl R., Himmelstein M., Foster G. (2021): International comparisons of weight stigma: addressing a void in the field. International Journal of Obesity 45(9):1976-1985. https://doi.org/10.1038/s41366-021-00860-z
  1409. Puhl R., Lessard L., Pearl R., Grupski A., Foster G. (2021): Policies to address weight discrimination and bullying: Perspectives of adults engaged in weight management from six nations. Obesity 29(11):1787-1798. https://doi.org/10.1002/oby.23275
  1410. Schwaiger R., Hueber L. (2021): Do MTurkers exhibit myopic loss aversion?. Economics Letters 209:110137. https://doi.org/10.1016/j.econlet.2021.110137
  1411. Hopkins R., Campbell G., Degenhardt L., Nielsen S., Blyth F., Cohen M., et al. (2021): Use of pharmacological and nonpharmacological treatments for chronic noncancer pain among people using opioids: a longitudinal cohort study. Pain 163(6):1049-1059. https://doi.org/10.1097/j.pain.0000000000002484
  1412. Fusaroli R., Grossman R., Bilenberg N., Cantio C., Jepsen J., Weed E. (2021): Toward a cumulative science of vocal markers of autism: A cross‐linguistic meta‐analysis‐based investigation of acoustic markers in American and Danish autistic children. Autism Research 15(4):653-664. https://doi.org/10.1002/aur.2661
  1413. Fusaroli R., Grossman R., Bilenberg N., Cantio C., Jepsen J., Weed E. (2021): Towards a cumulative science of vocal markers of autism: a cross-linguistic meta-analysis-based investigation of acoustic markers in American and Danish autistic children. https://doi.org/10.1101/2021.07.13.452165
  1414. Berk R. (2021): Post-Model-Selection Statistical Inference with Interrupted Time Series Designs: An Evaluation of an Assault Weapons Ban in California. arXiv. https://doi.org/10.48550/arxiv.2105.10624
  1415. Torkar R., Furia C., Feldt R. (2021): Bayesian Data Analysis for Software Engineering. 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). https://doi.org/10.1109/icse-companion52605.2021.00140
  1416. Kelter R. (2021): Power analysis and type I and type II error rates of Bayesian nonparametric two-sample tests for location-shifts based on the Bayes factor under Cauchy priors. Computational Statistics & Data Analysis 165:107326. https://doi.org/10.1016/j.csda.2021.107326
  1417. Kelter R. (2021): How to Choose between Different Bayesian Posterior Indices for Hypothesis Testing in Practice. Multivariate Behavioral Research 58(1):160-188. https://doi.org/10.1080/00273171.2021.1967716
  1418. Kelter R. (2021): fbst: An R package for the Full Bayesian Significance Test for testing a sharp null hypothesis against its alternative via the e value. Behavior Research Methods 54(3):1114-1130. https://doi.org/10.3758/s13428-021-01613-6
  1419. Heirene R., Wang A., Gainsbury S. (2021): Accuracy of self-reported gambling frequency and outcomes: Comparisons with account data. https://doi.org/10.31234/osf.io/5hs7j
  1420. Hudson R. (2021): Should We Strive to Make Science Bias-Free? A Philosophical Assessment of the Reproducibility Crisis. Journal for General Philosophy of Science 52(3):389-405. https://doi.org/10.1007/s10838-020-09548-w
  1421. Leigh R., Murphy R., Walsh F. (2021): uniForest: an unsupervised machine learning technique to detect outliers and restrict variance in microbiome studies. https://doi.org/10.1101/2021.05.17.444491
  1422. Leigh R., Murphy R., Walsh F. (2021): statSuma: automated selection and performance of statistical comparisons for microbiome studies. https://doi.org/10.1101/2021.06.15.448299
  1423. Parisien R., Constant M., Saltzman B., Popkin C., Ahmad C., Li X., et al. (2021): The Fragility of Statistical Significance in Cartilage Restoration of the Knee: A Systematic Review of Randomized Controlled Trials. CARTILAGE 13(1_suppl):147S-155S. https://doi.org/10.1177/19476035211012458
  1424. Matthews R. (2021): Thep-value Statement, Five Years On. Significance 18(2):16-19. https://doi.org/10.1111/1740-9713.01505
  1425. Pastorino R., Pezzullo A., Villani L., Causio F., Axfors C., Contopoulos-Ioannidis D., et al. (2021): Change in age distribution of COVID-19 deaths with the introduction of COVID-19 vaccination. Environmental Research 204:112342. https://doi.org/10.1016/j.envres.2021.112342
  1426. Pastorino R., Pezzullo A., Villani L., Causio F., Axfors C., Contopoulos-Ioannidis D., et al. (2021): Change in age distribution of COVID-19 deaths with the introduction of COVID-19 vaccination. https://doi.org/10.1101/2021.07.20.21260842
  1427. Ince R., Paton A., Kay J., Schyns P. (2021): Bayesian inference of population prevalence. eLife 10. https://doi.org/10.7554/elife.62461
  1428. Robin Tim Dreher, Leona Hoffmann, Arne Kramer-Sunderbrink, Peter Pütz, Robin Werner (2021): A Proposed Hybrid Effect Size Plus $p$-Value Criterion: A Replication of Goodman et al. (2019). arXiv (Cornell University).
  1429. Ramírez-Vélez R., López-Gil J., de Asteasu M., Izquierdo M., García-Hermoso A. (2021): Handgrip strength as a moderator of the influence of age on olfactory impairment in US adult population ≥ 40 years of age. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-93355-w
  1430. Berman R., Van den Bulte C. (2021): False Discovery in A/B Testing. Management Science 68(9):6762-6782. https://doi.org/10.1287/mnsc.2021.4207
  1431. Wickes R. (2021): Trade deficits and trade conflict: The United States and Japan. Japan and the World Economy 60:101098. https://doi.org/10.1016/j.japwor.2021.101098
  1432. Walley R., Grieve A. (2021): Optimising the trade‐off between type I andIIerror rates in the Bayesian context. Pharmaceutical Statistics 20(4):710-720. https://doi.org/10.1002/pst.2102
  1433. Hervochon R., Vauterin A., Lahlou G., Nguyen Y., Lamas G., Tankéré F. (2021): Is preoperative bone conduction shape a prognostic factor in otosclerosis surgery?. Clinical Otolaryngology 47(1):234-237. https://doi.org/10.1111/coa.13885
  1434. Hoffmann S., Schönbrodt F., Elsas R., Wilson R., Strasser U., Boulesteix A. (2021): The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines. Royal Society Open Science 8(4). https://doi.org/10.1098/rsos.201925
  1435. Fasola S., Cilluffo G., Montalbano L., Malizia V., Ferrante G., La Grutta S. (2021): A Methodological Framework to Discover Pharmacogenomic Interactions Based on Random Forests. Genes 12(6):933. https://doi.org/10.3390/genes12060933
  1436. Samantha Chong (2021): Narcissism predicts facial muscle reactivity during a body comparison threat: the role of personality in shaping affiliative behaviour.
  1437. Fletcher S. (2021): The role of replication in psychological science. European Journal for Philosophy of Science 11(1). https://doi.org/10.1007/s13194-020-00329-2
  1438. Pramanik S., Johnson V., Bhattacharya A. (2021): A modified sequential probability ratio test. Journal of Mathematical Psychology 101:102505. https://doi.org/10.1016/j.jmp.2021.102505
  1439. Shelley S., Brusatte S., Williamson T. (2021): Quantitative assessment of tarsal morphology illuminates locomotor behaviour in Palaeocene mammals following the end-Cretaceous mass extinction. Proceedings of the Royal Society B: Biological Sciences 288(1950):20210393. https://doi.org/10.1098/rspb.2021.0393
  1440. Nostedt S., Joffe A. (2021): Reverse Bayesian Implications of p-Values Reported in Critical Care Randomized Trials. Journal of Intensive Care Medicine 37(7):954-964. https://doi.org/10.1177/08850666211053793
  1441. Voisin S., Jacques M., Landen S., Harvey N., Haupt L., Griffiths L., et al. (2021): Meta‐analysis of genome‐wide DNA methylation and integrative omics of age in human skeletal muscle. Journal of Cachexia, Sarcopenia and Muscle 12(4):1064-1078. https://doi.org/10.1002/jcsm.12741
  1442. Semenyna S., Gómez Jiménez F., Vasey P. (2021): Women’s Reaction to Opposite- and Same-Sex Infidelity in Three Cultures. Human Nature 32(2):450-469. https://doi.org/10.1007/s12110-021-09405-9
  1443. Semenyna S., Vasey P. (2021): Women’s trust in gay men: An experimental study. Personality and Individual Differences 175:110727. https://doi.org/10.1016/j.paid.2021.110727
  1444. Semenyna S., Gómez Jiménez F., Vasey P. (2021): Testing Women’s Trust in Other Women and Same-Sex Attracted Males in Three Cultures. Archives of Sexual Behavior 50(8):3479-3488. https://doi.org/10.1007/s10508-021-02139-w
  1445. Zenebe-Gete S., Salowe R., O’Brien J. (2021): Benefits of Cohort Studies in a Consortia-Dominated Landscape. Frontiers in Genetics 12. https://doi.org/10.3389/fgene.2021.801653
  1446. Landen S., Jacques M., Hiam D., Alvarez-Romero J., Harvey N., Haupt L., et al. (2021): Skeletal muscle methylome and transcriptome integration reveals profound sex differences related to muscle function and substrate metabolism. Clinical Epigenetics 13(1). https://doi.org/10.1186/s13148-021-01188-1
  1447. Landen S., Jacques M., Hiam D., Alvarez-Romero J., Harvey N., Haupt L., et al. (2021): Skeletal muscle methylome and transcriptome integration reveals profound sex differences related to muscle function and substrate metabolism. https://doi.org/10.1101/2021.03.16.435733
  1448. LIAO S., Qin S. (2021): Ultra-chaos: an insurmountable objective obstacle of reproducibility and replicability. https://doi.org/10.21203/rs.3.rs-890639/v1
  1449. Vasishth S., Gelman A. (2021): How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis. Linguistics 59(5):1311-1342. https://doi.org/10.1515/ling-2019-0051
  1450. Wan S., Liang X., Jiang H., Sun J., Djilali N., Zhao T. (2021): A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries. Applied Energy 298:117177. https://doi.org/10.1016/j.apenergy.2021.117177
  1451. Jurkatis S., Ferrara G. (2021): Non-standard Errors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4014383
  1452. Schwab S., Janiaud P., Dayan M., Amrhein V., Panczak R., Palagi P., et al. (2021): Ten simple rules for good research practice. https://doi.org/10.31219/osf.io/am5ck
  1453. Fuscone S., Favre B., Prévot L. (2021): Reproducibility in speech rate convergence experiments. Language Resources and Evaluation 55(3):817-832. https://doi.org/10.1007/s10579-021-09528-6
  1454. Nikolakopoulos S., Edmar B., Ntzoufras I. (2021): Bayesian Evidence Synthesis for the common effect model. arXiv. https://doi.org/10.48550/arxiv.2103.13236
  1455. Leach S., Sutton R., Douglas K., Dhont K. (2021): The ‘me’ in meat: Does affirming the self make eating animals seem more morally wrong?. Journal of Experimental Social Psychology 95:104135. https://doi.org/10.1016/j.jesp.2021.104135
  1456. Markun S., Gravestock I., Jäger L., Rosemann T., Pichierri G., Burgstaller J. (2021): Effects of Vitamin B12 Supplementation on Cognitive Function, Depressive Symptoms, and Fatigue: A Systematic Review, Meta-Analysis, and Meta-Regression. Nutrients 13(3):923. https://doi.org/10.3390/nu13030923
  1457. Papatheodorou S., Evangelou E. (2021): Umbrella Reviews: What They Are and Why We Need Them. Methods in Molecular Biology. https://doi.org/10.1007/978-1-0716-1566-9_8
  1458. Muff S., Nilsen E., O’Hara R., Nater C. (2021): Rewriting results sections in the language of evidence. Trends in Ecology & Evolution 37(3):203-210. https://doi.org/10.1016/j.tree.2021.10.009
  1459. Stephen Senn (2021): Determining the Sample Size. Statistical Issues in Drug Development. https://doi.org/10.1002/9781119238614.ch13
  1460. Stephen Senn (2021): One‐Sided and Two‐Sided Tests and Other Issues to Do with Significance and P‐values. Statistical Issues in Drug Development. https://doi.org/10.1002/9781119238614.ch12
  1461. Debrouwere S., Rosseel Y. (2021): The Conceptual, Cunning, and Conclusive Experiment in Psychology. Perspectives on Psychological Science 17(3):852-862. https://doi.org/10.1177/17456916211026947
  1462. Debrouwere S., Rosseel Y. (2021): The conceptual, cunning and conclusive experiment in psychology. https://doi.org/10.31234/osf.io/y2rd7
  1463. Sorenson S., Sinko L., Berk R. (2021): The Endemic Amid the Pandemic: Seeking Help for Violence Against Women in the Initial Phases of COVID-19. Journal of Interpersonal Violence 36(9-10):4899-4915. https://doi.org/10.1177/0886260521997946
  1464. Smith S., Ferguson C., Henderson H. (2021): An Exploratory Study of Environmental Stress in Four High Violent Crime Cities: What Sets Them Apart?. Crime & Delinquency 68(11):2092-2114. https://doi.org/10.1177/00111287211057858
  1465. Marcotte S., Lefrançois P. (2021): Quelles stratégies pédagogiques participent au développement de la compétence scripturale ? Analyse secondaire d’une enquête à grande échelle. Revue des sciences de l’éducation 47(2):27-59. https://doi.org/10.7202/1082075ar
  1466. Igarashi T., Okuda S., Sasahara K. (2021): Development of the Japanese Version of the Linguistic Inquiry and Word Count Dictionary 2015 (J-LIWC2015). https://doi.org/10.31234/osf.io/5hq7d
  1467. Devos T., Sadler M., Perry D., Yogeeswaran K. (2021): Temporal Fluctuations in Context Ethnic Diversity Over Three Decades Predict Implicit National Inclusion of Asian Americans. https://doi.org/10.31234/osf.io/w4x7t
  1468. Buser T., Ahlskog R., Johanneson M., Koellinger P., Oskarsson S. (2021): Using Genes to Explore the Effects of Cognitive and Non-cognitive Skills on Education and Labor Market Outcomes. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3938154
  1469. Lash T. (2021): Getting Over TOP. Epidemiology 33(1):1-6. https://doi.org/10.1097/ede.0000000000001424
  1470. Errington T., Mathur M., Soderberg C., Denis A., Perfito N., Iorns E., et al. (2021): Investigating the replicability of preclinical cancer biology. eLife 10. https://doi.org/10.7554/elife.71601
  1471. Bhatta T. (2021): False Negative/False Positive. Encyclopedia of Gerontology and Population Aging. https://doi.org/10.1007/978-3-030-22009-9_572
  1472. Wingen T., Dohle S. (2021): Exploring Negative Beliefs About Power. Social Psychology 52(4):250-263. https://doi.org/10.1027/1864-9335/a000453
  1473. Prike T. (2021): Open Science, Replicability, and Transparency in Modelling. Methodos Series. https://doi.org/10.1007/978-3-030-83039-7_10
  1474. Ugai T., Väyrynen J., Lau M., Borowsky J., Akimoto N., Väyrynen S., et al. (2021): Immune cell profiles in the tumor microenvironment of early-onset, intermediate-onset, and later-onset colorectal cancer. Cancer Immunology, Immunotherapy 71(4):933-942. https://doi.org/10.1007/s00262-021-03056-6
  1475. Ugai T., Zhao M., Shimizu T., Akimoto N., Shi S., Takashima Y., et al. (2021): Association of PIK3CA mutation and PTEN loss with expression of CD274 (PD-L1) in colorectal carcinoma. OncoImmunology 10(1). https://doi.org/10.1080/2162402x.2021.1956173
  1476. Ugai T., Väyrynen J., Haruki K., Akimoto N., Lau M., Zhong R., et al. (2021): Smoking and Incidence of Colorectal Cancer Subclassified by Tumor-Associated Macrophage Infiltrates. JNCI: Journal of the National Cancer Institute 114(1):68-77. https://doi.org/10.1093/jnci/djab142
  1477. Diviák T. (2021): Není hvězda jako hvězda: identifikace klíčových aktérů v sociálních sítích. Sociální studia / Social Studies 18(1):101-120. https://doi.org/10.5817/soc2021-1-101
  1478. Hamasaki T., Bretz F., LaVange L., Müller P., Pennello G., Pinheiro J. (2021): Editorial: Roles of Hypothesis Testing, p-Values and Decision Making in Biopharmaceutical Research. Statistics in Biopharmaceutical Research 13(1):1-5. https://doi.org/10.1080/19466315.2021.1874803
  1479. Proulx T., Morey R. (2021): Beyond Statistical Ritual: Theory in Psychological Science. Perspectives on Psychological Science 16(4):671-681. https://doi.org/10.1177/17456916211017098
  1480. Schimmack U. (2021): Invalid Claims About the Validity of Implicit Association Tests by Prisoners of the Implicit Social-Cognition Paradigm. Perspectives on Psychological Science 16(2):435-442. https://doi.org/10.1177/1745691621991860
  1481. Kämmerer U., Klement R., Joos F., Sütterlin M., Reuss-Borst M. (2021): Low Carb and Ketogenic Diets Increase Quality of Life, Physical Performance, Body Composition, and Metabolic Health of Women with Breast Cancer. Nutrients 13(3):1029. https://doi.org/10.3390/nu13031029
  1482. Onkhar V., Bazilinskyy P., Dodou D., de Winter J. (2021): The effect of drivers’ eye contact on pedestrians’ perceived safety. Transportation Research Part F: Traffic Psychology and Behaviour 84:194-210. https://doi.org/10.1016/j.trf.2021.10.017
  1483. Olefir V., Bosniuk V. (2021): Calculation of the sample size as the cornerstone of planning scientific research. Lviv University Herald. Series: Psychological sciences. https://doi.org/10.30970/ps.2021.9.24
  1484. Bellou V., Belbasis L., Evangelou E. (2021): Tobacco Smoking and Risk for Pulmonary Fibrosis. Chest 160(3):983-993. https://doi.org/10.1016/j.chest.2021.04.035
  1485. Bellou V., Tzoulaki I., van Smeden M., Moons K., Evangelou E., Belbasis L. (2021): Prognostic factors for adverse outcomes in patients with COVID-19: a field-wide systematic review and meta-analysis. European Respiratory Journal 59(2):2002964. https://doi.org/10.1183/13993003.02964-2020
  1486. Bal V., Wilkinson E., White L., Law J., Feliciano P., Chung W. (2021): Early Pandemic Experiences of Autistic Adults: Predictors of Psychological Distress. Autism Research 14(6):1209-1219. https://doi.org/10.1002/aur.2480
  1487. Bal V., Wilkinson E., Fok M. (2021): Cognitive profiles of children with autism spectrum disorder with parent-reported extraordinary talents and personal strengths. Autism 26(1):62-74. https://doi.org/10.1177/13623613211020618
  1488. Forgetta V., Jiang L., Vulpescu N., Hogan M., Chen S., Morris J., et al. (2021): An Effector Index to Predict Target Genes at GWAS Loci. https://doi.org/10.21203/rs.3.rs-629030/v1
  1489. Vitiello V., Williford A. (2021): Alignment of teacher ratings and child direct assessments in preschool: A closer look at teaching strategies GOLD. Early Childhood Research Quarterly 56:114-123. https://doi.org/10.1016/j.ecresq.2021.03.004
  1490. Ajdacic-Gross V., Ajdacic L., Xu Y., Müller M., Rodgers S., Wyss C., et al. (2021): Backtracing persistent biomarker shifts to the age of onset: A novel procedure applied to men’s and women’s white blood cell counts in post-traumatic stress disorder. Biomarkers in Neuropsychiatry 4:100030. https://doi.org/10.1016/j.bionps.2021.100030
  1491. Stahel W. (2021): New relevance and significance measures to replace p-values. PLOS ONE 16(6):e0252991. https://doi.org/10.1371/journal.pone.0252991
  1492. Otte W., Vinkers C., Habets P., van IJzendoorn D., Tijdink J. (2021): Almost significant: trends and P values in the use of phrases describing marginally significant results in 567,758 randomized controlled trials published between 1990 and 2020. https://doi.org/10.1101/2021.03.01.21252701
  1493. Chopik W., Francis J. (2021): Partner influences on ICT use variety among middle-aged and older adults: The role of need for cognition. Computers in Human Behavior 126:107028. https://doi.org/10.1016/j.chb.2021.107028
  1494. Yuen W., Bruno R., Chan G., McCambridge J., Slade T., Clare P., et al. (2021): The experience of physiological and psychosocial alcohol‐related harms across adolescence and its association with alcohol use disorder in early adulthood: A prospective cohort study. Alcoholism: Clinical and Experimental Research 45(12):2518-2527. https://doi.org/10.1111/acer.14726
  1495. Chang X., Gao H., Li W. (2021): P-Hacking in Experimental Accounting Studies. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3762342
  1496. Kang X., Deng D., Crielaard W., Brandt B. (2021): Reprocessing 16S rRNA Gene Amplicon Sequencing Studies: (Meta)Data Issues, Robustness, and Reproducibility. Frontiers in Cellular and Infection Microbiology 11. https://doi.org/10.3389/fcimb.2021.720637
  1497. Li X., Celotto S., Pizzol D., Gasevic D., Ji M., Barnini T., et al. (2021): Metformin and health outcomes: An umbrella review of systematic reviews with meta‐analyses. European Journal of Clinical Investigation 51(7). https://doi.org/10.1111/eci.13536
  1498. Liao Y., Tang J., McNeill A., Kelly B., Cohen J. (2021): Impact of cigarette package warnings on attitudes towards sharing and gifting cigarettes in China: a nationwide study of smokers and non-smokers. Tobacco Control 31(6):750-753. https://doi.org/10.1136/tobaccocontrol-2020-056160
  1499. Hoga Y. (2021): Quantifying the data-dredging bias in structural break tests. Statistical Papers 63(1):143-155. https://doi.org/10.1007/s00362-021-01233-4
  1500. Wu Y., Schunn C. (2021): From plans to actions: A process model for why feedback features influence feedback implementation. Instructional Science 49(3):365-394. https://doi.org/10.1007/s11251-021-09546-5
  1501. Goto Y., Funada A., Maeda T., Goto Y. (2021): Time boundaries of the three-phase time-sensitive model for ventricular fibrillation cardiac arrest. Resuscitation Plus 6:100095. https://doi.org/10.1016/j.resplu.2021.100095
  1502. Zha Y., Chen C., Jiao Q., Zeng X., Cui X., Ning K. (2021): Ontology-aware deep learning for antibiotic resistance gene prediction: novel function discovery and comprehensive profiling from metagenomic data. https://doi.org/10.1101/2021.07.30.454403
  1503. Pavlov Y., Adamian N., Appelhoff S., Arvaneh M., Benwell C., Beste C., et al. (2021): #EEGManyLabs: Investigating the replicability of influential EEG experiments. Cortex 144:213-229. https://doi.org/10.1016/j.cortex.2021.03.013
  1504. Hu Z., Li F., Wang X., Lin Q. (2021): Description Length Guided Unified Granger Causality Analysis. IEEE Access 9:13704-13716. https://doi.org/10.1109/access.2021.3051985
  1505. Chen Z., Boehnke M., Wen X., Mukherjee B. (2021): Revisiting the genome-wide significance threshold for common variant GWAS. G3 Genes|Genomes|Genetics 11(2). https://doi.org/10.1093/g3journal/jkaa056
  1506. Riffo-Campos A., Ayala G., Domingo J. (2021): Ordering of Omics Features Using Beta Distributions on Montecarlo p-Values. Mathematics 9(11):1307. https://doi.org/10.3390/math9111307
  1507. Machery E. (2021): A mistaken confidence in data. European Journal for Philosophy of Science 11(2). https://doi.org/10.1007/s13194-021-00354-9
  1508. Machery E., Doris J. (2021): An Open Letter to Our Students. Character Trouble. https://doi.org/10.1093/oso/9780198719601.005.0001
  1509. Kuzovlev A., Yadgarov M., Berikashvili L., Ryabova E., Goncharova D., Perehodov S., et al. (2021): Choosing the right statistical test. Anesteziologiya i reanimatologiya. https://doi.org/10.17116/anaesthesiology202103188
  1510. Unknown authors (2020): References. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.030
  1511. Unknown authors (2020): Developmental Psychopathology and Longitudinal Methods. The Cambridge Handbook of Research Methods in Clinical Psychology. https://doi.org/10.1017/9781316995808.018
  1512. Banerjee A., Chassang S., Montero S., Snowberg E. (2020): A Theory of Experimenters: Robustness, Randomization, and Balance. American Economic Review 110(4):1206-1230. https://doi.org/10.1257/aer.20171634
  1513. Finkel A., Gray G. (2020): The Pebble Remains in the Master’s Hand: Two Careers Spent Learning (Still) from John Evans. Risk Analysis 41(4):678-693. https://doi.org/10.1111/risa.13649
  1514. Cortés-Gómez A., Romero D., Santos J., Rivera-Hernández J., Girondot M. (2020): Inorganic elements in live vs dead nesting olive ridley marine turtles in the Mexican Pacific: Introducing a new statistical methodology in ecotoxicology. Science of The Total Environment 761:143249. https://doi.org/10.1016/j.scitotenv.2020.143249
  1515. Xing A., Chu H., Lin L. (2020): Fragility index of network meta-analysis with application to smoking cessation data. Journal of Clinical Epidemiology 127:29-39. https://doi.org/10.1016/j.jclinepi.2020.07.003
  1516. Suzuki A. (2020): Presenting the Probabilities of Different Effect Sizes: Towards a Better Understanding and Communication of Statistical Uncertainty. arXiv. https://doi.org/10.48550/arxiv.2008.07478
  1517. Vexler A. (2020): Valid p-values and expectations of p-values revisited. Annals of the Institute of Statistical Mathematics 73(2):227-248. https://doi.org/10.1007/s10463-020-00747-2
  1518. Bolli A., Di Domenico P., Pastorino R., Busby G., Bottà G. (2020): Polygenic Risk Score Modifies Risk of Coronary Artery Disease Conferred by Low-Density Lipoprotein Cholesterol. https://doi.org/10.1101/2020.03.01.20029454
  1519. Orlando A., Rubin B., Panchal R., Tanner A., Hudson J., Harken K., et al. (2020): In Patients Over 50 Years, Increased Age Is Associated With Decreased Odds of Documented Loss of Consciousness After a Concussion. Frontiers in Neurology 11. https://doi.org/10.3389/fneur.2020.00039
  1520. Shen A. (2020): Randomness Tests: Theory and Practice. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-48006-6_18
  1521. Smetanin A., Moshkov N., Tatarinova T. (2020): Local Ancestry Prediction with PyLAE. https://doi.org/10.1101/2020.11.13.380105
  1522. Williams A., Botanov Y., Kilshaw R., Wong R., Sakaluk J. (2020): Potentially harmful therapies: A meta-scientific review of evidential value. Clinical Psychology: Science and Practice 28(1):5-18. https://doi.org/10.1111/cpsp.12331
  1523. Aiken A., Clare P., Boland V., Degenhardt L., Yuen W., Hutchinson D., et al. (2020): Parental supply of sips and whole drinks of alcohol to adolescents and associations with binge drinking and alcohol-related harms: A prospective cohort study. Drug and Alcohol Dependence 215:108204. https://doi.org/10.1016/j.drugalcdep.2020.108204
  1524. Macacu A., Guillot G. (2020): Dose–response analysis of toxicological and pharmacological mixtures with the model deviation ratio method: Problems and solutions. Toxicology Letters 325:62-66. https://doi.org/10.1016/j.toxlet.2020.02.005
  1525. Tackman A., Baranski E., Danvers A., Sbarra D., Raison C., Moseley S., et al. (2020): ‘Personality in its Natural Habitat’ Revisited: A Pooled, Multi–sample Examination of the Relationships between the Big Five Personality Traits and Daily Behaviour and Language Use. European Journal of Personality 34(5):753-776. https://doi.org/10.1002/per.2283
  1526. Vigren A., Pyddoke R. (2020): The impact on bus ridership of passenger incentive contracts in public transport. Transportation Research Part A: Policy and Practice 135:144-159. https://doi.org/10.1016/j.tra.2020.03.003
  1527. Carvalho A., Solmi M., Sanches M., Machado M., Stubbs B., Ajnakina O., et al. (2020): Evidence-based umbrella review of 162 peripheral biomarkers for major mental disorders. Translational Psychiatry 10(1). https://doi.org/10.1038/s41398-020-0835-5
  1528. Potochnik A. (2020): What Constitutes an Explanation in Biology?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.003
  1529. Somo A., Sadler M., Devos T. (2020): Implicit black‐weapon associations weakened over time in increasingly multiethnic metropolitan areas. Analyses of Social Issues and Public Policy 21(1):520-540. https://doi.org/10.1111/asap.12228
  1530. Somo A., Sadler M., Devos T. (2020): Implicit Black-Weapon Associations Weakened Over Time in Increasingly Multiethnic Metropolitan Areas. https://doi.org/10.31234/osf.io/4rs2t
  1531. Lantian A. (2020): Les pratiques de recherche ouvertes en psychologie. Psychologie Française 66(1):71-90. https://doi.org/10.1016/j.psfr.2020.09.001
  1532. Lantian A. (2020): Les pratiques de recherche ouvertes en psychologie. https://doi.org/10.31234/osf.io/tdy62
  1533. De Comite A., Crevecoeur F., Lefèvre P. (2020): Online modification of goal-directed control in human reaching movements. https://doi.org/10.1101/2020.09.03.280784
  1534. Fritsch A., Lenggenhager B., Bekrater-Bodmann R. (2020): Prosthesis embodiment and attenuation of prosthetic touch in upper limb amputees – A proof-of-concept study. Consciousness and Cognition 88:103073. https://doi.org/10.1016/j.concog.2020.103073
  1535. Khamesi A., Musmeci R., Silvestri S., Baker D. (2020): Reproducibility of Survey Results: A New Method to Quantify Similarity of Human Subject Pools. GLOBECOM 2020 – 2020 IEEE Global Communications Conference. https://doi.org/10.1109/globecom42002.2020.9348076
  1536. Afiaz A., Biswas R., Shamma R., Ananna N. (2020): Intimate partner violence (IPV) with miscarriages, stillbirths and abortions: Identifying vulnerable households for women in Bangladesh. PLOS ONE 15(7):e0236670. https://doi.org/10.1371/journal.pone.0236670
  1537. Stähli B., Roffi M., Eberli F., Rickli H., Erne P., Maggiorini M., et al. (2020): Temporal trends in in-hospital complications of acute coronary syndromes: Insights from the nationwide AMIS Plus registry. International Journal of Cardiology 313:16-24. https://doi.org/10.1016/j.ijcard.2020.04.003
  1538. Kurdi B., Ferguson M. (2020): Does the surveillance paradigm provide evidence for unconscious evaluative conditioning? A Bayesian perspective. https://doi.org/10.31234/osf.io/n6w7c
  1539. Farrar B., Boeckle M., Clayton N. (2020): Replications in Comparative Cognition: What Should We Expect and How Can We Improve?. Animal Behavior and Cognition 7(1):1-22. https://doi.org/10.26451/abc.07.01.02.2020
  1540. Farrar B., Altschul D., Fischer J., van der Mescht J., Placì S., Troisi C., et al. (2020): Claims and statistical inference in animal physical cognition research. https://doi.org/10.31234/osf.io/x5ya3
  1541. Kleinberg B. (2020): Manipulating emotions for ground truth emotion analysis. arXiv. https://doi.org/10.48550/arxiv.2006.08952
  1542. Schmid B. (2020): Faculty Opinions recommendation of Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature. https://doi.org/10.3410/f.736726087.793578052
  1543. Fritz B., King C., Ben Abdallah A., Lin N., Mickle A., Budelier T., et al. (2020): Preoperative Cognitive Abnormality, Intraoperative Electroencephalogram Suppression, and Postoperative Delirium. Anesthesiology 132(6):1458-1468. https://doi.org/10.1097/aln.0000000000003181
  1544. Alger B. (2020): Scientific Hypothesis-Testing Strengthens Neuroscience Research. eneuro 7(4):ENEURO.0357-19.2020. https://doi.org/10.1523/eneuro.0357-19.2020
  1545. Hughes B., Costello C., Pearman J., Razavi P., Bedford-Petersen C., Ludwig R., et al. (2020): The Big Five Across Socioeconomic Status: Measurement Invariance, Relationships, and Age Trends – Stage 1 Registered Report. https://doi.org/10.31234/osf.io/4jema
  1546. Hoover B., Miller J., Long J. (2020): Mapping areas of asynchronous‐temporal interaction in animal‐telemetry data. Transactions in GIS 24(3):573-586. https://doi.org/10.1111/tgis.12622
  1547. Wilson B., Harris C., Wixted J. (2020): Science is not a signal detection problem. Proceedings of the National Academy of Sciences 117(11):5559-5567. https://doi.org/10.1073/pnas.1914237117
  1548. Asken B., Houck Z., Schmidt J., Bauer R., Broglio S., McCrea M., et al. (2020): A Normative Reference vs. Baseline Testing Compromise for ImPACT: The CARE Consortium Multiple Variable Prediction (CARE-MVP) Norms. Sports Medicine 50(8):1533-1547. https://doi.org/10.1007/s40279-020-01263-2
  1549. Gahl B., Stanger O. (2020): Wissenschaftliche Grundlagen der herzchirurgischen Fachliteratur. Kompendium der modernen Herzchirurgie beim Erwachsenen. https://doi.org/10.1007/978-3-7091-0451-4_20
  1550. Bertol B., Dias F., Debortoli G., Souto B., Mendonça P., Araújo R., et al. (2020): HLA-G liver expression and HLA-G extended haplotypes are associated with chronic hepatitis C in HIV-negative and HIV-coinfected patients. Clinical Immunology 217:108482. https://doi.org/10.1016/j.clim.2020.108482
  1551. Ergün B., Demir İ., Özdamar T., Gasser B., Mattanovich D., Çalık P. (2020): Engineered Deregulation of Expression in Yeast with Designed Hybrid‐Promoter Architectures in Coordination with Discovered Master Regulator Transcription Factor. Advanced Biosystems 4(4). https://doi.org/10.1002/adbi.201900172
  1552. de la Vega C., Mahaffey C., Tuerena R., Yurkowski D., Ferguson S., Stenson G., et al. (2020): Arctic seals as tracers of environmental and ecological change. Limnology and Oceanography Letters 6(1):24-32. https://doi.org/10.1002/lol2.10176
  1553. Alós-Ferrer C., Yechiam E. (2020): At the eve of the 40th anniversary of the Journal of Economic Psychology: Standards, practices, and challenges. Journal of Economic Psychology 80:102309. https://doi.org/10.1016/j.joep.2020.102309
  1554. Cinelli C., Ferwerda J., Hazlett C. (2020): Sensemakr: Sensitivity Analysis Tools for OLS in R and Stata. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3588978
  1555. Perez-Ruixo C., Rossenu S., Zannikos P., Nandy P., Singh J., Drevets W., et al. (2020): Population Pharmacokinetics of Esketamine Nasal Spray and its Metabolite Noresketamine in Healthy Subjects and Patients with Treatment-Resistant Depression. Clinical Pharmacokinetics 60(4):501-516. https://doi.org/10.1007/s40262-020-00953-4
  1556. Cleland C. (2020): Is It Possible to Scientifically Reconstruct the History of Life on Earth?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.011
  1557. Friese C., Prainsack B. (2020): What Is the Relation between Facts and Values in Biological Science?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.014
  1558. Salgado C., Oosting J., Janssen B., Kumar R., Gruis N., van Doorn R. (2020): Genome‐wide characterization of 5‐hydoxymethylcytosine in melanoma reveals major differences with nevus. Genes, Chromosomes and Cancer 59(6):366-374. https://doi.org/10.1002/gcc.22837
  1559. Claus C., Lytle E., Carr D., Tong D. (2020): Big data registries in spine surgery research: the lurking dangers. BMJ Evidence-Based Medicine 26(3):103-105. https://doi.org/10.1136/bmjebm-2019-111333
  1560. Beranek C. (2020): Increased house mouse (Mus musculus) abundance in wetlands in response to Typha sp. flowering: implications for understanding wetland occupancy patterns of the eastern grass owl (Tyto longimembris). Australian Journal of Zoology 67(4):210-214. https://doi.org/10.1071/zo20063
  1561. Liu C., Dong W., Xia L., Lv J., Jiang D., Wang Q., et al. (2020): Safety and tolerability of a humanized rabbit monoclonal antibody (SSS07) in healthy adults: Randomized double-blind placebo-controlled single ascending dose trial. International Immunopharmacology 91:107263. https://doi.org/10.1016/j.intimp.2020.107263
  1562. Ebersole C., Mathur M., Baranski E., Bart-Plange D., Buttrick N., Chartier C., et al. (2020): Many Labs 5: Testing Pre-Data-Collection Peer Review as an Intervention to Increase Replicability. Advances in Methods and Practices in Psychological Science 3(3):309-331. https://doi.org/10.1177/2515245920958687
  1563. Ebersole C., Nosek B., Kidwell M., Buttrick N., Baranski E., Chartier C., et al. (2020): Many Labs 5: Testing pre-data collection peer review as an intervention to increase replicability. https://doi.org/10.31234/osf.io/jmnsq
  1564. Jørgensen C., Forsare C., Bendahl P., Falck A., Fernö M., Lövgren K., et al. (2020): Expression of epithelial-mesenchymal transition-related markers and phenotypes during breast cancer progression. Breast Cancer Research and Treatment 181(2):369-381. https://doi.org/10.1007/s10549-020-05627-0
  1565. Li C., Shen H., Sheng X., Wei H., Chen J. (2020): Kinetics and fractionation of carbon and oxygen isotopes during the solid-phase transformation of biogenic aragonite to calcite: The effect of organic matter. Palaeogeography, Palaeoclimatology, Palaeoecology 556:109876. https://doi.org/10.1016/j.palaeo.2020.109876
  1566. Chen C., Zarazua de Rubens G., Noel L., Kester J., Sovacool B. (2020): Assessing the socio-demographic, technical, economic and behavioral factors of Nordic electric vehicle adoption and the influence of vehicle-to-grid preferences. Renewable and Sustainable Energy Reviews 121:109692. https://doi.org/10.1016/j.rser.2019.109692
  1567. Limbachia C., Morrow K., Khibovska A., Meyer C., Padmala S., Pessoa L. (2020): Controllability over stressor decreases responses in key threat-related brain areas. https://doi.org/10.1101/2020.07.11.198762
  1568. Chiru T., Fursenco C., Ciobanu N., Dinu M., Popescu E., Ancuceanu R., et al. (2020): Use of medicinal plants in complementary treatment of the common cold and influenza – Perception of pharmacy customers in Moldova and Romania. Journal of Herbal Medicine 21:100346. https://doi.org/10.1016/j.hermed.2020.100346
  1569. van der Lee C., Gatt A., van Miltenburg E., Krahmer E. (2020): Human evaluation of automatically generated text: Current trends and best practice guidelines. Computer Speech & Language 67:101151. https://doi.org/10.1016/j.csl.2020.101151
  1570. Keysers C., Gazzola V., Wagenmakers E. (2020): Using Bayes factor hypothesis testing in neuroscience to establish evidence of absence. Nature Neuroscience 23(7):788-799. https://doi.org/10.1038/s41593-020-0660-4
  1571. Kißler C., Schwenk C., Kuhn J. (2020): Zur Additivität kognitiver Defizitprofile bei komorbiden Lernstörungen. Lernen und Lernstörungen 10(2):89-101. https://doi.org/10.1024/2235-0977/a000310
  1572. Zhang C., Mei Z., Pei J., Abe M., Zeng X., Huang Q., et al. (2020): A Modified Tumor-Node-Metastasis Classification for Primary Operable Colorectal Cancer. JNCI Cancer Spectrum 5(1). https://doi.org/10.1093/jncics/pkaa093
  1573. Azarian C., Foster S., Devloo‐Delva F., Feutry P. (2020): Population differentiation from environmental DNA: Investigating the potential of haplotype presence/absence‐based analysis of molecular variance. Environmental DNA 3(3):541-552. https://doi.org/10.1002/edn3.143
  1574. Burns C., Thomason J., Tansey W. (2020): Interpreting Black Box Models via Hypothesis Testing. Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference. https://doi.org/10.1145/3412815.3416889
  1575. Keating C., Sowden S., Fraser D., Cook J. (2020): Differences Between Autistic and Non-autistic Adults in the Recognition of Anger From Dynamic Expressions Remain After Controlling for Alexithymia. https://doi.org/10.21203/rs.3.rs-88768/v2
  1576. Keating C., Sowden S., Fraser D., Cook J. (2020): Differences Between Autistic and Non-autistic Adults in the Recognition of Anger From Dynamic Expressions Remain After Controlling for Alexithymia. https://doi.org/10.21203/rs.3.rs-88768/v3
  1577. Keating C., Sowden S., Fraser D., Cook J. (2020): Differences Between Autistic and Non-autistic Adults in the Recognition of Anger From Dynamic Expressions Remain After Controlling for Alexithymia. https://doi.org/10.21203/rs.3.rs-88768/v1
  1578. Walters C., Meyer C., Fladie I., Wayant C., Vassar M. (2020): Lowering the threshold of statistical significance in gastroenterology trials. Indian Journal of Gastroenterology 39(1):92-96. https://doi.org/10.1007/s12664-019-01007-9
  1579. Rietveld C., Slob E., Thurik A. (2020): A decade of research on the genetics of entrepreneurship: a review and view ahead. Small Business Economics 57(3):1303-1317. https://doi.org/10.1007/s11187-020-00349-5
  1580. Hyatt C., Hallowell E., Owens M., Weiss B., Sweet L., Miller J. (2020): An fMRI investigation of the relations between Extraversion, internalizing psychopathology, and neural activation following reward receipt in the Human Connectome Project sample. Personality Neuroscience 3. https://doi.org/10.1017/pen.2020.11
  1581. Batailler C., Muller D., Nurra C., Rougier M., Trouilloud D. (2020): Math approach training changes implicit identification with math: A close preregistered replication. Journal of Experimental Social Psychology 92:104059. https://doi.org/10.1016/j.jesp.2020.104059
  1582. Kamei D., Kamei Y., Nagano M., Mineshima M., Nitta K., Tsuchiya K. (2020): Elobixibat alleviates chronic constipation in hemodialysis patients: a questionnaire-based study. BMC Gastroenterology 20(1). https://doi.org/10.1186/s12876-020-1179-6
  1583. Turner D., Deng H., Houle T. (2020): Statistical Hypothesis Testing: Overview and Application. Headache: The Journal of Head and Face Pain 60(2):302-308. https://doi.org/10.1111/head.13706
  1584. Wright D. (2020): Improving Trust in Research: Supporting Claims with Evidence. Open Education Studies 2(1):1-8. https://doi.org/10.1515/edu-2020-0106
  1585. Boyce D., Lotze H., Tittensor D., Carozza D., Worm B. (2020): Future ocean biomass losses may widen socioeconomic equity gaps. Nature Communications 11(1). https://doi.org/10.1038/s41467-020-15708-9
  1586. Whitney D., Warschausky S., Whibley D., Kratz A., Murphy S., Hurvitz E., et al. (2020): Clinical factors associated with mood affective disorders among adults with cerebral palsy. Neurology Clinical Practice 10(3):206-213. https://doi.org/10.1212/cpj.0000000000000721
  1587. Leising D., Thielmann I., Glöckner A., Gärtner A., Schönbrodt F. (2020): Ten steps toward a better personality science – how quality may be rewarded more in research evaluation. https://doi.org/10.31234/osf.io/5zvmh
  1588. Leising D., Thielmann I., Glöckner A., Gärtner A., Schönbrodt F. (2020): Ten steps toward a better personality science – how quality may be rewarded more in research evaluation. https://doi.org/10.31234/osf.io/6btc3
  1589. Rice D., Raffoul H., Ioannidis J., Moher D. (2020): Academic criteria for promotion and tenure in biomedical sciences faculties: cross sectional analysis of international sample of universities. BMJ. https://doi.org/10.1136/bmj.m2081
  1590. Krpan D. (2020): Unburdening the Shoulders of Giants: A Quest for Disconnected Academic Psychology. Perspectives on Psychological Science 15(4):1042-1053. https://doi.org/10.1177/1745691620904775
  1591. Krpan D. (2020): Beyond a Dream: The Practical Foundations of Disconnected Psychology. https://doi.org/10.31234/osf.io/mw8fs
  1592. Rodriguez D., Berry M. (2020): Sensitizing jurors to eyewitness evidence using a counterfactual mindset induction. Applied Cognitive Psychology 34(3):768-775. https://doi.org/10.1002/acp.3667
  1593. Yeo D., Srivathsan A., Puniamoorthy J., Maosheng F., Grootaert P., Chan L., et al. (2020): Mangroves are an overlooked hotspot of insect diversity despite low plant diversity. https://doi.org/10.1101/2020.12.17.423191
  1594. Wang D., Liu L. (2020): The Science of Science. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020. https://doi.org/10.1145/3383583.3398500
  1595. Coker D. (2020): Risk Ratios and Special Education: The Cure Is Worse than the Disability. World Journal of Education 10(4):1. https://doi.org/10.5430/wje.v10n4p1
  1596. Depew D. (2020): How Do Concepts Contribute to Scientific Advancement?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.008
  1597. Buss D., Durkee P., Shackelford T., Bowdle B., Schmitt D., Brase G., et al. (2020): Human status criteria: Sex differences and similarities across 14 nations. Journal of Personality and Social Psychology 119(5):979-998. https://doi.org/10.1037/pspa0000206
  1598. Lovell D. (2020): Null hypothesis significance testing and effect sizes: can we ‘effect’ everything … or … anything?. Current Opinion in Pharmacology 51:68-77. https://doi.org/10.1016/j.coph.2019.12.001
  1599. Bickel D. (2020): Confidence distributions and empirical Bayes posterior distributions unified as distributions of evidential support. Communications in Statistics – Theory and Methods 51(10):3142-3163. https://doi.org/10.1080/03610926.2020.1790004
  1600. Bickel D. (2020): Null Hypothesis Significance Testing Interpreted and Calibrated by Estimating Probabilities of Sign Errors: A Bayes-Frequentist Continuum. The American Statistician 75(1):104-112. https://doi.org/10.1080/00031305.2020.1816214
  1601. Bickel D. (2020): Testing prediction algorithms as null hypotheses: Application to assessing the performance of deep neural networks. Stat 9(1). https://doi.org/10.1002/sta4.270
  1602. Bickel D. (2020): Departing from Bayesian inference toward minimaxity to the extent that the posterior distribution is unreliable. Statistics & Probability Letters 164:108802. https://doi.org/10.1016/j.spl.2020.108802
  1603. Willer D., Emanuelson P. (2020): Theory and the Replication Problem. Sociological Methodology 51(1):146-165. https://doi.org/10.1177/0081175020955216
  1604. Szucs D., Ioannidis J. (2020): Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals. NeuroImage 221:117164. https://doi.org/10.1016/j.neuroimage.2020.117164
  1605. Lee E. (2020): Statistical analysis of long-term trends in UK effective rainfall: implications for deep-seated landsliding. Quarterly Journal of Engineering Geology and Hydrogeology 53(4):587-597. https://doi.org/10.1144/qjegh2019-169
  1606. Rigdon E., Sarstedt M., Becker J. (2020): Quantify uncertainty in behavioral research. Nature Human Behaviour 4(4):329-331. https://doi.org/10.1038/s41562-019-0806-0
  1607. Green E., Finley A., Strawderman W. (2020): Introduction. Introduction to Bayesian Methods in Ecology and Natural Resources. https://doi.org/10.1007/978-3-030-60750-0_1
  1608. Green E., Finley A., Strawderman W. (2020): Hypothesis Testing and Model Choice. Introduction to Bayesian Methods in Ecology and Natural Resources. https://doi.org/10.1007/978-3-030-60750-0_5
  1609. Erosheva E., Grant S., Chen M., Lindner M., Nakamura R., Lee C. (2020): NIH peer review: Criterion scores completely account for racial disparities in overall impact scores. Science Advances 6(23). https://doi.org/10.1126/sciadv.aaz4868
  1610. Parslow E., Rose J. (2020): Stress and Risk – Preferences and Noise. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3733379
  1611. Pudney E., Himmelstein M., Puhl R., Foster G. (2020): Distressed or not distressed? A mixed methods examination of reactions to weight stigma and implications for emotional wellbeing and internalized weight bias. Social Science & Medicine 249:112854. https://doi.org/10.1016/j.socscimed.2020.112854
  1612. Doluk E., Rudawska A., Kuczmaszewski J., Pieśko P. (2020): Influence of Cutting Parameters on the Surface Quality of Two-Layer Sandwich Structures. Materials 13(7):1664. https://doi.org/10.3390/ma13071664
  1613. Parke E., Plutynski A. (2020): What Is the Nature of Theories and Models in Biology?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.005
  1614. Parke E., Du Bois S., Woodward H. (2020): Exploring political diversity in relation to health and stress, among graduate students in the mental health field. Journal of American College Health 70(4):1247-1256. https://doi.org/10.1080/07448481.2020.1791130
  1615. Dennis E., Baron D., Bartnik‐Olson B., Caeyenberghs K., Esopenko C., Hillary F., et al. (2020): ENIGMA brain injury: Framework, challenges, and opportunities. Human Brain Mapping 43(1):149-166. https://doi.org/10.1002/hbm.25046
  1616. Armstrong-Carter E., Trejo S., Hill L., Crossley K., Mason D., Domingue B. (2020): The Earliest Origins of Genetic Nurture: The Prenatal Environment Mediates the Association Between Maternal Genetics and Child Development. Psychological Science 31(7):781-791. https://doi.org/10.1177/0956797620917209
  1617. Feschet-Chassot E., Chennell P., Cueff R., Mailhot-Jensen B., Sautou V. (2020): Anodic alumina oxide surfaces prepared by dual hard and mild anodization at subzero temperature: Surface microscopic characterization and influence on wettability. Surfaces and Interfaces 19:100473. https://doi.org/10.1016/j.surfin.2020.100473
  1618. Gibson E. (2020): The Role of p -Values in Judging the Strength of Evidence and Realistic Replication Expectations. Statistics in Biopharmaceutical Research 13(1):6-18. https://doi.org/10.1080/19466315.2020.1724560
  1619. Sercy E., Orlando A., Carrick M., Lieser M., Madayag R., Vasquez D., et al. (2020): Long-term mortality and causes of death among patients with mild traumatic brain injury: a 5-year multicenter study. Brain Injury 34(4):556-566. https://doi.org/10.1080/02699052.2020.1725981
  1620. Wagenmakers E., Lee M., Rouder J., Morey R. (2020): The Principle of Predictive Irrelevance or Why Intervals Should Not be Used for Model Comparison Featuring a Point Null Hypothesis. The Theory of Statistics in Psychology. https://doi.org/10.1007/978-3-030-48043-1_8
  1621. Peterson E. (2020): What Methods Do Life Scientists Use?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.010
  1622. Lundkvist E., Wagnsson S., Davis L., Ivarsson A. (2020): Integration of immigrant youth in Sweden: does sport participation really have an impact?. International Journal of Adolescence and Youth 25(1):891-906. https://doi.org/10.1080/02673843.2020.1775099
  1623. Hartman E. (2020): Equivalence Testing for Regression Discontinuity Designs. Political Analysis 29(4):505-521. https://doi.org/10.1017/pan.2020.43
  1624. De Longis E., Alessandri G. (2020): Temporal dependency of emotional states at work and its relationship with dynamic performance. Social Psychological Bulletin 15(2). https://doi.org/10.32872/spb.2975
  1625. Steyerberg E., Van Calster B. (2020): Redefining significance and reproducibility for medical research: A plea for higherP‐value thresholds for diagnostic and prognostic models. European Journal of Clinical Investigation 50(5). https://doi.org/10.1111/eci.13229
  1626. Romero F., Sprenger J. (2020): Scientific self-correction: the Bayesian way. Synthese 198(S23):5803-5823. https://doi.org/10.1007/s11229-020-02697-x
  1627. Holzmeister F., Huber J., Kirchler M., Lindner F., Weitzel U., Zeisberger S. (2020): What Drives Risk Perception? A Global Survey with Financial Professionals and Laypeople. Management Science 66(9):3977-4002. https://doi.org/10.1287/mnsc.2019.3526
  1628. Zimmer F., Imhoff R. (2020): Abstinence from Masturbation and Hypersexuality. Archives of Sexual Behavior 49(4):1333-1343. https://doi.org/10.1007/s10508-019-01623-8
  1629. Zhang F., Wang Y., Mukiibi R., Chen L., Vinsky M., Plastow G., et al. (2020): Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: I: feed efficiency and component traits. BMC Genomics 21(1). https://doi.org/10.1186/s12864-019-6362-1
  1630. Rivadeneira F., Loder R., McGuire A., Chitwood J., Duffy K., Civitelli R., et al. (2020): Gender and Geographic Origin as Determinants of Manuscript Publication Outcomes: JBMR® Bibliometric Analysis from 2017 to 2019. Journal of Bone and Mineral Research 37(12):2420-2434. https://doi.org/10.1002/jbmr.4696
  1631. Squazzoni F., Bravo G., Grimaldo F., Garcıa-Costa D., Farjam M., Mehmani B. (2020): No Tickets for Women in the COVID-19 Race? A Study on Manuscript Submissions and Reviews in 2347 Elsevier Journals during the Pandemic. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3712813
  1632. Kock F. (2020): The Behavioral Ecology of Sex Tourism: The Consequences of Skewed Sex Ratios. Journal of Travel Research 60(6):1252-1264. https://doi.org/10.1177/0047287520946106
  1633. Cicconardi F., Krapf P., D’Annessa I., Gamisch A., Wagner H., Nguyen A., et al. (2020): Genomic Signature of Shifts in Selection in a Subalpine Ant and Its Physiological Adaptations. Molecular Biology and Evolution 37(8):2211-2227. https://doi.org/10.1093/molbev/msaa076
  1634. Piroli F., Angelini F., D’Ascenzo F., De Ferrari G. (2020): Does lowering p value threshold to 0.005 impact on evidence-based medicine? An analysis of current European Society of Cardiology guidelines on STEMI. European Journal of Internal Medicine 79:147-148. https://doi.org/10.1016/j.ejim.2020.05.036
  1635. Mann F., Atherton O., DeYoung C., Krueger R., Robins R. (2020): Big five personality traits and common mental disorders within a hierarchical taxonomy of psychopathology: A longitudinal study of Mexican-origin youth. Journal of Abnormal Psychology 129(8):769-787. https://doi.org/10.1037/abn0000633
  1636. Diaz-Quijano F. (2020): Estimating and testing an index of bias attributable to composite outcomes in comparative studies. Journal of Clinical Epidemiology 132:1-9. https://doi.org/10.1016/j.jclinepi.2020.12.003
  1637. Diaz-Quijano F., Calixto F., da Silva J. (2020): How feasible is it to abandon statistical significance? A reflection based on a short survey. BMC Medical Research Methodology 20(1). https://doi.org/10.1186/s12874-020-01030-x
  1638. Diaz-Quijano F. (2020): Estimating and Testing an Index of Bias Attributable to Composite Outcomes in Comparative Studies. https://doi.org/10.1101/2020.02.13.20020966
  1639. Diaz-Quijano F., Calixto F., Silva J. (2020): How feasible is it to abandon statistical significance? A reflection based on a short survey. https://doi.org/10.21203/rs.2.14217/v2
  1640. Götz F., Gvirtz A., Galinsky A., Jachimowicz J. (2020): How personality and policy predict pandemic behavior: Understanding sheltering-in-place in 54 countries at the onset of COVID-19. American Psychologist 76(1):39-49. https://doi.org/10.1037/amp0000740
  1641. Götz F., Gvirtz A., galinsky a., Jachimowicz J. (2020): How Personality and Policy Predict Pandemic Behavior: Understanding Sheltering-in-Place in 55 Countries at the Onset of COVID-19. https://doi.org/10.31234/osf.io/c7sj2
  1642. Björk Gunnarsdottir F., Auoja N., Bendahl P., Rydén L., Fernö M., Leandersson K. (2020): Co-localization of CD169 + macrophages and cancer cells in lymph node metastases of breast cancer patients is linked to improved prognosis and PDL1 expression. OncoImmunology 9(1). https://doi.org/10.1080/2162402x.2020.1848067
  1643. Silva F. (2020): A probabilistic framework and significance test for the analysis of structural orientations in skyscape archaeology. Journal of Archaeological Science 118:105138. https://doi.org/10.1016/j.jas.2020.105138
  1644. Potter G. (2020): Dismantling the Fragility Index: A demonstration of statistical reasoning. Statistics in Medicine 39(26):3720-3731. https://doi.org/10.1002/sim.8689
  1645. Sweeten G. (2020): Standard Errors in Quantitative Criminology: Taking Stock and Looking Forward. Journal of Quantitative Criminology 36(2):263-272. https://doi.org/10.1007/s10940-020-09463-9
  1646. Stark G., Pincheira‐Donoso D., Meiri S. (2020): No evidence for the ‘rate‐of‐living’ theory across the tetrapod tree of life. Global Ecology and Biogeography 29(5):857-884. https://doi.org/10.1111/geb.13069
  1647. Stark G., Schwarz R., Meiri S. (2020): Does nocturnal activity prolong gecko longevity?. Israel Journal of Ecology and Evolution 66(3-4):231-238. https://doi.org/10.1163/22244662-20191074
  1648. Schweizer G., Furley P., Rost N., Barth K. (2020): Reliable measurement in sport psychology: The case of performance outcome measures. Psychology of Sport and Exercise 48:101663. https://doi.org/10.1016/j.psychsport.2020.101663
  1649. Di Leo G., Sardanelli F. (2020): Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach. European Radiology Experimental 4(1). https://doi.org/10.1186/s41747-020-0145-y
  1650. Pino G., Zhang C., Wang Z. (2020): “(S)he’s so hearty”: Gender cues, stereotypes, and expectations of warmth in peer-to-peer accommodation services. International Journal of Hospitality Management 91:102650. https://doi.org/10.1016/j.ijhm.2020.102650
  1651. Albert G., Richardson G., Arnocky S., Senveli Z., Hodges-Simeon C. (2020): The Development and Psychometric Evaluation of a New Mating Effort Questionnaire. Archives of Sexual Behavior 50(2):511-530. https://doi.org/10.1007/s10508-020-01799-4
  1652. Coqueret G. (2020): Stock-specific sentiment and return predictability. Quantitative Finance 20(9):1531-1551. https://doi.org/10.1080/14697688.2020.1736314
  1653. Marqués G., Pengo T., Sanders M. (2020): Imaging methods are vastly underreported in biomedical research. eLife 9. https://doi.org/10.7554/elife.55133
  1654. Hoffman G., Zhao X. (2020): A Primer for Conducting Experiments in Human–Robot Interaction. ACM Transactions on Human-Robot Interaction 10(1):1-31. https://doi.org/10.1145/3412374
  1655. Almgren H., Parsons N., Van Den Bossche S., Marinazzo D. (2020): A review of ideas and strategies to improve scientific research and its dissemination in life and social sciences. OSF Preprints. https://doi.org/10.31222/osf.io/9hg3j
  1656. Yazici H. (2020): The P-value crisis and the issue of causality. Rheumatology 59(7):1467-1468. https://doi.org/10.1093/rheumatology/keaa152
  1657. Carlson H., Leitão J., Delplanque S., Cayeux I., Sander D., Vuilleumier P. (2020): Sustained effects of pleasant and unpleasant smells on resting state brain activity. Cortex 132:386-403. https://doi.org/10.1016/j.cortex.2020.06.017
  1658. Han H., Lee K., Soylu F. (2020): Applying the Deep Learning Method for Simulating Outcomes of Educational Interventions. SN Computer Science 1(2). https://doi.org/10.1007/s42979-020-0075-z
  1659. Han H., Lee K., Soylu F. (2020): Applying the Deep Learning Method for Simulating Outcomes of Educational Interventions. https://doi.org/10.31235/osf.io/xs2n8
  1660. Hussey I., Hughes S. (2020): Hidden Invalidity Among 15 Commonly Used Measures in Social and Personality Psychology. Advances in Methods and Practices in Psychological Science 3(2):166-184. https://doi.org/10.1177/2515245919882903
  1661. Brigandt I. (2020): How Are Biology Concepts Used and Transformed?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.006
  1662. Roden-Foreman J., Rapier N., Foreman M., Cribari C., Parsons M., Zagel A., et al. (2020): Avoiding Cribari gridlock 2: The standardized triage assessment tool outperforms the Cribari matrix method in 38 adult and pediatric trauma centers. Injury 52(3):443-449. https://doi.org/10.1016/j.injury.2020.09.027
  1663. Gómez-Ramírez J., Fernández-Blázquez M., González-Rosa J. (2020): A causal analysis of the effect of age and sex differences on brain atrophy in the elderly brain. https://doi.org/10.1101/2020.11.20.391623
  1664. Turna J., Balodis I., Munn C., Van Ameringen M., Busse J., MacKillop J. (2020): Overlapping patterns of recreational and medical cannabis use in a large community sample of cannabis users. Comprehensive Psychiatry 102:152188. https://doi.org/10.1016/j.comppsych.2020.152188
  1665. Turna J., Balodis I., Van Ameringen M., Busse J., MacKillop J. (2020): Attitudes and Beliefs Toward Cannabis Before Recreational Legalization: A Cross-Sectional Study of Community Adults in Ontario. Cannabis & Cannabinoid Research 7(4):526-536. https://doi.org/10.1089/can.2019.0088
  1666. Cavaillon J., Singer M., Skirecki T. (2020): Sepsis therapies: learning from 30 years of failure of translational research to propose new leads. EMBO Molecular Medicine 12(4). https://doi.org/10.15252/emmm.201810128
  1667. Salerno J., Campbell J., Phalen H., Bean S., Hans V., Spivack D., et al. (2020): The Impact of Minimal versus Extended Voir Dire and Judicial Rehabilitation in Civil Cases. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3733136
  1668. Sun J., Rhemtulla M., Vazire S. (2020): Eavesdropping on Missing Data: What Are University Students Doing When They Miss Experience Sampling Reports?. Personality and Social Psychology Bulletin 47(11):1535-1549. https://doi.org/10.1177/0146167220964639
  1669. Lin J., Yu Y., Zhou Y., Zhou Z., Shi X. (2020): How many preprints have actually been printed and why: a case study of computer science preprints on arXiv. Scientometrics 124(1):555-574. https://doi.org/10.1007/s11192-020-03430-8
  1670. Gao J. (2020): P-values – a chronic conundrum. BMC Medical Research Methodology 20(1). https://doi.org/10.1186/s12874-020-01051-6
  1671. Wang J., Iversen T., Hennig-Schmidt H., Godager G. (2020): Are patient-regarding preferences stable? Evidence from a laboratory experiment with physicians and medical students from different countries. European Economic Review 125:103411. https://doi.org/10.1016/j.euroecorev.2020.103411
  1672. Hua J., Wang G., Huang M., Hua S., Yang S. (2020): A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis. International Journal of Environmental Research and Public Health 17(11):3973. https://doi.org/10.3390/ijerph17113973
  1673. Jin-Woo Park, Jong‐Hyun Lee, KangHyun Shin (2020): Expansion of job demands-resources model by applying the circumplex model of affect: The role of dominant promotion-focus and LMX on the effects of job demands to two-dimensional view of work-related subjective well-being. Korean Journal of Industrial and Organizational Psychology.
  1674. Park J., Lee J., Shin K. (2020): Expansion of job demands-resources model by applying the circumplex model of affect. Korean Journal of Industrial and Organizational Psychology 33(4):501-543. https://doi.org/10.24230/kjiop.v33i4.501-543
  1675. Zhang J., Zheng L., Zhang S., Xu W., Zheng Y. (2020): Vocal characteristics predict infidelity intention and relationship commitment in men but not in women. Personality and Individual Differences 168:110389. https://doi.org/10.1016/j.paid.2020.110389
  1676. Zhong J., Wang D., Li C. (2020): A nonparametric health index and its statistical threshold for machine condition monitoring. Measurement 167:108290. https://doi.org/10.1016/j.measurement.2020.108290
  1677. Li J., Tong X. (2020): Statistical hypothesis testing versus machine-learning binary classification: distinctions and guidelines. Patterns 1(7):100115. https://doi.org/10.1016/j.patter.2020.100115
  1678. Li J., Tong X. (2020): Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines. Patterns 1(7):100115. https://doi.org/10.1016/j.patter.2020.100115
  1679. Flournoy J., Vijayakumar N., Cheng T., Cosme D., Flannery J., Pfeifer J. (2020): Improving practices and inferences in developmental cognitive neuroscience. Developmental Cognitive Neuroscience 45:100807. https://doi.org/10.1016/j.dcn.2020.100807
  1680. Pandolfi J., Staples T., Kiessling W. (2020): Increased extinction in the emergence of novel ecological communities. Science 370(6513):220-222. https://doi.org/10.1126/science.abb3996
  1681. Pandolfi J., Staples T., Kiessling W. (2020): The rise and fall of novel ecological communities. https://doi.org/10.1101/2020.06.02.131037
  1682. Svensson J., Schain M., Knudsen G., Ogden T., Plavén-Sigray P. (2020): Early stopping in clinical PET studies: how to reduce expense and exposure. https://doi.org/10.1101/2020.09.13.20192856
  1683. Dynako J., Owens G., Loder R., Frimpong T., Gerena R., Hasnain F., et al. (2020): Bibliometric and authorship trends over a 30 year publication history in two representative US sports medicine journals. Heliyon 6(3):e03698. https://doi.org/10.1016/j.heliyon.2020.e03698
  1684. Polanin J., Hennessy E., Tsuji S. (2020): Transparency and Reproducibility of Meta-Analyses in Psychology: A Meta-Review. Perspectives on Psychological Science 15(4):1026-1041. https://doi.org/10.1177/1745691620906416
  1685. Tschantret J. (2020): Democratic breakdown and terrorism. Conflict Management and Peace Science 38(4):369-390. https://doi.org/10.1177/0738894220911366
  1686. Arroyo-Barrigüete J., Carábias-López S., Curto-González T., Borrás-Pala F. (2020): Matemáticas en el doble grado ADE-Derecho: un análisis cuantitativo de las estrategias de estudio. Bordón. Revista de Pedagogía 72(4):27-42. https://doi.org/10.13042/bordon.2020.80306
  1687. Väyrynen J., Haruki K., Lau M., Väyrynen S., Zhong R., Dias Costa A., et al. (2020): The Prognostic Role of Macrophage Polarization in the Colorectal Cancer Microenvironment. Cancer Immunology Research 9(1):8-19. https://doi.org/10.1158/2326-6066.cir-20-0527
  1688. Väyrynen J., Lau M., Haruki K., Väyrynen S., Dias Costa A., Borowsky J., et al. (2020): Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin–Stained Sections. Clinical Cancer Research 26(16):4326-4338. https://doi.org/10.1158/1078-0432.ccr-20-0071
  1689. Kelemen J., Kaserer A., Jensen K., Stein P., Seifert B., Simmen H., et al. (2020): Prevalence and outcome of contrast-induced nephropathy in major trauma patients. European Journal of Trauma and Emergency Surgery 48(2):907-913. https://doi.org/10.1007/s00068-020-01496-w
  1690. Karch J. (2020): Improving on Adjusted R-Squared. Collabra: Psychology 6(1). https://doi.org/10.1525/collabra.343
  1691. Horstmann K., Rauthmann J., Sherman R., Ziegler M. (2020): Unveiling an exclusive link: Predicting behavior with personality, situation perception, and affect in a preregistered experience sampling study. Journal of Personality and Social Psychology 120(5):1317-1343. https://doi.org/10.1037/pspp0000357
  1692. Horstmann K., Rauthmann J., Sherman R., Ziegler M. (2020): Unveiling an Exclusive Link: Predicting Behavior with Personality, Situation Perception, and Affect in a Pre-Registered Experience Sampling Study. https://doi.org/10.31234/osf.io/ztw2n
  1693. Kari Gire Dahl (2020): Health literacy in the context of kidney transplant recipients: a multimethod study. Duo Research Archive (University of Oslo).
  1694. Vasilaky K., Brock J. (2020): Power(ful) guidelines for experimental economists. Journal of the Economic Science Association 6(2):189-212. https://doi.org/10.1007/s40881-020-00090-5
  1695. Ejima K., Brown A., Smith D., Beyaztas U., Allison D. (2020): Murine genetic models of obesity: type I error rates and the power of commonly used analyses as assessed by plasmode-based simulation. International Journal of Obesity 44(6):1440-1449. https://doi.org/10.1038/s41366-020-0554-2
  1696. Lohse K. (2020): Methodological Advances in Motor Learning and Development. Journal of Motor Learning and Development 8(1):1-13. https://doi.org/10.1123/jmld.2019-0054
  1697. Warren K. (2020): Senior therapeutic community members show greater consistency when affirming peers: evidence of social learning. Therapeutic Communities: The International Journal of Therapeutic Communities 41(1):5-13. https://doi.org/10.1108/tc-11-2019-0014
  1698. Fujiyoshi K., Väyrynen J., Borowsky J., Papke D., Arima K., Haruki K., et al. (2020): Tumour budding, poorly differentiated clusters, and T-cell response in colorectal cancer. EBioMedicine 57:102860. https://doi.org/10.1016/j.ebiom.2020.102860
  1699. Fujiyoshi K., Chen Y., Haruki K., Ugai T., Kishikawa J., Hamada T., et al. (2020): Smoking Status at Diagnosis and Colorectal Cancer Prognosis According to Tumor Lymphocytic Reaction. JNCI Cancer Spectrum 4(5). https://doi.org/10.1093/jncics/pkaa040
  1700. Hung K., Fithian W. (2020): Statistical methods for replicability assessment. The Annals of Applied Statistics 14(3). https://doi.org/10.1214/20-aoas1336
  1701. McCain K. (2020): What Is Biological Knowledge?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.004
  1702. Kevin P. O’Halloran, Sairam Tangirala, Fengjie Sun, Leonard E. Anagho, Gerald Agbegha, Clay Runck, et al. (2020): An Experiential Report on the Thayer Method of Teaching across College-Level Chemistry, Biology, Math, and Physics Courses. Bulletin of the Georgia Academy of Science.
  1703. Haruki K., Kosumi K., Li P., Arima K., Väyrynen J., Lau M., et al. (2020): An integrated analysis of lymphocytic reaction, tumour molecular characteristics and patient survival in colorectal cancer. British Journal of Cancer 122(9):1367-1377. https://doi.org/10.1038/s41416-020-0780-3
  1704. Kostas Kampourakis, Kostas Kampourakis (2020): Philosophy of Science for Biologists. Cambridge University Press eBooks. https://doi.org/10.1017/9781108648981
  1705. Kampourakis K. (2020): Why Does It Matter That Many Biology Concepts Are Metaphors?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.007
  1706. Kampourakis K., Uller T. (2020): How Can We Teach Philosophy of Science to Biologists?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.016
  1707. Kostas Kampourakis, Tobias Uller (2020): Preface. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.001
  1708. Kostas Kampourakis, Tobias Uller (2020): Index. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.018
  1709. Kostas Kampourakis, Tobias Uller (2020): Further Reading. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.017
  1710. Mima K., Miyanari N., Morito A., Yumoto S., Matsumoto T., Kosumi K., et al. (2020): Frailty is an independent risk factor for recurrence and mortality following curative resection of stage I–III colorectal cancer. Annals of Gastroenterological Surgery 4(4):405-412. https://doi.org/10.1002/ags3.12337
  1711. Cerdá Alberich L., Sangüesa Nebot C., Alberich-Bayarri A., Carot Sierra J., Martínez de las Heras B., Veiga Canuto D., et al. (2020): A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors. Cancers 12(12):3858. https://doi.org/10.3390/cancers12123858
  1712. Botzet L., Rohrer J., Arslan R. (2020): Analysing effects of birth order on intelligence, educational attainment, big five and risk aversion in an Indonesian sample. European Journal of Personality 35(2):234-248. https://doi.org/10.1002/per.2285
  1713. Vuillier L., Carter Z., Teixeira A., Moseley R. (2020): Alexithymia may explain the relationship between autistic traits and eating disorder psychopathology. Molecular Autism 11(1). https://doi.org/10.1186/s13229-020-00364-z
  1714. Belbasis L., Mavrogiannis M., Emfietzoglou M., Evangelou E. (2020): Environmental factors, serum biomarkers and risk of atrial fibrillation: an exposure-wide umbrella review of meta-analyses. European Journal of Epidemiology 35(3):223-239. https://doi.org/10.1007/s10654-020-00618-3
  1715. Backhausen L., Herting M., Tamnes C., Vetter N. (2020): Best practices in clinical developmental structural neuroimaging. https://doi.org/10.31234/osf.io/br38j
  1716. Fabrigar L., Wegener D., Petty R. (2020): A Validity-Based Framework for Understanding Replication in Psychology. Personality and Social Psychology Review 24(4):316-344. https://doi.org/10.1177/1088868320931366
  1717. Hides L., Quinn C., Chan G., Cotton S., Pocuca N., Connor J., et al. (2020): Telephone‐based motivational interviewing enhanced with individualised personality‐specific coping skills training for young people with alcohol‐related injuries and illnesses accessing emergency or rest/recovery services: a randomized controlled trial (QuikFix). Addiction 116(3):474-484. https://doi.org/10.1111/add.15146
  1718. Held L., Pawel S., Schwab S. (2020): Replication Power and Regression to The Mean. Significance 17(6):10-11. https://doi.org/10.1111/1740-9713.01462
  1719. Held L., Pawel S. (2020): Comment on “The Role of p-Values in Judging the Strength of Evidence and Realistic Replication Expectations”. Statistics in Biopharmaceutical Research 13(1):46-48. https://doi.org/10.1080/19466315.2020.1828161
  1720. Lautenbacher L., Neyse L. (2020): Depression, neuroticism and 2D:4D ratio: evidence from a large, representative sample. Scientific Reports 10(1). https://doi.org/10.1038/s41598-020-67882-x
  1721. Lima Portugal L., Alves R., Junior O., Sanchez T., Mocaiber I., Volchan E., et al. (2020): Interactions between emotion and action in the brain. NeuroImage 214:116728. https://doi.org/10.1016/j.neuroimage.2020.116728
  1722. Wang L., He X., Ugai T., Haruki K., Lo C., Hang D., et al. (2020): Risk Factors and Incidence of Colorectal Cancer According to Major Molecular Subtypes. JNCI Cancer Spectrum 5(1). https://doi.org/10.1093/jncics/pkaa089
  1723. Wang L., Hang D., He X., Lo C., Wu K., Chan A., et al. (2020): A prospective study of erythrocyte polyunsaturated fatty acids and risk of colorectal serrated polyps and conventional adenomas. International Journal of Cancer 148(1):57-66. https://doi.org/10.1002/ijc.33190
  1724. Soprun L., Utekhin V., Gvozdetskiy A., Akulin I., Churilov L. (2020): Anthropogenic environmental factors as triggers of type 1 diabetes mellitus in children. Pediatrician (St. Petersburg) 11(2):57-65. https://doi.org/10.17816/ped11257-65
  1725. Wei L., Mei Y., Zou L., Chen J., Tan M., Wang C., et al. (2020): Distribution Patterns for Bioactive Constituents in Pericarp, Stalk and Seed of Forsythiae Fructus. Molecules 25(2):340. https://doi.org/10.3390/molecules25020340
  1726. Lin L. (2020): Factors that impact fragility index and their visualizations. Journal of Evaluation in Clinical Practice 27(2):356-364. https://doi.org/10.1111/jep.13428
  1727. Lin L., Chu H. (2020): fragility: Assessing and Visualizing Fragility of Clinical Results with Binary Outcomes. CRAN: Contributed Packages. https://doi.org/10.32614/cran.package.fragility
  1728. ZHANG L., WEI X., LU J., PAN J. (2020): Lasso回归：从解释到预测. Advances in Psychological Science 28(10):1777-1788. https://doi.org/10.3724/sp.j.1042.2020.01777
  1729. Wang L., Wang Y., Chen Y., Pan X., Zhang W. (2020): Performance shaping factors dependence assessment through moderating and mediating effect analysis. Reliability Engineering & System Safety 202:107034. https://doi.org/10.1016/j.ress.2020.107034
  1730. Hall L., Hendricks A. (2020): High-throughput analysis suggests differences in journal false discovery rate by subject area and impact factor but not open access status. BMC Bioinformatics 21(1). https://doi.org/10.1186/s12859-020-03817-7
  1731. Desquilbet L. (2020): Enhancing Clinical Decision-Making: Challenges of making decisions on the basis of significant statistical associations. Journal of the American Veterinary Medical Association 256(2):187-193. https://doi.org/10.2460/javma.256.2.187
  1732. Philipp L., Matias C., Thalheimer S., Mehta S., Sharan A., Wu C. (2020): Robot-Assisted Stereotaxy Reduces Target Error: A Meta-Analysis and Meta-Regression of 6056 Trajectories. Neurosurgery 88(2):222-233. https://doi.org/10.1093/neuros/nyaa428
  1733. De Capitani L., De Martini D. (2020): Improving reproducibility probability estimation and preserving RP-testing. Statistical Methods & Applications 30(1):49-77. https://doi.org/10.1007/s10260-020-00513-x
  1734. Kcomt L., Evans-Polce R., Engstrom C., West B., McCabe S. (2020): Discrimination, Sexual Orientation Discrimination, and Severity of Tobacco Use Disorder in the United States: Results From the National Epidemiologic Survey on Alcohol and Related Conditions-III. Nicotine & Tobacco Research 23(6):920-930. https://doi.org/10.1093/ntr/ntaa197
  1735. Smillie L., Katic M., Laham S. (2020): Personality and moral judgment: Curious consequentialists and polite deontologists. Journal of Personality 89(3):549-564. https://doi.org/10.1111/jopy.12598
  1736. Said M., van de Vegte Y., Verweij N., van der Harst P. (2020): Associations of Observational and Genetically Determined Caffeine Intake With Coronary Artery Disease and Diabetes Mellitus. Journal of the American Heart Association 9(24). https://doi.org/10.1161/jaha.120.016808
  1737. Donnellan M., Kashy D. (2020): Designing and Managing Longitudinal Studies. The Cambridge Handbook of Research Methods in Clinical Psychology. https://doi.org/10.1017/9781316995808.022
  1738. Shaw M., Cloos L., Luong R., Elbaz S., Flake J. (2020): Measurement practices in large-scale replications: Insights from Many Labs 2. Canadian Psychology / Psychologie canadienne 61(4):289-298. https://doi.org/10.1037/cap0000220
  1739. Shaw M., Cloos L., Luong R., Elbaz S., Flake J. (2020): Measurement Practices in Large-Scale Replications: Insights from Many Labs 2. https://doi.org/10.31234/osf.io/kdurz
  1740. Kai M., Elmassry M., Farag M. (2020): Sampling, Detection, Identification, and Analysis of Bacterial Volatile Organic Compounds (VOCs). Bacterial Volatile Compounds as Mediators of Airborne Interactions. https://doi.org/10.1007/978-981-15-7293-7_12
  1741. Bendtsen M. (2020): The P Value Line Dance: When Does the Music Stop?. Journal of Medical Internet Research 22(8):e21345. https://doi.org/10.2196/21345
  1742. Margaret Samahita, Håkan J. Holm (2020): Mining for Mood Effect in the Field. RePEc: Research Papers in Economics.
  1743. Gardner M., Hutt S., Kamentz D., Duckworth A., D’Mello S. (2020): How Does High School Extracurricular Participation Predict Bachelor’s Degree Attainment? It is Complicated. Journal of Research on Adolescence 30(3):753-768. https://doi.org/10.1111/jora.12557
  1744. Feldt M., Menard J., Rosendahl A., Lettiero B., Bendahl P., Belting M., et al. (2020): The effect of statin treatment on intratumoral cholesterol levels and LDL receptor expression: a window-of-opportunity breast cancer trial. Cancer & Metabolism 8(1). https://doi.org/10.1186/s40170-020-00231-8
  1745. de Koning M., Assa S., Maagdenberg C., van Veldhuisen D., Pasch A., van Goor H., et al. (2020): Safety and Tolerability of Sodium Thiosulfate in Patients with an Acute Coronary Syndrome Undergoing Coronary Angiography: A Dose-Escalation Safety Pilot Study (SAFE-ACS). Journal of Interventional Cardiology 2020:1-8. https://doi.org/10.1155/2020/6014915
  1746. Wenzel M., Staab D., Rowland Z., van Scheppingen M. (2020): Relationship Satisfaction Can Help to Maintain the Positive Effect of Childbirth on Parental Self-Esteem. Social Psychological and Personality Science 12(7):1358-1368. https://doi.org/10.1177/1948550620971532
  1747. Rubin M. (2020): Does preregistration improve the credibility of research findings?. https://doi.org/10.31234/osf.io/bndj8
  1748. Rubin M. (2020): Does preregistration improve the credibility of research findings?. https://doi.org/10.31235/osf.io/mf7vj
  1749. Granberg M., Andersson P., Ahmed A. (2020): Hiring Discrimination Against Transgender People: Evidence from a Field Experiment. Labour Economics 65:101860. https://doi.org/10.1016/j.labeco.2020.101860
  1750. Brandt M., Turner-Zwinkels F. (2020): No Additional Evidence that Proximity to the July 4th Holiday Affects Affective Polarization. Collabra: Psychology 6(1). https://doi.org/10.1525/collabra.368
  1751. Brandt M., Turner-Zwinkels F. (2020): No Additional Evidence that Proximity to the July 4th Holiday Affects Affective Polarization. https://doi.org/10.31234/osf.io/7yqkd
  1752. Rubin M. (2020): Does preregistration improve the credibility of research findings?. The Quantitative Methods for Psychology 16(4):376-390. https://doi.org/10.20982/tqmp.16.4.p376
  1753. Rubin M. (2020): Does preregistration improve the credibility of research findings?. OSF Preprints. https://doi.org/10.31222/osf.io/vgr89
  1754. Spitzer M., Spitzer M. (2020): Die Replikationskrise in der Psychologie. Nervenheilkunde 39(06):404-416. https://doi.org/10.1055/a-1095-0144
  1755. Köllner M., Bleck K. (2020): Exploratory Evidence of Sex-Dimorphic Associations of the Ulna-to-Fibula Ratio, a Potential Marker of Pubertal Sex Steroid Exposure, with the Implicit Need for Power. Adaptive Human Behavior and Physiology 6(1):93-118. https://doi.org/10.1007/s40750-020-00130-8
  1756. Rydén M., Englund M., Ali N. (2020): ProteoMill: Efficient network-based functional analysis portal for proteomics data. https://doi.org/10.1101/2020.11.09.374579
  1757. Stachaczyk M., Atashzar S., Farina D. (2020): Adaptive Spatial Filtering of High-Density EMG for Reducing the Influence of Noise and Artefacts in Myoelectric Control. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(7):1511-1517. https://doi.org/10.1109/tnsre.2020.2986099
  1758. Himmelstein M., Puhl R., Pearl R., Pinto A., Foster G. (2020): Coping with Weight Stigma Among Adults in a Commercial Weight Management Sample. International Journal of Behavioral Medicine 27(5):576-590. https://doi.org/10.1007/s12529-020-09895-4
  1759. Frias‐Navarro D., Pascual‐Llobell J., Pascual‐Soler M., Perezgonzalez J., Berrios‐Riquelme J. (2020): Replication crisis or an opportunity to improve scientific production?. European Journal of Education 55(4):618-631. https://doi.org/10.1111/ejed.12417
  1760. Williams M., Marshall-Edwards S. (2020): Conceptual replication of Seo (2008), “Self-efficacy as a mediator in the relationship between self-oriented perfectionism and academic procrastination”. https://doi.org/10.31234/osf.io/vzsfa
  1761. Andreoletti M. (2020): Replicability Crisis and Scientific Reforms: Overlooked Issues and Unmet Challenges. International Studies in the Philosophy of Science 33(3):135-151. https://doi.org/10.1080/02698595.2021.1943292
  1762. Owens M., Sweet L., MacKillop J. (2020): Recent cannabis use is associated with smaller hippocampus volume: High‐resolution segmentation of structural subfields in a large non‐clinical sample. Addiction Biology 26(1). https://doi.org/10.1111/adb.12874
  1763. Linde M., Tendeiro J., Selker R., Wagenmakers E., van Ravenzwaaij D. (2020): Decisions About Equivalence: A Comparison of TOST, HDI-ROPE, and the Bayes Factor. https://doi.org/10.31234/osf.io/bh8vu
  1764. Ganz M., Nørgaard M., Beliveau V., Svarer C., Knudsen G., Greve D. (2020): False positive rates in positron emission tomography (PET) voxelwise analyses. Journal of Cerebral Blood Flow & Metabolism 41(7):1647-1657. https://doi.org/10.1177/0271678×20974961
  1765. Sadler M., Somo A., Devos T. (2020): Would that it were so simple: Dimensions of context diversity differentially relate to four implicit interethnic associations. The Journal of Social Psychology 161(6):731-752. https://doi.org/10.1080/00224545.2020.1845594
  1766. Dai M., Hu X., Gu F. (2020): Citizen Characteristics, Neighbourhood Conditions, and Prior Contacts with the Police: A Comparative Study of Public Satisfaction with the Police. Canadian Journal of Criminology and Criminal Justice 62(4):77-101. https://doi.org/10.3138/cjccj.2020-0026
  1767. Dietrich M. (2020): What Is the Nature of Scientific Controversies in the Biological Sciences?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.013
  1768. Krämer M., Rodgers J. (2020): The impact of having children on domain-specific life satisfaction: A quasi-experimental longitudinal investigation using the Socio-Economic Panel (SOEP) data. https://doi.org/10.31234/osf.io/bj3hw
  1769. Gordon M., Viganola D., Bishop M., Chen Y., Dreber A., Goldfedder B., et al. (2020): Are replication rates the same across academic fields? Community forecasts from the DARPA SCORE programme. Royal Society Open Science 7(7):200566. https://doi.org/10.1098/rsos.200566
  1770. Kent M., Schiavon S. (2020): Evaluation of the effect of landscape distance seen in window views on visual satisfaction. Building and Environment 183:107160. https://doi.org/10.1016/j.buildenv.2020.107160
  1771. Parkinson M., Sorzano C. (2020): Why Do We Need a Statistical Experiment Design?. Laboratory Animal Science and Medicine. https://doi.org/10.1007/978-3-030-66147-2_6
  1772. Ruse M. (2020): A Philosopher in the Age of Creationism. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.015
  1773. Bono M., Beasley S., Hanhauser E., Hart A., Karnik R., Vaishnav C. (2020): Fieldwork-based determination of design priorities for point-of-use drinking water quality sensors for use in resource-limited environments. PLOS ONE 15(1):e0228140. https://doi.org/10.1371/journal.pone.0228140
  1774. Meyer M., Gjorgjieva T., Rosica D. (2020): Healthcare worker intentions to receive a COVID-19 vaccine and reasons for hesitancy: A survey of 16,158 health system employees on the eve of vaccine distribution. https://doi.org/10.31234/osf.io/ge6uh
  1775. Meyer M., Gjorgjieva T., Rosica D. (2020): Healthcare worker intentions to receive a COVID-19 vaccine and reasons for hesitancy: A survey of 16,158 health system employees on the eve of vaccine distribution. https://doi.org/10.1101/2020.12.19.20248555
  1776. Felgenhauer M., Xu F. (2020): THE FACE VALUE OF ARGUMENTS WITH AND WITHOUT MANIPULATION. International Economic Review 62(1):277-293. https://doi.org/10.1111/iere.12479
  1777. Lovric M. (2020): On the Authentic Notion, Relevance, and Solution of the Jeffreys-Lindley Paradox in the Zettabyte Era. Journal of Modern Applied Statistical Methods 18(1):2-35. https://doi.org/10.22237/jmasm/1556670180
  1778. Sean M., Coulombe-Lévêque A., Vincenot M., Martel M., Gendron L., Marchand S., et al. (2020): Transcutaneous electrical nerve stimulation (TENS): towards the development of a clinic-friendly method for the evaluation of excitatory and inhibitory pain mechanisms. Canadian Journal of Pain 5(1):56-65. https://doi.org/10.1080/24740527.2020.1862624
  1779. Pilch M., O’Hora D., Jennings C., Caes L., McGuire B., Kainz V., et al. (2020): Perspective-taking influences attentional deployment towards facial expressions of pain: an eye-tracking study. Pain 161(6):1286-1296. https://doi.org/10.1097/j.pain.0000000000001827
  1780. Mahmud M., Soetanto D., Jack S. (2020): Environmental management and product innovation: The moderating role of the dynamic capability of small manufacturing firms. Journal of Cleaner Production 264:121633. https://doi.org/10.1016/j.jclepro.2020.121633
  1781. Muaz Mahmud (2020): Implementing environmental management. University of Lancaster. https://doi.org/10.17635/lancaster/thesis/1205
  1782. Cimci M., Witassek F., Radovanovic D., Rickli H., Pedrazzini G., Erne P., et al. (2020): Temporal trends in cardiovascular risk factors’ prevalence in patients with myocardial infarction. European Journal of Clinical Investigation 51(4). https://doi.org/10.1111/eci.13466
  1783. Mansour N., Balas E., Yang F., Vernon M. (2020): Prevalence and Prevention of Reproducibility Deficiencies in Life Sciences Research: Large-Scale Meta-Analyses. Medical Science Monitor 26. https://doi.org/10.12659/msm.922016
  1784. McLatchie N., Warmelink L., Tkacheva D. (2020): Reply to Mac Giolla and Ly (2019): On the reporting of Bayes factors in deception research. Legal and Criminological Psychology 25(2):72-79. https://doi.org/10.1111/lcrp.12177
  1785. McLatchie N., Warmelink L., Tkacheva D. (2020): Reply to Mac Giolla and Ly (2019): On the reporting of Bayes Factors in Deception Research. https://doi.org/10.31234/osf.io/kwy3q
  1786. Fraser N., Momeni F., Mayr P., Peters I. (2020): The relationship between bioRxiv preprints, citations and altmetrics. Quantitative Science Studies 1(2):618-638. https://doi.org/10.1162/qss_a_00043
  1787. Schmid N., Limère V., Raa B. (2020): Mixed model assembly line feeding with discrete location assignments and variable station space. Omega 102:102286. https://doi.org/10.1016/j.omega.2020.102286
  1788. Veronese N., Smith L., Bolzetta F., Cester A., Demurtas J., Punzi L. (2020): Efficacy of conservative treatments for hand osteoarthritis. Wiener klinische Wochenschrift 133(5-6):234-240. https://doi.org/10.1007/s00508-020-01702-0
  1789. Veronese N., Demurtas J., Thompson T., Solmi M., Pesolillo G., Celotto S., et al. (2020): Effect of low‐dose aspirin on health outcomes: An umbrella review of systematic reviews and meta‐analyses. British Journal of Clinical Pharmacology 86(8):1465-1475. https://doi.org/10.1111/bcp.14310
  1790. Bonneel N., Coeurjolly D., Digne J., Mellado N. (2020): Code replicability in computer graphics. ACM Transactions on Graphics 39(4). https://doi.org/10.1145/3386569.3392413
  1791. Bhagwat N., Barry A., Dickie E., Brown S., Devenyi G., Hatano K., et al. (2020): Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses. https://doi.org/10.1101/2020.05.22.100180
  1792. Rachev N., Han H., Lacko D., Gelpí R., Yamada Y., Lieberoth A. (2020): Replicating the Disease framing problem during the 2020 COVID-19 pandemic: A study of stress, worry, trust, and choice under risk. https://doi.org/10.31234/osf.io/rbfwp
  1793. Laccourreye O., Jankowski R., Lisan Q. (2020): Mastering the descriptive statistics used in otorhinolaryngology. European Annals of Otorhinolaryngology, Head and Neck Diseases 138(5):387-390. https://doi.org/10.1016/j.anorl.2020.12.004
  1794. Laccourreye O., Fakhry N., Franco-Vidal V., Jankowski R., Karkas A., Leboulanger N., et al. (2020): Statistics in scientific articles published in the European Annals of Otorhinolaryngology Head & Neck Diseases. European Annals of Otorhinolaryngology, Head and Neck Diseases 138(2):89-92. https://doi.org/10.1016/j.anorl.2020.06.015
  1795. Passon O., von der Twer T. (2020): Evidenz, Signifikanz und das kleine p. Zeitschrift für Bildungsforschung 10(3):377-395. https://doi.org/10.1007/s35834-020-00282-3
  1796. Borcan O. (2020): The Illicit Benefits of Local Party Alignment in National Elections. The Journal of Law, Economics, and Organization 36(3):461-494. https://doi.org/10.1093/jleo/ewaa005
  1797. Braganza O. (2020): A simple model suggesting economically rational sample-size choice drives irreproducibility. PLOS ONE 15(3):e0229615. https://doi.org/10.1371/journal.pone.0229615
  1798. Oliver Y. Chén, Raúl G. Saraiva, Guy Nagels, Huy P. Phan, Tom Schwantje, Hengyi Cao, et al. (2020): Thou Shalt Not Reject the P-value. arXiv (Cornell University). https://doi.org/10.13140/rg.2.2.18014.59206/1
  1799. Heffetz O. (2020): Are reference points merely lagged beliefs over probabilities?. Journal of Economic Behavior & Organization 181:252-269. https://doi.org/10.1016/j.jebo.2020.11.010
  1800. Palazzo O., Rass M., Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. Open Biology 10(12). https://doi.org/10.1098/rsob.200295
  1801. Palazzo O., Raß M., Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. https://doi.org/10.1101/2020.07.15.204677
  1802. Gureje O., Appiah-Poku J., Bello T., Kola L., Araya R., Chisholm D., et al. (2020): Effect of collaborative care between traditional and faith healers and primary health-care workers on psychosis outcomes in Nigeria and Ghana (COSIMPO): a cluster randomised controlled trial. The Lancet 396(10251):612-622. https://doi.org/10.1016/s0140-6736(20)30634-6
  1803. Tsigaris P., Teixeira da Silva J. (2020): Why blacklists are not reliable: A theoretical framework. The Journal of Academic Librarianship 47(1):102266. https://doi.org/10.1016/j.acalib.2020.102266
  1804. Tsigaris P., Teixeira da Silva J. (2020): Reproducibility issues with correlating Beall-listed publications and research awards at a small Canadian business school. Scientometrics 123(1):143-157. https://doi.org/10.1007/s11192-020-03353-4
  1805. Sonon P., Brito Ferreira M., Santos Almeida R., Saloum Deghaide N., Henrique Willcox G., Guimarães E., et al. (2020): Differential Frequencies of HLA-DRB1, DQA1, and DQB1 Alleles and Haplotypes Are Observed in the Arbovirus-Related Neurological Syndromes. The Journal of Infectious Diseases 224(3):517-525. https://doi.org/10.1093/infdis/jiaa764
  1806. Bazilinskyy P., Eisma Y., Dodou D., de Winter J. (2020): Risk perception: A study using dashcam videos and participants from different world regions. Traffic Injury Prevention 21(6):347-353. https://doi.org/10.1080/15389588.2020.1762871
  1807. Engzell P., Rohrer J. (2020): Improving Social Science: Lessons from the Open Science Movement. PS: Political Science & Politics 54(2):297-300. https://doi.org/10.1017/s1049096520000967
  1808. Peter Cahusac, A Nordestgaard, S Stender, B Nordestgaard, A Tybjaerg-Hansen, S Goodman, et al. (2020): Problems withpValues. Evidence‐Based Statistics. https://doi.org/10.1002/9781119549833.app3
  1809. Grunwald P., de Heide R., Koolen W. (2020): Safe Testing. 2020 Information Theory and Applications Workshop (ITA). https://doi.org/10.1109/ita50056.2020.9244948
  1810. Holtz P. (2020): Two Questions to Foster Critical Thinking in the Field of Psychology. Meta-Psychology 4. https://doi.org/10.15626/mp.2018.984
  1811. Biswas R., Rahman N., Islam H., Senserrick T., Bhowmik J. (2020): Exposure of mobile phones and mass media in maternal health services use in developing nations: evidence from Urban Health Survey 2013 of Bangladesh. Contemporary South Asia 29(3):460-473. https://doi.org/10.1080/09584935.2020.1770698
  1812. Zwolak R., Sih A. (2020): Animal personalities and seed dispersal: A conceptual review. Functional Ecology 34(7):1294-1310. https://doi.org/10.1111/1365-2435.13583
  1813. Klement R., Champ C., Kämmerer U., Koebrunner P., Krage K., Schäfer G., et al. (2020): Impact of a ketogenic diet intervention during radiotherapy on body composition: III—final results of the KETOCOMP study for breast cancer patients. Breast Cancer Research 22(1). https://doi.org/10.1186/s13058-020-01331-5
  1814. Klement R., Sonke J., Allgäuer M., Andratschke N., Appold S., Belderbos J., et al. (2020): Correlating Dose Variables with Local Tumor Control in Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer: A Modeling Study on 1500 Individual Treatments. International Journal of Radiation Oncology*Biology*Physics 107(3):579-586. https://doi.org/10.1016/j.ijrobp.2020.03.005
  1815. Klement R., Koebrunner P., Krage K., Sweeney R. (2020): Low Vitamin D Status in a Cancer Patient Population from Franconia, Germany. Complementary Medicine Research 28(4):300-307. https://doi.org/10.1159/000511993
  1816. Lal R., Ramaswami A., Russell A. (2020): Assessment of the Near-Road (monitoring) Network including comparison with nearby monitors within U.S. cities. Environmental Research Letters 15(11):114026. https://doi.org/10.1088/1748-9326/ab8156
  1817. Pearl R., Puhl R., Himmelstein M., Pinto A., Foster G. (2020): Weight Stigma and Weight-Related Health: Associations of Self-Report Measures Among Adults in Weight Management. Annals of Behavioral Medicine 54(11):904-914. https://doi.org/10.1093/abm/kaaa026
  1818. Samarei R., Mabarian S. (2020): A randomised trial comparing the subjective outcomes following septoplasty with or without inferior turbinoplasty. European Annals of Otorhinolaryngology, Head and Neck Diseases 137(4):277-283. https://doi.org/10.1016/j.anorl.2020.01.024
  1819. Samarei R., Mabarian S. (2020): Étude randomisée comparant les résultats subjectifs de patients ayant subi une septoplastie avec ou sans turbinoplastie. Annales françaises d’Oto-rhino-laryngologie et de Pathologie Cervico-faciale 137(4):256-262. https://doi.org/10.1016/j.aforl.2020.05.007
  1820. Bethlehem R., Seidlitz J., Romero-Garcia R., Trakoshis S., Dumas G., Lombardo M. (2020): A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder. Communications Biology 3(1). https://doi.org/10.1038/s42003-020-01212-9
  1821. Ramsey R. (2020): Ramsey_modesty. https://doi.org/10.31234/osf.io/hf5sv
  1822. Torkar R., Feldt R., Furia C. (2020): Bayesian Data Analysis in Empirical Software Engineering: The Case of Missing Data. Contemporary Empirical Methods in Software Engineering. https://doi.org/10.1007/978-3-030-32489-6_11
  1823. Kelter R. (2020): Analysis of Bayesian posterior significance and effect size indices for the two-sample t-test to support reproducible medical research. BMC Medical Research Methodology 20(1). https://doi.org/10.1186/s12874-020-00968-2
  1824. Kelter R. (2020): Bayesian and frequentist testing for differences between two groups with parametric and nonparametric two‐sample tests. WIREs Computational Statistics 13(6). https://doi.org/10.1002/wics.1523
  1825. Thibault R., Munafò M. (2020): Commentary: Improving our statistical inferences requires meta-research. International Journal of Epidemiology 49(3):894-895. https://doi.org/10.1093/ije/dyaa051
  1826. Ince R., Paton A., Kay J., Schyns P. (2020): Bayesian inference of population prevalence. https://doi.org/10.1101/2020.07.08.191106
  1827. Myte R., Harlid S., Sundkvist A., Gylling B., Häggström J., Zingmark C., et al. (2020): A longitudinal study of prediagnostic metabolic biomarkers and the risk of molecular subtypes of colorectal cancer. Scientific Reports 10(1). https://doi.org/10.1038/s41598-020-62129-1
  1828. Imhoff R., Lamberty P. (2020): A Bioweapon or a Hoax? The Link Between Distinct Conspiracy Beliefs About the Coronavirus Disease (COVID-19) Outbreak and Pandemic Behavior. Social Psychological and Personality Science 11(8):1110-1118. https://doi.org/10.1177/1948550620934692
  1829. Ulrich R., Miller J. (2020): Questionable research practices may have little effect on replicability. eLife 9. https://doi.org/10.7554/elife.58237
  1830. Berman R., Van den Bulte C. (2020): False Discovery in A/B Testing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3718802
  1831. Kohavi R., Tang D., Xu Y. (2020): Trustworthy Online Controlled Experiments. Cambridge University Press eBooks. https://doi.org/10.1017/9781108653985
  1832. Ron Kohavi, Diane Tang, Ya Xu (2020): Speed Matters. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.008
  1833. Ron Kohavi, Diane Tang, Ya Xu (2020): Introductory Topics for Everyone. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.002
  1834. Ron Kohavi, Diane Tang, Ya Xu (2020): Ethics in Controlled Experiments. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.012
  1835. Ron Kohavi, Diane Tang, Ya Xu (2020): The Statistics behind Online Controlled Experiments. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.023
  1836. Ron Kohavi, Diane Tang, Ya Xu (2020): Observational Causal Studies. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.015
  1837. Ron Kohavi, Diane Tang, Ya Xu (2020): Twyman’s Law and Experimentation Trustworthiness. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.005
  1838. Ron Kohavi, Diane Tang, Ya Xu (2020): Advanced Topics for Analyzing Experiments. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.022
  1839. Ron Kohavi, Diane Tang, Ya Xu (2020): Sample Ratio Mismatch and Other Trust-Related Guardrail Metrics. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.027
  1840. Ron Kohavi, Diane Tang, Ya Xu (2020): Complementary and Alternative Techniques to Controlled Experiments. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.013
  1841. Ron Kohavi, Diane Tang, Ya Xu (2020): Metrics for Experimentation and the Overall Evaluation Criterion. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.010
  1842. Ron Kohavi, Diane Tang, Ya Xu (2020): Experimentation Platform and Culture. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.006
  1843. Ron Kohavi, Diane Tang, Ya Xu (2020): The A/A Test. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.025
  1844. Ron Kohavi, Diane Tang, Ya Xu (2020): Institutional Memory and Meta-Analysis. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.011
  1845. Ron Kohavi, Diane Tang, Ya Xu (2020): Measuring Long-Term Treatment Effects. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.029
  1846. Ron Kohavi, Diane Tang, Ya Xu (2020): Running and Analyzing Experiments. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.004
  1847. Ron Kohavi, Diane Tang, Ya Xu (2020): Variance Estimation and Improved Sensitivity: Pitfalls and Solutions. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.024
  1848. Ron Kohavi, Diane Tang, Ya Xu (2020): Choosing a Randomization Unit. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.019
  1849. Ron Kohavi, Diane Tang, Ya Xu (2020): Client-Side Experiments. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.017
  1850. Ron Kohavi, Diane Tang, Ya Xu (2020): Selected Topics for Everyone. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.007
  1851. Ron Kohavi, Diane Tang, Ya Xu (2020): Advanced Topics for Building an Experimentation Platform. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.016
  1852. Ron Kohavi, Diane Tang, Ya Xu (2020): Triggering for Improved Sensitivity. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.026
  1853. Ron Kohavi, Diane Tang, Ya Xu (2020): Leakage and Interference between Variants. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.028
  1854. Ron Kohavi, Diane Tang, Ya Xu (2020): Ramping Experiment Exposure: Trading Off Speed, Quality, and Risk. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.020
  1855. Ron Kohavi, Diane Tang, Ya Xu (2020): Preface. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.001
  1856. Ron Kohavi, Diane Tang, Ya Xu (2020): Instrumentation. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.018
  1857. Ron Kohavi, Diane Tang, Ya Xu (2020): Scaling Experiment Analyses. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.021
  1858. Ron Kohavi, Diane Tang, Ya Xu (2020): Organizational Metrics. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.009
  1859. Ron Kohavi, Diane Tang, Ya Xu (2020): Complementary Techniques. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.014
  1860. Ron Kohavi, Diane Tang, Ya Xu (2020): Introduction and Motivation. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.003
  1861. Ron Kohavi, Diane Tang, Ya Xu (2020): Index. Trustworthy Online Controlled Experiments. https://doi.org/10.1017/9781108653985.031
  1862. Upshur R., Goldenberg M. (2020): Countering medical nihilism by reconnecting facts and values. Studies in History and Philosophy of Science Part A 84:75-83. https://doi.org/10.1016/j.shpsa.2020.08.005
  1863. Lasala R., Logreco A., Romagnoli A., Santoleri F., Musicco F., Costantini A. (2020): Cancer drugs for solid tumors approved by the EMA since 2014: an overview of pivotal clinical trials. European Journal of Clinical Pharmacology 76(6):843-850. https://doi.org/10.1007/s00228-020-02850-y
  1864. Nichols R., Slingerland E., Nielbo K., Kirby P., Logan C. (2020): Supernatural agents and prosociality in historical China: micro-modeling the cultural evolution of gods and morality in textual corpora. Religion, Brain & Behavior 11(1):46-64. https://doi.org/10.1080/2153599x.2020.1742778
  1865. Pillai S., Kobayashi K., Michael M., A M. (2020): Irreproducibility –The deadly sin of preclinical research in drug development. Journal of Pre-Clinical and Clinical Research 14(4):165-168. https://doi.org/10.26444/jpccr/131017
  1866. Aurtenetxe S., Molinaro N., Davidson D., Carreiras M. (2020): Early dissociation of numbers and letters in the human brain. Cortex 130:192-202. https://doi.org/10.1016/j.cortex.2020.03.030
  1867. Tahamont S., Jelveh Z., Chalfin A., Yan S., Hansen B. (2020): Dude, Where’s My Treatment Effect? Errors in Administrative Data Linking and the Destruction of Statistical Power in Randomized Experiments. Journal of Quantitative Criminology 37(3):715-749. https://doi.org/10.1007/s10940-020-09461-x
  1868. Voisin S., Harvey N., Haupt L., Griffiths L., Ashton K., Coffey V., et al. (2020): An epigenetic clock for human skeletal muscle. Journal of Cachexia, Sarcopenia and Muscle 11(4):887-898. https://doi.org/10.1002/jcsm.12556
  1869. Voisin S., Jacques M., Landen S., Harvey N., Haupt L., Griffiths L., et al. (2020): Meta-analysis of genome-wide DNA methylation and integrative OMICs in human skeletal muscle. https://doi.org/10.1101/2020.09.28.315838
  1870. Semenyna S., Gómez Jiménez F., VanderLaan D., Vasey P. (2020): Inter-sexual mate competition in three cultures. PLOS ONE 15(7):e0236549. https://doi.org/10.1371/journal.pone.0236549
  1871. Grant S., Wendt K., Leadbeater B., Supplee L., Mayo-Wilson E., Gardner F., et al. (2020): Transparent, Open, and Reproducible Prevention Science. OSF Preprints. https://doi.org/10.31222/osf.io/d2y43
  1872. Hong S., Xu T., Nikolaidis A., Smallwood J., Margulies D., Bernhardt B., et al. (2020): Toward a connectivity gradient-based framework for reproducible biomarker discovery. NeuroImage 223:117322. https://doi.org/10.1016/j.neuroimage.2020.117322
  1873. Meiri S., Avila L., Bauer A., Chapple D., Das I., Doan T., et al. (2020): The global diversity and distribution of lizard clutch sizes. Global Ecology and Biogeography 29(9):1515-1530. https://doi.org/10.1111/geb.13124
  1874. Evans S., Anderson J., Johnson A., Checketts J., Scott J., Middlemist K., et al. (2020): The Potential Effect of Lowering the Threshold of Statistical Significance From P < .05 to P < .005 in Orthopaedic Sports Medicine. Arthroscopy: The Journal of Arthroscopic & Related Surgery 37(4):1068-1074. https://doi.org/10.1016/j.arthro.2020.11.041
  1875. Gates S., Brock K., Ryan E. (2020): Bayesian statistical methods and their application to resuscitation trials. Resuscitation 149:60-64. https://doi.org/10.1016/j.resuscitation.2020.01.030
  1876. Han S., Stieha V. (2020): Growth Mindset for Human Resource Development: A Scoping Review of the Literature with Recommended Interventions. Human Resource Development Review 19(3):309-331. https://doi.org/10.1177/1534484320939739
  1877. Lemonnier S., Désiré L., Brémond R., Baccino T. (2020): Drivers’ visual attention: A field study at intersections. Transportation Research Part F: Traffic Psychology and Behaviour 69:206-221. https://doi.org/10.1016/j.trf.2020.01.012
  1878. Chabert S., Sénéchal C., Fougeroux A., Pousse J., Richard F., Nozières E., et al. (2020): Effect of environmental conditions and genotype on nectar secretion in sunflower (Helianthus annuusL.). OCL 27:51. https://doi.org/10.1051/ocl/2020040
  1879. Leach S., Weick M. (2020): When smiles (and frowns) speak words: Does power impact the correspondence between self‐reported affect and facial expressions?. British Journal of Psychology 111(4):683-701. https://doi.org/10.1111/bjop.12433
  1880. Hoehl S., Fairhurst M., Schirmer A. (2020): Interactional synchrony: signals, mechanisms and benefits. Social Cognitive and Affective Neuroscience 16(1-2):5-18. https://doi.org/10.1093/scan/nsaa024
  1881. Lewandowsky S., Oberauer K. (2020): Low replicability can support robust and efficient science. Nature Communications 11(1). https://doi.org/10.1038/s41467-019-14203-0
  1882. Bland S. (2020): An Interactionist Approach to Cognitive Debiasing. Episteme 19(1):66-88. https://doi.org/10.1017/epi.2020.9
  1883. Ho S., Wong L., Goh W. (2020): Avoid Oversimplifications in Machine Learning: Going beyond the Class-Prediction Accuracy. Patterns 1(2):100025. https://doi.org/10.1016/j.patter.2020.100025
  1884. Segerstrom S. (2020): Statistical Guideline No. 5. Include Results of a Power Analysis; if a Power Analysis Was Not Performed, Describe the Stopping Rule for Recruitment. International Journal of Behavioral Medicine 27(2):140-141. https://doi.org/10.1007/s12529-020-09868-7
  1885. Segerstrom S. (2020): Statistical Guideline #6. Indicate magnitude and precision in your estimation and use “new statistics”. International Journal of Behavioral Medicine 27(5):487-489. https://doi.org/10.1007/s12529-020-09929-x
  1886. Hoogeveen S., Sarafoglou A., Wagenmakers E. (2020): Laypeople Can Predict Which Social-Science Studies Will Be Replicated Successfully. Advances in Methods and Practices in Psychological Science 3(3):267-285. https://doi.org/10.1177/2515245920919667
  1887. Power S., Velez G. (2020): The MOVE Framework: Meanings, Observations, Viewpoints, and Experiences in processes of Social Change. Review of General Psychology 24(4):321-334. https://doi.org/10.1177/1089268020915841
  1888. TARG Meta-Research Group .. (2020): Statistics education in undergraduate psychology: A survey of UK curricula. https://doi.org/10.31234/osf.io/jv8x3
  1889. Yarkoni T. (2020): The generalizability crisis. Behavioral and Brain Sciences 45. https://doi.org/10.1017/s0140525x20001685
  1890. Pfeiler T., Egloff B. (2020): Personality and eating habits revisited: Associations between the big five, food choices, and Body Mass Index in a representative Australian sample. Appetite 149:104607. https://doi.org/10.1016/j.appet.2020.104607
  1891. Kantonen T., Karjalainen T., Isojärvi J., Nuutila P., Tuisku J., Rinne J., et al. (2020): Interindividual variability and lateralization of μ-opioid receptors in the human brain. NeuroImage 217:116922. https://doi.org/10.1016/j.neuroimage.2020.116922
  1892. Reydon T. (2020): What Is the Basis of Biological Classification?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.012
  1893. Vilgis T. (2020): Zurück zum Genuss. Biophysik der Ernährung. https://doi.org/10.1007/978-3-662-61151-7_6
  1894. Vilgis T. (2020): Fazit – oder: Was bleibt?. Biophysik der Ernährung. https://doi.org/10.1007/978-3-662-61151-7_7
  1895. Perneger T., Brindel P., Combescure C., Gayet-Ageron A. (2020): Evidence of survival benefit was often ambiguous in randomized trials of cancer treatments. Journal of Clinical Epidemiology 127:1-8. https://doi.org/10.1016/j.jclinepi.2020.06.026
  1896. Lewens T. (2020): How Can Conceptual Analysis Contribute to Scientific Practice?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.009
  1897. Ziermans T., Schirmbeck F., Oosterwijk F., Geurts H., de Haan L. (2020): Autistic traits in psychotic disorders: prevalence, familial risk, and impact on social functioning. Psychological Medicine 51(10):1704-1713. https://doi.org/10.1017/s0033291720000458
  1898. Ballard T., Evans N., Fisher G., Sewell D. (2020): Using mixture modeling to examine differences in perceptual decision-making as a function of the time and method of participant recruitment. https://doi.org/10.31234/osf.io/w9d67
  1899. Uller T., Kampourakis K. (2020): Why Should Biologists Care about the Philosophy of Science?. Philosophy of Science for Biologists. https://doi.org/10.1017/9781108648981.002
  1900. Wibble T., Engström J., Pansell T. (2020): Visual and Vestibular Integration Express Summative Eye Movement Responses and Reveal Higher Visual Acceleration Sensitivity than Previously Described. Investigative Opthalmology & Visual Science 61(5):4. https://doi.org/10.1167/iovs.61.5.4
  1901. Puoliväli T., Palva S., Palva J. (2020): Influence of multiple hypothesis testing on reproducibility in neuroimaging research: A simulation study and Python-based software. Journal of Neuroscience Methods 337:108654. https://doi.org/10.1016/j.jneumeth.2020.108654
  1902. VanderWeele T., Mathur M., Chen Y. (2020): Outcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical Studies. Statistical Science 35(3). https://doi.org/10.1214/19-sts728
  1903. Romanov V., Silvani G., Zhu H., Cox C., Martinac B. (2020): An Acoustic Platform for Single‐Cell, High‐Throughput Measurements of the Viscoelastic Properties of Cells. Small 17(3). https://doi.org/10.1002/smll.202005759
  1904. Romanov V., Silvani G., Zhu H., Cox C., Martinac B. (2020): An acoustic platform for single-cell, high-throughput measurements of the viscoelastic properties of cells. https://doi.org/10.1101/2020.09.07.286898
  1905. Orozco V., Bontemps C., Maigné E., Piguet V., Hofstetter A., Lacroix A., et al. (2020): HOW TO MAKE A PIE: REPRODUCIBLE RESEARCH FOR EMPIRICAL ECONOMICS AND ECONOMETRICS. Journal of Economic Surveys 34(5):1134-1169. https://doi.org/10.1111/joes.12389
  1906. Bellou V., Tzoulaki I., Evangelou E., Belbasis L. (2020): Risk factors for adverse clinical outcomes in patients with COVID-19: A systematic review and meta-analysis. https://doi.org/10.1101/2020.05.13.20100495
  1907. Calonga‐Solís V., Amorim L., Farias T., Petzl‐Erler M., Malheiros D., Augusto D. (2020): Variation in genes implicated in B‐cell development and antibody production affects susceptibility to pemphigus. Immunology 162(1):58-67. https://doi.org/10.1111/imm.13259
  1908. Forgetta V., Jiang L., Vulpescu N., Hogan M., Chen S., Morris J., et al. (2020): An Effector Index to Predict Causal Genes at GWAS Loci. https://doi.org/10.1101/2020.06.28.171561
  1909. Dempsey W., Mukherjee B. (2020): Reflecting on “A Statistician in Medicine” in 2020. Statistics in Medicine 40(1):42-48. https://doi.org/10.1002/sim.8830
  1910. Tierney W., Hardy J., Ebersole C., Viganola D., Clemente E., Gordon M., et al. (2020): A creative destruction approach to replication: Implicit work and sex morality across cultures. Journal of Experimental Social Psychology 93:104060. https://doi.org/10.1016/j.jesp.2020.104060
  1911. Gervais W., McKee S., Malik S. (2020): Do Religious Primes Increase Risk Taking? Evidence Against “Anticipating Divine Protection” in Two Preregistered Direct Replications of Kupor, Laurin, and Levav (2015). Psychological Science 31(7):858-864. https://doi.org/10.1177/0956797620922477
  1912. Thompson W., Wright J., Bissett P. (2020): Open exploration. eLife 9. https://doi.org/10.7554/elife.52157
  1913. Hersh W. (2020): Information. Health Informatics. https://doi.org/10.1007/978-3-030-47686-1_2
  1914. Yuen W., Chan G., Bruno R., Clare P., Mattick R., Aiken A., et al. (2020): Adolescent Alcohol Use Trajectories: Risk Factors and Adult Outcomes. Pediatrics 146(4). https://doi.org/10.1542/peds.2020-0440
  1915. Świątkowski W., Carrier A. (2020): There is Nothing Magical about Bayesian Statistics: An Introduction to Epistemic Probabilities in Data Analysis for Psychology Starters. Basic and Applied Social Psychology 42(6):387-412. https://doi.org/10.1080/01973533.2020.1792297
  1916. Kong X., Francks C. (2020): Reproducibility in the absence of selective reporting: An illustration from large‐scale brain asymmetry research. Human Brain Mapping 43(1):244-254. https://doi.org/10.1002/hbm.25154
  1917. Lyu X., Xu Y., Zhao X., Zuo X., Hu C. (2020): Beyond psychology: prevalence of p value and confidence interval misinterpretation across different fields. Journal of Pacific Rim Psychology 14. https://doi.org/10.1017/prp.2019.28
  1918. Lin X. (2020): Learning Lessons on Reproducibility and Replicability in Large Scale Genome-Wide Association Studies. Harvard Data Science Review 2(4). https://doi.org/10.1162/99608f92.33703976
  1919. Pan X. (2020): Calculation of sampling size for non-zero tolerance level. Global Ecology and Conservation 22:e00982. https://doi.org/10.1016/j.gecco.2020.e00982
  1920. Wang Y., Zhang F., Mukiibi R., Chen L., Vinsky M., Plastow G., et al. (2020): Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: II: carcass merit traits. BMC Genomics 21(1). https://doi.org/10.1186/s12864-019-6273-1
  1921. Choi Y., Chong S. (2020): Effects of Selective Attention on Mean-Size Computation: Weighted Averaging and Perceptual Enlargement. Psychological Science 31(10):1261-1271. https://doi.org/10.1177/0956797620943834
  1922. Ye Y., Shrestha S., Burkholder G., Bansal A., Erdmann N., Wiener H., et al. (2020): Rates and Correlates of Incident Type 2 Diabetes Mellitus Among Persons Living With HIV-1 Infection. Frontiers in Endocrinology 11. https://doi.org/10.3389/fendo.2020.555401
  1923. Pawitan Y. (2020): Defending the P-value. arXiv. https://doi.org/10.48550/arxiv.2009.02099
  1924. Kang Y., Kwon H., Stammen J., Moorhouse K., Agnew A. (2020): Biomechanical Response Targets of Adult Human Ribs in Frontal Impacts. Annals of Biomedical Engineering 49(2):900-911. https://doi.org/10.1007/s10439-020-02613-x
  1925. Pavlov Y., Kotchoubey B. (2020): The electrophysiological underpinnings of variation in verbal working memory capacity. Scientific Reports 10(1). https://doi.org/10.1038/s41598-020-72940-5
  1926. Pavlov Y., Adamian N., Appelhoff S., Arvaneh M., Benwell C., Beste C., et al. (2020): #EEGManyLabs: Investigating the Replicability of Influential EEG Experiments. https://doi.org/10.31234/osf.io/528nr
  1927. Pavlov Y., Kotchoubey B. (2020): The electrophysiological underpinnings of variation in verbal working memory capacity. https://doi.org/10.1101/2020.05.02.073825
  1928. Fukuda Y., Masani K., Yamaguchi T. (2020): Comparison of lower limb joint moment and power during turning gait between young and old adults using hierarchical Bayesian inference. Journal of Biomechanics 103:109702. https://doi.org/10.1016/j.jbiomech.2020.109702
  1929. Breig Z. (2020): Prediction and Model Selection in Experiments. Economic Record 96(313):153-176. https://doi.org/10.1111/1475-4932.12533
  1930. Rafi Z., Greenland S. (2020): Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise. BMC Medical Research Methodology 20(1). https://doi.org/10.1186/s12874-020-01105-9
  1931. Fang Z., He M., Song M. (2020): Serum lipid profiles and risk of colorectal cancer: a prospective cohort study in the UK Biobank. British Journal of Cancer 124(3):663-670. https://doi.org/10.1038/s41416-020-01143-6
  1932. Lu Z., Happee R., de Winter J. (2020): Take over! A video-clip study measuring attention, situation awareness, and decision-making in the face of an impending hazard. Transportation Research Part F: Traffic Psychology and Behaviour 72:211-225. https://doi.org/10.1016/j.trf.2020.05.013
  1933. Dienes Z. (2020): How to use and report Bayesian hypothesis tests. Psychology of Consciousness: Theory, Research, and Practice 8(1):9-26. https://doi.org/10.1037/cns0000258
  1934. Dienes Z. (2020): How to use and report Bayesian hypothesis tests. https://doi.org/10.31234/osf.io/bua5n
  1935. Dienes Z. (2020): The inner workings of Registered Reports. https://doi.org/10.31234/osf.io/yhp2a
  1936. Demir İ., Çalık P. (2020): Hybrid-architectured double-promoter expression systems enhance and upregulate-deregulated gene expressions in Pichia pastoris in methanol-free media. Applied Microbiology and Biotechnology 104(19):8381-8397. https://doi.org/10.1007/s00253-020-10796-5
  1937. Bahník Š., Vranka M. (2020): Punishment and corruption. https://doi.org/10.31234/osf.io/ew436
  1938. Дем’яненко В., Мар’єнко М., Носенко Ю., Семеріков С., Шишкіна М. (2020): Адаптивна хмаро орієнтована система навчання та професійного розвитку вчителів закладів загальної середньої освіти. Педагогічна думка eBooks. https://doi.org/10.31812/123456789/4348
  1939. Fowlie A. (2019): Bayesian and frequentist approaches to resonance searches. Journal of Instrumentation 14(10):P10031-P10031. https://doi.org/10.1088/1748-0221/14/10/p10031
  1940. Chalfin A., Hansen B., Lerner J., Parker L. (2019): Reducing Crime Through Environmental Design: Evidence from a Randomized Experiment of Street Lighting in New York City. https://doi.org/10.3386/w25798
  1941. Shafi A., Nguyen T., Peyvandipour A., Draghici S. (2019): GSMA: an approach to identify robust global and test Gene Signatures using Meta-Analysis. Bioinformatics 36(2):487-495. https://doi.org/10.1093/bioinformatics/btz561
  1942. Kshirsagar A., Dreyfuss B., Ishai G., Heffetz O., Hoffman G. (2019): Monetary-Incentive Competition Between Humans and Robots: Experimental Results. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). https://doi.org/10.1109/hri.2019.8673201
  1943. Slavenko A., Feldman A., Allison A., Bauer A., Böhm M., Chirio L., et al. (2019): Global patterns of body size evolution in squamate reptiles are not driven by climate. Global Ecology and Biogeography 28(4):471-483. https://doi.org/10.1111/geb.12868
  1944. Isakov A., Fowler J., Airoldi E., Christakis N. (2019): The Structure of Negative Social Ties in Rural Village Networks. Sociological Science 6:197-218. https://doi.org/10.15195/v6.a8
  1945. Koplenig A. (2019): A non-parametric significance test to compare corpora. PLOS ONE 14(9):e0222703. https://doi.org/10.1371/journal.pone.0222703
  1946. Alexander Skulmowski (2019): Bridging the gap between embodied cognition and cognitive load theory. Qucosa – Monarch (Chemnitz University of Technology).
  1947. Sarafoglou A., Hoogeveen S., Matzke D., Wagenmakers E. (2019): Teaching Good Research Practices: Protocol of a Research Master Course. Psychology Learning & Teaching 19(1):46-59. https://doi.org/10.1177/1475725719858807
  1948. Sarafoglou A., Hoogeveen S., Matzke D., Wagenmakers E. (2019): Teaching Good Research Practices: Protocol of a Research Master Course. https://doi.org/10.31234/osf.io/gvesh
  1949. Theadom A., McDonald S., Starkey N., Barker-Collo S., Jones K., Ameratunga S., et al. (2019): Social cognition four years after mild-TBI: An age-matched prospective longitudinal cohort study. Neuropsychology 33(4):560-567. https://doi.org/10.1037/neu0000516
  1950. Kvarven A., Strømland E., Johannesson M. (2019): Comparing meta-analyses and preregistered multiple-laboratory replication projects. Nature Human Behaviour 4(4):423-434. https://doi.org/10.1038/s41562-019-0787-z
  1951. Thalmayer A., Saucier G., Flournoy J., Srivastava S. (2019): Ethics-Relevant Values as Antecedents of Personality Change: Longitudinal Findings from the Life and Time Study. Collabra: Psychology 5(1). https://doi.org/10.1525/collabra.244
  1952. Adibi A., Sin D., Sadatsafavi M. (2019): Lowering the P Value Threshold. JAMA 321(15):1532. https://doi.org/10.1001/jama.2019.0566
  1953. Flouris A., Friesen B., Herry C., Seely A., Notley S., Kenny G. (2019): Heart rate variability dynamics during treatment for exertional heat strain when immediate response is not possible. Experimental Physiology 104(6):845-854. https://doi.org/10.1113/ep087297
  1954. Rădoi A., Poca M., Gándara D., Castro L., Cevallos M., Pacios M., et al. (2019): The Sport Concussion Assessment Tool (SCAT2) for evaluating civilian mild traumatic brain injury. A pilot normative study. PLOS ONE 14(2):e0212541. https://doi.org/10.1371/journal.pone.0212541
  1955. Bate A., Trifirò G., Avillach P., Evans S. (2019): Data Mining and Other Informatics Approaches to Pharmacoepidemiology. Pharmacoepidemiology. https://doi.org/10.1002/9781119413431.ch27
  1956. Gelman A. (2019): When we make recommendations for scientific practice, we are (at best) acting as social scientists. European Journal of Clinical Investigation 49(10). https://doi.org/10.1111/eci.13165
  1957. Martin A., Su P., Meinzer M. (2019): Common and unique effects of HD-tDCS to the social brain across cultural groups. Neuropsychologia 133:107170. https://doi.org/10.1016/j.neuropsychologia.2019.107170
  1958. Ramírez-Hassan A. (2019): Dynamic variable selection in dynamic logistic regression: an application to Internet subscription. Empirical Economics 59(2):909-932. https://doi.org/10.1007/s00181-019-01644-1
  1959. Krypotos A., Klugkist I., Mertens G., Engelhard I. (2019): A step-by-step guide on preregistration and effective data sharing for psychopathology research. Journal of Abnormal Psychology 128(6):517-527. https://doi.org/10.1037/abn0000424
  1960. Olsson-Collentine A., van Assen M., Hartgerink C. (2019): The Prevalence of Marginally Significant Results in Psychology Over Time. Psychological Science 30(4):576-586. https://doi.org/10.1177/0956797619830326
  1961. Joffe A., Wong K., Bond G., Khodayari Moez E., Acton B., Dinu I., et al. (2019): Kindergarten‐age neurocognitive, functional, and quality‐of‐life outcomes after liver transplantation at under 6 years of age. Pediatric Transplantation 24(2). https://doi.org/10.1111/petr.13624
  1962. Joffe A., Brin G., Farrow S. (2019): Unreliable Early Neuroprognostication After Severe Carbon Monoxide Poisoning Is Likely Due to Cytopathic Hypoxia: A Case Report and Discussion. Journal of Child Neurology 35(2):111-115. https://doi.org/10.1177/0883073819879833
  1963. Rosinger A., Ice G. (2019): Secondary data analysis to answer questions in human biology. American Journal of Human Biology 31(3). https://doi.org/10.1002/ajhb.23232
  1964. Johnson A., Evans S., Checketts J., Scott J., Wayant C., Johnson M., et al. (2019): Effects of a proposal to alter the statistical significance threshold on previously published orthopaedic trauma randomized controlled trials. Injury 50(11):1934-1937. https://doi.org/10.1016/j.injury.2019.08.012
  1965. Baselmans B., van de Weijer M., Abdellaoui A., Vink J., Hottenga J., Willemsen G., et al. (2019): A Genetic Investigation of the Well-Being Spectrum. Behavior Genetics 49(3):286-297. https://doi.org/10.1007/s10519-019-09951-0
  1966. Conradie B., Piesse J., Stephens J. (2019): The changing environment: Efficiency, vulnerability and changes in land use in the South African Karoo, 2012–2014. Environmental Development 32:100453. https://doi.org/10.1016/j.envdev.2019.07.003
  1967. Hutchinson J., Barrett L. (2019): The Power of Predictions: An Emerging Paradigm for Psychological Research. Current Directions in Psychological Science 28(3):280-291. https://doi.org/10.1177/0963721419831992
  1968. Englert B. (2019): Evidence in quantum data. Rochester Conference on Coherence and Quantum Optics (CQO-11). https://doi.org/10.1364/cqo.2019.w2a.1
  1969. Voelkl B. (2019): Multiple testing: correcting for alpha error inflation with false discovery rate (FDR) or family-wise error rate?. Animal Behaviour 155:173-177. https://doi.org/10.1016/j.anbehav.2019.07.001
  1970. McShane B., Gal D., Gelman A., Robert C., Tackett J. (2019): Abandon Statistical Significance. The American Statistician 73(sup1):235-245. https://doi.org/10.1080/00031305.2018.1527253
  1971. Alger B. (2019): Hypothesis-Testing Improves the Predicted Reliability of Neuroscience Research. https://doi.org/10.1101/537365
  1972. Byrne B., Jones D., Strong K., Polavarapu S., Harper A., Baker D., et al. (2019): On what scales can GOSAT flux inversions constrain anomalies in terrestrial ecosystems?. Atmospheric Chemistry and Physics 19(20):13017-13035. https://doi.org/10.5194/acp-19-13017-2019
  1973. Asken B., Thomas K., Lee A., Davis J., Malloy P., Salloway S. (2019): Discrepancy-Based Evidence for Loss of Thinking Abilities (DELTA): Development and Validation of a Novel Approach to Identifying Cognitive Changes. Journal of the International Neuropsychological Society 26(5):464-479. https://doi.org/10.1017/s1355617719001346
  1974. Brian D. Segal (2019): Toward Replicability With Confidence Intervals for the Exceedance Probability. Figshare. https://doi.org/10.6084/m9.figshare.10762796.v1
  1975. Verschuere B., te Kaat L. (2019): What Are the Core Features of Psychopathy? A Prototypicality Analysis Using the Psychopathy Checklist-Revised (PCL-R). Journal of Personality Disorders 34(3):410-419. https://doi.org/10.1521/pedi_2019_33_396
  1976. Palmer C., Cameron P., Gabbe B. (2019): Comparison of revised Functional Capacity Index scores with Abbreviated Injury Scale 2008 scores in predicting 12-month severe trauma outcomes. Injury Prevention 26(2):138-146. https://doi.org/10.1136/injuryprev-2018-043085
  1977. Maringe C., Belot A., Rubio F., Rachet B. (2019): Comparison of model-building strategies for excess hazard regression models in the context of cancer epidemiology. BMC Medical Research Methodology 19(1). https://doi.org/10.1186/s12874-019-0830-9
  1978. Furia C., Feldt R., Torkar R. (2019): Bayesian Data Analysis in Empirical Software Engineering Research. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2019.2935974
  1979. Albers C. (2019): The problem with unadjusted multiple and sequential statistical testing. Nature Communications 10(1). https://doi.org/10.1038/s41467-019-09941-0
  1980. Albers C. (2019): A note on the alpha-level: Not all alphas can be justified. https://doi.org/10.31234/osf.io/erwvk
  1981. Ammitzbøll C., Börnsen L., Petersen E., Oturai A., Søndergaard H., Grandjean P., et al. (2019): Perfluorinated substances, risk factors for multiple sclerosis and cellular immune activation. Journal of Neuroimmunology 330:90-95. https://doi.org/10.1016/j.jneuroim.2019.03.002
  1982. Shen C., Li X. (2019): Towards More Flexible False Positive Control in Phase III Randomized Clinical Trials. arXiv. https://doi.org/10.48550/arxiv.1902.08229
  1983. Ebersole C., Mathur M., Baranski E., Bart-Plange D., Buttrick N., Chartier C., et al. (2019): Many Labs 5: Testing pre-data collection peer review as an intervention to increase replicability. https://doi.org/10.31234/osf.io/sxfm2
  1984. Brand C., Ounsley J., Van der Post D., Morgan T. (2019): Cumulative Science via Bayesian Posterior Passing. Meta-Psychology 3. https://doi.org/10.15626/mp.2017.840
  1985. Chambers C. (2019): The Registered Reports Revolution Lessons in Cultural Reform. Significance 16(4):23-27. https://doi.org/10.1111/j.1740-9713.2019.01299.x
  1986. van der Lee C., Gatt A., van Miltenburg E., Wubben S., Krahmer E. (2019): Best practices for the human evaluation of automatically generated text. Proceedings of the 12th International Conference on Natural Language Generation. https://doi.org/10.18653/v1/w19-8643
  1987. Franck C., Gramacy R. (2019): Assessing Bayes Factor Surfaces Using Interactive Visualization and Computer Surrogate Modeling. The American Statistician 74(4):359-369. https://doi.org/10.1080/00031305.2019.1671219
  1988. Christopher T. Franck, Robert B. Gramacy (2019): Assessing Bayes factor surfaces using interactive visualization and computer surrogate modeling. Figshare. https://doi.org/10.6084/m9.figshare.9915803.v1
  1989. Lino de Oliveira C. (2019): Basic antidepressant research: a brief assay on how to justify your alpha. Bionatura 02(Bionatura Conference Serie). https://doi.org/10.21931/rb/cs/2019.02.01.3
  1990. Webb C., Linn S., Lebo M. (2019): A Bounds Approach to Inference Using the Long Run Multiplier. Political Analysis 27(3):281-301. https://doi.org/10.1017/pan.2019.3
  1991. Wayant C., Meyer C., Gupton R., Som M., Baker D., Vassar M. (2019): The Fragility Index in a Cohort of HIV/AIDS Randomized Controlled Trials. Journal of General Internal Medicine 34(7):1236-1243. https://doi.org/10.1007/s11606-019-04928-5
  1992. Wayant C., Scott J., Vassar M. (2019): Lowering the P Value Threshold—Reply. JAMA 321(15):1533. https://doi.org/10.1001/jama.2019.0574
  1993. Williams C. (2019): How redefining statistical significance can worsen the replication crisis. Economics Letters 181:65-69. https://doi.org/10.1016/j.econlet.2019.05.007
  1994. Hyatt C., Owens M., Crowe M., Carter N., Lynam D., Miller J. (2019): The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables. NeuroImage 205:116225. https://doi.org/10.1016/j.neuroimage.2019.116225
  1995. Geerards D., Pusic A., Hoogbergen M., van der Hulst R., Sidey-Gibbons C. (2019): Computerized Quality of Life Assessment: A Randomized Experiment to Determine the Impact of Individualized Feedback on Assessment Experience. Journal of Medical Internet Research 21(7):e12212. https://doi.org/10.2196/12212
  1996. Pollard D., Pollard T., Pollard K. (2019): Empowering statistical methods for cellular and molecular biologists. Molecular Biology of the Cell 30(12):1359-1368. https://doi.org/10.1091/mbc.e15-02-0076
  1997. Wright D. (2019): Research Methods for Education With Technology: Four Concerns, Examples, and Recommendations. Frontiers in Education 4. https://doi.org/10.3389/feduc.2019.00147
  1998. Bischof D., van der Velden M. (2019): The Use and Usefulness of p‐Values in Political Science: Introduction. Swiss Political Science Review 25(3):276-280. https://doi.org/10.1111/spsr.12376
  1999. Fellman D., Jylkkä J., Waris O., Soveri A., Ritakallio L., Haga S., et al. (2019): The role of strategy use in working memory training outcomes. Journal of Memory and Language 110:104064. https://doi.org/10.1016/j.jml.2019.104064
  2000. Whitney D. (2019): Racial differences in skeletal fragility but not osteoarthritis among women and men with cerebral palsy. Bone Reports 11:100219. https://doi.org/10.1016/j.bonr.2019.100219
  2001. Benjamin D., Berger J. (2019): Three Recommendations for Improving the Use of p -Values. The American Statistician 73(sup1):186-191. https://doi.org/10.1080/00031305.2018.1543135
  2002. Goldenholz D., Sun H., Westover B. (2019): Commentary on “Predicting seizure freedom after epilepsy surgery, a challenge in clinical practice”. Epilepsy & Behavior 99:106408. https://doi.org/10.1016/j.yebeh.2019.07.009
  2003. Sullivan D. (2019): Social Psychological Theory as History: Outlining the Critical-Historical Approach to Theory. Personality and Social Psychology Review 24(1):78-99. https://doi.org/10.1177/1088868319883174
  2004. Tawfik D., Scheid A., Profit J., Shanafelt T., Trockel M., Adair K., et al. (2019): Evidence Relating Health Care Provider Burnout and Quality of Care. Annals of Internal Medicine 171(8):555-567. https://doi.org/10.7326/m19-1152
  2005. Guillen Gonzalez D., Bittlinger M., Erk S., Müller S. (2019): Neuroscientific and Genetic Evidence in Criminal Cases: A Double-Edged Sword in Germany but Not in the United States?. Frontiers in Psychology 10. https://doi.org/10.3389/fpsyg.2019.02343
  2006. Rice D., Raffoul H., Ioannidis J., Moher D. (2019): Academic criteria for promotion and tenure in faculties of biomedical sciences: a cross-sectional analysis of 146 universities. https://doi.org/10.1101/802850
  2007. Landy D., Utset-Ward T., Lee M. (2019): What Are the Implications of Alternative Alpha Thresholds for Hypothesis Testing in Orthopaedics?. Clinical Orthopaedics & Related Research 477(10):2358-2363. https://doi.org/10.1097/corr.0000000000000843
  2008. Bickel D. (2019): Sharpen statistical significance: Evidence thresholds and Bayes factors sharpened into Occam’s razor. Stat 8(1). https://doi.org/10.1002/sta4.215
  2009. Bickel D. (2019): Null Hypothesis Significance Testing Defended and Calibrated by Bayesian Model Checking. The American Statistician 75(3):249-255. https://doi.org/10.1080/00031305.2019.1699443
  2010. Bickel D., Rahal A. (2019): Model fusion and multiple testing in the likelihood paradigm: shrinkage and evidence supporting a point null hypothesis. Statistics 53(6):1187-1209. https://doi.org/10.1080/02331888.2019.1660342
  2011. Schubring D., Schupp H. (2019): Affective picture processing: Alpha‐ and lower beta‐band desynchronization reflects emotional arousal. Psychophysiology 56(8). https://doi.org/10.1111/psyp.13386
  2012. Trafimow D. (2019): A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals. Econometrics 7(2):26. https://doi.org/10.3390/econometrics7020026
  2013. Trafimow D. (2019): Why successful replications across contexts and Operationalizations might not be good for theory building or testing. Journal for the Theory of Social Behaviour 49(3):359-368. https://doi.org/10.1111/jtsb.12211
  2014. Billheimer D. (2019): Predictive Inference and Scientific Reproducibility. The American Statistician 73(sup1):291-295. https://doi.org/10.1080/00031305.2018.1518270
  2015. Infanger D., Schmidt‐Trucksäss A. (2019): P value functions: An underused method to present research results and to promote quantitative reasoning. Statistics in Medicine 38(21):4189-4197. https://doi.org/10.1002/sim.8293
  2016. Gorman D. (2019): Use of publication procedures to improve research integrity by addiction journals. Addiction 114(8):1478-1486. https://doi.org/10.1111/add.14604
  2017. Koletsi D., Solmi M., Pandis N., Fleming P., Correll C., Ioannidis J. (2019): Most recommended medical interventions reach P &lt; 0.005 for their primary outcomes in meta-analyses. International Journal of Epidemiology 49(3):885-893. https://doi.org/10.1093/ije/dyz241
  2018. Makowski D., Ben-Shachar M., Chen S., Lüdecke D. (2019): Indices of Effect Existence and Significance in the Bayesian Framework. Frontiers in Psychology 10. https://doi.org/10.3389/fpsyg.2019.02767
  2019. van Ravenzwaaij D., Ioannidis J. (2019): True and false positive rates for different criteria of evaluating statistical evidence from clinical trials. BMC Medical Research Methodology 19(1). https://doi.org/10.1186/s12874-019-0865-y
  2020. Chen D., Dai L., Li D. (2019): A Delicate Balance for Innovation: Competition and Collaboration in R&D Consortia. Management and Organization Review 15(1):145-176. https://doi.org/10.1017/mor.2018.49
  2021. Wahlsten D. (2019): Intelligence. Genes, Brain Function, and Behavior. https://doi.org/10.1016/b978-0-12-812832-9.00015-4
  2022. Fife D. (2019): Flexplot: graphically-based data analysis. https://doi.org/10.31234/osf.io/kh9c3
  2023. Steel E., Liermann M., Guttorp P. (2019): Beyond Calculations: A Course in Statistical Thinking. The American Statistician 73(sup1):392-401. https://doi.org/10.1080/00031305.2018.1505657
  2024. Parslow E., Ranehill E., Zethraeus N., Blomberg L., von Schoultz B., Hirschberg A., et al. (2019): The digit ratio (2D:4D) and economic preferences: no robust associations in a sample of 330 women. Journal of the Economic Science Association 5(2):149-169. https://doi.org/10.1007/s40881-019-00076-y
  2025. Duffy E., Kay M., Jacquier E., Catellier D., Hampton J., Anater A., et al. (2019): Trends in Food Consumption Patterns of US Infants and Toddlers from Feeding Infants and Toddlers Studies (FITS) in 2002, 2008, 2016. Nutrients 11(11):2807. https://doi.org/10.3390/nu11112807
  2026. Sercy E., Carrick M., Orlando A., Bar-Or D. (2019): Factors to Consider When Evaluating Rates of Pharmacologic Venous Thromboembolism Prophylaxis Administration Among Trauma Patients. Journal for Healthcare Quality 42(6):304-314. https://doi.org/10.1097/jhq.0000000000000230
  2027. Sperling E., Tecklenburg S., Duncan L. (2019): Statistical inference and reproducibility in geobiology. Geobiology 17(3):261-271. https://doi.org/10.1111/gbi.12333
  2028. Domellöf E., Bäckström A., Johansson A., Rönnqvist L., von Hofsten C., Rosander K. (2019): Kinematic characteristics of second‐order motor planning and performance in 6‐ and 10‐year‐old children and adults: Effects of age and task constraints. Developmental Psychobiology 62(2):250-265. https://doi.org/10.1002/dev.21911
  2029. Kahle E., Veliz P., McCabe S., Boyd C. (2019): Functional and structural social support, substance use and sexual orientation from a nationally representative sample of US adults. Addiction 115(3):546-558. https://doi.org/10.1111/add.14819
  2030. Czibor E., Jimenez‐Gomez D., List J. (2019): The Dozen Things Experimental Economists Should Do (More of). Southern Economic Journal 86(2):371-432. https://doi.org/10.1002/soej.12392
  2031. Czibor E., Jimenez-Gomez D., List J. (2019): The Dozen Things Experimental Economists Should Do (More of). https://doi.org/10.3386/w25451
  2032. Czibor E., Jimenez-Gomez D., List J. (2019): The Dozen Things Experimental Economists Should Do (More Of). SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3313734
  2033. Chen E., Chang J. (2019): Investigating implicit and explicit attitudes toward sexual minorities in Taiwan. Psychology of Sexual Orientation and Gender Diversity 7(2):197-207. https://doi.org/10.1037/sgd0000362
  2034. Bonnier E., Dreber A., Hederos K., Sandberg A. (2019): Exposure to half-dressed women and economic behavior. Journal of Economic Behavior & Organization 168:393-418. https://doi.org/10.1016/j.jebo.2019.10.017
  2035. Rigat F., Bartolini E., Dalsass M., Kumar N., Marchi S., Speziale P., et al. (2019): Retrospective Identification of a Broad IgG Repertoire Differentiating Patients With S. aureus Skin and Soft Tissue Infections From Controls. Frontiers in Immunology 10. https://doi.org/10.3389/fimmu.2019.00114
  2036. Chen F., Ye K., Wang M. (2019): The minimum Bayes factor hypothesis test for correlations and partial correlations. Communications in Statistics – Theory and Methods 50(11):2467-2480. https://doi.org/10.1080/03610926.2019.1667397
  2037. Romero F. (2019): Philosophy of science and the replicability crisis. Philosophy Compass 14(11). https://doi.org/10.1111/phc3.12633
  2038. Romero F. (2019): Philosophy of Science and The Replicability Crisis. https://doi.org/10.31234/osf.io/x37nq
  2039. Romero F., Sprenger J. (2019): Scientific Self-Correction: The Bayesian Way. https://doi.org/10.31234/osf.io/daw3q
  2040. Holzmeister F., Huber J., Kirchler M., Lindner F., Weitzel U., Zeisberger S. (2019): What Drives Risk Perception? A Global Survey with Financial Professionals and Lay People. https://doi.org/10.31219/osf.io/v6r9n
  2041. Holzmeister F., Huber J., Kirchler M., Lindner F., Weitzel U., Zeisberger S. (2019): What Drives Risk Perception? A Global Survey with Financial Professionals and Lay People. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3374893
  2042. Zhang F., Hughes C. (2019): Beyond p-value: the Rigor and Power of Study. Global Clinical and Translational Research. https://doi.org/10.36316/gcatr.02.0021
  2043. Zhang F., Hughes C. (2019): Beyond p-value: the Rigor and Power of Study. Global Clinical and Translational Research. https://doi.org/10.36316/gcatr.01.0021
  2044. Fernanda Reistenbach-Goltz (2019): Interindividual variability in perceived appetite and appetite-related hormone responses to eating and exercise in humans. Figshare. https://doi.org/10.26174/thesis.lboro.9741896.v1
  2045. Manno F., Fernandez-Ruiz J., Manno S., Cheng S., Lau C., Barrios F. (2019): Sparse Sampling of Silence Type I Errors With an Emphasis on Primary Auditory Cortex. Frontiers in Neuroscience 13. https://doi.org/10.3389/fnins.2019.00516
  2046. de Oliveira Neto F., Torkar R., Feldt R., Gren L., Furia C., Huang Z. (2019): Evolution of statistical analysis in empirical software engineering research: Current state and steps forward. Journal of Systems and Software 156:246-267. https://doi.org/10.1016/j.jss.2019.07.002
  2047. Hillary F., Medaglia J. (2019): What the replication crisis means for intervention science. International Journal of Psychophysiology 154:3-5. https://doi.org/10.1016/j.ijpsycho.2019.05.006
  2048. MacMaster F., Croarkin P., Wilkes T., McLellan Q., Langevin L., Jaworska N., et al. (2019): Repetitive Transcranial Magnetic Stimulation in Youth With Treatment Resistant Major Depression. Frontiers in Psychiatry 10. https://doi.org/10.3389/fpsyt.2019.00170
  2049. Bartoš F., Maier M. (2019): Power or Alpha? The Better Way of Decreasing the False Discovery Rate. https://doi.org/10.31234/osf.io/ev29a
  2050. Diaz-Quijano F., Calixto F., Silva J. (2019): How feasible is it to abandon statistical significance? A reflection based on a short survey. Research Square (Research Square). https://doi.org/10.21203/rs.2.14217/v1
  2051. Crevecoeur F., Scott S., Cluff T. (2019): Robust Control in Human Reaching Movements: A Model-Free Strategy to Compensate for Unpredictable Disturbances. The Journal of Neuroscience 39(41):8135-8148. https://doi.org/10.1523/jneurosci.0770-19.2019
  2052. Ciasca G., Mazzini A., Sassun T., Nardini M., Minelli E., Papi M., et al. (2019): Efficient Spatial Sampling for AFM-Based Cancer Diagnostics: A Comparison between Neural Networks and Conventional Data Analysis. Condensed Matter 4(2):58. https://doi.org/10.3390/condmat4020058
  2053. Chen G., Xiao Y., Taylor P., Rajendra J., Riggins T., Geng F., et al. (2019): Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling. Neuroinformatics 17(4):515-545. https://doi.org/10.1007/s12021-018-9409-6
  2054. Xiong G., Dong D., Cheng C., Jiang Y., Sun X., He J., et al. (2019): State-independent and -dependent structural alterations in limbic-cortical regions in patients with current and remitted depression. Journal of Affective Disorders 258:1-10. https://doi.org/10.1016/j.jad.2019.07.065
  2055. Bravo G., Grimaldo F., López-Iñesta E., Mehmani B., Squazzoni F. (2019): The effect of publishing peer review reports on referee behavior in five scholarly journals. Nature Communications 10(1). https://doi.org/10.1038/s41467-018-08250-2
  2056. Dutilh G., Sarafoglou A., Wagenmakers E. (2019): Flexible yet fair: blinding analyses in experimental psychology. Synthese 198(S23):5745-5772. https://doi.org/10.1007/s11229-019-02456-7
  2057. Dutilh G., Sarafoglou A., Wagenmakers E. (2019): Flexible Yet Fair: Blinding Analyses in Experimental Psychology. https://doi.org/10.31234/osf.io/d79r8
  2058. Francis G. (2019): Hypothesis Testing Reconsidered. Cambridge University Press eBooks. https://doi.org/10.1017/9781108582995
  2059. Zhou G., Soufan O., Ewald J., Hancock R., Basu N., Xia J. (2019): NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Research 47(W1):W234-W241. https://doi.org/10.1093/nar/gkz240
  2060. Baltussen G., Swinkels L., van Vliet P. (2019): Global Factor Premiums. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3325720
  2061. Campbell H., Gustafson P. (2019): The World of Research Has Gone Berserk: Modeling the Consequences of Requiring “Greater Statistical Stringency” for Scientific Publication. The American Statistician 73(sup1):358-373. https://doi.org/10.1080/00031305.2018.1555101
  2062. Heinrich H., Gevensleben H., Becker A., Rothenberger A. (2019): Effects of neurofeedback on the dysregulation profile in children with ADHD: SCP NF meets SDQ-DP – a retrospective analysis. Psychological Medicine 50(2):258-263. https://doi.org/10.1017/s0033291718004130
  2063. Kennedy H., Baker B., Jordan J., Funk D. (2019): Running Recession: A Trend Analysis of Running Involvement and Runner Characteristics to Understand Declining Participation. Journal of Sport Management 33(3):215-228. https://doi.org/10.1123/jsm.2018-0261
  2064. Hoijtink H., Mulder J., van Lissa C., Gu X. (2019): A tutorial on testing hypotheses using the Bayes factor. Psychological Methods 24(5):539-556. https://doi.org/10.1037/met0000201
  2065. Hoijtink H., Mulder J., Van Lissa C., Gu X. (2019): A tutorial on testing hypotheses using the Bayes factor. https://doi.org/10.31234/osf.io/v3shc
  2066. Aguinis H., Vassar M., Wayant C. (2019): On reporting and interpreting statistical significance and p values in medical research. BMJ Evidence-Based Medicine 26(2):39-42. https://doi.org/10.1136/bmjebm-2019-111264
  2067. Groot H., van Blokland I., Lipsic E., Karper J., van der Harst P. (2019): Leukocyte profiles across the cardiovascular disease continuum: A population-based cohort study. Journal of Molecular and Cellular Cardiology 138:158-164. https://doi.org/10.1016/j.yjmcc.2019.11.156
  2068. Kurashige H., Yamashita Y., Hanakawa T., Honda M. (2019): Effective Augmentation of Creativity-Involving Productivity Consequent to Spontaneous Selectivity in Knowledge Acquisition. Frontiers in Psychology 10. https://doi.org/10.3389/fpsyg.2019.00600
  2069. Liu H., Koch C., Haller A., Joosse S., Kumar R., Vellekoop M., et al. (2019): Evaluation of Microfluidic Ceiling Designs for the Capture of Circulating Tumor Cells on a Microarray Platform. Advanced Biosystems 4(2). https://doi.org/10.1002/adbi.201900162
  2070. Llewelyn H. (2019): Replacing P-values with frequentist posterior probabilities of replication—When possible parameter values must have uniform marginal prior probabilities. PLOS ONE 14(2):e0212302. https://doi.org/10.1371/journal.pone.0212302
  2071. Han H., Glenn A., Dawson K. (2019): Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research. Brain Sciences 9(8):198. https://doi.org/10.3390/brainsci9080198
  2072. Han H., Dawson K., Thoma S., Glenn A. (2019): Developmental Level of Moral Judgment Influences Behavioral Patterns During Moral Decision-Making. The Journal of Experimental Education 88(4):660-675. https://doi.org/10.1080/00220973.2019.1574701
  2073. Han H., Dawson K., Thoma S., Glenn A. (2019): Developmental Level of Moral Judgment Influences Behavioral Patterns during Moral Decision-making. https://doi.org/10.31234/osf.io/qvkuj
  2074. Han H., Glenn A., Dawson K. (2019): Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research. https://doi.org/10.31234/osf.io/ak75x
  2075. König I. (2019): Presidential address: Six open questions to genetic epidemiologists. Genetic Epidemiology 43(3):242-249. https://doi.org/10.1002/gepi.22191
  2076. Lawler I., Zimmermann G. (2019): Misalignment Between Research Hypotheses and Statistical Hypotheses: A Threat to Evidence-Based Medicine?. Topoi 40(2):307-318. https://doi.org/10.1007/s11245-019-09667-0
  2077. Hannikainen I., Machery E., Rose D., Stich S., Olivola C., Sousa P., et al. (2019): For Whom Does Determinism Undermine Moral Responsibility? Surveying the Conditions for Free Will Across Cultures. Frontiers in Psychology 10. https://doi.org/10.3389/fpsyg.2019.02428
  2078. Rougier J. (2019): p -Values, Bayes Factors, and Sufficiency. The American Statistician 73(sup1):148-151. https://doi.org/10.1080/00031305.2018.1502684
  2079. Roden-Foreman J., Foreman M., Funk G., Powers M. (2019): Driver see, driver crash: Associations between televised stock car races’ audience size and the incidence of speed-related motor vehicle collisions in the United States. Baylor University Medical Center Proceedings 32(1):37-42. https://doi.org/10.1080/08998280.2018.1512275
  2080. Kim J., Choi I. (2019): Choosing the Level of Significance: A Decision‐theoretic Approach. Abacus 57(1):27-71. https://doi.org/10.1111/abac.12172
  2081. Kim J., Rahman M., Shamsuddin A. (2019): Can energy prices predict stock returns? An extreme bounds analysis. Energy Economics 81:822-834. https://doi.org/10.1016/j.eneco.2019.05.029
  2082. Charlesworth J., Weinert L., Araujo E., Welch J. (2019): Wolbachia , Cardinium and climate: an analysis of global data. Biology Letters 15(8). https://doi.org/10.1098/rsbl.2019.0273
  2083. Jane Hutton, Diggle, Peter John, Bird, Sheila M., Hennig, Christian, Longford, Nick, B. Mathur , Maya, et al. (2019): Discussion on the Meeting on ‘Signs and Sizes:Understanding and Replicating Statistical Findings’. Journal of the Royal Statistical Society Series A: Statistics in Society 183(2):449-469. https://doi.org/10.1111/rssa.12544
  2084. Turna J., Grosman Kaplan K., Patterson B., Bercik P., Anglin R., Soreni N., et al. (2019): Higher prevalence of irritable bowel syndrome and greater gastrointestinal symptoms in obsessive-compulsive disorder. Journal of Psychiatric Research 118:1-6. https://doi.org/10.1016/j.jpsychires.2019.08.004
  2085. Miller J., Ulrich R. (2019): The quest for an optimal alpha. PLOS ONE 14(1):e0208631. https://doi.org/10.1371/journal.pone.0208631
  2086. Blume J., Greevy R., Welty V., Smith J., Dupont W. (2019): An Introduction to Second-Generation p -Values. The American Statistician 73(sup1):157-167. https://doi.org/10.1080/00031305.2018.1537893
  2087. Rouder J., Haaf J., Snyder H. (2019): Minimizing Mistakes in Psychological Science. Advances in Methods and Practices in Psychological Science 2(1):3-11. https://doi.org/10.1177/2515245918801915
  2088. Witt J. (2019): Insights into Criteria for Statistical Significance from Signal Detection Analysis. Meta-Psychology 3. https://doi.org/10.15626/mp.2018.871
  2089. Sun J., Rhemtulla M., Vazire S. (2019): Eavesdropping on Missing Data: What Are University Students Doing When They Miss Experience Sampling Reports?. https://doi.org/10.31234/osf.io/5tcwd
  2090. Krueger J., Heck P. (2019): Putting the P -Value in its Place. The American Statistician 73(sup1):122-128. https://doi.org/10.1080/00031305.2018.1470033
  2091. Flournoy J., Vijayakumar N., Cheng T., Cosme D., Flannery J., Pfeifer J. (2019): Improving Practices and Inferences in Developmental Cognitive Neuroscience. https://doi.org/10.31234/osf.io/ez5sf
  2092. Sakaluk J. (2019): Expanding Statistical Frontiers in Sexual Science: Taxometric, Invariance, and Equivalence Testing. The Journal of Sex Research 56(4-5):475-510. https://doi.org/10.1080/00224499.2019.1568377
  2093. Ioannidis J. (2019): What Have We (Not) Learnt from Millions of Scientific Papers with P Values?. The American Statistician 73(sup1):20-25. https://doi.org/10.1080/00031305.2018.1447512
  2094. Quiggin J. (2019): The Replication Crisis as Market Failure. Econometrics 7(4):44. https://doi.org/10.3390/econometrics7040044
  2095. van Doorn J., Matzke D., Wagenmakers E. (2019): An In-Class Demonstration of Bayesian Inference. Psychology Learning & Teaching 19(1):36-45. https://doi.org/10.1177/1475725719848574
  2096. Dushoff J., Kain M., Bolker B. (2019): I can see clearly now: Reinterpreting statistical significance. Methods in Ecology and Evolution 10(6):756-759. https://doi.org/10.1111/2041-210x.13159
  2097. McPhetres J. (2019): Commentary: Acetaminophen Enhances the Reflective Learning Process. Frontiers in Psychology 10. https://doi.org/10.3389/fpsyg.2019.00705
  2098. Tendeiro J., Kiers H. (2019): A review of issues about null hypothesis Bayesian testing. Psychological Methods 24(6):774-795. https://doi.org/10.1037/met0000221
  2099. Adedeji J., Fadamiro J., Odeyale T. (2019): Design toolkits for campus open spaces from post-occupancy evaluations of federal universities in South-west Nigeria. Built Environment Project and Asset Management 10(2):296-311. https://doi.org/10.1108/bepam-11-2018-0138
  2100. Buxbaum J., Baron-Cohen S., Anagnostou E., Ashwin C., Betancur C., Chakrabarti B., et al. (2019): Rigor in science and science reporting: updated guidelines for submissions to Molecular Autism. Molecular Autism 10(1). https://doi.org/10.1186/s13229-018-0249-x
  2101. Ho J., Tumkaya T., Aryal S., Choi H., Claridge-Chang A. (2019): Moving beyond P values: data analysis with estimation graphics. Nature Methods 16(7):565-566. https://doi.org/10.1038/s41592-019-0470-3
  2102. Väyrynen J., Väyrynen S., Sirniö P., Minkkinen I., Klintrup K., Karhu T., et al. (2019): Platelet count, aspirin use, and characteristics of host inflammatory responses in colorectal cancer. Journal of Translational Medicine 17(1). https://doi.org/10.1186/s12967-019-1950-z
  2103. Karch J. (2019): Improving on Adjusted R-Squared. https://doi.org/10.31234/osf.io/v8dz5
  2104. Hoffman J. (2019): Hypothesis Testing: The Null Hypothesis, Significance and Type I Error. Basic Biostatistics for Medical and Biomedical Practitioners. https://doi.org/10.1016/b978-0-12-817084-7.00010-3
  2105. Tanniou J., Smid S., van der Tweel I., Teerenstra S., Roes K. (2019): Level of evidence for promising subgroup findings: The case of trends and multiple subgroups. Statistics in Medicine 38(14):2561-2572. https://doi.org/10.1002/sim.8133
  2106. Bruner J., Holman B. (2019): Self-correction in science: Meta-analysis, bias and social structure. Studies in History and Philosophy of Science Part A 78:93-97. https://doi.org/10.1016/j.shpsa.2019.02.001
  2107. Maekawa K., Ri M., Nakajima M., Sekine A., Ueda R., Tohkin M., et al. (2019): Serum lipidomics for exploring biomarkers of bortezomib therapy in patients with multiple myeloma. Cancer Science 110(10):3267-3274. https://doi.org/10.1111/cas.14178
  2108. Kosumi K., Hamada T., Zhang S., Liu L., da Silva A., Koh H., et al. (2019): Prognostic association of PTGS2 (COX-2) over-expression according to BRAF mutation status in colorectal cancer: Results from two prospective cohorts and CALGB 89803 (Alliance) trial. European Journal of Cancer 111:82-93. https://doi.org/10.1016/j.ejca.2019.01.022
  2109. Kosumi K., Baba Y., Okadome K., Yagi T., Kiyozumi Y., Yoshida N., et al. (2019): Tumor Long-interspersed Nucleotide Element-1 Methylation Level and Immune Response to Esophageal Cancer. Annals of Surgery 272(6):1025-1034. https://doi.org/10.1097/sla.0000000000003264
  2110. Berry K., Johnston J., Mielke P. (2019): One-Sample Tests. A Primer of Permutation Statistical Methods. https://doi.org/10.1007/978-3-030-20933-9_5
  2111. Rice K., Bonnett T., Krakauer C. (2019): Knowing the Signs: A Direct and Generalizable Motivation of Two-Sided Tests. Journal of the Royal Statistical Society Series A: Statistics in Society 183(2):411-430. https://doi.org/10.1111/rssa.12496
  2112. King K., Pullmann M., Lyon A., Dorsey S., Lewis C. (2019): Using implementation science to close the gap between the optimal and typical practice of quantitative methods in clinical science. Journal of Abnormal Psychology 128(6):547-562. https://doi.org/10.1037/abn0000417
  2113. Mullane K., Williams M. (2019): Preclinical Models of Alzheimer’s Disease: Relevance and Translational Validity. Current Protocols in Pharmacology 84(1). https://doi.org/10.1002/cpph.57
  2114. Jaffe K. (2019): Quantifying the prevalence of assortative mating in a human population. https://doi.org/10.1101/848911
  2115. Munkholm K., Faurholt-Jepsen M., Ioannidis J., Hemkens L. (2019): Consideration of confounding was suboptimal in the reporting of observational studies in psychiatry: a meta-epidemiological study. Journal of Clinical Epidemiology 119:75-84. https://doi.org/10.1016/j.jclinepi.2019.12.002
  2116. Oberauer K., Lewandowsky S. (2019): Addressing the theory crisis in psychology. Psychonomic Bulletin & Review 26(5):1596-1618. https://doi.org/10.3758/s13423-019-01645-2
  2117. Haruki K., Kosumi K., Hamada T., Twombly T., Väyrynen J., Kim S., et al. (2019): Association of autophagy status with amount of Fusobacterium nucleatum in colorectal cancer. The Journal of Pathology 250(4):397-408. https://doi.org/10.1002/path.5381
  2118. Mima K., Sakamoto Y., Kosumi K., Ogata Y., Miyake K., Hiyoshi Y., et al. (2019): Mucosal cancer-associated microbes and anastomotic leakage after resection of colorectal carcinoma. Surgical Oncology 32:63-68. https://doi.org/10.1016/j.suronc.2019.11.005
  2119. MIMA K., KURASHIGE J., MIYANARI N., MORITO A., YUMOTO S., MATSUMOTO T., et al. (2019): Advanced Age Is a Risk Factor for Recurrence After Resection in Stage II Colorectal Cancer. In Vivo 34(1):339-346. https://doi.org/10.21873/invivo.11779
  2120. Duppong Hurley K., Lambert M., Patwardhan I., Ringle J., Thompson R., Farley J. (2019): Parental report of outcomes from a randomized trial of in-home family services. Journal of Family Psychology 34(1):79-89. https://doi.org/10.1037/fam0000594
  2121. Kooijman L., Happee R., de Winter J. (2019): How Do eHMIs Affect Pedestrians’ Crossing Behavior? A Study Using a Head-Mounted Display Combined with a Motion Suit. Information 10(12):386. https://doi.org/10.3390/info10120386
  2122. Liebst L. (2019): Exploring the Sources of Collective Effervescence: A Multilevel Study. Sociological Science 6:27-42. https://doi.org/10.15195/v6.a2
  2123. Belbasis L., Bellou V., Evangelou E., Tzoulaki I. (2019): Environmental factors and risk of multiple sclerosis: Findings from meta-analyses and Mendelian randomization studies. Multiple Sclerosis Journal 26(4):397-404. https://doi.org/10.1177/1352458519872664
  2124. Kennedy-Shaffer L. (2019): Before p  < 0.05 to Beyond p  < 0.05: Using History to Contextualize p -Values and Significance Testing. The American Statistician 73(sup1):82-90. https://doi.org/10.1080/00031305.2018.1537891
  2125. Held L. (2019): The assessment of intrinsic credibility and a new argument for p < 0.005. Royal Society Open Science 6(3):181534. https://doi.org/10.1098/rsos.181534
  2126. Leonhard Held (2019): The assessment of intrinsic credibility and a new argument for p < 0.005. Zurich Open Repository and Archive (University of Zurich). https://doi.org/10.5167/uzh-175102
  2127. Held L. (2019): A New Standard for the Analysis and Design of Replication Studies. Journal of the Royal Statistical Society Series A: Statistics in Society 183(2):431-448. https://doi.org/10.1111/rssa.12493
  2128. Bailey L., Ens B., Both C., Heg D., Oosterbeek K., van de Pol M. (2019): Habitat selection can reduce effects of extreme climatic events in a long‐lived shorebird. Journal of Animal Ecology 88(10):1474-1485. https://doi.org/10.1111/1365-2656.13041
  2129. Portugal L., Alves R., Fernandes-Junior O., Sanchez T., Mocaiber I., Volchan E., et al. (2019): Interactions between emotion and action in the brain. https://doi.org/10.1101/812594
  2130. Lincoln Colling, Dénes Szűcs (2019): Statistical Inference and the Replication Crisis. Apollo (University of Cambridge). https://doi.org/10.17863/cam.35452
  2131. Besançon L., Dragicevic P. (2019): The Continued Prevalence of Dichotomous Inferences at CHI. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290607.3310432
  2132. Pace L., Salvan A. (2019): Likelihood, Replicability and Robbins’ Confidence Sequences. International Statistical Review 88(3):599-615. https://doi.org/10.1111/insr.12355
  2133. Ahtiainen M., Wirta E., Kuopio T., Seppälä T., Rantala J., Mecklin J., et al. (2019): Combined prognostic value of CD274 (PD-L1)/PDCDI (PD-1) expression and immune cell infiltration in colorectal cancer as per mismatch repair status. Modern Pathology 32(6):866-883. https://doi.org/10.1038/s41379-019-0219-7
  2134. Wittlin M. (2019): Meta-Evidence and Preliminary Injunctions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3356215
  2135. Marienko M. (2019): Наукові платформи та хмарні сервіси, їх місце у системі наукової освіти вчителя. Physical and Mathematical Education 22(4):93-99. https://doi.org/10.31110/2413-1571-2019-022-4-015
  2136. Rickard M., Lorenzo A., Hannick J., Blais A., Koyle M., Bägli D. (2019): Over-reliance on P Values in Urology: Fragility of Findings in the Hydronephrosis Literature Calls for Systematic Reporting of Robustness Indicators. Urology 133:204-210. https://doi.org/10.1016/j.urology.2019.03.045
  2137. Brysbaert M. (2019): How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables. Journal of Cognition 2(1). https://doi.org/10.5334/joc.72
  2138. Goulet M., Cousineau D. (2019): The Power of Replicated Measures to Increase Statistical Power. Advances in Methods and Practices in Psychological Science 2(3):199-213. https://doi.org/10.1177/2515245919849434
  2139. Steenbergen M. (2019): What Is In a (Non‐) Significant Finding? Moving Beyond False Dichotomies. Swiss Political Science Review 25(3):300-311. https://doi.org/10.1111/spsr.12373
  2140. Bendtsen M. (2019): An Electronic Screening and Brief Intervention for Hazardous and Harmful Drinking Among Swedish University Students: Reanalysis of Findings From a Randomized Controlled Trial Using a Bayesian Framework. Journal of Medical Internet Research 21(12):e14420. https://doi.org/10.2196/14420
  2141. Bendtsen M. (2019): Electronic Screening for Alcohol Use and Brief Intervention by Email for University Students: Reanalysis of Findings From a Randomized Controlled Trial Using a Bayesian Framework. Journal of Medical Internet Research 21(11):e14419. https://doi.org/10.2196/14419
  2142. Kwiatkowska M., Rogoza R. (2019): A modest proposal to link shyness and modesty: Investigating the relation within the framework of Big Five personality traits. Personality and Individual Differences 149:8-13. https://doi.org/10.1016/j.paid.2019.05.026
  2143. Rougier M., Muller D., Courset R., Smeding A., Devos T., Batailler C. (2019): Toward the use of approach/avoidance tendencies as attitude measures: Individual‐ and group‐level variability of the ingroup bias. European Journal of Social Psychology 50(4):857-875. https://doi.org/10.1002/ejsp.2653
  2144. Rougier M., Muller D., Courset R., Devos T., Batailler C. (2019): Toward the Use of Approach/Avoidance Tendencies as Attitude Measures: Individual- and Group-Level Variability of the Ingroup Bias. https://doi.org/10.31234/osf.io/9h5ap
  2145. Rubin M. (2019): What type of Type I error? Contrasting the Neyman-Pearson and Fisherian approaches in the context of exact and direct replications. https://doi.org/10.31234/osf.io/3hcgv
  2146. Gannon M., de Bragança Pereira C., Polpo A. (2019): Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels. The American Statistician 73(sup1):213-222. https://doi.org/10.1080/00031305.2018.1518268
  2147. Rubin M. (2019): What type of Type I error? Contrasting the Neyman–Pearson and Fisherian approaches in the context of exact and direct replications. Synthese 198(6):5809-5834. https://doi.org/10.1007/s11229-019-02433-0
  2148. Talvio M., Hietajärvi L., Matischek-Jauk M., Lonka K. (2019): Do Lions Quest (LQ) workshops have systematic impact on teachers’ social and emotional learning (SEL)? Samples from nine different countries. Electronic Journal of Research in Education Psychology 17(48). https://doi.org/10.25115/ejrep.v17i48.2166
  2149. Michel M., Murphy T., Motulsky H. (2019): New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology. Molecular Pharmacology 97(1):49-60. https://doi.org/10.1124/mol.119.118927
  2150. Michel M., Murphy T., Motulsky H. (2019): New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology. The Journal of Pharmacology and Experimental Therapeutics 372(1):136-147. https://doi.org/10.1124/jpet.119.264143
  2151. Michel M., Murphy T., Motulsky H. (2019): New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology. Drug Metabolism and Disposition 48(1):64-74. https://doi.org/10.1124/dmd.119.090027
  2152. Gorges M., Müller H., Liepelt-Scarfone I., Storch A., Dodel R., Hilker-Roggendorf R., et al. (2019): Structural brain signature of cognitive decline in Parkinson’s disease: DTI-based evidence from the LANDSCAPE study. Therapeutic Advances in Neurological Disorders 12. https://doi.org/10.1177/1756286419843447
  2153. Nørgaard M., Ganz M., Svarer C., Frokjaer V., Greve D., Strother S., et al. (2019): Different preprocessing strategies lead to different conclusions: A [11C]DASB-PET reproducibility study. Journal of Cerebral Blood Flow & Metabolism 40(9):1902-1911. https://doi.org/10.1177/0271678×19880450
  2154. Vilas M., Santilli M., Mikulan E., Adolfi F., Martorell Caro M., Manes F., et al. (2019): Reading Shakespearean tropes in a foreign tongue: Age of L2 acquisition modulates neural responses to functional shifts. Neuropsychologia 124:79-86. https://doi.org/10.1016/j.neuropsychologia.2019.01.007
  2155. Stoddard M., Sheard C., Akkaynak D., Yong E., Mahadevan L., Tobias J. (2019): Evolution of avian egg shape: underlying mechanisms and the importance of taxonomic scale. Ibis 161(4):922-925. https://doi.org/10.1111/ibi.12755
  2156. Amon M., Holden J. (2019): The Mismatch of Intrinsic Fluctuations and the Static Assumptions of Linear Statistics. Review of Philosophy and Psychology 12(1):149-173. https://doi.org/10.1007/s13164-018-0428-x
  2157. Costello M., Li Y., Remers S., MacKillop J., Sousa S., Ropp C., et al. (2019): Effects of 12-step mutual support and professional outpatient services on short-term substance use outcomes among adults who received inpatient treatment. Addictive Behaviors 98:106055. https://doi.org/10.1016/j.addbeh.2019.106055
  2158. Podlogar M., Gutierrez P., Joiner T. (2019): Improving Our Understanding of the Death/Life Implicit Association Test. Journal of Personality Assessment 102(6):845-857. https://doi.org/10.1080/00223891.2019.1663357
  2159. Matthias Grawehr, Þórarinn Gunnarsson (2019): From Recognition to Adaptation: How does Forecasting relate to International Aid Funding in Food Security?. Lund University Publications Student Papers (Lund University).
  2160. Kramer M., Barkmin M., Brinda T. (2019): Identifying Predictors for Code Highlighting Skills. Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education. https://doi.org/10.1145/3304221.3319745
  2161. Andreoletti M., Rescigno M. (2019): Microbiota-gut-brain research: A plea for an interdisciplinary approach and standardization. Behavioral and Brain Sciences 42. https://doi.org/10.1017/s0140525x18002868
  2162. Canavari M., Drichoutis A., Lusk J., Nayga R. (2019): How to run an experimental auction: a review of recent advances. European Review of Agricultural Economics 46(5):862-922. https://doi.org/10.1093/erae/jbz038
  2163. Owens M., MacKillop J., Sweet L. (2019): Cannabis Use is Associated with Lower Hippocampus and Amygdala Volume: High-Resolution Segmentation of Structural Subfields in a Large Non-clinical Sample. https://doi.org/10.31234/osf.io/3xytq
  2164. Maximilian Linde, Don van Ravenzwaaij (2019): baymedr: An R Package for the Calculation of Bayes Factors for Equivalence, Non-Inferiority, and Superiority Designs. arXiv (Cornell University).
  2165. Rahman M., Shoaib S., Amin M., Toma R., Moni M., Awal M. (2019): A Bayesian Optimization Framework for the Prediction of Diabetes Mellitus. 2019 5th International Conference on Advances in Electrical Engineering (ICAEE). https://doi.org/10.1109/icaee48663.2019.8975480
  2166. Wevers M., Gao J., Nielbo K. (2019): Tracking the Consumption Junction: Temporal Dependencies between Articles and Advertisements in Dutch Newspapers. arXiv. https://doi.org/10.48550/arxiv.1903.11461
  2167. Williams M., Trist D. (2019): Editorial. Current Opinion in Pharmacology 51:66-67. https://doi.org/10.1016/j.coph.2019.11.001
  2168. Lew M. (2019): A Reckless Guide to P-values. Handbook of Experimental Pharmacology. https://doi.org/10.1007/164_2019_286
  2169. Lew M. (2019): A reckless guide to P-values: local evidence, global errors. arXiv. https://doi.org/10.48550/arxiv.1910.02042
  2170. Lesk M., Mattern J., Moulaison Sandy H. (2019): Are Papers with Open Data More Credible? An Analysis of Open Data Availability in Retracted PLoS Articles. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-15742-5_14
  2171. Watson M., Christoforou P., Herrera P., Preece D., Carrell J., Harmon M., et al. (2019): An analysis of the quality of experimental design and reliability of results in tribology research. Wear 426-427:1712-1718. https://doi.org/10.1016/j.wear.2018.12.028
  2172. Bailey M., Thomas A., Francis O., Stokes C., Smidt H. (2019): The dark side of technological advances in analysis of microbial ecosystems. Journal of Animal Science and Biotechnology 10(1). https://doi.org/10.1186/s40104-019-0357-2
  2173. Szreder M. (2019): Statistical significance in the era of big data. Wiadomości Statystyczne. The Polish Statistician 64(11):42-57. https://doi.org/10.5604/01.3001.0013.7583
  2174. Gaber M., Garas S., Lusk E. (2019): Evidence on the Impact of Internal Control over Financial Reporting on Audit Fees. International Journal of Accounting and Financial Reporting 9(3):1. https://doi.org/10.5296/ijafr.v9i3.15001
  2175. Gaber M., Lusk E. (2019): A Vetting Protocol for the Analytical Procedures Platform for the AP-Phase of PCAOB Audits. Accounting and Finance Research 8(4):43. https://doi.org/10.5430/afr.v8n4p43
  2176. Atari M., Afhami R., Swami V. (2019): Psychometric assessments of Persian translations of three measures of conspiracist beliefs. PLOS ONE 14(4):e0215202. https://doi.org/10.1371/journal.pone.0215202
  2177. Atari M., Chaudhary N., Al-Shawaf L. (2019): Mate Preferences in Three Muslim-Majority Countries: Sex Differences and Personality Correlates. Social Psychological and Personality Science 11(4):533-545. https://doi.org/10.1177/1948550619866187
  2178. Atari M., Chaudhary N., Al-Shawaf L. (2019): Mate Preferences in Three Muslim-Majority Countries: Sex Differences and Personality Correlates. https://doi.org/10.31234/osf.io/2ubtx
  2179. de Barra M. (2019): Decision letter: Open exploration. https://doi.org/10.7554/elife.52157.sa1
  2180. Roden-Foreman J., Rapier N., Foreman M., Zagel A., Sexton K., Beck W., et al. (2019): Rethinking the definition of major trauma: The need for trauma intervention outperforms Injury Severity Score and Revised Trauma Score in 38 adult and pediatric trauma centers. Journal of Trauma and Acute Care Surgery 87(3):658-665. https://doi.org/10.1097/ta.0000000000002402
  2181. Ishikawa N., Ohishi Y., Maeda K. (2019): Nulls in the Air: Passive and Low-Complexity QoS Estimation Method for a Large-Scale Wi-Fi Network Based on Null Function Data Frames. IEEE Access 7:28581-28591. https://doi.org/10.1109/access.2019.2902182
  2182. Lubold N., Walker E., Pon-Barry H., Ogan A. (2019): Comfort with Robots Influences Rapport with a Social, Entraining Teachable Robot. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-23204-7_20
  2183. Fraser N., Momeni F., Mayr P., Peters I. (2019): The effect of bioRxiv preprints on citations and altmetrics. https://doi.org/10.1101/673665
  2184. Holtzman N., Tackman A., Carey A., Brucks M., Küfner A., Deters F., et al. (2019): Linguistic Markers of Grandiose Narcissism: A LIWC Analysis of 15 Samples. Journal of Language and Social Psychology 38(5-6):773-786. https://doi.org/10.1177/0261927×19871084
  2185. Holtzman N., Tackman A., große Deters F., Back M., Donnellan B., Pennebaker J., et al. (2019): Linguistic markers of grandiose narcissism: A LIWC analysis of 15 samples. https://doi.org/10.31234/osf.io/aeuzk
  2186. Byrd N. (2019): What we can (and can’t) infer about implicit bias from debiasing experiments. Synthese 198(2):1427-1455. https://doi.org/10.1007/s11229-019-02128-6
  2187. Parsons N., Carey-Smith R., Dritsaki M., Griffin X., Metcalfe D., Perry D., et al. (2019): Statistical significance and p-values. The Bone & Joint Journal 101-B(10):1179-1183. https://doi.org/10.1302/0301-620x.101b10.bjj-2019-0890
  2188. Veronese N., Demurtas J., Pesolillo G., Celotto S., Barnini T., Calusi G., et al. (2019): Magnesium and health outcomes: an umbrella review of systematic reviews and meta-analyses of observational and intervention studies. European Journal of Nutrition 59(1):263-272. https://doi.org/10.1007/s00394-019-01905-w
  2189. Nattrass N., Stephens J., Loubser J. (2019): Animal welfare and ecology in the contested ethics of rodent control in Cape Town. Journal of Urban Ecology 5(1). https://doi.org/10.1093/jue/juz008
  2190. Barbieri N., Marzucchi A., Rizzo U. (2019): Knowledge sources and impacts on subsequent inventions: Do green technologies differ from non-green ones?. Research Policy 49(2):103901. https://doi.org/10.1016/j.respol.2019.103901
  2191. Johannes N., Dora J., Rusz D. (2019): Social Smartphone Apps Do Not Capture Attention Despite Their Perceived High Reward Value. Collabra: Psychology 5(1). https://doi.org/10.1525/collabra.207
  2192. Christie N., Hsu E., Iskiwitch C., Iyer R., Graham J., Schwartz B., et al. (2019): The Moral Foundations of Needle Exchange Attitudes. Social Cognition 37(3):229-246. https://doi.org/10.1521/soco.2019.37.3.229
  2193. van Dongen N., van Doorn J., Gronau Q., van Ravenzwaaij D., Hoekstra R., Haucke M., et al. (2019): Multiple Perspectives on Inference for Two Simple Statistical Scenarios. The American Statistician 73(sup1):328-339. https://doi.org/10.1080/00031305.2019.1565553
  2194. Krepel N., Rush A., Iseger T., Sack A., Arns M. (2019): Can psychological features predict antidepressant response to rTMS? A Discovery–Replication approach. Psychological Medicine 50(2):264-272. https://doi.org/10.1017/s0033291718004191
  2195. Laccourreye O., Marret G., Rubin F., Fabre E., Badoual C., Oudard S., et al. (2019): Ten‐year outcome of curative “exclusive” chemotherapy in N0M0 squamous cell carcinoma of the larynx and pharynx with complete clinical response. Head & Neck 41(7):2190-2196. https://doi.org/10.1002/hed.25674
  2196. Chén O., Cao H., Reinen J., Qian T., Gou J., Phan H., et al. (2019): Resting-state brain information flow predicts cognitive flexibility in humans. Scientific Reports 9(1). https://doi.org/10.1038/s41598-019-40345-8
  2197. Chen O., Yang Z., Tan X., Gu Z., Chen J. (2019): Powerful postures do not lead to risky behaviors. Journal of Pacific Rim Psychology 13. https://doi.org/10.1017/prp.2019.17
  2198. Gómez P., Semmler M., Schützenberger A., Bohr C., Döllinger M. (2019): Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network. Medical & Biological Engineering & Computing 57(7):1451-1463. https://doi.org/10.1007/s11517-019-01965-4
  2199. Harms P. (2019): Automated Usability Evaluation of Virtual Reality Applications. ACM Transactions on Computer-Human Interaction 26(3):1-36. https://doi.org/10.1145/3301423
  2200. Harms P. (2019): VR Interaction Modalities for the Evaluation of Technical Device Prototypes. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-29390-1_23
  2201. Heck P., Meyer M. (2019): Information Avoidance in Genetic Health: Perceptions, Norms, and Preferences. Social Cognition 37(3):266-293. https://doi.org/10.1521/soco.2019.37.3.266
  2202. Heck P., Meyer M. (2019): Information Avoidance in Genetic Health: Perceptions, Norms, and Preferences. https://doi.org/10.31234/osf.io/v2awq
  2203. Forscher P., Lai C., Axt J., Ebersole C., Herman M., Devine P., et al. (2019): A meta-analysis of procedures to change implicit measures. Journal of Personality and Social Psychology 117(3):522-559. https://doi.org/10.1037/pspa0000160
  2204. Lodder P., Ong H., Grasman R., Wicherts J. (2019): A comprehensive meta-analysis of money priming. Journal of Experimental Psychology: General 148(4):688-712. https://doi.org/10.1037/xge0000570
  2205. Sonon P., Gomes R., Brelaz-de-Castro M., da Costa L., Pereira V., de Brito M., et al. (2019): Human leukocyte antigen-G 3′ untranslated region polymorphism +3142G/C (rs1063320) and haplotypes are associated with manifestations of the American Tegumentary Leishmaniasis in a Northeastern Brazilian population. Human Immunology 80(11):908-916. https://doi.org/10.1016/j.humimm.2019.08.001
  2206. Sonon P. (2019): Co-infecção Plasmodium falciparum e Schistosoma haematobium: papel dos genes HLA não-clássicos de classe I (HLA-G, -E e -F) na suscetibilidade à malária. https://doi.org/10.11606/t.17.2019.tde-01022019-110350
  2207. Peter Grünwald, Rianne de Heide, Wouter M. Koolen (2019): Safe Testing. arXiv (Cornell University).
  2208. Peter Lugtig, Vera Toepoel, Marieke Haan, Robbert Zandvliet, Laurens Klein Kranenburg (2019): Recruiting Young and Urban Groups into a Probability-Based Online Panel by Promoting Smartphone Use. University of Groningen research database (University of Groningen / Centre for Information Technology). https://doi.org/10.12758/mda.2019.04
  2209. Clare P., Aiken A., Yuen W., Peacock A., Boland V., Wadolowski M., et al. (2019): Parental supply of alcohol as a predictor of adolescent alcohol consumption patterns: A prospective cohort. Drug and Alcohol Dependence 204:107529. https://doi.org/10.1016/j.drugalcdep.2019.06.031
  2210. DeShong P. (2019): Responsible Conduct of Research (RCR). Handbook of Research Ethics and Scientific Integrity. https://doi.org/10.1007/978-3-319-76040-7_69-1
  2211. Hyland P., Shevlin M., Kerig P. (2019): Journal of Traumatic Stress P Value Guidelines. Journal of Traumatic Stress 32(5):651-652. https://doi.org/10.1002/jts.22460
  2212. Vlisides P., Thompson A., Kunkler B., Maybrier H., Avidan M., Mashour G. (2019): Perioperative Epidural Use and Risk of Delirium in Surgical Patients: A Secondary Analysis of the PODCAST Trial. Anesthesia & Analgesia 128(5):944-952. https://doi.org/10.1213/ane.0000000000004038
  2213. Quatto P., Ripamonti E., Marasini D. (2019): Best uses ofp-values and complementary measures in medical research: Recent developments in the frequentist and Bayesian frameworks. Journal of Biopharmaceutical Statistics 30(1):121-142. https://doi.org/10.1080/10543406.2019.1632874
  2214. Dragicevic P., Jansen Y., Sarma A., Kay M., Chevalier F. (2019): Increasing the Transparency of Research Papers with Explorable Multiverse Analyses. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300295
  2215. Ni Q., Yang G., Brandt W., Alexander D., Chen C., Luo B., et al. (2019): Does black hole growth depend fundamentally on host-galaxy compactness?. Monthly Notices of the Royal Astronomical Society 490(1):1135-1155. https://doi.org/10.1093/mnras/stz2623
  2216. Coleman R., Aguirre K., Spiegel H., Pecos C., Carr J., Harris B. (2019): The plus maze and scototaxis test are not valid behavioral assays for anxiety assessment in the South African clawed frog. Journal of Comparative Physiology A 205(4):567-582. https://doi.org/10.1007/s00359-019-01351-3
  2217. Holm Hansen R., Højsgaard Chow H., Christensen J., Sellebjerg F., von Essen M. (2019): Dimethyl fumarate therapy reduces memory T cells and the CNS migration potential in patients with multiple sclerosis. Multiple Sclerosis and Related Disorders 37:101451. https://doi.org/10.1016/j.msard.2019.101451
  2218. Biswas R., Kabir E., Khan H. (2019): Causes of Urban Migration in Bangladesh: Evidence from the Urban Health Survey. Population Research and Policy Review 38(4):593-614. https://doi.org/10.1007/s11113-019-09532-3
  2219. Biswas R., Sarker E., Kabir E., Senserrick T. (2019): Presence of Books for Children in the Households of Bangladesh: A District-wise Distribution. Reading & Writing Quarterly 36(1):65-79. https://doi.org/10.1080/10573569.2019.1624665
  2220. Valentine K., Buchanan E., Scofield J., Beauchamp M. (2019): Beyond p values: utilizing multiple methods to evaluate evidence. Behaviormetrika 46(1):121-144. https://doi.org/10.1007/s41237-019-00078-4
  2221. Gana R., Vasudevan S. (2019): Ridge regression estimated linear probability model predictions of O-glycosylation in proteins with structural and sequence data. BMC Molecular and Cell Biology 20(1). https://doi.org/10.1186/s12860-019-0200-9
  2222. Kellner R., Rösch D. (2019): A Bayesian Re-Interpretation of “significant” empirical financial research. Finance Research Letters 38:101402. https://doi.org/10.1016/j.frl.2019.101402
  2223. Betensky R. (2019): The p -Value Requires Context, Not a Threshold. The American Statistician 73(sup1):115-117. https://doi.org/10.1080/00031305.2018.1529624
  2224. Pearl R., Himmelstein M., Puhl R., Wadden T., Wojtanowski A., Foster G. (2019): Weight bias internalization in a commercial weight management sample: prevalence and correlates. Obesity Science & Practice 5(4):342-353. https://doi.org/10.1002/osp4.354
  2225. Puhl R., Himmelstein M., Pearl R., Wojtanowski A., Foster G. (2019): Weight Stigma Among Sexual Minority Adults: Findings from a Matched Sample of Adults Engaged in Weight Management. Obesity 27(11):1906-1915. https://doi.org/10.1002/oby.22633
  2226. Werth R. (2019): What causes dyslexia? Identifying the causes and effective compensatory therapy. Restorative Neurology and Neuroscience 37(6):591-608. https://doi.org/10.3233/rnn-190939
  2227. Heesen R., Bright L. (2019): Is Peer Review a Good Idea?. The British Journal for the Philosophy of Science 72(3):635-663. https://doi.org/10.1093/bjps/axz029
  2228. Schwaiger R., Kirchler M., Lindner F., Weitzel U. (2019): Determinants of investor expectations and satisfaction. A study with financial professionals. Journal of Economic Dynamics and Control 110:103675. https://doi.org/10.1016/j.jedc.2019.03.002
  2229. NOROUZIAN R., MIRANDA M., PLONSKY L. (2019): A Bayesian Approach to Measuring Evidence in L2 Research: An Empirical Investigation. The Modern Language Journal 103(1):248-261. https://doi.org/10.1111/modl.12543
  2230. Matland R., Murray G. (2019): A second look at partisanship’s effect on receptivity to social pressure to vote. Social Influence 14(1):1-13. https://doi.org/10.1080/15534510.2019.1572536
  2231. Born R. (2019): Banishing “Black/White Thinking”: A Trio of Teaching Tricks. eneuro 6(6):ENEURO.0456-19.2019. https://doi.org/10.1523/eneuro.0456-19.2019
  2232. Price R., Bethune R., Massey L. (2019): Problem with p values: why p values do not tell you if your treatment is likely to work. Postgraduate Medical Journal 96(1131):1-3. https://doi.org/10.1136/postgradmedj-2019-137079
  2233. Calin-Jageman R., Cumming G. (2019): The New Statistics for Better Science: Ask How Much, How Uncertain, and What Else Is Known. The American Statistician 73(sup1):271-280. https://doi.org/10.1080/00031305.2018.1518266
  2234. Matthews R. (2019): Moving Towards the Post p  < 0.05 Era via the Analysis of Credibility. The American Statistician 73(sup1):202-212. https://doi.org/10.1080/00031305.2018.1543136
  2235. Myte R., Gylling B., Häggström J., Häggström C., Zingmark C., Löfgren Burström A., et al. (2019): Metabolic factors and the risk of colorectal cancer by KRAS and BRAF mutation status. International Journal of Cancer 145(2):327-337. https://doi.org/10.1002/ijc.32104
  2236. Nunkoo R., Seetanah B., Jaffur Z., Moraghen P., Sannassee R. (2019): Tourism and Economic Growth: A Meta-regression Analysis. Journal of Travel Research 59(3):404-423. https://doi.org/10.1177/0047287519844833
  2237. Martin R. (2019): False confidence, non-additive beliefs, and valid statistical inference. International Journal of Approximate Reasoning 113:39-73. https://doi.org/10.1016/j.ijar.2019.06.005
  2238. Van Patten R., Greif T., Britton K., Tremont G. (2019): Single-photon emission computed tomography (SPECT) perfusion and neuropsychological performance in mild cognitive impairment. Journal of Clinical and Experimental Neuropsychology 41(5):530-543. https://doi.org/10.1080/13803395.2019.1586838
  2239. Voisin S., Harvey N., Haupt L., Griffiths L., Ashton K., Coffey V., et al. (2019): An epigenetic clock for human skeletal muscle. https://doi.org/10.1101/821009
  2240. Anderson S. (2019): Misinterpreting p: The discrepancy between p values and the probability the null hypothesis is true, the influence of multiple testing, and implications for the replication crisis. Psychological Methods 25(5):596-609. https://doi.org/10.1037/met0000248
  2241. Rubinstein S., Sigworth E., Etemad S., Martin R., Chen Q., Warner J. (2019): Indication of Measures of Uncertainty for Statistical Significance in Abstracts of Published Oncology Trials. JAMA Network Open 2(12):e1917530. https://doi.org/10.1001/jamanetworkopen.2019.17530
  2242. Mathias S., Knowles E., Mollon J., Rodrigue A., Koenis M., Alexander-Bloch A., et al. (2019): Minimal Relationship between Local Gyrification and General Cognitive Ability in Humans. Cerebral Cortex 30(6):3439-3450. https://doi.org/10.1093/cercor/bhz319
  2243. Roels S., Loeys T., Moerkerke B. (2019): Including Data Analytical Stability in Cluster-based Inference. https://doi.org/10.1101/844860
  2244. Weston S., Ritchie S., Rohrer J., Przybylski A. (2019): Recommendations for Increasing the Transparency of Analysis of Preexisting Data Sets. Advances in Methods and Practices in Psychological Science 2(3):214-227. https://doi.org/10.1177/2515245919848684
  2245. Mitra S., Mehta U., Binukumar B., Venkatasubramanian G., Thirthalli J. (2019): Statistical power estimation in non-invasive brain stimulation studies and its clinical implications: An exploratory study of the meta-analyses. Asian Journal of Psychiatry 44:29-34. https://doi.org/10.1016/j.ajp.2019.07.006
  2246. Peters S., Rambo-Hernandez K., Makel M., Matthews M., Plucker J. (2019): Effect of Local Norms on Racial and Ethnic Representation in Gifted Education. AERA Open 5(2). https://doi.org/10.1177/2332858419848446
  2247. Bartell S. (2019): Understanding and Mitigating the Replication Crisis, for Environmental Epidemiologists. Current Environmental Health Reports 6(1):8-15. https://doi.org/10.1007/s40572-019-0225-4
  2248. McCabe S., Hughes T., West B., Veliz P., Boyd C. (2019): DSM‐5 Alcohol Use Disorder Severity as a Function of Sexual Orientation Discrimination: A National Study. Alcoholism: Clinical and Experimental Research 43(3):497-508. https://doi.org/10.1111/acer.13960
  2249. McCabe S., Veliz P., Wilens T., West B., Schepis T., Ford J., et al. (2019): Sources of Nonmedical Prescription Drug Misuse Among US High School Seniors: Differences in Motives and Substance Use Behaviors. Journal of the American Academy of Child & Adolescent Psychiatry 58(7):681-691. https://doi.org/10.1016/j.jaac.2018.11.018
  2250. Mills S., Howard A., Petigura E., Fulton B., Isaacson H., Weiss L. (2019): The California-Kepler Survey. VIII. Eccentricities of Kepler Planets and Tentative Evidence of a High-metallicity Preference for Small Eccentric Planets. The Astronomical Journal 157(5):198. https://doi.org/10.3847/1538-3881/ab1009
  2251. Larivée S., Sénéchal C., St-Onge Z., Sauvé M. (2019): Le biais de confirmation en recherche. Revue de psychoéducation 48(1):245-263. https://doi.org/10.7202/1060013ar
  2252. Lynch S. (2019): Towards Systematic Methods in an Era of Big Data: Neighborhood Wide Association Studies. Energy Balance and Cancer. https://doi.org/10.1007/978-3-030-18408-7_5
  2253. Shao S., Chan Y., Kao Yang Y., Lin S., Hung M., Chien R., et al. (2019): The Chang Gung Research Database—A multi‐institutional electronic medical records database for real‐world epidemiological studies in Taiwan. Pharmacoepidemiology and Drug Safety 28(5):593-600. https://doi.org/10.1002/pds.4713
  2254. Liu S., Sun D. (2019): Discussion of ‘Prior-based Bayesian Information Criterion (PBIC)’. Statistical Theory and Related Fields 3(1):24-25. https://doi.org/10.1080/24754269.2019.1611142
  2255. Joosse S., Beyer B., Gasch C., Nastały P., Kuske A., Isbarn H., et al. (2019): Tumor-Associated Release of Prostatic Cells into the Blood after Transrectal Ultrasound-Guided Biopsy in Patients with Histologically Confirmed Prostate Cancer. Clinical Chemistry 66(1):161-168. https://doi.org/10.1373/clinchem.2019.310912
  2256. Lilburn S., Little D., Osth A., Smith P. (2019): Cultural Problems Cannot Be Solved with Technical Solutions Alone. Computational Brain & Behavior 2(3-4):170-175. https://doi.org/10.1007/s42113-019-00036-z
  2257. Gates S., Ealing E. (2019): Reporting and interpretation of results from clinical trials that did not claim a treatment difference: survey of four general medical journals. BMJ Open 9(9):e024785. https://doi.org/10.1136/bmjopen-2018-024785
  2258. Hug S. (2019): Just Say No to p < x (∀x ∊ (0, 1]), *s and Other Evil Things. Swiss Political Science Review 25(3):312-321. https://doi.org/10.1111/spsr.12374
  2259. Vaillancourt S., Coulombe-Lévêque A., Fradette J., Martel S., Naour W., da Silva R., et al. (2019): Combining transcutaneous electrical nerve stimulation with therapeutic exercise to reduce pain in an elderly population: a pilot study. Disability and Rehabilitation 43(15):2141-2148. https://doi.org/10.1080/09638288.2019.1693639
  2260. Lakić S. (2019): BAYESOV FAKTOR: OPIS I RAZLOZI ZA UPOTREBU U PSIHOLOŠKIM ISTRAŽIVANJIMA. ГОДИШЊАК ЗА ПСИХОЛОГИЈУ 16(18):39-58. https://doi.org/10.46630/gpsi.18.2019.03
  2261. Buhelt S., Søndergaard H., Oturai A., Ullum H., von Essen M., Sellebjerg F. (2019): Relationship between Multiple Sclerosis-Associated IL2RA Risk Allele Variants and Circulating T Cell Phenotypes in Healthy Genotype-Selected Controls. Cells 8(6):634. https://doi.org/10.3390/cells8060634
  2262. Richter S., Stevenson S., Newman T., Wilson L., Menon D., Maas A., et al. (2019): Handling of Missing Outcome Data in Traumatic Brain Injury Research: A Systematic Review. Journal of Neurotrauma 36(19):2743-2752. https://doi.org/10.1089/neu.2018.6216
  2263. Papatheodorou S. (2019): Umbrella reviews: what they are and why we need them. European Journal of Epidemiology 34(6):543-546. https://doi.org/10.1007/s10654-019-00505-6
  2264. Papatheodorou S. (2019): Author Reply: A critical reflection on the grading of the certainty of evidence in umbrella reviews. European Journal of Epidemiology 34(9):891-892. https://doi.org/10.1007/s10654-019-00535-0
  2265. Hollenbach S., Slocum B., Kailasam A., Chescheir N. (2019): Connect the Dots—October 2019. Obstetrics & Gynecology 134(4):878-879. https://doi.org/10.1097/aog.0000000000003482
  2266. Noble S., Scheinost D., Constable R. (2019): Cluster failure or power failure? Evaluating sensitivity in cluster-level inference. NeuroImage 209:116468. https://doi.org/10.1016/j.neuroimage.2019.116468
  2267. Neely S. (2019): Science V. Significance: Examining the Role and Application of Statistical Significance Testing in Public Administration Research. Public Administration Quarterly 43(2):185-221. https://doi.org/10.1177/073491491904300202
  2268. Du Bois S., Legate N., Kendall A. (2019): Examining Partnership–Health Associations Among Lesbian Women and Gay Men Using Population-Level Data. LGBT Health 6(1):23-33. https://doi.org/10.1089/lgbt.2018.0158
  2269. Shikano S. (2019): Hypothesis Testing in the Bayesian Framework. Swiss Political Science Review 25(3):288-299. https://doi.org/10.1111/spsr.12375
  2270. Hoogeveen S., Sarafoglou A., Wagenmakers E. (2019): Laypeople Can Predict Which Social Science Studies Replicate. https://doi.org/10.31234/osf.io/egw9d
  2271. Yarkoni T. (2019): The Generalizability Crisis. https://doi.org/10.31234/osf.io/jqw35
  2272. Makin T., Orban de Xivry J. (2019): Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. eLife 8. https://doi.org/10.7554/elife.48175
  2273. Petker T., Owens M., Amlung M., Oshri A., Sweet L., MacKillop J. (2019): Cannabis involvement and neuropsychological performance: findings from the Human Connectome Project. Journal of Psychiatry and Neuroscience 44(6):414-422. https://doi.org/10.1503/jpn.180115
  2274. Kantonen T., Karjalainen T., Isojärvi J., Nuutila P., Tuisku J., Rinne J., et al. (2019): Interindividual variability and lateralization of µ-opioid receptors in the human brain. https://doi.org/10.1101/821223
  2275. Syriopoulos T., Bakos G. (2019): Investor herding behaviour in globally listed shipping stocks. Maritime Policy & Management 46(5):545-564. https://doi.org/10.1080/03088839.2019.1597288
  2276. Devos T., Sadler M., Perry D., Yogeeswaran K. (2019): Temporal fluctuations in context ethnic diversity over three decades predict implicit national inclusion of Asian Americans. Group Processes & Intergroup Relations 24(1):3-25. https://doi.org/10.1177/1368430219887440
  2277. Koster T., Wetterslev J., Gluud C., Jakobsen J., Kaufmann T., Eck R., et al. (2019): Apparently conclusive meta-analyses on interventions in critical care may be inconclusive—a meta-epidemiological study. Journal of Clinical Epidemiology 114:1-10. https://doi.org/10.1016/j.jclinepi.2019.05.011
  2278. Pincez T., Neven B., Le Pointe H., Varlet P., Fernandes H., Gareton A., et al. (2019): Neurological Involvement in Childhood Evans Syndrome. Journal of Clinical Immunology 39(2):171-181. https://doi.org/10.1007/s10875-019-0594-3
  2279. Roettger T. (2019): Researcher degrees of freedom in phonetic research. Laboratory Phonology: Journal of the Association for Laboratory Phonology 10(1). https://doi.org/10.5334/labphon.147
  2280. Bhatta T. (2019): False Negative/False Positive. Encyclopedia of Gerontology and Population Aging. https://doi.org/10.1007/978-3-319-69892-2_572-1
  2281. Hardwicke T., Serghiou S., Janiaud P., Danchev V., Crüwell S., Goodman S., et al. (2019): Calibrating the Scientific Ecosystem Through Meta-Research. Annual Review of Statistics and Its Application 7(1):11-37. https://doi.org/10.1146/annurev-statistics-031219-041104
  2282. Hardwicke T., Serghiou S., Janiaud P., Danchev V., Crüwell S., Goodman S., et al. (2019): Calibrating the scientific ecosystem through meta-research. OSF Preprints. https://doi.org/10.31222/osf.io/krb58
  2283. Dirnagl U. (2019): The p value wars (again). European Journal of Nuclear Medicine and Molecular Imaging 46(12):2421-2423. https://doi.org/10.1007/s00259-019-04467-5
  2284. Gunter U., Önder I., Smeral E. (2019): Scientific value of econometric tourism demand studies. Annals of Tourism Research 78:102738. https://doi.org/10.1016/j.annals.2019.06.005
  2285. Cheung V., Yuen V., Wong G., Choi S. (2019): The effect of sleep deprivation and disruption on DNA damage and health of doctors. Anaesthesia 74(4):434-440. https://doi.org/10.1111/anae.14533
  2286. Johnson V. (2019): Evidence From Marginally Significant t Statistics. The American Statistician 73(sup1):129-134. https://doi.org/10.1080/00031305.2018.1518788
  2287. Bumiller-Bini V., Cipolla G., Spadoni M., Augusto D., Petzl-Erler M., Beltrame M., et al. (2019): Condemned or Not to Die? Gene Polymorphisms Associated With Cell Death in Pemphigus Foliaceus. Frontiers in Immunology 10. https://doi.org/10.3389/fimmu.2019.02416
  2288. Bal V., Fok M., Lord C., Smith I., Mirenda P., Szatmari P., et al. (2019): Predictors of longer‐term development of expressive language in two independent longitudinal cohorts of language‐delayed preschoolers with Autism Spectrum Disorder. Journal of Child Psychology and Psychiatry 61(7):826-835. https://doi.org/10.1111/jcpp.13117
  2289. Kosmidou V., Ahuja M. (2019): A Configurational Approach to Family Firm Innovation. Family Business Review 32(2):154-173. https://doi.org/10.1177/0894486519827738
  2290. Troeger V. (2019): To P or not to P? The Usefulness of P‐values in Quantitative Political Science Research. Swiss Political Science Review 25(3):281-287. https://doi.org/10.1111/spsr.12377
  2291. Lewitzki V., Klement R., Kosmala R., Lisowski D., Flentje M., Polat B. (2019): Accelerated hyperfractionated radiochemotherapy with temozolomide is equivalent to normofractionated radiochemotherapy in a retrospective analysis of patients with glioblastoma. Radiation Oncology 14(1). https://doi.org/10.1186/s13014-019-1427-5
  2292. Vitiello V., Pianta R., Whittaker J., Ruzek E. (2019): Alignment and misalignment of classroom experiences from Pre-K to kindergarten. Early Childhood Research Quarterly 52:44-56. https://doi.org/10.1016/j.ecresq.2019.06.014
  2293. Thompson W., Wright J., Bissett P. (2019): Author response: Open exploration. https://doi.org/10.7554/elife.52157.sa2
  2294. Chopik W., Weaver J. (2019): Old dog, new tricks: Age differences in dog personality traits, associations with human personality traits, and links to important outcomes. Journal of Research in Personality 79:94-108. https://doi.org/10.1016/j.jrp.2019.01.005
  2295. Chopik W., Weaver J. (2019): Old dog, new tricks: Age differences in dog personality traits, associations with human personality traits, and links to important outcomes. https://doi.org/10.31234/osf.io/r3m4f
  2296. Goodman W., Spruill S., Komaroff E. (2019): A Proposed Hybrid Effect Size Plus p -Value Criterion: Empirical Evidence Supporting its Use. The American Statistician 73(sup1):168-185. https://doi.org/10.1080/00031305.2018.1564697
  2297. Horneff W., Pezolano N. (2019): Data Analytics in Internal Audit 2.0. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3964559
  2298. Kong X., Francks C. (2019): An illustration of reproducibility in neuroscience research in the absence of selective reporting. https://doi.org/10.1101/866301
  2299. Yang X., Wu W., Peng M., Shen Q., Feng J., Lai W., et al. (2019): Identity-by-Descent Analysis Reveals Susceptibility Loci for Severe Acne in Chinese Han Cohort. Journal of Investigative Dermatology 139(9):2049-2051.e20. https://doi.org/10.1016/j.jid.2019.03.1132
  2300. Liu Y., Sun J., Wu T., Lu X., Du Y., Duan H., et al. (2019): Effects of serum from breast cancer surgery patients receiving perioperative dexmedetomidine on breast cancer cell malignancy: A prospective randomized controlled trial. Cancer Medicine 8(18):7603-7612. https://doi.org/10.1002/cam4.2654
  2301. Gu Y., Li W., Evans M., Englert B. (2019): Very strong evidence in favor of quantum mechanics and against local hidden variables from a Bayesian analysis. Physical Review A 99(2). https://doi.org/10.1103/physreva.99.022112
  2302. Xu Y., Norton S., Rahman Q. (2019): A longitudinal birth cohort study of early life conditions, psychosocial factors, and emerging adolescent sexual orientation. Developmental Psychobiology 62(1):5-20. https://doi.org/10.1002/dev.21894
  2303. Goto Y., Funada A., Maeda T., Okada H., Goto Y. (2019): Sex-specific differences in survival after out-of-hospital cardiac arrest: a nationwide, population-based observational study. Critical Care 23(1). https://doi.org/10.1186/s13054-019-2547-x
  2304. Luo Y., Liang J., Zeng G., Li X., Chen M., Jiang L., et al. (2019): Responses of seeds of typical Brassica crops to tetracycline stress: Sensitivity difference and source analysis. Ecotoxicology and Environmental Safety 184:109597. https://doi.org/10.1016/j.ecoenv.2019.109597
  2305. Liu Y., Yan W., Tan C., Li J., Xu W., Cao X., et al. (2019): Common Variant in TREM1 Influencing Brain Amyloid Deposition in Mild Cognitive Impairment and Alzheimer’s Disease. Neurotoxicity Research 37(3):661-668. https://doi.org/10.1007/s12640-019-00105-y
  2306. Xu Y., Li Y., Liang Y., Cai L. (2019): Topic-sentiment evolution over time: a manifold learning-based model for online news. Journal of Intelligent Information Systems 55(1):27-49. https://doi.org/10.1007/s10844-019-00586-5
  2307. Hu Y., Hoffman G. (2019): Using Skin Texture Change to Design Emotion Expression in Social Robots. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). https://doi.org/10.1109/hri.2019.8673012
  2308. Itescu Y., Foufopoulos J., Pafilis P., Meiri S. (2019): The diverse nature of island isolation and its effect on land bridge insular faunas. Global Ecology and Biogeography 29(2):262-280. https://doi.org/10.1111/geb.13024
  2309. Ma Y., Mockus A., Milhollin B., Zaretzki R., Bradley R., Bichescu B. (2019): A Methodology for Analyzing Uptake of Software Technologies Among Developers. arXiv. https://doi.org/10.48550/arxiv.1908.11431
  2310. French Z., Caird M., Whitney D. (2019): Osteoporosis Epidemiology Among Adults With Cerebral Palsy: Findings From Private and Public Administrative Claims Data. JBMR Plus 3(11). https://doi.org/10.1002/jbm4.10231
  2311. Houck Z., Asken B., Bauer R., Caccese J., Buckley T., McCrea M., et al. (2019): Academic aptitude mediates the relationship between socioeconomic status and race in predicting ImPACT scores in college athletes. The Clinical Neuropsychologist 34(3):561-579. https://doi.org/10.1080/13854046.2019.1666923
  2312. Zheng Z., Li C., Ha P., Chang G., Yang P., Zhang X., et al. (2019): CDKN2B upregulation prevents teratoma formation in multipotent fibromodulin-reprogrammed cells. Journal of Clinical Investigation 129(8):3236-3251. https://doi.org/10.1172/jci125015
  2313. Luan Z., Bleidorn W. (2019): Self–other personality agreement and internalizing problems in adolescence. Journal of Personality 88(3):568-583. https://doi.org/10.1111/jopy.12511
  2314. Dienes Z. (2019): How Do I Know What My Theory Predicts?. Advances in Methods and Practices in Psychological Science 2(4):364-377. https://doi.org/10.1177/2515245919876960
  2315. Dienes Z. (2019): How do I know what my theory predicts?. https://doi.org/10.31234/osf.io/yqaj4
  2316. Dienes Z. (2019): How do I know what my theory predicts. https://doi.org/10.31234/osf.io/kryqt
  2317. Kekecs Z., Palfi B., Szaszi B., Szecsi P., Zrubka M., Kovacs M., et al. (2019): Raising the value of research studies in psychological science by increasing the credibility of research reports: The Transparent Psi Project – Preprint. https://doi.org/10.31234/osf.io/uwk7y
  2318. Machery E. (2019): The Alpha War. Review of Philosophy and Psychology 12(1):75-99. https://doi.org/10.1007/s13164-019-00440-1
  2319. Charbonneau É., St-Amant P. (2019): P-Values are not Sufficient but they are Necessary: Probing the Role and Application of Statistical Significance Testing in Public Administration Research. Public Administration Quarterly 43(2):229-243. https://doi.org/10.1177/073491491904300204
  2320. Rubanovich A. (2019): Redefining the Critical Value of Significance Level (0.005 instead of 0.05): The Bayes Trace. Biology Bulletin 46(11):1449-1457. https://doi.org/10.1134/s1062359019110086
  2321. Сопрун Л., Акулин И., Утехин В., Гвоздецкий А., Чурилов Л. (2019): Связанные с урбанизацией факторы заболеваемости сахарным диабетом первого типа. Biosfera. https://doi.org/10.24855/biosfera.v10i4.464
  2322. Unknown authors (2018): How Not to Corrupt Power. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.014
  2323. Janssens A., Penders B. (2018): Lowering the P Value Threshold. JAMA 320(9):936. https://doi.org/10.1001/jama.2018.8725
  2324. Oldehinkel A. (2018): The importance of taking no for an answer. Nature Human Behaviour 2(8):533-534. https://doi.org/10.1038/s41562-018-0393-5
  2325. Abadie A. (2018): Statistical Non-Significance in Empirical Economics. https://doi.org/10.3386/w24403
  2326. Bird A. (2018): Understanding the Replication Crisis as a Base Rate Fallacy. The British Journal for the Philosophy of Science 72(4):965-993. https://doi.org/10.1093/bjps/axy051
  2327. Skulmowski A., Rey G. (2018): Adjusting Sample Sizes for Different Categories of Embodied Cognition Research. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.02384
  2328. Lebis A., Lefevre M., Luengo V., Guin N. (2018): Capitalisation of analysis processes. Proceedings of the 8th International Conference on Learning Analytics and Knowledge. https://doi.org/10.1145/3170358.3170408
  2329. Distefano A., Jackson F., Levinson A., Infantolino Z., Jarcho J., Nelson B. (2018): A comparison of the electrocortical response to monetary and social reward. Social Cognitive and Affective Neuroscience 13(3):247-255. https://doi.org/10.1093/scan/nsy006
  2330. Agnew A., Murach M., Dominguez V., Sreedhar A., Misicka E., Harden A., et al. (2018): Sources of Variability in Structural Bending Response of Pediatric and Adult Human Ribs in Dynamic Frontal Impacts. SAE Technical Paper Series. https://doi.org/10.4271/2018-22-0004
  2331. Solebo A., Cumberland P., Rahi J. (2018): 5-year outcomes after primary intraocular lens implantation in children aged 2 years or younger with congenital or infantile cataract: findings from the IoLunder2 prospective inception cohort study. The Lancet Child & Adolescent Health 2(12):863-871. https://doi.org/10.1016/s2352-4642(18)30317-1
  2332. Polonioli A., Vega-Mendoza M., Blankinship B., Carmel D. (2018): Reporting in Experimental Philosophy: Current Standards and Recommendations for Future Practice. Review of Philosophy and Psychology 12(1):49-73. https://doi.org/10.1007/s13164-018-0414-3
  2333. Fowlie A. (2018): DAMPE squib? Significance of the 1.4 TeV DAMPE excess. Physics Letters B 780:181-184. https://doi.org/10.1016/j.physletb.2018.03.006
  2334. Guerin A., Clare A. (2018): Mini-review: effect sizes and meta-analysis for antifouling research. Biofouling 34(10):1185-1199. https://doi.org/10.1080/08927014.2018.1550196
  2335. Martin A., Su P., Meinzer M. (2018): Common and unique effects of HD-tDCS to the social brain across cultural groups. https://doi.org/10.1101/408799
  2336. Cockburn A., Gutwin C., Dix A. (2018): HARK No More. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3173574.3173715
  2337. Krypotos A., Klugkist I., Mertens G., Engelhard I. (2018): A Step-By-Step Guide on Preregistration and Effective Data Sharing for Psychopathology Research. https://doi.org/10.31234/osf.io/ysgfa
  2338. Sandberg A. (2018): Undressed for Success? The Effects of Half-Naked Women on Economic Behavior. AEA Randomized Controlled Trials. https://doi.org/10.1257/rct.2989
  2339. Schirmer A., Ng T., Ebstein R. (2018): Vicarious social touch biases gazing at faces and facial emotions. Emotion 18(8):1097-1105. https://doi.org/10.1037/emo0000393
  2340. Cursan A. (2018): Un chercheur sachant chercher : de l’importance scientifique des résultats « nuls » et négatifs en psychologie. Pratiques Psychologiques 24(3):309-324. https://doi.org/10.1016/j.prps.2018.03.001
  2341. Krefeld-Schwalb A., Witte E., Zenker F. (2018): Hypothesis-Testing Demands Trustworthy Data—A Simulation Approach to Inferential Statistics Advocating the Research Program Strategy. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.00460
  2342. Totomoch-Serra A., Muñoz M., Burgueño J., Revilla-Monsalve M., Perez-Muñoz A., Diaz-Badillo Á. (2018): The ADRA2A rs553668 variant is associated with type 2 diabetes and five variants were associated at nominal significance levels in a population-based case–control study from Mexico City. Gene 669:28-34. https://doi.org/10.1016/j.gene.2018.05.078
  2343. Govindaswami B., Jegatheesan P., Nudelman M., Narasimhan S. (2018): Prevention of Prematurity. Clinics in Perinatology 45(3):579-595. https://doi.org/10.1016/j.clp.2018.05.013
  2344. Aczel B., Palfi B., Szollosi A., Kovacs M., Szaszi B., Szecsi P., et al. (2018): Quantifying Support for the Null Hypothesis in Psychology: An Empirical Investigation. Advances in Methods and Practices in Psychological Science 1(3):357-366. https://doi.org/10.1177/2515245918773742
  2345. Aczel B., Hoekstra R., Gelman A., Wagenmakers E., Klugkist I., Rouder J., et al. (2018): Discussion points for Bayesian inference. https://doi.org/10.31234/osf.io/23m7f
  2346. Baselmans B., Willems Y., van Beijsterveldt C., Ligthart L., Willemsen G., Dolan C., et al. (2018): Unraveling the Genetic and Environmental Relationship Between Well-Being and Depressive Symptoms Throughout the Lifespan. Frontiers in Psychiatry 9. https://doi.org/10.3389/fpsyt.2018.00261
  2347. Coker B., Rudin C., King G. (2018): A Theory of Statistical Inference for Ensuring the Robustness of\n Scientific Results. Management Science 67(10):6174-6197. https://doi.org/10.1287/mnsc.2020.3818
  2348. Van Calster B., Steyerberg E., Collins G., Smits T. (2018): Consequences of relying on statistical significance: Some illustrations. European Journal of Clinical Investigation 48(5). https://doi.org/10.1111/eci.12912
  2349. Voelkl B., Vogt L., Sena E., Würbel H. (2018): Reproducibility of preclinical animal research improves with heterogeneity of study samples. PLOS Biology 16(2):e2003693. https://doi.org/10.1371/journal.pbio.2003693
  2350. Wilson B., Wixted J. (2018): The Prior Odds of Testing a True Effect in Cognitive and Social Psychology. Advances in Methods and Practices in Psychological Science 1(2):186-197. https://doi.org/10.1177/2515245918767122
  2351. Nosek B., Ebersole C., DeHaven A., Mellor D. (2018): The preregistration revolution. Proceedings of the National Academy of Sciences 115(11):2600-2606. https://doi.org/10.1073/pnas.1708274114
  2352. Cesana B. (2018): What p-value must be used as the Statistical Significance Threshold? P<0.005, P<0.01, P<0.05 or no value at all?. Biomedical Journal of Scientific & Technical Research 6(3). https://doi.org/10.26717/bjstr.2018.06.001359
  2353. Sevcik C. (2018): First derivatives at the optimum analysis (\textit{fdao}): An approach to estimate the uncertainty in nonlinear regression involving stochastically independent variables. arXiv. https://doi.org/10.48550/arxiv.1802.09057
  2354. Albers C., Kiers H., van Ravenzwaaij D. (2018): Credible Confidence: A Pragmatic View on the Frequentist vs Bayesian Debate. Collabra: Psychology 4(1). https://doi.org/10.1525/collabra.149
  2355. Albers C., Kiers H., van Ravenzwaaij D. (2018): Credible Confidence: A pragmatic view on the frequentist vs Bayesian debate. https://doi.org/10.31234/osf.io/we9h6
  2356. Li C., Wang X., Wen C., Tan H. (2018): Association of degree of loss aversion and grey matter volume in superior frontal gyrus by voxel-based morphometry. Brain Imaging and Behavior 14(1):89-99. https://doi.org/10.1007/s11682-018-9962-5
  2357. Qian C., Zhang X., Li Z. (2018): Linear trends in temperature extremes in China, with an emphasis on non-Gaussian and serially dependent characteristics. Climate Dynamics 53(1-2):533-550. https://doi.org/10.1007/s00382-018-4600-x
  2358. Van Le C. (2018): Detection of Structural Changes Without Using P Values. Studies in Computational Intelligence. https://doi.org/10.1007/978-3-030-04200-4_41
  2359. Meyer C., Padmala S., Pessoa L. (2018): Dynamic Threat Processing. Journal of Cognitive Neuroscience 31(4):522-542. https://doi.org/10.1162/jocn_a_01363
  2360. Janssen C. (2018): Peer Review #2 of “Adaptive automation: automatically (dis)engaging automation during visually distracted driving (v0.1)”. https://doi.org/10.7287/peerj-cs.166v0.1/reviews/2
  2361. Damrau C., Colomb J., Brembs B. (2018): Differential dominance of an allele of the Drosophila tßh gene challenges standard genetic techniques. https://doi.org/10.1101/504332
  2362. Cabrall C., Janssen N., de Winter J. (2018): Adaptive automation: automatically (dis)engaging automation during visually distracted driving. PeerJ Computer Science 4:e166. https://doi.org/10.7717/peerj-cs.166
  2363. Chabris C., Heck P., Mandart J., Benjamin D., Simons D. (2018): No Evidence That Experiencing Physical Warmth Promotes Interpersonal Warmth. Social Psychology 50(2):127-132. https://doi.org/10.1027/1864-9335/a000361
  2364. Brydges C. (2018): Publication bias and statistical power in gerontological psychology. https://doi.org/10.31234/osf.io/ruwxt
  2365. Winship C., Zhuo X. (2018): Interpreting t-Statistics Under Publication Bias: Rough Rules of Thumb. Journal of Quantitative Criminology 36(2):329-346. https://doi.org/10.1007/s10940-018-9387-8
  2366. Wayant C., Scott J., Vassar M. (2018): Evaluation of Lowering the P Value Threshold for Statistical Significance From .05 to .005 in Previously Published Randomized Clinical Trials in Major Medical Journals. JAMA 320(17):1813. https://doi.org/10.1001/jama.2018.12288
  2367. Camerer C., Dreber A., Holzmeister F., Ho T., Huber J., Johannesson M., et al. (2018): Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nature Human Behaviour 2(9):637-644. https://doi.org/10.1038/s41562-018-0399-z
  2368. Camerer C., Dreber A., Holzmeister F., Ho T., Huber J., Johannesson M., et al. (2018): Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. https://doi.org/10.31235/osf.io/4hmb6
  2369. Hyatt C., Owens M., Gray J., CARTER N., MacKillop J., Sweet L., et al. (2018): Personality traits share overlapping neuroanatomical correlates with internalizing and externalizing psychopathology. https://doi.org/10.31234/osf.io/he738
  2370. Bol D. (2018): Putting Politics in the Lab: A Review of Lab Experiments in Political Science. Government and Opposition 54(1):167-190. https://doi.org/10.1017/gov.2018.14
  2371. Schuette D., Moore L., Robert M., Taddei T., Ehrlich B. (2018): Hepatocellular Carcinoma Outcome Is Predicted by Expression of Neuronal Calcium Sensor 1. Cancer Epidemiology, Biomarkers & Prevention 27(9):1091-1100. https://doi.org/10.1158/1055-9965.epi-18-0167
  2372. Lakens D., Adolfi F., Albers C., Anvari F., Apps M., Argamon S., et al. (2018): Justify your alpha. Nature Human Behaviour 2(3):168-171. https://doi.org/10.1038/s41562-018-0311-x
  2373. Buller D., Walkosz B., Buller M., Wallis A., Andersen P., Scott M., et al. (2018): Implementation of Occupational Sun Safety at a 2-Year Follow-Up in a Randomized Trial: Comparison of Sun Safe Workplaces Policy Intervention to Attention Control. American Journal of Health Promotion 33(5):683-697. https://doi.org/10.1177/0890117118814398
  2374. Huber D., Potter K., Huszar L. (2018): Less “story” and more “reliability” in cognitive neuroscience. Cortex 113:347-349. https://doi.org/10.1016/j.cortex.2018.10.030
  2375. Huber D., Potter K., Huszar L. (2018): Less “Story” and more “Reliability” in cognitive neuroscience. https://doi.org/10.31234/osf.io/5q9ma
  2376. Lederer D., Bell S., Branson R., Chalmers J., Marshall R., Maslove D., et al. (2018): Control of Confounding and Reporting of Results in Causal Inference Studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals. Annals of the American Thoracic Society 16(1):22-28. https://doi.org/10.1513/annalsats.201808-564ps
  2377. Trafimow D., Amrhein V., Areshenkoff C., Barrera-Causil C., Beh E., Bilgiç Y., et al. (2018): Manipulating the Alpha Level Cannot Cure Significance Testing. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.00699
  2378. Mayo D. (2018): Statistical inference as severe testing how to get beyond the statistics wars. https://doi.org/10.1017/9781107286184
  2379. Mayo D. (2018): Statistical Inference as Severe Testing. Cambridge University Press eBooks. https://doi.org/10.1017/9781107286184
  2380. Deborah G. Mayo (2018): Beyond Probabilism and Performance. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.002
  2381. Deborah G. Mayo (2018): The Myth of “The Myth of Objectivity”. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.009
  2382. Deborah G. Mayo (2018): Falsification, Pseudoscience, Induction. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.005
  2383. Deborah G. Mayo (2018): Induction and Confirmation. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.004
  2384. Deborah G. Mayo (2018): Pragmatic and Error Statistical Bayesians. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.017
  2385. Deborah G. Mayo (2018): What Ever Happened to Bayesian Foundations?. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.016
  2386. Deborah G. Mayo (2018): Power: Pre-data and Post-data. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.013
  2387. Deborah G. Mayo (2018): Rejection Fallacies: Who’s Exaggerating What?. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.010
  2388. Deborah G. Mayo (2018): More Auditing: Objectivity and Model Checking. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.012
  2389. Deborah G. Mayo (2018): Souvenirs. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.018
  2390. Deborah G. Mayo (2018): Ingenious and Severe Tests. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.006
  2391. Deborah G. Mayo (2018): Error Probing Tools versus Logics of Evidence. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.003
  2392. Deborah G. Mayo (2018): Auditing: Biasing Selection Effects and Randomization. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.011
  2393. Deborah G. Mayo (2018): Capability and Severity: Deeper Concepts. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.008
  2394. Deborah G. Mayo (2018): It’s The Methods, Stupid. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.007
  2395. Deborah G. Mayo (2018): Preface. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.001
  2396. Deborah G. Mayo (2018): Deconstructing the N-P versus Fisher Debates. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.015
  2397. Deborah G. Mayo (2018): Index. Statistical Inference as Severe Testing. https://doi.org/10.1017/9781107286184.019
  2398. Elliott D. (2018): Adversarial Evaluation of Multimodal Machine Translation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d18-1329
  2399. Lorca-Puls D., Gajardo-Vidal A., White J., Seghier M., Leff A., Green D., et al. (2018): The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings. Neuropsychologia 115:101-111. https://doi.org/10.1016/j.neuropsychologia.2018.03.014
  2400. Dirk Ostwald, Sebastian O. Schneider, Rasmus Bruckner, Lilla Horváth (2018): Random field theory-based p-values: A review of the SPM implementation. arXiv (Cornell University). https://doi.org/10.17605/osf.io/3dx9w
  2401. Krasnienkov D., Khalangot M., Kravchenko V., Kovtun V., Guryanov V., Chizhova V., et al. (2018): Hyperglycemia attenuates the association between telomere length and age in Ukrainian population. Experimental Gerontology 110:247-252. https://doi.org/10.1016/j.exger.2018.06.027
  2402. Dragos D., Gilca M. (2018): Taste of phytocompounds: A better predictor for ethnopharmacological activities of medicinal plants than the phytochemical class?. Journal of Ethnopharmacology 220:129-146. https://doi.org/10.1016/j.jep.2018.03.034
  2403. Parslow E., Ranehill E., Zethraeus N., Blomberg L., von Schoultz B., Lindén Hirschberg A., et al. (2018): The Digit Ratio (2D:4D) and Economic Preferences: No Robust Associations in a Sample of 330 Women. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3238048
  2404. Olivetti E., Benozzo D., Bím J., Panzeri S., Avesani P. (2018): Classification-Based Prediction of Effective Connectivity Between Timeseries With a Realistic Cortical Network Model. Frontiers in Computational Neuroscience 12. https://doi.org/10.3389/fncom.2018.00038
  2405. Ripamonti E., Frustaci M., Zonca G., Aggujaro S., Molteni F., Luzzatti C. (2018): Disentangling phonological and articulatory processing: A neuroanatomical study in aphasia. Neuropsychologia 121:175-185. https://doi.org/10.1016/j.neuropsychologia.2018.10.015
  2406. Otárola-Castillo E., Torquato M. (2018): Bayesian Statistics in Archaeology. Annual Review of Anthropology 47(1):435-453. https://doi.org/10.1146/annurev-anthro-102317-045834
  2407. Miller E. (2018): Peer Review #1 of “Adaptive automation: automatically (dis)engaging automation during visually distracted driving (v0.1)”. https://doi.org/10.7287/peerj-cs.166v0.1/reviews/1
  2408. Buchanan E., Foreman R., Johnson B., Pavlacic J., Swadley R., Schulenberg S. (2018): Does the delivery matter? Examining randomization at the item level. Behaviormetrika 45(2):295-316. https://doi.org/10.1007/s41237-018-0055-y
  2409. Forsell E., Viganola D., Pfeiffer T., Almenberg J., Wilson B., Chen Y., et al. (2018): Predicting replication outcomes in the Many Labs 2 study. Journal of Economic Psychology 75:102117. https://doi.org/10.1016/j.joep.2018.10.009
  2410. Forsell E., Viganola D., Pfeiffer T., Almenberg J., Wilson B., Chen Y., et al. (2018): Predicting replication outcomes in the Many Labs 2 study. https://doi.org/10.31234/osf.io/sgjaz
  2411. Schott E., Rhemtulla M., Byers-Heinlein K. (2018): Should I test more babies? Solutions for transparent data peeking. Infant Behavior and Development 54:166-176. https://doi.org/10.1016/j.infbeh.2018.09.010
  2412. Schott E., Rhemtulla M., Byers-Heinlein K. (2018): Should I test more babies? Solutions for transparent data peeking. https://doi.org/10.31234/osf.io/gxfaj
  2413. Bonnier E., Dreber A., Hederos Eriksson K., Sandberg A. (2018): Undressed for Success? The Effects of Half-Naked Women on Economic Behavior. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3168626
  2414. Chekalin E., Rubanovich A., Tatarinova T., Kasianov A., Bender N., Chekalina M., et al. (2018): Changes in Biological Pathways During 6,000 Years of Civilization in Europe. Molecular Biology and Evolution 36(1):127-140. https://doi.org/10.1093/molbev/msy201
  2415. Sedaghat F., Cheraghpour M., Hosseini S., Pourvali K., Teimoori-Toolabi L., Mehrtash A., et al. (2018): Hypomethylation of NANOG promoter in colonic mucosal cells of obese patients: a possible role of NF-κB. British Journal of Nutrition 122(5):499-508. https://doi.org/10.1017/s000711451800212x
  2416. Cova F., Strickland B., Abatista A., Allard A., Andow J., Attie M., et al. (2018): Estimating the Reproducibility of Experimental Philosophy. Review of Philosophy and Psychology 12(1):9-44. https://doi.org/10.1007/s13164-018-0400-9
  2417. Cova F., Strickland B., Abatista A., Allard A., Andow J., Attie M., et al. (2018): Estimating the Reproducibility of Experimental Philosophy. https://doi.org/10.31234/osf.io/sxdah
  2418. Loesche F., Goslin J., Bugmann G. (2018): Paving the Way to Eureka—Introducing “Dira” as an Experimental Paradigm to Observe the Process of Creative Problem Solving. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.01773
  2419. Stark G., Tamar K., Itescu Y., Feldman A., Meiri S. (2018): Cold and isolated ectotherms: drivers of reptilian longevity. Biological Journal of the Linnean Society 125(4):730-740. https://doi.org/10.1093/biolinnean/bly153
  2420. Stark G., Meiri S. (2018): Cold and dark captivity: Drivers of amphibian longevity. Global Ecology and Biogeography 27(11):1384-1397. https://doi.org/10.1111/geb.12804
  2421. Banks G., Field J., Oswald F., O’Boyle E., Landis R., Rupp D., et al. (2018): Answers to 18 Questions About Open Science Practices. Journal of Business and Psychology 34(3):257-270. https://doi.org/10.1007/s10869-018-9547-8
  2422. Gigerenzer G. (2018): Statistical Rituals: The Replication Delusion and How We Got There. Advances in Methods and Practices in Psychological Science 1(2):198-218. https://doi.org/10.1177/2515245918771329
  2423. Prochilo G., Louis W., Bode S., Zacher H., Molenberghs P. (2018): An Extended Commentary on Post-Publication Peer Review In Organizational Neuroscience. https://doi.org/10.31234/osf.io/hv3rm
  2424. Arnold G., Farrer B., Holahan R. (2018): How do landowners learn about high-volume hydraulic fracturing? A survey of Eastern Ohio landowners in active or proposed drilling units. Energy Policy 114:455-464. https://doi.org/10.1016/j.enpol.2017.12.026
  2425. Campbell H., Gustafson P. (2018): Conditional equivalence testing: An alternative remedy for publication bias. PLOS ONE 13(4):e0195145. https://doi.org/10.1371/journal.pone.0195145
  2426. Crane H. (2018): The Impact of P -hacking on “Redefine Statistical Significance”. Basic and Applied Social Psychology 40(4):219-235. https://doi.org/10.1080/01973533.2018.1474111
  2427. Hoijtink H., Gu X., Mulder J. (2018): Bayesian evaluation of informative hypotheses for multiple populations. British Journal of Mathematical and Statistical Psychology 72(2):219-243. https://doi.org/10.1111/bmsp.12145
  2428. Koh H., Hamada T., Song M., Liu L., Cao Y., Nowak J., et al. (2018): Physical Activity and Colorectal Cancer Prognosis According to Tumor-Infiltrating T Cells. JNCI Cancer Spectrum 2(4). https://doi.org/10.1093/jncics/pky058
  2429. Groot H., Al Ali L., van der Horst I., Schurer R., van der Werf H., Lipsic E., et al. (2018): Plasma interleukin 6 levels are associated with cardiac function after ST-elevation myocardial infarction. Clinical Research in Cardiology 108(6):612-621. https://doi.org/10.1007/s00392-018-1387-z
  2430. HU C., KONG X., Er i., Al E., PENG K. (2018): 贝叶斯因子及其在JASP中的实现. Advances in Psychological Science 26(6):951-965. https://doi.org/10.3724/sp.j.1042.2018.00951
  2431. Han H., Park J., Thoma S. (2018): Why do we need to employ Bayesian statistics and how can we employ it in studies of moral education?: With practical guidelines to use JASP for educators and researchers. Journal of Moral Education. https://doi.org/10.1080/03057240.2018.1463204
  2432. Han H., Park J., Thoma S. (2018): Why Do We Need to Employ Bayesian Statistics and How Can We Employ it in Studies of Moral Education?: With Practical Guidelines to Use JASP for Educators and Researchers. https://doi.org/10.31234/osf.io/mruz9
  2433. Hussey I., Hughes S. (2018): Hidden invalidity among fifteen commonly used measures in social and personality psychology. https://doi.org/10.31234/osf.io/7rbfp
  2434. Davidson I. (2018): The Ouroboros of Psychological Methodology: The Case of Effect Sizes (Mechanical Objectivity vs. Expertise). Review of General Psychology 22(4):469-476. https://doi.org/10.1037/gpr0000154
  2435. Moaniba I., Su H., Lee P. (2018): Knowledge recombination and technological innovation: the important role of cross-disciplinary knowledge. Innovation 20(4):326-352. https://doi.org/10.1080/14479338.2018.1478735
  2436. Gich Saladich I. (2018): ¿Sería conveniente reducir el valor p considerado significativo?. Endocrinología, Diabetes y Nutrición 65(9):477-478. https://doi.org/10.1016/j.endinu.2018.09.001
  2437. Gich Saladich I. (2018): It would be desirable to reduce the p value considered significant?. Endocrinología, Diabetes y Nutrición (English ed.) 65(9):477-478. https://doi.org/10.1016/j.endien.2018.11.002
  2438. Kuzovkin I., Vicente R., Petton M., Lachaux J., Baciu M., Kahane P., et al. (2018): Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex. Communications Biology 1(1). https://doi.org/10.1038/s42003-018-0110-y
  2439. Hernandez I., Gellad W., Good C. (2018): Lowering the P Value Threshold. JAMA 320(9):935. https://doi.org/10.1001/jama.2018.8733
  2440. Cristea I., Ioannidis J. (2018): P values in display items are ubiquitous and almost invariably significant: A survey of top science journals. PLOS ONE 13(5):e0197440. https://doi.org/10.1371/journal.pone.0197440
  2441. Martin I., Nations J. (2018): Taxation and Citizen Voice in School District Parcel Tax Elections. Sociological Science 5:653-668. https://doi.org/10.15195/v5.a27
  2442. Perezgonzalez J., Frías-Navarro M. (2018): Retract p < 0.005 and propose using JASP, instead. F1000Research 6:2122. https://doi.org/10.12688/f1000research.13389.2
  2443. Ørmen J. (2018): From Consumer Demand to User Engagement: Comparing the Popularity and Virality of Election Coverage on the Internet. The International Journal of Press/Politics 24(1):49-68. https://doi.org/10.1177/1940161218809160
  2444. Kim J., Ahmed K., Ji P. (2018): Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence. Abacus 54(4):524-546. https://doi.org/10.1111/abac.12141
  2445. Kim J. (2018): TACKLING FALSE POSITIVES IN BUSINESS RESEARCH: A STATISTICAL TOOLBOX WITH APPLICATIONS. Journal of Economic Surveys 33(3):862-895. https://doi.org/10.1111/joes.12303
  2446. Teixeira da Silva J., Tsigaris P. (2018): What Value Do Journal Whitelists and Blacklists Have in Academia?. The Journal of Academic Librarianship 44(6):781-792. https://doi.org/10.1016/j.acalib.2018.09.017
  2447. Derringer J. (2018): A simple correction for non-independent tests. https://doi.org/10.31234/osf.io/f2tyw
  2448. Sprenger J. (2018): The objectivity of Subjective Bayesianism. European Journal for Philosophy of Science 8(3):539-558. https://doi.org/10.1007/s13194-018-0200-1
  2449. de Ruiter J. (2018): Redefine or justify? Comments on the alpha debate. Psychonomic Bulletin & Review 26(2):430-433. https://doi.org/10.3758/s13423-018-1523-9
  2450. Charlesworth J., Weinert L., Araujo-Jnr. E., Welch J. (2018): Wolbachia, Cardinium and climate: an analysis of global data. https://doi.org/10.1101/490284
  2451. Spence J., Stanley D. (2018): Concise, Simple, and Not Wrong: In Search of a Short-Hand Interpretation of Statistical Significance. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.02185
  2452. Freese J., Peterson D. (2018): The Emergence of Statistical Objectivity: Changing Ideas of Epistemic Vice and Virtue in Science. Sociological Theory 36(3):289-313. https://doi.org/10.1177/0735275118794987
  2453. Witt J. (2018): Witt_Criteria_For_Statistical_Significance_v1. https://doi.org/10.31234/osf.io/x587f
  2454. Schoch J., Noser E., Ehlert U. (2018): Do Implicit Motives Influence Perceived Chronic Stress and Vital Exhaustion?. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.01149
  2455. Schroeder J., Karkar R., Fogarty J., Kientz J., Munson S., Kay M. (2018): A Patient-Centered Proposal for Bayesian Analysis of Self-Experiments for Health. Journal of Healthcare Informatics Research 3(1):124-155. https://doi.org/10.1007/s41666-018-0033-x
  2456. Krueger J., Heck P. (2018): Testing Significance Testing. Collabra: Psychology 4(1). https://doi.org/10.1525/collabra.108
  2457. Haushofer J., Reisinger J. (2018): Atheist primes reduce religiosity and subjective wellbeing. Religion, Brain & Behavior 9(2):126-142. https://doi.org/10.1080/2153599x.2018.1436585
  2458. Ashton J. (2018): It has not been proven why or that most research findings are false. Medical Hypotheses 113:27-29. https://doi.org/10.1016/j.mehy.2018.02.004
  2459. Kruschke J. (2018): Rejecting or Accepting Parameter Values in Bayesian Estimation. Advances in Methods and Practices in Psychological Science 1(2):270-280. https://doi.org/10.1177/2515245918771304
  2460. Kruschke J. (2018): Rejecting or accepting parameter values in Bayesian estimation. https://doi.org/10.31219/osf.io/s5vdy
  2461. Ioannidis J. (2018): The Proposal to Lower P Value Thresholds to .005. JAMA 319(14):1429. https://doi.org/10.1001/jama.2018.1536
  2462. Ioannidis J., Kim B., Trounson A. (2018): How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation. Nature Biomedical Engineering 2(11):797-809. https://doi.org/10.1038/s41551-018-0314-y
  2463. Ioannidis J. (2018): Why replication has more scientific value than original discovery. Behavioral and Brain Sciences 41. https://doi.org/10.1017/s0140525x18000729
  2464. Sweedler J. (2018): Science Research — Looking in the Mirror. Analytical Chemistry 90(21):12323-12324. https://doi.org/10.1021/acs.analchem.8b04786
  2465. Axt J., Casola G., Nosek B. (2018): Reducing Social Judgment Biases May Require Identifying the Potential Source of Bias. Personality and Social Psychology Bulletin 45(8):1232-1251. https://doi.org/10.1177/0146167218814003
  2466. Axt J., Casola G., Nosek B. (2018): Reducing social judgment biases may require identifying the potential source of bias. https://doi.org/10.31234/osf.io/ngxks
  2467. Ho J., Tumkaya T., Aryal S., Choi H., Claridge-Chang A. (2018): Moving beyond P values: Everyday data analysis with estimation plots. https://doi.org/10.1101/377978
  2468. Wright J., Esses V. (2018): It’s security, stupid! Voters’ perceptions of immigrants as a security risk predicted support for Donald Trump in the 2016 US presidential election. Journal of Applied Social Psychology 49(1):36-49. https://doi.org/10.1111/jasp.12563
  2469. Polanin J., Nuijten M. (2018): Verifying the accuracy of statistical significance testing in Campbell Collaboration systematic reviews through the use of the R package statcheck. Campbell Systematic Reviews 14(1):1-36. https://doi.org/10.4073/csrm.2018.1
  2470. Kim J., Schulz W., Zimmermann T., Hahlweg K. (2018): Parent–child interactions and child outcomes: Evidence from randomized intervention. Labour Economics 54:152-171. https://doi.org/10.1016/j.labeco.2018.08.003
  2471. Sienicki K. (2018): COMMENTS ON “AN EXCEPTIONAL SUMMER DURING THE SOUTH POLE RACE OF 1911/12”. Bulletin of the American Meteorological Society 99(10):2139-2143. https://doi.org/10.1175/bams-d-17-0282.1
  2472. Kosumi K., Hamada T., Koh H., Borowsky J., Bullman S., Twombly T., et al. (2018): The Amount of Bifidobacterium Genus in Colorectal Carcinoma Tissue in Relation to Tumor Characteristics and Clinical Outcome. The American Journal of Pathology 188(12):2839-2852. https://doi.org/10.1016/j.ajpath.2018.08.015
  2473. Tsoi K., Ho J., Chan F., Sung J. (2018): Long‐term use of low‐dose aspirin for cancer prevention: A 10‐year population cohort study in Hong Kong. International Journal of Cancer 145(1):267-273. https://doi.org/10.1002/ijc.32083
  2474. Gillingham K., Keyes A., Palmer K. (2018): Advances in Evaluating Energy Efficiency Policies and Programs. Annual Review of Resource Economics 10(1):511-532. https://doi.org/10.1146/annurev-resource-100517-023028
  2475. Smith K., Ruhl H., Huffard C., Messié M., Kahru M. (2018): Episodic organic carbon fluxes from surface ocean to abyssal depths during long-term monitoring in NE Pacific. Proceedings of the National Academy of Sciences 115(48):12235-12240. https://doi.org/10.1073/pnas.1814559115
  2476. Berlemont K., Nadal J. (2018): Perceptual Decision-Making: Biases in Post-Error Reaction Times Explained by Attractor Network Dynamics. The Journal of Neuroscience 39(5):833-853. https://doi.org/10.1523/jneurosci.1015-18.2018
  2477. Berlemont K., Nadal J. (2018): Perceptual decision making: Biases in post-error reaction times\n explained by attractor network dynamics. arXiv. https://doi.org/10.48550/arxiv.1803.00795
  2478. Huang C., Chiang S., Ke T., Chen T., You Y., Chen W., et al. (2018): Clinical significance of programmed death 1 ligand-1 (CD274/PD-L1) and intra-tumoral CD8+ T-cell infiltration in stage II–III colorectal cancer. Scientific Reports 8(1). https://doi.org/10.1038/s41598-018-33927-5
  2479. King K., Pullmann M., Lyon A., Dorsey S., Lewis C. (2018): Using implementation science to close the gap between the optimal and typical practice of quantitative methods in clinical science. https://doi.org/10.31234/osf.io/n2v68
  2480. Kevin Parent (2018): What English Verb Errors do Korean University Students Make? A Corpus Examination of Learner-Produced Verb Phrases. Korean Journal of English Language and Linguistics 18(4):423-441. https://doi.org/10.15738/kjell.18.4.201812.423
  2481. Metze K. (2018): Lowering the p-value from 0.05 to 0.005 conflicts with the 3R rules – an advocacy for alternatives to hypothesis testing with the p-value approach. ALTEX. https://doi.org/10.14573/altex.1807091
  2482. Sainani K. (2018): Response. Medicine & Science in Sports & Exercise 51(3):600-600. https://doi.org/10.1249/mss.0000000000001824
  2483. Fronczyk K. (2018): Congruence and measurement invariance of self-report and informant-ratings of the Big Five dimensions. Personality and Individual Differences 139:7-12. https://doi.org/10.1016/j.paid.2018.10.036
  2484. Hall L., Hendricks A. (2018): Analysis of >30,000 abstracts suggests higher false discovery rates for oncology journals, especially those with low impact factors. https://doi.org/10.1101/500660
  2485. Tal-Or L., Trifonov T., Zucker S., Mazeh T., Zechmeister M. (2018): Correcting HIRES/Keck radial velocities for small systematic errors. Monthly Notices of the Royal Astronomical Society: Letters 484(1):L8-L13. https://doi.org/10.1093/mnrasl/sly227
  2486. Tal-Or L., Zechmeister M., Reiners A., Jeffers S., Schöfer P., Quirrenbach A., et al. (2018): The CARMENES search for exoplanets around M dwarfs. Astronomy & Astrophysics 614:A122. https://doi.org/10.1051/0004-6361/201732362
  2487. Tal-Or L., Zechmeister M., Reiners A., Jeffers S., Schöfer P., Quirrenbach A., et al. (2018): The CARMENES search for exoplanets around M dwarfs: Radial-velocity variations of active stars in visual-channel spectra. arXiv. https://doi.org/10.48550/arxiv.1803.02338
  2488. Botzet L., Rohrer J., Arslan R. (2018): Effects of Birth Order on Intelligence, Educational Attainment, Personality, and Risk Aversion in an Indonesian Sample. https://doi.org/10.31234/osf.io/5387k
  2489. Hietajärvi L., Salmela-Aro K., Tuominen H., Hakkarainen K., Lonka K. (2018): Beyond screen time: Multidimensionality of socio-digital participation and relations to academic well-being in three educational phases. Computers in Human Behavior 93:13-24. https://doi.org/10.1016/j.chb.2018.11.049
  2490. Belbasis L., Dosis V., Evangelou E. (2018): Elucidating the environmental risk factors for rheumatic diseases: An umbrella review of meta‐analyses. International Journal of Rheumatic Diseases 21(8):1514-1524. https://doi.org/10.1111/1756-185x.13356
  2491. Gallo L., Cristallini E., Svarc M. (2018): A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds. Journal of Geophysical Research: Solid Earth 123(11). https://doi.org/10.1029/2018jb016319
  2492. Moyé L., Cohen M. (2018): Liberation From the P Value’s Tyranny. Circulation Research 122(8):1046-1048. https://doi.org/10.1161/circresaha.117.312227
  2493. Ma L., Christensen T. (2018): Government Trust, Social Trust, and Citizens’ Risk Concerns: Evidence from Crisis Management in China. Public Performance & Management Review 42(2):383-404. https://doi.org/10.1080/15309576.2018.1464478
  2494. Colling L., Szűcs D. (2018): Statistical Inference and the Replication Crisis. Review of Philosophy and Psychology 12(1):121-147. https://doi.org/10.1007/s13164-018-0421-4
  2495. Fang L., Gao P., Bao H., Tang X., Wang B., Feng Y., et al. (2018): Chronic obstructive pulmonary disease in China: a nationwide prevalence study. The Lancet Respiratory Medicine 6(6):421-430. https://doi.org/10.1016/s2213-2600(18)30103-6
  2496. Anselin L. (2018): A Local Indicator of Multivariate Spatial Association: Extending Geary’s c. Geographical Analysis 51(2):133-150. https://doi.org/10.1111/gean.12164
  2497. Cardoso L., Gomes G., Vieira T. (2018): Validity Evidence of the Zulliger-SC Test to children’s assessment. Psico-USF 23(3):451-460. https://doi.org/10.1590/1413-82712018230305
  2498. Silva Aycaguer L. (2018): Errores metodológicos frecuentes en la investigación clínica. Medicina Intensiva 42(9):541-546. https://doi.org/10.1016/j.medin.2017.12.012
  2499. Silva Aycaguer L. (2018): Frequent methodological errors in clinical research. Medicina Intensiva (English Edition) 42(9):541-546. https://doi.org/10.1016/j.medine.2018.10.001
  2500. Jäncke L., Leipold S., Burkhard A. (2018): The neural underpinnings of music listening under different attention conditions. NeuroReport 29(7):594-604. https://doi.org/10.1097/wnr.0000000000001019
  2501. Said M., Verweij N., van der Harst P. (2018): Associations of Combined Genetic and Lifestyle Risks With Incident Cardiovascular Disease and Diabetes in the UK Biobank Study. JAMA Cardiology 3(8):693. https://doi.org/10.1001/jamacardio.2018.1717
  2502. Koch M. (2018): Probiotics and Evidence-based Medicine. Journal of Clinical Gastroenterology 52(Supplement 1):S4-S6. https://doi.org/10.1097/mcg.0000000000001106
  2503. Van Der Ende M., Hendriks T., Van Veldhuisen D., Snieder H., Verweij N., Van Der Harst P. (2018): Causal Pathways from Blood Pressure to Larger QRS Amplitudes: a Mendelian Randomization Study. Scientific Reports 8(1). https://doi.org/10.1038/s41598-018-24002-0
  2504. Perugini M., Gallucci M., Costantini G. (2018): A Practical Primer To Power Analysis for Simple Experimental Designs. International Review of Social Psychology 31(1). https://doi.org/10.5334/irsp.181
  2505. Solmi M., Köhler C., Stubbs B., Koyanagi A., Bortolato B., Monaco F., et al. (2018): Environmental risk factors and nonpharmacological and nonsurgical interventions for obesity: An umbrella review of meta‐analyses of cohort studies and randomized controlled trials. European Journal of Clinical Investigation 48(12). https://doi.org/10.1111/eci.12982
  2506. Bendtsen M. (2018): A Gentle Introduction to the Comparison Between Null Hypothesis Testing and Bayesian Analysis: Reanalysis of Two Randomized Controlled Trials. Journal of Medical Internet Research 20(10):e10873. https://doi.org/10.2196/10873
  2507. Sarstedt M., Mooi E. (2018): Hypothesis Testing and ANOVA. Springer Texts in Business and Economics. https://doi.org/10.1007/978-3-662-56707-4_6
  2508. Christen M. (2018): Comparing cultural differences with domain-specific differences of appreciating and understanding values. Journal of Moral Education 47(3):333-345. https://doi.org/10.1080/03057240.2018.1469477
  2509. Mayer M. (2018): Research integrity and the law that never was. BMJ Evidence-Based Medicine 23(6):218-224. https://doi.org/10.1136/bmjebm-2018-110993
  2510. Meier M. (2018): Can research participants comment authoritatively on the validity of their self-reports of mind wandering and task engagement? A replication and extension of Seli, Jonker, Cheyne, Cortes, and Smilek (2015). Journal of Experimental Psychology: Human Perception and Performance 44(10):1567-1585. https://doi.org/10.1037/xhp0000556
  2511. Davidson M., Alais D., van Boxtel J., Tsuchiya N. (2018): Attention periodically samples competing stimuli during binocular rivalry. eLife 7. https://doi.org/10.7554/elife.40868
  2512. Davidson M., Alais D., Tsuchiya N., van Boxtel J. (2018): Attention periodically samples competing stimuli during binocular rivalry. https://doi.org/10.1101/253740
  2513. Morey M., Yadav S. (2018): Documentation of the File Drawer Problem in Academic Finance Journals. The Journal of Investing 27(1):143-147. https://doi.org/10.3905/joi.2018.27.1.143
  2514. Steinfath M., Vogl S., Violet N., Schwarz F., Mielke H., Selhorst T., et al. (2018): Simple changes of individual studies can improve the reproducibility of the biomedical scientific process as a whole. PLOS ONE 13(9):e0202762. https://doi.org/10.1371/journal.pone.0202762
  2515. Heino M., Vuorre M., Hankonen N. (2018): Bayesian evaluation of behavior change interventions: a brief introduction and a practical example. Health Psychology and Behavioral Medicine 6(1):49-78. https://doi.org/10.1080/21642850.2018.1428102
  2516. Mullarkey M., Stewart R., Wells T., Shumake J., Beevers C. (2018): Self-Dislike and Sadness are Central Symptoms of Depression in College Students: A Network Analysis. https://doi.org/10.31234/osf.io/fujmb
  2517. Kim M., Kim C. (2018): Personality Basis for Partisan News Media Use: Openness to Experience and Consumption of Liberal News Media. Mass Communication and Society 21(6):814-833. https://doi.org/10.1080/15205436.2018.1506035
  2518. Khan M., Trønnes P. (2018): p-Hacking in Experimental Audit Research. Behavioral Research in Accounting 31(1):119-131. https://doi.org/10.2308/bria-52183
  2519. Song N., Kim K., Shin A., Park J., Chang H., Shi J., et al. (2018): Colorectal cancer susceptibility loci and influence on survival. Genes, Chromosomes and Cancer 57(12):630-637. https://doi.org/10.1002/gcc.22674
  2520. Cox N., Below J. (2018): Critical Evaluation of Data Requires Rigorous but Broadly Based Statistical Inference. Circulation Research 122(8):1049-1051. https://doi.org/10.1161/circresaha.118.312530
  2521. Kavish N., Fu Q., Vaughn M., Qian Z., Boutwell B. (2018): Resting Heart Rate and Psychopathy Revisited: Findings From the Add Health Survey. International Journal of Offender Therapy and Comparative Criminology 63(4):543-557. https://doi.org/10.1177/0306624×18806748
  2522. Holtzman N., Tackman A., Kuefner A., große Deters F., Back M., Donnellan B., et al. (2018): Linguistic Markers of Narcissism: An Exploratory LIWC Analysis of 15 Samples. https://doi.org/10.31234/osf.io/yvna6
  2523. Barbieri N., Marzucchi A., Rizzo U. (2018): Knowledge Sources and Impacts on Subsequent Inventions: Do Green Technologies Differ from Non-Green Ones?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3164197
  2524. Johannes N., Dora J., Rusz D. (2018): Social Smartphone Apps Do Not Capture Attention Despite Their Perceived High Reward Value. https://doi.org/10.31234/osf.io/rxsf7
  2525. Bello N., Renter D. (2018): Invited review: Reproducible research from noisy data: Revisiting key statistical principles for the animal sciences. Journal of Dairy Science 101(7):5679-5701. https://doi.org/10.3168/jds.2017-13978
  2526. Gundersen O., Kjensmo S. (2018): State of the Art: Reproducibility in Artificial Intelligence. Proceedings of the AAAI Conference on Artificial Intelligence 32(1). https://doi.org/10.1609/aaai.v32i1.11503
  2527. Possatto Junior O., Bertagna F., Peterlini E., Baleroni A., Rossi R., Zeni Neto H. (2018): Survey of statistical methods applied in articles published in Acta Scientiarum. Agronomy from 1998 to 2016. Acta Scientiarum. Agronomy 41(1):42641. https://doi.org/10.4025/actasciagron.v41i1.42641
  2528. Efthimiou O. (2018): Practical guide to the meta-analysis of rare events. Evidence Based Mental Health 21(2):72-76. https://doi.org/10.1136/eb-2018-102911
  2529. Heffetz O. (2018): Are Reference Points Merely Lagged Beliefs Over Probabilities?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3051829
  2530. Skippen P., Matzke D., Heathcote A., Fulham W., Michie P., Karayanidis F. (2018): Reliability of triggering inhibitory process is a better predictor of impulsivity than SSRT. Acta Psychologica 192:104-117. https://doi.org/10.1016/j.actpsy.2018.10.016
  2531. Bauer P. (2018): Unemployment, Trust in Government, and Satisfaction with Democracy: An Empirical Investigation. Socius: Sociological Research for a Dynamic World 4. https://doi.org/10.1177/2378023117750533
  2532. De Boeck P., Jeon M. (2018): Perceived crisis and reforms: Issues, explanations, and remedies. Psychological Bulletin 144(7):757-777. https://doi.org/10.1037/bul0000154
  2533. Smith P., Little D. (2018): Small is beautiful: In defense of the small-N design. Psychonomic Bulletin & Review 25(6):2083-2101. https://doi.org/10.3758/s13423-018-1451-8
  2534. Plavén-Sigray P., Matheson G., Gustavsson P., Stenkrona P., Halldin C., Farde L., et al. (2018): Is dopamine D1 receptor availability related to social behavior? A positron emission tomography replication study. PLOS ONE 13(3):e0193770. https://doi.org/10.1371/journal.pone.0193770
  2535. Holm Hansen R., Højsgaard Chow H., Sellebjerg F., Rode von Essen M. (2018): Dimethyl fumarate therapy suppresses B cell responses and follicular helper T cells in relapsing-remitting multiple sclerosis. Multiple Sclerosis Journal 25(9):1289-1297. https://doi.org/10.1177/1352458518790417
  2536. Sapra R., Nundy S. (2018): Why the p-value is under fire?. Current Medicine Research and Practice 8(6):222-229. https://doi.org/10.1016/j.cmrp.2018.10.003
  2537. Holbert R., Hardy B., Park E., Robinson N., Jung H., Zeng C., et al. (2018): Addressing a statistical power-alpha level blind spot in political- and health-related media research: discontinuous criterion power analyses. Annals of the International Communication Association 42(2):75-92. https://doi.org/10.1080/23808985.2018.1459198
  2538. Robertson R., Cease A., Simpson S. (2018): Anoxia tolerance of the adult Australian Plague Locust (Chortoicetes terminifera). Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 229:81-92. https://doi.org/10.1016/j.cbpa.2018.12.005
  2539. Vijayakumar R., Cheung M. (2018): Replicability of Machine Learning Models in the Social Sciences. Zeitschrift für Psychologie 226(4):259-273. https://doi.org/10.1027/2151-2604/a000344
  2540. Valentine K., Buchanan E., Wynn A., Hopke T., (Wikowsky) Clark A., Wilson H. (2018): Have psychologists increased reporting of outliers in response to the reproducibility crisis?. https://doi.org/10.31219/osf.io/jq8eu
  2541. Ranney R., Behar E., Bartoszek G. (2018): Individuals Intolerant of Uncertainty: The Maintenance of Worry and Distress Despite Reduced Uncertainty. Behavior Therapy 50(3):489-503. https://doi.org/10.1016/j.beth.2018.08.006
  2542. Dingledine R. (2018): Why Is It so Hard to Do Good Science?. eneuro 5(5):ENEURO.0188-18.2018. https://doi.org/10.1523/eneuro.0188-18.2018
  2543. Morton R., Ou K., Qin X. (2018): The effect of religion on Muslims’ charitable contributions to members of a non‐Muslim majority. Journal of Public Economic Theory 22(2):433-448. https://doi.org/10.1111/jpet.12352
  2544. Morton R., Ou K., Qin X. (2018): The Effect of Religion on Muslims’ Charitable Contributions to Members of a Non-Muslim Majority. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3231623
  2545. Schwaiger R., Kirchler M., Lindner F., Weitzel U. (2018): Determinants of Investor Expectations and Satisfaction: A Study With Financial Professionals. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3265719
  2546. Berk R. (2018): Some Concluding Observations About Actuarial Justice and More. Machine Learning Risk Assessments in Criminal Justice Settings. https://doi.org/10.1007/978-3-030-02272-3_9
  2547. Bethlehem R., Seidlitz J., Romero-Garcia R., Dumas G., Lombardo M. (2018): Normative age modelling of cortical thickness in autistic males. https://doi.org/10.1101/252593
  2548. Morey R., Homer S., Proulx T. (2018): Beyond Statistics: Accepting the Null Hypothesis in Mature Sciences. Advances in Methods and Practices in Psychological Science 1(2):245-258. https://doi.org/10.1177/2515245918776023
  2549. Morey R., Homer S., Proulx T. (2018): Beyond statistics: accepting the null hypothesis in mature sciences. https://doi.org/10.31219/osf.io/ek9rm
  2550. Echodu R., Edema H., Malinga G., Hendy A., Colebunders R., Moriku Kaducu J., et al. (2018): Is nodding syndrome in northern Uganda linked to consumption of mycotoxin contaminated food grains?. BMC Research Notes 11(1). https://doi.org/10.1186/s13104-018-3774-y
  2551. Smith R. (2018): The continuing misuse of null hypothesis significance testing in biological anthropology. American Journal of Physical Anthropology 166(1):236-245. https://doi.org/10.1002/ajpa.23399
  2552. Klein R., Vianello M., Hasselman F., Adams B., Adams R., Alper S., et al. (2018): Many Labs 2: Investigating Variation in Replicability Across Samples and Settings. Advances in Methods and Practices in Psychological Science 1(4):443-490. https://doi.org/10.1177/2515245918810225
  2553. Kelter R., Kramer M., Brinda T. (2018): Statistical Frequency-Analysis of Misconceptions In Object-Oriented-Programming. Proceedings of the 18th Koli Calling International Conference on Computing Education Research. https://doi.org/10.1145/3279720.3279727
  2554. Stockley R., Halpin D., Celli B., Singh D. (2018): Chronic Obstructive Pulmonary Disease Biomarkers and Their Interpretation. American Journal of Respiratory and Critical Care Medicine 199(10):1195-1204. https://doi.org/10.1164/rccm.201810-1860so
  2555. Calin-Jageman R., Cumming G. (2018): The New Statistics for Better Science: Ask How Much, How Uncertain, and What Else is Known. https://doi.org/10.31234/osf.io/3mztg
  2556. Matthews R. (2018): Beyond ‘significance’: principles and practice of the Analysis of Credibility. Royal Society Open Science 5(1):171047. https://doi.org/10.1098/rsos.171047
  2557. Berman R., Pekelis L., Scott A., Van den Bulte C. (2018): p-Hacking and False Discovery in A/B Testing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3204791
  2558. Kirsch S., Meryash D., González-Arévalo B. (2018): Determinants of Parent Satisfaction with Emergency or Urgent Care When the Patient Has Autism. Journal of Developmental & Behavioral Pediatrics 39(5):365-375. https://doi.org/10.1097/dbp.0000000000000573
  2559. Mustillo S., Lizardo O., McVeigh R. (2018): Editors’ Comment: A Few Guidelines for Quantitative Submissions. American Sociological Review 83(6):1281-1283. https://doi.org/10.1177/0003122418806282
  2560. Glover S. (2018): Likelihood Ratios: A Tutorial. OSF Preprints. https://doi.org/10.31222/osf.io/g3j2k
  2561. Grant S., Spears A., Pedersen E. (2018): Video Games as a Potential Modality for Behavioral Health Services for Young Adult Veterans: Exploratory Analysis. JMIR Serious Games 6(3):e15. https://doi.org/10.2196/games.9327
  2562. Tavallaei V., Rezapour-Mirsaleh Y., Rezaiemaram P., Saadat S. (2018): Mindfulness for female outpatients with chronic primary headaches: an internet-based bibliotherapy. European Journal of Translational Myology 28(2). https://doi.org/10.4081/ejtm.2018.7380
  2563. Genc S., Malpas C., Ball G., Silk T., Seal M. (2018): Age, sex, and puberty related development of the corpus callosum: a multi-technique diffusion MRI study. Brain Structure and Function 223(6):2753-2765. https://doi.org/10.1007/s00429-018-1658-5
  2564. Vazire S. (2018): Implications of the Credibility Revolution for Productivity, Creativity, and Progress. Perspectives on Psychological Science 13(4):411-417. https://doi.org/10.1177/1745691617751884
  2565. Gates S. (2018): Reporting and interpretation of results from clinical trials that did not claim a treatment difference. https://doi.org/10.31219/osf.io/725sz
  2566. Fourati S., Talla A., Mahmoudian M., Burkhart J., Klén R., Henao R., et al. (2018): A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nature Communications 9(1). https://doi.org/10.1038/s41467-018-06735-8
  2567. Fourati S., Talla A., Mahmoudian M., Burkhart J., Klén R., Henao R., et al. (2018): A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. https://doi.org/10.1101/311696
  2568. Lazic S. (2018): Four simple ways to increase power without increasing the sample size. Laboratory Animals 52(6):621-629. https://doi.org/10.1177/0023677218767478
  2569. Herbold S. (2018): Benchmarking cross-project defect prediction approaches with costs metrics. arXiv. https://doi.org/10.48550/arxiv.1801.04107
  2570. Serghiou S., Ioannidis J. (2018): Altmetric Scores, Citations, and Publication of Studies Posted as Preprints. JAMA 319(4):402. https://doi.org/10.1001/jama.2017.21168
  2571. Gruener S. (2018): Sample size calculations in economic RCTs: following clinical studies?. https://doi.org/10.31235/osf.io/43zbg
  2572. Pineda S., Sirota M. (2018): Determining Significance in the New Era for P Values. Journal of Pediatric Gastroenterology and Nutrition 67(5):547-548. https://doi.org/10.1097/mpg.0000000000002120
  2573. Sekine T., Hirata T., Ishikawa S., Ito S., Ishimori K., Matsumura K., et al. (2018): Regulation of NRF2, AP‐1 and NF‐κB by cigarette smoke exposure in three‐dimensional human bronchial epithelial cells. Journal of Applied Toxicology 39(5):717-725. https://doi.org/10.1002/jat.3761
  2574. Pfeiler T., Egloff B. (2018): Personality and meat consumption: The importance of differentiating between type of meat. Appetite 130:11-19. https://doi.org/10.1016/j.appet.2018.07.007
  2575. Könen T., Karbach J. (2018): Self-Reported Cognitive Failures in Everyday Life: A Closer Look at Their Relation to Personality and Cognitive Performance. Assessment 27(5):982-995. https://doi.org/10.1177/1073191118786800
  2576. Miwa T. (2018): Informal Comments on the ASA 2016 Statement. Japanese Journal of Biometrics 38(2):163-170. https://doi.org/10.5691/jjb.38.163
  2577. Thomas Michael McCabe (2018): An Analysis of Prey Resistance and Long-Term Temporal Changes in Venom Composition Within Rattlesnake Populations. Scholarship & Creative Works – Digital UNC a service of University Libraries (University of Northern Colorado).
  2578. Ziermans T., de Bruijn Y., Dijkhuis R., Staal W., Swaab H. (2018): Impairments in cognitive empathy and alexithymia occur independently of executive functioning in college students with autism. Autism 23(6):1519-1530. https://doi.org/10.1177/1362361318817716
  2579. Roettger T. (2018): Researcher degrees of freedom in phonetic research. https://doi.org/10.31234/osf.io/fp4jr
  2580. Gnambs T., Appel M. (2018): Are robots becoming unpopular? Changes in attitudes towards autonomous robotic systems in Europe. Computers in Human Behavior 93:53-61. https://doi.org/10.1016/j.chb.2018.11.045
  2581. Gnambs T., Appel M. (2018): Are robots becoming unpopular? Changes in attitudes towards autonomous robotic systems in europe. https://doi.org/10.31234/osf.io/aurce
  2582. Lash T., Collin L., Van Dyke M. (2018): The Replication Crisis in Epidemiology: Snowball, Snow Job, or Winter Solstice?. Current Epidemiology Reports 5(2):175-183. https://doi.org/10.1007/s40471-018-0148-x
  2583. Parker T., Griffith S., Bronstein J., Fidler F., Foster S., Fraser H., et al. (2018): Empowering peer reviewers with a checklist to improve transparency. Nature Ecology & Evolution 2(6):929-935. https://doi.org/10.1038/s41559-018-0545-z
  2584. Mattei T. (2018): Practical Effects of Lowering the P Value in Neurosurgery: Restricting Evidence-Based Medicine to Big Business. World Neurosurgery 117:460-462. https://doi.org/10.1016/j.wneu.2018.07.112
  2585. Czaczkes T., Beckwith J., Horsch A. (2018): Information synergy: adding unambiguous quality information rescues social information use in ants. https://doi.org/10.1101/219980
  2586. Mallard T., Harden K., Fromme K. (2018): Genetic risk for schizophrenia is associated with substance use in emerging adulthood: an event-level polygenic prediction model. Psychological Medicine 49(12):2027-2035. https://doi.org/10.1017/s0033291718002817
  2587. Kato T. (2018): We Encourage You to Submit Your Negative or Nonsignificant Results for Publication in the <i>Japanese Journal of Personality</i>. The Japanese Journal of Personality 27(2):99-124. https://doi.org/10.2132/personality.27.2.11
  2588. Hamada T., Liu L., Nowak J., Mima K., Cao Y., Ng K., et al. (2018): Vitamin D status after colorectal cancer diagnosis and patient survival according to immune response to tumour. European Journal of Cancer 103:98-107. https://doi.org/10.1016/j.ejca.2018.07.130
  2589. Puoliväli T., Palva S., Palva J. (2018): Influence of multiple hypothesis testing on reproducibility in neuroimaging research. https://doi.org/10.1101/488353
  2590. VanderWeele T., Mathur M., Ding P. (2018): Correcting Misinterpretations of the E-Value. Annals of Internal Medicine 170(2):131-132. https://doi.org/10.7326/m18-3112
  2591. Marigorta U., Rodríguez J., Gibson G., Navarro A. (2018): Replicability and Prediction: Lessons and Challenges from GWAS. Trends in Genetics 34(7):504-517. https://doi.org/10.1016/j.tig.2018.03.005
  2592. van Hees V., Sabia S., Jones S., Wood A., Anderson K., Kivimäki M., et al. (2018): Estimating sleep parameters using an accelerometer without sleep diary. Scientific Reports 8(1). https://doi.org/10.1038/s41598-018-31266-z
  2593. van Hees V., Sabia S., Jones S., Wood A., Anderson K., Kivimäki M., et al. (2018): Estimating sleep parameters using an accelerometer without sleep diary. https://doi.org/10.1101/257972
  2594. Thompson W. (2018): Alpha Is Not the False Alarm Rate: An Activity to Dispel a Common Statistical Misconception. Teaching of Psychology 46(1):72-79. https://doi.org/10.1177/0098628318816156
  2595. Reed W. (2018): A Primer on the ‘Reproducibility Crisis’ and Ways to Fix It. Australian Economic Review 51(2):286-300. https://doi.org/10.1111/1467-8462.12262
  2596. Durand W., Ruddell J., Eltorai A., DePasse J., Daniels A. (2018): Ileus Following Adult Spinal Deformity Surgery. World Neurosurgery 116:e806-e813. https://doi.org/10.1016/j.wneu.2018.05.099
  2597. Gervais W., McKee S., Malik S. (2018): Do Religious Primes Increase Risk Taking? Evidence Against “Anticipating Divine Protection” in Two Preregistered Direct Replications. https://doi.org/10.31234/osf.io/8f7qd
  2598. Otte W., Tijdink J., Weerheim P., Lamberink H., Vinkers C. (2018): Adequate statistical power in clinical trials is associated with the combination of a male first author and a female last author. eLife 7. https://doi.org/10.7554/elife.34412
  2599. Davis W., Giner-Sorolla R., Lindsay D., Lougheed J., Makel M., Meier M., et al. (2018): Peer-Review Guidelines Promoting Replicability and Transparency in Psychological Science. Advances in Methods and Practices in Psychological Science 1(4):556-573. https://doi.org/10.1177/2515245918806489
  2600. Chopik W., Lucas R. (2018): Actor, partner, and similarity effects of personality on global and experienced well-being. Journal of Research in Personality 78:249-261. https://doi.org/10.1016/j.jrp.2018.12.008
  2601. Skylark W., Carr J., McComas C. (2018): Who says “larger” and who says “smaller”? Individual differences in the language of comparison. Judgment and Decision Making 13(6):547-561. https://doi.org/10.1017/s1930297500006586
  2602. Briggs W. (2018): Everything Wrong with P-Values Under One Roof. Studies in Computational Intelligence. https://doi.org/10.1007/978-3-030-04200-4_2
  2603. Goh W., Wong L. (2018): Dealing with Confounders in Omics Analysis. Trends in Biotechnology 36(5):488-498. https://doi.org/10.1016/j.tibtech.2018.01.013
  2604. Yan X., Dvir N., Jacques M., Cavalcante L., Papadimitriou I., Munson F., et al. (2018): ACEI/D gene variant predicts ACE enzyme content in blood but not the ACE, UCP2, and UCP3 protein content in human skeletal muscle in the Gene SMART study. Journal of Applied Physiology 125(3):923-930. https://doi.org/10.1152/japplphysiol.00344.2018
  2605. Liu Y., Mi Y., Mueller T., Kreibich S., Williams E., Van Drogen A., et al. (2018): Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results. https://doi.org/10.1101/307421
  2606. Georgie Y., Porcaro C., Mayhew S., Bagshaw A., Ostwald D. (2018): A perceptual decision making EEG/fMRI data set. https://doi.org/10.1101/253047
  2607. Zhang Y., Wen C. (2018): Statistics as Part of Scientific Reasoning in Plant Sciences: Overlooked Issues and Recommended Solutions. Molecular Plant 12(1):7-9. https://doi.org/10.1016/j.molp.2018.11.001
  2608. Wei Y., Chen F. (2018): Lowering the P Value Threshold. JAMA 320(9):934. https://doi.org/10.1001/jama.2018.8721
  2609. Hanazuka Y., Shimizu M., Takaoka H., Midorikawa A. (2018): Orangutans ( Pongo pygmaeus ) recognize their own past actions. Royal Society Open Science 5(12):181497. https://doi.org/10.1098/rsos.181497
  2610. Itescu Y., Schwarz R., Donihue C., Slavenko A., Roussos S., Sagonas K., et al. (2018): Inconsistent patterns of body size evolution in co‐occurring island reptiles. Global Ecology and Biogeography 27(5):538-550. https://doi.org/10.1111/geb.12716
  2611. Lyu Z., Peng K., Hu C. (2018): P-Value, Confidence Intervals, and Statistical Inference: A New Dataset of Misinterpretation. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.00868
  2612. Khudokormov A., Nelyubina A., Safina M., Andronova E., Temirbulatova K. (2018): New studies on modern Economic Theory of the West. Scientific Research of Faculty of Economics. Electronic Journal 10(1):7-73. https://doi.org/10.38050/2078-3809-2018-10-1-7-73
  2613. Olsson-Collentine A., van Assen M., Hartgerink C. (2017): Preprint – The prevalence of marginal significance in psychology over time. https://doi.org/10.31234/osf.io/4dpq3
  2614. Verschuere B., Kaat L. (2017): What are the core features of psychopathy? A prototypicality analysis using the Psychopathy Checklist-Revised (PCL-R). https://doi.org/10.31219/osf.io/c7heu
  2615. Pereira C., Nakano E., Fossaluza V., Esteves L., Gannon M., Polpo A. (2017): Hypothesis Tests for Bernoulli Experiments: Ordering the Sample Space by Bayes Factors and Using Adaptive Significance Levels for Decisions. Entropy 19(12):696. https://doi.org/10.3390/e19120696
  2616. Van Houtte C., Ktenidou O., Larkin T., Holden C. (2017): A continuous map of near-surface S-wave attenuation in New Zealand. Geophysical Journal International 213(1):408-425. https://doi.org/10.1093/gji/ggx559
  2617. Meyer C., Padmala S., Pessoa L. (2017): Dynamic threat processing. https://doi.org/10.1101/183798
  2618. Singh Chawla D. (2017): ‘One-size-fits-all’ threshold for P values under fire. Nature. https://doi.org/10.1038/nature.2017.22625
  2619. Liu D., Jiang X., Zheng H., Xie B., Wang H., He T., et al. (2017): The modularity of microbial interaction network in healthy human saliva: Stability and specificity. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). https://doi.org/10.1109/bibm.2017.8217976
  2620. Lakens D., Adolfi F., Albers C., Anvari F., Apps M., Argamon S., et al. (2017): Justify Your Alpha. https://doi.org/10.31234/osf.io/9s3y6
  2621. Spurlock D. (2017): The Purpose and Power of Reporting Effect Sizes in Nursing Education Research. Journal of Nursing Education 56(11):645-647. https://doi.org/10.3928/01484834-20171020-02
  2622. Greve D., Fischl B. (2017): False positive rates in surface-based anatomical analysis. NeuroImage 171:6-14. https://doi.org/10.1016/j.neuroimage.2017.12.072
  2623. Witte E., Zenker F. (2017): From Discovery to Justification: Outline of an Ideal Research Program in Empirical Psychology. Frontiers in Psychology 8. https://doi.org/10.3389/fpsyg.2017.01847
  2624. Buchanan E., Foreman R., Huber B., Pavlacic J., Swadley R., Schulenberg S. (2017): Does the Delivery Matter? Examining Randomization at the Item Level. https://doi.org/10.31219/osf.io/p93df
  2625. Buchanan E., Scofield J. (2017): Bulletproof Bias? Considering the Type of Data in Common Proportion of Variance Effect Sizes. https://doi.org/10.31219/osf.io/cs4vy
  2626. Neto F., Torkar R., Feldt R., Gren L., Furia C., Huang Z. (2017): Evolution of statistical analysis in empirical software engineering research: Current state and steps forward. arXiv. https://doi.org/10.48550/arxiv.1706.00933
  2627. Chen G., Xiao Y., Taylor P., Rajendra J., Riggins T., Geng F., et al. (2017): Handling Multiplicity in Neuroimaging through Bayesian Lenses with Multilevel Modeling. https://doi.org/10.1101/238998
  2628. Crane H. (2017): Why ‘Redefining Statistical Significance’ Will Not Improve Reproducibility and Could Make the Replication Crisis Worse. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3074083
  2629. Kuzovkin I., Vicente R., Petton M., Lachaux J., Baciu M., Kahane P., et al. (2017): Activations of Deep Convolutional Neural Network are Aligned with Gamma Band Activity of Human Visual Cortex. https://doi.org/10.1101/133694
  2630. Hannikainen I., Machery E., Cushman F. (2017): Is utilitarian sacrifice becoming more morally permissible?. Cognition 170:95-101. https://doi.org/10.1016/j.cognition.2017.09.013
  2631. Perezgonzalez J., Frías-Navarro M. (2017): Retract p < 0.005 and propose using JASP, instead. F1000Research 6:2122. https://doi.org/10.12688/f1000research.13389.1
  2632. Perezgonzalez J., Frias-Navarro D. (2017): Retract 0.005 and propose using JASP, instead. https://doi.org/10.31234/osf.io/t2fn8
  2633. Leek J., McShane B., Gelman A., Colquhoun D., Nuijten M., Goodman S. (2017): Five ways to fix statistics. Nature 551(7682):557-559. https://doi.org/10.1038/d41586-017-07522-z
  2634. Wicherts J. (2017): The Weak Spots in Contemporary Science (and How to Fix Them). Animals 7(12):90. https://doi.org/10.3390/ani7120090
  2635. Birch J. (2017): Refining the precautionary framework. Animal Sentience 2(16). https://doi.org/10.51291/2377-7478.1279
  2636. Rodgers J., Shrout P. (2017): Psychology’s Replication Crisis as Scientific Opportunity: A Précis for Policymakers. Policy Insights from the Behavioral and Brain Sciences 5(1):134-141. https://doi.org/10.1177/2372732217749254
  2637. Held L., Ott M. (2017): Onp-Values and Bayes Factors. Annual Review of Statistics and Its Application 5(1):393-419. https://doi.org/10.1146/annurev-statistics-031017-100307
  2638. Bradley M., Brand A. (2017): Out with .05, in with Replication and Measurement: Isolating and Working with the Particular Effect Sizes that are Troublesome for Inferential Statistics. The Journal of General Psychology 144(4):309-316. https://doi.org/10.1080/00221309.2017.1381496
  2639. Kovic M. (2017): The epistemic value of p-values. https://doi.org/10.31235/osf.io/g6jtx
  2640. Stoddard M., Yong E., Akkaynak D., Sheard C., Tobias J., Mahadevan L. (2017): Avian egg shape: Form, function, and evolution. Science 356(6344):1249-1254. https://doi.org/10.1126/science.aaj1945
  2641. Meier M. (2017): Can Research Participants Comment Authoritatively on the Validity of Their Self-Reports of Mind Wandering and Task Engagement? A Replication and Extension of Seli, Jonker, Cheyne, Cortes, and Smilek. https://doi.org/10.31234/osf.io/qzu26
  2642. Heino M., Vuorre M., Hankonen N. (2017): Bayesian evaluation of behavior change interventions: A brief introduction and a practical example. https://doi.org/10.31234/osf.io/xmgwv
  2643. Borchers M. (2017): Sind die meisten publizierten Forschungsbefunde falsch?. Im Focus Onkologie 20(11):8-11. https://doi.org/10.1007/s15015-017-3612-4
  2644. Kavish N., Fu Q., Vaughn M., Qian Z., Boutwell B. (2017): Resting heart rate and psychopathy: Findings from the Add Health Survey. https://doi.org/10.1101/205005
  2645. Hauser O., Linos E., Rogers T. (2017): Innovation with field experiments: Studying organizational behaviors in actual organizations. Research in Organizational Behavior 37:185-198. https://doi.org/10.1016/j.riob.2017.10.004
  2646. Paul F. Smith (2017): A Guerilla Guide to Common Problems in ‘Neurostatistics’: Essential Statistical Topics in Neuroscience. PubMed.
  2647. Biswas R., Rahman N., Kabir E., Raihan F. (2017): Women’s opinion on the justification of physical spousal violence: A quantitative approach to model the most vulnerable households in Bangladesh. PLOS ONE 12(11):e0187884. https://doi.org/10.1371/journal.pone.0187884
  2648. Valentine K., Buchanan E., Scofield J., Beauchamp M. (2017): Beyond p-values: Utilizing Multiple Estimates to Evaluate Evidence. https://doi.org/10.31219/osf.io/9hp7y
  2649. Piraino S. (2017): Estimating the false finding rate across scientific fields. https://doi.org/10.31234/osf.io/97cwq
  2650. Piraino S. (2017): Estimating the false finding rate across scientific fields. https://doi.org/10.31234/osf.io/97cwq_v1
  2651. Paparrizos S., Schindler D., Potouridis S., Matzarakis A. (2017): Spatio-temporal analysis of present and future precipitation responses over South Germany. Journal of Water and Climate Change 9(3):490-499. https://doi.org/10.2166/wcc.2017.009
  2652. TT Mallard (2017): Genetic risk for schizophrenia influences substance use in emerging adulthood : an event-level polygenic prediction model. Texas ScholarWorks (Texas Digital Library). https://doi.org/10.15781/t2jd4q74f
  2653. Mallard T., Harden K., Fromme K. (2017): Genetic risk for schizophrenia influences substance use in emerging adulthood: An event-level polygenic prediction model. https://doi.org/10.1101/157636
  2654. Briggs W. (2017): Testing, Prediction, and Cause in Econometric Models. Studies in Computational Intelligence. https://doi.org/10.1007/978-3-319-73150-6_1
  2655. Machery E., Doris J. (2017): An Open Letter to Our Students: Doing Interdisciplinary Moral Psychology. Moral Psychology. https://doi.org/10.1007/978-3-319-61849-4_7
  2656. Lyócsa Š., Výrost T., Baumöhl E. (2017): Return spillovers around the globe: A network approach. Economic Modelling 77:133-146. https://doi.org/10.1016/j.econmod.2017.11.003
  2657. 友永 雅己 (2017): 心理学の社会貢献に関する私見：教育・発達領域の論文を読んで. 心理学評論. https://doi.org/10.24602/sjpr.60.4_419
  2658. 刘 帆. (2017): Biocontrol Activity of Three Endolichenic Fungi from Peltigera. Advances in Microbiology 06(04):91-97. https://doi.org/10.12677/amb.2017.64012
  2659. Eisend M., Kuß A. (2016): Hypothesen und Modelle beim Theorietest. Grundlagen empirischer Forschung. https://doi.org/10.1007/978-3-658-09705-9_7
  2660. Brock J., Vasilaky K. (2015): Traversing the Landscape of Experimental Power. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2692696
  2661. Teah G., Conner T. (1994): [Quantitative structure-activity relationship of P-amino-diphenyl ether analogues to inhibit cytochrome P-450]. Frontiers in Psychology 12. https://doi.org/10.3389/fpsyg.2021.659206
  2662. Jiin‐Haur Chuang, H M Chan, Yanwen Huang, Jan Sing Hsieh, Tai‐Chien Huang (1993): Enterolith ileus as a complication of duodenal diverticulosis–one case report and review of the literature. PubMed.
  2663. Francesco Cicconardi, James J. Lewis, Simon H. Martin, Robert D. Reed, Charles G. Danko, Stephen H. Montgomery, et al. (): . Edinburgh Research Explorer (University of Edinburgh).

Brembs B. (2017): Operant Behavior in Model Systems. In Learning and Memory: A Comprehensive Reference pp. 505–516. Elsevier.

  1. Van Damme S., De Fruyt N., Watteyne J., Kenis S., Peymen K., Schoofs L., et al. (2020): Neuromodulatory pathways in learning and memory: Lessons from invertebrates. Journal of Neuroendocrinology 33(1). https://doi.org/10.1111/jne.12911
  2. Godfrey-Smith P. (2019): Evolving Across the Explanatory Gap. Philosophy, Theory, and Practice in Biology 11(20220112). https://doi.org/10.3998/ptpbio.16039257.0011.001
  3. Damrau C., Toshima N., Tanimura T., Brembs B., Colomb J. (2018): Octopamine and Tyramine Contribute Separately to the Counter-Regulatory Response to Sugar Deficit in Drosophila. Frontiers in Systems Neuroscience 11. https://doi.org/10.3389/fnsys.2017.00100
  4. Brembs B. (2017): Genetic Analysis of Behavior in Drosophila. The Oxford Handbook of Invertebrate Neurobiology. https://doi.org/10.1093/oxfordhb/9780190456757.013.37
  5. Verasztó C., Ueda N., Bezares-Calderón L., Panzera A., Williams E., Shahidi R., et al. (2017): Ciliomotor circuitry underlying whole-body coordination of ciliary activity in the Platynereis larva. eLife 6. https://doi.org/10.7554/elife.26000
  6. Edelman S., Moyal R. (2017): Fundamental computational constraints on the time course of perception and action. Progress in Brain Research. https://doi.org/10.1016/bs.pbr.2017.05.006

Brembs B. (2017): Genetic Analysis of Behavior in Drosophila. In The Oxford Handbook of Invertebrate Neurobiology pp. 171–184. Oxford University Press.

  1. Nagpal J., Cryan J. (2021): Microbiota-brain interactions: Moving toward mechanisms in model organisms. Neuron 109(24):3930-3953. https://doi.org/10.1016/j.neuron.2021.09.036
  2. B. Brembs, Ottavia Palazzo (2020): Molecular and behavioral study of the FoxP locus in Drosophila melanogaster. https://www.semanticscholar.org/paper/bf0849db3033b893abc7420598124e0ecbd2f881
  3. Chang O. (2018): Self-programming Robots Boosted by Neural Agents. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-05587-5_42
  4. Chang O. (2017): Autonomous Robots and Behavior Initiators. Human-Robot Interaction – Theory and Application. https://doi.org/10.5772/INTECHOPEN.71958

Gorostiza EA, Colomb J, Brembs B. (2016): A decision underlies phototaxis in an insect. Royal Society Open Biology 6:160229.

  1. Liu F., Li W., Huang Z. (2026): Bee swimming is adaptive but disrupted by insecticide. Communications Biology. https://doi.org/10.1038/s42003-026-09669-w
  2. Andersson P., Jägerbrand A. (2026): Ecosystem-specific moth attraction to warm-coloured LED simulating road lighting conditions. Lighting Research & Technology. https://doi.org/10.1177/14771535251400296
  3. Han R., Zhang J., Guo Z., Guo X., Hou S., Feng K., et al. (2026): Developmental light colour influences adult phototaxis and locomotion in Drosophila melanogaster. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.6140213
  4. Han R., Zhang J., Guo Z., Guo X., Hou S., Feng K., et al. (2026): Developmental light colour influences adult phototaxis and locomotion in Drosophila melanogaster. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.6140212
  5. Bambaradeniya T., Magni P., Dadour I. (2026): A Comparative Analysis of the Responses of Lucilia cuprina (Wiedemann) and Chrysomya rufifacies (Macqart) (Calliphoridae) to Different Reflectance Levels of Green and Yellow Light Hues. Insects 17(3):283. https://doi.org/10.3390/insects17030283
  6. Du Z., Yang G., Zheng X., Jiang L., Zong L., Li C., et al. (2026): Histaminergic regulation of phototactic divergence between two forms of Callosobruchus maculatus via the HisCl2 receptor. Pest Management Science. https://doi.org/10.1002/ps.70491
  7. Sancho G., Albacete S., Azpiazu C., Sgolastra F., Rodrigo A., Bosch J. (2025): A phototaxis assay to measure sublethal effects of pesticides on bees. Scientific Reports 15(1). https://doi.org/10.1038/s41598-025-05400-7
  8. Sancho G., Albacete S., Azpiazu C., Sgolastra F., Rodrigo A., Bosch J. (2025): A phototaxis assay to measure sublethal effects of pesticides on bees. https://doi.org/10.1101/2025.02.11.637502
  9. Matsui H., Hata Y. (2025): Long-term continuous light exposure alters phototaxis in the acoel flatworm Praesagittifera naikaiensis. https://doi.org/10.21203/rs.3.rs-7802934/v1
  10. Yang J., He J., Peng J. (2025): Broad-spectrum light-induced bending, fracture, delamination, and jumping in a phenanthrene-modified barbituric acid crystal. Dyes and Pigments 246:113347. https://doi.org/10.1016/j.dyepig.2025.113347
  11. Meschenmoser M., Dürr V. (2025): Contrast and luminance dependence of target choice and visual orientation in walking stick insects. Scientific Reports 15(1). https://doi.org/10.1038/s41598-025-90650-8
  12. Araújo P., Schlindwein C., Mota T. (2025): Light wavelength and intensity modulate phototaxis in the nocturnal bee Megalopta aegis. Journal of Experimental Biology 229(1). https://doi.org/10.1242/jeb.251038
  13. Triphan T., Ferreira C., Huetteroth W. (2025): Play-like behavior exhibited by the vinegar fly Drosophila melanogaster. Current Biology 35(5):1145-1155.e2. https://doi.org/10.1016/j.cub.2025.01.025
  14. Yang X., Chen Y., Wang F., Chen S., Cao Z., Feng Y., et al. (2025): Bioinspired artificial phototaxis and phototropism enabled by photoresponsive smart materials. Materials Today 87:348-377. https://doi.org/10.1016/j.mattod.2025.05.004
  15. Notomi Y., Dobata S., Kazawa T., Maezawa S., Namiki S., Kanzaki R., et al. (2025): Innate visual attraction before, during and after escape from adverse substrates in carpenter ants. Journal of Experimental Biology 228(13). https://doi.org/10.1242/jeb.250278
  16. Bower C. (2024): Fixed non-random orientation to the Sun (conversotropism) in two window-flowered greenhood orchids, Diplodium spp. (Orchidaceae: Pterostylidinae); implications for other window flowers and pollinator behaviour. Botanical Journal of the Linnean Society 206(3):231-244. https://doi.org/10.1093/botlinnean/boae023
  17. Soley F. (2024): Predatory flexibility of an araneophagic assassin bug derives from a few behavioural rules. Animal Behaviour 221:122843. https://doi.org/10.1016/j.anbehav.2024.02.013
  18. Sancho Blanco G., Albacete S., Azpiazu C., Sgolastra F., Rodrigo A., Bosch J. (2024): A Phototaxis Method to Measure Sublethal Effects of Pesticides on Bees. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4844357
  19. Cheng J., Zhang R., Li H., Wang Z., Lin C., Jin P., et al. (2024): Soft Crawling Microrobot Based on Flexible Optoelectronics Enabling Autonomous Phototaxis in Terrestrial and Aquatic Environments. Soft Robotics 12(1):45-55. https://doi.org/10.1089/soro.2023.0112
  20. Lebovich L., Alisch T., Redhead E., Parker M., Loewenstein Y., Couzin I., et al. (2024): Spatiotemporal dynamics of locomotor decisions in Drosophila melanogaster. https://doi.org/10.1101/2024.09.04.611038
  21. Kim S., Badhiwala K., Duret G., Robinson J. (2024): Phototaxis is a satiety-dependent behavioral sequence in Hydra vulgaris. Journal of Experimental Biology 227(18). https://doi.org/10.1242/jeb.247503
  22. Notomi Y., Dobata S., Kazawa T., Maezawa S., Namiki S., Kanzaki R., et al. (2024): Adaptive decision-making by ants in response to past, imminent, and predicted adversity. https://doi.org/10.1101/2024.12.17.628737
  23. Jägerbrand A., Andersson P., Nilsson Tengelin M. (2023): Dose–effects in behavioural responses of moths to light in a controlled lab experiment. Scientific Reports 13(1). https://doi.org/10.1038/s41598-023-37256-0
  24. Alicea B., Gordon R., Parent J. (2023): The Psychophysical World of the Motile DiatomBacillaria paradoxa. The Mathematical Biology of Diatoms. https://doi.org/10.1002/9781119751939.ch9
  25. Farhoudi F., Sahragard A., Hosseini R., Saboori A. (2023): Comparative function of blacklight and visual light sources in capturing Harmonia axyridis in the field and laboratory conditions; effect of color polymorphism. https://doi.org/10.21203/rs.3.rs-2472082/v1
  26. Wu F., Du Z., Zhang T., Jiang L., Zhang L., Ge S. (2023): A neurotransmitter histamine mediating phototransduction and photopreference in Callosobruchus maculatus. Pest Management Science 79(9):3002-3011. https://doi.org/10.1002/ps.7475
  27. Lakhiani R., Shanavas S., Melnattur K. (2023): Comparative biology of sleep in diverse animals. Journal of Experimental Biology 226(14). https://doi.org/10.1242/jeb.245677
  28. Kim S., Robinson J. (2023): Phototaxis is a state-dependent behavioral sequence in Hydra vulgaris. https://doi.org/10.1101/2023.05.12.540432
  29. Pachghare V., Chandra M., Surve A., Kulkarni A. (2023): Evaluating Toxicity of Lithium to Hydra viridissima. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences 93(4):819-826. https://doi.org/10.1007/s40011-023-01488-x
  30. Devineni A., Scaplen K. (2022): Neural Circuits Underlying Behavioral Flexibility: Insights From Drosophila. Frontiers in Behavioral Neuroscience 15. https://doi.org/10.3389/fnbeh.2021.821680
  31. Horn C., Wasylenko J., Luong L. (2022): Scared of the dark? Phototaxis as behavioural immunity in a host–parasite system. Biology Letters 18(1). https://doi.org/10.1098/rsbl.2021.0531
  32. Bruno J., Udoh U., Landen J., Osborn P., Asher C., Hunt J., et al. (2022): A circadian-dependent preference for light displayed by Xenopus tadpoles is modulated by serotonin. iScience 25(11):105375. https://doi.org/10.1016/j.isci.2022.105375
  33. Poetini M., Musachio E., Araujo S., Bortolotto V., Meichtry L., Silva N., et al. (2022): Improvement of non-motor and motor behavioral alterations associated with Parkinson-like disease in Drosophila melanogaster: Comparative effects of treatments with hesperidin and L-dopa. NeuroToxicology 89:174-183. https://doi.org/10.1016/j.neuro.2022.02.004
  34. Shang X., Wei J., Liu W., Pan X., Huang C., Nikpay A., et al. (2022): Investigating Population Dynamics and Sex Structure of Exolontha castanea Chang (Coleoptera: Melolonthidae) Using Light Traps in Sugarcane Fields in China. Sugar Tech 24(5):1441-1448. https://doi.org/10.1007/s12355-021-01081-4
  35. Chen A., Deb D., Bahl A., Engert F. (2021): Algorithms underlying flexible phototaxis in larval zebrafish. Journal of Experimental Biology 224(10). https://doi.org/10.1242/jeb.238386
  36. Bijayalaxmi Swain, Anne C. von Philipsborn (2021): Sound production in Drosophila melanogaster: Behaviour and neurobiology. Advances in Insect Physiology. https://doi.org/10.1016/bs.aiip.2021.08.001
  37. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. https://doi.org/10.1101/2021.10.04.463010
  38. Damrau C., Colomb J., Brembs B. (2021): Sensitivity to expression levels underlies differential dominance of a putative null allele of the Drosophila tβh gene in behavioral phenotypes. PLOS Biology 19(5):e3001228. https://doi.org/10.1371/journal.pbio.3001228
  39. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. PLOS ONE 16(8):e0256560. https://doi.org/10.1371/journal.pone.0256560
  40. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. https://doi.org/10.1101/2021.01.15.426803
  41. Uda M., Fujiwara J., Seike M., Segami S., Higashimoto S., Hirai T., et al. (2021): Controllable Positive/Negative Phototaxis of Millimeter-Sized Objects with Sensing Function. Langmuir 37(37):11093-11101. https://doi.org/10.1021/acs.langmuir.1c01833
  42. Grob R., el Jundi B., Fleischmann P. (2021): Towards a common terminology for arthropod spatial orientation. Ethology Ecology & Evolution 33(3):338-358. https://doi.org/10.1080/03949370.2021.1905075
  43. Goulard R., Buehlmann C., Niven J., Graham P., Webb B. (2021): A unified mechanism for innate and learned visual landmark guidance in the insect central complex. PLOS Computational Biology 17(9):e1009383. https://doi.org/10.1371/journal.pcbi.1009383
  44. Goulard R., Buehlmann C., Niven J., Graham P., Webb B. (2021): A unified mechanism for innate and learned visual landmark guidance in the insect central complex. https://doi.org/10.1101/2021.01.28.428620
  45. Han R., Wei T., Tseng S., Lo C. (2021): Characterizing approach behavior of Drosophila melanogaster in Buridan’s paradigm. PLOS ONE 16(1):e0245990. https://doi.org/10.1371/journal.pone.0245990
  46. Hu Y., Yang L., Yan Q., Ji Q., Chang L., Zhang C., et al. (2021): Self-Locomotive Soft Actuator Based on Asymmetric Microstructural Ti3C2Tx MXene Film Driven by Natural Sunlight Fluctuation. ACS Nano 15(3):5294-5306. https://doi.org/10.1021/acsnano.0c10797
  47. Hu Y., Ji Q., Huang M., Chang L., Zhang C., Wu G., et al. (2021): Light‐Driven Self‐Oscillating Actuators with Phototactic Locomotion Based on Black Phosphorus Heterostructure. Angewandte Chemie International Edition 60(37):20511-20517. https://doi.org/10.1002/anie.202108058
  48. Hu Y., Ji Q., Huang M., Chang L., Zhang C., Wu G., et al. (2021): Light‐Driven Self‐Oscillating Actuators with Phototactic Locomotion Based on Black Phosphorus Heterostructure. Angewandte Chemie 133(37):20674-20680. https://doi.org/10.1002/ange.202108058
  49. Spierer A., Rand D. (2020): The Genetic Architecture of Robustness for Flight Performance in Drosophila. https://doi.org/10.1101/2020.12.04.412395
  50. Dimitriadou A., Chatzianastasi N., Zacharaki P., O’Connor M., Goldsmith S., O’Connor M., et al. (2020): Adult Movement Defects Associated with a CORL Mutation in Drosophila Display Behavioral Plasticity. G3 Genes|Genomes|Genetics 10(5):1697-1706. https://doi.org/10.1534/g3.120.400648
  51. Brembs B. (2020): The brain as a dynamically active organ. Biochemical and Biophysical Research Communications 564:55-69. https://doi.org/10.1016/j.bbrc.2020.12.011
  52. Brembs B. (2020): The brain as a dynamically active organ. https://doi.org/10.31234/osf.io/j37av
  53. Vogt K. (2020): Towards a functional connectome in Drosophila. Journal of Neurogenetics 34(1):156-161. https://doi.org/10.1080/01677063.2020.1712598
  54. Melnattur K., Zhang B., Shaw P. (2020): Disrupting flight increases sleep and identifies a novel sleep-promoting pathway in Drosophila. Science Advances 6(19). https://doi.org/10.1126/sciadv.aaz2166
  55. Yang L., Chang L., Hu Y., Huang M., Ji Q., Lu P., et al. (2020): An Autonomous Soft Actuator with Light‐Driven Self‐Sustained Wavelike Oscillation for Phototactic Self‐Locomotion and Power Generation. Advanced Functional Materials 30(15). https://doi.org/10.1002/adfm.201908842
  56. Nouvian M., Galizia C. (2020): Complexity and plasticity in honey bee phototactic behaviour. Scientific Reports 10(1). https://doi.org/10.1038/s41598-020-64782-y
  57. Cossovich R., Virgint S., Khakhar D., Garg Y., Lu L. (2020): Robotario. Proceedings of the 2020 3rd International Conference on Robot Systems and Applications. https://doi.org/10.1145/3402597.3402598
  58. Budaev S., Kristiansen T., Giske J., Eliassen S. (2020): Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing. Royal Society Open Science 7(12):201886. https://doi.org/10.1098/rsos.201886
  59. Sawada Y., Sasaki T., Nishio K., Kurata M., Honryo T., Agawa Y. (2020): Positive phototaxis as the cause of jaw malformations in larval greater amberjack, Seriola dumerili (Risso, 1810): Mitigation by rearing in tanks with low‐brightness walls. Aquaculture Research 51(6):2261-2274. https://doi.org/10.1111/are.14571
  60. Sancer G., Kind E., Uhlhorn J., Volkmann J., Hammacher J., Pham T., et al. (2019): Cellular and synaptic adaptations of neural circuits processing skylight polarization in the fly. Journal of Comparative Physiology A 206(2):233-246. https://doi.org/10.1007/s00359-019-01389-3
  61. Sancer G., Kind E., Uhlhorn J., Volkmann J., Hammacher J., Pham T., et al. (2019): Cellular and synaptic adaptations of neural circuits processing skylight polarization in the fly. https://doi.org/10.1101/838300
  62. Dhar G., Mukherjee S., Nayak N., Sahu S., Bag J., Rout R., et al. (2019): Various Behavioural Assays to Detect the Neuronal Abnormality in Flies. Springer Protocols Handbooks. https://doi.org/10.1007/978-1-4939-9756-5_18
  63. Yang J., Li D., Pun E., Lin H., Zhao X. (2019): Superiority quantitation of laser-driving in Sm3+ doped germanium tellurite glass phosphors as bio-friendly lighting sources. Optics & Laser Technology 120:105685. https://doi.org/10.1016/j.optlastec.2019.105685
  64. Melnattur K., Zhang B., Shaw P. (2019): Plasticity in a Drosophila wing circuit supports an adaptive sleep function. https://doi.org/10.1101/691451
  65. Skutt-Kakaria K., Reimers P., Currier T., Werkhoven Z., de Bivort B. (2019): A neural circuit basis for context-modulation of individual locomotor behavior. https://doi.org/10.1101/797126
  66. Caipo L., González-Ramírez M., Guzmán-Palma P., Contreras E., Palominos T., Fuenzalida-Uribe N., et al. (2019): Slit neuronal secretion coordinates optic lobe morphogenesis in Drosophila. Developmental Biology 458(1):32-42. https://doi.org/10.1016/j.ydbio.2019.10.004
  67. Zhao Y., Xuan C., Qian X., Alsaid Y., Hua M., Jin L., et al. (2019): Soft phototactic swimmer based on self-sustained hydrogel oscillator. Science Robotics 4(33). https://doi.org/10.1126/scirobotics.aax7112
  68. Poehlmann A., Soselisa S., Fenk L., Straw A. (2018): A unifying model to predict multiple object orienting behaviors in tethered flies. https://doi.org/10.1101/379651
  69. Damrau C., Toshima N., Tanimura T., Brembs B., Colomb J. (2018): Octopamine and Tyramine Contribute Separately to the Counter-Regulatory Response to Sugar Deficit in Drosophila. Frontiers in Systems Neuroscience 11. https://doi.org/10.3389/fnsys.2017.00100
  70. Damrau C., Colomb J., Brembs B. (2018): Differential dominance of an allele of the Drosophila tßh gene challenges standard genetic techniques. https://doi.org/10.1101/504332
  71. Perry C., Chittka L. (2018): How foresight might support the behavioral flexibility of arthropods. Current Opinion in Neurobiology 54:171-177. https://doi.org/10.1016/j.conb.2018.10.014
  72. Gorostiza E. (2018): Does Cognition Have a Role in Plasticity of “Innate Behavior”? A Perspective From Drosophila. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.01502
  73. Frighetto G., Zordan M., Castiello U., Megighian A. (2018): Mechanisms of selection for the control of action in Drosophila melanogaster. https://doi.org/10.1101/296962
  74. Borycz J., Ziegler A., Borycz J., Uhlenbrock G., Tapken D., Caceres L., et al. (2018): Location and functions of Inebriated in theDrosophilaeye. Biology Open 7(7). https://doi.org/10.1242/bio.034926
  75. Bhattarai M., Bhattarai U., Feng J., Wang D. (2018): Effect of Different Light Spectrum in Helicoverpa armigera Larvae during HearNPV Induced Tree-Top Disease. Insects 9(4):183. https://doi.org/10.3390/insects9040183
  76. Grabowska M., Steeves J., Alpay J., van de Poll M., Ertekin D., van Swinderen B. (2018): Innate visual preferences and behavioral flexibility in Drosophila. Journal of Experimental Biology. https://doi.org/10.1242/jeb.185918
  77. Alexandre N., Humphrey P., Gloss A., Lee J., Frazier J., Affeldt H., et al. (2018): Habitat preference of an herbivore shapes the habitat distribution of its host plant. Ecosphere 9(9). https://doi.org/10.1002/ecs2.2372
  78. Budaev S., Giske J., Eliassen S. (2018): AHA: A general cognitive architecture for Darwinian agents. Biologically Inspired Cognitive Architectures 25:51-57. https://doi.org/10.1016/j.bica.2018.07.009
  79. Edelman S. (2018): Damasio, Antonio, 2018. The Strange Order of Things: Life, Feeling, and the Making of Cultures. New York: Pantheon. 336 pages. Evolutionary Studies in Imaginative Culture 2(2):119-124. https://doi.org/10.26613/esic.2.2.98
  80. Yuan Y., Yuan J., Tan H., Song X., Tu Y., Zhang T., et al. (2018): A Highly Stretchable Tough Polymer Actuator Driven by Acetone Vapors. Macromolecular Materials and Engineering 304(1). https://doi.org/10.1002/mame.201800501
  81. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  82. Vorontsov D., Dyakonova V. (2017): Light-dark decision making in snails: Do preceding light conditions matter?. Communicative & Integrative Biology 10(5-6):e1356515. https://doi.org/10.1080/19420889.2017.1356515
  83. Hall H., Medina P., Cooper D., Escobedo S., Rounds J., Brennan K., et al. (2017): Transcriptome profiling of aging Drosophila photoreceptors reveals gene expression trends that correlate with visual senescence. BMC Genomics 18(1). https://doi.org/10.1186/s12864-017-4304-3
  84. Alexandre N., Humphrey P., Gloss A., Lee J., Frazier J., Affeldt H., et al. (2017): Habitat preference of an herbivore shapes the habitat distribution of its host plant. https://doi.org/10.1101/156240

Colomb J, Brembs B. (2016): PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila. PeerJ 4:e1971.

  1. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  2. Croteau-Chonka E., Clayton M., Venkatasubramanian L., Harris S., Jones B., Narayan L., et al. (2022): High-throughput automated methods for classical and operant conditioning of Drosophila larvae. eLife 11. https://doi.org/10.7554/eLife.70015
  3. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. bioRxiv. https://doi.org/10.1101/2021.10.04.463010
  4. Spierer A., Rand D. (2020): The Genetic Architecture of Robustness for Flight Performance in Drosophila. bioRxiv. https://doi.org/10.1101/2020.12.04.412395
  5. B. Brembs, Ottavia Palazzo (2020): Molecular and behavioral study of the FoxP locus in Drosophila melanogaster. https://www.semanticscholar.org/paper/bf0849db3033b893abc7420598124e0ecbd2f881
  6. Kristina T Klein (2020): High-Throughput Operant Conditioning in Drosophila Larvae. https://doi.org/10.17863/CAM.47681
  7. Palazzo O., Raß M., Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. bioRxiv. https://doi.org/10.1101/2020.07.15.204677
  8. Wiggin T., Hsiao Y., Liu J., Huber R., Griffith L. (2020): Rest Is Required to Learn an Appetitively-Reinforced Operant Task in Drosophila. Frontiers in Behavioral Neuroscience 15. https://doi.org/10.3389/fnbeh.2021.681593
  9. Gao Y., Zhu C., Li K., Cheng X., Du Y., Yang D., et al. (2020): Comparative proteomics analysis of dietary restriction in Drosophila. PLOS ONE 15(10):e0240596. https://doi.org/10.1371/journal.pone.0240596
  10. Papanikolopoulou K., Roussou I., Gouzi J., Samiotaki M., Panayotou G., Turin L., et al. (2019): Drosophila Tau Negatively Regulates Translation and Olfactory Long-Term Memory, But Facilitates Footshock Habituation and Cytoskeletal Homeostasis. The Journal of Neuroscience 39(42):8315-8329. https://doi.org/10.1523/JNEUROSCI.0391-19.2019
  11. Nargeot R., Puygrenier L. (2019): Operant Learning in Invertebrates. Reference Module in Life Sciences. https://doi.org/10.1016/b978-0-12-809633-8.90788-x
  12. B. Brembs (2017): Genetic Analysis of Behavior in Drosophila-Oxford Handbooks. https://www.semanticscholar.org/paper/16aa0067215f06a381fc39b3a4e61282f6c99320
  13. Lehmann F. (2017): Neural Control and Precision of Spike Phasing in Flight Muscles. Journal of Neurology and Neuromedicine 2(9):15-19. https://doi.org/10.29245/2572.942X/2017/9.1153
  14. Brembs B. (2016): Genetic Analysis of Behavior in Drosophila. The Oxford Handbook of Invertebrate Neurobiology. https://doi.org/10.1093/OXFORDHB/9780190456757.013.37
  15. Lehmann F., Bartussek J. (2016): Neural control and precision of flight muscle activation in Drosophila. Journal of Comparative Physiology A 203(1):1-14. https://doi.org/10.1007/s00359-016-1133-9

Rahman R, Chirn G, Kanodia A, Sytnikova YA, Brembs B, Bergman CM, Lau NC. (2015): Unique transposon landscapes are pervasive across Drosophila melanogaster genomes. Nucl. Acids Res. 43(22):10655–10672.

  1. Bodelón A., Chillida V., de Oliveira D., Vieira C., Guerreiro M. (2026): Male sterility in Drosophila hybrids revealed by a multi-generational transcriptomic analysis of genes and transposable elements in testes. BMC Biology. https://doi.org/10.1186/s12915-026-02566-y
  2. Rezvykh A., Kulikova D., Zelentsova E., Protsenko L., Bespalova A., Guseva I., et al. (2026): Transposable elements as drivers of genome evolution in Drosophila virilis. Nucleic Acids Research 54(4). https://doi.org/10.1093/nar/gkag139
  3. Choucri M., Treiber C. (2026): Transposons contribute to splice-isoform diversity in the Drosophila brain. https://doi.org/10.64898/2026.01.22.701052
  4. Merenciano M., Larue A., Garambois C., Nunes W., Vieira C. (2025): Exploring the Relationship of Transposable Elements and Ageing: Causes and Consequences. Genome Biology and Evolution 17(6). https://doi.org/10.1093/gbe/evaf088
  5. Rubanova N., Singh D., Barolle L., Chalvet F., Netter S., Poidevin M., et al. (2025): An endogenous retroviral element co-opts an upstream regulatory sequence to achieve somatic expression and mobility. Nucleic Acids Research 53(11). https://doi.org/10.1093/nar/gkaf485
  6. Rubanova N., Singh D., Barolle L., Chalvet F., Netter S., Poidevin M., et al. (2025): An endogenous retroviral element co-opts an upstream regulatory sequence to achieve somatic expression and mobility. https://doi.org/10.1101/2025.01.02.631056
  7. Streeck R., Stephenson H., Herzig A. (2025): Reversible repression of inducible genes by Polycomb Repressive Complex 2 and H3K27me3 in Drosophila melanogaster. https://doi.org/10.1101/2025.06.09.658596
  8. Huang Y., Sahu S., Liu X. (2025): Deciphering recent transposition patterns in plants through comparison of 811 genome assemblies. Plant Biotechnology Journal 23(4):1121-1132. https://doi.org/10.1111/pbi.14570
  9. Groza C., Chen X., Wheeler T., Bourque G., Goubert C. (2024): A unified framework to analyze transposable element insertion polymorphisms using graph genomes. Nature Communications 15(1). https://doi.org/10.1038/s41467-024-53294-2
  10. Jansen G., Gebert D., Kumar T., Simmons E., Murphy S., Teixeira F. (2024): Tolerance thresholds underlie responses to DNA damage during germline development. https://doi.org/10.1101/2024.01.07.574510
  11. Jansen G., Gebert D., Kumar T., Simmons E., Murphy S., Teixeira F. (2024): Tolerance thresholds underlie responses to DNA damage during germline development. Genes & Development. https://doi.org/10.1101/gad.351701.124
  12. Jia H., Tan S., Cai Y., Guo Y., Shen J., Zhang Y., et al. (2024): Low-input PacBio sequencing generates high-quality individual fly genomes and characterizes mutational processes. Nature Communications 15(1). https://doi.org/10.1038/s41467-024-49992-6
  13. Azad M., Tong T., Lau N. (2024): Transposable Element (TE) insertion predictions from RNAseq inputs and TE impact on RNA splicing and gene expression in Drosophila brain transcriptomes. Mobile DNA 15(1). https://doi.org/10.1186/s13100-024-00330-z
  14. Azad M., Tong T., Lau N. (2024): Transposable Element (TE) insertion predictions from RNAseq inputs and TE impact on RNA splicing and gene expression in Drosophila brain transcriptomes. https://doi.org/10.1101/2024.06.07.597839
  15. Liu X., Zhao L., Majid M., Huang Y. (2024): Orthoptera-TElib: a library of Orthoptera transposable elements for TE annotation. Mobile DNA 15(1). https://doi.org/10.1186/s13100-024-00316-x
  16. Eugénio A., Marialva M., Beldade P. (2023): Effects ofWolbachiaon Transposable Element Expression Vary BetweenDrosophila melanogasterHost Genotypes. Genome Biology and Evolution 15(3). https://doi.org/10.1093/gbe/evad036
  17. Nuss A., Lomas J., Reyes J., Garcia-Cruz O., Lei W., Sharma A., et al. (2023): The highly improved genome ofIxodes scapulariswith X and Y pseudochromosomes. Life Science Alliance 6(12):e202302109. https://doi.org/10.26508/lsa.202302109
  18. Groza C., Chen X., Wheeler T., Bourque G., Goubert C. (2023): A Unified Framework to Analyze Transposable Element Insertion Polymorphisms using Graph Genomes. https://doi.org/10.1101/2023.09.11.557209
  19. Courret C., Larracuente A. (2023): High levels of intra-strain structural variation in Drosophila simulans X pericentric heterochromatin. GENETICS 225(4). https://doi.org/10.1093/genetics/iyad176
  20. Oliveira D., Fablet M., Larue A., Vallier A., Carareto C., Rebollo R., et al. (2023): ChimeraTE: a pipeline to detect chimeric transcripts derived from genes and transposable elements. Nucleic Acids Research 51(18):9764-9784. https://doi.org/10.1093/nar/gkad671
  21. Galbraith J., Hayward A. (2023): The influence of transposable elements on animal colouration. Trends in Genetics 39(8):624-638. https://doi.org/10.1016/j.tig.2023.04.005
  22. Cao J., Yu T., Xu B., Hu Z., Zhang X., Theurkauf W., et al. (2023): Epigenetic and chromosomal features drive transposon insertion inDrosophila melanogaster. Nucleic Acids Research 51(5):2066-2086. https://doi.org/10.1093/nar/gkad054
  23. Zhang S., Wang R., Zhu X., Zhang L., Liu X., Sun L. (2023): Characteristics and expression of lncRNA and transposable elements in Drosophila aneuploidy. iScience 26(12):108494. https://doi.org/10.1016/j.isci.2023.108494
  24. Eugénio A., Marialva M., Beldade P. (2022): Effects of Wolbachia on transposable element activity largely depend on Drosophila melanogaster host genotype. https://doi.org/10.1101/2022.07.21.500779
  25. Carlson C., ter Horst A., Johnston J., Henry E., Falk B., Kuo Y. (2022): High-quality, chromosome-scale genome assemblies: comparisons of three Diaphorina citri (Asian citrus psyllid) geographic populations. DNA Research 29(4). https://doi.org/10.1093/dnares/dsac027
  26. Oliveira D., Fablet M., Larue A., Vallier A., Carareto C., Rebollo R., et al. (2022): ChimeraTE: A pipeline to detect chimeric transcripts derived from genes and transposable elements. https://doi.org/10.1101/2022.09.05.505575
  27. Rech G., Radío S., Guirao-Rico S., Aguilera L., Horvath V., Green L., et al. (2022): Population-scale long-read sequencing uncovers transposable elements associated with gene expression variation and adaptive signatures in Drosophila. Nature Communications 13(1). https://doi.org/10.1038/s41467-022-29518-8
  28. Nicolas J., Tempel S., Fiston-Lavier A., Cherif E. (2022): Finding and Characterizing Repeats in Plant Genomes. Methods in Molecular Biology. https://doi.org/10.1007/978-1-0716-2067-0_18
  29. Rigal J., Martin Anduaga A., Bitman E., Rivellese E., Kadener S., Marr M. (2022): Artificially stimulating retrotransposon activity increases mortality and accelerates a subset of aging phenotypes in Drosophila. eLife 11. https://doi.org/10.7554/elife.80169
  30. Rigal J., Anduaga A., Bitman E., Rivellese E., Kadener S., Marr M. (2022): Artificially stimulating retrotransposon activity increases mortality and accelerates a subset of aging phenotypes in Drosophila. https://doi.org/10.1101/2022.05.23.493120
  31. Wei K., Mai D., Chatla K., Bachtrog D. (2022): Dynamics and Impacts of Transposable Element Proliferation in the Drosophila nasuta Species Group Radiation. Molecular Biology and Evolution 39(5). https://doi.org/10.1093/molbev/msac080
  32. Hemmer L., Negm S., Geng X., Courret C., Navarro-Domínguez B., Speece I., et al. (2022): Centromere-associated retroelement evolution in Drosophila melanogaster reveals an underlying conflict. https://doi.org/10.1101/2022.11.25.518008
  33. van den Beek M., Rubanova N., Siudeja K. (2022): Experimental Approaches to Study Somatic Transposition in Drosophila Using Whole-Genome DNA Sequencing. Methods in Molecular Biology. https://doi.org/10.1007/978-1-0716-2883-6_14
  34. Yang N., Srivastav S., Rahman R., Ma Q., Dayama G., Li S., et al. (2022): Transposable element landscapes in aging Drosophila. PLOS Genetics 18(3):e1010024. https://doi.org/10.1371/journal.pgen.1010024
  35. Han S., Dias G., Basting P., Viswanatha R., Perrimon N., Bergman C. (2022): Local assembly of long reads enables phylogenomics of transposable elements in a polyploid cell line. Nucleic Acids Research 50(21):e124-e124. https://doi.org/10.1093/nar/gkac794
  36. Han S., Dias G., Basting P., Nelson M., Patel S., Marzo M., et al. (2022): Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sublines. Genetics 221(3). https://doi.org/10.1093/genetics/iyac077
  37. Han S., Dias G., Basting P., Viswanatha R., Perrimon N., Bergman C. (2022): Local assembly of long reads enables phylogenomics of transposable elements in a polyploid cell line. https://doi.org/10.1101/2022.01.04.471818
  38. Tong X., Han M., Lu K., Tai S., Liang S., Liu Y., et al. (2022): High-resolution silkworm pan-genome provides genetic insights into artificial selection and ecological adaptation. Nature Communications 13(1). https://doi.org/10.1038/s41467-022-33366-x
  39. Liu X., Majid M., Yuan H., Chang H., Zhao L., Nie Y., et al. (2022): Transposable element expansion and low-level piRNA silencing in grasshoppers may cause genome gigantism. BMC Biology 20(1). https://doi.org/10.1186/s12915-022-01441-w
  40. Huang Y., Shukla H., Lee Y. (2022): Species-specific chromatin landscape determines how transposable elements shape genome evolution. https://doi.org/10.1101/2022.03.11.484033
  41. Ullastres A., Merenciano M., González J. (2021): Regulatory regions in natural transposable element insertions drive interindividual differences in response to immune challenges in Drosophila. Genome Biology 22(1). https://doi.org/10.1186/s13059-021-02471-3
  42. Mariyappa D., Rusch D., Han S., Luhur A., Overton D., Miller D., et al. (2021): A novel transposable element-based authentication protocol for Drosophila cell lines. G3 Genes|Genomes|Genetics 12(2). https://doi.org/10.1093/g3journal/jkab403
  43. Rech G., Radío S., Guirao-Rico S., Aguilera L., Horvath V., Green L., et al. (2021): Population-scale long-read sequencing uncovers transposable elements contributing to gene expression variation and associated with adaptive signatures in Drosophila melanogaster. https://doi.org/10.1101/2021.10.08.463646
  44. Amorim I., Sotero-Caio C., Costa R., Xavier C., de Moura R. (2021): Comprehensive mapping of transposable elements reveals distinct patterns of element accumulation on chromosomes of wild beetles. Chromosome Research 29(2):203-218. https://doi.org/10.1007/s10577-021-09655-4
  45. Wei K., Mai D., Chatla K., Bachtrog D. (2021): Dynamics and impacts of transposable element proliferation during the Drosophila nasuta species group radiation. https://doi.org/10.1101/2021.08.12.456169
  46. Lawlor M., Cao W., Ellison C. (2021): A transposon expression burst accompanies the activation of Y-chromosome fertility genes during Drosophila spermatogenesis. Nature Communications 12(1). https://doi.org/10.1038/s41467-021-27136-4
  47. Lawlor M., Cao W., Ellison C. (2021): A burst of transposon expression accompanies the activation of Y chromosome fertility genes during Drosophila spermatogenesis. https://doi.org/10.1101/2021.05.10.443472
  48. Yang N., Srivastav S., Rahman R., Ma Q., Dayama G., Chinen M., et al. (2021): Transposable element landscape changes are buffered by RNA silencing in aging Drosophila. https://doi.org/10.1101/2021.01.08.425853
  49. Himmel N., Letcher J., Sakurai A., Gray T., Benson M., Donaldson K., et al. (2021): Identification of a neural basis for cold acclimation in Drosophila larvae. iScience 24(6):102657. https://doi.org/10.1016/j.isci.2021.102657
  50. Himmel N., Letcher J., Sakurai A., Gray T., Benson M., Donaldson K., et al. (2021): The evolution of cold nociception in drosophilid larvae and identification of a neural basis for cold acclimation. https://doi.org/10.1101/2021.01.04.425280
  51. Tan S., Ma H., Wang J., Wang M., Wang M., Yin H., et al. (2021): DNA transposons mediate duplications via transposition-independent and -dependent mechanisms in metazoans. Nature Communications 12(1). https://doi.org/10.1038/s41467-021-24585-9
  52. Han S., Basting P., Dias G., Luhur A., Zelhof A., Bergman C. (2021): Transposable element profiles reveal cell line identity and loss of heterozygosity in Drosophila cell culture. Genetics 219(2). https://doi.org/10.1093/genetics/iyab113
  53. Han S., Dias G., Basting P., Nelson M., Patel S., Marzo M., et al. (2021): Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sub-lines. https://doi.org/10.1101/2021.12.08.471819
  54. Han S., Basting P., Dias G., Luhur A., Zelhof A., Bergman C. (2021): Transposable element profiles reveal cell line identity and loss of heterozygosity in Drosophila cell culture. https://doi.org/10.1101/2021.04.24.441253
  55. Lee Y. (2021): Synergistic epistasis of the deleterious effects of transposable elements. Genetics 220(2). https://doi.org/10.1093/genetics/iyab211
  56. Lee Y. (2021): Synergistic epistasis of the deleterious effects of transposable elements. https://doi.org/10.1101/2021.05.21.444727
  57. Tiedeman Z., Signor S. (2021): The Transposable Elements of the Drosophila serrata Reference Panel. Genome Biology and Evolution 13(9). https://doi.org/10.1093/gbe/evab100
  58. Ellison C., Kagda M., Cao W. (2020): Telomeric TART elements target the piRNA machinery in Drosophila. PLOS Biology 18(12):e3000689. https://doi.org/10.1371/journal.pbio.3000689
  59. Ellison C., Kagda M., Cao W. (2020): The Drosophila TART transposon manipulates the piRNA pathway as a counter-defense strategy to limit host silencing. https://doi.org/10.1101/2020.02.20.957324
  60. Gilbert C., Peccoud J., Cordaux R. (2020): Transposable Elements and the Evolution of Insects. Annual Review of Entomology 66(1):355-372. https://doi.org/10.1146/annurev-ento-070720-074650
  61. Schwarz F., Wierzbicki F., Senti K., Kofler R. (2020): Tirant Stealthily Invaded Natural Drosophila melanogaster Populations during the Last Century. Molecular Biology and Evolution 38(4):1482-1497. https://doi.org/10.1093/molbev/msaa308
  62. Schwarz F., Wierzbicki F., Senti K., Kofler R. (2020): Tirant stealthily invaded natural Drosophila melanogaster populations during the last century. https://doi.org/10.1101/2020.06.10.144378
  63. Choi J., Lee Y. (2020): Double-edged sword: The evolutionary consequences of the epigenetic silencing of transposable elements. PLOS Genetics 16(7):e1008872. https://doi.org/10.1371/journal.pgen.1008872
  64. Lin K., Wang W., Lin C., Rastegari E., Su Y., Chang Y., et al. (2020): Piwi reduction in the aged niche eliminates germline stem cells via Toll-GSK3 signaling. Nature Communications 11(1). https://doi.org/10.1038/s41467-020-16858-6
  65. Mohamed M., Dang N., Ogyama Y., Burlet N., Mugat B., Boulesteix M., et al. (2020): A Transposon Story: From TE Content to TE Dynamic Invasion of Drosophila Genomes Using the Single-Molecule Sequencing Technology from Oxford Nanopore. Cells 9(8):1776. https://doi.org/10.3390/cells9081776
  66. Signor S. (2020): Transposable elements in individual genotypes of Drosophila simulans. Ecology and Evolution 10(7):3402-3412. https://doi.org/10.1002/ece3.6134
  67. Luo S., Zhang H., Duan Y., Yao X., Clark A., Lu J. (2020): The evolutionary arms race between transposable elements and piRNAs in Drosophila melanogaster. BMC Evolutionary Biology 20(1). https://doi.org/10.1186/s12862-020-1580-3
  68. Zhang S., Pointer B., Kelleher E. (2020): Rapid evolution of piRNA-mediated silencing of an invading transposable element was driven by abundant de novo mutations. Genome Research 30(4):566-575. https://doi.org/10.1101/gr.251546.119
  69. Gamez S., Srivastav S., Akbari O., Lau N. (2020): Diverse Defenses: A Perspective Comparing Dipteran Piwi-piRNA Pathways. Cells 9(10):2180. https://doi.org/10.3390/cells9102180
  70. Hatkevich T., Miller D., Turcotte C., Miller M., Sekelsky J. (2020): A pathway for error-free non-homologous end joining of resected meiotic double-strand breaks. Nucleic Acids Research 49(2):879-890. https://doi.org/10.1093/nar/gkaa1205
  71. Lee Y., Ogiyama Y., Martins N., Beliveau B., Acevedo D., Wu C., et al. (2020): Pericentromeric heterochromatin is hierarchically organized and spatially contacts H3K9me2 islands in euchromatin. PLOS Genetics 16(3):e1008673. https://doi.org/10.1371/journal.pgen.1008673
  72. Tiedeman Z., Signor S. (2020): The transposable elements of the Drosophila serrata reference panel. https://doi.org/10.1101/2020.06.11.146431
  73. Erwin A., Blumenstiel J. (2019): Aging in the Drosophila ovary: contrasting changes in the expression of the piRNA machinery and mitochondria but no global release of transposable elements. BMC Genomics 20(1). https://doi.org/10.1186/s12864-019-5668-3
  74. Ullastres A., Merenciano M., González J. (2019): Regulatory regions in natural transposable element insertions drive interindividual differences in response to immune challenges in Drosophila. https://doi.org/10.1101/655225
  75. Borredá C., Pérez-Román E., Ibanez V., Terol J., Talon M. (2019): Reprogramming of retrotransposon activity during speciation of the genus Citrus. Genome Biology and Evolution. https://doi.org/10.1093/gbe/evz246
  76. Ellison C., Cao W. (2019): Nanopore sequencing and Hi-C scaffolding provide insight into the evolutionary dynamics of transposable elements and piRNA production in wild strains of Drosophila melanogaster. Nucleic Acids Research 48(1):290-303. https://doi.org/10.1093/nar/gkz1080
  77. Rech G., Bogaerts-Márquez M., Barrón M., Merenciano M., Villanueva-Cañas J., Horváth V., et al. (2019): Stress response, behavior, and development are shaped by transposable element-induced mutations in Drosophila. PLOS Genetics 15(2):e1007900. https://doi.org/10.1371/journal.pgen.1007900
  78. Guio L., González J. (2019): New Insights on the Evolution of Genome Content: Population Dynamics of Transposable Elements in Flies and Humans. Methods in Molecular Biology. https://doi.org/10.1007/978-1-4939-9074-0_16
  79. Ninova M., Chen Y., Godneeva B., Rogers A., Luo Y., Fejes Tóth K., et al. (2019): Su(var)2-10 and the SUMO Pathway Link piRNA-Guided Target Recognition to Chromatin Silencing. Molecular Cell 77(3):556-570.e6. https://doi.org/10.1016/j.molcel.2019.11.012
  80. Ninova M., Godneeva B., Chen Y., Luo Y., Prakash S., Jankovics F., et al. (2019): The SUMO Ligase Su(var)2-10 Controls Hetero- and Euchromatic Gene Expression via Establishing H3K9 Trimethylation and Negative Feedback Regulation. Molecular Cell 77(3):571-585.e4. https://doi.org/10.1016/j.molcel.2019.09.033
  81. Ninova M., Chen Y., Godneeva B., Rogers A., Luo Y., Aravin A., et al. (2019): The SUMO ligase Su(var)2-10 links piRNA-guided target recognition to chromatin silencing. https://doi.org/10.1101/533091
  82. Ninova M., Godneeva B., Ariel Chen Y., Luo Y., Prakash S., Jankovics F., et al. (2019): The SUMO ligase Su(var)2-10 controls eu- and heterochromatic gene expression via establishment of H3K9 trimethylation and negative feedback regulation. https://doi.org/10.1101/533232
  83. Bogaerts-Márquez M., Barrón M., Fiston-Lavier A., Vendrell-Mir P., Castanera R., Casacuberta J., et al. (2019): T-lex3: an accurate tool to genotype and estimate population frequencies of transposable elements using the latest short-read whole genome sequencing data. Bioinformatics 36(4):1191-1197. https://doi.org/10.1093/bioinformatics/btz727
  84. Hartmann M., Umbanhowar J., Sekelsky J. (2019): Centromere-Proximal Meiotic Crossovers in Drosophila melanogaster Are Suppressed by Both Highly Repetitive Heterochromatin and Proximity to the Centromere. Genetics 213(1):113-125. https://doi.org/10.1534/genetics.119.302509
  85. Hartmann M., Umbanhowar J., Sekelsky J. (2019): Centromere-proximal meiotic crossovers in Drosophila melanogaster are suppressed by both highly-repetitive heterochromatin and the centromere effect. https://doi.org/10.1101/637561
  86. Merenciano M., Iacometti C., González J. (2019): A unique cluster of roo insertions in the promoter region of a stress response gene in Drosophila melanogaster. Mobile DNA 10(1). https://doi.org/10.1186/s13100-019-0152-9
  87. Vendrell-Mir P., Barteri F., Merenciano M., González J., Casacuberta J., Castanera R. (2019): A benchmark of transposon insertion detection tools using real data. Mobile DNA 10(1). https://doi.org/10.1186/s13100-019-0197-9
  88. Signor S. (2019): Transposable elements in individual genotypes of Drosophila simulans. https://doi.org/10.1101/781419
  89. Srivastav S., Rahman R., Ma Q., Pierre J., Bandyopadhyay S., Lau N. (2019): Har-P, a short P-element variant, weaponizes P-transposase to severely impair Drosophila development. eLife 8. https://doi.org/10.7554/elife.49948
  90. Srivastav S., Rahman R., Ma Q., Lau N. (2019): Har-P, a short P -element variant, weaponizes P -transposase to severely impair Drosophila development. https://doi.org/10.1101/700211
  91. Zhang S., Kelleher E. (2019): Rapid evolution of piRNA-mediated silencing of an invading transposable element was driven by abundant de novo mutations. https://doi.org/10.1101/611350
  92. Hill T., Unckless R. (2019): A Deep Learning Approach for Detecting Copy Number Variation in Next-Generation Sequencing Data. G3 Genes|Genomes|Genetics 9(11):3575-3582. https://doi.org/10.1534/g3.119.400596
  93. Hill T. (2019): Transposable element dynamics are consistent across the Drosophila phylogeny, despite drastically differing content. https://doi.org/10.1101/651059
  94. Bourgeois Y., Boissinot S. (2019): On the Population Dynamics of Junk: A Review on the Population Genomics of Transposable Elements. Genes 10(6):419. https://doi.org/10.3390/genes10060419
  95. Lee Y., Ogiyama Y., Martins N., Beliveau B., Acevedo D., Wu C., et al. (2019): Pericentromeric heterochromatin is hierarchically organized and spatially contacts H3K9me2 islands in euchromatin. https://doi.org/10.1101/525873
  96. Erwin A., Blumenstiel J. (2018): Contrasting effects of aging on the expression of transposons, the piRNA machinery and mitochondrial transcripts in the Drosophila ovary. https://doi.org/10.1101/342105
  97. Serrato-Capuchina A., Matute D. (2018): The Role of Transposable Elements in Speciation. Genes 9(5):254. https://doi.org/10.3390/genes9050254
  98. Luhur A., Klueg K., Zelhof A. (2018): Generating and working with Drosophila cell cultures: Current challenges and opportunities. WIREs Developmental Biology 8(3). https://doi.org/10.1002/wdev.339
  99. Solares E., Chakraborty M., Miller D., Kalsow S., Hall K., Perera A., et al. (2018): Rapid Low-Cost Assembly of the Drosophila melanogaster Reference Genome Using Low-Coverage, Long-Read Sequencing. G3 Genes|Genomes|Genetics 8(10):3143-3154. https://doi.org/10.1534/g3.118.200162
  100. Solares E., Chakraborty M., Miller D., Kalsow S., Hall K., Perera A., et al. (2018): Rapid low-cost assembly of the Drosophila melanogaster reference genome using low-coverage, long-read sequencing. https://doi.org/10.1101/267401
  101. Lerat E., Goubert C., Guirao‐Rico S., Merenciano M., Dufour A., Vieira C., et al. (2018): Population‐specific dynamics and selection patterns of transposable element insertions in European natural populations. Molecular Ecology 28(6):1506-1522. https://doi.org/10.1111/mec.14963
  102. Lerat E. (2018): Repeat in Genomes: How and Why You Should Consider Them in Genome Analyses?. Encyclopedia of Bioinformatics and Computational Biology. https://doi.org/10.1016/b978-0-12-809633-8.20227-6
  103. Tsetsos F., Drineas P., Paschou P. (2018): Genetics and Population Analysis. Encyclopedia of Bioinformatics and Computational Biology. https://doi.org/10.1016/b978-0-12-809633-8.20114-3
  104. Rech G., Bogaerts-Marquez M., Barrón M., Merenciano M., Villanueva-Cañas J., Horváth V., et al. (2018): Stress response, behavior, and development are shaped by transposable element-induced mutations in Drosophila. https://doi.org/10.1101/380618
  105. Bourque G., Burns K., Gehring M., Gorbunova V., Seluanov A., Hammell M., et al. (2018): Ten things you should know about transposable elements. Genome Biology 19(1). https://doi.org/10.1186/s13059-018-1577-z
  106. Manee M., Jackson J., Bergman C. (2018): Conserved Noncoding Elements Influence the Transposable Element Landscape in Drosophila. Genome Biology and Evolution 10(6):1533-1545. https://doi.org/10.1093/gbe/evy104
  107. Goerner-Potvin P., Bourque G. (2018): Computational tools to unmask transposable elements. Nature Reviews Genetics 19(11):688-704. https://doi.org/10.1038/s41576-018-0050-x
  108. Hill T., Betancourt A. (2018): Extensive exchange of transposable elements in the Drosophila pseudoobscura group. Mobile DNA 9(1). https://doi.org/10.1186/s13100-018-0123-6
  109. Hill T., Betancourt A. (2018): Extensive horizontal exchange of transposable elements in the Drosophila pseudoobscura group. https://doi.org/10.1101/284117
  110. Bergman C., Han S., Nelson M., Bondarenko V., Kozeretska I. (2017): Genomic analysis of P elements in natural populations of Drosophila melanogaster. PeerJ 5:e3824. https://doi.org/10.7717/peerj.3824
  111. Bergman C., Nelson M., Bondarenko V., Kozeretska I. (2017): Genomic analysis of P elements in natural populations of Drosophila melanogaster. https://doi.org/10.1101/107169
  112. Disdero E., Filée J. (2017): LoRTE: Detecting transposon-induced genomic variants using low coverage PacBio long read sequences. Mobile DNA 8(1). https://doi.org/10.1186/s13100-017-0088-x
  113. Clark J., Rahman R., Yang N., Yang L., Lau N. (2017): Drosophila PAF1 Modulates PIWI/piRNA Silencing Capacity. Current Biology 27(17):2718-2726.e4. https://doi.org/10.1016/j.cub.2017.07.052
  114. Ninova M., Griffiths-Jones S., Ronshaugen M. (2017): Abundant expression of somatic transposon-derived piRNAs throughout Tribolium castaneum embryogenesis. Genome Biology 18(1). https://doi.org/10.1186/s13059-017-1304-1
  115. Nelson M., Linheiro R., Bergman C. (2017): McClintock: An Integrated Pipeline for Detecting Transposable Element Insertions in Whole-Genome Shotgun Sequencing Data. G3 Genes|Genomes|Genetics 7(8):2763-2778. https://doi.org/10.1534/g3.117.043893
  116. Schmidt R. (2017): Faculty Opinions recommendation of The Arabidopsis thaliana mobilome and its impact at the species level. Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature. https://doi.org/10.3410/f.726397587.793531533
  117. Srivastav S., Kelleher E. (2017): Paternal Induction of Hybrid Dysgenesis in Drosophila melanogaster Is Weakly Correlated with Both P-Element and hobo Element Dosage. G3 Genes|Genomes|Genetics 7(5):1487-1497. https://doi.org/10.1534/g3.117.040634
  118. Luo S., Lu J. (2017): Silencing of Transposable Elements by piRNAs in Drosophila: An Evolutionary Perspective. Genomics, Proteomics & Bioinformatics 15(3):164-176. https://doi.org/10.1016/j.gpb.2017.01.006
  119. Zhang S., Kelleher E. (2017): Targeted identification of TE insertions in a Drosophila genome through hemi-specific PCR. Mobile DNA 8(1). https://doi.org/10.1186/s13100-017-0092-1
  120. Horváth V., Merenciano M., González J. (2017): Revisiting the Relationship between Transposable Elements and the Eukaryotic Stress Response. Trends in Genetics 33(11):832-841. https://doi.org/10.1016/j.tig.2017.08.007
  121. Lee Y., Karpen G. (2017): Pervasive epigenetic effects of Drosophila euchromatic transposable elements impact their evolution. eLife 6. https://doi.org/10.7554/elife.25762
  122. Disdero E., Filée J. (2016): LoRTE: Detecting transposon-induced genomic variants using low coverage PacBio long read sequences. https://doi.org/10.1101/073551
  123. Kelleher E. (2016): Reexamining the P-Element Invasion of Drosophila melanogaster Through the Lens of piRNA Silencing. Genetics 203(4):1513-1531. https://doi.org/10.1534/genetics.115.184119
  124. Arkhipova I., Rice P. (2016): Mobile genetic elements: in silico, in vitro, in vivo. Molecular Ecology 25(5):1027-1031. https://doi.org/10.1111/mec.13543
  125. Kozeretska I., Bondarenko V., Shulga V., Serga S., Rozhok A., Protsenko A., et al. (2016): Phenotypic and genomic analysis of P elements in natural populations of Drosophila melanogaster. https://doi.org/10.1101/047910
  126. Quadrana L., Bortolini Silveira A., Mayhew G., LeBlanc C., Martienssen R., Jeddeloh J., et al. (2016): The Arabidopsis thaliana mobilome and its impact at the species level. eLife 5. https://doi.org/10.7554/elife.15716
  127. Nelson M., Linheiro R., Bergman C. (2016): McClintock: An integrated pipeline for detecting transposable element insertions in whole genome shotgun sequencing data. https://doi.org/10.1101/095372
  128. Kofler R., Gómez-Sánchez D., Schlötterer C. (2016): PoPoolationTE2: Comparative Population Genomics of Transposable Elements Using Pool-Seq. Molecular Biology and Evolution 33(10):2759-2764. https://doi.org/10.1093/molbev/msw137
  129. Kofler R., Gómez-Sánchez D., Schlöetterer C. (2016): PoPoolationTE2: comparative population genomics of transposable elements using Pool-Seq. https://doi.org/10.1101/038745
  130. Penke T., McKay D., Strahl B., Matera A., Duronio R. (2016): Direct interrogation of the role of H3K9 in metazoan heterochromatin function. Genes & Development 30(16):1866-1880. https://doi.org/10.1101/gad.286278.116

Mendoza E, Colomb J, Rybak J, Pflüger H-J, Zars T, Scharff C, Brembs B. (2014): Drosophila FoxP mutants are deficient in operant self-learning. PLoS One 9(6):e100648.

  1. Latorre-Estivalis J., Ares M., Farina W. (2026): Experience-dependent modulation of Fox transcription factors in the stingless bee Tetragonisca fiebrigi. https://doi.org/10.21203/rs.3.rs-8872933/v1
  2. Ladd C., Simpson J. (2025): Behavior choices amongst grooming, feeding and courting in Drosophila show contextual flexibility, not an absolute hierarchy of needs. Journal of Experimental Biology 228(23). https://doi.org/10.1242/jeb.250826
  3. Ladd C., Simpson J. (2025): Behavior choices amongst grooming, feeding, and courting in Drosophila show contextual flexibility, not an absolute hierarchy of needs. https://doi.org/10.1101/2025.05.09.653186
  4. Tsai F., Lin C., Su Y., Yu J., Kuo D. (2025): Evolutionary History of Bilaterian FoxP Genes: Complex Ancestral Functions and Evolutionary Changes Spanning 2R-WGD in the Vertebrate Lineage. Molecular Biology and Evolution 42(4). https://doi.org/10.1093/molbev/msaf072
  5. Adnan Hassan R., Al-Fatlawi A. (2025): Genomic Insights Into Developmental Language Disorders: Biomarkers and Their Interactions. Frontiers in Bioscience-Scholar 17(4). https://doi.org/10.31083/fbs38706
  6. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  7. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.1
  8. Lebovich L., Alisch T., Redhead E., Parker M., Loewenstein Y., Couzin I., et al. (2024): Spatiotemporal dynamics of locomotor decisions in Drosophila melanogaster. https://doi.org/10.1101/2024.09.04.611038
  9. Ehweiner A., Duch C., Brembs B. (2022): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant selflearning in Drosophila. https://doi.org/10.1101/2022.12.16.520755
  10. Croteau-Chonka E., Clayton M., Venkatasubramanian L., Harris S., Jones B., Narayan L., et al. (2022): High-throughput automated methods for classical and operant conditioning of Drosophila larvae. eLife 11. https://doi.org/10.7554/elife.70015
  11. Cabana-Domínguez J., Antón-Galindo E., Fernàndez-Castillo N., Singgih E., O’Leary A., Norton W., et al. (2022): The translational genetics of ADHD and related phenotypes in model organisms. Neuroscience & Biobehavioral Reviews 144:104949. https://doi.org/10.1016/j.neubiorev.2022.104949
  12. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. https://doi.org/10.1101/2021.10.04.463010
  13. Rabin D., Rankin C. (2021): Learning and Memory in Fruit Flies and Nematodes. Encyclopedia of Life Sciences. https://doi.org/10.1002/9780470015902.a0029238
  14. Klein K., Croteau-Chonka E., Narayan L., Winding M., Masson J., Zlatic M. (2021): Serotonergic Neurons Mediate Operant Conditioning in Drosophila Larvae. https://doi.org/10.1101/2021.06.14.448341
  15. Smith B., Cook C. (2020): Experimental psychology meets behavioral ecology: what laboratory studies of learning polymorphisms mean for learning under natural conditions, and vice versa. Journal of Neurogenetics 34(1):178-183. https://doi.org/10.1080/01677063.2020.1718674
  16. Gao J., Geng R., Deng H., Zuo H., Weng S., He J., et al. (2020): A Novel Forkhead Box Protein P (FoxP) From Litopenaeus vannamei Plays a Positive Role in Immune Response. Frontiers in Immunology 11. https://doi.org/10.3389/fimmu.2020.593987
  17. Palazzo O., Rass M., Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. Open Biology 10(12). https://doi.org/10.1098/rsob.200295
  18. Palazzo O., Raß M., Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. https://doi.org/10.1101/2020.07.15.204677
  19. Wolf R., Heisenberg M., Brembs B., Waddell S., Mishra A., Kehrer A., et al. (2020): Memory, anticipation, action – working with Troy D. Zars. Journal of Neurogenetics 34(1):9-20. https://doi.org/10.1080/01677063.2020.1715976
  20. Castells-Nobau A., Eidhof I., Fenckova M., Brenman-Suttner D., Scheffer-de Gooyert J., Christine S., et al. (2019): Conserved regulation of neurodevelopmental processes and behavior by FoxP in Drosophila. PLOS ONE 14(2):e0211652. https://doi.org/10.1371/journal.pone.0211652
  21. Kottler B., Faville R., Bridi J., Hirth F. (2019): Inverse Control of Turning Behavior by Dopamine D1 Receptor Signaling in Columnar and Ring Neurons of the Central Complex in Drosophila. Current Biology 29(4):567-577.e6. https://doi.org/10.1016/j.cub.2019.01.017
  22. Brembs B. (2019): Faculty Opinions recommendation of Conserved regulation of neurodevelopmental processes and behavior by FoxP in Drosophila. Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature. https://doi.org/10.3410/f.735080766.793556526
  23. Schatton A., Mendoza E., Grube K., Scharff C. (2018): FoxP in bees: A comparative study on the developmental and adult expression pattern in three bee species considering isoforms and circuitry. Journal of Comparative Neurology 526(9):1589-1610. https://doi.org/10.1002/cne.24430
  24. Schatton A., Agoro J., Mardink J., Leboulle G., Scharff C. (2018): Identification of the neurotransmitter profile of AmFoxP expressing neurons in the honeybee brain using double-label in situ hybridization. BMC Neuroscience 19(1). https://doi.org/10.1186/s12868-018-0469-1
  25. Kottler B., Faville R., Bridi J., Hirth F. (2018): Dopamine D1 receptor signalling differentially regulates action sequences and turning behaviour in freely moving Drosophila. https://doi.org/10.1101/385674
  26. Hung Y., Stopfer M. (2018): Decision Making: How Fruit Flies Integrate Olfactory Evidence. Current Biology 28(13):R757-R759. https://doi.org/10.1016/j.cub.2018.05.065
  27. Schatton A., Scharff C. (2017): FoxP expression identifies a Kenyon cell subtype in the honeybee mushroom bodies linking them to fruit fly αβc neurons. European Journal of Neuroscience 46(9):2534-2541. https://doi.org/10.1111/ejn.13713
  28. Widmann A., Eichler K., Selcho M., Thum A., Pauls D. (2017): Odor-taste learning in Drosophila larvae. Journal of Insect Physiology 106:47-54. https://doi.org/10.1016/j.jinsphys.2017.08.004
  29. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  30. Brembs B. (2017): Genetic Analysis of Behavior in Drosophila. The Oxford Handbook of Invertebrate Neurobiology. https://doi.org/10.1093/oxfordhb/9780190456757.013.37
  31. Foley B., Marjoram P., Nuzhdin S. (2017): Basic reversal-learning capacity in flies suggests rudiments of complex cognition. PLOS ONE 12(8):e0181749. https://doi.org/10.1371/journal.pone.0181749
  32. Mendoza E., Scharff C. (2017): Protein-Protein Interaction Among the FoxP Family Members and their Regulation of Two Target Genes, VLDLR and CNTNAP2 in the Zebra Finch Song System. Frontiers in Molecular Neuroscience 10. https://doi.org/10.3389/fnmol.2017.00112
  33. Itachi Mills (2017): The Evolution of Fear Ecology: A Fruit Fly (Drosophila melanogaster) Perspective. IRL – University of Missouri, St. Louis (University of Missouri–St. Louis).
  34. Balari S., Lorenzo G. (2017): What Lenneberg Got Right: A Homological Program for the Study of Language Evolution. Biolinguistics 11:139-170. https://doi.org/10.5964/bioling.9083
  35. Unknown authors (2016): Peer Review #3 of “PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila (v0.2)”. https://doi.org/10.7287/peerj.1971v0.2/reviews/3
  36. Unknown authors (2016): Peer Review #3 of “PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila (v0.1)”. https://doi.org/10.7287/peerj.1971v0.1/reviews/3
  37. Schatton A., Scharff C. (2016): Next stop: Language. The ‘FOXP2’ gene’s journey through time. Metode Science Studies Journal. https://doi.org/10.7203/metode.7.7248
  38. Brembs B. (2016): Operant Behavior in Model Systems. https://doi.org/10.1101/058719
  39. Colomb J., Brembs B. (2016): PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila. PeerJ 4:e1971. https://doi.org/10.7717/peerj.1971
  40. Julien Colomb, Björn Brembs, Bj Örn Brembs, B Auguie, A Bedecarrats, C Cornet, et al. (2016): Peer Review #1 of “PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila (v0.1)”. https://doi.org/10.7287/peerj.1971v0.1/reviews/1
  41. Zlatic M. (2016): Peer Review #2 of “PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila (v0.1)”. https://doi.org/10.7287/peerj.1971v0.1/reviews/2
  42. Ostrowski D., Kahsai L., Kramer E., Knutson P., Zars T. (2015): Place memory retention in Drosophila. Neurobiology of Learning and Memory 123:217-224. https://doi.org/10.1016/j.nlm.2015.06.015
  43. Engvild K. (2015): Possible evolution of teleological bias, language acquisition, and search for meaning from primitive agency detection. Ideas in Ecology and Evolution 8. https://doi.org/10.4033/iee.2015.8.2.n
  44. Heisenberg M. (2015): Outcome learning, outcome expectations, and intentionality inDrosophila. Learning & Memory 22(6):294-298. https://doi.org/10.1101/lm.037481.114
  45. Cedric Boeckx, Constantina Theofanopoulou (2014): A Multidimensional Interdisciplinary Framework for Linguistics: The Lexicon as a Case Study. Journal of Cognitive Science 15(4):403-420. https://doi.org/10.17791/jcs.2014.15.4.403
  46. Weiss S., Rosales-Ruiz J. (2014): Operant/Classical Conditioning: Comparisons,Intersections and Interactions The 2014 Winter Conference on Animal Learning and Behavior Focus and Research Seminar Sessions. International Journal of Comparative Psychology 27(4). https://doi.org/10.46867/ijcp.2014.27.04.07

Colomb J, Brembs B. (2014): Sub-strains of Drosophila Canton-S differ markedly in their locomotor behavior. F1000 Research 3:176.

  1. Jung N., Xia C., Jang Y., Kim H., Chung Y., Chon T. (2025): Movement and Dispersion Parameters Characterizing the Group Behavior of Drosophila melanogaster in Micro-Areas of an Observation Arena. Animals 15(11):1515. https://doi.org/10.3390/ani15111515
  2. Nunes R., Drummond-Barbosa D. (2025): Brain dopamine imbalance causes follicle death and underlies negative effect of high sugar diet during Drosophila oogenesis. https://doi.org/10.1101/2025.04.25.650701
  3. Dutra Nunes R., Drummond-Barbosa D. (2025): Dopamine production in the central nervous system is important for follicle survival and interacts with genetic background and a high sugar diet during Drosophila oogenesis. GENETICS 232(1). https://doi.org/10.1093/genetics/iyaf239
  4. Yanan Wei, Hongyu Miao, Hadi Najafi, Woo Jae Kim (2025): Precise measurement of motor neuron dysfunction in Drosophila ALS model via climbing assay and leg imaging. Methods in Cell Biology. https://doi.org/10.1016/bs.mcb.2025.02.008
  5. Li W., Pan X., Li M., ling L., Zhang M., liu Z., et al. (2023): Impact of age on the rotenone-induced sporadic Parkinson’s disease model using Drosophila melanogaster. Neuroscience Letters 805:137187. https://doi.org/10.1016/j.neulet.2023.137187
  6. Hibicke M., Nichols C. (2022): Validation of the forced swim test in Drosophila, and its use to demonstrate psilocybin has long-lasting antidepressant-like effects in flies. Scientific Reports 12(1). https://doi.org/10.1038/s41598-022-14165-2
  7. Malacrida S., De Lazzari F., Mrakic-Sposta S., Vezzoli A., Zordan M., Bisaglia M., et al. (2022): Lifespan and ROS levels in different Drosophila melanogaster strains after 24 h hypoxia exposure. Biology Open 11(6). https://doi.org/10.1242/bio.059386
  8. Damrau C., Colomb J., Brembs B. (2021): Sensitivity to expression levels underlies differential dominance of a putative null allele of the Drosophila tβh gene in behavioral phenotypes. PLOS Biology 19(5):e3001228. https://doi.org/10.1371/journal.pbio.3001228
  9. Samota E., Davey R. (2021): Knowledge and Attitudes Among Life Scientists Toward Reproducibility Within Journal Articles: A Research Survey. Frontiers in Research Metrics and Analytics 6. https://doi.org/10.3389/frma.2021.678554
  10. Wiebels K., Moreau D. (2021): Dynamic Data Visualizations to Enhance Insight and Communication Across the Lifecycle of a Scientific Project. https://doi.org/10.31234/osf.io/uatyw
  11. Himmel N., Letcher J., Sakurai A., Gray T., Benson M., Donaldson K., et al. (2021): Identification of a neural basis for cold acclimation in Drosophila larvae. iScience 24(6):102657. https://doi.org/10.1016/j.isci.2021.102657
  12. Himmel N., Letcher J., Sakurai A., Gray T., Benson M., Donaldson K., et al. (2021): The evolution of cold nociception in drosophilid larvae and identification of a neural basis for cold acclimation. https://doi.org/10.1101/2021.01.04.425280
  13. Mitra S., Pinch M., Kandel Y., Li Y., Rodriguez S., Hansen I. (2021): Olfaction-Related Gene Expression in the Antennae of Female Mosquitoes From Common Aedes aegypti Laboratory Strains. Frontiers in Physiology 12. https://doi.org/10.3389/fphys.2021.668236
  14. Bath E., Thomson J., Perry J. (2020): Anxiety-like behaviour is regulated independently from sex, mating status and the sex peptide receptor in Drosophila melanogaster. Animal Behaviour 166:1-7. https://doi.org/10.1016/j.anbehav.2020.05.011
  15. Evanthia Kaimaklioti Samota (2020): Knowledge and attitudes among life scientists towards reproducibility within journal articles. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.6084/m9.figshare.c.4436912.v8
  16. Hague M., Caldwell C., Cooper B. (2020): Pervasive Effects of Wolbachia on Host Temperature Preference. mBio 11(5). https://doi.org/10.1128/mbio.01768-20
  17. Hague M., Caldwell C., Cooper B. (2020): Divergent effects of Wolbachia on host temperature preference. https://doi.org/10.1101/2020.06.11.146977
  18. Palazzo O., Rass M., Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. Open Biology 10(12). https://doi.org/10.1098/rsob.200295
  19. Palazzo O., Raß M., Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. https://doi.org/10.1101/2020.07.15.204677
  20. Ramaekers A., Claeys A., Kapun M., Mouchel-Vielh E., Potier D., Weinberger S., et al. (2019): Altering the Temporal Regulation of One Transcription Factor Drives Evolutionary Trade-Offs between Head Sensory Organs. Developmental Cell 50(6):780-792.e7. https://doi.org/10.1016/j.devcel.2019.07.027
  21. Samota E., Davey R. (2019): Knowledge and attitudes among life scientists towards reproducibility within journal articles: a research survey. https://doi.org/10.1101/581033
  22. Konkol M., Kray C., Suleiman J. (2019): Creating Interactive Scientific Publications using Bindings. Proceedings of the ACM on Human-Computer Interaction 3(EICS):1-18. https://doi.org/10.1145/3331158
  23. Konkol M. (2019): Publishing Reproducible Geoscientific Papers: Status quo, benefits, and opportunities. https://doi.org/10.31237/osf.io/mcdrn
  24. Dolan M., Frechter S., Bates A., Dan C., Huoviala P., Roberts R., et al. (2019): Neurogenetic dissection of the Drosophila lateral horn reveals major outputs, diverse behavioural functions, and interactions with the mushroom body. eLife 8. https://doi.org/10.7554/elife.43079
  25. Isaac R. (2019): The Effect of Mating and the Male Sex Peptide on Group Behaviour of Post-mated Female Drosophila melanogaster. Neurochemical Research 44(6):1508-1516. https://doi.org/10.1007/s11064-019-02722-7
  26. Marques-Smith A., Neto J., Lopes G., Nogueira J., Calcaterra L., Frazão J., et al. (2018): Recording from the same neuron with high-density CMOS probes and patch-clamp: a ground-truth dataset and an experiment in collaboration. https://doi.org/10.1101/370080
  27. Ramaekers A., Weinberger S., Claeys A., Kapun M., Yan J., Wolf R., et al. (2018): Altering the temporal regulation of one transcription factor drives sensory trade-offs. https://doi.org/10.1101/348375
  28. Qiao B., Li C., Allen V., Shirasu-Hiza M., Syed S. (2018): Automated analysis of long-term grooming behavior in Drosophila using a k-nearest neighbors classifier. eLife 7. https://doi.org/10.7554/elife.34497
  29. Damrau C., Colomb J., Brembs B. (2018): Differential dominance of an allele of the Drosophila tßh gene challenges standard genetic techniques. https://doi.org/10.1101/504332
  30. Coelho D., Schwartz S., Merino M., Hauert B., Topfel B., Tieche C., et al. (2018): Culling Less Fit Neurons Protects against Amyloid-β-Induced Brain Damage and Cognitive and Motor Decline. Cell Reports 25(13):3661-3673.e3. https://doi.org/10.1016/j.celrep.2018.11.098
  31. Coelho D., Schwartz S., Merino M., Hauert B., Topfel B., Tieche C., et al. (2018): Culling less fit neurons protects against amyloid-β-induced brain damage and cognitive and motor decline. https://doi.org/10.1101/468868
  32. Pomatto L., Wong S., Tower J., Davies K. (2018): Sex-specific adaptive homeostasis in D. melanogaster depends on increased proteolysis by the 20S Proteasome: Data-in-Brief. Data in Brief 17:653-661. https://doi.org/10.1016/j.dib.2018.01.044
  33. Xiao C., Qiu S., Robertson R. (2017): Persistent one-way walking in a circular arena in Drosophila melanogaster Canton-S strain. https://doi.org/10.1101/145888
  34. Velazquez-Ulloa N. (2017): A Drosophila model for developmental nicotine exposure. PLOS ONE 12(5):e0177710. https://doi.org/10.1371/journal.pone.0177710
  35. Signor S., Abbasi M., Marjoram P., Nuzhdin S. (2017): Conservation of social effects (Ψ) between two species of Drosophila despite reversal of sexual dimorphism. Ecology and Evolution 7(23):10031-10041. https://doi.org/10.1002/ece3.3523
  36. Qiu S., Xiao C., Meldrum Robertson R. (2017): Different age-dependent performance in Drosophila wild-type Canton-S and the white mutant w1118 flies. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 206:17-23. https://doi.org/10.1016/j.cbpa.2017.01.003
  37. Hampel S., Seeds A. (2017): Targeted Manipulation of Neuronal Activity in Behaving Adult Flies. Decoding Neural Circuit Structure and Function. https://doi.org/10.1007/978-3-319-57363-2_7
  38. Tennant J., Waldner F., Jacques D., Masuzzo P., Collister L., Hartgerink C. (2016): The academic, economic and societal impacts of Open Access: an evidence-based review. F1000Research 5:632. https://doi.org/10.12688/f1000research.8460.3
  39. Morgan S., Seggio J., Nascimento N., Huh D., Hicks J., Sharp K., et al. (2016): The Phenotypic Effects of Royal Jelly on Wild-Type D. melanogaster Are Strain-Specific. PLOS ONE 11(8):e0159456. https://doi.org/10.1371/journal.pone.0159456
  40. Xiao C., Robertson R. (2015): Locomotion Induced by Spatial Restriction in Adult Drosophila. PLOS ONE 10(9):e0135825. https://doi.org/10.1371/journal.pone.0135825
  41. Christine Damrau (2015): Aminergic control of Drosophila behavior. Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver. https://doi.org/10.17169/refubium-17177
  42. Watson M. (2015): When will ‘open science’ become simply ‘science’?. Genome Biology 16(1). https://doi.org/10.1186/s13059-015-0669-2
  43. Haddaway N., Hedlund K., Jackson L., Kätterer T., Lugato E., Thomsen I., et al. (2015): What are the effects of agricultural management on soil organic carbon in boreo-temperate systems?. Environmental Evidence 4(1). https://doi.org/10.1186/s13750-015-0049-0
  44. Zalucki O., Day R., Kottler B., Karunanithi S., van Swinderen B. (2015): Behavioral and electrophysiological analysis of general anesthesia in 3 background strains ofDrosophila melanogaster. Fly 9(1):7-15. https://doi.org/10.1080/19336934.2015.1072663
  45. Rahman R., Chirn G., Kanodia A., Sytnikova Y., Brembs B., Bergman C., et al. (2015): Unique transposon landscapes are pervasive acrossDrosophila melanogastergenomes. Nucleic Acids Research 43(22):10655-10672. https://doi.org/10.1093/nar/gkv1193
  46. West R., Elliott C., Wade A. (2015): Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision. Scientific Reports 5(1). https://doi.org/10.1038/srep16933
  47. Pinfield S. (2015): Making Open Access work. Online Information Review 39(5):604-636. https://doi.org/10.1108/oir-05-2015-0167

Brembs B, Button K, Munafò M. (2013): Deep impact: unintended consequences of journal rank. Front. Neurosci. 7:291.

  1. Teymourifar A. (2026): Understanding the ABS journal ranking system: a critical review. Frontiers in Education 11. https://doi.org/10.3389/feduc.2026.1773655
  2. Nosek B., Allison D., Jamieson K., McNutt M., Nielsen A., Wolf S. (2026): A framework for assessing the trustworthiness of scientific research findings. Proceedings of the National Academy of Sciences 123(6). https://doi.org/10.1073/pnas.2536736123
  3. Knudson D. (2026): Bibliometrics of Measurement in Physical Education and Exercise Science Over the Last Twenty-Five Years. Measurement in Physical Education and Exercise Science. https://doi.org/10.1080/1091367x.2026.2621420
  4. Balyakina E. (2026): Regional academic journals as a component of Russia’s research infrastructure. Science Editor and Publisher 10(2):241-253. https://doi.org/10.24069/sep-251045
  5. Lemaitre J., Popelka D., Ribotta B., Westlake H., Chakrabarti S., Xiaoxue L., et al. (2026): A retrospective analysis of 400 publications reveals patterns of irreproducibility across an entire life sciences research field. https://doi.org/10.7554/elife.108403
  6. Lemaitre J., Popelka D., Ribotta B., Westlake H., Chakrabarti S., Xiaoxue L., et al. (2026): A retrospective analysis of 400 publications reveals patterns of irreproducibility across an entire life sciences research field. https://doi.org/10.7554/elife.108403.1
  7. Nejad N., Alvarado-Vargas M., Sepehr M. (2026): Strategic Journal Tier Selection for Literature Reviews: A Bibliometric Analysis Using Topic Modeling and Diversity Metrics. https://doi.org/10.21203/rs.3.rs-8717398/v1
  8. Anikin A. (2025): Can I trust this paper?. Psychonomic Bulletin & Review 32(6):2633-2647. https://doi.org/10.3758/s13423-025-02740-3
  9. Ramalho A., Duarte-Mendes P., Paulo R., Petrica J. (2025): The Tempo Giusto : A Call for Recalibrating Time in Human Movement Research. Quest. https://doi.org/10.1080/00336297.2025.2586504
  10. Marcum C. (2025): Drinking from the firehose? Write more and Publish Less (Version 2). https://doi.org/10.54900/vr8ax-nz653
  11. Marcum C. (2025): Drinking from the firehose? Write more and Publish Less (Version 2). https://doi.org/10.54900/tvby7-zsx87
  12. Dunleavy D. (2025): On the Dearth of Retractions in Social Work: A Cross-Sectional Study of Ten Leading Journals. Metrics 2(3):16. https://doi.org/10.3390/metrics2030016
  13. Dawn N. Albertson, Derek K. Tracy, Dan W. Joyce, Sukhwinder S. Shergill (2025): The Foundations. Research Methods in Mental Health. https://doi.org/10.1017/9781009028967.001
  14. Knudson D. (2025): Properties of Kinesiology-Related Journals According to Cabells Journalytics Not Apparent in Other Journal Metrics. Quest. https://doi.org/10.1080/00336297.2025.2603938
  15. Brown E., Vega Castillo M., Hordósy R. (2025): How ‘International’ Are Sociology Journals? Analysis of Stated Aims and Editorial Board Networks. Sociological Research Online 30(4):922-941. https://doi.org/10.1177/13607804241281455
  16. Tabe H., Nwosu L., Bechuke A. (2025): Higher Educational Transformation and Institutional Policies in the use of Bibliography for Peer Review Journals. E-Journal of Humanities, Arts and Social Sciences. https://doi.org/10.38159/ehass.2025665
  17. Lemaitre J., Popelka D., Ribotta B., Westlake H., Chakrabarti S., Xiaoxue L., et al. (2025): A retrospective analysis of 400 publications reveals patterns of irreproducibility across an entire life sciences research field. https://doi.org/10.1101/2025.07.07.663460
  18. Brauer K. (2025): Estimating the N-pact Factor for the International Journal of Applied Positive Psychology from 2019 To 2024: Reasons To Be Optimistic. International Journal of Applied Positive Psychology 10(3). https://doi.org/10.1007/s41042-025-00241-1
  19. Solberg Söilen K. (2025): The Academic Ecosystem. The Researcher’s Journey. https://doi.org/10.1007/978-3-031-91565-9_3
  20. Maxwell L., Shreedhar P., Krishnan A. (2025): How do we measure the costs, benefits, and harms of sharing data from biomedical studies? A protocol for a scoping review. Open Research Europe 3:151. https://doi.org/10.12688/openreseurope.16063.2
  21. Knöchelmann M. (2025): Formal Authorship in the Wake of Uncertain Futures: The Narrative of Publish or Perish in the Humanities. https://doi.org/10.31235/osf.io/dt2m3_v1
  22. Knöchelmann M., Schendzielorz C. (2025): Writing in the Sciences: Scientists, Scientific Writers, and the Division of Writing Labour. Minerva. https://doi.org/10.1007/s11024-025-09606-x
  23. Miłkowski M., Nowakowski P. (2025): Tracking norms: a plea for a normative digital philosophy of science. Synthese 206(3). https://doi.org/10.1007/s11229-025-05200-6
  24. Scafetta N. (2025): Measuring scholarly performance using comprehensive standardized research-teaching (RT) score. Scientometrics 130(5):2595-2616. https://doi.org/10.1007/s11192-025-05317-y
  25. Bekkers R. (2025): A Modular Approach to Research Quality. https://doi.org/10.59350/x5h3c-54450
  26. Bekkers R. (2025): A Modular Approach to Research Quality. https://doi.org/10.59350/yaj6t-tcx28
  27. Wongvorachan T. (2025): Rethinking Academic Publishing: A Call for Inclusive, Transparent, and Sustainable Reforms. Preprints.org. https://doi.org/10.20944/preprints202505.1897.v1
  28. Zantout Z., Azad A., Gleason K., de Castro V., Smith D. (2025): Has scientific progress in accounting slowed down?. Journal of Contemporary Accounting & Economics 22(1):100531. https://doi.org/10.1016/j.jcae.2025.100531
  29. Gärtner A., Leising D., Schönbrodt F. (2024): Towards responsible research assessment: How to reward research quality. PLOS Biology 22(2):e3002553. https://doi.org/10.1371/journal.pbio.3002553
  30. Allard A., Clavien C. (2024): Teaching epistemic integrity to promote reliable scientific communication. Frontiers in Psychology 15. https://doi.org/10.3389/fpsyg.2024.1308304
  31. Cañibano C., Woolley R., Iversen E., Corona-Sobrino C. (2024): The state-of-the-art of research on science research careers. https://doi.org/10.31235/osf.io/dnxvp
  32. Marcum C. (2024): Drinking from the Firehose? Write More and Publish Less. https://doi.org/10.54900/r8zwg-62003
  33. Hagiopol C., Leru P. (2024): Scientific Truth in a Post-Truth Era: A Review*. Science & Education 34(5):2923-2956. https://doi.org/10.1007/s11191-024-00527-x
  34. Dunleavy D. (2024): On the Dearth of Retractions in Social Work: A Preliminary Analysis of Ten Leading Journals. OSF Preprints. https://doi.org/10.31222/osf.io/y9pvm
  35. Knudson D. (2024): Are There Meaningful Prestige Metrics of Kinesiology-Related Journals?. Measurement in Physical Education and Exercise Science 28(4):316-337. https://doi.org/10.1080/1091367x.2024.2341849
  36. Knudson D. (2024): Scopus Citation Metrics for Top and Bottom Quintile Kinesiology-Related Journals. International Journal of Kinesiology in Higher Education 8(4):343-354. https://doi.org/10.1080/24711616.2024.2416184
  37. McKiernan E., Carter C., Dougherty M., Tananbaum G. (2024): A framework for values-based assessment in promotion, tenure, and other academic evaluations. https://doi.org/10.31219/osf.io/s4vc5
  38. Dames H., Musfeld P., Popov V., Oberauer K., Frischkorn G. (2024): Responsible Research Assessment Should Prioritize Theory Development and Testing Over Ticking Open Science Boxes. Meta-Psychology 8. https://doi.org/10.15626/mp.2023.3735
  39. Ogihara H., Yamamoto N., Kurasawa Y., Kamo T., Hagiyama A., Hayashi S., et al. (2024): Characteristics and Methodological Quality of the Top 50 Most Influential Articles on Stroke Rehabilitation. American Journal of Physical Medicine & Rehabilitation 103(4):363-369. https://doi.org/10.1097/phm.0000000000002412
  40. Koskinen J., Kimppa K., Lahtiranta J., Hyrynsalmi S. (2024): Quantified academics: Heideggerian technology critical analysis of the academic ranking competition. Information Technology & People 37(8):25-42. https://doi.org/10.1108/itp-01-2023-0032
  41. Mališ J., Baloun L. (2024): The replication crisis in science and its overlap with kinanthropology: an introduction (Part 2). Tělesná kultura 47. https://doi.org/10.5507/tk.2024.003
  42. Yates K., Copping J., Tweddle J., O’Leary B. (2024): Benefits and barriers for researcher-practitioner collaboration on marine and coastal management issues. Environmental Science & Policy 155:103713. https://doi.org/10.1016/j.envsci.2024.103713
  43. Andreassen K., Denise Mason L., Chen J. (2024): Engendering ethics: recognition and inclusion of intersectional identities in queer communities when conducting population survey research. Continuum 38(3):292-306. https://doi.org/10.1080/10304312.2024.2338478
  44. Kulikowski K., Przytuła S., Sułkowski Ł. (2024): ‘Homo Metricus’: The New Academic Worker. How Quantitative Research Evaluation Practices Reshape the Intellectual Capital Needed to Succeed in Contemporary Universities?. Higher Education Policy. https://doi.org/10.1057/s41307-024-00383-y
  45. Balafoutas L., Celse J., Karakostas A., Umashev N. (2024): Incentives and the replication crisis in social sciences: A critical review of open science practices. Journal of Behavioral and Experimental Economics 114:102327. https://doi.org/10.1016/j.socec.2024.102327
  46. Sallam M. (2024): Bibliometric top ten healthcare-related ChatGPT publications in the first ChatGPT anniversary. Narra J 4(2):e917. https://doi.org/10.52225/narra.v4i2.917
  47. Sallam M. (2024): Bibliometric Top Ten Healthcare-Related ChatGPT Publications in the First ChatGPT Anniversary. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-4241528/v1
  48. Knöchelmann M. (2024): Formal authorship in the wake of uncertain futures: the narrative of publish or perish in the humanities. Research Evaluation 33. https://doi.org/10.1093/reseval/rvae044
  49. Knöchelmann M. (2024): Science in Formation: The Indexed Scientist and the Inaccessibility of Scientific Information. https://doi.org/10.31235/osf.io/xwpq6
  50. Rabbani M. (2024): Dollars and megabits: A comparative analysis of Telecom and Healthcare Connect Fund. Information Economics and Policy 67:101082. https://doi.org/10.1016/j.infoecopol.2024.101082
  51. Behera P., Jain S., Kumar A. (2024): Examining retraction counts to evaluate journal quality in psychology. Current Psychology 43(26):22436-22443. https://doi.org/10.1007/s12144-024-06044-y
  52. Chen X., Liu Z. (2024): Field‐specific gold open access dynamics in the Chinese mainland: Overviews, disparities, and strategic insights. Learned Publishing 37(4). https://doi.org/10.1002/leap.1630
  53. Höller Y., Urbschat M., Bathke A. (2024): Sustainable scientific publishing: a pilot survey on stakeholder motivations and opinions. Discover Sustainability 5(1). https://doi.org/10.1007/s43621-023-00175-1
  54. Unknown authors (2023): Praise Page. Distrust. https://doi.org/10.1093/oso/9780192868459.002.0001
  55. Unknown authors (2023): Copyright Page. Distrust. https://doi.org/10.1093/oso/9780192868459.002.0004
  56. Ahmed A., Al-Khatib A., Boum Y., Debat H., Gurmendi Dunkelberg A., Hinchliffe L., et al. (2023): The future of academic publishing. Nature Human Behaviour 7(7):1021-1026. https://doi.org/10.1038/s41562-023-01637-2
  57. Rovetta A., Castaldo L. (2023): An Epistemological and Infodemiological Framework for Health-Communication During COVID-19 and Future Crises (Preprint). https://doi.org/10.2196/preprints.49200
  58. Heidenreich A., Eisemann N., Katalinic A., Hübner J. (2023): Study results from journals with a higher impact factor are closer to “truth”: a meta-epidemiological study. Systematic Reviews 12(1). https://doi.org/10.1186/s13643-023-02167-8
  59. Gorman D. (2023): COVID-19 publications in top-ranked public health journals during the first phase of the pandemic. Quantitative Science Studies 4(2):535-546. https://doi.org/10.1162/qss_a_00257
  60. Rodrigues E. (2023): A Necessária e Difícil Reforma da Avaliação da Investigação. Políticas de Ciência e da Língua, Publicação Científica e Rankings Académicos. https://doi.org/10.21814/uminho.ed.66.9
  61. Henderson E., Darby R., Farran E. (2023): The Responsible Research(er) Recruitment Checklist: A best practice guide for applying principles of responsible research assessment in researcher recruitment materials. https://doi.org/10.31219/osf.io/2kgny
  62. Smith G. (2023): Distrust. https://doi.org/10.1093/oso/9780192868459.001.0001
  63. Smith G. (2023): The Paranormal Is Normal. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0002
  64. Smith G. (2023): A Post-Fact World. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0005
  65. Smith G. (2023): Looking for Needles in Haystacks. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0009
  66. Smith G. (2023): Squeezing Blood from Rocks. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0006
  67. Smith G. (2023): Beat the Market. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0010
  68. Smith G. (2023): Provocative, but Wrong. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0008
  69. Smith G. (2023): Irreproducible Research. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0014
  70. Smith G. (2023): Too Much Data. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0011
  71. Smith G. (2023): Restoring the Luster of Science. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0016
  72. Smith G. (2023): Flying Saucers and Space Tourists. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0003
  73. Smith G. (2023): Overpromising and Underdelivering. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0012
  74. Smith G. (2023): Elite Conspiracies. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0004
  75. Smith G. (2023): Most Medicines Disappoint. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0007
  76. Smith G. (2023): Introduction. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0001
  77. Smith G. (2023): The Replication Crisis. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0015
  78. Smith G. (2023): Artificial Unintelligence. Distrust. https://doi.org/10.1093/oso/9780192868459.003.0013
  79. Sjuls G., Vulchanova M., Assaneo M. (2023): Replication of population-level differences in auditory-motor synchronization ability in a Norwegian-speaking population. Communications Psychology 1(1). https://doi.org/10.1038/s44271-023-00049-2
  80. Hiraba H., Takeuchi Y., Nishio K., Koizumi H., Yoneyama T., Matsumura H. (2023): Current status of dental journals published by Japanese organization. Japanese Dental Science Review 60:40-43. https://doi.org/10.1016/j.jdsr.2023.12.001
  81. Jacques Balthazart, A Bhargava, A Arnold, D Bangasser, K Denton, A Gupta, et al. (2023): References. Fifty Years of Evolution in Biological Research. https://doi.org/10.1002/9781394236633.refs
  82. Dora J., Piccirillo M., Foster K., King K. (2023): Accelerating addiction research via Open Science and Team Science. https://doi.org/10.31234/osf.io/pbkrx
  83. Dora J., Piccirillo M., Foster K., King K. (2023): Accelerating addiction research via Open Science and Team Science. Psychology of Learning and Motivation. https://doi.org/10.1016/bs.plm.2023.06.004
  84. Bottesini J., Aschwanden C., Rhemtulla M., Vazire S. (2023): How Do Science Journalists Evaluate Psychology Research?. Advances in Methods and Practices in Psychological Science 6(3). https://doi.org/10.1177/25152459231183912
  85. Beiter K. (2023): Access to scholarly publications in the global North and the global South—Copyright and the need for a paradigm shift under the right to science. Frontiers in Sociology 8. https://doi.org/10.3389/fsoc.2023.1277292
  86. Mhlongo L. (2023): Prospects and Challenges of Extracting Scientific Publications from a Doctoral Thesis in International Economic Law: An Academic Excursion. South African Yearbook of International Law. https://doi.org/10.25159/2521-2583/15123
  87. Dzhukaeva M., Muslimova M., Mamedova G. (2023): Digital Technology and Practices of Humanities Research. SHS Web of Conferences 172:05001. https://doi.org/10.1051/shsconf/202317205001
  88. Jeyaraman M., Selvaraj P., Vaish A., Iyengar K., Vaishya R. (2023): Journal metrics of the top-ranked Orthopaedic, Medical, and Surgical journals – A cross-sectional, comparative study. International Orthopaedics 48(2):357-364. https://doi.org/10.1007/s00264-023-06010-6
  89. Sallam M. (2023): Bibliometric Top Ten Healthcare Related ChatGPT Publications in Scopus, Web of Science, and Google Scholar in the First ChatGPT Anniversary (Preprint). https://doi.org/10.2196/preprints.55085
  90. Rabbani M. (2023): Dollars and megabits: a comparative analysis of Telecom and Healthcare Connect Fund. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4372621
  91. Wood M. (2023): Beyond Journals and Peer Review: Towards a More Flexible Ecosystem For Scholarly Communication. https://doi.org/10.32388/swkkoc
  92. PENG M. (2023): Changes in Scholars’ Ways of Knowledge Production Shaped by Systematic Measures. Gaziantep University Journal of Social Sciences 22(1):30-45. https://doi.org/10.21547/jss.1209134
  93. Martins M., Pires H. (2023): Políticas de Ciência e da Língua, Publicação Científica e Rankings Académicos. UMinho Editora/CECS eBooks. https://doi.org/10.21814/uminho.ed.66
  94. Shomoossi N., Fiezabadi M., Vaziri E., Shabani E., Amiri M. (2023): International aspects in healthcare and medical education: Scientometric trends and knowledge maps before the COVID-19 pandemic. Journal of Education and Health Promotion 12(1). https://doi.org/10.4103/jehp.jehp_53_23
  95. Giannos P., Katsikas Triantafyllidis K., Paraskevaidi M., Kyrgiou M., Kechagias K. (2023): Female Dynamics in Authorship of Scientific Publications in the Public Library of Science: A 10-year Bibliometric Analysis of Biomedical Research. European Journal of Investigation in Health, Psychology and Education 13(2):228-237. https://doi.org/10.3390/ejihpe13020018
  96. Galbraith Q., Carlile Butterfield A., Cardon C. (2023): Judging Journals: How Impact Factor and Other Metrics Differ across Disciplines. College & Research Libraries 84(6). https://doi.org/10.5860/crl.84.6.888
  97. Dawson S. (2023): An Open Access Strategy for the Drug Repurposing Community. https://doi.org/10.14293/s2199-1006.1.sor-.ppxiw81.v1
  98. Dawson S. (2023): An Open Access Strategy for the Drug Repurposing Community. https://doi.org/10.58647/drugarxiv.pr000001.v1
  99. Setrojoyo S., Sutrisno, Ng S., Darmo I., Astuti N. (2023): Differences in Perceptions Between Small Businesses and Large Businesses on the Effectiveness of HR Management in Achieving Business Goals: Based on Field Evidence. International Journal of Professional Business Review 8(6):e02285. https://doi.org/10.26668/businessreview/2023.v8i6.2285
  100. Ortner T., Kretzschmar A., Rauthmann J., Tibubos A. (2023): Fachgruppe Differentielle Psychologie, Persönlichkeitspsychologie und psychologische Diagnostik. Psychologische Rundschau 74(3):182-184. https://doi.org/10.1026/0033-3042/a000638
  101. Unknown authors (2022): Archives, Access and Artificial Intelligence. Bielefeld University Press eBooks. https://doi.org/10.1515/9783839455845
  102. Aarts A. (2022): Making the Most of Tenure in Two Acts: An Additional Way to Help Change Incentives in Psychological Science?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4176916
  103. Aarts A. (2022): The Natural Selection of Bad Psychological Scientists. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4176925
  104. Gärtner A., Leising D., Freyer N., Musfeld P., Lange J., Schönbrodt F. (2022): Responsible Research Assessment II: A specific proposal for hiring and promotion in psychology. https://doi.org/10.31234/osf.io/5yexm
  105. Costa A., Martins S., Pintassilgo S., Nunes N., Carvalho H. (2022): Publicação científica e ciências sociais: 100 números da revista Sociologia, Problemas e Práticas. Sociologia, Problemas e Práticas. https://doi.org/10.7458/spp202210028003
  106. Brand C. (2022): An outdated publishing system threatens both research integrity and the retention of rigorous early career researchers. OSF Preprints. https://doi.org/10.31222/osf.io/nr3vt
  107. Berner D., Amrhein V. (2022): Why and how we should join the shift from significance testing to estimation. Journal of Evolutionary Biology 35(6):777-787. https://doi.org/10.1111/jeb.14009
  108. Berner D., Amrhein V. (2022): Why and How We Should Join the Shift From Significance Testing to Estimation. Preprints.org. https://doi.org/10.20944/preprints202112.0235.v2
  109. Dunleavy D. (2022): Progressive and degenerative journals: on the growth and appraisal of knowledge in scholarly publishing. European Journal for Philosophy of Science 12(4). https://doi.org/10.1007/s13194-022-00492-8
  110. Dunleavy D. (2022): Progressive and Degenerative Journals: On the Growth and Appraisal of Knowledge in Scholarly Publishing. OSF Preprints. https://doi.org/10.31222/osf.io/yskhj
  111. Leising D., Thielmann I., Glöckner A., Gärtner A., Schönbrodt F. (2022): Ten steps toward a better personality science – how quality may be rewarded more in research evaluation. Personality Science 3. https://doi.org/10.5964/ps.6029
  112. Gorman D., Huber C. (2022): Ranking of addiction journals in eight widely used impact metrics. Journal of Behavioral Addictions 11(2):348-360. https://doi.org/10.1556/2006.2022.00020
  113. Villalobos D., Povedano-Montero J., Fernández S., López-Muñoz F., Pacios J., del Río D. (2022): Scientific research on verbal fluency tests: A bibliometric analysis. Journal of Neurolinguistics 63:101082. https://doi.org/10.1016/j.jneuroling.2022.101082
  114. Schönbrodt F., Gärtner A., Frank M., Gollwitzer M., Ihle M., Mischkowski D., et al. (2022): Responsible Research Assessment I: Implementing DORA and CoARA for hiring and promotion in psychology. https://doi.org/10.31234/osf.io/rgh5b
  115. Zhang J., Jin B., Sha J., Chen Y., Zhang Y. (2022): SentenceLDA- and ConNetClus-Based Heterogeneous Academic Network Analysis for Publication Ranking. Algorithms 15(5):159. https://doi.org/10.3390/a15050159
  116. Yeh J., Shulruf B., Lee H., Huang P., Kuo W., Hwang T., et al. (2022): Faculty appointment and promotion in Taiwan’s medical schools, a systematic analysis. BMC Medical Education 22(1). https://doi.org/10.1186/s12909-022-03435-2
  117. Bottesini J., Aschwanden C., Rhemtulla M., Vazire S. (2022): How Do Science Journalists Evaluate Psychology Research?. https://doi.org/10.31234/osf.io/26kr3
  118. Wenaas L. (2022): Choices of immediate open access and the relationship to journal ranking and publish-and-read deals. Frontiers in Research Metrics and Analytics 7. https://doi.org/10.3389/frma.2022.943932
  119. Stoy L. (2022): Future pathways of a sector in transition. Septentrio Conference Series. https://doi.org/10.7557/5.6590
  120. Lise Jaillant (1384974) (2022): Archives, Access and Artificial Intelligence. Digital Humanities Research. https://doi.org/10.14361/9783839455845
  121. Knöchelmann M., Schendzielorz C. (2022): Writing in the Sciences: Scientists as Writers, Scientific Writing, and the Persuasive Story. https://doi.org/10.31235/osf.io/fmcsp
  122. Maria Imaculada Cardoso Sampaio (2022): Produção Científica: um Guia Prático. Universidade de São Paulo. Instituto de Psicologia eBooks. https://doi.org/10.11606/9786587596280
  123. Kaguhangire-Barifaijo M., Kyohairwe S., Komakech R. (2022): Academics’ Enthusiasm for Scholarly Research Engagement: Perspectives on Selected Universities in Uganda. Open Journal of Social Sciences 10(13):284-305. https://doi.org/10.4236/jss.2022.1013023
  124. Aly M., Colunga E., Crockett M., Goldrick M., Gomez P., Kung F., et al. (2022): Changing the Culture of Peer Review for a More Inclusive and Equitable Psychological Science. https://doi.org/10.31234/osf.io/435xz
  125. Héroux M., Butler A., Cashin A., McCaughey E., Affleck A., Green M., et al. (2022): Quality Output Checklist and Content Assessment (QuOCCA): a new tool for assessing research quality and reproducibility. BMJ Open 12(9):e060976. https://doi.org/10.1136/bmjopen-2022-060976
  126. Eve M., Gadie R., Odeniyi V., Parvin S. (2022): Chapter 5: Reviewing the Reviewers: Training Neural Networks to Read Peer Review Reports. Archives, Access and Artificial Intelligence. https://doi.org/10.1515/9783839455845-006
  127. Reichel M., Greer A., Nielsen M., de Waal T. (2022): How to publish a great scientific paper – A guide for publishing successfully in Veterinary Parasitology. Veterinary Parasitology 304:109697. https://doi.org/10.1016/j.vetpar.2022.109697
  128. Dougherty M., Horne Z. (2022): Citation counts and journal impact factors do not capture some indicators of research quality in the behavioural and brain sciences. Royal Society Open Science 9(8). https://doi.org/10.1098/rsos.220334
  129. Hosseini M., Senabre Hidalgo E., Horbach S., Güttinger S., Penders B. (2022): Messing with Merton: The intersection between open science practices and Mertonian values. Accountability in Research 31(5):428-455. https://doi.org/10.1080/08989621.2022.2141625
  130. Aubert Bonn N., De Vries R., Pinxten W. (2022): The failure of success: four lessons learned in five years of research on research integrity and research assessments. BMC Research Notes 15(1). https://doi.org/10.1186/s13104-022-06191-0
  131. Ellis R. (2022): Questionable Research Practices, Low Statistical Power, and Other Obstacles to Replicability: Why Preclinical Neuroscience Research Would Benefit from Registered Reports. eneuro 9(4):ENEURO.0017-22.2022. https://doi.org/10.1523/eneuro.0017-22.2022
  132. Eagle S., Okonkwo D. (2022): Response to Daneshvar and Rovito, “ Letter to the Editor: Telling the Whole Story: Articles Linking Chronic Traumatic Encephalopathy and Repetitive Head Impacts Have Higher Journal Impact Factors” (DOI: 10.1089/neu.2022.0374). Journal of Neurotrauma 40(3-4):413-413. https://doi.org/10.1089/neu.2022.0395
  133. Moskovkin V., Saprykina T., Boichuk I. (2022): Transformative agreements in the development of open access. Journal of Electronic Resources Librarianship 34(3):165-207. https://doi.org/10.1080/1941126x.2022.2099000
  134. Serenko A., Bontis N. (2021): Global ranking of knowledge management and intellectual capital academic journals: a 2021 update. Journal of Knowledge Management 26(1):126-145. https://doi.org/10.1108/jkm-11-2020-0814
  135. Heidenreich A., Eisemann N., Katalinic A., Huebner J. (2021): The Association Between The Impact Factor of A Journal And The Trueness of Findings Published Therein: A Meta-Epidemiological Study. https://doi.org/10.21203/rs.3.rs-861273/v1
  136. D’Ippoliti C. (2021): “MANY‐CITEDNESS”: CITATIONS MEASURE MORE THAN JUST SCIENTIFIC QUALITY. Journal of Economic Surveys 35(5):1271-1301. https://doi.org/10.1111/joes.12416
  137. Chambers C., Tzavella L. (2021): The past, present and future of Registered Reports. Nature Human Behaviour 6(1):29-42. https://doi.org/10.1038/s41562-021-01193-7
  138. Triggle C., MacDonald R., Triggle D., Grierson D. (2021): Requiem for impact factors and high publication charges. Accountability in Research 29(3):133-164. https://doi.org/10.1080/08989621.2021.1909481
  139. Clarice de Medeiros Chaves Ferreira, Rafaela Ferreira Guatimosim, Ana Luiza Silva Teles (2021): Medidas Cienciométricas: o que são, para que servem, e para que não servem?.
  140. Berner D., Amrhein V. (2021): Why and How We Should Join the Shift From Significance Testing to Estimation. Preprints.org. https://doi.org/10.20944/preprints202112.0235.v1
  141. Rowbottom D. (2021): Peer Review May Not Be Such a Bad Idea: Response to Heesen and Bright. The British Journal for the Philosophy of Science 73(4):927-940. https://doi.org/10.1086/714787
  142. Morales E., McKiernan E., Niles M., Schimanski L., Alperin J. (2021): How faculty define quality, prestige, and impact of academic journals. PLOS ONE 16(10):e0257340. https://doi.org/10.1371/journal.pone.0257340
  143. Morales E., McKiernan E., Niles M., Schimanski L., Alperin J. (2021): How faculty define quality, prestige, and impact in research. https://doi.org/10.1101/2021.04.14.439880
  144. Piron F., Olyhoek T., Vilchis I., Smith I., Liré Z. (2021): Saying ‘No’ to Rankings and Metrics. Socially Responsible Higher Education. https://doi.org/10.1163/9789004459076_007
  145. López-Muñoz F., Weinreb R., Moghimi S., Povedano-Montero F. (2021): A Bibliometric and Mapping Analysis of Glaucoma Research between 1900 and 2019. Ophthalmology Glaucoma 5(1):16-25. https://doi.org/10.1016/j.ogla.2021.05.008
  146. Lopez-Munoz F., Eremchenko O., Fernandez-Lopez M., Rodriguez-Sanchez B., Povedano-Montero F. (2021): International Scientific Research on Venture Capital: a Bibliometric and Mapping Analysis from the Period 1978–2020. Economics of Science 7(1):66-84. https://doi.org/10.22394/2410-132x-2021-7-1-66-84
  147. Génova G., Astudillo H., Fraga A. (2021): La burbuja de publicaciones científicas alimenta la infodemia. https://doi.org/10.64628/aao.wqxr97aqj
  148. J Adema, B Schmidt, E Aguado-Lpez, A Becerril-Garca, D Aksnes, L Langfeldt, et al. (2021): Opening the record of science: making scholarly publishing work for science in the digital era. https://doi.org/10.24948/2021.01
  149. Manzano-Patrón J., López-Neira I., Izquierdo P. (2021): Open Science in Spain: Towards a Coordinated Strategy. Journal of Science Policy & Governance 18(01). https://doi.org/10.38126/jspg180108
  150. Teixeira da Silva J. (2021): The Matthew effect impacts science and academic publishing by preferentially amplifying citations, metrics and status. Scientometrics 126(6):5373-5377. https://doi.org/10.1007/s11192-021-03967-2
  151. Teixeira da Silva J., Dunleavy D., Moradzadeh M., Eykens J. (2021): A credit-like rating system to determine the legitimacy of scientific journals and publishers. Scientometrics 126(10):8589-8616. https://doi.org/10.1007/s11192-021-04118-3
  152. Teixeira da Silva J. (2021): The i100-index, i1000-index and i10,000-index: expansion and fortification of the Google Scholar h-index for finer-scale citation descriptions and researcher classification. Scientometrics 126(4):3667-3672. https://doi.org/10.1007/s11192-020-03831-9
  153. Bragg K., Marchand G., Hilpert J., Cummings J. (2021): Using bibliometrics to evaluate outcomes and influence of translational biomedical research centers. Journal of Clinical and Translational Science 6(1). https://doi.org/10.1017/cts.2021.863
  154. Tiokhin L., Panchanathan K., Lakens D., Vazire S., Morgan T., Zollman K. (2021): Honest signaling in academic publishing. PLOS ONE 16(2):e0246675. https://doi.org/10.1371/journal.pone.0246675
  155. Marcel Knöchelmann (2021): The Democratisation Myth. Science & Technology Studies 34(2):65-89. https://doi.org/10.23987/sts.94964
  156. Knöchelmann M. (2021): Systemimmanenz und Transformation: Die Bibliothek der Zukunft als lokale Verwalterin?. Bibliothek Forschung und Praxis 45(1):151-162. https://doi.org/10.1515/bfp-2020-0101
  157. Allen M., Iliescu D. (2021): Impact Factor Wars. European Journal of Psychological Assessment 37(5):341-343. https://doi.org/10.1027/1015-5759/a000679
  158. Lizotte M. (2021): If you do not deign to quantify, someone else will do it for you: In support of a balanced approach to the evaluation of science. Social Science Information 60(3):363-371. https://doi.org/10.1177/05390184211021364
  159. Orhan M. (2021): Dynamic interactionism between research fraud and research culture: a commentary to Harvey’s analysis. Quality in Higher Education 27(1):134-146. https://doi.org/10.1080/13538322.2021.1857900
  160. Vergara-Fernández M. (2021): The Journal Impact Factor Might Be Useful But, for What, Precisely?. OEconomia 11-3:473-484. https://doi.org/10.4000/oeconomia.11593
  161. Wood M. (2021): Beyond Journals and Peer Review: Towards a More Flexible Ecosystem for Scholarly Communication. Preprints.org. https://doi.org/10.20944/preprints202012.0612.v2
  162. Banasik-Jemielniak N., Jemielniak D., Wilamowski M. (2021): Psychology and Wikipedia: Measuring Psychology Journals’ Impact by Wikipedia Citations. Social Science Computer Review 40(3):756-774. https://doi.org/10.1177/0894439321993836
  163. Raja N., Dunne E. (2021): Publication pressure threatens the integrity of palaeontological research. https://doi.org/10.31223/x5v32z
  164. Amaral O., Neves K. (2021): Reproducibility: expect less of the scientific paper. Nature 597(7876):329-331. https://doi.org/10.1038/d41586-021-02486-7
  165. Palavalli-Nettimi R. (2021): Toward a Sustainable Model of Scientific Publishing. Journal of Science Policy & Governance 18(01). https://doi.org/10.38126/jspg180111
  166. Dudley R. (2021): The Changing Landscape of Open Access Publishing: Can Open Access Publishing Make the Scholarly World More Equitable and Productive?. Journal of Librarianship and Scholarly Communication 9(1). https://doi.org/10.7710/2162-3309.2345
  167. Hilbert S., Coors S., Kraus E., Bischl B., Lindl A., Frei M., et al. (2021): Machine learning for the educational sciences. Review of Education 9(3). https://doi.org/10.1002/rev3.3310
  168. Hilbert S., Coors S., Kraus E., Bischl B., Frei M., Lindl A., et al. (2021): Machine Learning for the Educational Sciences. https://doi.org/10.31234/osf.io/3hnr6
  169. Larsson Å. (2021): Science and Prehistory: Are we Mature enough to Handle It?. Current Swedish Archaeology 21(1):27-33. https://doi.org/10.37718/csa.2013.03
  170. van der Weel A., Praal F. (2020): 2. Publishing in the Digital Humanities. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.02
  171. Hernández A. (2020): Taming the Big Green Elephant. Globale Gesellschaft und internationale Beziehungen. https://doi.org/10.1007/978-3-658-31821-5
  172. Smedsrød B., Longva L. (2020): The costly prestige ranking of scholarly journals. Ravnetrykk. https://doi.org/10.7557/15.5507
  173. Leachman C., Anderson T. (2020): Publishing Behavior of Engineering Faculty. 2020 ASEE Virtual Annual Conference Content Access Proceedings. https://doi.org/10.18260/1-2–35110
  174. Chambers C., Tzavella L. (2020): The past, present, and future of Registered Reports. OSF Preprints. https://doi.org/10.31222/osf.io/43298
  175. Warwick C., Bailey-Ross C. (2020): 4. The Impact of Digital Resources. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.04
  176. Leising D., Thielmann I., Glöckner A., Gärtner A., Schönbrodt F. (2020): Ten steps toward a better personality science – how quality may be rewarded more in research evaluation. https://doi.org/10.31234/osf.io/5zvmh
  177. Leising D., Thielmann I., Glöckner A., Gärtner A., Schönbrodt F. (2020): Ten steps toward a better personality science – how quality may be rewarded more in research evaluation. https://doi.org/10.31234/osf.io/6btc3
  178. O’Donnell D. (2020): 8. Critical Mass. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.08
  179. Costello E., Farrelly T., Murphy T. (2020): Open and Shut: Open Access in Hybrid Educational Technology Journals 2010 – 2017. The International Review of Research in Open and Distributed Learning 21(1):112-133. https://doi.org/10.19173/irrodl.v20i5.4383
  180. ElSabry E., Sumikura K. (2020): Does open access to academic research help small, science-based companies?. Journal of Industry-University Collaboration 2(3):95-109. https://doi.org/10.1108/jiuc-04-2020-0004
  181. Rodrigues E. (2020): A pandemia e a emergência da Ciência Aberta. A Universidade do Minho em tempos de pandemia. https://doi.org/10.21814/uminho.ed.24.12
  182. Tóth-Czifra E. (2020): 10. The Risk of Losing the Thick Description. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.10
  183. Büttner F., Toomey E., McClean S., Roe M., Delahunt E. (2020): Are questionable research practices facilitating new discoveries in sport and exercise medicine? The proportion of supported hypotheses is implausibly high. British Journal of Sports Medicine 54(22):1365-1371. https://doi.org/10.1136/bjsports-2019-101863
  184. Azevedo F. (2020): Not So Simple: Science is in the Details. Psychological Inquiry 31(1):61-65. https://doi.org/10.1080/1047840x.2020.1722001
  185. Da Costa G., Alves C., Luizeti B. (2020): Os Princípios de Hong Kong e sua importância para o ecossistema científico atual. Journal of Evidence-Based Healthcare 2(2):159-166. https://doi.org/10.17267/2675-021xevidence.v2i2.3247
  186. Fraumann G., D’Souza J., Holmberg K. (2020): 4.7 Eigenfactor. Handbook Bibliometrics. https://doi.org/10.1515/9783110646610-025
  187. Balthazart J. (2020): How technical progress reshaped behavioral neuroendocrinology during the last 50 years… and some methodological remarks. Hormones and Behavior 118:104682. https://doi.org/10.1016/j.yhbeh.2020.104682
  188. Edmond J., Tasovac T., Fischer F., Romary L. (2020): 9. Springing the Floor for a Different Kind of Dance. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.09
  189. Edmond J., Romary L. (2020): 3. Academic Publishing. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.03
  190. Edmond J. (2020): 1. Introduction. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.01
  191. Tennant J., Agarwal R., Baždarić K., Brassard D., Crick T., Dunleavy D., et al. (2020): A tale of two ‘opens’: intersections between Free and Open Source Software and Open Scholarship. https://doi.org/10.31235/osf.io/2kxq8
  192. Tennant J., Wien C. (2020): Fixing the crisis state of scientific evaluation. https://doi.org/10.31235/osf.io/f4zk9
  193. Tennant J. (2020): How open science is fighting against private, proprietary publishing platforms. https://doi.org/10.31235/osf.io/wq4x8
  194. Van Zundert J., Antonijević S., Andrews T. (2020): 6. ‘Black Boxes’ and True Colour — A Rhetoric of Scholarly Code. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.06
  195. Mwelwa J., Boulton G., Wafula J., Loucoubar C. (2020): Developing Open Science in Africa: Barriers, Solutions and Opportunities. Data Science Journal 19(1):31. https://doi.org/10.5334/dsj-2020-031
  196. Nyhan J. (2020): 7. The Evaluation and Peer Review of Digital Scholarship in the Humanities. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.07
  197. Moustafa K. (2020): Reforming science publishing. Learned Publishing 33(4):437-440. https://doi.org/10.1002/leap.1315
  198. Moustafa K. (2020): Reforming Science Publishing. https://doi.org/10.31221/osf.io/mfhx7
  199. Kulikowski K., Antipow E. (2020): Niezamierzone konsekwencje punktozy jako wartości kulturowej polskiej społeczności akademickiej. Studia Socjologiczne. https://doi.org/10.24425/sts.2020.132476
  200. Louis, Christine (2020): . Western Journal of Emergency Medicine 21(4). https://doi.org/10.5811/westjem.2020.7.48908
  201. Barnsbee L. (2020): The capacity of health services researchers to engage with research impact. https://doi.org/10.5204/thesis.eprints.180823
  202. Martins M., Rodrigues E. (2020): A Universidade do Minho em tempos de pandemia: Tomo II: Re(Ações). A Universidade do Minho em tempos de pandemia. https://doi.org/10.21814/uminho.ed.24
  203. Knöchelmann M. (2020): The Democratisation Myth: Open Access and the Solidification of Epistemic Injustices. https://doi.org/10.31235/osf.io/hw7at
  204. Seeber M. (2020): How do journals of different rank instruct peer reviewers? Reviewer guidelines in the field of management. Scientometrics 122(3):1387-1405. https://doi.org/10.1007/s11192-019-03343-1
  205. Eve M. (2020): 5. Violins in the Subway. Digital Technology and the Practices of Humanities Research. https://doi.org/10.11647/obp.0192.05
  206. Eve M., Neylon C., O’Donnell D., Moore S., Gadie R., Odeniyi V., et al. (2020): Reading Peer Review. Cambridge University Press eBooks. https://doi.org/10.1017/9781108783521
  207. Ertaş M., Kozak M. (2020): Publish or perish: The proportion of articles versus additional sections in tourism and hospitality journals. Journal of Hospitality and Tourism Management 43:149-156. https://doi.org/10.1016/j.jhtm.2020.03.001
  208. Orhan M. (2020): Pardon my French: On superfluous journal rankings, incentives, and impacts on industrial-organizational psychology publication practices in French business schools. Industrial and Organizational Psychology 13(3):295-306. https://doi.org/10.1017/iop.2020.59
  209. Niles M., Schimanski L., McKiernan E., Alperin J. (2020): Why we publish where we do: Faculty publishing values and their relationship to review, promotion and tenure expectations. PLOS ONE 15(3):e0228914. https://doi.org/10.1371/journal.pone.0228914
  210. Charnine M., Khakimova A., Klokov A. (2020): Impact Factor of a Term: a Tool for Assessing Article’s Future Citations and Author’s Influence Based on PubMed and DBLP Collections. Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2. https://doi.org/10.51130/graphicon-2020-2-3-74
  211. Wood M. (2020): Beyond Journals and Peer Review: Towards a More Flexible Ecosystem for Scholarly Communication. Preprints.org. https://doi.org/10.20944/preprints202012.0612.v1
  212. Braganza O. (2020): A simple model suggesting economically rational sample-size choice drives irreproducibility. PLOS ONE 15(3):e0229615. https://doi.org/10.1371/journal.pone.0229615
  213. Hanel P. (2020): Conducting High Impact Research With Limited Financial Resources (While Working from Home). Meta-Psychology 4. https://doi.org/10.15626/mp.2020.2560
  214. Paul Kudlow (2020): Increasing the Reach, Usage, and Impact of Scholarly Content. TSpace.
  215. Vuong Q., La V., Ho M., Vuong T., Ho M. (2020): Characteristics of retracted articles based on retraction data from online sources through February 2019. Science Editing 7(1):34-44. https://doi.org/10.6087/kcse.187
  216. Vuong Q., La V., Ho T., Vuong T., Ho M. (2020): Characteristics of retracted articles based on retraction data from online sources through February 2019. https://doi.org/10.31219/osf.io/njsy8
  217. Hunter Jr. R., Shannon J. (2020): A Primer on the Role of the University’s Attorney. Education Quarterly Reviews 3(1). https://doi.org/10.31014/aior.1993.03.01.113
  218. Rodriguez R., Chan V., Wong A., Montoy J. (2020): A Review of Journal Impact Metrics and Characteristics to Assist Emergency Medicine Investigators with Manuscript Submission Decisions. Western Journal of Emergency Medicine 21(4). https://doi.org/10.5811/westjem.2020.4.47030
  219. Gray R. (2020): Sorry, we’re open: Golden open-access and inequality in non-human biological sciences. Scientometrics 124(2):1663-1675. https://doi.org/10.1007/s11192-020-03540-3
  220. Gray R. (2020): Sorry, we’re open: Golden Open Access and inequality in the natural sciences. https://doi.org/10.1101/2020.03.12.988493
  221. Sedghi S., Razmgir M., Moradzadeh M. (2020): Contribution of Iranian scholars to medical sciences: A holistic overview of 140-years publication. Medical Journal of The Islamic Republic of Iran. https://doi.org/10.47176/mjiri.34.158
  222. Beck S., Bergenholtz C., Bogers M., Brasseur T., Conradsen M., Di Marco D., et al. (2020): The Open Innovation in Science research field: a collaborative conceptualisation approach. Industry and Innovation 29(2):136-185. https://doi.org/10.1080/13662716.2020.1792274
  223. Huffman A. (2019): Climate Change and the Emergency Department. Annals of Emergency Medicine 73(5):A19-A22. https://doi.org/10.1016/j.annemergmed.2019.03.014
  224. Griffiths A., Modinou I., Heslop C., Brand C., Weatherill A., Baker K., et al. (2019): AccessLab: Workshops to broaden access to scientific research. PLOS Biology 17(5):e3000258. https://doi.org/10.1371/journal.pbio.3000258
  225. Fombuena A. (2019): Evaluación de la transferencia de conocimiento e innovación de las universidades españolas. Revista Española de Documentación Científica 42(3):e240. https://doi.org/10.3989/redc.2019.3.1596
  226. Balaji B., Dhanamjaya M. (2019): Preprints in Scholarly Communication: Re-Imagining Metrics and Infrastructures. Publications 7(1):6. https://doi.org/10.3390/publications7010006
  227. Brembs B. (2019): Reliable novelty: New should not trump true. PLOS Biology 17(2):e3000117. https://doi.org/10.1371/journal.pbio.3000117
  228. Greenhow C., Gleason B., Staudt Willet K. (2019): Social scholarship revisited: Changing scholarly practices in the age of social media. British Journal of Educational Technology 50(3):987-1004. https://doi.org/10.1111/bjet.12772
  229. Claudia G., Achim S., Oliver K., Benjamin M., Thomas W., Thomas B., et al. (2019): Welche Beiträge können strukturierte Promotionsprogramme zur Qualitätssicherung medizinischer Promotionen und wissenschaftlichen Karriereförderung/ Nachwuchsförderung leisten? Eine Evaluation am Beispiel der Programminitiative „Experimentelle Medizin“ der Universität Ulm. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen 147-148:110-119. https://doi.org/10.1016/j.zefq.2019.10.001
  230. Kulczycki E., Rozkosz E., Drabek A. (2019): Internationalization of Polish Journals in the Social Sciences and Humanities: Transformative Role of The Research Evaluation System. Canadian Journal of Sociology 44(1):9-38. https://doi.org/10.29173/cjs28794
  231. Kulczycki E. (2019): Książkowe publikacje naukowe w europejskich systemach ewaluacji nauki. Nauka. https://doi.org/10.24425/nauka.2019.126190
  232. Kulczycki E., Rozkosz E., Drabek A. (2019): Umiędzynarodowienie polskich czasopism w naukach społecznych i humanistycznych – transformacyjna rola systemu ewaluacji nauki. Nauka i Szkolnictwo Wyższe. https://doi.org/10.14746/nisw.2019.1-2.11
  233. McKiernan E., Schimanski L., Muñoz Nieves C., Matthias L., Niles M., Alperin J. (2019): Use of the Journal Impact Factor in academic review, promotion, and tenure evaluations. eLife 8. https://doi.org/10.7554/elife.47338
  234. Singh G., Farjalla V., Chen B., Pelling A., Ceyhan E., Dominik M., et al. (2019): Researcher engagement in policy deemed societally beneficial yet unrewarded. Frontiers in Ecology and the Environment 17(7):375-382. https://doi.org/10.1002/fee.2084
  235. Baffy G., Burns M., Hoffmann B., Ramani S., Sabharwal S., Borus J., et al. (2019): Scientific Authors in a Changing World of Scholarly Communication: What Does the Future Hold?. The American Journal of Medicine 133(1):26-31. https://doi.org/10.1016/j.amjmed.2019.07.028
  236. Campbell H., Gustafson P. (2019): The World of Research Has Gone Berserk: Modeling the Consequences of Requiring “Greater Statistical Stringency” for Scientific Publication. The American Statistician 73(sup1):358-373. https://doi.org/10.1080/00031305.2018.1555101
  237. Hopf H., Matlin S., Mehta G., Krief A. (2019): Blocking the Hype‐Hypocrisy‐Falsification‐Fakery Pathway is Needed to Safeguard Science. Angewandte Chemie International Edition 59(6):2150-2154. https://doi.org/10.1002/anie.201911889
  238. Hopf H., Matlin S., Mehta G., Krief A. (2019): Blocking the Hype‐Hypocrisy‐Falsification‐Fakery Pathway is Needed to Safeguard Science. Angewandte Chemie 132(6):2170-2174. https://doi.org/10.1002/ange.201911889
  239. Tennan J., Crane H., Crick T., Davila J., Enkhbayar A., Havemann J., et al. (2019): Ten hot topics around scholarly publishing. Bibliosphere. https://doi.org/10.20913/1815-3186-2019-3-3-25
  240. Tennant J., Crane H., Crick T., Davila J., Enkhbayar A., Havemann J., et al. (2019): Ten Hot Topics around Scholarly Publishing. Publications 7(2):34. https://doi.org/10.3390/publications7020034
  241. Tennant J., Beamer J., Bosman J., Brembs B., Chung N., Clement G., et al. (2019): Foundations for Open Scholarship Strategy Development. OSF Preprints. https://doi.org/10.31222/osf.io/b4v8p
  242. Perez Velazquez J. (2019): Corporate Culture in Academia and The Current Standards of Research Appraisal. The Rise of the Scientist-Bureaucrat. https://doi.org/10.1007/978-3-030-12326-0_3
  243. Perez Velazquez J. (2019): The Tragicomedy of Peer Review—The Publication Game and the Lottery of Grants. The Rise of the Scientist-Bureaucrat. https://doi.org/10.1007/978-3-030-12326-0_5
  244. Akers K. (2019): Biomedical Journals: Scientific Quality, Reputation, and Impact Factor. A Guide to the Scientific Career. https://doi.org/10.1002/9781118907283.ch40
  245. Siler K., Larivière V., Sugimoto C. (2019): The diverse niches of megajournals: Specialism within generalism. Journal of the Association for Information Science and Technology 71(7):800-816. https://doi.org/10.1002/asi.24299
  246. Siler K., Sugimoto C., Larivière V. (2019): The Diverse Niches of Megajournals: Specialism within Generalism. https://doi.org/10.31235/osf.io/px8kt
  247. Kim L., Portenoy J., West J., Stovel K. (2019): Scientific journals still matter in the era of academic search engines and preprint archives. Journal of the Association for Information Science and Technology 71(10):1218-1226. https://doi.org/10.1002/asi.24326
  248. Wenaas L. (2019): Open Access: A Remedy to the Crisis in Scientific Inquiry?. Theory and History in the Human and Social Sciences. https://doi.org/10.1007/978-3-030-33099-6_13
  249. Tiokhin L., Panchanathan K., Lakens D., Vazire S., Morgan T., Zollman K. (2019): Honest signaling in academic publishing. https://doi.org/10.31219/osf.io/gyeh8
  250. Niles M., Schimanski L., McKiernan E., Alperin J. (2019): Why we publish where we do: Faculty publishing values and their relationship to review, promotion and tenure expectations. https://doi.org/10.1101/706622
  251. Kossmeier M., Vilsmeier J., Dittrich R., Fritz T., Kolmanz C., Plessen C., et al. (2019): Long-Term Trends (1980–2017) in the N-Pact Factor of Journals in Personality Psychology and Individual Differences Research. Zeitschrift für Psychologie 227(4):293-302. https://doi.org/10.1027/2151-2604/a000384
  252. Dougherty M., Horne Z. (2019): Citation counts and journal impact factors do not capture some indicators research quality in the behavioral and brain sciences. https://doi.org/10.31234/osf.io/9g5wk
  253. Schreiber M. (2019): Bibliometric Epilogue: Measuring the Works of D.R.T. Zahn. physica status solidi (b) 256(2). https://doi.org/10.1002/pssb.201800748
  254. Wang M., Jiao S., Chai K., Chen G. (2019): Building journal’s long-term impact: using indicators detected from the sustained active articles. Scientometrics 121(1):261-283. https://doi.org/10.1007/s11192-019-03196-8
  255. Stergiou N. (2019): Writing manuscripts. Advice for the Novice Investigator. https://doi.org/10.1201/b22034-4
  256. Nigel R. F. Burnell (2019): Handling multiple demands in academia : does gender play a role?. https://doi.org/10.15126/thesis.00851831
  257. Oliver Braganza (2019): Economically rational sample-size choice and irreproducibility. arXiv (Cornell University).
  258. Open Book Publishers, Edmond, Jennifer (2019): Digital Technology and the Practices of Humanities Research. Open Book Publishers. https://doi.org/10.11647/obp.0192
  259. Heesen R., Bright L. (2019): Is Peer Review a Good Idea?. The British Journal for the Philosophy of Science 72(3):635-663. https://doi.org/10.1093/bjps/axz029
  260. Steiner T. (2019): Grundlagen für die Entwicklung einer Open Scholarship-Strategie. https://doi.org/10.59350/9gfjs-pey70
  261. Larivière V. (2019): Le français, langue seconde ? De l’évolution des lieux et langues de publication des chercheurs au Québec, en France et en Allemagne. Recherches sociographiques 59(3):339-363. https://doi.org/10.7202/1058718ar
  262. Millard W. (2019): Jefferson’s Taper in the Digital Hall of Mirrors. Annals of Emergency Medicine 73(5):A15-A19. https://doi.org/10.1016/j.annemergmed.2019.03.013
  263. Katz Y., Matter U. (2019): Metrics of Inequality: The Concentration of Resources in the U.S. Biomedical Elite. Science as Culture 29(4):475-502. https://doi.org/10.1080/09505431.2019.1694882
  264. Zhu Y., Fu K. (2019): The Relationship Between Interdisciplinarity and Journal Impact Factor in the Field of Communication During 1997–2016. Journal of Communication 69(3):273-297. https://doi.org/10.1093/joc/jqz012
  265. Katchanov Y., Markova Y., Shmatko N. (2019): Comparing the topological rank of journals in Web of Science and Mendeley. Heliyon 5(7):e02089. https://doi.org/10.1016/j.heliyon.2019.e02089
  266. Muraille E. (2019): Ethical control of innovation in a globalized and liberal world: Is good science still science?. Endeavour 43(4):100709. https://doi.org/10.1016/j.endeavour.2020.100709
  267. Unknown authors (2018): References. Evaluating Scholarship and Research Impact. https://doi.org/10.1108/978-1-78756-387-220181010
  268. Unknown authors (2018): . Nauka ta innovacii 14(1). https://doi.org/10.15407/scin14.01
  269. Stewart A., Cotton J. (2018): Does “Evaluating Journal Quality and the Association for Information Systems Senior Scholars Journal Basket…” Support the Basket with Bibliometric Measures?. AIS Transactions on Replication Research 4:1-41. https://doi.org/10.17705/1atrr.00032
  270. Polonioli A., Vega-Mendoza M., Blankinship B., Carmel D. (2018): Reporting in Experimental Philosophy: Current Standards and Recommendations for Future Practice. Review of Philosophy and Psychology 12(1):49-73. https://doi.org/10.1007/s13164-018-0414-3
  271. Eder A., Frings C. (2018): What Makes a Quality Journal?. Experimental Psychology 65(5):257-262. https://doi.org/10.1027/1618-3169/a000426
  272. FOMBUENA VALERO A. (2018): Geospatial Social Network lnnovation Assessment of the Spanish Higher Education. https://doi.org/10.4995/thesis/10251/107943
  273. Brembs B. (2018): Prestigious Science Journals Struggle to Reach Even Average Reliability. Frontiers in Human Neuroscience 12. https://doi.org/10.3389/fnhum.2018.00037
  274. Brembs B. (2018): Corrigendum: Prestigious Science Journals Struggle to Reach Even Average Reliability. Frontiers in Human Neuroscience 12. https://doi.org/10.3389/fnhum.2018.00376
  275. Fernando B., José César P., Miguel A. V. (2018): Pot la psicologia rescatar-se a si mateixa?. Incentius, biaix i replicabilitat. Anuari de Psicologia de la Societat Valenciana de Psicologia 18(2). https://doi.org/10.7203/anuari.psicologia.18.2.231
  276. Kotchoubey B., Pavlov Y. (2018): A Systematic Review and Meta-Analysis of the Relationship Between Brain Data and the Outcome in Disorders of Consciousness. Frontiers in Neurology 9. https://doi.org/10.3389/fneur.2018.00315
  277. Forest C. (2018): PubPeer contre “fake news” en Sciences ?. Ethics, Medicine and Public Health 4:9-11. https://doi.org/10.1016/j.jemep.2018.01.009
  278. Moher D., Naudet F., Cristea I., Miedema F., Ioannidis J., Goodman S. (2018): Assessing scientists for hiring, promotion, and tenure. PLOS Biology 16(3):e2004089. https://doi.org/10.1371/journal.pbio.2004089
  279. Pojani D., Olvera-Garcia J., Sipe N., Byrne J. (2018): Research Productivity of Australian Planning Academics: A Bibliometric Analysis. Journal of Planning Education and Research 42(1):90-101. https://doi.org/10.1177/0739456×18804330
  280. Paulus F., Cruz N., Krach S. (2018): The Impact Factor Fallacy. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.01487
  281. Coelho G. (2018): Avaliação de impacto de periódicos brasileiros de extensão universitária. Biblios Journal of Librarianship and Information Science. https://doi.org/10.5195/biblios.2018.468
  282. Bowden J., Sargent N., Wesselingh S., Size L., Donovan C., Miller C. (2018): Measuring research impact: a large cancer research funding programme in Australia. Health Research Policy and Systems 16(1). https://doi.org/10.1186/s12961-018-0311-3
  283. Rehman J. (2018): Novelty in science – real necessity or distracting obsession?. https://doi.org/10.64628/aai.hkt4pq49a
  284. Tennant J. (2018): The state of the art in peer review. FEMS Microbiology Letters 365(19). https://doi.org/10.1093/femsle/fny204
  285. Schimanski L., Alperin J. (2018): The evaluation of scholarship in academic promotion and tenure processes: Past, present, and future. F1000Research 7:1605. https://doi.org/10.12688/f1000research.16493.1
  286. Leydesdorff L., Wagner C., Bornmann L. (2018): Discontinuities in citation relations among journals: self-organized criticality as a model of scientific revolutions and change. Scientometrics 116(1):623-644. https://doi.org/10.1007/s11192-018-2734-6
  287. Scerbo M. (2018): Some Exciting News and Changes for the Journal. Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare 13(5):303-305. https://doi.org/10.1097/sih.0000000000000346
  288. Brueckner M., Spencer R., Paull M. (2018): Teaching for Tomorrow: Preparing Responsible Citizens. CSR, Sustainability, Ethics & Governance. https://doi.org/10.1007/978-3-319-71449-3_1
  289. Brueckner M., Paull M., Spencer R. (2018): Corporate Social Responsibility an australischen Hochschulen. Management-Reihe Corporate Social Responsibility. https://doi.org/10.1007/978-3-662-56314-4_19
  290. Sharpe M., Turner K. (2018): Bibliopolitics: The History of Notation and the Birth of the Citational Academic Subject. Foucault Studies. https://doi.org/10.22439/fs.v25i2.5578
  291. Sharpe M., Turner K. (2018): Bibliopolitics: The History of Notation and the Birth of the Citational Academic Subject. Foucault Studies. https://doi.org/10.22439/fs.v0i25.5578
  292. Whitley M., Massey W., Camiré M., Blom L., Chawansky M., Forde S., et al. (2018): A systematic review of sport for development interventions across six global cities. Sport Management Review 22(2):181-193. https://doi.org/10.1016/j.smr.2018.06.013
  293. Schreiber M. (2018): A skeptical view on the Hirsch index and its predictive power. Physica Scripta 93(10):102501. https://doi.org/10.1088/1402-4896/aad959
  294. Carl N., Kirkegaard E., Dalliard M., Frost P., Kura K., Meisenberg G., et al. (2018): Editorial: A Response to Criticisms of the OpenPsych Journals. Open Differential Psychology. https://doi.org/10.26775/odp.2018.11.02
  295. Braganza O. (2018): Proxyeconomics, the inevitable corruption of proxy-based competition. arXiv. https://doi.org/10.48550/arxiv.1803.00345
  296. Muñoz-Tamayo R. (2018): A brief tribute to slowness in science. https://doi.org/10.31235/osf.io/n9wpg
  297. Jan R., Zainab T. (2018): The Impact Story of Retracted Articles Altmetric it!. 2018 5th International Symposium on Emerging Trends and Technologies in Libraries and Information Services (ETTLIS). https://doi.org/10.1109/ettlis.2018.8485245
  298. Barriga S., Barbón O., Buenaño C., Barriga L. (2018): Impacto en la Producción Científica de un Programa Experiencial de Preparación para la Investigación Dirigido a Docentes Universitarios. Formación universitaria 11(3):41-48. https://doi.org/10.4067/s0718-50062018000300041
  299. Jiménez García S., Moreles Vázquez J., Mayoral Sánchez E. (2018): Poder y vulnerabilidad en las evaluaciones de pares en revistas académicas. Culturales 6:1-28. https://doi.org/10.22234/recu.20180601.e364
  300. Cuschieri S. (2018): WASP: Is open access publishing the way forward? A review of the different ways in which research papers can be published. Early Human Development 121:54-57. https://doi.org/10.1016/j.earlhumdev.2018.02.017
  301. Sauer S., Sülzenbrück S. (2018): Die Arbeitsweise der Forschung zu Zeiten von Digitalisierung und Reproduzierbarkeitskrise: Neue Methoden, alte Probleme. FOM-Edition. https://doi.org/10.1007/978-3-658-23397-6_11
  302. Vogl S., Scherndl T., Kühberger A. (2018): #Psychology: a bibliometric analysis of psychological literature in the online media. Scientometrics 115(3):1253-1269. https://doi.org/10.1007/s11192-018-2727-5
  303. D’Antonio Maceiras S. (2018): El circulo vicioso de las revistas científicas y la progresiva irrelevancia de la ciencia pública. Política y Sociedad 55(2):467-490. https://doi.org/10.5209/poso.57222
  304. Ceci S., Williams W. (2018): Journal Publishing Strategies. Guide to Publishing in Psychology Journals. https://doi.org/10.1017/9781108304443.018
  305. Cochrane T., Redmond P., Corrin L. (2018): Technology Enhanced Learning, Research Impact and Open Scholarship. Australasian Journal of Educational Technology 34(3). https://doi.org/10.14742/ajet.4640
  306. Strielkowski W., Gryshova I. (2018): Academic Publishing and «Predatory» Journals. Nauka ta innovacii 14(1):5-12. https://doi.org/10.15407/scin14.01.005
  307. Strielkowski W., Gryshova I. (2018): Academic Publishing and «Predatory» Journals. Science and innovation 14(1):5-12. https://doi.org/10.15407/scine14.01.005
  308. Unknown authors (2017): Peer Review #3 of “The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research (v0.1)”. https://doi.org/10.7287/peerj.3544v0.1/reviews/3
  309. Molchanovа A., Chunikhina N., Strielkowski W. (2017): Innovations and academic publishing: who will cast the first stone?. Marketing and Management of Innovations. https://doi.org/10.21272/mmi.2017.4-03
  310. Meyer A., Starbuck W. (2017): Mahalo: Sustaining JMI’s Positive Spirit. Journal of Management Inquiry 27(2):154-157. https://doi.org/10.1177/1056492617726272
  311. Copenhaver A., Mitrofan O., Ferguson C. (2017): For Video Games, Bad News Is Good News: News Reporting of Violent Video Game Studies. Cyberpsychology, Behavior, and Social Networking 20(12):735-739. https://doi.org/10.1089/cyber.2017.0364
  312. Polonioli A. (2017): A plea for minimally biased naturalistic philosophy. Synthese 196(9):3841-3867. https://doi.org/10.1007/s11229-017-1628-0
  313. Buttliere B., Buder J. (2017): Personalizing papers using Altmetrics: comparing paper ‘Quality’ or ‘Impact’ to person ‘Intelligence’ or ‘Personality’. Scientometrics 111(1):219-239. https://doi.org/10.1007/s11192-017-2246-9
  314. Reider B. (2017): Brace for Impact. The American Journal of Sports Medicine 45(10):2213-2216. https://doi.org/10.1177/0363546517721707
  315. D’Ippoliti C. (2017): Many-Citednesss: Citations Measure More than Just Scientific Impact. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2993971
  316. Are C., Yanala U., Malhotra G., Hall B., Smith L., Wyld L., et al. (2017): Global Curriculum in Research Literacy for the Surgical Oncologist. Annals of Surgical Oncology 25(3):604-616. https://doi.org/10.1245/s10434-017-6277-5
  317. Are C., Yanala U., Malhotra G., Hall B., Smith L., Cummings C., et al. (2017): Global curriculum in research literacy for the surgical oncologist. European Journal of Surgical Oncology 44(1):31-42. https://doi.org/10.1016/j.ejso.2017.07.017
  318. Craig Anderson (2017): Framing employment research using behavioural science. Stirling Online Research Repository (University of Stirling).
  319. Lakens D. (2017): Peer Review #2 of “The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research (v0.1)”. https://doi.org/10.7287/peerj.3544v0.1/reviews/2
  320. Gent D., Esker P., Kriss A. (2017): Statistical Power in Plant Pathology Research. Phytopathology® 108(1):15-22. https://doi.org/10.1094/phyto-03-17-0098-le
  321. Greenblatt D., Shader R. (2017): The Impact Non-Factor. Journal of Clinical Psychopharmacology 37(4):389-390. https://doi.org/10.1097/jcp.0000000000000743
  322. Kulczycki E., Rozkosz E. (2017): Does an expert-based evaluation allow us to go beyond the Impact Factor? Experiences from building a ranking of national journals in Poland. Scientometrics 111(1):417-442. https://doi.org/10.1007/s11192-017-2261-x
  323. McKiernan E. (2017): Imagining the “open” university: Sharing scholarship to improve research and education. PLOS Biology 15(10):e1002614. https://doi.org/10.1371/journal.pbio.1002614
  324. McKiernan E. (2017): Imagining the ‘open’ university: Sharing science to improve research and education. https://doi.org/10.7287/peerj.preprints.2711v1
  325. Paulus F., Cruz N., Krach S. (2017): The impact factor fallacy. https://doi.org/10.1101/108027
  326. Peters G., Kok G., Crutzen R., Sanderman R. (2017): Health Psychology Bulletin: Improving Publication Practices to Accelerate Scientific Progress. Health Psychology Bulletin 1:1-6. https://doi.org/10.5334/hpb.2
  327. Peters G. (2017): Why not to use the journal impact factor as a criterion for the selection of junior researchers: A comment on Bornmann and Williams (2017). Journal of Informetrics 11(3):888-891. https://doi.org/10.1016/j.joi.2017.08.001
  328. Cremers H. (2017): Chapter 2. Experimental research. Crossroads Semantics. https://doi.org/10.1075/z.210.02cre
  329. Armstrong J., Green K. (2017): Guidelines for Science: Evidence and Checklists. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3055874
  330. van Mil J., Green J. (2017): Citations and science. International Journal of Clinical Pharmacy 39(5):977-979. https://doi.org/10.1007/s11096-017-0539-y
  331. Laman J., Kooistra S., Clausen B. (2017): Reproducibility Issues: Avoiding Pitfalls in Animal Inflammation Models. Methods in Molecular Biology. https://doi.org/10.1007/978-1-4939-6786-5_1
  332. Tennant J., Dugan J., Graziotin D., Jacques D., Waldner F., Mietchen D., et al. (2017): A multi-disciplinary perspective on emergent and future innovations in peer review. F1000Research 6:1151. https://doi.org/10.12688/f1000research.12037.3
  333. Tennant J., Dugan J., Graziotin D., Jacques D., Waldner F., Mietchen D., et al. (2017): A multi-disciplinary perspective on emergent and future innovations in peer review. F1000Research 6:1151. https://doi.org/10.12688/f1000research.12037.1
  334. Tennant J., Dugan J., Graziotin D., Jacques D., Waldner F., Mietchen D., et al. (2017): A multi-disciplinary perspective on emergent and future innovations in peer review. F1000Research 6:1151. https://doi.org/10.12688/f1000research.12037.2
  335. Campos L., Feres Júnior J., Guarnieri F. (2017): 50 Anos da Revista DADOS: Uma Análise Bibliométrica do seu Perfil Disciplinar e Temático. Dados 60(3):623-661. https://doi.org/10.1590/001152582017131
  336. Taylor L., Willett P. (2017): Comparison of US and UK rankings of LIS journals. Aslib Journal of Information Management 69(3):354-367. https://doi.org/10.1108/ajim-08-2016-0136
  337. Manuelle Freire (2017): What is new in new media art education?A critical discourse analysis of the mythologies of media art education at the university.
  338. Munafò M. (2017): Promoting reproducibility in addiction research. Addiction 112(9):1519-1520. https://doi.org/10.1111/add.13853
  339. Eve M., Priego E. (2017): Who is Actually Harmed by Predatory Publishers?. tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 15(2):755-770. https://doi.org/10.31269/triplec.v15i2.867
  340. Tsimilli-Michael M., Haldimann P. (2017): Sustainability of photosynthesis research-when research is impeded by the cults of audit and management. Photosynthetica 55(2):391-400. https://doi.org/10.1007/s11099-017-0686-3
  341. Song M., Kim S., Lee K. (2017): Ensemble analysis of topical journal ranking in bioinformatics. Journal of the Association for Information Science and Technology 68(6):1564-1583. https://doi.org/10.1002/asi.23840
  342. Hanel P., Haase J. (2017): Predictors of Citation Rate in Psychology: Inconclusive Influence of Effect and Sample Size. Frontiers in Psychology 8. https://doi.org/10.3389/fpsyg.2017.01160
  343. Serghiou S. (2017): Peer Review #1 of “The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research (v0.1)”. https://doi.org/10.7287/peerj.3544v0.1/reviews/1
  344. Moore S., Neylon C., Paul Eve M., Paul O’Donnell D., Pattinson D. (2017): “Excellence R Us”: university research and the fetishisation of excellence. Palgrave Communications 3(1). https://doi.org/10.1057/palcomms.2016.105
  345. Arsène S. (2017): Editorial. China Perspectives 2017(3):3-5. https://doi.org/10.4000/chinaperspectives.7373
  346. Wall T., Bellamy L., Evans V., Hopkins S. (2017): Revisiting impact in the context of workplace research: a review and possible directions. Journal of Work-Applied Management 9(2):95-109. https://doi.org/10.1108/jwam-07-2017-0018
  347. Herb U. (2017): “The Future of Scholarly Publishing: Open Access and the Economics of Digitisation”. https://doi.org/10.59350/j3374-faf75
  348. Amrhein V., Korner-Nievergelt F., Roth T. (2017): The earth is flat ( p > 0.05): significance thresholds and the crisis of unreplicable research. PeerJ 5:e3544. https://doi.org/10.7717/peerj.3544
  349. Spezi V., Wakeling S., Pinfield S., Creaser C., Fry J., Willett P. (2017): Open-access mega-journals. Journal of Documentation 73(2):263-283. https://doi.org/10.1108/jd-06-2016-0082
  350. Katz Y., Matter U. (2017): On the Biomedical Elite: Inequality and Stasis in Scientific Knowledge Production. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3000628
  351. Unknown authors (2016): The Future of Scholarly Publishing. F1000Research Channels. https://doi.org/10.12688/f1000research.channels.103
  352. Unknown authors (2016): BIBLIOGRAPHY. Handbook of Bibliometric Indicators. https://doi.org/10.1002/9783527681969.biblio
  353. Unknown authors (2016): Peer Review #2 of “Auto-correlation of journal impact factor for consensus research reporting statements: a cohort study (v0.1)”. https://doi.org/10.7287/peerj.1887v0.1/reviews/2
  354. Abele-Brehm A., Bühner M. (2016): Wer soll die Professur bekommen?. Psychologische Rundschau 67(4):250-261. https://doi.org/10.1026/0033-3042/a000335
  355. Polonioli A. (2016): New Issues for New Methods: Ethical and Editorial Challenges for an Experimental Philosophy. Science and Engineering Ethics 23(4):1009-1034. https://doi.org/10.1007/s11948-016-9838-2
  356. Polonioli A. (2016): Metrics, flawed indicators, and the case of philosophy journals. Scientometrics 108(2):987-994. https://doi.org/10.1007/s11192-016-1941-2
  357. Rentier B. (2016): Open science: a revolution in sight?. Interlending & Document Supply 44(4):155-160. https://doi.org/10.1108/ilds-06-2016-0020
  358. Knuteson B. (2016): The Solution to Science’s Replication Crisis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2835131
  359. Shanahan D. (2016): Auto-correlation of journal impact factor for consensus research reporting statements: a cohort study. PeerJ 4:e1887. https://doi.org/10.7717/peerj.1887
  360. Towpik E. (2016): IF-mania: Journal Impact Factor nie jest właściwym wskaźnikiem oceniania wyników badań naukowych, indywidualnych uczonych ani ośrodków badawczych. Nowotwory. Journal of Oncology 65(6):465-475. https://doi.org/10.5603/njo.2015.0092
  361. McKiernan E., Bourne P., Brown C., Buck S., Kenall A., Lin J., et al. (2016): How open science helps researchers succeed. eLife 5. https://doi.org/10.7554/elife.16800
  362. Wimmer E., Rethlefsen M., Jarvis C., Shipman J. (2016): Understanding Research Impact: A Review of Existing and Emerging Tools for Nursing. Journal of Professional Nursing 32(6):401-411. https://doi.org/10.1016/j.profnurs.2016.05.005
  363. Daniels J., Thistlethwaite P. (2016): Being a Scholar in the Digital Era: Transforming Scholarly Practice for the Public Good. https://doi.org/10.46692/9781447329299
  364. Daniels J., Thistlethwaite P. (2016): References. Being a Scholar in the Digital Era. https://doi.org/10.51952/9781447329299.bm001
  365. Tennant J., Waldner F., Jacques D., Masuzzo P., Collister L., Hartgerink C. (2016): The academic, economic and societal impacts of Open Access: an evidence-based review. F1000Research 5:632. https://doi.org/10.12688/f1000research.8460.3
  366. Tennant J., Waldner F., Jacques D., Masuzzo P., Collister L., Hartgerink C. (2016): The academic, economic and societal impacts of Open Access: an evidence-based review. F1000Research 5:632. https://doi.org/10.12688/f1000research.8460.2
  367. Fiedler K. (2016): Empfehlungen der DGPs-Kommission „Qualität der psychologischen Forschung“. Psychologische Rundschau 67(1):59-74. https://doi.org/10.1026/0033-3042/a000316
  368. Siler K., Strang D. (2016): Peer Review and Scholarly Originality. Science, Technology, & Human Values 42(1):29-61. https://doi.org/10.1177/0162243916656919
  369. Christopher M. (2016): Peer Review #1 of “Auto-correlation of journal impact factor for consensus research reporting statements: a cohort study (v0.1)”. https://doi.org/10.7287/peerj.1887v0.1/reviews/1
  370. Pievatolo M. (2016): Metajournals. A federalist proposal for scholarly communication and data aggregation. https://doi.org/10.31235/osf.io/xtycn
  371. Meadows M., Dietz T., Vandermotten C. (2016): A perspective on problems and prospects for academic publishing in Geography. Geo: Geography and Environment 3(1). https://doi.org/10.1002/geo2.16
  372. Poirazi P., Belin D., Gräff J., Hanganu‐Opatz I., López‐Bendito G. (2016): Balancing family with a successful career in neuroscience. European Journal of Neuroscience 44(2):1797-1803. https://doi.org/10.1111/ejn.13280
  373. Smaldino P., McElreath R. (2016): The natural selection of bad science. Royal Society Open Science 3(9):160384. https://doi.org/10.1098/rsos.160384
  374. Ralph P. (2016): Practical Suggestions for Improving Scholarly Peer Review Quality and Reducing Cycle Times. Communications of the Association for Information Systems 38:274-283. https://doi.org/10.17705/1cais.03813
  375. Hassmén P., Keegan R., Piggott D. (2016): Planning a Post-revolutionary World. Rethinking Sport and Exercise Psychology Research. https://doi.org/10.1057/978-1-137-48338-6_10
  376. Carey R. (2016): Quantifying Scientific Merit. Circulation Research 119(12):1273-1275. https://doi.org/10.1161/circresaha.116.309883
  377. Soziologie in Österreich – Internationale Verflechtungen 2015 Innsbruck (2016): Soziologie in Österreich – Internationale Verflechtungen. innsbruck university press eBooks. https://doi.org/10.15203/3122-56-7
  378. Stanley E. Lazic (2016): Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility.
  379. Branch T., Linnell A. (2016): What makes some fisheries references highly cited?. Fish and Fisheries 17(4):1094-1133. https://doi.org/10.1111/faf.12160
  380. Tracz V., Lawrence R. (2016): Towards an open science publishing platform. F1000Research 5:130. https://doi.org/10.12688/f1000research.7968.1
  381. Starbuck W. (2016): 60th Anniversary Essay. Administrative Science Quarterly 61(2):165-183. https://doi.org/10.1177/0001839216629644
  382. LeHuray A. (2015): In response to Bales (2014). Integrated Environmental Assessment and Management 11(2):185-187. https://doi.org/10.1002/ieam.1619
  383. Bowen A., Casadevall A. (2015): Increasing disparities between resource inputs and outcomes, as measured by certain health deliverables, in biomedical research. Proceedings of the National Academy of Sciences 112(36):11335-11340. https://doi.org/10.1073/pnas.1504955112
  384. Casadevall A., Fang F. (2015): Impacted Science: Impact Is Not Importance. mBio 6(5). https://doi.org/10.1128/mbio.01593-15
  385. Wolf B., Häring A., Heß J. (2015): Strategies towards Evaluation beyond Scientific Impact. Pathways not only for Agricultural Research. Organic Farming 1(1):3-18. https://doi.org/10.12924/of2015.01010003
  386. Wolf B., Häring A., Heß J. (2015): Strategies towards Evaluation beyond Scientific Impact. Pathways not only for Agricultural Research. https://doi.org/10.12924/of2014.01010003
  387. Frey B. (2015): Fruitful and Barren Developments in Economics. Journal of Contextual Economics – Schmollers Jahrbuch 135(2):143-154. https://doi.org/10.3790/schm.135.2.143
  388. Carl Lagoze, Paul Edwards, Christian Sandvig, Jean‐Christophe Plantin (2015): Should I stay or should I go? Alternative infrastructures in scholarly publishing. London School of Economics and Political Science Research Online (London School of Economics and Political Science).
  389. Madan C. (2015): Every scientist is a memory researcher: Suggestions for making research more memorable. F1000Research 4:19. https://doi.org/10.12688/f1000research.6053.1
  390. Jane E. (2015): Flaming? What flaming? The pitfalls and potentials of researching online hostility. Ethics and Information Technology 17(1):65-87. https://doi.org/10.1007/s10676-015-9362-0
  391. Contreras F., Buzeta L., Pedraja-Rejas L. (2015): Importancia de las publicaciones académicas: algunos problemas y recomendaciones a tener en cuenta. Idesia (Arica) 33(4):111-119. https://doi.org/10.4067/s0718-34292015000400014
  392. Génova G., Astudillo H., Fraga A. (2015): The Scientometric Bubble Considered Harmful. Science and Engineering Ethics 22(1):227-235. https://doi.org/10.1007/s11948-015-9632-6
  393. Ian McCullough (2015): Journal Impact Factor – The Worst Metric in Science.
  394. Teixeira da Silva J. (2015): Issues in Science Publishing. What’a Hot and What’s not?. KOME 3(1):81-87. https://doi.org/10.17646/kome.2015.16
  395. Monteiro J. (2015): Organic Farming Volume 1 (2015) | Issue 1. https://doi.org/10.12924/librello.of2015.0101
  396. Louys J. (2015): Palaeontologia Electronica in an increasingly open-access world. Palaeontologia Electronica. https://doi.org/10.26879/152e
  397. Axel-Berg J. (2015): Competing on the world stage: the Universidade de Sa?o Paulo and global universities rankings. https://doi.org/10.11606/d.101.2015.tde-12082015-161448
  398. Margraf J. (2015): Zur Lage der Psychologie. Psychologische Rundschau 66(1):1-30. https://doi.org/10.1026/0033-3042/a000247
  399. Munafo M. (2015): A New Editor-in-Chief for Nicotine & Tobacco Research. Nicotine & Tobacco Research 17(1):1-1. https://doi.org/10.1093/ntr/ntu222
  400. Erikson M., Erlandson P., Erikson M. (2015): Academic misconduct in teaching portfolios. International Journal for Academic Development 20(4):345-354. https://doi.org/10.1080/1360144x.2015.1083435
  401. Eve M. (2015): Open Access publishing and scholarly communications in non-scientific disciplines. Online Information Review 39(5):717-732. https://doi.org/10.1108/oir-04-2015-0103
  402. Eslami M., Rickman A., Vaccaro K., Aleyasen A., Vuong A., Karahalios K., et al. (2015): “I always assumed that I wasn’t really that close to [her]”. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. https://doi.org/10.1145/2702123.2702556
  403. Myriam Hernández Álvarez (2015): Concit-corpus context citation analysis to learn function, polarity and influence.
  404. HERNÁNDEZ-ALVAREZ M., GOMEZ J. (2015): Survey about citation context analysis: Tasks, techniques, and resources. Natural Language Engineering 22(3):327-349. https://doi.org/10.1017/s1351324915000388
  405. Hernandez-Alvarez M., Gomez J. (2015): Citation Impact Categorization: For Scientific Literature. 2015 IEEE 18th International Conference on Computational Science and Engineering. https://doi.org/10.1109/cse.2015.21
  406. Pentti Nieminen, Khaled Abass, Kirsi Vähäkanga, Arja Rautio (2015): Statistically Non-significant Papers in Environmental Health Studies included more Outcome Variables. PubMed. https://doi.org/10.3967/bes2015.093
  407. Hunter P. (2015): Web 2.0 and academic debate. EMBO reports 16(7):787-790. https://doi.org/10.15252/embr.201540721
  408. Jones R. (2015): Presidential Address: Truth and error in scientific publishing. Journal of the Southern African Institute of Mining and Metallurgy. https://doi.org/10.17159/2411-9717/2015/v115n9a1
  409. Probst T., Hagger M. (2015): Advancing the Rigour and Integrity of Our Science: The Registered Reports Initiative. Stress and Health 31(3):177-179. https://doi.org/10.1002/smi.2645
  410. Teixeira da Silva, Asunción Jaime (2015): Issues in Science Publishing. What’s Hot and What’s not?.
  411. Fresco-Santalla A., Hernández-Pérez T. (2014): Current and Evolving Models of Peer Review. The Serials Librarian 67(4):373-398. https://doi.org/10.1080/0361526x.2014.985415
  412. Gasparyan A., Ayvazyan L., Akazhanov N., Kitas G. (2014): Self-correction in biomedical publications and the scientific impact. Croatian Medical Journal 55(1):61-72. https://doi.org/10.3325/cmj.2014.55.61
  413. Casadevall A., Fang F. (2014): Causes for the Persistence of Impact Factor Mania. mBio 5(2). https://doi.org/10.1128/mbio.00064-14
  414. Casadevall A., Fang F. (2014): Specialized Science. Infection and Immunity 82(4):1355-1360. https://doi.org/10.1128/iai.01530-13
  415. Lagoze C. (2014): Big Data, data integrity, and the fracturing of the control zone. Big Data & Society 1(2). https://doi.org/10.1177/2053951714558281
  416. D. Chambers C., Feredoes E., D. Muthukumaraswamy S., J. Etchells P. (2014): Instead of “playing the game” it is time to change the rules: Registered Reports at <em>AIMS Neuroscience</em> and beyond. AIMS Neuroscience 1(1):4-17. https://doi.org/10.3934/neuroscience.2014.1.4
  417. Knudson D. (2014): What is a kinesiology journal?1. Comprehensive Psychology 3(1):Article 20. https://doi.org/10.2466/03.cp.3.20
  418. Emmanuel Chinamasa (2014): JOURNAL IMPACT FACTOR: EXPIRED PRESCRIPTION FOR ACADEMICS RESEARCH OUTPUT. International Journal of Advanced Research in Management and Social Sciences.
  419. Liao H., Xiao R., Cimini G., Medo M. (2014): Network-Driven Reputation in Online Scientific Communities. PLoS ONE 9(12):e112022. https://doi.org/10.1371/journal.pone.0112022
  420. Coates H. (2014): Ensuring research integrity: The role of data management in current crises. College & Research Libraries News 75(11):598-601. https://doi.org/10.5860/crln.75.11.9224
  421. Engler J., Husemann M. (2014): Data constraints, bias and the (mis-)use of scientometrics for predicting academic success: A comment on van Dijk et al. Proceedings of Peerage of Science. https://doi.org/10.14726/procpos.2014.e8
  422. Jaume Singla Valls (2014): Analysis of the Impact Factor of scientific journals.
  423. Ware J., Munafò M. (2014): Significance chasing in research practice: causes, consequences and possible solutions. Addiction 110(1):4-8. https://doi.org/10.1111/add.12673
  424. Campanario J. (2014): Analysis of the distribution of cited journals according to their positions in the h-core of citing journal listed in Journal Citation Reports. Journal of Informetrics 8(3):534-545. https://doi.org/10.1016/j.joi.2014.04.007
  425. Hamilton K., Karahalios K., Sandvig C., Eslami M. (2014): A path to understanding the effects of algorithm awareness. CHI ’14 Extended Abstracts on Human Factors in Computing Systems. https://doi.org/10.1145/2559206.2578883
  426. Kevin Hamilton, Karrie Karahalios, Christian Sandvig, Motahhare Eslami (2014): the Effects of Algorithm Awareness.
  427. Black K. (2014): F1000Research: Tics welcomes you to 21st century biomedical publishing. F1000Research 3:272. https://doi.org/10.12688/f1000research.5664.1
  428. Moustafa K. (2014): The Disaster of the Impact Factor. Science and Engineering Ethics 21(1):139-142. https://doi.org/10.1007/s11948-014-9517-0
  429. Mario Infelise (2014): «Lavoro e conoscenza» dieci anni dopo. Carte, Studi e Opere – Centro Trentin di Venezia. https://doi.org/10.36253/978-88-6655-516-2
  430. Erikson M., Erlandson P. (2014): A taxonomy of motives to cite. Social Studies of Science 44(4):625-637. https://doi.org/10.1177/0306312714522871
  431. Nagib Callaos, Bekis Callaos (2014): Academic Ethos, Pathos, and Logos. Research Ethos. SHILAP Revista de lepidopterología.
  432. Fraley R., Vazire S. (2014): The N-Pact Factor: Evaluating the Quality of Empirical Journals with Respect to Sample Size and Statistical Power. PLoS ONE 9(10):e109019. https://doi.org/10.1371/journal.pone.0109019
  433. Mott R., Yuan W., Kaisaki P., Gan X., Cleak J., Edwards A., et al. (2014): The Architecture of Parent-of-Origin Effects in Mice. Cell 156(1-2):332-342. https://doi.org/10.1016/j.cell.2013.11.043
  434. Arlinghaus R. (2014): Are Current Research Evaluation Metrics Causing a Tragedy of the Scientific Commons and the Extinction of University-Based Fisheries Programs?. Fisheries 39(5):212-215. https://doi.org/10.1080/03632415.2014.903837
  435. Huber W. (2014): Deep Impact: Impact Factors and Accounting Research. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2441340
  436. Yì Wáng, Richa Arora, Yongdoo Choi, Hsiao‐Wen Chung, V. Egorov, Jens Frahm, et al. (2014): Implications of Web of Science journal impact factor for scientific output evaluation in 16 institutions and investigators’ opinion. PubMed. https://doi.org/10.3978/j.issn.2223-4292.2014.11.16
  437. Björn Brembs (2013): What ranking journals has in common with astrology. Riviste UNIMI (Università degli studi di Milano). https://doi.org/10.13130/2282-5398/3378
  438. Vaudano E. (2013): THE INNOVATIVE MEDICINES INITIATIVE: A PUBLIC PRIVATE PARTNERSHIP MODEL TO FOSTER DRUG DISCOVERY. Computational and Structural Biotechnology Journal 6(7):e201303017. https://doi.org/10.5936/csbj.201303017
  439. Götz Fabry, Martin R. Fischer (2013): Die ZMA und der Impact Factor. PubMed. https://doi.org/10.3205/zma000882
  440. Vessuri H., Guédon J., Cetto A. (2013): Excellence or quality? Impact of the current competition regime on science and scientific publishing in Latin America and its implications for development. Current Sociology 62(5):647-665. https://doi.org/10.1177/0011392113512839
  441. Park I., Peacey M., Munafò M. (2013): Modelling the effects of subjective and objective decision making in scientific peer review. Nature 506(7486):93-96. https://doi.org/10.1038/nature12786
  442. Mani J., Makarević J., Juengel E., Ackermann H., Nelson K., Bartsch G., et al. (2013): I Publish in I Edit? – Do Editorial Board Members of Urologic Journals Preferentially Publish Their Own Scientific Work?. PLoS ONE 8(12):e83709. https://doi.org/10.1371/journal.pone.0083709
  443. Eisen J., MacCallum C., Neylon C. (2013): Expert Failure: Re-evaluating Research Assessment. PLoS Biology 11(10):e1001677. https://doi.org/10.1371/journal.pbio.1001677
  444. Maria Chiara Pievatolo (2013): Metajournals. A federalist proposal for scholarly communication and data aggregation. Pisa (Department of Political Sciences). https://doi.org/10.13130/2282-5398/2942
  445. Pievatolo M. (2013): Metajournals: A Federalist Proposal for Scholarly Communication and Data Aggregation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2255775
  446. Richard Sproat (2013): TALIP Perspectives. ACM Transactions on Asian Language Information Processing 12(4):1-2. https://doi.org/10.1145/2523057.2523058
  447. Wellen R. (2013): Open Access, Megajournals, and MOOCs. Sage Open 3(4). https://doi.org/10.1177/2158244013507271
  448. Jawaid S. (2013): Striving for improved visibility and increased citation through coverage by PubMed Central. Pakistan Journal of Medical Sciences 30(1). https://doi.org/10.12669/pjms.301.4878
  449. Drew, Barbara (2003): The Future of Scholarly Publishing. adfl. https://doi.org/10.1632/adfl.34.3.54

Brembs B. (2013): Invertebrate behavior — actions or responses? Front. Neurosci. 7:221.

  1. Pietrzak B. (2025): How Invertebrate Personalities Unfold. Zoophilologica. Polish Journal of Animal Studies. https://doi.org/10.31261/zoophilologica.2025.16.09
  2. Pasquini E., Brouwer J., Di Rollo V., Baracchi D., Messina A., Frasnelli E. (2025): Central GABAergic neuromodulation of nocifensive behaviors in bumble bees. iScience 28(3):112024. https://doi.org/10.1016/j.isci.2025.112024
  3. GIOVANETTI M., ZAVATTA L., ALBERTAZZI S., FLAMINIO S., RANALLI R., BORTOLOTTI L. (2025): Exploring behavioural plasticity in the nesting biology of Megachile sculpturalis (Hymenoptera: Megachilidae) and its role in invasion success. European Journal of Entomology 122:198-209. https://doi.org/10.14411/eje.2025.025
  4. Macali A., Ferretti S., Scozzafava S., Gatto E., Carere C. (2024): Different behavioral profiles between invasive and native nudibranchs: means for invasion success?. Current Zoology 70(3):406-417. https://doi.org/10.1093/cz/zoae028
  5. Mandal F. (2024): Why does ritualization exist in animals, including humans?. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4770835
  6. Cohen-Bodénès S., Neri P. (2024): State-dependent dynamics of cuttlefish mantle activity. Journal of Experimental Biology 227(14). https://doi.org/10.1242/jeb.247457
  7. Macali A., Ferretti S., Scozzafava S., Carere C. (2023): Different behavioural profiles between invasive and native nudibranchs: means for invasion success?. https://doi.org/10.1101/2023.04.13.536773
  8. Cohen-Bodénès S., Neri P. (2023): State-dependent dynamics of cuttlefish mantle activity. https://doi.org/10.1101/2023.06.19.545578
  9. Jardim V. (2022): Individual behavioural differences in social species a multidisciplinary study with mice and ants. https://doi.org/10.11606/t.47.2022.tde-07122022-092630
  10. Arican C., Bulk J., Deisig N., Nawrot M. (2020): Cockroaches Show Individuality in Learning and Memory During Classical and Operant Conditioning. Frontiers in Physiology 10. https://doi.org/10.3389/fphys.2019.01539
  11. Matthew Olenski (2020): INCREASED DOPAMINE LEVELS DO NOT INFLUENCE AGGRESSIVE BEHAVIOR IN BLACK WIDOW SPIDERS (LATRODECTUS HESPERUS). UA Campus Repository (The University of Arizona).
  12. Arican C., Bulk J., Deisig N., Nawrot M. (2019): Cockroaches show individuality in learning and memory during classical and operant conditioning. https://doi.org/10.1101/825265
  13. Mather J., Carere C. (2019): Consider the Individual: Personality and Welfare in Invertebrates. Animal Welfare. https://doi.org/10.1007/978-3-030-13947-6_10
  14. Tonna M., Marchesi C., Parmigiani S. (2019): The biological origins of rituals: An interdisciplinary perspective. Neuroscience & Biobehavioral Reviews 98:95-106. https://doi.org/10.1016/j.neubiorev.2018.12.031
  15. Damrau C., Toshima N., Tanimura T., Brembs B., Colomb J. (2018): Octopamine and Tyramine Contribute Separately to the Counter-Regulatory Response to Sugar Deficit in Drosophila. Frontiers in Systems Neuroscience 11. https://doi.org/10.3389/fnsys.2017.00100
  16. Zaguri M., Zohar Y., Hawlena D. (2018): Considerations Used by Desert Isopods to Assess Scorpion Predation Risk. The American Naturalist 192(5):630-643. https://doi.org/10.1086/699840
  17. Avila-Núñez J., Naya M., Otero L., Alonso-Amelot M. (2017): Sticky trap predation in the Neotropical resin bug Heniartes stali (Wygodzinsky) (Hemiptera: Reduviidae: Harpactorinae). Journal of Ethology 35(2):213-219. https://doi.org/10.1007/s10164-017-0512-1
  18. Shah S. (2017): Effects of sleep-deprivation on decision-making and action selection. https://doi.org/10.22371/02.2017.006
  19. Rodrigues A., Botina L., Nascimento C., Gontijo L., Torres J., Guedes R. (2016): Ontogenic behavioral consistency, individual variation and fitness consequences among lady beetles. Behavioural Processes 131:32-39. https://doi.org/10.1016/j.beproc.2016.08.003
  20. Gyuris E. (2015): Personality traits in arthropods. Állattani Közlemények 100(1-2):101-110. https://doi.org/10.20331/allkoz.2015.100.1-2.101
  21. Kralj-Fišer S., Schuett W. (2014): Studying personality variation in invertebrates: why bother?. Animal Behaviour 91:41-52. https://doi.org/10.1016/j.anbehav.2014.02.016

Brembs B. (2013): Kin Selection. In Brenner’s Encyclopedia of Genetics pp. 163–165. Elsevier.

No citations found.

Colomb J, Reiter L, Blaszkiewicz J, Wessnitzer J, Brembs B. (2012): Open source tracking and analysis of adult Drosophila locomotion in Buridan’s paradigm with and without visual targets. PLoS ONE 7(8):e42247.

  1. Mohylyak I., Andriatsilavo M., Bengochea M., Pascual-Caro C., Asfogo N., Fonseca-Topp S., et al. (2025): Temporal transcriptional regulation of mitochondrial morphology primes activity-dependent circuit connectivity. Nature Communications 16(1). https://doi.org/10.1038/s41467-025-62908-2
  2. Horiuchi J., Uemura N., Horiuchi S., Saitoe M. (2025): Forgetting in Drosophila consists of an increase in uncertainty rather than a stochastic loss of memory. https://doi.org/10.1101/2025.06.26.661725
  3. Han R., Zhang J., Huang G., Yuan R., Lian Y., Zhao M., et al. (2025): The Effects of Blue Light on Locomotion and Cognition in Early Adult Drosophila melanogaster. Journal of Experimental Zoology Part A: Ecological and Integrative Physiology 343(4):511-520. https://doi.org/10.1002/jez.2900
  4. Han R., Zhang J., Huang H., Chen Y., Lu Y., Wang Y., et al. (2025): Effects of Coffee on Drosophila melanogaster: Concentration‐Dependent Enhancement and Sleep Deprivation Recovery. Ethology 131(9):107-121. https://doi.org/10.1111/eth.70002
  5. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2025): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.2
  6. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2025): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.3
  7. Linneweber G., Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., et al. (2024): Individuality across environmental context in Drosophila melanogaster. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-3329039/v1
  8. Sen S. (2024): eLife Assessment: Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1.sa3
  9. Thomas F Mathejczyk, Cara Knief, Muhammad A Haidar, Florian Freitag, Tydings McClary, Mathias F Wernet, et al. (2024): Reviewer #3 (Public Review): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1.sa0
  10. Thomas F Mathejczyk, Cara Knief, Muhammad A Haidar, Florian Freitag, Tydings McClary, Mathias F Wernet, et al. (2024): Reviewer #1 (Public Review): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1.sa2
  11. Thomas F Mathejczyk, Cara Knief, Muhammad A Haidar, Florian Freitag, Tydings McClary, Mathias F Wernet, et al. (2024): Reviewer #2 (Public Review): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1.sa1
  12. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2024): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171
  13. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2024): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.7554/elife.98171.1
  14. Kwon W., Lee K. (2024): How does parental diet affect offspring locomotor capacity in the bean bug, Riptortus pedestris?. Entomologia Experimentalis et Applicata 173(2):118-128. https://doi.org/10.1111/eea.13522
  15. Mohylyak I., Bengochea M., Pascual-Caro C., Asfogo N., Fonseca-Topp S., Danda N., et al. (2023): Developmental transcriptional control of mitochondrial homeostasis is required for activity-dependent synaptic connectivity. https://doi.org/10.1101/2023.06.11.544500
  16. Mi K., Li Y., Yang Y., Secombe J., Liu X. (2023): DVT: a high-throughput analysis pipeline for locomotion and social behavior in adult Drosophila melanogaster. Cell & Bioscience 13(1). https://doi.org/10.1186/s13578-023-01125-0
  17. Bengochea M., Sitt J., Izard V., Preat T., Cohen L., Hassan B. (2023): Numerical discrimination in Drosophila melanogaster. Cell Reports 42(7):112772. https://doi.org/10.1016/j.celrep.2023.112772
  18. Bengochea M., Preat T., Hassan B. (2023): A New Behavioral Paradigm for Visual Classical Conditioning in <em>Drosophila</em>. BIO-PROTOCOL 13(21). https://doi.org/10.21769/bioprotoc.4875
  19. Mathejczyk T., Knief C., Haidar M., Freitag F., McClary T., Wernet M., et al. (2023): Individuality across environmental context in Drosophila melanogaster. https://doi.org/10.1101/2023.11.26.568741
  20. Huda A., Omelchenko A., Vaden T., Castaneda A., Ni L. (2022): Responses of differentDrosophilaspecies to temperature changes. Journal of Experimental Biology 225(11). https://doi.org/10.1242/jeb.243708
  21. Bengochea M., Sitt J., Preat T., Izard V., Cohen L., Hassan B. (2022): Numerical discrimination in Drosophila melanogaster. https://doi.org/10.1101/2022.02.26.482107
  22. Zárate R., Hidalgo S., Navarro N., Molina-Mateo D., Arancibia D., Rojo-Cortés F., et al. (2022): An Early Disturbance in Serotonergic Neurotransmission Contributes to the Onset of Parkinsonian Phenotypes in Drosophila melanogaster. Cells 11(9):1544. https://doi.org/10.3390/cells11091544
  23. Sanchez Marco S., Buhl E., Firth R., Zhu B., Gainsborough M., Beleza‐Meireles A., et al. (2022): Hereditary spastic paraparesis presenting as cerebral palsy due to ADD3 variant with mechanistic insight provided by a Drosophila γ‐adducin model. Clinical Genetics 102(6):494-502. https://doi.org/10.1111/cge.14220
  24. Omelchenko A., Huda A., Castaneda A., Vaden T., Ni L. (2021): Using TrackMate to Analyze Drosophila Larval and Adult Locomotion. https://doi.org/10.1101/2021.09.28.462241
  25. Carvajal-Oliveros A., Domínguez-Baleón C., Zárate R., Campusano J., Narváez-Padilla V., Reynaud E. (2021): Nicotine suppresses Parkinson’s disease like phenotypes induced by Synphilin-1 overexpression in Drosophila melanogaster by increasing tyrosine hydroxylase and dopamine levels. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-88910-4
  26. Ahronberg A., Scharf I. (2021): Social isolation interaction with the feeding regime differentially affects survival and results in a hump-shaped pattern in movement activity. Behavioural Processes 190:104460. https://doi.org/10.1016/j.beproc.2021.104460
  27. Damrau C., Colomb J., Brembs B. (2021): Sensitivity to expression levels underlies differential dominance of a putative null allele of the Drosophila tβh gene in behavioral phenotypes. PLOS Biology 19(5):e3001228. https://doi.org/10.1371/journal.pbio.3001228
  28. Kiral F., Dutta S., Linneweber G., Hilgert S., Poppa C., Duch C., et al. (2021): Brain connectivity inversely scales with developmental temperature in Drosophila. Cell Reports 37(12):110145. https://doi.org/10.1016/j.celrep.2021.110145
  29. Kiral F., Dutta S., Linneweber G., Poppa C., von Kleist M., Hassan B., et al. (2021): Variable brain wiring through scalable and relative synapse formation in Drosophila. https://doi.org/10.1101/2021.05.12.443860
  30. Haoyang Rong (2021): Neural Coding and Organization Principles in the Drosophila Olfactory System. Open Scholarship Institutional Repository (Washington University in St. Louis). https://doi.org/10.7936/jfww-z726
  31. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. PLOS ONE 16(8):e0256560. https://doi.org/10.1371/journal.pone.0256560
  32. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. https://doi.org/10.1101/2021.01.15.426803
  33. Mollá-Albaladejo R., Sánchez-Alcañiz J. (2021): Behavior Individuality: A Focus on Drosophila melanogaster. Frontiers in Physiology 12. https://doi.org/10.3389/fphys.2021.719038
  34. Han R., Wei T., Tseng S., Lo C. (2021): Characterizing approach behavior of Drosophila melanogaster in Buridan’s paradigm. PLOS ONE 16(1):e0245990. https://doi.org/10.1371/journal.pone.0245990
  35. Hidalgo S., Campusano J., Hodge J. (2021): Assessing olfactory, memory, social and circadian phenotypes associated with schizophrenia in a genetic model based on Rim. Translational Psychiatry 11(1). https://doi.org/10.1038/s41398-021-01418-3
  36. Benmaamar S., Brembs B. (2021): Allelic variants at the foraging locus respond differentially to changes in larval density or food composition. In&Vertebrates. https://doi.org/10.52732/torh8261
  37. Zhuravlev A., Vetrovoy O., Ivanova P., Savvateeva-Popova E. (2020): 3-Hydroxykynurenine in Regulation of Drosophila Behavior: The Novel Mechanisms for Cardinal Phenotype Manifestations. Frontiers in Physiology 11. https://doi.org/10.3389/fphys.2020.00971
  38. Khakhalin A. (2020): Analysis of Visual Collision Avoidance inXenopusTadpoles. Cold Spring Harbor Protocols 2021(4):pdb.prot106914. https://doi.org/10.1101/pdb.prot106914
  39. Khakhalin A., Lopez V., Aizenman C. (2020): Behavioral assays to study neural development in Xenopus laevis tadpoles. https://doi.org/10.1101/2020.08.21.261669
  40. Ertekin D., Kirszenblat L., Faville R., van Swinderen B. (2020): Down-regulation of a cytokine secreted from peripheral fat bodies improves visual attention while reducing sleep in Drosophila. PLOS Biology 18(8):e3000548. https://doi.org/10.1371/journal.pbio.3000548
  41. Kiral F., Linneweber G., Mathejczyk T., Georgiev S., Wernet M., Hassan B., et al. (2020): Autophagy-dependent filopodial kinetics restrict synaptic partner choice during Drosophila brain wiring. Nature Communications 11(1). https://doi.org/10.1038/s41467-020-14781-4
  42. Linneweber G., Andriatsilavo M., Dutta S., Bengochea M., Hellbruegge L., Liu G., et al. (2020): A neurodevelopmental origin of behavioral individuality in the Drosophila visual system. Science 367(6482):1112-1119. https://doi.org/10.1126/science.aaw7182
  43. de Bruijn J. (2020): Information reliability shaping parasitoid foraging behaviour. https://doi.org/10.18174/506702
  44. Leismann J., Spagnuolo M., Pradhan M., Wacheul L., Vu M., Musheev M., et al. (2020): The 18S ribosomal RNA m 6 A methyltransferase Mettl5 is required for normal walking behavior in Drosophila. EMBO reports 21(7). https://doi.org/10.15252/embr.201949443
  45. Melnattur K., Kirszenblat L., Morgan E., Militchin V., Sakran B., English D., et al. (2020): A conserved role for sleep in supporting Spatial Learning in Drosophila. Sleep 44(3). https://doi.org/10.1093/sleep/zsaa197
  46. Tainton-Heap L., Kirszenblat L., Notaras E., Grabowska M., Jeans R., Feng K., et al. (2020): A Paradoxical Kind of Sleep in Drosophila melanogaster. Current Biology 31(3):578-590.e6. https://doi.org/10.1016/j.cub.2020.10.081
  47. Mathias Raß, Svenja Oestreich, Ardi Manaj, Stephan Schneuwly (2020): Loss of fuss in Drosophila melanogaster results in decreased locomotor activity due to an increased number of pauses. PubMed. https://doi.org/10.17912/micropub.biology.000230
  48. Palazzo O., Rass M., Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. Open Biology 10(12). https://doi.org/10.1098/rsob.200295
  49. Palazzo O., Raß M., Brembs B. (2020): Identification of FoxP circuits involved in locomotion and object fixation in Drosophila. https://doi.org/10.1101/2020.07.15.204677
  50. Takagi S., Benton R. (2020): Animal Behavior: A Neural Basis of Individuality. Current Biology 30(12):R710-R712. https://doi.org/10.1016/j.cub.2020.04.052
  51. Hidalgo S., Castro C., Zárate R., Valderrama B., Hodge J., Campusano J. (2020): The behavioral and neurochemical characterization of a Drosophila dysbindin mutant supports the contribution of serotonin to schizophrenia negative symptoms. Neurochemistry International 138:104753. https://doi.org/10.1016/j.neuint.2020.104753
  52. Moulin T., Covill L., Itskov P., Williams M., Schiöth H. (2020): Rodent and fly models in behavioral neuroscience: An evaluation of methodological advances, comparative research, and future perspectives. Neuroscience & Biobehavioral Reviews 120:1-12. https://doi.org/10.1016/j.neubiorev.2020.11.014
  53. Ertekin D., Kirszenblat L., Faville R., van Swinderen B. (2019): Downregulation of a satiety signal from peripheral fat bodies improves visual attention while reducing sleep need in Drosophila. https://doi.org/10.1101/808998
  54. Kiral F., Linneweber G., Georgiev S., Hassan B., von Kleist M., Hiesinger P. (2019): Autophagy-dependent filopodial kinetics restrict synaptic partner choice during Drosophila brain wiring. https://doi.org/10.1101/762179
  55. Linneweber G., Andriatsilavo M., Dutta S., Hellbruegge L., Liu G., Ejsmont R., et al. (2019): A neurodevelopmental origin of behavioral individuality. https://doi.org/10.1101/540880
  56. Yen H., Han R., Lo C. (2019): Quantification of Visual Fixation Behavior and Spatial Orientation Memory in Drosophila melanogaster. Frontiers in Behavioral Neuroscience 13. https://doi.org/10.3389/fnbeh.2019.00215
  57. Scaplen K., Mei N., Bounds H., Song S., Azanchi R., Kaun K. (2019): Automated real-time quantification of group locomotor activity in Drosophila melanogaster. Scientific Reports 9(1). https://doi.org/10.1038/s41598-019-40952-5
  58. Kirszenblat L., Yaun R., van Swinderen B. (2019): Visual experience drives sleep need in Drosophila. Sleep 42(7). https://doi.org/10.1093/sleep/zsz102
  59. Damrau C., Toshima N., Tanimura T., Brembs B., Colomb J. (2018): Octopamine and Tyramine Contribute Separately to the Counter-Regulatory Response to Sugar Deficit in Drosophila. Frontiers in Systems Neuroscience 11. https://doi.org/10.3389/fnsys.2017.00100
  60. Damrau C., Colomb J., Brembs B. (2018): Differential dominance of an allele of the Drosophila tßh gene challenges standard genetic techniques. https://doi.org/10.1101/504332
  61. Ostrowski D., Salari A., Zars M., Zars T. (2018): A biphasic locomotor response to acute unsignaled high temperature exposure in Drosophila. PLOS ONE 13(6):e0198702. https://doi.org/10.1371/journal.pone.0198702
  62. Coelho D., Schwartz S., Merino M., Hauert B., Topfel B., Tieche C., et al. (2018): Culling Less Fit Neurons Protects against Amyloid-β-Induced Brain Damage and Cognitive and Motor Decline. Cell Reports 25(13):3661-3673.e3. https://doi.org/10.1016/j.celrep.2018.11.098
  63. Coelho D., Schwartz S., Merino M., Hauert B., Topfel B., Tieche C., et al. (2018): Culling less fit neurons protects against amyloid-β-induced brain damage and cognitive and motor decline. https://doi.org/10.1101/468868
  64. Liu G., Nath T., Linneweber G., Claeys A., Guo Z., Li J., et al. (2018): A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila. PLOS Computational Biology 14(8):e1006410. https://doi.org/10.1371/journal.pcbi.1006410
  65. Kirszenblat L., Ertekin D., Goodsell J., Zhou Y., Shaw P., van Swinderen B. (2018): Sleep regulates visual selective attention in Drosophila. Journal of Experimental Biology. https://doi.org/10.1242/jeb.191429
  66. Kirszenblat L., Ertekin D., Goodsell J., Zhou Y., Shaw P., Swinderen B. (2018): Sleep regulates visual selective attention in Drosophila. https://doi.org/10.1101/403246
  67. Rohde P., Østergaard S., Kristensen T., Sørensen P., Loeschcke V., Mackay T., et al. (2018): Functional Validation of Candidate Genes Detected by Genomic Feature Models. G3 Genes|Genomes|Genetics 8(5):1659-1668. https://doi.org/10.1534/g3.118.200082
  68. Karunanithi S., Troup M., van Swinderen B. (2018): Using Drosophila to Understand General Anesthesia: From Synapses to Behavior. Methods in Enzymology. https://doi.org/10.1016/bs.mie.2018.02.003
  69. Gilad T., Koren R., Moalem Y., Subach A., Scharf I. (2018): Effect of continuous and alternating episodes of starvation on behavior and reproduction in the red flour beetle. Journal of Zoology 305(4):213-222. https://doi.org/10.1111/jzo.12556
  70. Weinrich T., Hogg C., Jeffery G. (2018): The temporal sequence of improved mitochondrial function on the dynamics of respiration, mobility, and cognition in aged Drosophila. Neurobiology of Aging 70:140-147. https://doi.org/10.1016/j.neurobiolaging.2018.06.010
  71. Xiao C., Qiu S., Robertson R. (2017): Persistent One-Way Walking in a Circular Arena in Drosophila melanogaster Canton-S Strain. Behavior Genetics 48(1):80-93. https://doi.org/10.1007/s10519-017-9881-z
  72. Xiao C., Qiu S., Robertson R. (2017): Persistent one-way walking in a circular arena in Drosophila melanogaster Canton-S strain. https://doi.org/10.1101/145888
  73. Molina-Mateo D., Fuenzalida-Uribe N., Hidalgo S., Molina-Fernández C., Abarca J., Zárate R., et al. (2017): Characterization of a presymptomatic stage in a Drosophila Parkinson’s disease model: Unveiling dopaminergic compensatory mechanisms. Biochimica et Biophysica Acta (BBA) – Molecular Basis of Disease 1863(11):2882-2890. https://doi.org/10.1016/j.bbadis.2017.07.013
  74. Scharf I., Wertheimer K., Xin J., Gilad T., Goldenberg I., Subach A. (2017): Context‐dependent effects of cold stress on behavioral, physiological, and life‐history traits of the red flour beetle. Insect Science 26(1):142-153. https://doi.org/10.1111/1744-7917.12497
  75. Gris K., Coutu J., Gris D. (2017): Supervised and Unsupervised Learning Technology in the Study of Rodent Behavior. Frontiers in Behavioral Neuroscience 11. https://doi.org/10.3389/fnbeh.2017.00141
  76. Corthals K., Heukamp A., Kossen R., Großhennig I., Hahn N., Gras H., et al. (2017): Neuroligins Nlg2 and Nlg4 Affect Social Behavior in Drosophila melanogaster. Frontiers in Psychiatry 8. https://doi.org/10.3389/fpsyt.2017.00113
  77. Ferguson L., Petty A., Rohrscheib C., Troup M., Kirszenblat L., Eyles D., et al. (2017): Transient Dysregulation of Dopamine Signaling in a Developing Drosophila Arousal Circuit Permanently Impairs Behavioral Responsiveness in Adults. Frontiers in Psychiatry 8. https://doi.org/10.3389/fpsyt.2017.00022
  78. Fuenzalida-Uribe N., Campusano J. (2017): Unveiling the Dual Role of the Dopaminergic System on Locomotion and the Innate Value for an Aversive Olfactory Stimulus in Drosophila. Neuroscience 371:433-444. https://doi.org/10.1016/j.neuroscience.2017.12.032
  79. Geissmann Q., Garcia Rodriguez L., Beckwith E., French A., Jamasb A., Gilestro G. (2017): Ethoscopes: An open platform for high-throughput ethomics. PLOS Biology 15(10):e2003026. https://doi.org/10.1371/journal.pbio.2003026
  80. Geissmann Q., Rodriguez L., Beckwith E., French A., Jamasb A., Gilestro G. (2017): Ethoscopes: an open platform for high-throughput ethomics. https://doi.org/10.1101/113647
  81. Hidalgo S., Molina-Mateo D., Escobedo P., Zárate R., Fritz E., Fierro A., et al. (2017): Characterization of a Novel Drosophila SERT Mutant: Insights on the Contribution of the Serotonin Neural System to Behaviors. ACS Chemical Neuroscience 8(10):2168-2179. https://doi.org/10.1021/acschemneuro.7b00089
  82. Qiu S., Xiao C., Meldrum Robertson R. (2017): Different age-dependent performance in Drosophila wild-type Canton-S and the white mutant w1118 flies. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 206:17-23. https://doi.org/10.1016/j.cbpa.2017.01.003
  83. Qiu S., Xiao C. (2017): Behavioral decoding of Drosophila locomotion in a circular arena. https://doi.org/10.7287/peerj.preprints.2908v2
  84. Qiu S., Robertson R., Xiao C. (2017): Behavioral decoding of Drosophila locomotion in a circular arena. https://doi.org/10.7287/peerj.preprints.2908v1
  85. Wexler Y., Wertheimer K., Subach A., Pruitt J., Scharf I. (2017): Mating alters the link between movement activity and pattern in the red flour beetle. Physiological Entomology 42(4):299-306. https://doi.org/10.1111/phen.12195
  86. Wexler Y., Scharf I. (2017): Distinct effects of two separately applied stressors on behavior in the red flour beetle. Behavioural Processes 145:86-92. https://doi.org/10.1016/j.beproc.2017.10.008
  87. Gorostiza E., Colomb J., Brembs B. (2016): A decision underlies phototaxis in an insect. Open Biology 6(12):160229. https://doi.org/10.1098/rsob.160229
  88. Rose J., Cullen D., Simpson S., Stevenson P. (2016): Born to win or bred to lose: aggressive and submissive behavioural profiles in crickets. Animal Behaviour 123:441-450. https://doi.org/10.1016/j.anbehav.2016.11.021
  89. Ehaideb S., Wignall E., Kasuya J., Evans W., Iyengar A., Koerselman H., et al. (2016): Mutation of orthologous prickle genes causes a similar epilepsy syndrome in flies and humans. Annals of Clinical and Translational Neurology 3(9):695-707. https://doi.org/10.1002/acn3.334
  90. Wexler Y., Subach A., Pruitt J., Scharf I. (2016): Behavioral repeatability of flour beetles before and after metamorphosis and throughout aging. Behavioral Ecology and Sociobiology 70(5):745-753. https://doi.org/10.1007/s00265-016-2098-y
  91. Xiao C., Robertson R. (2015): Locomotion Induced by Spatial Restriction in Adult Drosophila. PLOS ONE 10(9):e0135825. https://doi.org/10.1371/journal.pone.0135825
  92. Christine Damrau (2015): Aminergic control of Drosophila behavior. Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver. https://doi.org/10.17169/refubium-17177
  93. Axel Gorostiza E., Colomb J., Brembs B. (2015): A decision underlies phototaxis in an insect. https://doi.org/10.1101/023846
  94. Colomb J., Brembs B. (2015): Sub-strains of Drosophila Canton-S differ markedly in their locomotor behavior. F1000Research 3:176. https://doi.org/10.12688/f1000research.4263.2
  95. Girdhar K., Gruebele M., Chemla Y. (2015): The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog. PLOS ONE 10(7):e0128668. https://doi.org/10.1371/journal.pone.0128668
  96. Freeman A., Dai H., Sanyal S. (2014): Use of Drosophila to Study Restless Legs Syndrome. Movement Disorders. https://doi.org/10.1016/b978-0-12-405195-9.00078-0
  97. Zenger B., Wetzel S., Duncan J. (2014): Acquisition of High-Quality Digital Video of <em>Drosophila</em> Larval and Adult Behaviors from a Lateral Perspective. Journal of Visualized Experiments. https://doi.org/10.3791/51981
  98. Colomb J., Brembs B. (2014): Sub-strains of Drosophila Canton-S differ markedly in their locomotor behavior. F1000Research 3:176. https://doi.org/10.12688/f1000research.4263.1
  99. Tao J., Risse B., Jiang X. (2014): Stereo and Motion Based 3D High Density Object Tracking. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-53842-1_12
  100. Giannoni-Guzmán M., Avalos A., Marrero J., Otero-Loperena E., Kayım M., Medina J., et al. (2014): Measuring individual locomotor rhythms in honey bees, paper wasps and similar sized insects. Journal of Experimental Biology. https://doi.org/10.1242/jeb.096180
  101. Kodama N., Kimura T., Yonemura S., Kaneda S., Ohashi M., Ikeno H. (2014): Automated Analysis of Two-Dimensional Positions and Body Lengths of Earthworms (Oligochaeta); MimizuTrack. PLoS ONE 9(6):e97986. https://doi.org/10.1371/journal.pone.0097986
  102. Risse B., Berh D., Tao J., Jiang X., Klette R., Klämbt C. (2013): Comparison of two 3D tracking paradigms for freely flying insects. EURASIP Journal on Image and Video Processing 2013(1). https://doi.org/10.1186/1687-5281-2013-57
  103. Timmerman C., Suppiah S., Gurudatta B., Yang J., Banerjee C., Sandstrom D., et al. (2013): The Drosophila Transcription Factor Adf-1 (nalyot) Regulates Dendrite Growth by Controlling FasII and Staufen Expression Downstream of CaMKII and Neural Activity. Journal of Neuroscience 33(29):11916-11931. https://doi.org/10.1523/jneurosci.1760-13.2013
  104. Sathishkumar Raja (2013): The neuronal basis of spontaneous flight behavior in Drosophila. Refubium (Universitätsbibliothek der Freien Universität Berlin). https://doi.org/10.17169/refubium-13718

Brembs B. (2011): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proc. Roy. Soc. B. 278(1707):930–939.

  1. Drummond A., Nash S., Holloway T., Turner L., Wilson A., Briffa M. (2025): A sensory investment syndrome hypothesis: personality and predictability are linked to sensory capacity in the hermit crab Pagurus bernhardus. Proceedings of the Royal Society B: Biological Sciences 292(2050). https://doi.org/10.1098/rspb.2025.0932
  2. Winter G., Schielzeth H. (2025): Genetic and habitat complexity effects on unpredictability in escape behaviour of a grasshopper species. Journal of Evolutionary Biology 38(5):618-629. https://doi.org/10.1093/jeb/voaf030
  3. Lemos J., Gholami T., Kane R. (2025): Muslim Debates on Free Will and Robert Kane’s Libertarian View. Philosophia 52(5):1341-1360. https://doi.org/10.1007/s11406-024-00805-6
  4. Cheng K. (2025): Random-rate processing in navigation in bacteria, archaea, and desert ants. Psihologijske teme 34(1):79-96. https://doi.org/10.31820/pt.34.1.4
  5. Westphal K. (2025): Kant’s Cognitive Architecture & Bickhard’s Interactivism. Phenomenology and the Cognitive Sciences. https://doi.org/10.1007/s11097-025-10070-x
  6. Mochalov K., Voskresensky A. (2025): Symbolic activity as a mechanism of semiotic mediation between the real and the possible. Semiotic studies 5(4):29-38. https://doi.org/10.18287/2782-2966-2025-5-4-29-38
  7. Chittka L., Skeels S., Dyakova O., Janbon M. (2025): The exploration of consciousness in insects. Philosophical Transactions of the Royal Society B: Biological Sciences 380(1939). https://doi.org/10.1098/rstb.2024.0302
  8. Kastrinos N. (2025): State of the Future 20.0 By Jerome C.Glenn, TheodoreGordon, ElizabethFlorescu, and the Millenium Project Team, The Millenium ProjectWashington DC, 470 p text plus appendixes with sources and further information, available through https://millennium-project.org/. FUTURES & FORESIGHT SCIENCE 7(1). https://doi.org/10.1002/ffo2.70004
  9. Philippe Faure (2025): Exploration and behavioral variability. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-443-29867-7.00026-8
  10. Hendijani R. (2025): Motivation and Time: Motivational Congruence Theory’s Stance. Integrative Psychological and Behavioral Science 59(3). https://doi.org/10.1007/s12124-025-09928-1
  11. Deeti S., Cheng K. (2025): Desert ants (Melophorus bagoti) oscillate and scan more in navigation when the visual scene changes. Animal Cognition 28(1). https://doi.org/10.1007/s10071-025-01936-3
  12. Deeti S., Cheng K. (2025): Ants oscillate and scan more in navigation when the visual scene changes. https://doi.org/10.1101/2025.01.13.632872
  13. Mochalov K., Voskresensky A. (2025): Symbolic Activity and Agency. Integrative Psychological and Behavioral Science 59(2). https://doi.org/10.1007/s12124-025-09911-w
  14. Poddiakov A. (2024): Possibilities of Free Will in Different Physical, Social, and Technological Worlds: An Introduction to a Thematic Issue. Integrative Psychological and Behavioral Science 58(3):884-893. https://doi.org/10.1007/s12124-024-09843-x
  15. Walsh A. (2024): References. Understanding Biosocial Criminology. https://doi.org/10.4337/9781035322879.00023
  16. Winter G., Schielzeth H. (2024): Ontogeny of unpredictability in escape behaviour of a grasshopper species. Animal Behaviour 219:123029. https://doi.org/10.1016/j.anbehav.2024.11.007
  17. Mitchell J. (2024): Animals in public service: A narrative inquiry. Administrative Theory & Praxis 46(3):259-279. https://doi.org/10.1080/10841806.2023.2282902
  18. Tønnessen M. (2024): Ecological Semiotics. Reference Module in Earth Systems and Environmental Sciences. https://doi.org/10.1016/b978-0-443-21964-1.00018-5
  19. Tse P. (2024): A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.001.0001
  20. Tse P. (2024): Scientific Arguments against Free Will. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.003.0004
  21. Tse P. (2024): Dedication. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.002.0004
  22. Tse P. (2024): Epigraph. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.002.0005
  23. Tse P. (2024): Frontispiece. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.002.0009
  24. Tse P. (2024): The Philosophy of Free Will. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.003.0003
  25. Tse P. (2024): The Neuroscience of Free Will. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.003.0005
  26. Tse P. (2024): The Basic Questions. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.003.0002
  27. Tse P. (2024): Introduction. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.003.0001
  28. Tse P. (2024): Notes. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.002.0008
  29. Tse P. (2024): Copyright Page. A Neurophilosophy of Libertarian Free Will. https://doi.org/10.1093/oso/9780198876953.002.0003
  30. Tarbutton R. (2024): Neo-Darwinism and Teleological Synchronicity. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4720028
  31. Tarbutton R. (2024): <p>Determinism and Criminal Culpability&nbsp;</p>. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4962888
  32. Baumeister R. (2024): The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.001.0001
  33. Baumeister R. (2024): Do Conscious Thoughts Cause Behavior?. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0009
  34. Baumeister R. (2024): How Free Will Operates, in Practice. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0018
  35. Baumeister R. (2024): Summing Up and Looking Ahead. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0021
  36. Baumeister R. (2024): Free Will Gone Rogue. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0013
  37. Baumeister R. (2024): Positive Psychology and Free Will. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0020
  38. Baumeister R. (2024): Does Free Will Mean Random Action?. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0006
  39. Baumeister R. (2024): What Free Will Feels Like. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0014
  40. Baumeister R. (2024): Meaning Changes Everything. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0011
  41. Baumeister R. (2024): Why Free Will Evolved. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0003
  42. Baumeister R. (2024): Free Will in the Flesh. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0005
  43. Baumeister R. (2024): How Children Acquire Free Will. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0016
  44. Baumeister R. (2024): Wanting Comes First. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0007
  45. Baumeister R. (2024): Copyright Page. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.002.0004
  46. Baumeister R. (2024): What Should a Scientific Theory of Free Will Look Like?. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0002
  47. Baumeister R. (2024): Free Will Is No Free Lunch. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0012
  48. Baumeister R. (2024): How Conscious Thoughts Cause Behavior. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0010
  49. Baumeister R. (2024): Desire to Have or Use Free Will?. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0019
  50. Baumeister R. (2024): Owning Your Actions as an Individual. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0017
  51. Baumeister R. (2024): Whether People Believe in Free Will or Not. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0015
  52. Baumeister R. (2024): The Time Dimension. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0004
  53. Baumeister R. (2024): What Is Choice?. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0008
  54. Baumeister R. (2024): Why Does Free Will Matter?. The Science of Free Will. https://doi.org/10.1093/oso/9780197693520.003.0001
  55. Ilan Y. (2024): Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making. Integrative Psychological and Behavioral Science 58(4):1843-1875. https://doi.org/10.1007/s12124-024-09853-9
  56. 罗 曼. (2024): The Predictive Effect of Subjective Social Status on Negative Attitudes towards Artificial Intelligence: A Moderated Mediation Model. Advances in Psychology 14(06):578-586. https://doi.org/10.12677/ap.2024.146442
  57. Smith B., Lei H. (2023): Decision-making: A new role for insect mushroom bodies. Current Biology 33(19):R1004-R1006. https://doi.org/10.1016/j.cub.2023.08.047
  58. Frith C., Frith U. (2023): What Makes Us Social?. The MIT Press eBooks. https://doi.org/10.7551/mitpress/10400.001.0001
  59. Lee D. (2023): Responsible Robots and AI Via Moral Conditioning. Online Journal of Robotics & Automation Technology 2(2). https://doi.org/10.33552/ojrat.2023.02.000534
  60. Jablonka E., Ginsburg S. (2023): From Teleonomy to Mentally Driven Goal-Directed Behavior: Evolutionary Considerations. Evolution “On Purpose”. https://doi.org/10.7551/mitpress/14642.003.0010
  61. Kormas P., Moutzouri A., Protopapadakis E. (2023): Implications of Neuroplasticity to the Philosophical Debate of Free Will and Determinism. Handbook of Computational Neurodegeneration. https://doi.org/10.1007/978-3-319-75922-7_21
  62. Nichelli P., Grafman J. (2023): The place of Free Will: the freedom of the prisoner. Neurological Sciences 45(3):861-871. https://doi.org/10.1007/s10072-023-07138-4
  63. T Aquino, J Minxha, S Dunne, I Ross, A Mamelak, U Rutishauser, et al. (2023): References. What Makes Us Social?. https://doi.org/10.7551/mitpress/10400.003.0029
  64. Nilufar Y. (2023): DIFFERENCES IN STUDENTS’ VOLITIONAL REGULATION AND WILLPOWER ACCORDING TO EDUCATIONAL DIRECTIONS. Frontline Social Sciences and History Journal 03(05):87-95. https://doi.org/10.37547/social-fsshj-03-05-12
  65. Hutfluss A., Bermúdez‐Cuamatzin E., Mouchet A., Briffa M., Slabbekoorn H., Dingemanse N. (2022): Male song stability shows cross‐year repeatability but does not affect reproductive success in a wild passerine bird. Journal of Animal Ecology 91(7):1507-1520. https://doi.org/10.1111/1365-2656.13736
  66. Botero Bernal A. (2022): Moral, derecho y sociedad. Reflexiones interdisciplinarias sobre la crisis moral en el caso latinoamericano. Problema. Anuario de Filosofía y Teoría del Derecho. https://doi.org/10.22201/iij.24487937e.2022.16.17038
  67. Dresp-Langley B. (2022): From Biological Synapses to “Intelligent” Robots. Electronics 11(5):707. https://doi.org/10.3390/electronics11050707
  68. Austin E. (2022): Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.001.0001
  69. Austin E. (2022): Why Can’t We Be Friends?. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0006
  70. Austin E. (2022): Building the Tranquil Child. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0017
  71. Austin E. (2022): Living Unnoticed: Politics and Power. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0011
  72. Austin E. (2022): Science and Anxiety. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0019
  73. Austin E. (2022): Of Sex, Love, and Harmless Pleasure. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0016
  74. Austin E. (2022): Happiness, Theirs and Ours. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0003
  75. Austin E. (2022): Notes. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.002.0009
  76. Austin E. (2022): What Do You Want?. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0005
  77. Austin E. (2022): Living Unnoticed: The Tyranny of the “Like”. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0012
  78. Austin E. (2022): That Old Time Religion. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0020
  79. Austin E. (2022): Misfortune and Resilience. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0015
  80. Austin E. (2022): Epicureanism, the Original Cast. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0002
  81. Austin E. (2022): Imposter Syndrome. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0009
  82. Austin E. (2022): Copyright Page. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.002.0004
  83. Austin E. (2022): Wealth and What It Costs. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0010
  84. Austin E. (2022): Practicing Epicureanism. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0024
  85. Austin E. (2022): Experiencing Death. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0021
  86. Austin E. (2022): Greed for Life. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0014
  87. Austin E. (2022): Let Me Be Frank. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0007
  88. Austin E. (2022): Pandemics and Other Comforting Horrors. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0022
  89. Austin E. (2022): Natural Hedonism. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0004
  90. Austin E. (2022): Dedication. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.002.0005
  91. Austin E. (2022): Foodies, Dinner Parties, and Wine Snobs. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0018
  92. Austin E. (2022): The Pleasures of Virtue. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0008
  93. Austin E. (2022): Maybe We’re Doing It Wrong. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0001
  94. Austin E. (2022): The Fourfold Remedy. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0023
  95. Austin E. (2022): Ambition, Work, and Success. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.003.0013
  96. Jablonka E., Ginsburg S. (2022): Learning and the Evolution of Conscious Agents. Biosemiotics 15(3):401-437. https://doi.org/10.1007/s12304-022-09501-y
  97. van Hateren J. (2022): The estimator theory of life and mind: how agency and consciousness can emerge. https://doi.org/10.31219/osf.io/mrhzb
  98. Schultz J., Frith C. (2022): Animacy and the prediction of behaviour. Neuroscience & Biobehavioral Reviews 140:104766. https://doi.org/10.1016/j.neubiorev.2022.104766
  99. Schultz J., Frith C. (2022): Animacy and the prediction of behaviour. https://doi.org/10.31234/osf.io/w8x59
  100. Christensen J., Farahi F., Vartanian M., Yazdi S. (2022): Choice Hygiene for “Consumer Neuroscientists”? Ethical Considerations and Proposals for Future Endeavours. Frontiers in Neuroscience 15. https://doi.org/10.3389/fnins.2021.612639
  101. Kull K. (2022): Choices by organisms: on the role of freedom in behaviour and evolution. Biological Journal of the Linnean Society 139(4):555-562. https://doi.org/10.1093/biolinnean/blac077
  102. Cornejo Plaza M. (2022): Perspectiva neurocientífica de la agencia: ¿es problemática para el derecho?. Problema. Anuario de Filosofía y Teoría del Derecho. https://doi.org/10.22201/iij.24487937e.2022.16.17039
  103. Kormas P., Moutzouri A., Protopapadakis E. (2022): Implications of Neuroplasticity to the Philosophical Debate of Free Will and Determinism. Handbook of Computational Neurodegeneration. https://doi.org/10.1007/978-3-319-75479-6_21-1
  104. Anuario de Filosofía y Teoría del Derecho P. (2022): Revista completa. Problema. Anuario de Filosofía y Teoría del Derecho. https://doi.org/10.22201/iij.24487937e.2022.16.17042
  105. Yurchenko S. (2022): A systematic approach to brain dynamics: cognitive evolution theory of consciousness. Cognitive Neurodynamics 17(3):575-603. https://doi.org/10.1007/s11571-022-09863-6
  106. Yurchenko S. (2022): From the origins to the stream of consciousness and its neural correlates. Frontiers in Integrative Neuroscience 16. https://doi.org/10.3389/fnint.2022.928978
  107. Grimm S. (2022): Series Editor Foreword. Living for Pleasure. https://doi.org/10.1093/oso/9780197558324.002.0007
  108. Cornwell T., Mitchell D., Beckmann C., Joynson A., Biro P. (2022): Multilevel repeatability shows selection may act on both personality and predictability, but neither is state dependent. Animal Behaviour 195:85-92. https://doi.org/10.1016/j.anbehav.2022.11.004
  109. MIHALACHE A., ZĂGREAN L. (2021): Free will – an approach from the perspective of neuroscience. Romanian Journal of Medical Practice 16(3):327-337. https://doi.org/10.37897/rjmp.2021.3.6
  110. Elisa Martn-Arvalo, Carole Guedj, Franois Cotton, Gilles Rode, Karen Reilly, Fadila Hadj, et al. (2021): . OBM Neurobiology 05(03). https://doi.org/10.21926/obm.neurobiol.2103
  111. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. PLOS ONE 16(8):e0256560. https://doi.org/10.1371/journal.pone.0256560
  112. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. https://doi.org/10.1101/2021.01.15.426803
  113. Malatesti L., McMillan J. (2021): Some Methodological Issues in Neuroethics: The Case of Responsibility and Psychopathy. Cambridge Quarterly of Healthcare Ethics 30(4):681-693. https://doi.org/10.1017/s0963180121000153
  114. Luke Armstrong (2021): Autonomy in political liberalism. https://doi.org/10.5525/gla.thesis.82094
  115. van Loon M., Bovenkerk B. (2021): 32. The ethics and mindedness of insects. Justice and food security in a changing climate. https://doi.org/10.3920/978-90-8686-915-2_32
  116. Osman M., Bechlivanidis C. (2021): Public perceptions of manipulations on behavior outside of awareness. Psychology of Consciousness: Theory, Research, and Practice 11(2):154-176. https://doi.org/10.1037/cns0000308
  117. Westfal M., Crusius J., Genschow O. (2021): Imitation and interindividual differences: Belief in free will is not related to automatic imitation. Acta Psychologica 219:103374. https://doi.org/10.1016/j.actpsy.2021.103374
  118. Westfal M., Crusius J., Genschow O. (2021): Imitation and interindividual differences: Belief in free will is not related to automatic imitation. https://doi.org/10.31234/osf.io/bk2j4
  119. Yurchenko S. (2021): Why the Quantum Brain?. OBM Neurobiology 05(03):1-18. https://doi.org/10.21926/obm.neurobiol.2103103
  120. Sendova-Franks A., Worley A., Franks N. (2020): Post-contact immobility and half-lives that save lives. Proceedings of the Royal Society B: Biological Sciences 287(1930):20200881. https://doi.org/10.1098/rspb.2020.0881
  121. Martinig A., Mathot K., Lane J., Dantzer B., Boutin S. (2020): Selective disappearance does not underlie age-related changes in trait repeatability in red squirrels. Behavioral Ecology 32(2):306-315. https://doi.org/10.1093/beheco/araa136
  122. Brembs B. (2020): The brain as a dynamically active organ. Biochemical and Biophysical Research Communications 564:55-69. https://doi.org/10.1016/j.bbrc.2020.12.011
  123. Broom D. (2020): Brain complexity, sentience and welfare. Animal Sentience 5(29). https://doi.org/10.51291/2377-7478.1613
  124. Travers E., Friedemann M., Haggard P. (2020): The Readiness Potential reflects planning-based expectation, not uncertainty, in the timing of action. Cognitive Neuroscience 12(1):14-27. https://doi.org/10.1080/17588928.2020.1824176
  125. Travers E., Friedemann M., Haggard P. (2020): The Readiness Potential reflects expectation, not uncertainty, in the timing of action. https://doi.org/10.1101/2020.04.16.045344
  126. Shine J. (2020): The thalamus integrates the macrosystems of the brain to facilitate complex, adaptive brain network dynamics. Progress in Neurobiology 199:101951. https://doi.org/10.1016/j.pneurobio.2020.101951
  127. Schultz J., Frith C. (2020): Animacy and the prediction of behaviour. https://doi.org/10.31234/osf.io/2k4mj
  128. Vervoort L., Blusiewicz T. (2020): The CMT Model of Free Will. Dialogue 59(3):415-435. https://doi.org/10.1017/s0012217320000104
  129. Vervoort L., Blusiewicz T. (2020): Free will and (in)determinism in the brain: a case for naturalized philosophy. THEORIA. An International Journal for Theory, History and Foundations of Science 35(3):345-364. https://doi.org/10.1387/theoria.21302
  130. Abe M. (2020): Functional advantages of Lévy walks emerging near a critical point. Proceedings of the National Academy of Sciences 117(39):24336-24344. https://doi.org/10.1073/pnas.2001548117
  131. Abe M., Kasada M. (2020): Optimal Random Avoidance Strategy in Prey-Predator Interactions. https://doi.org/10.1101/2020.03.04.976076
  132. Abe M. (2020): Functional advantages of Lévy walks emerging near a critical point. https://doi.org/10.1101/2020.01.27.920801
  133. Chauhan M. (2020): Biologically Inspired Intelligent Machine and Its Correlation to Free Will. Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-981-15-6876-3_21
  134. Henriksen R., Höglund A., Fogelholm J., Abbey-Lee R., Johnsson M., Dingemanse N., et al. (2020): Intra-Individual Behavioural Variability: A Trait under Genetic Control. International Journal of Molecular Sciences 21(21):8069. https://doi.org/10.3390/ijms21218069
  135. Metten R. (2020): Ich will, also bin ich. Ich will, also bin ich. https://doi.org/10.1007/978-3-662-59827-6_4
  136. Metten R. (2020): Laying down a path in walking. Ich will, also bin ich. https://doi.org/10.1007/978-3-662-59827-6_3
  137. Metten R. (2020): Leben gedeiht in Freiheit. Ich will, also bin ich. https://doi.org/10.1007/978-3-662-59827-6_2
  138. Metten R. (2020): Wenn mal was schief geht …. Ich will, also bin ich. https://doi.org/10.1007/978-3-662-59827-6_5
  139. Budaev S., Kristiansen T., Giske J., Eliassen S. (2020): Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing. Royal Society Open Science 7(12):201886. https://doi.org/10.1098/rsos.201886
  140. Edelman S. (2020): Life, Death, and Other Inconvenient Truths. The MIT Press eBooks. https://doi.org/10.7551/mitpress/12282.001.0001
  141. Beliavsky V. (2020): On Freedom. Freedom, Responsibility, and Therapy. https://doi.org/10.1007/978-3-030-41571-6_1
  142. van Woudenberg, René, Peels, R., de Ridder, Jeroen (2020): Scientific Challenges to Common Sense Philosophy. https://doi.org/10.4324/9781351064224
  143. Deli E. (2020): Can the Fermionic Mind Hypothesis (FMH) Explain Consciousness? The Physics of Selfhood. Activitas Nervosa Superior 62(2):35-47. https://doi.org/10.1007/s41470-020-00070-4
  144. Deli E. (2020): Thermodynamic Implications of the Fermionic Mind Hypothesis. Activitas Nervosa Superior 62(3):96-103. https://doi.org/10.1007/s41470-020-00074-0
  145. Fisher D., Pruitt J. (2019): Insights from the study of complex systems for the ecology and evolution of animal populations. Current Zoology 66(1):1-14. https://doi.org/10.1093/cz/zoz016
  146. Horváth G., Rodríguez‐Ruiz G., Martín J., López P., Herczeg G. (2019): Maternal diet affects juvenile Carpetan rock lizard performance and personality. Ecology and Evolution 9(24):14476-14488. https://doi.org/10.1002/ece3.5882
  147. Slors M. (2019): Two Distinctions That Help to Chart the Interplay Between Conscious and Unconscious Volition. Frontiers in Psychology 10. https://doi.org/10.3389/fpsyg.2019.00552
  148. Perquin M., Yang J., Teufel C., Sumner P., Hedge C., Bompas A. (2019): Inability to improve performance with control shows limited access to inner states. Journal of Experimental Psychology: General 149(2):249-274. https://doi.org/10.1037/xge0000641
  149. Perquin M., Yang J., Teufel C., Sumner P., Hedge C., Bompas A. (2019): Inability to improve performance with control shows limited access to inner states. https://doi.org/10.1101/535187
  150. Kokkoris M., Baumeister R., Kühnen U. (2019): Freeing or freezing decisions? Belief in free will and indecisiveness. Organizational Behavior and Human Decision Processes 154:49-61. https://doi.org/10.1016/j.obhdp.2019.08.002
  151. Ebenhöh O. (2019): Faculty Opinions recommendation of A unifying theory of biological function. Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature. https://doi.org/10.3410/f.727894402.793561699
  152. Kane R. (2019): DIMENSIONS OF RESPONSIBILITY: FREEDOM OF ACTION AND FREEDOM OF WILL. Social Philosophy and Policy 36(01):114-131. https://doi.org/10.1017/s0265052519000232
  153. Baddeley R., Franks N., Hunt E. (2019): Optimal foraging and the information theory of gambling. Journal of The Royal Society Interface 16(157):20190162. https://doi.org/10.1098/rsif.2019.0162
  154. Uithol S. (2019): Representaties in cognitieve neurowetenschap. Algemeen Nederlands Tijdschrift voor Wijsbegeerte 111(3):405-417. https://doi.org/10.5117/antw2019.3.006.uith
  155. Budaev S., Jørgensen C., Mangel M., Eliassen S., Giske J. (2019): Decision-Making From the Animal Perspective: Bridging Ecology and Subjective Cognition. Frontiers in Ecology and Evolution 7. https://doi.org/10.3389/fevo.2019.00164
  156. Hills T. (2019): Neurocognitive free will. Proceedings of the Royal Society B: Biological Sciences 286(1908):20190510. https://doi.org/10.1098/rspb.2019.0510
  157. Meincke A. (2018): Bio-Agency and the Possibility of Artificial Agents. European Studies in Philosophy of Science. https://doi.org/10.1007/978-3-319-72577-2_5
  158. Ragagnin M. (2018): Efeitos de estressores múltiplos no impacto da acidificação oceânica na biota marinha. https://doi.org/10.11606/d.21.2018.tde-13032018-155525
  159. Kessler N. (2018): Posthumanism’s Material Problem. AESS Interdisciplinary Environmental Studies and Sciences Series. https://doi.org/10.1007/978-3-319-99274-7_3
  160. Baddeley R., Franks N., Hunt E. (2018): Optimal foraging and the information theory of gambling. https://doi.org/10.1101/497198
  161. Budaev S., Giske J., Eliassen S. (2018): AHA: A general cognitive architecture for Darwinian agents. Biologically Inspired Cognitive Architectures 25:51-57. https://doi.org/10.1016/j.bica.2018.07.009
  162. Müller T. (2018): A Stochastic Process Model for Free Agency under Indeterminism. Dialectica 72(2):219-252. https://doi.org/10.1111/1746-8361.12222
  163. Cornwell T., McCarthy I., Snyder C., Biro P. (2018): The influence of environmental gradients on individual behaviour: Individual plasticity is consistent across risk and temperature gradients. Journal of Animal Ecology 88(4):511-520. https://doi.org/10.1111/1365-2656.12935
  164. Cao Y., Li Y., Zou W., Li Z., Shen Q., Liao S., et al. (2018): Bell Test over Extremely High-Loss Channels: Towards Distributing Entangled Photon Pairs between Earth and the Moon. Physical Review Letters 120(14). https://doi.org/10.1103/physrevlett.120.140405
  165. Kiroi V., Bakhtin O., Minyaeva N., Shaposhnikov D., Aslanyan E., Lazurenko D. (2018): Electrographic Correlates of Predictions of the Time Course of Events. Neuroscience and Behavioral Physiology 48(8):990-998. https://doi.org/10.1007/s11055-018-0660-y
  166. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  167. Chang C., Teo H., Norma-Rashid Y., Li D. (2017): Predator personality and prey behavioural predictability jointly determine foraging performance. Scientific Reports 7(1). https://doi.org/10.1038/srep40734
  168. Perry C., Barron A., Chittka L. (2017): The frontiers of insect cognition. Current Opinion in Behavioral Sciences 16:111-118. https://doi.org/10.1016/j.cobeha.2017.05.011
  169. Feldman G. (2017): Making sense of agency: Belief in free will as a unique and important construct. Social and Personality Psychology Compass 11(1). https://doi.org/10.1111/spc3.12293
  170. Feldman G., Farh J., Wong K. (2017): Agency Beliefs Over Time and Across Cultures: Free Will Beliefs Predict Higher Job Satisfaction. Personality and Social Psychology Bulletin 44(3):304-317. https://doi.org/10.1177/0146167217739261
  171. Feldman G., Chandrashekar S. (2017): Laypersons’ Beliefs and Intuitions About Free Will and Determinism. Social Psychological and Personality Science 9(5):539-549. https://doi.org/10.1177/1948550617713254
  172. Brown H. (2017): Universal History and the Emergence of Species Being. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2894913
  173. van Hateren J. (2017): A Unifying Theory of Biological Function. Biological Theory 12(2):112-126. https://doi.org/10.1007/s13752-017-0261-y
  174. Rigato J. (2017): Downward causation and supervenience: the non-reductionist’s extra argument for incompatibilism. Philosophical Explorations 21(3):384-399. https://doi.org/10.1080/13869795.2017.1390146
  175. Avila-Núñez J., Naya M., Otero L., Alonso-Amelot M. (2017): Sticky trap predation in the Neotropical resin bug Heniartes stali (Wygodzinsky) (Hemiptera: Reduviidae: Harpactorinae). Journal of Ethology 35(2):213-219. https://doi.org/10.1007/s10164-017-0512-1
  176. Westphal K. (2017): How Kant Justifies Freedom of Agency (without Transcendental Idealism). European Journal of Philosophy 25(4):1695-1717. https://doi.org/10.1111/ejop.12264
  177. Westphal K. (2017): Empiricism, Pragmatic Realism, and the A Priori in Mind and the World Order. Pragmatism in Transition. https://doi.org/10.1007/978-3-319-52863-2_8
  178. Fukutomi M., Ogawa H. (2017): Crickets alter wind-elicited escape strategies depending on acoustic context. Scientific Reports 7(1). https://doi.org/10.1038/s41598-017-15276-x
  179. Larrosa P., Ojea A., Ojea I., Molina V., Zorrilla-Zubilete M., Delorenzi A. (2017): Retrieval under stress decreases the long-term expression of a human declarative memory via reconsolidation. Neurobiology of Learning and Memory 142:135-145. https://doi.org/10.1016/j.nlm.2017.03.005
  180. Edelman S. (2017): Language and other complex behaviors: Unifying characteristics, computational models, neural mechanisms. Language Sciences 62:91-123. https://doi.org/10.1016/j.langsci.2017.04.003
  181. Heinze S., von Philipsborn A. (2017): Editorial overview: Recent advances in insect neuroethology: from sensory processing to circuits controlling internal states. Current Opinion in Insect Science 24:iv-vi. https://doi.org/10.1016/j.cois.2017.11.006
  182. Stefan Bode (2017): Uncovering contextual biases in human decision-making. A multivariate analysis approach for patterns of functional magnetic resonance imaging data and event-related potentials. Kölner Universitäts PublikationsServer (Universität zu Köln).
  183. Déli E., Tozzi A., Peters J. (2017): Relationships between short and fast brain timescales. Cognitive Neurodynamics 11(6):539-552. https://doi.org/10.1007/s11571-017-9450-4
  184. Roskies A. (2016): Decision-Making and Self-Governing Systems. Neuroethics 11(3):245-257. https://doi.org/10.1007/s12152-016-9280-9
  185. Lavazza A. (2016): Free Will and Neuroscience: From Explaining Freedom Away to New Ways of Operationalizing and Measuring It. Frontiers in Human Neuroscience 10. https://doi.org/10.3389/fnhum.2016.00262
  186. Mohan A., De Ridder D., Vanneste S. (2016): Graph theoretical analysis of brain connectivity in phantom sound perception. Scientific Reports 6(1). https://doi.org/10.1038/srep19683
  187. Brembs B. (2016): Operant Behavior in Model Systems. https://doi.org/10.1101/058719
  188. Merker B. (2016): Insects join the consciousness fray. Animal Sentience 1(9). https://doi.org/10.51291/2377-7478.1131
  189. Tomsic D. (2016): Visual motion processing subserving behavior in crabs. Current Opinion in Neurobiology 41:113-121. https://doi.org/10.1016/j.conb.2016.09.003
  190. Abed-Vieillard D., Cortot J. (2016): When Choice Makes Sense: Menthol Influence on Mating, Oviposition and Fecundity in Drosophila melanogaster. Frontiers in Integrative Neuroscience 10. https://doi.org/10.3389/fnint.2016.00005
  191. De Ridder D., Vanneste S., Gillett G., Manning P., Glue P., Langguth B. (2016): Psychosurgery Reduces Uncertainty and Increases Free Will? A Review. Neuromodulation: Technology at the Neural Interface 19(3):239-248. https://doi.org/10.1111/ner.12405
  192. Hunt E., Baddeley R., Worley A., Sendova-Franks A., Franks N. (2016): Ants determine their next move at rest: motor planning and causality in complex systems. Royal Society Open Science 3(1):150534. https://doi.org/10.1098/rsos.150534
  193. Feldman G., Wong K., Baumeister R. (2016): Bad is freer than good: Positive–negative asymmetry in attributions of free will. Consciousness and Cognition 42:26-40. https://doi.org/10.1016/j.concog.2016.03.005
  194. Turri J. (2016): Exceptionalist naturalism: Human agency and the causal order. Quarterly Journal of Experimental Psychology 71(2):396-410. https://doi.org/10.1080/17470218.2016.1251472
  195. Velasque M., Briffa M. (2016): The opposite effects of routine metabolic rate and metabolic rate during startle responses on variation in the predictability of behaviour in hermit crabs. Behaviour 153(13-14):1545-1566. https://doi.org/10.1163/1568539x-00003371
  196. Der R., Martius G. (2016): Dynamical self-consistency leads to behavioral development and emergent social interactions in robots. 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). https://doi.org/10.1109/devlrn.2016.7846789
  197. Kane R. (2016): The complex tapestry of free will: striving will, indeterminism and volitional streams. Synthese 196(1):145-160. https://doi.org/10.1007/s11229-016-1046-8
  198. Kane R. (2016): Moral Responsibility, Reactive Attitudes and Freedom of Will. The Journal of Ethics 20(1-3):229-246. https://doi.org/10.1007/s10892-016-9234-9
  199. Feldman G., Chandrashekar S., Wong K. (2015): The freedom to excel: Belief in free will predicts better academic performance. Personality and Individual Differences 90:377-383. https://doi.org/10.1016/j.paid.2015.11.043
  200. Briegel H., Müller T. (2015): A Chance for Attributable Agency. Minds and Machines 25(3):261-279. https://doi.org/10.1007/s11023-015-9381-y
  201. ID Loram (2015): Postural Control and Sensorimotor Integration.
  202. Osvath M. (2015): Putting flexible animal prospection into context: escaping the theoretical box. WIREs Cognitive Science 7(1):5-18. https://doi.org/10.1002/wcs.1372
  203. Nadin M., Kurismaa A. (2015): From Russia with Love / Russian experimental and empirical contributions informed by an anticipatory perspective. International Journal of General Systems 44(6):615-620. https://doi.org/10.1080/03081079.2015.1032074
  204. Der R., Martius G. (2015): Novel plasticity rule can explain the development of sensorimotor intelligence. Proceedings of the National Academy of Sciences 112(45). https://doi.org/10.1073/pnas.1508400112
  205. Kane R. (2015): On the role of indeterminism in libertarian free will. Philosophical Explorations 19(1):2-16. https://doi.org/10.1080/13869795.2016.1085594
  206. Delorenzi A., Maza F., Suárez L., Barreiro K., Molina V., Stehberg J. (2014): Memory beyond expression. Journal of Physiology-Paris 108(4-6):307-322. https://doi.org/10.1016/j.jphysparis.2014.07.002
  207. Neuringer A. (2014): Operant Variability and the Evolution of Volition. International Journal of Comparative Psychology 27(2). https://doi.org/10.46867/ijcp.2014.27.02.09
  208. Khakhalin A., Koren D., Gu J., Xu H., Aizenman C. (2014): Excitation and inhibition in recurrent networks mediate collision avoidance in Xenopus tadpoles. European Journal of Neuroscience 40(6):2948-2962. https://doi.org/10.1111/ejn.12664
  209. Shields G. (2014): Neuroscience and Conscious Causation: Has Neuroscience Shown that We Cannot Control Our Own Actions?. Review of Philosophy and Psychology 5(4):565-582. https://doi.org/10.1007/s13164-014-0200-9
  210. Loram I., van de Kamp C., Lakie M., Gollee H., Gawthrop P. (2014): Does the Motor System Need Intermittent Control?. Exercise and Sport Sciences Reviews 42(3):117-125. https://doi.org/10.1249/jes.0000000000000018
  211. van Hateren J. (2014): The origin of agency, consciousness, and free will. Phenomenology and the Cognitive Sciences 14(4):979-1000. https://doi.org/10.1007/s11097-014-9396-5
  212. van Hateren J. (2014): Active causation and the origin of meaning. Biological Cybernetics 109(1):33-46. https://doi.org/10.1007/s00422-014-0622-6
  213. Rigato J., Murakami M., Mainen Z. (2014): Spontaneous Decisions and Free Will: Empirical Results and Philosophical Considerations. Cold Spring Harbor Symposia on Quantitative Biology 79:177-184. https://doi.org/10.1101/sqb.2014.79.024810
  214. Smiley J., Fernie S., Dainty A. (2014): Understanding construction reform discourses. Construction Management and Economics 32(7-8):804-815. https://doi.org/10.1080/01446193.2014.909049
  215. Westphal K. (2014): Autonomy, Freedom & Embodiment: Hegel’s Critique of Contemporary Biologism. Hegel Bulletin 35(1):56-83. https://doi.org/10.1017/hgl.2014.4
  216. Christensen K., Papavassiliou D., de Figueiredo A., Franks N., Sendova-Franks A. (2014): Universality in ant behaviour. Journal of The Royal Society Interface 12(102):20140985. https://doi.org/10.1098/rsif.2014.0985
  217. Salvador L., Bartumeus F., Levin S., Ryu W. (2014): Mechanistic analysis of the search behaviour ofCaenorhabditis elegans. Journal of The Royal Society Interface 11(92):20131092. https://doi.org/10.1098/rsif.2013.1092
  218. Gawthrop P., Loram I., Gollee H., Lakie M. (2014): Intermittent control models of human standing: similarities and differences. Biological Cybernetics 108(2):159-168. https://doi.org/10.1007/s00422-014-0587-5
  219. Freitas P., Andrade F., Novais P. (2014): Criminal Liability of Autonomous Agents: From the Unthinkable to the Plausible. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-662-45960-7_11
  220. Kane R. (2014): II-Acting ‘of One’s Own Free Will’: Modern Reflections on an Ancient Philosophical Problem. Proceedings of the Aristotelian Society (Hardback) 114(1pt1):35-55. https://doi.org/10.1111/j.1467-9264.2014.00363.x
  221. Bode S., Murawski C., Soon C., Bode P., Stahl J., Smith P. (2014): Demystifying “free will”: The role of contextual information and evidence accumulation for predictive brain activity. Neuroscience & Biobehavioral Reviews 47:636-645. https://doi.org/10.1016/j.neubiorev.2014.10.017
  222. Stahlman W., Blaisdell A. (2014): Selections on the Empirical and Theoretical Investigations of Behavioral Variability: An Introduction to the Special Issue. International Journal of Comparative Psychology 27(2). https://doi.org/10.46867/ijcp.2014.27.02.11
  223. Unknown authors (2013): Neuroscience of Creativity. The MIT Press eBooks. https://doi.org/10.7551/mitpress/9780262019583.001.0001
  224. Gelperin A. (2013): Associative Memory Mechanisms in Terrestrial Slugs and Snails. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00022-8
  225. Grandpierre A. (2013): Biologically Organized Quantum Vacuum and the Cosmic Origin of Cellular Life. Phenomenology of Space and Time. https://doi.org/10.1007/978-3-319-02015-0_10
  226. Frith C. (2013): The psychology of volition. Experimental Brain Research 229(3):289-299. https://doi.org/10.1007/s00221-013-3407-6
  227. Zilio D. (2013): Behavioral Unit of Selection and the Operant-Respondent Distinction: The Role of Neurophysiological Events in Controlling the Verbal Behavior of Theorizing About Behavior. The Psychological Record 63(4):895-918. https://doi.org/10.11133/j.tpr.2013.63.4.011
  228. De Ridder D., Verplaetse J., Vanneste S. (2013): The predictive brain and the “free will” illusion. Frontiers in Psychology 4. https://doi.org/10.3389/fpsyg.2013.00131
  229. Martius G., Der R., Ay N. (2013): Information Driven Self-Organization of Complex Robotic Behaviors. PLoS ONE 8(5):e63400. https://doi.org/10.1371/journal.pone.0063400
  230. Stamps J., Saltz J., Krishnan V. (2013): Genotypic differences in behavioural entropy: unpredictable genotypes are composed of unpredictable individuals. Animal Behaviour 86(3):641-649. https://doi.org/10.1016/j.anbehav.2013.07.012
  231. Wong K., Cheng C. (2013): Predictable or Not? Individuals’ Risk Decisions Do Not Necessarily Predict Their Next Ones. PLoS ONE 8(2):e56811. https://doi.org/10.1371/journal.pone.0056811
  232. Briffa M., Bridger D., Biro P. (2013): How does temperature affect behaviour? Multilevel analysis of plasticity, personality and predictability in hermit crabs. Animal Behaviour 86(1):47-54. https://doi.org/10.1016/j.anbehav.2013.04.009
  233. Briffa M. (2013): Plastic proteans: reduced predictability in the face of predation risk in hermit crabs. Biology Letters 9(5):20130592. https://doi.org/10.1098/rsbl.2013.0592
  234. Biro P., Adriaenssens B. (2013): Predictability as a Personality Trait: Consistent Differences in Intraindividual Behavioral Variation. The American Naturalist 182(5):621-629. https://doi.org/10.1086/673213
  235. Nepomnyashchikh V. (2013): Variability in invertebrate behavior and the problem of free will. Biology Bulletin Reviews 3(5):406-411. https://doi.org/10.1134/s2079086413050083
  236. Neuringer A., Jensen G. (2012): The Predictably Unpredictable Operant. Comparative Cognition & Behavior Reviews 7:55-84. https://doi.org/10.3819/ccbr.2012.70004
  237. Warzecha A., Rosner R., Grewe J. (2012): Impact and sources of neuronal variability in the fly’s motion vision pathway. Journal of Physiology-Paris 107(1-2):26-40. https://doi.org/10.1016/j.jphysparis.2012.10.002
  238. Rigoni D., Kühn S., Gaudino G., Sartori G., Brass M. (2012): Reducing self-control by weakening belief in free will. Consciousness and Cognition 21(3):1482-1490. https://doi.org/10.1016/j.concog.2012.04.004
  239. Hong F. (2012): On Microscopic Irreversibility and Non-deterministic Chaos: Resolving the Conflict between Determinism and Free Will. Integral Biomathics. https://doi.org/10.1007/978-3-642-28111-2_21
  240. Radder H., Meynen G. (2012): Does the brain “initiate” freely willed processes? A philosophy of science critique of Libet-type experiments and their interpretation. Theory & Psychology 23(1):3-21. https://doi.org/10.1177/0959354312460926
  241. Barham J. (2012): Normativity, agency, and life. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43(1):92-103. https://doi.org/10.1016/j.shpsc.2011.05.008
  242. Stamps J., Briffa M., Biro P. (2012): Unpredictable animals: individual differences in intraindividual variability (IIV). Animal Behaviour 83(6):1325-1334. https://doi.org/10.1016/j.anbehav.2012.02.017
  243. Nargeot R., Simmers J. (2012): Functional Organization and Adaptability of a Decision-Making Network in Aplysia. Frontiers in Neuroscience 6. https://doi.org/10.3389/fnins.2012.00113
  244. Zhang S., Si A., Pahl M. (2012): Visually Guided Decision Making in Foraging Honeybees. Frontiers in Neuroscience 6. https://doi.org/10.3389/fnins.2012.00088
  245. Miller S., Ngo T., van Swinderen B. (2012): Attentional Switching in Humans and Flies: Rivalry in Large and Miniature Brains. Frontiers in Human Neuroscience 5. https://doi.org/10.3389/fnhum.2011.00188
  246. Tomina Y., Takahata M. (2012): Discrimination learning with light stimuli in restrained American lobster. Behavioural Brain Research 229(1):91-105. https://doi.org/10.1016/j.bbr.2011.12.044
  247. Unknown authors (2011): References. Physics of Self‐Organization and Evolution. https://doi.org/10.1002/9783527636792.refs
  248. Mele A. (2011): Libertarianism and Human Agency. Philosophy and Phenomenological Research 87(1):72-92. https://doi.org/10.1111/j.1933-1592.2011.00529.x
  249. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  250. Schüür F., Haggard P. (2011): What are self-generated actions?. Consciousness and Cognition 20(4):1697-1704. https://doi.org/10.1016/j.concog.2011.09.006
  251. Chittka L., Skorupski P. (2011): Information processing in miniature brains. Proceedings of the Royal Society B: Biological Sciences 278(1707):885-888. https://doi.org/10.1098/rspb.2010.2699
  252. Cobbe N. (2011): Interspecies Mixtures and the Status of Humanity. Is this Cell a Human Being?. https://doi.org/10.1007/978-3-642-20772-3_9
  253. Nihat Ay, Keyan Ghazi-Zahedi (2011): An Information Theoretic Approach to Prediction and Deliberative Decision Making of Embodied Systems.
  254. Ansorge U., Horstmann G., Scharlau I. (2011): Top-down contingent feature-specific orienting with and without awareness of the visual input. Advances in Cognitive Psychology 7(-1):108-119. https://doi.org/10.2478/v10053-008-0087-z

Pauly D, Chacana PA, Calzado EG, Brembs B, Schade R. (2011): IgY Technology: Extraction of Chicken Antibodies from Egg Yolk by Polyethylene Glycol (PEG) Precipitation. Journal of Visualized Experiments (51).

  1. Patel D., K. G., Nayaka S., Gacem A., Kumar P., Sharma A., et al. (2025): Comprehensive analysis of the major IgY antibody extraction strategies from chicken egg yolk. Veterinary Immunology and Immunopathology 283:110928. https://doi.org/10.1016/j.vetimm.2025.110928
  2. Pramod G., Havlad P., Murthy N., Majid A., Thomas J., Enayathullah M., et al. (2025): Peptide Epitope-Based Avian IgY Antibodies against SARS-CoV-2 Spike: A Cost Effective Approach for Viral Detection and Neutralization. International Journal of Peptide Research and Therapeutics 31(5). https://doi.org/10.1007/s10989-025-10739-6
  3. Qiu H., Jin X., Zhang X., Chen K., Wang L., Huang J. (2025): Egg Yolk Immunoglobulins (IgY) Purification, Activity Enhancement, and Potential Benefits for Human Health. Nutrients 17(17):2890. https://doi.org/10.3390/nu17172890
  4. Aslam M., Khalid S., Dar N., Abbas Z., Gull I., Khan Z., et al. (2025): Production of IgY in egg yolk of Gallus gallus Domesticus by oral vaccination and its characterization with outer membrane of Ornithobacterium rhinotracheale. Veterinary Immunology and Immunopathology 281:110899. https://doi.org/10.1016/j.vetimm.2025.110899
  5. Moosavi M., Rahimi S., Karimi Torshizi M., Zahraei Salehi T., Ashrafi Tamai I., Grimes J. (2025): In vitro evaluation of the inhibitory potential of specific egg yolk antibodies induced by different antigens of Salmonella Typhimurium. Poultry Science 104(12):105934. https://doi.org/10.1016/j.psj.2025.105934
  6. Xia M., Ichou M., Landivar M., Zhou P., Vadlamudi S., Leruth A., et al. (2025): Avian Immunoglobulin Y Antibodies Targeting the Protruding or Shell Domain of Norovirus Capsid Protein Neutralize Norovirus Replication in the Human Intestinal Enteroid System. Vaccines 13(12):1228. https://doi.org/10.3390/vaccines13121228
  7. Bhate M., Sharma S. (2025): Standardization of extraction, identification, and characterization of an immunoglobulin Y antibody: A potential inhibitor of cariogenic and endodontic microbiome. Journal of Conservative Dentistry and Endodontics 28(8):772-782. https://doi.org/10.4103/JCDE.JCDE_185_25
  8. Soltani N., Rahimi S., Khaki P., Karimi Torshizi M., Eskandari B., Grimes J. (2025): Efficacy of hyperimmunized egg yolk antibodies (IgY) against Campylobacter jejuni: In Vitro and In Vivo evaluations. Poultry Science 104(2):104718. https://doi.org/10.1016/j.psj.2024.104718
  9. Martínez P., Flores A., Bonilla K., Urdaneta N., Benaím G., Canudas N., et al. (2025): Synthesis, Photophysical Characterization, and DFT Analysis of (E)-1-(4-aminophenyl)-3-(1-benzyl-pyrrol-2-yl) Prop-2-en-1-one, a Novel Aminochalcone that can be Used as a Fluorescent Probe for Protein Labeling. Journal of Fluorescence. https://doi.org/10.1007/s10895-025-04645-9
  10. Setthawong P., Yamkasem J., Khemthong M., Tattiyapong P., Metheenukul P., Prasertsincharoen N., et al. (2025): Development of IgY-Based Passive Immunization Against Tilapia Lake Virus: Development and In Vitro Neutralization Assays. Viruses 17(3):448. https://doi.org/10.3390/v17030448
  11. Shahsavar S., Esmaeili Z., Ariannejad H., Ghazvini K. (2025): Production of Polyclonal Whole-Cell Anti-IgY against Helicobacter pylori and Determination of Its Stability in Environmental Conditions and Interaction with Salmonella and E. coli. Molecular Genetics, Microbiology and Virology 40(1):75-82. https://doi.org/10.3103/S0891416825700089
  12. Lei S., Yang Y., Zhao C., Liu A., He P. (2025): Innovative Approaches to Combat Duck Viral Hepatitis: Dual-Specific Anti-DHAV-1 and DHAV-3 Yolk Antibodies. Vaccines 13(2):154. https://doi.org/10.3390/vaccines13020154
  13. Kerdput V., Yodkamol V., Sookprasong M., Wongwadhunyoo W., Khunsri I., Masrinoul P., et al. (2025): Production and evaluation of rabies immunoglobulin extracted from chicken egg yolk. Biotechnology Reports 46:e00897. https://doi.org/10.1016/j.btre.2025.e00897
  14. Qi X., Zhao S., Huang Q., Wang Y., Pei Q., Chen Y., et al. (2025): Preparation and application of specific chicken yolk antibodies in detecting Brucella. Frontiers in Veterinary Science 12. https://doi.org/10.3389/fvets.2025.1552097
  15. Fibriani A., Naisanu K., Yamahoki N., Kinanti D. (2024): Development of Polyclonal Chicken Egg Yolk Immunoglobulin Y (IgY) Antibodies Targeting SARS-CoV-2 Multi-Epitope Antigen. Journal of Virological Methods 331:115062. https://doi.org/10.1016/j.jviromet.2024.115062
  16. Fan B., Zhuang X., Wei J., Bu L., Liu Y., Zhang L., et al. (2024): On-site detection of Sporothrix globosa using immunoglobulin Y, magnetic separation and loop-mediated isothermal amplification. Analytica Chimica Acta 1289:342216. https://doi.org/10.1016/j.aca.2024.342216
  17. Idar I., Yusuf M., Anshori J., Subroto T. (2024): Purification of IgY Anti-SARS-CoV-2-Chicken Nucleocapsid and Elimination of Its Low-temperature Storage-induced Aggregates. Trends in Sciences 21(5):7649. https://doi.org/10.48048/tis.2024.7649
  18. Al-Qaoud K., Obeidat Y., Al-Omari T., Okour M., Al-Omari M., Ahmad M., et al. (2024): The development of an electrochemical immunosensor utilizing chicken IgY anti-spike antibody for the detection of SARS-CoV-2. Scientific Reports 14(1). https://doi.org/10.1038/s41598-023-50501-w
  19. Gellhorn Serra M., Meier L., Sauerhering L., Wilhelm J., Kupke A. (2024): Organotypic brain slices as a model to study the neurotropism of the highly pathogenic Nipah and Ebola viruses. Journal of General Virology 105(10). https://doi.org/10.1099/jgv.0.002038
  20. A’yun R., Hakim M., Giri-Rachman E., Tan M., Niloperbowo W. (2024): Anti-HBsAg IgY polyclonal antibodies potential as capture antibody for HBsAg Detection Kit development. Current Research on Biosciences and Biotechnology 5(2):1-4. https://doi.org/10.5614/crbb.2024.5.2/ubvbmkpz
  21. Yaseen S., Khan A. (2024): Antivenom production in chicken against Sind krait (Bungarus sindanus) venom and its efficacy assessment using different immunoassays. Ciência Rural 54(2). https://doi.org/10.1590/0103-8478cr20220639
  22. Suresh L., Indhuprakash S., Gandhi S., Diraviyam T. (2023): Amalgamation of nanotechnology with chicken IgY to enrich therapeutic and diagnostic applications: a systematic review. Immunotherapy 15(11):867-884. https://doi.org/10.2217/imt-2022-0304
  23. Saputri M., Esfandiari A., Rachmawan W., Soejoedono R., Handharyani E., Jupisa C., et al. (2023): Influenza Immunoglobulin (Ig) Y Derived from Chicken Egg Yolk: Production, Characterization, and Its Cross-Reactivity. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences 94(1):23-30. https://doi.org/10.1007/s40011-023-01510-2
  24. Grzywa R., Łupicka-Słowik A., Sieńczyk M. (2023): IgYs: on her majesty’s secret service. Frontiers in Immunology 14. https://doi.org/10.3389/fimmu.2023.1199427
  25. Qiu T., Zhang H., Lei H., Zhang L., Zhang Y., Shen X., et al. (2023): Preparation of Anti-Zearalenone IgY and Development of an Indirect Competitive ELISA Method for the Measurement of Zearalenone in Post-Fermented Tea. Foods 12(24):4478. https://doi.org/10.3390/foods12244478
  26. Gómez Osorio V., González Rodríguez S., Contreras Rodríguez L., Díaz Gonzalez G., Ramírez Hernández M. (2023): Obtención de la proteína verde fluorescente recombinante y su anticuerpo policlonal Igy. Revista Colombiana de Biotecnología 25(1):57-68. https://doi.org/10.15446/rev.colomb.biote.v25n1.91675
  27. Kupke A., Volz A., Dietzel E., Freudenstein A., Schmidt J., Shams-Eldin H., et al. (2022): Protective CD8+ T Cell Response Induced by Modified Vaccinia Virus Ankara Delivering Ebola Virus Nucleoprotein. Vaccines 10(4):533. https://doi.org/10.3390/vaccines10040533
  28. Mubarak A., Alturaiki W., Dawoud T., El-Tayeb M., Elbadawi Y., Moussa I. (2022): Production of Anti-Camel IgY for Diagnosis of Infectious Diseases Affecting Camels in Saudi Arabia. Journal of King Saud University – Science 35(1):102421. https://doi.org/10.1016/j.jksus.2022.102421
  29. Otterbeck A., Skorup P., Hanslin K., Larsson A., Stålberg J., Hjelmqvist H., et al. (2022): Intravenous anti-P. aeruginosa IgY-antibodies do not decrease pulmonary bacterial concentrations in a porcine model of ventilator-associated pneumonia. Innate Immunity 28(7-8):224-234. https://doi.org/10.1177/17534259221114217
  30. Madera-Contreras A., Solano-Texta R., Cisneros-Sarabia A., Bautista-Santos I., Vences-Velázquez G., Vences-Velázquez A., et al. (2022): Optimized method for the extraction of contaminant-free IgY antibodies from egg yolk using PEG 6000. MethodsX 9:101874. https://doi.org/10.1016/j.mex.2022.101874
  31. Agurto-Arteaga A., Poma-Acevedo A., Rios-Matos D., Choque-Guevara R., Montesinos-Millán R., Montalván Á., et al. (2022): Preclinical Assessment of IgY Antibodies Against Recombinant SARS-CoV-2 RBD Protein for Prophylaxis and Post-Infection Treatment of COVID-19. Frontiers in Immunology 13. https://doi.org/10.3389/fimmu.2022.881604
  32. Chun C., Roth M., Welti R., Richards M., Hsu W., O’Quinn T., et al. (2022): Exploring the potential effect of phospholipase A2 antibody to extend beef shelf-life in a beef liposome model system. Meat Science 198:109091. https://doi.org/10.1016/j.meatsci.2022.109091
  33. Artman C., Idegwu N., Brumfield K., Lai K., Hauta S., Falzarano D., et al. (2022): Feasibility of Polyclonal Avian Immunoglobulins (IgY) as Prophylaxis against Human Norovirus Infection. Viruses 14(11):2371. https://doi.org/10.3390/v14112371
  34. Rohde C., Pfeiffer S., Baumgart S., Becker S., Krähling V. (2022): Ebola Virus Activates IRE1α-Dependent XBP1u Splicing. Viruses 15(1):122. https://doi.org/10.3390/v15010122
  35. León-Núñez D., Vizcaíno-López M., Escorcia M., Correa D., Pérez-Hernández E., Gómez-Chávez F. (2022): IgY Antibodies as Biotherapeutics in Biomedicine. Antibodies 11(4):62. https://doi.org/10.3390/antib11040062
  36. Sahoo D., Allenspach K., Mochel J., Parker V., Rudinsky A., Winston J., et al. (2022): Synbiotic-IgY Therapy Modulates the Mucosal Microbiome and Inflammatory Indices in Dogs with Chronic Inflammatory Enteropathy: A Randomized, Double-Blind, Placebo-Controlled Study. Veterinary Sciences 10(1):25. https://doi.org/10.3390/vetsci10010025
  37. Khalaf H., Al-Bouqaee H., Hwijeh M., Abbady A. (2022): Characterization of rabbit polyclonal antibody against camel recombinant nanobodies. Open Life Sciences 17(1):659-675. https://doi.org/10.1515/biol-2022-0065
  38. Magalhães I., Souza P., Marques L., Girão N., Araújo F., Guedes M. (2022): New insights into the recombinant proteins and monoclonal antibodies employed to immunodiagnosis and control of Zika virus infection: A review. International Journal of Biological Macromolecules 200:139-150. https://doi.org/10.1016/j.ijbiomac.2021.12.196
  39. Wang J., Tan L., Bi W., Shen H., Li D., Yu Z., et al. (2022): Ultrasensitive microfluidic immunosensor with stir bar enrichment for point-of-care test of Staphylococcus aureus in foods triggered by DNAzyme-assisted click reaction. Food Chemistry 378:132093. https://doi.org/10.1016/j.foodchem.2022.132093
  40. Li K., Bermudez O., Forciniti D. (2022): Poly (ethylene) glycol (PEG) precipitation of glycosylated and non-glycosylated monoclonal antibodies. Process Biochemistry 121:563-574. https://doi.org/10.1016/j.procbio.2022.07.030
  41. Brumfield K., Seo H., Idegwu N., Artman C., Gonyar L., Nataro J., et al. (2022): Feasibility of avian antibodies as prophylaxis against enterotoxigenic escherichia coli colonization. Frontiers in Immunology 13. https://doi.org/10.3389/fimmu.2022.1011200
  42. Frumkin L., Lucas M., Scribner C., Ortega-Heinly N., Rogers J., Yin G., et al. (2022): Egg-Derived Anti-SARS-CoV-2 Immunoglobulin Y (IgY) With Broad Variant Activity as Intranasal Prophylaxis Against COVID-19. Frontiers in Immunology 13. https://doi.org/10.3389/fimmu.2022.899617
  43. Sahar Sabahi, S. Mortazavi, Mohamadreza Nassiri, K. Ghazvini, Fakhri, Shahidi, et al. (2022): Production of Functional Ice Cream Fortified by Immunoglobulin Y against Escherichia coli O157:H7 and Helicobacter Pylori. Biointerface Research in Applied Chemistry 13(2):188. https://doi.org/10.33263/briac132.188
  44. El-Kafrawy S., Odle A., Abbas A., Hassan A., Abdel-dayem U., Qureshi A., et al. (2022): SARS-CoV-2-specific immunoglobulin Y antibodies are protective in infected mice. PLOS Pathogens 18(9):e1010782. https://doi.org/10.1371/journal.ppat.1010782
  45. Otterbeck A., Skorup P., Hanslin K., Larsson A., Stålberg J., Hjelmqvist H., et al. (2021): Bronchially instilled IgY‐antibodies did not decrease pulmonary p. aeruginosa concentration in experimental porcine pneumonia. Acta Anaesthesiologica Scandinavica 65(5):656-663. https://doi.org/10.1111/aas.13784
  46. Vansofla A., Nazarian S., Kordbache E., Fathi J. (2021): An IgG/IgY sandwich-ELISA for the detection of heat-labile enterotoxin B subunit of enterotoxigenic Escherichia coli. Gene Reports 23:101099. https://doi.org/10.1016/J.GENREP.2021.101099
  47. Choraria A., Somasundaram R., Janani S., Rajendran S., Oukkache N., Michael A. (2021): Chicken egg yolk antibodies (IgY)-based antivenom for neutralization of snake venoms: a review. Toxin Reviews 41(3):1018-1029. https://doi.org/10.1080/15569543.2021.1942063
  48. Artman C., Brumfield K., Khanna S., Goepp J. (2021): Avian antibodies (IgY) targeting spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inhibit receptor binding and viral replication. PLOS ONE 16(5):e0252399. https://doi.org/10.1371/journal.pone.0252399
  49. Karachaliou C., Vassilakopoulou V., Livaniou E. (2021): IgY technology: Methods for developing and evaluating avian immunoglobulins for the in vitro detection of biomolecules. World Journal of Methodology 11(5):243-262. https://doi.org/10.5662/wjm.v11.i5.243
  50. Aston E., Wallach M., Narayanan A., Egaña-Labrin S., Gallardo R. (2021): Hyperimmunized Chickens Produce Neutralizing Antibodies against SARS-CoV-2. Viruses 14(7):1510. https://doi.org/10.3390/v14071510
  51. Zhang G., Xue J., Li X. (2021): The Influence of Newcastle Disease Virus Major Proteins on Virulence. Veterinary Science Research 3(2). https://doi.org/10.30564/vsr.v3i2.4098
  52. Wang H., Zeng X., Lin J. (2021): Ex Vivo Evaluation of Egg Yolk IgY Degradation in Chicken Gastrointestinal Tract. Frontiers in Immunology 12. https://doi.org/10.3389/fimmu.2021.746831
  53. Rehan I., Elnagar A. (2021): Chicken Egg Yolk-IgY: Passive Immunization Promising Targeted Therapy of COVID-19 Pandemic. Journal of Applied Veterinary Sciences 6(2):67-91. https://doi.org/10.21608/JAVS.2021.164324
  54. Do K., Vi T., Le H., Do T., Do D., Nguyen D., et al. (2021): The inhibitory effect of anti-urease IgY on Helicobacter pylori infection in Swiss albino mice. Pharmaceutical Sciences Asia 48(3):255-268. https://doi.org/10.29090/psa.2021.03.19.123
  55. Zhang L., Xiao Y., Ji L., Lin M., Zou Y., Zhao J., et al. (2021): Potential Therapeutic Effects of Egg Yolk Antibody (IgY) in Helicobacter pylori Infections─A Review. Journal of Agricultural and Food Chemistry 69(46):13691-13699. https://doi.org/10.1021/acs.jafc.1c05398
  56. Mafi M., Mousavi gargari l., nazarian s., mohammad khani f. (2021): Production of Egg Yolk Immunoglobulin (IgY) Against Recombinant LTB of Enterotoxigenic Escherichia coli (ETEC) and Evaluation of Its Protective Effect in Animal Model. Scientific Journal of Kurdistan University of Medical Sciences 26(6):107-123. https://doi.org/10.52547/sjku.26.6.107
  57. Morgan P., Freire M., Tavares A., Michael A., Zhang X. (2021): Extraction and Purification of IgY. IgY-Technology: Production and Application of Egg Yolk Antibodies. https://doi.org/10.1007/978-3-030-72688-1_11
  58. Villamil-Silva S., Ortiz-Joya L., Contreras-Rodríguez L., Díaz- Gonzalez G., Ramírez-Hernández M. (2021): Identificación de una triparedoxina peroxidasa citoplasmática en Leishmania braziliensis. Revista Colombiana de Química 50(2):3-14. https://doi.org/10.15446/rev.colomb.quim.v50n2.91721
  59. Quynh Lan T., Kha T. (2021): Investigation of Two Precipitation Methods for Extracting Immunoglobulin Y (IgY) from Egg Yolks. Veterinary Science Research 3(2). https://doi.org/10.30564/vsr.v3i2.4074
  60. Ferreira Júnior Á., Morgan P., Zhang X., Schade R. (2021): Biology and Molecular Structure of Avian IgY Antibody. IgY-Technology: Production and Application of Egg Yolk Antibodies. https://doi.org/10.1007/978-3-030-72688-1_5
  61. A. Iqbal, Syed Qaswar Ali Shah, İ. Çetingül, E. Gultepe, A. Qudoos, I. Bayram (2020): The Use of Egg Yolk Antibodies for Food Protection and Immunity. https://www.semanticscholar.org/paper/045ad40feaaf9b2c2f68c1d1b3f3539b452d5a8c
  62. Mondal B., Ramlal S., Setlem K., Mahadeva A., Aradhya S., Parida M. (2020): A real-time immunocapture PCR (RT-IPCR) without interference of protein A for convenient detection of staphylococcal enterotoxin B from food and environmental samples. Annals of Microbiology 70(1). https://doi.org/10.1186/s13213-020-01567-8
  63. Constantin C., Neagu M., Supeanu T., Chiurciu V., Spandidos D. (2020): IgY – turning the page toward passive immunization in COVID-19 infection (Review). Experimental and Therapeutic Medicine 20(1):151-158. https://doi.org/10.3892/etm.2020.8704
  64. Mary D., Mathew M. (2020): Effect of Streptococcal IgY on Quantity of Streptococcus mutans in High Caries Risk Children. Journal of Pharmaceutical Research International. https://doi.org/10.9734/jpri/2020/v32i1630643
  65. D. Pauly, K. Hanack (2020): How to avoid pitfalls in antibody use [version 1; peer review: 2 approved]. https://www.semanticscholar.org/paper/be8926a4cac333813ea24bedd0b4636ccef858be
  66. Redwan E., Aljadawi A., Uversky V. (2020): Simple and efficient protocol for immunoglobulin Y purification from chicken egg yolk. Poultry Science 100(3):100956. https://doi.org/10.1016/j.psj.2020.12.053
  67. Antenucci F., Arak H., Gao J., Allahgadry T., Thøfner I., Bojesen A. (2020): Hydrostatic Filtration Enables Large-Scale Production of Outer Membrane Vesicles That Effectively Protect Chickens against Gallibacterium anatis. Vaccines 8(1):40. https://doi.org/10.3390/vaccines8010040
  68. Pérez de la Lastra J., Baca-González V., Asensio-Calavia P., González-Acosta S., Morales-delaNuez A. (2020): Can Immunization of Hens Provide Oral-Based Therapeutics against COVID-19?. Vaccines 8(3):486. https://doi.org/10.3390/vaccines8030486
  69. Fathi J., Ebrahimi F., Nazarian S., Hajizade A., Malekzadegan Y., Abdi A. (2020): Production of egg yolk antibody (IgY) against shiga-like toxin (stx) and evaluation of its prophylaxis potency in mice. Microbial Pathogenesis 145:104199. https://doi.org/10.1016/j.micpath.2020.104199
  70. Zhang J., Li H., Chen Y., Chen L., Tang H., Kong F., et al. (2020): Microencapsulation of immunoglobulin Y: optimization with response surface morphology and controlled release during simulated gastrointestinal digestion. Journal of Zhejiang University-SCIENCE B 21(8):611-627. https://doi.org/10.1631/jzus.B2000172
  71. Cortés-Sarabia K., Bautista-Santos I., Cisneros-Sarabia A., Parra-Rojas I., Estrada-Moreno A., Flores-Alfaro E., et al. (2020): Gardnerella vaginalis Vaginolysin (VLY)-Derived MAP8 Peptide (VLY-MAP8) Induced the Production of Egg Yolk IgY Antibodies that Inhibit Erythrocytes Lysis. International Journal of Peptide Research and Therapeutics 27(1):413-420. https://doi.org/10.1007/s10989-020-10099-3
  72. Cortés-Sarabia K., Bautista-Santos I., Cisneros-Sarabia A., Parra-Rojas I., Estrada-Moreno A., Flores-Alfaro E., et al. (2020): Gardnerella vaginalis Vaginolysin (VLY)-Derived MAP8 Peptide (VLY-MAP8) Induced the Production of Egg Yolk IgY Antibodies that Inhibit Erythrocytes Lysis. International Journal of Peptide Research and Therapeutics 27(1):413-420. https://doi.org/10.1007/s10989-020-10099-3
  73. Petrov K., Wierbowski B., Liu J., Salic A. (2020): Distinct Cation Gradients Power Cholesterol Transport at Different Key Points in the Hedgehog Signaling Pathway. Developmental Cell 55(3):314-327.e7. https://doi.org/10.1016/j.devcel.2020.08.002
  74. Roushani M., Rahmati Z., Golchin M., Lotfi Z., Nemati M. (2020): Electrochemical immunosensor for determination of Staphylococcus aureus bacteria by IgY immobilized on glassy carbon electrode with electrodeposited gold nanoparticles. Microchimica Acta 187(10). https://doi.org/10.1007/s00604-020-04547-6
  75. Sivaprasad M., Vinod V., Jisna K., Nair P., Parmar N. (2020): Egg yolk antibodies (IgY) and its relevance in animal and human health-An updated review. Journal of Food and Animal Sciences 1(2):81-86. https://doi.org/10.51128/JFAS.2020.A015
  76. Hatamzade Isfahani N., Rahimi S., Rasaee M., Karimi Torshizi M., Zahraei Salehi T., Grimes J. (2020): The effect of capsulated and noncapsulated egg-yolk–specific antibody to reduce colonization in the intestine of Salmonella enterica ssp. enterica serovar Infantis–challenged broiler chickens. Poultry Science 99(3):1387-1394. https://doi.org/10.1016/j.psj.2019.11.019
  77. Zhang Q., He D., Xu L., Ge S., Wang J., Zhang X. (2020): Generation and evaluation of anti-mouse IgG IgY as secondary antibody. Preparative Biochemistry & Biotechnology 50(8):788-793. https://doi.org/10.1080/10826068.2020.1737940
  78. Kota R., Reddy P., Sreerama K. (2020): Application of IgY antibodies against staphylococcal protein A (SpA) of Staphylococcus aureus for detection and prophylactic functions. Applied Microbiology and Biotechnology 104(21):9387-9398. https://doi.org/10.1007/s00253-020-10912-5
  79. Oliveira T., Bezerra F., Gambarini M., Teles A., Cunha P., Brazil D., et al. (2020): Immunoconjugates to increase photoinactivation of bovine alphaherpesvirus 1 in semen. Veterinary Microbiology 247:108780. https://doi.org/10.1016/j.vetmic.2020.108780
  80. Li X., He P., Yu L., He Q., Jia C., Yang H., et al. (2020): Production and characteristics of a novel chicken egg yolk antibody (IgY) against periodontitis-associated pathogens. Journal of Oral Microbiology 12(1):1831374. https://doi.org/10.1080/20002297.2020.1831374
  81. Lu Y., Wang Y., Zhang Z., Huang J., Yao M., Huang G., et al. (2020): Generation of Chicken IgY against SARS-COV-2 Spike Protein and Epitope Mapping. Journal of Immunology Research 2020(1). https://doi.org/10.1155/2020/9465398
  82. Otterbeck A., Hanslin K., Lantz E., Larsson A., Stålberg J., Lipcsey M. (2019): Inhalation of specific anti-Pseudomonas aeruginosa IgY antibodies transiently decreases P. aeruginosa colonization of the airway in mechanically ventilated piglets. Intensive Care Medicine Experimental 7(1). https://doi.org/10.1186/s40635-019-0246-1
  83. Ozkan B., Budama-Kilinc Y., Cakir-Koc R., Mese S., Badur S. (2019): Application of an immunoglobulin Y-alkaline phosphatase bioconjugate as a diagnostic tool for influenza A virus. Bioengineered 10(1):33-42. https://doi.org/10.1080/21655979.2019.1586054
  84. Leiva C., Cangelosi A., Mariconda V., Farace M., Geoghegan P., Brero L., et al. (2019): IgY‐based antivenom against Bothrops alternatus: Production and neutralization efficacy. Toxicon 163:84-92. https://doi.org/10.1016/j.toxicon.2019.03.020
  85. Fernandes D., Eto S., Funnicelli M., Fernandes C., Charlie-Silva I., Belo M., et al. (2019): Immunoglobulin Y in the diagnosis of Aeromonas hydrophila infection in Nile tilapia (Oreochromis niloticus). Aquaculture 500:576-585. https://doi.org/10.1016/J.AQUACULTURE.2018.10.045
  86. Pereira E., van Tilburg M., Florean E., Guedes M. (2019): Egg yolk antibodies (IgY) and their applications in human and veterinary health: A review. International Immunopharmacology 73:293-303. https://doi.org/10.1016/j.intimp.2019.05.015
  87. ABDOLMALEKI F., ZAMANI Z., TALEBI S. (2019): Evaluation of Human Anti IgG Polyclonal Antibody Production Conjugated with Peroxidase in Egg Yolk. Iranian Journal of Public Health. https://doi.org/10.18502/IJPH.V48I7.2962
  88. Kowalczyk J., Śmiałek M., Tykałowski B., Dziewulska D., Stenzel T., Koncicki A. (2019): Field evaluation of maternal antibody transfer from breeder turkey hens to egg yolks, egg whites, and poults. Poultry Science 98(8):3150-3157. https://doi.org/10.3382/ps/pez126
  89. M. Dallal, M. Zamani, Laya Kafami Khorasani, M. Tehrani, M. S. Yazdi, S. Vahedi, et al. (2019): Characterization of Anti-E.Coli Antibody Exteracted From Immunized Hen Eggs By Polyethylene Glycol (PEG) Precipitation. https://www.semanticscholar.org/paper/ca7b831a3adc1649f0ebdac103e77dce3525db80
  90. Santiago-Martínez M., Marín-Hernández Á., Gallardo-Pérez J., Yoval-Sánchez B., Feregrino-Mondragón R., Rodríguez-Zavala J., et al. (2019): FruBPase II and ADP-PFK1 are involved in the modulation of carbon flow in the metabolism of carbohydrates in Methanosarcina acetivorans. Archives of Biochemistry and Biophysics 669:39-49. https://doi.org/10.1016/j.abb.2019.05.012
  91. Jahandar M., Nassiri M., Nasiri K., Haghparast A. (2019): Production and Purification of Specific IgY Against InvG Protein of Salmonella typhimurium. International Journal of Infection In Press(In Press). https://doi.org/10.5812/IJI.87683
  92. Silva R., Almeida M., Marialva E., Balieiro A., Castro D., Rios-Velasquez C., et al. (2019): Chicken eggs as a surveillance tool for malaria and leishmaniasis vector presence. Revista da Sociedade Brasileira de Medicina Tropical 52. https://doi.org/10.1590/0037-8682-0415-2018
  93. Kota R., Srirama K., Reddy P. (2019): IgY antibodies of chicken do not bind staphylococcal binder of immunoglobulin (Sbi) from Staphylococcus aureus. Annals of Microbiology 69(5):531-540. https://doi.org/10.1007/s13213-019-1441-8
  94. Eto S., Fernandes D., Yunis-Aguinaga J., Claudiano G., Shimada M., Salvador R., et al. (2019): Characterization and production of IgY antibodies anti-Photobacterium damselae subsp. piscicida: Therapeutic and prophylactic use in Rachycentron canadum. Aquaculture 513:734424. https://doi.org/10.1016/j.aquaculture.2019.734424
  95. Das S., Majumder S., Nag M., Kingston J. (2019): A sandwich duplex immuno PCR for rapid and sensitive identification of Clostridium perfringens alpha and enterotoxin. Anaerobe 57:63-74. https://doi.org/10.1016/j.anaerobe.2019.03.015
  96. Thirumalai D., Visaga Ambi S., Vieira-Pires R., Xiaoying Z., Sekaran S., Krishnan U. (2019): Chicken egg yolk antibody (IgY) as diagnostics and therapeutics in parasitic infections – A review. International Journal of Biological Macromolecules 136:755-763. https://doi.org/10.1016/j.ijbiomac.2019.06.118
  97. Tran T., Do B., Nguyen T., Tran T., Tran S., Nguyen B., et al. (2019): Development of an IgY-based lateral flow immunoassay for detection of fumonisin B in maize. F1000Research 8:1042. https://doi.org/10.12688/f1000research.19643.2
  98. Sushma U., Srivastava A., Krishnan M. (2019): Melamine Detection in Food matrices employing Chicken Antibody (IgY): A Comparison between Colorimetric and Chemiluminescent Methods. Current Analytical Chemistry 15(6):668-677. https://doi.org/10.2174/1573411015666181205120323
  99. Nie W., Zhao C., Guo X., Sun L., Meng T., Liu Y., et al. (2019): Preparation and identification of chicken egg yolk immunoglobulins against human enterovirus 71 for diagnosis of hand-foot-and-mouth disease. Analytical Biochemistry 573:44-50. https://doi.org/10.1016/j.ab.2019.02.029
  100. Zhu Y., Ma Y., Lu M., Zhang Y., Li A., Liang X., et al. (2019): Efficient Production of Human Norovirus-Specific IgY in Egg Yolks by Vaccination of Hens with a Recombinant Vesicular Stomatitis Virus Expressing VP1 Protein. Viruses 11(5):444. https://doi.org/10.3390/v11050444
  101. A. Khabiri, Mahdieh Bayat, B. Hemati (2018): Production of IgY antibody against Vibrio cholerae cytotoxin B and development of an ELISA for detection of cholera toxin. https://www.semanticscholar.org/paper/c56b7889ee5ad40865f615723139c5d52c17a1df
  102. Agil Darmawan (2018): DETEKSI IMMUNOGLOBULIN YOLK (IgY) Anti-plasmodium falciparum Lactate Dehydrogenase (pfLDH) PADA TELUR AYAM MENGGUNAKAN TEKNIK IN-HOUSE INDIRECT ELISA. https://www.semanticscholar.org/paper/3e2ea65e94a855937056012395cb42afbcf9ddb6
  103. Amukelani Marivate (2018): Recombinant expression optimisation and characterisation of peptidases for diagnosis of animal African Trypanosomosis. https://www.semanticscholar.org/paper/c7b6713a2c6fde6b54150fe527cd136a419bb890
  104. Sun L., Li M., Fei D., Diao Q., Wang J., Li L., et al. (2018): Preparation and Application of Egg Yolk Antibodies Against Chinese Sacbrood Virus Infection. Frontiers in Microbiology 9. https://doi.org/10.3389/fmicb.2018.01814
  105. Sheng L., He Z., Liu Y., Ma M., Cai Z. (2018): Mass spectrometry characterization for N-glycosylation of immunoglobulin Y from hen egg yolk. International Journal of Biological Macromolecules 108:277-283. https://doi.org/10.1016/j.ijbiomac.2017.12.012
  106. Bayat M., Khabiri A., Hemati B. (2018): Development of IgY-Based Sandwich ELISA as a Robust Tool for Rapid Detection and Discrimination of Toxigenic Vibrio cholerae. Canadian Journal of Infectious Diseases and Medical Microbiology 2018:1-9. https://doi.org/10.1155/2018/4032531
  107. Wang N., Xu Q., Liu Y., Jin Y., Harlina P., Ma M. (2018): Highly efficient extraction and purification of low‐density lipoprotein from hen egg yolk. Poultry Science 97(6):2230-2238. https://doi.org/10.3382/ps/pey059
  108. Aradhya S., Reddy P., Ramlal S., Nagaraj S., Mondal B., Murali H. (2018): Development and Evaluation of IgY Immunocapture PCR for Detection of Enteropathogenic E. coli Devoid of Protein A Interference. Journal of Pure and Applied Microbiology 12(3):1109-1118. https://doi.org/10.22207/jpam.12.3.09
  109. Kruse T., Biedenkopf N., Hertz E., Dietzel E., Stalmann G., López-Méndez B., et al. (2018): The Ebola Virus Nucleoprotein Recruits the Host PP2A-B56 Phosphatase to Activate Transcriptional Support Activity of VP30. Molecular Cell 69(1):136-145.e6. https://doi.org/10.1016/j.molcel.2017.11.034
  110. Budama-Kilinc Y., Cakir-Koc R., Ozdemir B., Kaya Z., Badur S. (2018): Production and characterization of a conserved M2e peptide-based specific IgY antibody: evaluation of the diagnostic potential via conjugation with latex nanoparticles. Preparative Biochemistry & Biotechnology 48(10):930-939. https://doi.org/10.1080/10826068.2018.1525564
  111. Łupicka-Słowik A., Psurski M., Grzywa R., Bobrek K., Smok P., Walczak M., et al. (2017): Development of Adenosine Deaminase-Specific IgY Antibodies: Diagnostic and Inhibitory Application. Applied Biochemistry and Biotechnology 184(4):1358-1374. https://doi.org/10.1007/s12010-017-2626-x
  112. Gaetani C., Ambrosi E., Ugo P., Moretto L. (2017): Electrochemical Immunosensor for Detection of IgY in Food and Food Supplements. Chemosensors 5(1):10. https://doi.org/10.3390/CHEMOSENSORS5010010
  113. Choudhury Nafisa Binte Hussain, Mohammad Chhiddikur Rahman (2017): Comparative Study on Genetic Variations in Maternal Antibody (IgY) Transfer from Dam to Egg-yolk in Different Meat Lines of Chickens. https://www.semanticscholar.org/paper/4419954c62e0e08ad1478e943140a946f8a71590
  114. Choudhury Nafisa Binte Hussain (2017): Isolation and Estimation of Chicken Immunoglobulins (IgY) from Egg Yolk by Optimizing Polyethylene Glycol (PEG) Precipitation Method. https://www.semanticscholar.org/paper/980e7f7ce4e6d99d54cc0c7ad2679eb4efebc8fa
  115. Li C., Zhang Y., Eremin S., Yakup O., Yao G., Zhang X. (2017): Detection of kanamycin and gentamicin residues in animal-derived food using IgY antibody based ic-ELISA and FPIA. Food Chemistry 227:48-54. https://doi.org/10.1016/j.foodchem.2017.01.058
  116. Li C., Ren H., Schade R., Zhang X. (2017): A novel and efficient immunoglobulin Y extraction method using poloxamer-polyethylene glycol. Preparative Biochemistry & Biotechnology 47(7):739-743. https://doi.org/10.1080/10826068.2017.1315598
  117. Song D., Qu X., Liu Y., Li L., Yin D., Li J., et al. (2017): A Rapid Detection Method of Brucella with Quantum Dots and Magnetic Beads Conjugated with Different Polyclonal Antibodies. Nanoscale Research Letters 12(1). https://doi.org/10.1186/s11671-017-1941-z
  118. da Rocha D., Fernandez J., de Almeida C., da Silva C., Magnoli F., da Silva O., et al. (2017): Development of IgY antibodies against anti‐snake toxins endowed with highly lethal neutralizing activity. European Journal of Pharmaceutical Sciences 106:404-412. https://doi.org/10.1016/j.ejps.2017.05.069
  119. Sheng L., He Z., Chen J., Liu Y., Ma M., Cai Z. (2017): The impact of N-glycosylation on conformation and stability of immunoglobulin Y from egg yolk. International Journal of Biological Macromolecules 96:129-136. https://doi.org/10.1016/j.ijbiomac.2016.12.043
  120. Akbari M., Ahmadi A., Mirkalantari S., Salimian J. (2017): Anti-Vibriocholerae IgY Antibody Inhibits Mortality in Suckling Mice Model. Journal of the National Medical Association 110(1):84-87. https://doi.org/10.1016/j.jnma.2017.04.001
  121. Sert M., Cakir Koc R., Budama Kilinc Y. (2017): Novel Fitc-Labeled Igy Antibody: Fluorescence Imaging Toxoplasma Gondii In Vitro. Scientific Reports 7(1). https://doi.org/10.1038/s41598-017-00930-1
  122. Shetty P., D’Souza A., CP G. (2017): Conjugation of Peroxidase from Brassica oleracea gongylodes for Use as a Label- Prospect of a Novel Enzyme Tag for Immunoassay Systems. International Journal of Applied Sciences and Biotechnology 5(1):59-65. https://doi.org/10.3126/IJASBT.V5I1.17009
  123. Solhi R., Alebouyeh M., Khafri A., Rezaeifard M., Aminian M. (2017): In vitro evaluation of cross-strain inhibitory effects of IgY polyclonal antibody against H. pylori. Microbial Pathogenesis 110:682-687. https://doi.org/10.1016/j.micpath.2017.03.025
  124. Teimoori S., Arimatsu Y., Laha T., Kaewkes S., Sereerak P., Sripa M., et al. (2017): Chicken IgY-based coproantigen capture ELISA for diagnosis of human opisthorchiasis. Parasitology International 66(4):443-447. https://doi.org/10.1016/j.parint.2015.10.011
  125. Torii T., Kanemitsu K., Hagiwara A. (2017): Sialic acid level is significantly elevated in IgM enriched protein fraction in sera of cancer patients. Journal of Immunoassay and Immunochemistry 38(2):127-139. https://doi.org/10.1080/15321819.2016.1224973
  126. Kousted T., Kalliokoski O., Christensen S., Winther J., Hau J. (2017): Exploring the antigenic response to multiplexed immunizations in a chicken model of antibody production. Heliyon 3(3):e00267. https://doi.org/10.1016/j.heliyon.2017.e00267
  127. Cahyaningsih T., Hasmi Pasaribu F., Indrawati A. (2017): Produksi IgY Spesifik Staphylococcus aureus dari Isolat Asal Kasus Staphylococcosis pada Kelinci. Jurnal Ilmu Pertanian Indonesia 22(1):1-5. https://doi.org/10.18343/JIPI.22.1.1
  128. Amro W., Al-Qaisi W., Al-Razem F. (2017): Production and purification of IgY antibodies from chicken egg yolk. Journal of Genetic Engineering and Biotechnology 16(1):99-103. https://doi.org/10.1016/j.jgeb.2017.10.003
  129. Lee W., Syed Atif A., Tan S., Leow C. (2017): Insights into the chicken IgY with emphasis on the generation and applications of chicken recombinant monoclonal antibodies. Journal of Immunological Methods 447:71-85. https://doi.org/10.1016/j.jim.2017.05.001
  130. Budama-Kilinc Y., Cakir-Koc R., Ozgun Eren G. (2017): Synthesis and Characterization of Gold Nanoparticles-AntibodyEnzyme Conjugate for use in Influenza a Spesific Nano-ELISA. World Congress on New Technologies. https://doi.org/10.11159/ICNFA17.140
  131. Liu Y., Zhao C., Fu K., Song X., Xu K., Wang J., et al. (2017): Selective turn-on fluorescence detection of Vibrio parahaemolyticus in food based on charge-transfer between CdSe/ZnS quantum dots and gold nanoparticles. Food Control 80:380-387. https://doi.org/10.1016/J.FOODCONT.2017.05.032
  132. Júnior A., Santos J., Bassi P., Bittar J., Bittar E. (2017): IgY-Technology Applied to Studies of Toxoplasma gondii Infection. Toxoplasmosis. https://doi.org/10.5772/67997
  133. Chris Brown (2016): Treatment and Prevention of Human Rotavirus (HRV) in Developing Countries: The Potential of Avian Immunoglobulin Y. https://www.semanticscholar.org/paper/ce09ff619f3c0ab69844f833acf7df413292e961
  134. Christine L. Ferguson (2016): Open Peer Review. Serials review. https://doi.org/10.6084/m9.figshare.2066868.v1
  135. Brandon D., Korn A. (2016): Immunosorbent analysis of toxin contamination in milk and ground beef using IgY-based ELISA†. Food and Agricultural Immunology 27(4):496-508. https://doi.org/10.1080/09540105.2015.1126809
  136. Dietzel E., Schudt G., Krähling V., Matrosovich M., Becker S. (2016): Functional Characterization of Adaptive Mutations during the West African Ebola Virus Outbreak. Journal of Virology 91(2). https://doi.org/10.1128/JVI.01913-16
  137. Börstler J., Engel D., Petersen M., Poggensee C., Jansen S., Schmidt‐Chanasit J., et al. (2016): Surveillance of maternal antibodies against West Nile virus in chicken eggs in South‐West Germany. Tropical Medicine & International Health 21(5):687-690. https://doi.org/10.1111/tmi.12676
  138. Borhani K., Mohabati Mobarez A., Khabiri A., Behmanesh M., Khoramabadi N. (2016): Inhibitory effects of rHP-NAP IgY against Helicobacter pylori attachment to AGS cell line. Microbial Pathogenesis 97:231-235. https://doi.org/10.1016/j.micpath.2016.06.004
  139. M. Naidoo (2016): Trypanosoma congolense invariant surface glycoprotein: a potential diagnostic antigen for animal African trypanosomiasis. https://www.semanticscholar.org/paper/2adb552c13f0def32704bfa653dd1a1e88fefd9d
  140. Toso R., Neher B., Alvarez Rubianes N., Toribio M., Boeris M., Gastaldo M., et al. (2016): Obtaining, purification and characterization of specific immunoglobulin Y with diagnostic and prophylactic aims. Ciencia Veterinaria 18(1):49-58. https://doi.org/10.19137/CIENVET2016-1814
  141. Cakir-Koc R. (2016): Production of anti-SAG1 IgY antibody against Toxoplasma gondii parasites and evaluation of antibody activity by ELISA method. Parasitology Research 115(8):2947-2952. https://doi.org/10.1007/s00436-016-5047-9
  142. Agrawal R., Hirpurkar S., Sannat C., Gupta A. (2016): Comparative study on immunoglobulin Y transfer from breeding hens to egg yolk and progeny chicks in different breeds of poultry. Veterinary World 9(4):425-431. https://doi.org/10.14202/vetworld.2016.425-431
  143. Nagaraj S., Ramlal S., Kingston J., Batra H. (2016): Development of IgY based sandwich ELISA for the detection of staphylococcal enterotoxin G (SEG), an egc toxin. International Journal of Food Microbiology 237:136-141. https://doi.org/10.1016/j.ijfoodmicro.2016.08.009
  144. Krähling V., Becker D., Rohde C., Eickmann M., Eroğlu Y., Herwig A., et al. (2016): Development of an antibody capture ELISA using inactivated Ebola Zaire Makona virus. Medical Microbiology and Immunology 205(2):173-183. https://doi.org/10.1007/s00430-015-0438-6
  145. Zhang X., Diraviyam T., Li X., Yao G., Michael A. (2016): Preparation of chicken IgY against recombinant E2 protein of bovine viral diarrhea virus (BVDV) and development of ELISA and ICA for BVDV detection. Bioscience, Biotechnology, and Biochemistry 80(12):2467-2472. https://doi.org/10.1080/09168451.2016.1217144
  146. Korzyukov Y., Hetzel U., Kipar A., Vapalahti O., Hepojoki J. (2016): Generation of Anti-Boa Immunoglobulin Antibodies for Serodiagnostic Applications, and Their Use to Detect Anti-Reptarenavirus Antibodies in Boa Constrictor. PLOS ONE 11(6):e0158417. https://doi.org/10.1371/journal.pone.0158417
  147. Winkelbach A., Schade R., Schulz C., Wuertz S. (2015): Comparison of oral, rectal and intraperitoneal administration of IgY antibodies in passive immunization of rainbow trout (Oncorhynchus mykiss). Aquaculture International 23(2):427-438. https://doi.org/10.1007/s10499-014-9823-1
  148. Winkelbach A., Günzel D., Schulz C., Wuertz S. (2015): Differences in IgY gut absorption in gastric rainbow trout (Oncorhynchus mykiss) and agastric common carp (Cyprinus carpio) assessed in vivo and in vitro. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology 167:58-64. https://doi.org/10.1016/j.cbpc.2014.09.001
  149. C. G. Michael (2015): EFFICACY OF ORAL PASSIVE IMMUNOTHERAPY AGAINST DENTAL CARIES IN HUMANS USING CHICKEN EGG YOLK ANTIBODIES GENERATED AGAINST STREPTOCOCCUS MUTANS. https://www.semanticscholar.org/paper/3426a5b18f21613df79531b126cab9f528913f0f
  150. Tong C., Geng F., He Z., Cai Z., Ma M. (2015): A simple method for isolating chicken egg yolk immunoglobulin using effective delipidation solution and ammonium sulfate. Poultry Science 94(1):104-110. https://doi.org/10.3382/ps/peu005
  151. D. Bollschweiler (2015): Study of the archaeal motility system of Halobacterium salinarum by cryo-electron tomography. https://www.semanticscholar.org/paper/4a3d395efb18397c778b9c66a07a6398f4001655
  152. Pauly D., Hanack K. (2015): How to avoid pitfalls in antibody use. F1000Research 4:691. https://doi.org/10.12688/f1000research.6894.1
  153. Gandhimathi, A. Michael (2015): PROTECTION AGAINST EXPERIMENTAL DENTAL CARIES IN RATS WITH CHICKEN EGG YOLK ANTIBODIES (IgY) GENERATED AGAINST STREPTOCOCCUS MUTANS. https://www.semanticscholar.org/paper/56c1ad29f22837650d64d815b71964f264eadd07
  154. J. Gomes (2015): Purification of IgY using aqueous biphasic systems composed of good’s buffers ionic liquids. https://www.semanticscholar.org/paper/f24f43c0c8ee549f5e64287ab114e05ea93cf748
  155. Borhani K., Mobarez A., Khabiri A., Behmanesh M., Khoramabadi N. (2015): Production of specific IgY Helicobacter pylori recombinant OipA protein and assessment of its inhibitory effects towards attachment of H. pylori to AGS cell line. Clinical and Experimental Vaccine Research 4(2):177. https://doi.org/10.7774/cevr.2015.4.2.177
  156. M. Dallal, E. Kalantar, Laya Kafami Khorasani, M. Tehrani, M. Zamani (2015): Identification and Extraction of Chicken Egg Yolk Immunoglobulin from Egg by Polyethylene Glycol (PEG) Precipitation. https://www.semanticscholar.org/paper/eccaf0c99c92b5672a94d39234d4cab04f78ac4b
  157. Teimoori S., Arimatsu Y., Laha T., Kaewkes S., Sereerak P., Tangkawattana S., et al. (2015): Immunodiagnosis of opisthorchiasis using parasite cathepsin F. Parasitology Research 114(12):4571-4578. https://doi.org/10.1007/s00436-015-4703-9
  158. Müller S., Schubert A., Zajac J., Dyck T., Oelkrug C. (2015): IgY antibodies in human nutrition for disease prevention. Nutrition Journal 14(1). https://doi.org/10.1186/s12937-015-0067-3
  159. Mudili V., Makam S., Sundararaj N., Siddaiah C., Gupta V., Rao P. (2015): A novel IgY-Aptamer hybrid system for cost-effective detection of SEB and its evaluation on food and clinical samples. Scientific Reports 5(1). https://doi.org/10.1038/srep15151
  160. Júnior W., Cano R., Totola A., Carvalho L., Cerri M., Coimbra J., et al. (2015): Adsorption of immunoglobulin Y in supermacroporous continuous cryogel with immobilized Cu(2+) ions. Journal of Chromatography A 1395:16-22. https://doi.org/10.1016/j.chroma.2015.03.052
  161. Спиридонов В., Хоменко Я., Іщенко В., Гончаренко В., Шимко Н., Рибальченко Д. (2015): Elisa development for detection of glyphosat resistant gm soybean. ScienceRise 11(6 (16)):12. https://doi.org/10.15587/2313-8416.2015.53844
  162. Priyanka B., Abhijith K., Rastogi N., Raghavarao K., Thakur M. (2014): Integrated Approach for the Extraction and Purification of IgY from Chicken Egg Yolk. Separation Science and Technology 49(4):562-568. https://doi.org/10.1080/01496395.2013.855231
  163. C. Gandhimathi, A. Michael (2014): IN VITRO NEUTRALIZATION OF VIRULENCE PROPERTIES OF STREPTOCOCCUS MUTANS USING CHICKEN EGGYOLK ANTIBODIES (IGY). https://www.semanticscholar.org/paper/bf12bac5fcb38b073418c757ba48cd1306530fd6
  164. C. Sampaio, M. Baldissera, M. Sagrillo, T. Heres, C. Oliveira, D. R. Stainki, et al. (2014): IN VITRO CYTOTOXICITY AND GENOTOXICITY OF CHICKEN EGG YOLK ANTIBODIES ( IGY ) AGAINST TRYPANOSOMA EVANSI IN HUMAN LYMPHOCYTES LUZIA. https://www.semanticscholar.org/paper/a447a0ecc56ca4c379618224def72b9a3aa420e1
  165. Gabriel F., Accoceberry I., Bessoule J., Salin B., Lucas-Guérin M., Manon S., et al. (2014): A Fox2-Dependent Fatty Acid ß-Oxidation Pathway Coexists Both in Peroxisomes and Mitochondria of the Ascomycete Yeast Candida lusitaniae. PLoS ONE 9(12):e114531. https://doi.org/10.1371/journal.pone.0114531
  166. Lee H., Abeyrathne E., Choi I., Suh J., Ahn D. (2014): Sequential separation of immunoglobulin Y and phosvitin from chicken egg yolk without using organic solvents. Poultry Science 93(10):2668-2677. https://doi.org/10.3382/ps.2014-04093
  167. L. Cristina, M. Baldissera, M. Sagrillo, C. Belmonte, S. G. Monteiro (2014): IN VITRO CYTOTOXICITY AND GENOTOXICITY OF CHICKEN EGG YOLK ANTIBODIES (IGY) AGAINST TRYPANOSOMA EVANSI IN HUMAN LYMPHOCYTES. https://www.semanticscholar.org/paper/65ecd1b816c002f9e295e56eaa6c02d590cd7ce4
  168. Sampaio L., Baldissera M., Grando T., Gressler L., Capeleto D., de Sa M., et al. (2014): Production, purification and therapeutic potential of egg yolk antibodies for treating Trypanosoma evansi infection. Veterinary Parasitology 204(3-4):96-103. https://doi.org/10.1016/j.vetpar.2014.05.032
  169. L. Sampaio (2014): Imunoterapia com igY aviária em ratos experimentalmente infectados porTrypanosoma evansi. https://www.semanticscholar.org/paper/a3ce8c9bf852d5cc9f9541e483421e12600d80b0
  170. O. Baiyegunhi (2014): Heterologous expression of invariant surface glycoproteins, ISG75 of Trypanosoma brucei brucei and T.b. gambiense, for antibody production and diagnosis of African Trypanosomiasis. https://www.semanticscholar.org/paper/252fe5f769535b625374a0ce0ae1b4dafd7d38a4
  171. Phumzile Mkhize (2014): Development of an enzyme-linked immunosorbent assay (ELISA) for field detection and discrimination of Fusarium circinatum from Fusarium oxysporum and Diplodia pinea in pine seedlings. https://www.semanticscholar.org/paper/f93961fa63956bf60e9712fb2908980809548dac
  172. Cátedra de Inmunología (2013): Caracterización de las inmunoglobulinas del ñandú (Rhea americana) Characterization of Rhea americana immunoglobulins. https://www.semanticscholar.org/paper/d6e2073553fa119fab64fe7a14cd20db1da84d77
  173. D. Montagna, V. Rigo, L. Ramayo, L. H. Goldman, M. Huguet, N. Maceira, et al. (2013): Caracterización de las inmunoglobulinas del ñandú (Rhea americana). https://www.semanticscholar.org/paper/a189445ca79a21ddb21f4b7010cc376dc87115c5
  174. Suzette Curtello H. (2013): Purification of Avian IgY with Trichloroacetic Acid (TCA). Journal of Chromatography & Separation Techniques 04(09). https://doi.org/10.4172/2157-7064.1000205
  175. Reddy P., Shekar A., Kingston J., Sripathy M., Batra H. (2013): Evaluation of IgY capture ELISA for sensitive detection of alpha hemolysin of Staphylococcus aureus without staphylococcal protein A interference. Journal of Immunological Methods 391(1-2):31-38. https://doi.org/10.1016/j.jim.2013.02.004
  176. Romina V., Lucila B., Fabrisio E., Natalia Y., Daniela P., Mariacute a C., et al. (2013): Characterization of egg yolk immunoglobulin (IgY) against enterotoxigenic Escherichia coli and evaluation of its effects on bovine intestinal cells. African Journal of Microbiology Research 7(5):398-405. https://doi.org/10.5897/AJMR12.1935
  177. T. Diraviyam, M. Menaka, R. Mahenthiran, A. Michael (2013): GENERATION AND PHYSICOCHEMICAL CHARACTERIZATION OF CHICKEN EGG YOLK ANTIBODIES (IgY) AGAINST Salmonella typhimurium. https://www.semanticscholar.org/paper/070a702f38d857a8d23116ab54671108f47ffeaf
  178. Dai Y., Zhang X., Tan M., Huang P., Lei W., Fang H., et al. (2013): A dual chicken IgY against rotavirus and norovirus. Antiviral Research 97(3):293-300. https://doi.org/10.1016/j.antiviral.2012.12.011
  179. A. SALIH T., A. ALJEBORY S., A. AL-ALOOSI T. (2013): EXTRACTION, PURIFICATION AND ESTIMATE THE MOLECULAR WEIGHT OF IMMUNOGLOBULIN IGY AGAINST A SPECIALIST PARASITE EIMERIA TENELLA IN CHICKENS. Journal of University of Anbar for Pure Science 7(2):1-14. https://doi.org/10.37652/juaps.2013.84867
  180. Niederstadt L., Hohn O., Dorner B., Schade R., Bannert N. (2012): Stimulation of IgY responses in gene gun immunized laying hens by combined administration of vector DNA coding for the target antigen Botulinum toxin A1 and for avian cytokine adjuvants. Journal of Immunological Methods 382(1-2):58-67. https://doi.org/10.1016/j.jim.2012.05.005
  181. Niederstadt L., Schade R. (2012): Was sind und was können polyklonale aviäre Antikörper?. BIOspektrum 18(2):174-177. https://doi.org/10.1007/s12268-012-0162-3
  182. Lars Niederstadt (2012): Herstellung von DNA induzierten mono-/ polyklonalen Antikörpern, zur Schnelldetektion von hochpathogenen viralen Erregern und bioterroristisch relevanten Toxinen. https://www.semanticscholar.org/paper/064e0c9acc33893fcd73bd926e54193b5071fa10
  183. Tan S., Mohamedali A., Kapur A., Lukjanenko L., Baker M. (2012): A novel, cost-effective and efficient chicken egg IgY purification procedure. Journal of Immunological Methods 380(1-2):73-76. https://doi.org/10.1016/j.jim.2012.03.003
  184. Wala Ahmad Amro, Wael Al-Qaisi, Fawzi Al-Razem (): Production and purification of IgY antibodies from chicken egg yolk. https://www.semanticscholar.org/paper/c5ee51865167d98ed5adb6c105596c6de30368d5

Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157–164.

  1. Liu J., Wang X., Chen Z., Zeng Z., Lai J., Jiang M. (2026): Bionic Adaptive Decision-Making Memristive Circuit Based on Fight-or-Flight Response Reinforced by Environment Enrichment. IEEE Transactions on Circuits and Systems I: Regular Papers 73(3):1814-1827. https://doi.org/10.1109/TCSI.2025.3622672
  2. Yang L., Cai R., Cheng M., Ding Z., Li S., Zeng Z. (2025): Memristor-Based Circuit Design of Biological Behavior Chain. IEEE Transactions on Circuits and Systems I: Regular Papers 72(8):4127-4139. https://doi.org/10.1109/TCSI.2024.3504406
  3. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  4. Dou G., Guo W., Kong L., Sun J., Guo M., Wen S. (2024): Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning. IEEE Transactions on Biomedical Circuits and Systems 18(5):1166-1177. https://doi.org/10.1109/TBCAS.2024.3388673
  5. Sun J., Xu Y., Liu P., Wang Y. (2023): Memristor-Based Neural Network Circuit of Duple-Reward and Duple-Punishment Operant Conditioning With Time Delay. IEEE Transactions on Circuits and Systems I: Regular Papers 70(11):4369-4379. https://doi.org/10.1109/TCSI.2023.3305679
  6. Stahlman W., Leising K. (2023): The behavioral origins of phylogenic responses and ontogenic habits. Journal of the Experimental Analysis of Behavior 121(1):27-37. https://doi.org/10.1002/jeab.892
  7. Yang C., Wang X., Chen Z., Zhang S., Zeng Z. (2022): Memristive Circuit Implementation of Operant Cascaded With Classical Conditioning. IEEE Transactions on Biomedical Circuits and Systems 16(5):926-938. https://doi.org/10.1109/TBCAS.2022.3204742
  8. Croteau-Chonka E., Clayton M., Venkatasubramanian L., Harris S., Jones B., Narayan L., et al. (2022): High-throughput automated methods for classical and operant conditioning of Drosophila larvae. eLife 11. https://doi.org/10.7554/eLife.70015
  9. Menti G., Meda N., Zordan M., Megighian A. (2022): Towards a unified vision on animal navigation. European Journal of Neuroscience 57(12):1980-1997. https://doi.org/10.1111/ejn.15881
  10. Sun J., Han J., Wang Y., Liu P. (2022): Memristor-Based Neural Network Circuit of Operant Conditioning Accorded With Biological Feature. IEEE Transactions on Circuits and Systems I: Regular Papers 69(11):4475-4486. https://doi.org/10.1109/TCSI.2022.3194364
  11. Tonna M., Ottoni R., Pellegrini C., Mora L., Gambolo L., Di Donna A., et al. (2022): The motor profile of obsessive-compulsive rituals: psychopathological and evolutionary implications. CNS Spectrums 28(4):441-449. https://doi.org/10.1017/S1092852922000165
  12. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. bioRxiv. https://doi.org/10.1101/2021.10.04.463010
  13. Kristina T Klein (2020): High-Throughput Operant Conditioning in Drosophila Larvae. https://doi.org/10.17863/CAM.47681
  14. Skora L., Yeomans M., Crombag H., Scott R. (2020): Evidence that instrumental conditioning requires conscious awareness in humans. Cognition 208:104546. https://doi.org/10.1016/j.cognition.2020.104546
  15. Tonna M., Ponzi D., Palanza P., Marchesi C., Parmigiani S. (2020): Proximate and ultimate causes of ritual behavior. Behavioural Brain Research 393:112772. https://doi.org/10.1016/j.bbr.2020.112772
  16. Kuroda T., Gilroy S., Cançado C., Podlesnik C. (2020): Effects of Punishing Target Response During Extinction on Resurgence and Renewal in Zebrafish (Danio rerio). Behavioural Processes 178:104191. https://doi.org/10.1016/j.beproc.2020.104191
  17. Cloninger C., Cloninger K., Zwir I., Keltikangas-Järvinen L. (2019): The complex genetics and biology of human temperament: a review of traditional concepts in relation to new molecular findings. Translational Psychiatry 9(1). https://doi.org/10.1038/s41398-019-0621-4
  18. Miletto Petrazzini M., Pecunioso A., Dadda M., Agrillo C. (2019): The Impact of Brain Lateralization and Anxiety-Like Behaviour in an Extensive Operant Conditioning Task in Zebrafish (Danio rerio). Symmetry 11(11):1395. https://doi.org/10.3390/sym11111395
  19. Tonna M., Marchesi C., Parmigiani S. (2019): The biological origins of rituals: An interdisciplinary perspective. Neuroscience & Biobehavioral Reviews 98:95-106. https://doi.org/10.1016/j.neubiorev.2018.12.031
  20. Nargeot R., Puygrenier L. (2019): Operant Learning in Invertebrates. Reference Module in Life Sciences. https://doi.org/10.1016/b978-0-12-809633-8.90788-x
  21. Patrick S., Bullock D. (2019): Graded striatal learning factors enable switches between goal-directed and habitual modes, by reassigning behavior control to the fastest-computed representation that predicts reward. bioRxiv. https://doi.org/10.1101/619445
  22. A. Buatois (2018): Etudes comportementales et neurobiologiques de l’apprentissage visuel chez l’abeille (Apis mellifera) en réalité virtuelle. https://www.semanticscholar.org/paper/2cfc95f35267912cbd2050ec4e7001004b8d9b70
  23. Alexis Buatois (2018): Behavioral and neurobiological studies of visual learning in honey bees (Apis mellifera) in virtual reality. https://www.semanticscholar.org/paper/176fb2176c8703d70bf8e61849abb8d256ed1668
  24. Shahsavari H., Zare Z., Parsa-Yekta Z., Griffiths P., Vaismoradi M. (2018): Learning Situations in Nursing Education: A Concept Analysis. Research and Theory for Nursing Practice 32(1):23-45. https://doi.org/10.1891/1541-6577.32.1.23
  25. H. Shahsavari, Z. Zare, Z. Parsa-yekta, P. Griffiths, M. Vaismoradi (2018): Learning Situations in Nursing Education : A Concept Analysis. https://www.semanticscholar.org/paper/5b1a0cbbb86cb30adeed8217cabee4905d4c2a44
  26. Zwir I., Arnedo J., Del-Val C., Pulkki-Råback L., Konte B., Yang S., et al. (2018): Uncovering the complex genetics of human temperament. Molecular Psychiatry 25(10):2275-2294. https://doi.org/10.1038/s41380-018-0264-5
  27. B. Brembs (2017): Genetic Analysis of Behavior in Drosophila-Oxford Handbooks. https://www.semanticscholar.org/paper/16aa0067215f06a381fc39b3a4e61282f6c99320
  28. Schultheiss P., Buatois A., Avarguès-Weber A., Giurfa M. (2017): Using virtual reality to study visual performances of honeybees. Current Opinion in Insect Science 24:43-50. https://doi.org/10.1016/j.cois.2017.08.003
  29. Brembs B. (2016): Genetic Analysis of Behavior in Drosophila. The Oxford Handbook of Invertebrate Neurobiology. https://doi.org/10.1093/OXFORDHB/9780190456757.013.37
  30. Bartumeus F., Campos D., Ryu W., Lloret‐Cabot R., Méndez V., Catalan J. (2016): Foraging success under uncertainty: search tradeoffs and optimal space use. Ecology Letters 19(11):1299-1313. https://doi.org/10.1111/ele.12660
  31. Eilam D. (2015): The cognitive roles of behavioral variability: idiosyncratic acts as the foundation of identity and as transitional, preparatory, and confirmatory phases. Neuroscience & Biobehavioral Reviews 49:55-70. https://doi.org/10.1016/j.neubiorev.2014.11.023
  32. Burgos J., García-Leal Ó. (2015): Autoshaped choice in artificial neural networks: implications for behavioral economics and neuroeconomics. Behavioural Processes 114:63-71. https://doi.org/10.1016/j.beproc.2015.01.010
  33. Peckmezian T., Taylor P. (2015): A virtual reality paradigm for the study of visually mediated behaviour and cognition in spiders. Animal Behaviour 107:87-95. https://doi.org/10.1016/J.ANBEHAV.2015.06.018
  34. Cyr A., Boukadoum M., Thériault F. (2014): Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot’s controller. Frontiers in Neurorobotics 8. https://doi.org/10.3389/fnbot.2014.00021
  35. Chen Jing, Ling ZongShuai (2014): Basal Ganglia Cognitive Behavioral Model Based on Operant ConditioningReflex. https://www.semanticscholar.org/paper/d85d1c6d0f941016b257518c0620ce3047bf42d9
  36. Mendoza E., Colomb J., Rybak J., Pflüger H., Zars T., Scharff C., et al. (2014): Drosophila FoxP Mutants Are Deficient in Operant Self-Learning. PLoS ONE 9(6):e100648. https://doi.org/10.1371/journal.pone.0100648
  37. H. Bell (2014): Behavioral Variability in the Service of Constancy. https://www.semanticscholar.org/paper/1526ff6a60a3c43fb9a76eb1132078c7107ca0d1
  38. J. Stanley (2014): Operant/Classical Conditioning: Comparisons, Intersections and Interactions The 2014 Winter Conference on Animal Learning and Behavior Focus and Research Seminar Sessions. https://www.semanticscholar.org/paper/4f45f4727e5e1051a32989ce2b514c7e98854736
  39. Yapici N., Zimmer M., Domingos A. (2014): Cellular and molecular basis of decision‐making. EMBO reports 15(10):1023-1035. https://doi.org/10.15252/embr.201438993
  40. Méndez V., Campos D., Bartumeus F. (2014): Biological Searches and Random Animal Motility. Springer Series in Synergetics. https://doi.org/10.1007/978-3-642-39010-4_9
  41. W. D. Stahlman, A. Blaisdell (2014): Selections on the Empirical and Theoretical Investigations of Behavioral Variability: An Introduction to the Special Issue. https://www.semanticscholar.org/paper/1bb4d907a672b553d4e18919970a8109b91a9ee3
  42. H. Bell (2013): Control in living systems : an exploration of the cybernetic properties of interactive behaviour. https://www.semanticscholar.org/paper/b32ee0884d7c1f6c750e1ef58c461d3087ef171e
  43. MENDOZA E., COLOMB J., RYBAK J., PFLÜGER H., ZARS T., SCHARFF C., et al. (2012): THE DROSOPHILA FOXP GENE IS REQUIRED FOR OPERANT SELF-LEARNING: IMPLICATIONS FOR THE EVOLUTION OF LANGUAGE. The Evolution of Language. https://doi.org/10.1142/9789814401500_0100
  44. Nargeot R., Simmers J. (2012): Functional Organization and Adaptability of a Decision-Making Network in Aplysia. Frontiers in Neuroscience 6. https://doi.org/10.3389/fnins.2012.00113
  45. C. Eschbach (2011): Klassiches und operantes Lernen bei Larven der Drosophila. https://www.semanticscholar.org/paper/c4f06eaa9680c47a619b8a89b689f67d08db1d39
  46. Dorian S. Houser, J. Finneran, Sam H. Ridgway (2010): eScholarship International Journal of Comparative Psychology. https://www.semanticscholar.org/paper/9a61905f00a0775475b2f404dc73a8a6ebba3759
  47. Unknown authors (): Operant Conditioning in Drosophila.
  48. Veronika Kurchyna, Jan Ole Berndt, Ingo J. Timm (): Choosing Theories Matters: How Different Learning Theories Impact Simulation Results Using Equine Stereotypies as Example. https://www.semanticscholar.org/paper/8229f7e5c4370a199ab8f892c8fa3a1d3cc7435e
  49. Zongshuai Li, Chen Jing, Zongshuai Li (): An Autonomous Cognitive Model Simulating Basal Ganglia An Autonomous Cognitive Model Simulating Basal Ganglia Mechanism Mechanism. https://www.semanticscholar.org/paper/eaa9baea729b6d270138e685e13b61768791fae6

van Swinderen B, Brembs B. (2010): Attention-like deficit and hyperactivity in a Drosophila memory mutant. J. Neurosci. 30(3):1003–1014.

  1. Dougnon G., Matsui H. (2025): Three decades of neuroscience research using animal models of ADHD and ASD: a bibliometric analysis. Frontiers in Psychiatry 16. https://doi.org/10.3389/fpsyt.2025.1528205
  2. Grünblatt E., Homolak J., Babic Perhoc A., Davor V., Knezovic A., Osmanovic Barilar J., et al. (2023): From attention-deficit hyperactivity disorder to sporadic Alzheimer’s disease—Wnt/mTOR pathways hypothesis. Frontiers in Neuroscience 17. https://doi.org/10.3389/fnins.2023.1104985
  3. Qu S., Zhou X., Wang Z., Wei Y., Zhou H., Zhang X., et al. (2023): The effects of methylphenidate and atomoxetine on Drosophila brain at single-cell resolution and potential drug repurposing for ADHD treatment. Molecular Psychiatry 29(1):165-185. https://doi.org/10.1038/s41380-023-02314-6
  4. Cabana-Domínguez J., Antón-Galindo E., Fernàndez-Castillo N., Singgih E., O’Leary A., Norton W., et al. (2022): THE TRANSLATIONAL GENETICS OF ADHD AND RELATED PHENOTYPES IN MODEL ORGANISMS. Neuroscience & Biobehavioral Reviews 144:104949. https://doi.org/10.1016/j.neubiorev.2022.104949
  5. Qu S., Zhu Q., Zhou H., Gao Y., Wei Y., Ma Y., et al. (2022): EasyFlyTracker: A Simple Video Tracking Python Package for Analyzing Adult Drosophila Locomotor and Sleep Activity to Facilitate Revealing the Effect of Psychiatric Drugs. Frontiers in Behavioral Neuroscience 15. https://doi.org/10.3389/fnbeh.2021.809665
  6. Philyaw T., Rothenfluh A., Titos I. (2022): The Use of Drosophila to Understand Psychostimulant Responses. Biomedicines 10(1):119. https://doi.org/10.3390/biomedicines10010119
  7. Karam C., Williams B., Jones S., Javitch J. (2021): The Role of the Dopamine Transporter in the Effects of Amphetamine on Sleep and Sleep Architecture in Drosophila. Neurochemical Research 47(1):177-189. https://doi.org/10.1007/s11064-021-03275-4
  8. Hime G., Stonehouse S., Pang T. (2021): Alternative models for transgenerational epigenetic inheritance: Molecular psychiatry beyond mice and man. World Journal of Psychiatry 11(10):711-735. https://doi.org/10.5498/wjp.v11.i10.711
  9. Coll-Tané M., Gong N., Belfer S., van Renssen L., Kurtz-Nelson E., Szuperak M., et al. (2021): The CHD8/CHD7/Kismet family links blood-brain barrier glia and serotonin to ASD-associated sleep defects. Science Advances 7(23). https://doi.org/10.1126/sciadv.abe2626
  10. Grabowska M., Jeans R., Steeves J., van Swinderen B. (2020): Oscillations in the central brain of Drosophila are phase locked to attended visual features. Proceedings of the National Academy of Sciences 117(47):29925-29936. https://doi.org/10.1073/pnas.2010749117
  11. Carter O., van Swinderen B., Leopold D., Collin S., Maier A. (2020): Perceptual rivalry across animal species. Journal of Comparative Neurology 528(17):3123-3133. https://doi.org/10.1002/cne.24939
  12. Malloy C., Somasundaram E., Omar A., Bhutto U., Medley M., Dzubuk N., et al. (2019): Pharmacological identification of cholinergic receptor subtypes: modulation of locomotion and neural circuit excitability in Drosophila larvae. Neuroscience 411:47-64. https://doi.org/10.1016/j.neuroscience.2019.05.016
  13. Rohde P., Jensen I., Sarup P., Ørsted M., Demontis D., Sørensen P., et al. (2019): Genetic Signatures of Drug Response Variability in Drosophila melanogaster. Genetics 213(2):633-650. https://doi.org/10.1534/genetics.119.302381
  14. Lee A., Brandon C., Wang J., Frost W. (2018): An Argument for Amphetamine-Induced Hallucinations in an Invertebrate. Frontiers in Physiology 9. https://doi.org/10.3389/fphys.2018.00730
  15. Damrau C., Colomb J., Brembs B. (2018): Differential dominance of an allele of the Drosophila tßh gene challenges standard genetic techniques. https://doi.org/10.1101/504332
  16. Kirszenblat L., Ertekin D., Goodsell J., Zhou Y., Shaw P., van Swinderen B. (2018): Sleep regulates visual selective attention in Drosophila. Journal of Experimental Biology. https://doi.org/10.1242/jeb.191429
  17. Kittel-Schneider S. (2018): Biologische Grundlagen der Aufmerksamkeitsdefizits-/Hyperaktivitätsstörung (ADHS) des Erwachsenenalters. Handbuch Klinische Psychologie. https://doi.org/10.1007/978-3-662-45995-9_18-1
  18. Adrienn G. Varga (2017): The Neural Basis of Head Direction and Spatial Context in the Insect Central Complex. https://www.semanticscholar.org/paper/96f51ced9d277145f8d3c950e0486b86c21ba667
  19. Chakravarti L., Moscato E., Kayser M. (2017): Unraveling the Neurobiology of Sleep and Sleep Disorders Using Drosophila. Current Topics in Developmental Biology. https://doi.org/10.1016/bs.ctdb.2016.07.010
  20. Widmann A., Artinger M., Biesinger L., Boepple K., Peters C., Schlechter J., et al. (2016): Genetic Dissection of Aversive Associative Olfactory Learning and Memory in Drosophila Larvae. PLOS Genetics 12(10):e1006378. https://doi.org/10.1371/journal.pgen.1006378
  21. de Bivort B., van Swinderen B. (2016): Evidence for selective attention in the insect brain. Current Opinion in Insect Science 15:9-15. https://doi.org/10.1016/J.COIS.2016.02.007
  22. de Bivort B., van Swinderen B. (2016): Evidence for selective attention in the insect brain. bioRxiv. https://doi.org/10.1101/041889
  23. Bosch D. (2016): Analysing visual behaviour in Drosophila Dscam2 mutants using optimised optomotor and operant control assays. https://doi.org/10.14264/UQL.2016.454
  24. Rohde P., Madsen L., Neumann Arvidson S., Loeschcke V., Demontis D., Kristensen T. (2016): Testing candidate genes for attention-deficit/hyperactivity disorder in fruit flies using a high throughput assay for complex behavior. Fly 10(1):25-34. https://doi.org/10.1080/19336934.2016.1158365
  25. Zhang Q., Du G., John V. (2016): Alzheimer’s Model Develops Early ADHD Syndrome. Journal of Neurology & Neurophysiology 06(06). https://doi.org/10.4172/2155-9562.1000329
  26. Farris S. (2016): Insect societies and the social brain. Current Opinion in Insect Science 15:1-8. https://doi.org/10.1016/j.cois.2016.01.010
  27. Koenig S., Wolf R., Heisenberg M. (2016): Visual Attention in Flies—Dopamine in the Mushroom Bodies Mediates the After-Effect of Cueing. PLOS ONE 11(8):e0161412. https://doi.org/10.1371/journal.pone.0161412
  28. Koenig S., Wolf R., Heisenberg M. (2016): Vision in Flies: Measuring the Attention Span. PLOS ONE 11(2):e0148208. https://doi.org/10.1371/journal.pone.0148208
  29. Kim S., Tellez K., Buchan G., Lebestky T. (2016): Fly Stampede 2.0: A Next Generation Optomotor Assay for Walking Behavior in Drosophila Melanogaster. Frontiers in Molecular Neuroscience 9. https://doi.org/10.3389/fnmol.2016.00148
  30. Paulk A., Kirszenblat L., Zhou Y., van Swinderen B. (2015): Closed-Loop Behavioral Control Increases Coherence in the Fly Brain. Journal of Neuroscience 35(28):10304-10315. https://doi.org/10.1523/JNEUROSCI.0691-15.2015
  31. Christine Damrau (2015): Aminergic control of Drosophila behavior. https://www.semanticscholar.org/paper/3cb424aba0cca7ae9cd0494d14bc5ad3960d994f
  32. van der Voet M., Harich B., Franke B., Schenck A. (2015): ADHD-associated dopamine transporter, latrophilin and neurofibromin share a dopamine-related locomotor signature in Drosophila. Molecular Psychiatry 21(4):565-573. https://doi.org/10.1038/mp.2015.55
  33. Van De Poll M., Zajaczkowski E., Taylor G., Srinivasan M., van Swinderen B. (2015): Using an abstract geometry in virtual reality to explore choice behaviour: visual flicker preferences in honeybees. Journal of Experimental Biology. https://doi.org/10.1242/jeb.125138
  34. Farris S., Van Dyke J. (2015): Evolution and function of the insect mushroom bodies: contributions from comparative and model systems studies. Current Opinion in Insect Science 12:19-25. https://doi.org/10.1016/J.COIS.2015.08.006
  35. B. Brembs, H. Pflüger, Christine Damrau, Naoko Toshima, Sabrina Scholz-Kornehl, Martin Schwärzel, et al. (2014): COMPLEX INTERACTIONS OF OCTOPAMINE AND TYRAMINE ORCHESTRATE SUGAR RESPONSIVENESS AND STARVATION RESISTANCE IN DROSOPHILA. https://www.semanticscholar.org/paper/e59465d832f95a29f87c3022fa150a70119b3518
  36. Zhong C., Zhang Y., He W., Wei P., Lu Y., Zhu Y., et al. (2014): Multi-unit recording with iridium oxide modified stereotrodes in Drosophila melanogaster. Journal of Neuroscience Methods 222:218-229. https://doi.org/10.1016/j.jneumeth.2013.11.013
  37. van Alphen B., van Swinderen B. (2013): Drosophila strategies to study psychiatric disorders. Brain Research Bulletin 92:1-11. https://doi.org/10.1016/j.brainresbull.2011.09.007
  38. J. Riedl (2013): Identification of neurons controlling orientation behavior in the Drosophila melanogaster larva. https://www.semanticscholar.org/paper/fa47f2536a2648478aeb2d8a34dd186191cd6293
  39. heng Zhonga, Yuanyuan Zhanga, W. Hea, P. Weia, Yit-Yian Lua, Y. Zhub, et al. (2013): ulti-unit recording with iridium oxide modified stereotrodes in rosophila melanogaster. https://www.semanticscholar.org/paper/ed9a20e80491a18eff4ff27b060689fa2612d294
  40. van Swinderen B. (2012): Competing visual flicker reveals attention-like rivalry in the fly brain. Frontiers in Integrative Neuroscience 6. https://doi.org/10.3389/FNINT.2012.00096
  41. van Swinderen B. (2012): Competing visual flicker reveals attention-like rivalry in the fly brain. Frontiers in Integrative Neuroscience 6. https://doi.org/10.3389/fnint.2012.00096
  42. Baddeley B., Graham P., Husbands P., Philippides A. (2012): A Model of Ant Route Navigation Driven by Scene Familiarity. PLoS Computational Biology 8(1):e1002336. https://doi.org/10.1371/journal.pcbi.1002336
  43. Leboulle G. (2012): Glutamate Neurotransmission in the Honey Bee Central Nervous System. Honeybee Neurobiology and Behavior. https://doi.org/10.1007/978-94-007-2099-2_14
  44. Haendel M., Chesler E. (2012): Lost and found in behavioral informatics. International Review of Neurobiology. https://doi.org/10.1016/B978-0-12-388408-4.00001-0
  45. Arena P., Patane L., Termini P. (2012): Modeling attentional loop in the insect Mushroom Bodies. The 2012 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN.2012.6252833
  46. Yasmine Graf, B. Brembs (2012): Testing Drosophila learning and memory mutants with and without methylphenidate treatment in Buridan’s paradigm. https://www.semanticscholar.org/paper/03a1559be3e37794b60de8d9907711ca05ce9fe9
  47. B. Swinderen, R. Andretić (2011): Andretic miniature brain : setting arousal thresholds in a Drosophila Dopamine in. https://www.semanticscholar.org/paper/15a8d6235d55958aeb3d68506f32a03bdc697395
  48. van Swinderen B. (2011): Attention in Drosophila. International Review of Neurobiology. https://doi.org/10.1016/B978-0-12-387003-2.00003-3
  49. LaFerriere H., Speichinger K., Stromhaug A., Zars T. (2011): The Radish Gene Reveals a Memory Component with Variable Temporal Properties. PLoS ONE 6(9):e24557. https://doi.org/10.1371/journal.pone.0024557
  50. Evans O., Paulk A., van Swinderen B. (2011): An Automated Paradigm for Drosophila Visual Psychophysics. PLoS ONE 6(6):e21619. https://doi.org/10.1371/journal.pone.0021619
  51. Lee P., Lin H., Chang Y., Fu T., Dubnau J., Hirsh J., et al. (2011): Serotonin–mushroom body circuit modulating the formation of anesthesia-resistant memory in Drosophila. Proceedings of the National Academy of Sciences 108(33):13794-13799. https://doi.org/10.1073/pnas.1019483108
  52. Sareen P., Wolf R., Heisenberg M. (2011): Attracting the attention of a fly. Proceedings of the National Academy of Sciences 108(17):7230-7235. https://doi.org/10.1073/pnas.1102522108
  53. Farris S., Pettrey C., Daly K. (2011): A subpopulation of mushroom body intrinsic neurons is generated by protocerebral neuroblasts in the tobacco hornworm moth, Manduca sexta (Sphingidae, Lepidoptera). Arthropod Structure & Development 40(5):395-408. https://doi.org/10.1016/j.asd.2010.10.004
  54. Farris S. (2011): Are mushroom bodies cerebellum-like structures?. Arthropod Structure & Development 40(4):368-379. https://doi.org/10.1016/j.asd.2011.02.004
  55. Farris S., Schulmeister S. (2011): Parasitoidism, not sociality, is associated with the evolution of elaborate mushroom bodies in the brains of hymenopteran insects. Proceedings of the Royal Society B: Biological Sciences 278(1707):940-951. https://doi.org/10.1098/rspb.2010.2161
  56. Brembs B. (2010): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences 278(1707):930-939. https://doi.org/10.1098/rspb.2010.2325
  57. Blackiston D., Shomrat T., Nicolas C., Granata C., Levin M. (2010): A Second-Generation Device for Automated Training and Quantitative Behavior Analyses of Molecularly-Tractable Model Organisms. PLoS ONE 5(12):e14370. https://doi.org/10.1371/journal.pone.0014370
  58. S. M. Miller, Trung Thành Ngô, B. van Swinderen (): Human Neuroscience Hypothesis and Theory Article Attentional Switching in Humans and Flies: Rivalry in Large and Miniature Brains. https://www.semanticscholar.org/paper/f19f6d37316f70258a837832a3272745a73f72df

Brembs B. (2010): Q&A. Current Biology 20(14):R588–R589.

  1. Palavalli-Nettimi R. (2021): Toward a Sustainable Model of Scientific Publishing. Journal of Science Policy & Governance 18(01). https://doi.org/10.38126/jspg180111

Colomb J, Brembs B. (2010): The biology of psychology. Communicative & Integrative Biology 3(2):142–145.

  1. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  2. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.1
  3. Chandio Y., Interrante V., Anwar F. (2024): Human Factors at Play: Understanding the Impact of Conditioning on Presence and Reaction Time in Mixed Reality. IEEE Transactions on Visualization and Computer Graphics 30(5):2400-2410. https://doi.org/10.1109/tvcg.2024.3372120
  4. Bielecki J., Dam Nielsen S., Nachman G., Garm A. (2023): Associative learning in the box jellyfish Tripedalia cystophora. Current Biology 33(19):4150-4159.e5. https://doi.org/10.1016/j.cub.2023.08.056
  5. Ehweiner A., Duch C., Brembs B. (2022): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant selflearning in Drosophila. https://doi.org/10.1101/2022.12.16.520755
  6. Croteau-Chonka E., Clayton M., Venkatasubramanian L., Harris S., Jones B., Narayan L., et al. (2022): High-throughput automated methods for classical and operant conditioning of Drosophila larvae. eLife 11. https://doi.org/10.7554/elife.70015
  7. Sharov A., Tønnessen M. (2021): Agency in Non-human Organisms. Biosemiotics. https://doi.org/10.1007/978-3-030-89484-9_4
  8. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. https://doi.org/10.1101/2021.10.04.463010
  9. Thiede K., Born J., Vorster A. (2021): Sleep and conditioning of the siphon withdrawal reflex in Aplysia. Journal of Experimental Biology 224(16). https://doi.org/10.1242/jeb.242431
  10. Klein K., Croteau-Chonka E., Narayan L., Winding M., Masson J., Zlatic M. (2021): Serotonergic Neurons Mediate Operant Conditioning in Drosophila Larvae. https://doi.org/10.1101/2021.06.14.448341
  11. Ginsburg S., Jablonka E. (2021): Evolutionary transitions in learning and cognition. Philosophical Transactions of the Royal Society B 376(1821). https://doi.org/10.1098/rstb.2019.0766
  12. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  13. Brembs B. (2017): Genetic Analysis of Behavior in Drosophila. The Oxford Handbook of Invertebrate Neurobiology. https://doi.org/10.1093/oxfordhb/9780190456757.013.37
  14. Brembs B. (2016): Operant Behavior in Model Systems. https://doi.org/10.1101/058719
  15. Colomb J., Brembs B. (2016): PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila. PeerJ 4:e1971. https://doi.org/10.7717/peerj.1971
  16. Bronfman Z., Ginsburg S., Jablonka E. (2016): The Transition to Minimal Consciousness through the Evolution of Associative Learning. Frontiers in Psychology 7. https://doi.org/10.3389/fpsyg.2016.01954
  17. Christopher J., de Belle J. (2014): Olfactory learning and memory assays. Behavioral Genetics of the Fly (Drosophila Melanogaster). https://doi.org/10.1017/cbo9780511920585.019
  18. Mendoza E., Colomb J., Rybak J., Pflüger H., Zars T., Scharff C., et al. (2014): Drosophila FoxP Mutants Are Deficient in Operant Self-Learning. PLoS ONE 9(6):e100648. https://doi.org/10.1371/journal.pone.0100648
  19. Killeen P. (2014): Pavlov + Skinner = Premack. International Journal of Comparative Psychology 27(4). https://doi.org/10.46867/ijcp.2014.27.04.04
  20. Weiss S., Rosales-Ruiz J. (2014): Operant/Classical Conditioning: Comparisons,Intersections and Interactions The 2014 Winter Conference on Animal Learning and Behavior Focus and Research Seminar Sessions. International Journal of Comparative Psychology 27(4). https://doi.org/10.46867/ijcp.2014.27.04.07
  21. Nepomnyashchikh V. (2013): Variability in invertebrate behavior and the problem of free will. Biology Bulletin Reviews 3(5):406-411. https://doi.org/10.1134/s2079086413050083
  22. Noboa V., Gillette R. (2013): Selective prey avoidance learning in the predatory sea-slugPleurobranchaea californica. Journal of Experimental Biology. https://doi.org/10.1242/jeb.079384
  23. Scheich H., Brosch M. (2012): Task-Related Activation of Auditory Cortex. Springer Handbook of Auditory Research. https://doi.org/10.1007/978-1-4614-2350-8_3
  24. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  25. Claire Eschbach (2011): Klassiches und operantes Lernen bei Larven der Drosophila.
  26. Brembs B. (2010): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences 278(1707):930-939. https://doi.org/10.1098/rspb.2010.2325

Brembs B. (2009): Mushroom-bodies regulate habit formation in Drosophila. Curr. Biol. 19(16):1351–1355.

  1. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  2. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.1
  3. Rozenfeld E., Parnas M. (2024): Neuronal circuit mechanisms of competitive interaction between action-based and coincidence learning. Science Advances 10(49). https://doi.org/10.1126/sciadv.adq3016
  4. Farnworth M., Loupasaki T., Couto A., Montgomery S. (2024): Mosaic evolution of a learning and memory circuit in Heliconiini butterflies. Current Biology 34(22):5252-5262.e5. https://doi.org/10.1016/j.cub.2024.09.069
  5. Farnworth M., Loupasaki T., Couto A., Montgomery S. (2024): Mosaic evolution of a learning and memory circuit in Heliconiini butterflies. https://doi.org/10.1101/2024.04.21.590441
  6. Abubaker M., Hsu F., Feng K., Chu L., de Belle J., Chiang A. (2024): Asymmetric neurons are necessary for olfactory learning in the Drosophila brain. Current Biology 34(5):946-957.e4. https://doi.org/10.1016/j.cub.2024.01.037
  7. Suárez-Grimalt R., Grunwald Kadow I., Scheunemann L. (2024): An integrative sensor of body states: how the mushroom body modulates behavior depending on physiological context. Learning & Memory 31(5):a053918. https://doi.org/10.1101/lm.053918.124
  8. Jelen M., Musso P., Junca P., Gordon M. (2023): Optogenetic induction of appetitive and aversive taste memories in Drosophila. eLife 12. https://doi.org/10.7554/elife.81535
  9. Stahlman W., Leising K. (2023): The behavioral origins of phylogenic responses and ontogenic habits. Journal of the Experimental Analysis of Behavior 121(1):27-37. https://doi.org/10.1002/jeab.892
  10. Ehweiner A., Duch C., Brembs B. (2022): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant selflearning in Drosophila. https://doi.org/10.1101/2022.12.16.520755
  11. Thiagarajan D., Sachse S. (2022): Multimodal Information Processing and Associative Learning in the Insect Brain. Insects 13(4):332. https://doi.org/10.3390/insects13040332
  12. Kelly M., Barron A. (2022): The best of both worlds: Dual systems of reasoning in animals and AI. Cognition 225:105118. https://doi.org/10.1016/j.cognition.2022.105118
  13. Matilda Gibbons, Andrew Crump, Meghan Barrett, Sajedeh Sarlak, Jonathan Birch, Lars Chıttka (2022): Can insects feel pain? A review of the neural and behavioural evidence. Advances in Insect Physiology. https://doi.org/10.1016/bs.aiip.2022.10.001
  14. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. https://doi.org/10.1101/2021.10.04.463010
  15. Wiggin T., Hsiao Y., Liu J., Huber R., Griffith L. (2021): Rest Is Required to Learn an Appetitively-Reinforced Operant Task in Drosophila. Frontiers in Behavioral Neuroscience 15. https://doi.org/10.3389/fnbeh.2021.681593
  16. Li F., Lindsey J., Marin E., Otto N., Dreher M., Dempsey G., et al. (2020): The connectome of the adult Drosophila mushroom body provides insights into function. eLife 9. https://doi.org/10.7554/elife.62576
  17. Li F., Lindsey J., Marin E., Otto N., Dreher M., Dempsey G., et al. (2020): The connectome of the adult Drosophila mushroom body: implications for function. https://doi.org/10.1101/2020.08.29.273276
  18. Driscoll M., Buchert S., Coleman V., McLaughlin M., Nguyen A., Sitaraman D. (2020): Dopamine neurons promotes wakefulness via the DopR receptor in the Drosophila mushroom body. https://doi.org/10.1101/2020.04.29.069229
  19. Wolf R., Heisenberg M., Brembs B., Waddell S., Mishra A., Kehrer A., et al. (2020): Memory, anticipation, action – working with Troy D. Zars. Journal of Neurogenetics 34(1):9-20. https://doi.org/10.1080/01677063.2020.1715976
  20. Wiggin T., Hsiao Y., Liu J., Huber R., Griffith L. (2020): Rest is Required to Learn an Appetitively-Reinforced Operant Task in Drosophila. https://doi.org/10.1101/2020.08.28.272047
  21. Mizunami M., Hirohata S., Sato A., Arai R., Terao K., Sato M., et al. (2019): Development of behavioural automaticity by extended Pavlovian training in an insect. Proceedings of the Royal Society B: Biological Sciences 286(1894):20182132. https://doi.org/10.1098/rspb.2018.2132
  22. Coll-Tané M., Krebbers A., Castells-Nobau A., Zweier C., Schenck A. (2019): Intellectual disability and autism spectrum disorders ‘on the fly’: insights from Drosophila. Disease Models & Mechanisms 12(5). https://doi.org/10.1242/dmm.039180
  23. Nargeot R., Puygrenier L. (2019): Operant Learning in Invertebrates. Reference Module in Life Sciences. https://doi.org/10.1016/b978-0-12-809633-8.90788-x
  24. Wong A., Marvel C., Taylor J., Krakauer J. (2018): Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?. Brain 142(3):662-673. https://doi.org/10.1093/brain/awy334
  25. Wong A., Marvel C., Taylor J., Krakauer J. (2018): Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?. https://doi.org/10.1101/386466
  26. Schatton A., Scharff C. (2017): FoxP expression identifies a Kenyon cell subtype in the honeybee mushroom bodies linking them to fruit fly αβc neurons. European Journal of Neuroscience 46(9):2534-2541. https://doi.org/10.1111/ejn.13713
  27. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  28. Brembs B. (2017): Genetic Analysis of Behavior in Drosophila. The Oxford Handbook of Invertebrate Neurobiology. https://doi.org/10.1093/oxfordhb/9780190456757.013.37
  29. Foley B., Marjoram P., Nuzhdin S. (2017): Basic reversal-learning capacity in flies suggests rudiments of complex cognition. PLOS ONE 12(8):e0181749. https://doi.org/10.1371/journal.pone.0181749
  30. Arena E., Arena P., Strauss R., Patané L. (2017): Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System. Frontiers in Neurorobotics 11. https://doi.org/10.3389/fnbot.2017.00012
  31. de Bivort B., van Swinderen B. (2016): Evidence for selective attention in the insect brain. Current Opinion in Insect Science 15:9-15. https://doi.org/10.1016/j.cois.2016.02.007
  32. de Bivort B., van Swinderen B. (2016): Evidence for selective attention in the insect brain. https://doi.org/10.1101/041889
  33. Brembs B. (2016): Operant Behavior in Model Systems. https://doi.org/10.1101/058719
  34. Bradley Ong, Maria Clarice N. Villanueva (2016): Effects of Bacopa monnieri supplementation on the learning and short-term memory retention of sleep-deprived Drosophila melanogaster.
  35. Colomb J., Brembs B. (2016): PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila. PeerJ 4:e1971. https://doi.org/10.7717/peerj.1971
  36. Vogt K., Aso Y., Hige T., Knapek S., Ichinose T., Friedrich A., et al. (2016): Direct neural pathways convey distinct visual information to Drosophila mushroom bodies. eLife 5. https://doi.org/10.7554/elife.14009
  37. Marko Jurjako (2016): Reasons: A Naturalistic Explanation.
  38. Anders Vesterberg (2015): Gene-Environment Interplay on Oviposition Site Selection in Drosophila melanogaster. TSpace.
  39. Heisenberg M. (2015): Outcome learning, outcome expectations, and intentionality inDrosophila. Learning & Memory 22(6):294-298. https://doi.org/10.1101/lm.037481.114
  40. Parker M., Brock A., Sudwarts A., Teh M., Combe F., Brennan C. (2015): Developmental role of acetylcholinesterase in impulse control in zebrafish. Frontiers in Behavioral Neuroscience 9. https://doi.org/10.3389/fnbeh.2015.00271
  41. Solanki N., Wolf R., Heisenberg M. (2015): Central complex and mushroom bodies mediate novelty choice behavior inDrosophila. Journal of Neurogenetics 29(1):30-37. https://doi.org/10.3109/01677063.2014.1002661
  42. Farris S., Van Dyke J. (2015): Evolution and function of the insect mushroom bodies: contributions from comparative and model systems studies. Current Opinion in Insect Science 12:19-25. https://doi.org/10.1016/j.cois.2015.08.006
  43. Mendoza E., Colomb J., Rybak J., Pflüger H., Zars T., Scharff C., et al. (2014): Drosophila FoxP Mutants Are Deficient in Operant Self-Learning. PLoS ONE 9(6):e100648. https://doi.org/10.1371/journal.pone.0100648
  44. Vogt K., Schnaitmann C., Dylla K., Knapek S., Aso Y., Rubin G., et al. (2014): Shared mushroom body circuits underlie visual and olfactory memories in Drosophila. eLife 3. https://doi.org/10.7554/elife.02395
  45. Smith K., Graybiel A. (2014): Investigating habits: strategies, technologies and models. Frontiers in Behavioral Neuroscience 8. https://doi.org/10.3389/fnbeh.2014.00039
  46. Parker M., Evans A., Brock A., Combe F., Teh M., Brennan C. (2014): Moderate alcohol exposure during early brain development increases stimulus‐response habits in adulthood. Addiction Biology 21(1):49-60. https://doi.org/10.1111/adb.12176
  47. Killeen P. (2014): Pavlov + Skinner = Premack. International Journal of Comparative Psychology 27(4). https://doi.org/10.46867/ijcp.2014.27.04.04
  48. Weiss S., Rosales-Ruiz J. (2014): Operant/Classical Conditioning: Comparisons,Intersections and Interactions The 2014 Winter Conference on Animal Learning and Behavior Focus and Research Seminar Sessions. International Journal of Comparative Psychology 27(4). https://doi.org/10.46867/ijcp.2014.27.04.07
  49. Aso Y., Sitaraman D., Ichinose T., Kaun K., Vogt K., Belliart-Guérin G., et al. (2014): Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila. eLife 3. https://doi.org/10.7554/elife.04580
  50. Guo A., Lu H., Zhang K., Ren Q., Chiang Wong Y. (2013): Visual Learning and Decision Making in Drosophila melanogaster. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00028-9
  51. Perry C., Barron A., Cheng K. (2013): Invertebrate learning and cognition: relating phenomena to neural substrate. WIREs Cognitive Science 4(5):561-582. https://doi.org/10.1002/wcs.1248
  52. Parker M., Brock A., Walton R., Brennan C. (2013): The role of zebrafish (Danio rerio) in dissecting the genetics and neural circuits of executive function. Frontiers in Neural Circuits 7. https://doi.org/10.3389/fncir.2013.00063
  53. Arena P., Caccamo S., Patane L., Strauss R. (2013): A computational model for motor learning in insects. The 2013 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2013.6706897
  54. Arena P., Patane L., Strauss R. (2013): The Insect Mushroom Bodies: a Paradigm of Neural Reuse. Advances in Artificial Life, ECAL 2013. https://doi.org/10.7551/978-0-262-31709-2-ch109
  55. Nepomnyashchikh V. (2013): Variability in invertebrate behavior and the problem of free will. Biology Bulletin Reviews 3(5):406-411. https://doi.org/10.1134/s2079086413050083
  56. Zhang X., Ren Q., Guo A. (2013): Parallel Pathways for Cross-Modal Memory Retrieval inDrosophila. The Journal of Neuroscience 33(20):8784-8793. https://doi.org/10.1523/jneurosci.4631-12.2013
  57. Zanini D., Jallon J., Rabinow L., Samson M. (2012): Deletion of the Drosophila neuronal gene found in neurons disrupts brain anatomy and male courtship. Genes, Brain and Behavior 11(7):819-827. https://doi.org/10.1111/j.1601-183x.2012.00817.x
  58. Shmuelof L., Huang V., Haith A., Delnicki R., Mazzoni P., Krakauer J. (2012): Overcoming Motor “Forgetting” Through Reinforcement Of Learned Actions. The Journal of Neuroscience 32(42):14617-14621a. https://doi.org/10.1523/jneurosci.2184-12.2012
  59. Arena P., Patané L., Stornanti V., Termini P., Zäpf B., Strauss R. (2012): Modeling the insect mushroom bodies: Application to a delayed match-to-sample task. Neural Networks 41:202-211. https://doi.org/10.1016/j.neunet.2012.11.013
  60. Ren Q., Li H., Wu Y., Ren J., Guo A. (2012): A GABAergic Inhibitory Neural Circuit Regulates Visual Reversal Learning inDrosophila. The Journal of Neuroscience 32(34):11524-11538. https://doi.org/10.1523/jneurosci.0827-12.2012
  61. Joseph R., Heberlein U. (2012): Tissue-Specific Activation of a Single Gustatory Receptor Produces Opposing Behavioral Responses in Drosophila. Genetics 192(2):521-532. https://doi.org/10.1534/genetics.112.142455
  62. Namiki S., Takaguchi M., Seki Y., Kazawa T., Fukushima R., Iwatsuki C., et al. (2012): Concentric zones for pheromone components in the mushroom body calyx of the moth brain. Journal of Comparative Neurology 521(5):1073-1092. https://doi.org/10.1002/cne.23219
  63. Zhao X., Campos A. (2012): Insulin signalling in mushroom body neurons regulates feeding behaviour inDrosophilalarvae. Journal of Experimental Biology 215(15):2696-2702. https://doi.org/10.1242/jeb.066969
  64. Wu Y., Ren Q., Li H., Guo A. (2012): The GABAergic anterior paired lateral neurons facilitate olfactory reversal learning in Drosophila. Learning & Memory 19(10):478-486. https://doi.org/10.1101/lm.025726.112
  65. Tomina Y., Takahata M. (2012): Discrimination learning with light stimuli in restrained American lobster. Behavioural Brain Research 229(1):91-105. https://doi.org/10.1016/j.bbr.2011.12.044
  66. Sorribes A., Armendariz B., Lopez-Pigozzi D., Murga C., de Polavieja G. (2011): The Origin of Behavioral Bursts in Decision-Making Circuitry. PLoS Computational Biology 7(6):e1002075. https://doi.org/10.1371/journal.pcbi.1002075
  67. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  68. van Swinderen B. (2011): Attention in Drosophila. International Review of Neurobiology. https://doi.org/10.1016/b978-0-12-387003-2.00003-3
  69. Claire Eschbach (2011): Klassiches und operantes Lernen bei Larven der Drosophila.
  70. Farris S. (2011): Are mushroom bodies cerebellum-like structures?. Arthropod Structure & Development 40(4):368-379. https://doi.org/10.1016/j.asd.2011.02.004
  71. Brembs B. (2010): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences 278(1707):930-939. https://doi.org/10.1098/rspb.2010.2325
  72. van Swinderen B., Brembs B. (2010): Attention-Like Deficit and Hyperactivity in aDrosophilaMemory Mutant. The Journal of Neuroscience 30(3):1003-1014. https://doi.org/10.1523/jneurosci.4516-09.2010
  73. Abramson C., Nolf S., Mixson T., Wells H. (2010): Can Honey Bees Learn the Removal of a Stimulus as a Conditioning Cue?. Ethology 116(9):843-854. https://doi.org/10.1111/j.1439-0310.2010.01796.x
  74. Colomb J., Brembs B. (2010): The biology of psychology. Communicative & Integrative Biology 3(2):142-145. https://doi.org/10.4161/cib.3.2.10334
  75. Farris S., Schulmeister S. (2010): Parasitoidism, not sociality, is associated with the evolution of elaborate mushroom bodies in the brains of hymenopteran insects. Proceedings of the Royal Society B: Biological Sciences 278(1707):940-951. https://doi.org/10.1098/rspb.2010.2161
  76. Waddell S. (2010): Dopamine reveals neural circuit mechanisms of fly memory. Trends in Neurosciences 33(10):457-464. https://doi.org/10.1016/j.tins.2010.07.001
  77. Zars T. (2010): Short-term memories in Drosophila are governed by general and specific genetic systems. Learning & Memory 17(5):246-251. https://doi.org/10.1101/lm.1706110
  78. Foucaud J., Burns J., Mery F. (2010): Use of Spatial Information and Search Strategies in a Water Maze Analog in Drosophila melanogaster. PLoS ONE 5(12):e15231. https://doi.org/10.1371/journal.pone.0015231
  79. van Swinderen B. (2009): Fly Memory: A Mushroom Body Story in Parts. Current Biology 19(18):R855-R857. https://doi.org/10.1016/j.cub.2009.07.064
  80. Yu C., Gupta J., Chen J., Yin H. (2009): Genetic Deletion of A2AAdenosine Receptors in the Striatum Selectively Impairs Habit Formation: Figure 1. The Journal of Neuroscience 29(48):15100-15103. https://doi.org/10.1523/jneurosci.4215-09.2009
  81. Linnea R. Vose (): Novel cognitive impairments identified in a Drosophila model of Neurofibromatosis Type 1.

Brembs B. (2009): The importance of being active. J. Neurogenet. 23(1):120–126.

  1. Westphal K. (2025): Kant’s Cognitive Architecture & Bickhard’s Interactivism. Phenomenology and the Cognitive Sciences. https://doi.org/10.1007/s11097-025-10070-x
  2. Westphal K. (2025): Negativität, Alltagswahrnehmung und disjunktive Urteilsformen. Abhandlungen zur Philosophie. https://doi.org/10.1007/978-3-662-71844-5_9
  3. Wandrey M., Halina M. (2025): The Evolution of Animal Consciousness. Philosophy Compass 20(12). https://doi.org/10.1111/phc3.70069
  4. Craighero L. (2024): An embodied approach to fetal and newborn perceptual and sensorimotor development. Brain and Cognition 179:106184. https://doi.org/10.1016/j.bandc.2024.106184
  5. Rusch C., Alonso San Alberto D., Riffell J. (2021): Visuo-Motor Feedback Modulates Neural Activities in the Medulla of the Honeybee,Apis mellifera. The Journal of Neuroscience 41(14):3192-3203. https://doi.org/10.1523/jneurosci.1824-20.2021
  6. Jékely G., Godfrey-Smith P., Keijzer F. (2021): Reafference and the origin of the self in early nervous system evolution. Philosophical Transactions of the Royal Society B 376(1821). https://doi.org/10.1098/rstb.2019.0764
  7. Niemann H. (2021): Popper, Darwin, and Biology. Karl Popper’s Science and Philosophy. https://doi.org/10.1007/978-3-030-67036-8_13
  8. Pütz S., Kram J., Rauh E., Kaiser S., Toews R., Lueningschroer-Wang Y., et al. (2021): Loss of p21-activated kinase Mbt/PAK4 causes Parkinson-like phenotypes inDrosophila. Disease Models & Mechanisms 14(6). https://doi.org/10.1242/dmm.047811
  9. Keijzer F. (2020): Demarcating cognition: the cognitive life sciences. Synthese 198(S1):137-157. https://doi.org/10.1007/s11229-020-02797-8
  10. Jékely G., Godfrey-Smith P., Keijzer F. (2020): Reafference and the origin of the self in early nervous system evolution. https://doi.org/10.31234/osf.io/kvu64
  11. Godfrey-Smith P. (2020): Gradualism and the Evolution of Experience. Philosophical Topics 48(1):201-220. https://doi.org/10.5840/philtopics202048110
  12. Goulard R., Buehlmann C., Niven J., Graham P., Webb B. (2020): A motion compensation treadmill for untethered wood ants ( Formica rufa ): evidence for transfer of orientation memories from free-walking training. Journal of Experimental Biology 223(24). https://doi.org/10.1242/jeb.228601
  13. Goulard R., Buehlmann C., Niven J., Graham P., Webb B. (2020): Transfer of orientation memories in untethered wood ants ( Formica rufa ) from walking in an arena to walking on a motion compensation treadmill. https://doi.org/10.1101/2020.05.29.084905
  14. Ferris B., Green J., Maimon G. (2018): Abolishment of Spontaneous Flight Turns in Visually Responsive Drosophila. Current Biology 28(2):170-180.e5. https://doi.org/10.1016/j.cub.2017.12.008
  15. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  16. Brembs B. (2017): Genetic Analysis of Behavior in Drosophila. The Oxford Handbook of Invertebrate Neurobiology. https://doi.org/10.1093/oxfordhb/9780190456757.013.37
  17. Brembs B. (2016): Operant Behavior in Model Systems. https://doi.org/10.1101/058719
  18. Adler J., Vang L. (2016): Decision Making by Drosophila Flies. https://doi.org/10.1101/045666
  19. Bronfman Z., Ginsburg S., Jablonka E. (2016): The Transition to Minimal Consciousness through the Evolution of Associative Learning. Frontiers in Psychology 7. https://doi.org/10.3389/fpsyg.2016.01954
  20. Schneider S., Vogt T., Abeln V. (2015): Exercise in Space: Physical and Mental Benefit. Sports Performance. https://doi.org/10.1007/978-4-431-55315-1_19
  21. Christopher J., de Belle J. (2014): Olfactory learning and memory assays. Behavioral Genetics of the Fly (Drosophila Melanogaster). https://doi.org/10.1017/cbo9780511920585.019
  22. Thomas N. (2014): The Multidimensional Spectrum of Imagination: Images, Dreams, Hallucinations, and Active, Imaginative Perception. Humanities 3(2):132-184. https://doi.org/10.3390/h3020132
  23. Gomez-Marin A., Paton J., Kampff A., Costa R., Mainen Z. (2014): Big behavioral data: psychology, ethology and the foundations of neuroscience. Nature Neuroscience 17(11):1455-1462. https://doi.org/10.1038/nn.3812
  24. Gomez-Marin A., Paton J., Kampff A., Costa R., Mainen Z. (2014): Big Behavioral Data: Psychology, Ethology and the Foundations of Neuroscience. https://doi.org/10.1101/006809
  25. Cai J., Li L. (2013): Autonomous Navigation Strategy in Mobile Robot. Journal of Computers 8(8). https://doi.org/10.4304/jcp.8.8.2118-2125
  26. Schleyer M., Diegelmann S., Michels B., Saumweber T., Gerber B. (2013): ‘Decision Making’ in Larval Drosophila. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00005-8
  27. Nepomnyashchikh V. (2013): Increases in variations in animal behavior induced by autocorrelations. Biology Bulletin Reviews 3(1):49-56. https://doi.org/10.1134/s2079086413010064
  28. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  29. Claire Eschbach (2011): Klassiches und operantes Lernen bei Larven der Drosophila.
  30. Brembs B. (2010): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences 278(1707):930-939. https://doi.org/10.1098/rspb.2010.2325

Brembs B, Plendl W. (2008): Double dissociation of protein-kinase C and adenylyl cyclase manipulations on operant and classical learning in Drosophila. Curr. Biol. 18(15):1168–1171.

  1. Hernandez J., Le N., Oramas R., Azanchi R., Mei N., Long A., et al. (2025): Shaping of olfactory responses by taste in a new assay for operant learning in Drosophila melanogaster. Journal of Experimental Biology 228(24). https://doi.org/10.1242/jeb.251074
  2. Corcoran J., Storks L., Wong R. (2025): Bold zebrafish (Danio rerio) learn faster in a classical associative learning task. Scientific Reports 15(1). https://doi.org/10.1038/s41598-025-00423-6
  3. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  4. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.1
  5. Rozenfeld E., Parnas M. (2024): Neuronal circuit mechanisms of competitive interaction between action-based and coincidence learning. Science Advances 10(49). https://doi.org/10.1126/sciadv.adq3016
  6. Byrne J., Hochner B., Shomrat T., Kemenes G. (2024): Cellular and molecular mechanisms of memory in molluscs. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-443-15754-7.00031-6
  7. Jelen M., Musso P., Junca P., Gordon M. (2023): Optogenetic induction of appetitive and aversive taste memories in Drosophila. eLife 12. https://doi.org/10.7554/elife.81535
  8. Ehweiner A., Duch C., Brembs B. (2022): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant selflearning in Drosophila. https://doi.org/10.1101/2022.12.16.520755
  9. Croteau-Chonka E., Clayton M., Venkatasubramanian L., Harris S., Jones B., Narayan L., et al. (2022): High-throughput automated methods for classical and operant conditioning of Drosophila larvae. eLife 11. https://doi.org/10.7554/elife.70015
  10. Matilda Gibbons, Andrew Crump, Meghan Barrett, Sajedeh Sarlak, Jonathan Birch, Lars Chıttka (2022): Can insects feel pain? A review of the neural and behavioural evidence. Advances in Insect Physiology. https://doi.org/10.1016/bs.aiip.2022.10.001
  11. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. https://doi.org/10.1101/2021.10.04.463010
  12. Klein K., Croteau-Chonka E., Narayan L., Winding M., Masson J., Zlatic M. (2021): Serotonergic Neurons Mediate Operant Conditioning in Drosophila Larvae. https://doi.org/10.1101/2021.06.14.448341
  13. Schneider S., Sanguinetti A. (2021): Positive reinforcement is just the beginning: Associative learning principles for energy efficiency and climate sustainability. Energy Research & Social Science 74:101958. https://doi.org/10.1016/j.erss.2021.101958
  14. Wiggin T., Hsiao Y., Liu J., Huber R., Griffith L. (2021): Rest Is Required to Learn an Appetitively-Reinforced Operant Task in Drosophila. Frontiers in Behavioral Neuroscience 15. https://doi.org/10.3389/fnbeh.2021.681593
  15. Brembs B. (2020): The brain as a dynamically active organ. Biochemical and Biophysical Research Communications 564:55-69. https://doi.org/10.1016/j.bbrc.2020.12.011
  16. Brembs B. (2020): The brain as a dynamically active organ. https://doi.org/10.31234/osf.io/j37av
  17. Melnattur K., Kirszenblat L., Morgan E., Militchin V., Sakran B., English D., et al. (2020): A conserved role for sleep in supporting Spatial Learning in Drosophila. Sleep 44(3). https://doi.org/10.1093/sleep/zsaa197
  18. Melnattur K., Kirszenblat L., Morgan E., Militchin V., Sakran B., English D., et al. (2020): A conserved role for sleep in supporting spatial learning in Drosophila. https://doi.org/10.1101/2020.06.27.174656
  19. Wolf R., Heisenberg M., Brembs B., Waddell S., Mishra A., Kehrer A., et al. (2020): Memory, anticipation, action – working with Troy D. Zars. Journal of Neurogenetics 34(1):9-20. https://doi.org/10.1080/01677063.2020.1715976
  20. Boto T., Stahl A., Tomchik S. (2020): Cellular and circuit mechanisms of olfactory associative learning inDrosophila. Journal of Neurogenetics 34(1):36-46. https://doi.org/10.1080/01677063.2020.1715971
  21. Wiggin T., Hsiao Y., Liu J., Huber R., Griffith L. (2020): Rest is Required to Learn an Appetitively-Reinforced Operant Task in Drosophila. https://doi.org/10.1101/2020.08.28.272047
  22. Kuroda T., Gilroy S., Cançado C., Podlesnik C. (2020): Effects of punishing target response during extinction on resurgence and renewal in zebrafish (Danio rerio). Behavioural Processes 178:104191. https://doi.org/10.1016/j.beproc.2020.104191
  23. Nargeot R., Puygrenier L. (2019): Operant Learning in Invertebrates. Reference Module in Life Sciences. https://doi.org/10.1016/b978-0-12-809633-8.90788-x
  24. Widmann A., Eichler K., Selcho M., Thum A., Pauls D. (2017): Odor-taste learning in Drosophila larvae. Journal of Insect Physiology 106:47-54. https://doi.org/10.1016/j.jinsphys.2017.08.004
  25. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  26. Brembs B. (2017): Genetic Analysis of Behavior in Drosophila. The Oxford Handbook of Invertebrate Neurobiology. https://doi.org/10.1093/oxfordhb/9780190456757.013.37
  27. Foley B., Marjoram P., Nuzhdin S. (2017): Basic reversal-learning capacity in flies suggests rudiments of complex cognition. PLOS ONE 12(8):e0181749. https://doi.org/10.1371/journal.pone.0181749
  28. Byrne J., Hochner B., Kemenes G. (2017): Cellular and Molecular Mechanisms of Memory in Mollusks. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21097-3
  29. Kirkerud N., Schlegel U., Giovanni Galizia C. (2017): Aversive Learning of Colored Lights in Walking Honeybees. Frontiers in Behavioral Neuroscience 11. https://doi.org/10.3389/fnbeh.2017.00094
  30. Unknown authors (2016): Peer Review #3 of “PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila (v0.2)”. https://doi.org/10.7287/peerj.1971v0.2/reviews/3
  31. Unknown authors (2016): Peer Review #3 of “PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila (v0.1)”. https://doi.org/10.7287/peerj.1971v0.1/reviews/3
  32. Brembs B. (2016): Operant Behavior in Model Systems. https://doi.org/10.1101/058719
  33. Colomb J., Brembs B. (2016): PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila. PeerJ 4:e1971. https://doi.org/10.7717/peerj.1971
  34. Julien Colomb, Björn Brembs, Bj Örn Brembs, B Auguie, A Bedecarrats, C Cornet, et al. (2016): Peer Review #1 of “PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila (v0.1)”. https://doi.org/10.7287/peerj.1971v0.1/reviews/1
  35. Zlatic M. (2016): Peer Review #2 of “PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila (v0.1)”. https://doi.org/10.7287/peerj.1971v0.1/reviews/2
  36. Burgos J. (2015): Misbehavior in a Neural Network Model. International Journal of Comparative Psychology 28. https://doi.org/10.46867/ijcp.2015.28.01.02
  37. Heisenberg M. (2015): Outcome learning, outcome expectations, and intentionality inDrosophila. Learning & Memory 22(6):294-298. https://doi.org/10.1101/lm.037481.114
  38. Hawkins R., Byrne J. (2015): Associative Learning in Invertebrates. Cold Spring Harbor Perspectives in Biology 7(5):a021709. https://doi.org/10.1101/cshperspect.a021709
  39. Cyr A., Boukadoum M., Thériault F. (2014): Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot’s controller. Frontiers in Neurorobotics 8. https://doi.org/10.3389/fnbot.2014.00021
  40. Alvarez B., Morís J., Luque D., Loy I. (2014): Extinction, spontaneous recovery and reinstatement in the garden snail, Helix aspersa. Animal Behaviour 92:75-83. https://doi.org/10.1016/j.anbehav.2014.03.023
  41. Guo C., Du Y., Yuan D., Li M., Gong H., Gong Z., et al. (2014): A conditioned visual orientation requires the ellipsoid body in Drosophila. Learning & Memory 22(1):56-63. https://doi.org/10.1101/lm.036863.114
  42. Mendoza E., Colomb J., Rybak J., Pflüger H., Zars T., Scharff C., et al. (2014): Drosophila FoxP Mutants Are Deficient in Operant Self-Learning. PLoS ONE 9(6):e100648. https://doi.org/10.1371/journal.pone.0100648
  43. Byrne J., LaBar K., LeDoux J., Schafe G., Thompson R. (2014): Learning and Memory. From Molecules to Networks. https://doi.org/10.1016/b978-0-12-397179-1.00020-8
  44. Jozefowiez J. (2014): The Many Faces of Pavlovian Conditioning. International Journal of Comparative Psychology 27(4). https://doi.org/10.46867/ijcp.2014.27.04.06
  45. Vogt K., Schnaitmann C., Dylla K., Knapek S., Aso Y., Rubin G., et al. (2014): Shared mushroom body circuits underlie visual and olfactory memories in Drosophila. eLife 3. https://doi.org/10.7554/elife.02395
  46. Weiss S., Rosales-Ruiz J. (2014): Operant/Classical Conditioning: Comparisons,Intersections and Interactions The 2014 Winter Conference on Animal Learning and Behavior Focus and Research Seminar Sessions. International Journal of Comparative Psychology 27(4). https://doi.org/10.46867/ijcp.2014.27.04.07
  47. Guo A., Lu H., Zhang K., Ren Q., Chiang Wong Y. (2013): Visual Learning and Decision Making in Drosophila melanogaster. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00028-9
  48. Webb B. (2013): Issues in Invertebrate Learning Raised by Robot Models. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00008-3
  49. Perry C., Barron A., Cheng K. (2013): Invertebrate learning and cognition: relating phenomena to neural substrate. WIREs Cognitive Science 4(5):561-582. https://doi.org/10.1002/wcs.1248
  50. Cai J., Li L. (2013): Autonomous Navigation Strategy in Mobile Robot. Journal of Computers 8(8). https://doi.org/10.4304/jcp.8.8.2118-2125
  51. Garren M., Sexauer S., Page T. (2013): Effect of Circadian Phase on Memory Acquisition and Recall: Operant Conditioning vs. Classical Conditioning. PLoS ONE 8(3):e58693. https://doi.org/10.1371/journal.pone.0058693
  52. Hastings M., Farah C., Sossin W. (2013): Roles of Protein Kinase C and Protein Kinase M in Aplysia Learning. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00018-6
  53. Zisopoulou S., Asimaki O., Leondaritis G., Vasilaki A., Sakellaridis N., Pitsikas N., et al. (2013): PKC-epsilon activation is required for recognition memory in the rat. Behavioural Brain Research 253:280-289. https://doi.org/10.1016/j.bbr.2013.07.036
  54. Ueda A., Wu C. (2012): Cyclic Adenosine Monophosphate Metabolism in Synaptic Growth, Strength, and Precision: Neural and Behavioral Phenotype-Specific Counterbalancing Effects betweendncPhosphodiesterase andrutAdenylyl Cyclase Mutations. Journal of Neurogenetics 26(1):64-81. https://doi.org/10.3109/01677063.2011.652752
  55. MENDOZA E., COLOMB J., RYBAK J., PFLÜGER H., ZARS T., SCHARFF C., et al. (2012): THE DROSOPHILA FOXP GENE IS REQUIRED FOR OPERANT SELF-LEARNING: IMPLICATIONS FOR THE EVOLUTION OF LANGUAGE. The Evolution of Language. https://doi.org/10.1142/9789814401500_0100
  56. Jian Cai, Xuguang Sun, Rui Hong, Quan Sheng Wang (2012): Research autonomous motion control for snake robot based on bionic learning strategy. International Conference on Modelling, Identification and Control.
  57. Cai J., Yu R., Cheng L. (2012): Autonomous navigation research for mobile robot. Proceedings of the 10th World Congress on Intelligent Control and Automation. https://doi.org/10.1109/wcica.2012.6357893
  58. Tomina Y., Takahata M. (2012): Discrimination learning with light stimuli in restrained American lobster. Behavioural Brain Research 229(1):91-105. https://doi.org/10.1016/j.bbr.2011.12.044
  59. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  60. Kahsai L., Zars T. (2011): Learning and Memory in Drosophila: Behavior, Genetics, and Neural Systems. International Review of Neurobiology. https://doi.org/10.1016/b978-0-12-387003-2.00006-9
  61. Ruan X., Chen J., Dai L. (2011): Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot. Intelligent Control and Automation 02(03):214-225. https://doi.org/10.4236/ica.2011.23026
  62. Shuai Y., Hu Y., Qin H., Campbell R., Zhong Y. (2011): Distinct molecular underpinnings of Drosophila olfactory trace conditioning. Proceedings of the National Academy of Sciences 108(50):20201-20206. https://doi.org/10.1073/pnas.1107489109
  63. Brembs B. (2010): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences 278(1707):930-939. https://doi.org/10.1098/rspb.2010.2325
  64. van Swinderen B., Brembs B. (2010): Attention-Like Deficit and Hyperactivity in aDrosophilaMemory Mutant. The Journal of Neuroscience 30(3):1003-1014. https://doi.org/10.1523/jneurosci.4516-09.2010
  65. Colomb J., Brembs B. (2010): The biology of psychology. Communicative & Integrative Biology 3(2):142-145. https://doi.org/10.4161/cib.3.2.10334
  66. Zars T. (2010): Short-term memories in Drosophila are governed by general and specific genetic systems. Learning & Memory 17(5):246-251. https://doi.org/10.1101/lm.1706110
  67. Claridge-Chang A., Roorda R., Vrontou E., Sjulson L., Li H., Hirsh J., et al. (2009): Writing Memories with Light-Addressable Reinforcement Circuitry. Cell 139(2):405-415. https://doi.org/10.1016/j.cell.2009.08.034
  68. Brembs B. (2009): Mushroom Bodies Regulate Habit Formation in Drosophila. Current Biology 19(16):1351-1355. https://doi.org/10.1016/j.cub.2009.06.014
  69. Brembs B. (2009): The Importance of Being Active. Journal of Neurogenetics 23(1-2):120-126. https://doi.org/10.1080/01677060802471643
  70. Lorenzetti F., Baxter D., Byrne J. (2008): Molecular Mechanisms Underlying a Cellular Analog of Operant Reward Learning. Neuron 59(5):815-828. https://doi.org/10.1016/j.neuron.2008.07.019
  71. Linnea R. Vose (): Novel cognitive impairments identified in a Drosophila model of Neurofibromatosis Type 1.

Brembs B. (2008): Operant learning of Drosophila at the torque meter. J. Vis. Exp. 16.

  1. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  2. van Alphen B., Semenza E., Yap M., van Swinderen B., Allada R. (2021): A deep sleep stage in Drosophila with a functional role in waste clearance. Science Advances 7(4). https://doi.org/10.1126/sciadv.abc2999
  3. Thiede K., Born J., Vorster A. (2021): Sleep and conditioning of the siphon withdrawal reflex in Aplysia. Journal of Experimental Biology 224(16). https://doi.org/10.1242/jeb.242431
  4. Unknown authors (2020): Sub-strains of CantonS differ markedly in their Drosophila locomotor behavior. https://www.semanticscholar.org/paper/4ca6d29238943bf8e700439ae9b5a7d685bdf720
  5. Kuroda T., Mizutani Y., Cançado C., Podlesnik C. (2019): Predator videos and electric shock function as punishers for zebrafish (Danio rerio). Journal of the Experimental Analysis of Behavior 111(1):116-129. https://doi.org/10.1002/jeab.494
  6. Damrau C., Toshima N., Tanimura T., Brembs B., Colomb J. (2018): Octopamine and Tyramine Contribute Separately to the Counter-Regulatory Response to Sugar Deficit in Drosophila. Frontiers in Systems Neuroscience 11. https://doi.org/10.3389/fnsys.2017.00100
  7. Colomb J., Brembs B. (2016): PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila. PeerJ 4:e1971. https://doi.org/10.7717/peerj.1971
  8. Unknown authors (2015): PHOTOTACTIC BEHAVIOUR SUPPRESSION IN DROSOPHILA MELANOGASTER. https://www.semanticscholar.org/paper/fd2b8e6c3ec95bd77a4e123b6bfee4b378e081e4
  9. Guo C., Du Y., Yuan D., Li M., Gong H., Gong Z., et al. (2015): A conditioned visual orientation requires the ellipsoid body in Drosophila. Learning & Memory 22(1):56-63. https://doi.org/10.1101/lm.036863.114
  10. Van De Poll M., Zajaczkowski E., Taylor G., Srinivasan M., van Swinderen B. (2015): Using an abstract geometry in virtual reality to explore choice behaviour: visual flicker preferences in honeybees. Journal of Experimental Biology. https://doi.org/10.1242/jeb.125138
  11. Unknown authors (2014): Sub-strains of CantonS differ markedly in their Drosophila locomotor behavior.
  12. Mendoza E., Colomb J., Rybak J., Pflüger H., Zars T., Scharff C., et al. (2014): Drosophila FoxP Mutants Are Deficient in Operant Self-Learning. PLoS ONE 9(6):e100648. https://doi.org/10.1371/journal.pone.0100648
  13. Colomb J., Brembs B. (2014): Sub-strains of Drosophila Canton-S differ markedly in their locomotor behavior. F1000Research 3:176. https://doi.org/10.12688/f1000research.4263.2
  14. Kirkerud N., Wehmann H., Galizia C., Gustav D. (2013): APIS—a novel approach for conditioning honey bees. Frontiers in Behavioral Neuroscience 7. https://doi.org/10.3389/fnbeh.2013.00029
  15. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  16. B. Swinderen, R. Andretić (2011): Andretic miniature brain : setting arousal thresholds in a Drosophila Dopamine in. https://www.semanticscholar.org/paper/15a8d6235d55958aeb3d68506f32a03bdc697395
  17. Visvanathan K., Gianchandani Y. (2011): Locomotion response of airborne, ambulatory and aquatic insects to thermal stimulation using piezoceramic microheaters. Journal of Micromechanics and Microengineering 21(12):125002. https://doi.org/10.1088/0960-1317/21/12/125002
  18. van Swinderen B., Brembs B. (2010): Attention-Like Deficit and Hyperactivity in a Drosophila Memory Mutant. The Journal of Neuroscience 30(3):1003-1014. https://doi.org/10.1523/JNEUROSCI.4516-09.2010
  19. Colomb J., Brembs B. (2010): The biology of psychology. Communicative & Integrative Biology 3(2):142-145. https://doi.org/10.4161/cib.3.2.10334
  20. Carter M., Shieh J. (2010): Chapter 2 – Animal Behavior. Guide to Research Techniques in Neuroscience. https://doi.org/10.1016/B978-0-12-374849-2.00002-1
  21. Masek P., Scott K. (2010): Limited taste discrimination in Drosophila. Proceedings of the National Academy of Sciences 107(33):14833-14838. https://doi.org/10.1073/pnas.1009318107
  22. Björn Brembs (2009): Mushroom Bodies Regulate Habit Formation in Drosophila. https://www.semanticscholar.org/paper/6fc194f9840e722e0b9e5c75fa6d864edc4c4d82
  23. Brembs B. (2008): Mushroom bodies regulate habit formation in Drosophila. Current Biology 19(16):1351-1355. https://doi.org/10.1016/j.cub.2009.06.014
  24. Brembs B., Plendl W. (2008): Double dissociation of PKC and AC manipulations on operant and classical learning in Drosophila. Current Biology 18(15):1168-1171. https://doi.org/10.1016/j.cub.2008.07.041
  25. B. Brembs (2008): The neurobiology of operant learning: biophysical and molecular mechanisms in a hierarchical organization of multiple memory systems. https://www.semanticscholar.org/paper/e093d2a5cc2d2f3f0ffe7588f0b80e5e3993d330
  26. Richard Smiley, Jean Charchaflieh, Suny Downstate, Ivan Velickovic, J. Eloy (): Open Peer Review Invited Referee Responses. https://www.semanticscholar.org/paper/51bcae1b36f0c3cfae954c67c39510501c1b0b7c
  27. S. M. Miller, Trung Thành Ngô, B. van Swinderen (): Human Neuroscience Hypothesis and Theory Article Attentional Switching in Humans and Flies: Rivalry in Large and Miniature Brains. https://www.semanticscholar.org/paper/f19f6d37316f70258a837832a3272745a73f72df

Maye A, Hsieh C, Sugihara G, Brembs B. (2007): Order in spontaneous behavior. PLoS ONE 2(5):e443.

  1. Andrés Bendesky (2025): Genetic Variation in Neurotransmitter Receptors Generates Behavioral Diversity. Digital Commons – RU (Rockefeller University). https://doi.org/10.48496/3y0n-tx90
  2. Park C. (2025): Kinematic re-envision of haptic expertise through Bernstein’s problem: A comparative analysis of stochastic and deterministic features. Expert Systems with Applications 276:127146. https://doi.org/10.1016/j.eswa.2025.127146
  3. Kagaya K., Nakano T., Nakayama R. (2025): Multiple Power Laws and Scaling Relation in Exploratory Locomotion of the Snail Tegula nigerrima. Journal of Robotics and Mechatronics 37(1):99-104. https://doi.org/10.20965/jrm.2025.p0099
  4. Gerçeker K., Kilcik A., Ozguc A., Yurchyshyn V. (2025): Simplex Projection Predictions of the Remainder of Solar Cycle 25 and the Next Solar Cycle 26 Based on the Monthly Mean Sunspot Numbers. Solar Physics 300(12). https://doi.org/10.1007/s11207-025-02577-y
  5. Cheng K. (2025): Random-rate processing in navigation in bacteria, archaea, and desert ants. Psihologijske teme 34(1):79-96. https://doi.org/10.31820/pt.34.1.4
  6. Seuront L. (2025): What do the geometric and stochastic properties of swimming behaviour have to teach us about zooplankton behavioural ecology?. Journal of Plankton Research 47(2). https://doi.org/10.1093/plankt/fbae075
  7. Juusola M., Takalo J., Kemppainen J., Haghighi K., Scales B., McManus J., et al. (2025): Theory of morphodynamic information processing: Linking sensing to behaviour. Vision Research 227:108537. https://doi.org/10.1016/j.visres.2024.108537
  8. Minasandra P., Grout E., Brock K., Crofoot M., Demartsev V., Gersick A., et al. (2025): Behavioral sequences across multiple animal species in the wild share common structural features. Proceedings of the National Academy of Sciences 122(20). https://doi.org/10.1073/pnas.2503962122
  9. Mochalov K., Voskresensky A. (2025): Symbolic Activity and Agency. Integrative Psychological and Behavioral Science 59(2). https://doi.org/10.1007/s12124-025-09911-w
  10. Tzou J., Tzou L. (2024): Counterexample to the Lévy flight foraging hypothesis in the narrow capture framework. Physical Review Research 6(2). https://doi.org/10.1103/physrevresearch.6.023274
  11. Zakharov N., Belova E., Gamaleya A., Tomskiy A., Sedov A. (2024): Neuronal activity features of the subthalamic nucleus associated with optimal deep brain stimulation electrode insertion path in Parkinson’s disease. European Journal of Neuroscience 60(12):6987-7005. https://doi.org/10.1111/ejn.16630
  12. Zakharov N., Belova E., Gamaleya A., Tomskiy A., Sedov A. (2024): A novel approach to compute discrete nonlinear single unit activity features. https://doi.org/10.21203/rs.3.rs-5240162/v1
  13. Minasandra P., Grout E., Brock K., Crofoot M., Demartsev V., Gersick A., et al. (2024): Behavioral sequences across multiple animal species in the wild share common structural features. https://doi.org/10.1101/2024.01.20.576411
  14. Jaiton V., Manoonpong P. (2024): Neural Chaotic Dynamics for Adaptive Exploration Control of an Autonomous Flying Robot. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-71533-4_19
  15. Parker A., Cattell R., Alzahrani R., Hsu C., Irizarry-Valle Y., Joshi J., et al. (2023): Introduction. Neuromorphic Circuits. https://doi.org/10.1088/978-0-7503-5097-6ch1
  16. Koul A., Ahmar D., Iannetti G., Novembre G. (2023): Interpersonal synchronization of spontaneously generated body movements. iScience 26(3):106104. https://doi.org/10.1016/j.isci.2023.106104
  17. Beyts C., Cella M., Colegrave N., Downie R., Martin J., Walsh P. (2023): The effect of heterospecific and conspecific competition on inter-individual differences in tungara frog tadpole (Engystomops pustulosus) behavior. Behavioral Ecology 34(2):210-222. https://doi.org/10.1093/beheco/arac109
  18. Beyts C., Martin J., Colegrave N., Walsh P. (2023): Food availability early in life impacts among and within individual variation in behaviour. https://doi.org/10.1101/2023.02.23.529667
  19. Zenil H., Marshall J., Tegnér J. (2023): Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results. Frontiers in Computational Neuroscience 16. https://doi.org/10.3389/fncom.2022.956074
  20. Juusola M., Takalo J., Kemppainen J., Razban Haghighi K., Scales B., McManus J., et al. (2023): Theory of Morphodynamic Information Processing: Linking Sensing to Behaviour. Preprints.org. https://doi.org/10.20944/preprints202308.1210.v1
  21. Dipierro S., Giacomin G., Valdinoci E. (2023): Analysis of the Lévy Flight Foraging Hypothesis in \(\mathbb{R}^{n}\) and Unreliability of the Most Rewarding Strategies. SIAM Journal on Applied Mathematics 83(5):1935-1968. https://doi.org/10.1137/22m1526563
  22. Flammang B. (2022): Bioinspired Design in Research: Evolution as Beta-Testing. Integrative And Comparative Biology 62(5):1164-1173. https://doi.org/10.1093/icb/icac134
  23. Tan J., Tan C., Nurzaman S. (2022): An Embodied Intelligence-Based Biologically Inspired Strategy for Searching a Moving Target. Artificial Life 28(3):348-368. https://doi.org/10.1162/artl_a_00375
  24. Cornwell T., Mitchell D., Beckmann C., Joynson A., Biro P. (2022): Multilevel repeatability shows selection may act on both personality and predictability, but neither is state dependent. Animal Behaviour 195:85-92. https://doi.org/10.1016/j.anbehav.2022.11.004
  25. Kwon V., Cai P., Dixon C., Hamlin V., Spencer C., Rojas A., et al. (2022): Peripheral NOD-like receptor deficient inflammatory macrophages trigger neutrophil infiltration into the brain disrupting daytime locomotion. Communications Biology 5(1). https://doi.org/10.1038/s42003-022-03410-z
  26. Blanco A., Larrinaga A., Neto J., Troncoso J., Méndez G., Domínguez-Lapido P., et al. (2021): Spotting intruders: Species distribution models for managing invasive intertidal macroalgae. Journal of Environmental Management 281:111861. https://doi.org/10.1016/j.jenvman.2020.111861
  27. Minh B., Dang C., Vinh L., Lanfear R. (2021): QMaker: Fast and Accurate Method to Estimate Empirical Models of Protein Evolution. Systematic Biology 70(5):1046-1060. https://doi.org/10.1093/sysbio/syab010
  28. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. PLOS ONE 16(8):e0256560. https://doi.org/10.1371/journal.pone.0256560
  29. Steymans I., Pujol-Lereis L., Brembs B., Gorostiza E. (2021): Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila. https://doi.org/10.1101/2021.01.15.426803
  30. Castro J., Beviano V., Paço A., Leitão-Castro J., Cadete F., Francisco M., et al. (2021): Hoxd13/Bmp2-mediated mechanism involved in zebrafish finfold design. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-86621-4
  31. Fenk L., Kim A., Maimon G. (2021): Suppression of motion vision during course-changing, but not course-stabilizing, navigational turns. Current Biology 31(20):4608-4619.e3. https://doi.org/10.1016/j.cub.2021.09.068
  32. Cocconi L., Kuhn-Régnier A., Neuss M., Sendova-Franks A., Christensen K. (2021): Reconstructing the Intrinsic Statistical Properties of Intermittent Locomotion Through Corrections for Boundary Effects. Bulletin of Mathematical Biology 83(4). https://doi.org/10.1007/s11538-020-00848-2
  33. Höll M., Barkai E. (2021): Big jump principle for heavy-tailed random walks with correlated\n increments. The European Physical Journal B 94(10). https://doi.org/10.1140/epjb/s10051-021-00215-7
  34. Höll M., Barkai E. (2021): Big jump principle for heavy-tailed random walks with correlated increments. The European Physical Journal B 94(10). https://doi.org/10.1140/epjb/s10051-021-00215-7
  35. Walls R., Dulvy N. (2021): Tracking the rising extinction risk of sharks and rays in the Northeast Atlantic Ocean and Mediterranean Sea. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-94632-4
  36. Wilken S., Frazão V., Saadat N., Ebenhöh O. (2021): The view of microbes as energy converters illustrates the trade-off between growth rate and yield. Biochemical Society Transactions 49(4):1663-1674. https://doi.org/10.1042/bst20200977
  37. Kwon V., Cai P., Dixon C., Hamlin V., Spencer C., Rojas A., et al. (2021): Peripheral NOD-like receptor deficient inflammatory macrophages trigger neutrophil infiltration disrupting daytime locomotion. https://doi.org/10.1101/2021.10.27.466033
  38. Mathuru A., Libersat F., Vyas A., Teseo S. (2020): Why behavioral neuroscience still needs diversity?: A curious case of a persistent need. Neuroscience & Biobehavioral Reviews 116:130-141. https://doi.org/10.1016/j.neubiorev.2020.06.021
  39. Hammond A., Meyers L., Purcell S. (2020): Not so sluggish: movement and sediment turnover of the world’s heaviest holothuroid, Thelenota anax. Marine Biology 167(5). https://doi.org/10.1007/s00227-020-3671-5
  40. Martinig A., Mathot K., Lane J., Dantzer B., Boutin S. (2020): Selective disappearance does not underlie age-related changes in trait repeatability in red squirrels. Behavioral Ecology 32(2):306-315. https://doi.org/10.1093/beheco/araa136
  41. Cellini B., Mongeau J. (2020): Hybrid visual control in fly flight: insights into gaze shift via saccades. Current Opinion in Insect Science 42:23-31. https://doi.org/10.1016/j.cois.2020.08.009
  42. Brembs B. (2020): The brain as a dynamically active organ. Biochemical and Biophysical Research Communications 564:55-69. https://doi.org/10.1016/j.bbrc.2020.12.011
  43. de Paula Junior D., de Oliveira M., Bruscadin J., Pinheiro D., Bomtorin A., Coelho Júnior V., et al. (2020): Caste‐specific gene expression underlying the differential adult brain development in the honeybee Apis mellifera . Insect Molecular Biology 30(1):42-56. https://doi.org/10.1111/imb.12671
  44. Parés-Pujolràs E., Travers E., Ahmetoglu Y., Haggard P. (2020): Evidence accumulation under uncertainty – a neural marker of emerging choice and urgency. https://doi.org/10.1101/2020.06.30.179622
  45. Travers E., Friedemann M., Haggard P. (2020): The Readiness Potential reflects planning-based expectation, not uncertainty, in the timing of action. Cognitive Neuroscience 12(1):14-27. https://doi.org/10.1080/17588928.2020.1824176
  46. Travers E., Friedemann M., Haggard P. (2020): The Readiness Potential reflects expectation, not uncertainty, in the timing of action. https://doi.org/10.1101/2020.04.16.045344
  47. Sanabria F. (2020): Internal-Clock Models and Misguided Views of Mechanistic Explanations: A Reply to Eckard & Lattal (2020). Perspectives on Behavior Science 43(4):779-790. https://doi.org/10.1007/s40614-020-00268-6
  48. Manfredi F., Cianciotti B., Potenza A., Tassi E., Noviello M., Biondi A., et al. (2020): TCR Redirected T Cells for Cancer Treatment: Achievements, Hurdles, and Goals. Frontiers in Immunology 11. https://doi.org/10.3389/fimmu.2020.01689
  49. Evans J. (2020): Determinism. Encyclopedia of Personality and Individual Differences. https://doi.org/10.1007/978-3-319-24612-3_1127
  50. Gomathi K., Akshaya N., Srinaath N., Moorthi A., Selvamurugan N. (2020): Regulation of Runx2 by post-translational modifications in osteoblast differentiation. Life Sciences 245:117389. https://doi.org/10.1016/j.lfs.2020.117389
  51. Christensen K., Cocconi L., Sendova-Franks A. (2020): Animal intermittent locomotion: A null model for the probability of moving forward in bounded space. Journal of Theoretical Biology 510:110533. https://doi.org/10.1016/j.jtbi.2020.110533
  52. Bussière K. (2020): Survival is insufficient: Degenerate utopian nostalgia in popular culture post-apocalyptic fiction. Australasian Journal of Popular Culture 9(2):261-275. https://doi.org/10.1386/ajpc_00031_1
  53. Demin K., Lakstygal A., Volgin A., de Abreu M., Genario R., Alpyshov E., et al. (2020): Cross-species Analyses of Intra-species Behavioral Differences in Mammals and Fish. Neuroscience 429:33-45. https://doi.org/10.1016/j.neuroscience.2019.12.035
  54. Ghosh M., Rihel J. (2020): Hierarchical Compression Reveals Sub-Second to Day-Long Structure in Larval Zebrafish Behavior. eneuro 7(4):ENEURO.0408-19.2020. https://doi.org/10.1523/eneuro.0408-19.2020
  55. Abe M. (2020): Functional advantages of Lévy walks emerging near a critical point. Proceedings of the National Academy of Sciences 117(39):24336-24344. https://doi.org/10.1073/pnas.2001548117
  56. Abe M., Kasada M. (2020): Optimal Random Avoidance Strategy in Prey-Predator Interactions. https://doi.org/10.1101/2020.03.04.976076
  57. Abe M. (2020): Functional advantages of Lévy walks emerging near a critical point. https://doi.org/10.1101/2020.01.27.920801
  58. Chang O., Zhinin-Vera L. (2020): A Wise Up Visual Robot Driven by a Self-taught Neural Agent. Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-3-030-63128-4_47
  59. Brudar S., Gujt J., Spohr E., Hribar-Lee B. (2020): Studying the mechanism of phase separation in aqueous solutions of globular proteins via molecular dynamics computer simulations. Physical Chemistry Chemical Physics 23(1):415-424. https://doi.org/10.1039/d0cp05160h
  60. Shokaku T., Moriyama T., Murakami H., Shinohara S., Manome N., Morioka K. (2020): Development of an automatic turntable-type multiple T-maze device and observation of pill bug behavior. Review of Scientific Instruments 91(10). https://doi.org/10.1063/5.0009531
  61. Ahamed T., Costa A., Stephens G. (2020): Capturing the continuous complexity of behaviour in Caenorhabditis elegans. Nature Physics 17(2):275-283. https://doi.org/10.1038/s41567-020-01036-8
  62. Fisher D., Pruitt J. (2019): Insights from the study of complex systems for the ecology and evolution of animal populations. Current Zoology 66(1):1-14. https://doi.org/10.1093/cz/zoz016
  63. Sims D., Humphries N., Hu N., Medan V., Berni J. (2019): Optimal searching behaviour generated intrinsically by the central pattern generator for locomotion. eLife 8. https://doi.org/10.7554/elife.50316
  64. Krueger J. (2019): The Return of the Death Instinct. The American Journal of Psychology 132(2):256-259. https://doi.org/10.5406/amerjpsyc.132.2.0256
  65. Ghosh M., Rihel J. (2019): Hierarchical Compression Reveals Sub-Second to Day-Long Structure in Larval Zebrafish Behaviour. https://doi.org/10.1101/694471
  66. Le P., Kumar P., Ruiz M., Mbogo C., Muturi E. (2019): Predicting the direct and indirect impacts of climate change on malaria in coastal Kenya. PLOS ONE 14(2):e0211258. https://doi.org/10.1371/journal.pone.0211258
  67. Budaev S., Jørgensen C., Mangel M., Eliassen S., Giske J. (2019): Decision-Making From the Animal Perspective: Bridging Ecology and Subjective Cognition. Frontiers in Ecology and Evolution 7. https://doi.org/10.3389/fevo.2019.00164
  68. Ahamed T., Costa A., Stephens G. (2019): Capturing the Continuous Complexity of Behavior in C. elegans. https://doi.org/10.1101/827535
  69. Ferris B., Green J., Maimon G. (2018): Abolishment of Spontaneous Flight Turns in Visually Responsive Drosophila. Current Biology 28(2):170-180.e5. https://doi.org/10.1016/j.cub.2017.12.008
  70. Fisher D., Brachmann M., Burant J. (2018): Complex dynamics and the development of behavioural individuality. Animal Behaviour 138:e1-e6. https://doi.org/10.1016/j.anbehav.2018.02.015
  71. Toepfer F., Wolf R., Heisenberg M. (2018): Multi-stability with ambiguous visual stimuli in Drosophila orientation behavior. PLOS Biology 16(2):e2003113. https://doi.org/10.1371/journal.pbio.2003113
  72. Libersat F., Kaiser M., Emanuel S. (2018): Mind Control: How Parasites Manipulate Cognitive Functions in Their Insect Hosts. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.00572
  73. Moberly H., Lee D., Kessler M., Carrigan E. (2018): Supporting the next generation of Texas A&M University scholars. Library Management 39(8/9):597-604. https://doi.org/10.1108/lm-10-2017-0104
  74. Murano J., Mitsuishi M., Moriyama T. (2018): Behavioral pattern of pill bugs revealed in virtually infinite multiple T-maze. Artificial Life and Robotics 23(4):444-448. https://doi.org/10.1007/s10015-018-0457-7
  75. Hubert M., Hubert M., Linzmajer M., Riedl R., Kenning P. (2018): Trust me if you can – neurophysiological insights on the influence of consumer impulsiveness on trustworthiness evaluations in online settings. European Journal of Marketing 52(1/2):118-146. https://doi.org/10.1108/ejm-12-2016-0870
  76. Ragagnin M. (2018): Efeitos de estressores múltiplos no impacto da acidificação oceânica na biota marinha. https://doi.org/10.11606/d.21.2018.tde-13032018-155525
  77. Chang O. (2018): Self-programming Robots Boosted by Neural Agents. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-05587-5_42
  78. Chang O. (2018): Autonomous Robots and Behavior Initiators. Human-Robot Interaction – Theory and Application. https://doi.org/10.5772/intechopen.71958
  79. Grobstein P. (2018): The Brain as a Learner/Inquirer/Creator: Some Implications of Its Organization for Individual and Social Well Being. Learning To Live Together: Promoting Social Harmony. https://doi.org/10.1007/978-3-319-90659-1_14
  80. Le P., Kumar P., Ruiz M. (2018): Stochastic lattice-based modelling of malaria dynamics. Malaria Journal 17(1). https://doi.org/10.1186/s12936-018-2397-z
  81. Staszkiewicz P. (2018): The application of citation count regression to identify important papers in the literature on non-audit fees. Managerial Auditing Journal 34(1):96-115. https://doi.org/10.1108/maj-05-2017-1552
  82. Budaev S., Giske J., Eliassen S. (2018): AHA: A general cognitive architecture for Darwinian agents. Biologically Inspired Cognitive Architectures 25:51-57. https://doi.org/10.1016/j.bica.2018.07.009
  83. Cornwell T., McCarthy I., Snyder C., Biro P. (2018): The influence of environmental gradients on individual behaviour: Individual plasticity is consistent across risk and temperature gradients. Journal of Animal Ecology 88(4):511-520. https://doi.org/10.1111/1365-2656.12935
  84. Sarp V., Kilcik A., Yurchyshyn V., Rozelot J., Ozguc A. (2018): Prediction of solar cycle 25: a non-linear approach. Monthly Notices of the Royal Astronomical Society 481(3):2981-2985. https://doi.org/10.1093/mnras/sty2470
  85. Melott A., Pivarunas A., Meert J., Lieberman B. (2017): Does the planetary dynamo go cycling on? Re-examining the evidence for cycles in magnetic reversal rate. International Journal of Astrobiology 17(1):44-50. https://doi.org/10.1017/s1473550417000040
  86. Melanson A., Mejias J., Jun J., Maler L., Longtin A. (2017): Nonstationary Stochastic Dynamics Underlie Spontaneous Transitions between Active and Inactive Behavioral States. eneuro 4(2):ENEURO.0355-16.2017. https://doi.org/10.1523/eneuro.0355-16.2017
  87. Barchuk A., dos Santos G., Dias Caneschi R., de Paula Junior D., Moda L. (2017): The ontogenetic saga of a social brain. Apidologie 49(1):32-48. https://doi.org/10.1007/s13592-017-0540-4
  88. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  89. Ehrlich D., Schoppik D. (2017): Control of Movement Initiation Underlies the Development of Balance. Current Biology 27(3):334-344. https://doi.org/10.1016/j.cub.2016.12.003
  90. Maia G., Lima A., Kaefer I. (2017): Not just the river: genes, shapes, and sounds reveal population-structured diversification in the Amazonian frog Allobates tapajos (Dendrobatoidea). Biological Journal of the Linnean Society 121(1):95-108. https://doi.org/10.1093/biolinnean/blw017
  91. Daly I., Tetley A., Jared S., How M., Roberts N. (2017): Colour preference in Odontodactylus scyllarus (Linnaeus, 1758) (Stomatopoda). Journal of Crustacean Biology 37(4):374-379. https://doi.org/10.1093/jcbiol/rux038
  92. Evans J. (2017): Determinism. Encyclopedia of Personality and Individual Differences. https://doi.org/10.1007/978-3-319-28099-8_1127-1
  93. Shi L., Xiao A. (2017): Modeling Anomalous Diffusion by a Subordinated Integrated Brownian Motion. Advances in Mathematical Physics 2017:1-7. https://doi.org/10.1155/2017/7246865
  94. Durán-Carabali L., Arcego D., Odorcyk F., Reichert L., Cordeiro J., Sanches E., et al. (2017): Prenatal and Early Postnatal Environmental Enrichment Reduce Acute Cell Death and Prevent Neurodevelopment and Memory Impairments in Rats Submitted to Neonatal Hypoxia Ischemia. Molecular Neurobiology. https://doi.org/10.1007/s12035-017-0604-5
  95. Nagaya N., Mizumoto N., Abe M., Dobata S., Sato R., Fujisawa R. (2017): Anomalous diffusion on the servosphere: A potential tool for detecting inherent organismal movement patterns. PLOS ONE 12(6):e0177480. https://doi.org/10.1371/journal.pone.0177480
  96. David P., Thébault E., Anneville O., Duyck P., Chapuis E., Loeuille N. (2017): Impacts of Invasive Species on Food Webs. Advances in Ecological Research. https://doi.org/10.1016/bs.aecr.2016.10.001
  97. Lindsay T., Sustar A., Dickinson M. (2017): The Function and Organization of the Motor System Controlling Flight Maneuvers in Flies. Current Biology 27(3):345-358. https://doi.org/10.1016/j.cub.2016.12.018
  98. Ishida Y., Chiba R. (2017): Free Will and Turing Test with Multiple Agents: An Example of Chatbot Design. Procedia Computer Science 112:2506-2518. https://doi.org/10.1016/j.procs.2017.08.190
  99. Reynolds A., Bartumeus F., Kölzsch A., van de Koppel J. (2016): Signatures of chaos in animal search patterns. Scientific Reports 6(1). https://doi.org/10.1038/srep23492
  100. Campos D., Bartumeus F., Méndez V., Andrade J., Espadaler X. (2016): Variability in individual activity bursts improves ant foraging success. Journal of The Royal Society Interface 13(125):20160856. https://doi.org/10.1098/rsif.2016.0856
  101. Valenti D., Denaro G., Conversano F., Brunet C., Bonanno A., Basilone G., et al. (2016): The role of noise on the steady state distributions of phytoplankton populations. Journal of Statistical Mechanics: Theory and Experiment 2016(5):054044. https://doi.org/10.1088/1742-5468/2016/05/054044
  102. Calderon D., Kilinc M., Maritan A., Banavar J., Pfaff D. (2016): Generalized CNS arousal: An elementary force within the vertebrate nervous system. Neuroscience & Biobehavioral Reviews 68:167-176. https://doi.org/10.1016/j.neubiorev.2016.05.014
  103. Zilio D. (2016): On the Autonomy of Psychology from Neuroscience: A Case Study of Skinner’s Radical Behaviorism and Behavior Analysis. Review of General Psychology 20(2):155-170. https://doi.org/10.1037/gpr0000067
  104. Salem D., Abul Seoud R., Kadah Y. (2016): Prediction of binding peptides to class I Major Histocompatibility Complex using modified scoring matrices and data splitting strategies. Biocybernetics and Biomedical Engineering 36(3):509-520. https://doi.org/10.1016/j.bbe.2016.04.003
  105. Hunt E., Baddeley R., Worley A., Sendova-Franks A., Franks N. (2016): Ants determine their next move at rest: motor planning and causality in complex systems. Royal Society Open Science 3(1):150534. https://doi.org/10.1098/rsos.150534
  106. Devraj G., Beerlage C., Brüne B., Kempf V. (2016): Hypoxia and HIF-1 activation in bacterial infections. Microbes and Infection 19(3):144-156. https://doi.org/10.1016/j.micinf.2016.11.003
  107. Kariithi H., Boeren S., Murungi E., Vlak J., Abd-Alla A. (2016): A proteomics approach reveals molecular manipulators of distinct cellular processes in the salivary glands of Glossina m. morsitans in response to Trypanosoma b. brucei infections. Parasites & Vectors 9(1). https://doi.org/10.1186/s13071-016-1714-z
  108. Huang J., Huang J., Liu C., Zhang J., Lu X., Ma K. (2016): Diversity hotspots and conservation gaps for the Chinese endemic seed flora. Biological Conservation 198:104-112. https://doi.org/10.1016/j.biocon.2016.04.007
  109. Colomb J., Brembs B. (2016): PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila. PeerJ 4:e1971. https://doi.org/10.7717/peerj.1971
  110. Velasque M., Briffa M. (2016): The opposite effects of routine metabolic rate and metabolic rate during startle responses on variation in the predictability of behaviour in hermit crabs. Behaviour 153(13-14):1545-1566. https://doi.org/10.1163/1568539x-00003371
  111. Chmielarz P., Kreiner G., Kuśmierczyk J., Kowalska M., Roman A., Tota K., et al. (2016): Depressive-like immobility behavior and genotype × stress interactions in male mice of selected strains. Stress 19(2):206-213. https://doi.org/10.3109/10253890.2016.1150995
  112. Kane R. (2016): The complex tapestry of free will: striving will, indeterminism and volitional streams. Synthese 196(1):145-160. https://doi.org/10.1007/s11229-016-1046-8
  113. Kane R. (2016): Moral Responsibility, Reactive Attitudes and Freedom of Will. The Journal of Ethics 20(1-3):229-246. https://doi.org/10.1007/s10892-016-9234-9
  114. Dunn T., Mu Y., Narayan S., Randlett O., Naumann E., Yang C., et al. (2016): Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion. eLife 5. https://doi.org/10.7554/elife.12741
  115. Kudo T., Terashima S., Takaki Y., Tomita K., Saito M., Kanno M., et al. (2016): PlantExpress: A Database Integrating OryzaExpress and ArthaExpress for Single-species and Cross-species Gene Expression Network Analyses with Microarray-Based Transcriptome Data. Plant and Cell Physiology 58(1):e1-e1. https://doi.org/10.1093/pcp/pcw208
  116. Racine E., Nguyen V., Saigle V., Dubljevic V. (2016): Media Portrayal of a Landmark Neuroscience Experiment on Free Will. Science and Engineering Ethics 23(4):989-1007. https://doi.org/10.1007/s11948-016-9845-3
  117. Neuringer A. (2015): Reinforced (un)predictability and the voluntary operant. European Journal of Behavior Analysis 17(1):19-30. https://doi.org/10.1080/15021149.2015.1084767
  118. Kölzsch A., Alzate A., Bartumeus F., de Jager M., Weerman E., Hengeveld G., et al. (2015): Experimental evidence for inherent Lévy search behaviour in foraging animals. Proceedings of the Royal Society B: Biological Sciences 282(1807):20150424. https://doi.org/10.1098/rspb.2015.0424
  119. Kim A., Fitzgerald J., Maimon G. (2015): Cellular evidence for efference copy in Drosophila visuomotor processing. Nature Neuroscience 18(9):1247-1255. https://doi.org/10.1038/nn.4083
  120. Sullivan C., Loughlin R., Schank J., Joshi S. (2015): Genetic algorithms produce individual robotic rat pup behaviors that match Norway rat pup behaviors at multiple scales. Artificial Life and Robotics 20(2):93-102. https://doi.org/10.1007/s10015-015-0208-y
  121. Sims D. (2015): Intrinsic Lévy behaviour in organisms – searching for a mechanism. Physics of Life Reviews 14:111-114. https://doi.org/10.1016/j.plrev.2015.06.002
  122. Valenti D., Denaro G., Spagnolo B., Mazzola S., Basilone G., Conversano F., et al. (2015): Stochastic models for phytoplankton dynamics in Mediterranean Sea. Ecological Complexity 27:84-103. https://doi.org/10.1016/j.ecocom.2015.06.001
  123. Muijres F., Elzinga M., Iwasaki N., Dickinson M. (2015): Body saccades of Drosophila consist of stereotyped banked turns. Journal of Experimental Biology 218(6):864-875. https://doi.org/10.1242/jeb.114280
  124. Zenil H., Marshall J., Tegnér J. (2015): Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results. arXiv. https://doi.org/10.48550/arxiv.1509.06338
  125. Kurt Heininger (2015): Duality Of Stochasticity And Natural Selection: A Cybernetic Evolution Theory.
  126. Moy K., Li W., Tran H., Simonis V., Story E., Brandon C., et al. (2015): Computational Methods for Tracking, Quantitative Assessment, and Visualization of C. elegans Locomotory Behavior. PLOS ONE 10(12):e0145870. https://doi.org/10.1371/journal.pone.0145870
  127. LIU L., LUAN R., YIN F., ZHU X., LÜ Q. (2015): Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model. Epidemiology and Infection 144(1):144-151. https://doi.org/10.1017/s0950268815001144
  128. Abe M., Shimada M. (2015): Lévy Walks Suboptimal under Predation Risk. PLOS Computational Biology 11(11):e1004601. https://doi.org/10.1371/journal.pcbi.1004601
  129. Van De Poll M., Zajaczkowski E., Taylor G., Srinivasan M., van Swinderen B. (2015): Using an abstract geometry in virtual reality to explore choice behaviour: visual flicker preferences in honeybees. Journal of Experimental Biology. https://doi.org/10.1242/jeb.125138
  130. McGeoch M., Shaw J., Terauds A., Lee J., Chown S. (2015): Monitoring biological invasion across the broader Antarctic: A baseline and indicator framework. Global Environmental Change 32:108-125. https://doi.org/10.1016/j.gloenvcha.2014.12.012
  131. Ananjeva N., Uteshev V., Orlov N., Gakhova E. (2015): Strategies for conservation of endangered amphibian and reptile species. Biology Bulletin 42(5):432-439. https://doi.org/10.1134/s1062359015050027
  132. Lee R., Kuhn B., Stephens G. (2015): Prediction of spontaneous behavioral dynamics by neuronal population imaging in mouse neocortex. 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). https://doi.org/10.1109/iciibms.2015.7439527
  133. Schwarz R., Branicky R., Grundy L., Schafer W., Brown A. (2015): Changes in Postural Syntax Characterize Sensory Modulation and Natural Variation of C. elegans Locomotion. PLOS Computational Biology 11(8):e1004322. https://doi.org/10.1371/journal.pcbi.1004322
  134. Schwarz R., Branicky R., Grundy L., Schafer W., Brown A. (2015): Changes in postural syntax characterize sensory modulation and natural variation of C. elegans locomotion. https://doi.org/10.1101/017707
  135. Nurzaman S., Yu X., Kim Y., Iida F. (2015): Goal-directed multimodal locomotion through coupling between mechanical and attractor selection dynamics. Bioinspiration & Biomimetics 10(2):025004. https://doi.org/10.1088/1748-3190/10/2/025004
  136. Ishida Y. (2015): A Note on Continuous Self-Identification as Self-Awareness: An Example of Robot Navigation. Procedia Computer Science 60:1865-1874. https://doi.org/10.1016/j.procs.2015.08.297
  137. Bush A., Nipperess D., Theischinger G., Turak E., Hughes L. (2014): Testing for taxonomic bias in the future diversity of Australian Odonata. Diversity and Distributions 20(9):1016-1028. https://doi.org/10.1111/ddi.12196
  138. MacIntosh A. (2014): The Fractal Primate:. Primate Research 30(1):95-119. https://doi.org/10.2354/psj.30.011
  139. Chamero B., Buscalioni Á., Marugán‐Lobón J., Sarris I. (2014): 3D Geometry and Quantitative Variation of the Cervico‐Thoracic Region in Crocodylia. The Anatomical Record 297(7):1278-1291. https://doi.org/10.1002/ar.22926
  140. Mendoza E., Colomb J., Rybak J., Pflüger H., Zars T., Scharff C., et al. (2014): Drosophila FoxP Mutants Are Deficient in Operant Self-Learning. PLoS ONE 9(6):e100648. https://doi.org/10.1371/journal.pone.0100648
  141. Pyke G. (2014): Understanding movements of organisms: it’s time to abandon the Lévy foraging hypothesis. Methods in Ecology and Evolution 6(1):1-16. https://doi.org/10.1111/2041-210x.12298
  142. HF Hillebrandt (2014): Bayesian Hierarchical Predictive Coding of Human Social Behaviour. UCL Discovery (University College London).
  143. Hillebrandt H., Friston K., Blakemore S. (2014): Effective connectivity during animacy perception – dynamic causal modelling of Human Connectome Project data. Scientific Reports 4(1). https://doi.org/10.1038/srep06240
  144. Bell H. (2014): Behavioral Variability in the Service of Constancy. International Journal of Comparative Psychology 27(2). https://doi.org/10.46867/ijcp.2014.27.02.02
  145. Moritz J. (2014): ANIMAL SUFFERING, EVOLUTION, AND THE ORIGINS OF EVIL: TOWARD A “FREE CREATURES” DEFENSE. Zygon: Journal of Religion and Science 49(2). https://doi.org/10.1111/zygo.12085
  146. Jung K., Jang H., Kralik J., Jeong J. (2014): Bursts and Heavy Tails in Temporal and Sequential Dynamics of Foraging Decisions. PLoS Computational Biology 10(8):e1003759. https://doi.org/10.1371/journal.pcbi.1003759
  147. Christensen K., Papavassiliou D., de Figueiredo A., Franks N., Sendova-Franks A. (2014): Universality in ant behaviour. Journal of The Royal Society Interface 12(102):20140985. https://doi.org/10.1098/rsif.2014.0985
  148. Seuront L., Stanley H. (2014): Anomalous diffusion and multifractality enhance mating encounters in the ocean. Proceedings of the National Academy of Sciences 111(6):2206-2211. https://doi.org/10.1073/pnas.1322363111
  149. Mix L., Masel J. (2014): CHANCE, PURPOSE, AND PROGRESS IN EVOLUTION AND CHRISTIANITY. Evolution. https://doi.org/10.1111/evo.12434
  150. Heisenberg M. (2014): The Beauty of the Network in the Brain and the Origin of the Mind in the Control of Behavior. Journal of Neurogenetics 28(3-4):389-399. https://doi.org/10.3109/01677063.2014.912279
  151. Humphries N., Sims D. (2014): Optimal foraging strategies: Lévy walks balance searching and patch exploitation under a very broad range of conditions. Journal of Theoretical Biology 358:179-193. https://doi.org/10.1016/j.jtbi.2014.05.032
  152. Kane R. (2014): II-Acting ‘of One’s Own Free Will’: Modern Reflections on an Ancient Philosophical Problem. Proceedings of the Aristotelian Society (Hardback) 114(1pt1):35-55. https://doi.org/10.1111/j.1467-9264.2014.00363.x
  153. Wang R., Wang G., Zheng J. (2014): An Exploration of the Range of Noise Intensity That Affects the Membrane Potential of Neurons. Abstract and Applied Analysis 2014:1-11. https://doi.org/10.1155/2014/801642
  154. Nurzaman S., Yu X., Kim Y., Iida F. (2014): Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism. Entropy 16(5):2592-2610. https://doi.org/10.3390/e16052592
  155. Wearmouth V., McHugh M., Humphries N., Naegelen A., Ahmed M., Southall E., et al. (2014): Scaling laws of ambush predator ‘waiting’ behaviour are tuned to a common ecology. Proceedings of the Royal Society B: Biological Sciences 281(1782):20132997. https://doi.org/10.1098/rspb.2013.2997
  156. Gomez-Marin A., Paton J., Kampff A., Costa R., Mainen Z. (2014): Big behavioral data: psychology, ethology and the foundations of neuroscience. Nature Neuroscience 17(11):1455-1462. https://doi.org/10.1038/nn.3812
  157. Gomez-Marin A., Paton J., Kampff A., Costa R., Mainen Z. (2014): Big Behavioral Data: Psychology, Ethology and the Foundations of Neuroscience. https://doi.org/10.1101/006809
  158. Censi A., Straw A., Sayaman R., Murray R., Dickinson M. (2013): Discriminating External and Internal Causes for Heading Changes in Freely Flying Drosophila. PLoS Computational Biology 9(2):e1002891. https://doi.org/10.1371/journal.pcbi.1002891
  159. Nayakar C., Omkar S., Srikanth R. (2013): Consciousness, Libertarian Free Will and Quantum Randomness. Interdisciplinary Perspectives on Consciousness and the Self. https://doi.org/10.1007/978-81-322-1587-5_23
  160. Zilio D. (2013): Behavioral Unit of Selection and the Operant-Respondent Distinction: The Role of Neurophysiological Events in Controlling the Verbal Behavior of Theorizing About Behavior. The Psychological Record 63(4):895-918. https://doi.org/10.11133/j.tpr.2013.63.4.011
  161. Martius G., Der R., Ay N. (2013): Information Driven Self-Organization of Complex Robotic Behaviors. PLoS ONE 8(5):e63400. https://doi.org/10.1371/journal.pone.0063400
  162. Denaro G., Valenti D., Spagnolo B., Basilone G., Mazzola S., Zgozi S., et al. (2013): Dynamics of Two Picophytoplankton Groups in Mediterranean Sea: Analysis of the Deep Chlorophyll Maximum by a Stochastic Advection-Reaction-Diffusion Model. PLoS ONE 8(6):e66765. https://doi.org/10.1371/journal.pone.0066765
  163. Heather C. Bell (2013): Control in living systems : an exploration of the cybernetic properties of interactive behaviour. Open ULeth Scholarship (OPUS) (University of Lethbridge).
  164. SCHATZ J., OHLENDORF B., BUSSE P., PELZ G., DOLCH D., TEUBNER J., et al. (2013): Twenty years of active bat rabies surveillance in Germany: a detailed analysis and future perspectives. Epidemiology and Infection 142(6):1155-1166. https://doi.org/10.1017/s0950268813002185
  165. MORGAN-SHORT K., FARETTA-STUTENBERG M., BRILL-SCHUETZ K., CARPENTER H., WONG P. (2013): Declarative and procedural memory as individual differences in second language acquisition. Bilingualism: Language and Cognition 17(1):56-72. https://doi.org/10.1017/s1366728912000715
  166. Wong K., Cheng C. (2013): Predictable or Not? Individuals’ Risk Decisions Do Not Necessarily Predict Their Next Ones. PLoS ONE 8(2):e56811. https://doi.org/10.1371/journal.pone.0056811
  167. Schmidt L., Miskovic V. (2013): A New Perspective on Temperamental Shyness: Differential Susceptibility to Endoenvironmental Influences. Social and Personality Psychology Compass 7(3):141-157. https://doi.org/10.1111/spc3.12014
  168. Briffa M., Bridger D., Biro P. (2013): How does temperature affect behaviour? Multilevel analysis of plasticity, personality and predictability in hermit crabs. Animal Behaviour 86(1):47-54. https://doi.org/10.1016/j.anbehav.2013.04.009
  169. Briffa M. (2013): Plastic proteans: reduced predictability in the face of predation risk in hermit crabs. Biology Letters 9(5):20130592. https://doi.org/10.1098/rsbl.2013.0592
  170. Heisenberg M. (2013): Action Selection. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00002-2
  171. Dickinson M. (2013): Death Valley, Drosophila, and the Devonian Toolkit. Annual Review of Entomology 59(1):51-72. https://doi.org/10.1146/annurev-ento-011613-162041
  172. Kawabata M., Ueno T., Tomita J., Kawatani J., Tomoda A., Kume S., et al. (2013): Temporal organization of rest defined by actigraphy data in healthy and childhood chronic fatigue syndrome children. BMC Psychiatry 13(1). https://doi.org/10.1186/1471-244x-13-281
  173. Humphries N., Weimerskirch H., Sims D. (2013): A new approach for objective identification of turns and steps in organism movement data relevant to random walk modelling. Methods in Ecology and Evolution 4(10):930-938. https://doi.org/10.1111/2041-210x.12096
  174. Biro P., Adriaenssens B. (2013): Predictability as a Personality Trait: Consistent Differences in Intraindividual Behavioral Variation. The American Naturalist 182(5):621-629. https://doi.org/10.1086/673213
  175. Sathishkumar Raja (2013): The neuronal basis of spontaneous flight behavior in Drosophila. Refubium (Universitätsbibliothek der Freien Universität Berlin). https://doi.org/10.17169/refubium-13718
  176. Benhamou S. (2013): Of scales and stationarity in animal movements. Ecology Letters 17(3):261-272. https://doi.org/10.1111/ele.12225
  177. O’Brien T., Kinnaird M. (2013): The Wildlife Picture Index: A Biodiversity Indicator for Top Trophic Levels. Biodiversity Monitoring and Conservation. https://doi.org/10.1002/9781118490747.ch3
  178. Nepomnyashchikh V. (2013): Increases in variations in animal behavior induced by autocorrelations. Biology Bulletin Reviews 3(1):49-56. https://doi.org/10.1134/s2079086413010064
  179. Nepomnyashchikh V. (2013): Variability in invertebrate behavior and the problem of free will. Biology Bulletin Reviews 3(5):406-411. https://doi.org/10.1134/s2079086413050083
  180. Méndez V., Campos D., Bartumeus F. (2013): Biological Searches and Random Animal Motility. Springer Series in Synergetics. https://doi.org/10.1007/978-3-642-39010-4_9
  181. Jahan Z., Castelli S., Aversa G., Rufini S., Desideri A., Giovanetti A. (2013): Role of human topoisomerase IB on ionizing radiation induced damage. Biochemical and Biophysical Research Communications 432(3):545-548. https://doi.org/10.1016/j.bbrc.2013.02.032
  182. Proekt A., Banavar J., Maritan A., Pfaff D. (2012): Scale invariance in the dynamics of spontaneous behavior. Proceedings of the National Academy of Sciences 109(26):10564-10569. https://doi.org/10.1073/pnas.1206894109
  183. Paulk A., Millard S., van Swinderen B. (2012): Vision in Drosophila: Seeing the World Through a Model’s Eyes. Annual Review of Entomology 58(1):313-332. https://doi.org/10.1146/annurev-ento-120811-153715
  184. Warzecha A., Rosner R., Grewe J. (2012): Impact and sources of neuronal variability in the fly’s motion vision pathway. Journal of Physiology-Paris 107(1-2):26-40. https://doi.org/10.1016/j.jphysparis.2012.10.002
  185. van Swinderen B. (2012): Competing visual flicker reveals attention-like rivalry in the fly brain. Frontiers in Integrative Neuroscience 6. https://doi.org/10.3389/fnint.2012.00096
  186. Nayakar C., Srikanth R. (2012): Uncomputability and free will. arXiv. https://doi.org/10.48550/arxiv.1210.6301
  187. Nayakar C., Omkar S., Srikanth R. (2012): Libertarian free will and quantum indeterminism. arXiv. https://doi.org/10.48550/arxiv.1202.4440
  188. MENDOZA E., COLOMB J., RYBAK J., PFLÜGER H., ZARS T., SCHARFF C., et al. (2012): THE DROSOPHILA FOXP GENE IS REQUIRED FOR OPERANT SELF-LEARNING: IMPLICATIONS FOR THE EVOLUTION OF LANGUAGE. The Evolution of Language. https://doi.org/10.1142/9789814401500_0100
  189. Libersat F., Gal R. (2012): What can parasitoid wasps teach us about decision-making in insects?. Journal of Experimental Biology 216(1):47-55. https://doi.org/10.1242/jeb.073999
  190. Barham J. (2012): Normativity, agency, and life. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43(1):92-103. https://doi.org/10.1016/j.shpsc.2011.05.008
  191. Joseph J., Dunn F., Stopfer M. (2012): Spontaneous Olfactory Receptor Neuron Activity Determines Follower Cell Response Properties. The Journal of Neuroscience 32(8):2900-2910. https://doi.org/10.1523/jneurosci.4207-11.2012
  192. KAGAYA K. (2012): Neural circuit mechanisms for controlling voluntary behavior in crayfish. Hikaku seiri seikagaku(Comparative Physiology and Biochemistry) 29(1):3-10. https://doi.org/10.3330/hikakuseiriseika.29.3
  193. Magdziarz M., Metzler R., Szczotka W., Zebrowski P. (2012): Correlated continuous-time random walks—scaling limits and Langevin picture. Journal of Statistical Mechanics: Theory and Experiment 2012(04):P04010. https://doi.org/10.1088/1742-5468/2012/04/p04010
  194. Heisenberg M. (2012): The Origin of Freedom in Animal Behaviour. Is Science Compatible with Free Will?. https://doi.org/10.1007/978-1-4614-5212-6_7
  195. Crowther M., Lunney D., Parnaby H. (2012): Are Journal Impact Factors another key threatening process for Australian fauna?. Science Under Siege. https://doi.org/10.7882/fs.2012.049
  196. Humphries N., Weimerskirch H., Queiroz N., Southall E., Sims D. (2012): Foraging success of biological Lévy flights recorded in situ. Proceedings of the National Academy of Sciences 109(19):7169-7174. https://doi.org/10.1073/pnas.1121201109
  197. Okle O., Stemmer K., Deschl U., Dietrich D. (2012): L-BMAA Induced ER Stress and Enhanced Caspase 12 Cleavage in Human Neuroblastoma SH-SY5Y Cells at Low Nonexcitotoxic Concentrations. Toxicological Sciences 131(1):217-224. https://doi.org/10.1093/toxsci/kfs291
  198. KAPTIJN R., THOMESE F., LIEFBROER A., SILVERSTEIN M. (2012): TESTING EVOLUTIONARY THEORIES OF DISCRIMINATIVE GRANDPARENTAL INVESTMENT. Journal of Biosocial Science 45(3):289-310. https://doi.org/10.1017/s0021932012000612
  199. Bazazi S., Bartumeus F., Hale J., Couzin I. (2012): Intermittent Motion in Desert Locusts: Behavioural Complexity in Simple Environments. PLoS Computational Biology 8(5):e1002498. https://doi.org/10.1371/journal.pcbi.1002498
  200. Sreedhar M., Upadhyay N., Mishra S. (2012): Optimized solutions for an optimization technique based on minority charge carrier inspired algorithm applied to selective harmonic elimination in induction motor drive. 2012 1st International Conference on Recent Advances in Information Technology (RAIT). https://doi.org/10.1109/rait.2012.6194523
  201. Sreedhar M., Dasgupta A. (2012): Experimental verification of Minority Charge Carrier Inspired Algorithm applied to voltage source inverter. 2012 IEEE 5th India International Conference on Power Electronics (IICPE). https://doi.org/10.1109/iicpe.2012.6450395
  202. Miller S., Ngo T., van Swinderen B. (2012): Attentional Switching in Humans and Flies: Rivalry in Large and Miniature Brains. Frontiers in Human Neuroscience 5. https://doi.org/10.3389/fnhum.2011.00188
  203. Nurzaman S., Matsumoto Y., Nakamura Y., Shirai K., Ishiguro H. (2012): Bacteria-inspired underactuated mobile robot based on a biological fluctuation. Adaptive Behavior 20(4):225-236. https://doi.org/10.1177/1059712312445901
  204. Ueno T., Masuda N., Kume S., Kume K. (2012): Dopamine Modulates the Rest Period Length without Perturbation of Its Power Law Distribution in Drosophila melanogaster. PLoS ONE 7(2):e32007. https://doi.org/10.1371/journal.pone.0032007
  205. Diamantidis A., Carey J., Nakas C., Papadopoulos N. (2011): Population‐specific demography and invasion potential in medfly. Ecology and Evolution 1(4):479-488. https://doi.org/10.1002/ece3.33
  206. DIAMANTIDIS A., CAREY J., NAKAS C., PAPADOPOULOS N. (2011): Ancestral populations perform better in a novel environment: domestication of Mediterranean fruit fly populations from five global regions. Biological Journal of the Linnean Society 102(2):334-345. https://doi.org/10.1111/j.1095-8312.2010.01579.x
  207. Mele A. (2011): Libertarianism and Human Agency. Philosophy and Phenomenological Research 87(1):72-92. https://doi.org/10.1111/j.1933-1592.2011.00529.x
  208. Sorribes A., Armendariz B., Lopez-Pigozzi D., Murga C., de Polavieja G. (2011): The Origin of Behavioral Bursts in Decision-Making Circuitry. PLoS Computational Biology 7(6):e1002075. https://doi.org/10.1371/journal.pcbi.1002075
  209. Bendesky A., Bargmann C. (2011): Genetic contributions to behavioural diversity at the gene–environment interface. Nature Reviews Genetics 12(12):809-820. https://doi.org/10.1038/nrg3065
  210. He B. (2011): Scale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task. The Journal of Neuroscience 31(39):13786-13795. https://doi.org/10.1523/jneurosci.2111-11.2011
  211. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  212. Flammang B., Porter M. (2011): Bioinspiration: Applying Mechanical Design to Experimental Biology. Integrative and Comparative Biology 51(1):128-132. https://doi.org/10.1093/icb/icr014
  213. Claire Eschbach (2011): Klassiches und operantes Lernen bei Larven der Drosophila.
  214. Sims D., Humphries N., Bradford R., Bruce B. (2011): Lévy flight and Brownian search patterns of a free‐ranging predator reflect different prey field characteristics. Journal of Animal Ecology 81(2):432-442. https://doi.org/10.1111/j.1365-2656.2011.01914.x
  215. Hays G., Bastian T., Doyle T., Fossette S., Gleiss A., Gravenor M., et al. (2011): High activity and Lévy searches: jellyfish can search the water column like fish. Proceedings of the Royal Society B: Biological Sciences 279(1728):465-473. https://doi.org/10.1098/rspb.2011.0978
  216. Ikegami, Takashi, Oka, Mizuki, Abe, Hirotake (2011): Autonomy of the internet: Complexity of flow dynamics in a packet switching network. ECAL 2011: The 11th European Conference on Artificial Life. https://doi.org/10.7551/978-0-262-29714-1-ch056
  217. Huang J., Chen B., Liu C., Lai J., Zhang J., Ma K. (2011): Identifying hotspots of endemic woody seed plant diversity in China. Diversity and Distributions 18(7):673-688. https://doi.org/10.1111/j.1472-4642.2011.00845.x
  218. Lewejohann L., Zipser B., Sachser N. (2011): “Personality” in laboratory mice used for biomedical research: A way of understanding variability?. Developmental Psychobiology 53(6):624-630. https://doi.org/10.1002/dev.20553
  219. Windecker R. (2011): Stochastic Artificial Neural Networks and random walks. The 2011 International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn.2011.6033351
  220. Rosner R., Warzecha A. (2011): Relating Neuronal to Behavioral Performance: Variability of Optomotor Responses in the Blowfly. PLoS ONE 6(10):e26886. https://doi.org/10.1371/journal.pone.0026886
  221. Ranjitkar S., Schousboe M., Thomsen T., Adhikari M., Kapel C., Bygbjerg I., et al. (2011): Prevalence of molecular markers of anti-malarial drug resistance in Plasmodium vivax and Plasmodium falciparum in two districts of Nepal. Malaria Journal 10(1). https://doi.org/10.1186/1475-2875-10-75
  222. Glaser S., Ye H., Maunder M., MacCall A., Fogarty M., Sugihara G. (2011): Detecting and forecasting complex nonlinear dynamics in spatially structured catch-per-unit-effort time series for North Pacific albacore (Thunnus alalunga). Canadian Journal of Fisheries and Aquatic Sciences 68(3):400-412. https://doi.org/10.1139/f10-160
  223. Groom S., Schwarz M. (2011): Bees in the Southwest Pacific: Origins, diversity and conservation. Apidologie 42(6):759-770. https://doi.org/10.1007/s13592-011-0079-8
  224. Zou S., Liedo P., Altamirano-Robles L., Cruz-Enriquez J., Morice A., Ingram D., et al. (2011): Recording Lifetime Behavior and Movement in an Invertebrate Model. PLoS ONE 6(4):e18151. https://doi.org/10.1371/journal.pone.0018151
  225. Nurzaman S., Matsumoto Y., Nakamura Y., Shirai K., Koizumi S., Ishiguro H. (2011): From Lévy to Brownian: A Computational Model Based on Biological Fluctuation. PLoS ONE 6(2):e16168. https://doi.org/10.1371/journal.pone.0016168
  226. Nurzaman S., Matsumoto Y., Nakamura Y., Koizumi S., Ishiguro H. (2011): ‘Yuragi’-Based Adaptive Mobile Robot Search With and Without Gradient Sensing: From Bacterial Chemotaxis to a Levy Walk. Advanced Robotics 25(16):2019-2037. https://doi.org/10.1163/016918611×590229
  227. Zulkifli Zainal Abidin, Mohd Rizal Arshad, Umi Kalthum Ngah (2011): A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application.
  228. He B., Zempel J., Snyder A., Raichle M. (2010): The Temporal Structures and Functional Significance of Scale-free Brain Activity. Neuron 66(3):353-369. https://doi.org/10.1016/j.neuron.2010.04.020
  229. Brembs B. (2010): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences 278(1707):930-939. https://doi.org/10.1098/rspb.2010.2325
  230. van Swinderen B., Brembs B. (2010): Attention-Like Deficit and Hyperactivity in aDrosophilaMemory Mutant. The Journal of Neuroscience 30(3):1003-1014. https://doi.org/10.1523/jneurosci.4516-09.2010
  231. Nayakar C., Srikanth R. (2010): Quantum randomness and free will. arXiv. https://doi.org/10.48550/arxiv.1011.4898
  232. Stewart F., Baker D., Webb B. (2010): A model of visual–olfactory integration for odour localisation in free-flying fruit flies. Journal of Experimental Biology 213(11):1886-1900. https://doi.org/10.1242/jeb.026526
  233. Finlay J. Stewart (2010): Modelling visual-olfactory integration in free-flying Drosophila. ERA.
  234. Gardiner J., Overall R., Marc J. (2010): The Fractal Nature of the Brain: EEG Data Suggests That the Brain Functions as a “Quantum Computer” in 5-8 Dimensions. NeuroQuantology 8(2). https://doi.org/10.14704/nq.2010.8.2.279
  235. Kagaya K., Takahata M. (2010): Readiness Discharge for Spontaneous Initiation of Walking in Crayfish. The Journal of Neuroscience 30(4):1348-1362. https://doi.org/10.1523/jneurosci.4885-09.2010
  236. Frye M. (2010): Multisensory systems integration for high-performance motor control in flies. Current Opinion in Neurobiology 20(3):347-352. https://doi.org/10.1016/j.conb.2010.02.002
  237. Chang O., Campoy P., Martinez C., Olivares-Mendez M. (2010): A robotic eye controller based on cooperative neural agents. The 2010 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2010.5596985
  238. Gal R., Libersat F. (2010): A Wasp Manipulates Neuronal Activity in the Sub-Esophageal Ganglion to Decrease the Drive for Walking in Its Cockroach Prey. PLoS ONE 5(4):e10019. https://doi.org/10.1371/journal.pone.0010019
  239. Gal R., Libersat F. (2010): On predatory wasps and zombie cockroaches. Communicative & Integrative Biology 3(5):458-461. https://doi.org/10.4161/cib.3.5.12472
  240. Ram Gal, Frédéric Libersat, Parc Scientifique de Luminy (2010): Investigations of “free will” and spontaneous behavior in insects.
  241. Tang S., Juusola M. (2010): Intrinsic Activity in the Fly Brain Gates Visual Information during Behavioral Choices. PLoS ONE 5(12):e14455. https://doi.org/10.1371/journal.pone.0014455
  242. Shaviro S. (2010): Interstitial Life: Subtractive Vitalism in Whitehead and Deleuze. Deleuze Studies 4(1):107-119. https://doi.org/10.3366/e1750224110000863
  243. Nurzaman S., Matsumoto Y., Nakamura Y., Shirai K., Koizumi S., Ishiguro H. (2010): An adaptive switching behavior between levy and Brownian random search in a mobile robot based on biological fluctuation. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2010.5651671
  244. Tozan Y., Klein E., Darley S., Panicker R., Laxminarayan R., Breman J. (2010): Prereferral rectal artesunate for treatment of severe childhood malaria: a cost-effectiveness analysis. The Lancet 376(9756):1910-1915. https://doi.org/10.1016/s0140-6736(10)61460-2
  245. Tanimura Y., Yang M., Ottens A., Lewis M. (2010): Development and temporal organization of repetitive behavior in an animal model. Developmental Psychobiology 52(8):813-824. https://doi.org/10.1002/dev.20477
  246. Zulkifli Zainal Abidin, Umi Kalthum Ngah, Mohd Rizal Arshad, Ong Boon Ping (2010): A novel Fly Optimization Algorithm for swarming application. 2010 IEEE Conference on Robotics, Automation and Mechatronics. https://doi.org/10.1109/ramech.2010.5513157
  247. Abidin Z., Arshad M., Ngah U. (2010): Waypoint control of Drosobots: Swarms of mini ASVs. 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA). https://doi.org/10.1109/isiea.2010.5679401
  248. Chechkin A., Hofmann M., Sokolov I. (2009): Continuous-time random walk with correlated waiting times. Physical Review E 80(3). https://doi.org/10.1103/physreve.80.031112
  249. Gama B., Oliveira N., Souza J., Daniel-Ribeiro C., Ferreira-da-Cruz M. (2009): Characterisation of pvmdr1 and pvdhfr genes associated with chemoresistance in Brazilian Plasmodium vivax isolates. Memórias do Instituto Oswaldo Cruz 104(7):1009-1011. https://doi.org/10.1590/s0074-02762009000700012
  250. Brembs B. (2009): Mushroom Bodies Regulate Habit Formation in Drosophila. Current Biology 19(16):1351-1355. https://doi.org/10.1016/j.cub.2009.06.014
  251. Brembs B. (2009): The Importance of Being Active. Journal of Neurogenetics 23(1-2):120-126. https://doi.org/10.1080/01677060802471643
  252. Tepolt C., Darling J., Bagley M., Geller J., Blum M., Grosholz E. (2009): European green crabs (Carcinus maenas) in the northeastern Pacific: genetic evidence for high population connectivity and current‐mediated expansion from a single introduced source population. Diversity and Distributions 15(6):997-1009. https://doi.org/10.1111/j.1472-4642.2009.00605.x
  253. Koch C. (2009): Free Will, Physics, Biology, and the Brain. Understanding Complex Systems. https://doi.org/10.1007/978-3-642-03205-9_2
  254. Krstic D., Boll W., Noll M. (2009): Sensory Integration Regulating Male Courtship Behavior in Drosophila. PLoS ONE 4(2):e4457. https://doi.org/10.1371/journal.pone.0004457
  255. Bartumeus F., Catalan J. (2009): Optimal search behavior and classic foraging theory. Journal of Physics A: Mathematical and Theoretical 42(43):434002. https://doi.org/10.1088/1751-8113/42/43/434002
  256. Bartumeus F. (2009): Behavioral intermittence, Lévy patterns, and randomness in animal movement. Oikos 118(4):488-494. https://doi.org/10.1111/j.1600-0706.2009.17313.x
  257. Bartumeus F. (2009): Behavioral intermittence, Lévy patterns, and randomness in animal movement. Oikos 118(4):488-494. https://doi.org/10.1111/j.1600-0706.2008.17313.x
  258. Rosenkranz M., Davidson R. (2009): Affective neural circuitry and mind–body influences in asthma. NeuroImage 47(3):972-980. https://doi.org/10.1016/j.neuroimage.2009.05.042
  259. Eisele N., Anderson D. (2009): Dual-Function Antibodies to Yersinia pestis LcrV Required for Pulmonary Clearance of Plague. Clinical and Vaccine Immunology 16(12):1720-1727. https://doi.org/10.1128/cvi.00333-09
  260. Bleeker P., Diergaarde P., Ament K., Guerra J., Weidner M., Schütz S., et al. (2009): The Role of Specific Tomato Volatiles in Tomato-Whitefly Interaction. Plant Physiology 151(2):925-935. https://doi.org/10.1104/pp.109.142661
  261. Haverkamp S., Inta D., Monyer H., Wässle H. (2009): Expression analysis of green fluorescent protein in retinal neurons of four transgenic mouse lines. Neuroscience 160(1):126-139. https://doi.org/10.1016/j.neuroscience.2009.01.081
  262. Nurzaman S., Matsumoto Y., Nakamura Y., Koizumi S., Ishiguro H. (2009): Yuragi-based adaptive searching behavior in mobile robot: From bacterial chemotaxis to Levy walk. 2008 IEEE International Conference on Robotics and Biomimetics. https://doi.org/10.1109/robio.2009.4913103
  263. Nurzaman S., Matsumoto Y., Nakamura Y., Koizumi S., Ishiguro H. (2009): Biologically inspired adaptive mobile robot search with and without gradient sensing. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2009.5353998
  264. Yanagawa T., Mogi K. (2009): Analysis of ongoing dynamics in neural networks. Neuroscience Research 64(2):177-184. https://doi.org/10.1016/j.neures.2009.02.011
  265. Hong Y., Velegol D., Chaturvedi N., Sen A. (2009): Biomimetic behavior of synthetic particles: from microscopic randomness to macroscopic control. Phys. Chem. Chem. Phys. 12(7):1423-1435. https://doi.org/10.1039/b917741h
  266. Schoppik D., Nagel K., Lisberger S. (2008): Cortical Mechanisms of Smooth Eye Movements Revealed by Dynamic Covariations of Neural and Behavioral Responses. Neuron 58(2):248-260. https://doi.org/10.1016/j.neuron.2008.02.015
  267. Bienek D., Biagini R., Charlton D., Smith J., Sammons D., Robertson S. (2008): Rapid Point-of-Care Test To Detect Broad Ranges of Protective Antigen-Specific Immunoglobulin G Concentrations in Recipients of the U.S.-Licensed Anthrax Vaccine. Clinical and Vaccine Immunology 15(4):644-649. https://doi.org/10.1128/cvi.00473-07
  268. Bartumeus F., Levin S. (2008): Fractal reorientation clocks: Linking animal behavior to statistical patterns of search. Proceedings of the National Academy of Sciences 105(49):19072-19077. https://doi.org/10.1073/pnas.0801926105
  269. Takahashi H., Horibe N., Shimada M., Ikegami T. (2008): Analyzing the House Fly’s Exploratory Behavior with Autoregression Methods. Journal of the Physical Society of Japan 77(8):084802. https://doi.org/10.1143/jpsj.77.084802
  270. Carpenter K., Abrar M., Aeby G., Aronson R., Banks S., Bruckner A., et al. (2008): One-Third of Reef-Building Corals Face Elevated Extinction Risk from Climate Change and Local Impacts. Science 321(5888):560-563. https://doi.org/10.1126/science.1159196
  271. Chikamoto K., Kagaya K., Takahata M. (2008): Electromyographic Characterization of Walking Behavior Initiated Spontaneously in Crayfish. Zoological Science 25(8):783-792. https://doi.org/10.2108/zsj.25.783
  272. Dees N., Bahar S., Moss F. (2008): Stochastic resonance and the evolution ofDaphniaforaging strategy. Physical Biology 5(4):044001. https://doi.org/10.1088/1478-3975/5/4/044001
  273. Beum P., Lindorfer M., Taylor R. (2008): Within Peripheral Blood Mononuclear Cells, Antibody-Dependent Cellular Cytotoxicity of Rituximab-Opsonized Daudi cells Is Promoted by NK Cells and Inhibited by Monocytes due to Shaving. The Journal of Immunology 181(4):2916-2924. https://doi.org/10.4049/jimmunol.181.4.2916
  274. Schaeffer S., Bhutkar A., McAllister B., Matsuda M., Matzkin L., O’Grady P., et al. (2008): Polytene Chromosomal Maps of 11 Drosophila Species: The Order of Genomic Scaffolds Inferred From Genetic and Physical Maps. Genetics 179(3):1601-1655. https://doi.org/10.1534/genetics.107.086074
  275. Uwe Meixner (2008): New perspectives for a dualistic conception of mental causation. OPUS (Augsburg University).
  276. Gabriel Vacarìu (2007): Epistemologically different worlds. PhilPapers (PhilPapers Foundation). https://doi.org/10.26190/unsworks/6625
  277. Niven J. (2007): GHOST IN THE MACHINE?. Journal of Experimental Biology 210(19):v-v. https://doi.org/10.1242/jeb.001149
  278. Ball P. (2007): Do flies have free will?. Nature. https://doi.org/10.1038/news070514-8
  279. Hong Y., Blackman N., Kopp N., Sen A., Velegol D. (2007): Chemotaxis of Nonbiological Colloidal Rods. Physical Review Letters 99(17). https://doi.org/10.1103/physrevlett.99.178103

Brembs B, Christiansen F, Pflüger J, Duch C. (2007): Flight initiation and maintenance deficits in flies with genetically altered biogenic amine levels. J. Neurosci. 27(41):11122–11131.

  1. Malyshev A., Smirnov I., Volgushev M. (2025): Heterosynaptic Plasticity: History and Evolution of the Concept in Aplysia and Vertebrates. The Neuroscientist 32(1):20-37. https://doi.org/10.1177/10738584251390787
  2. Eichler A., Kruse P., Schob C., Lenz M. (2025): Synaptic transmission in supragranular layers of the human cortex – comparative review of structure, function, and plasticity. Frontiers in Synaptic Neuroscience 17. https://doi.org/10.3389/fnsyn.2025.1724377
  3. Chou C., Droogers W., Lalanne T., Fineberg E., Klimenko T., Owens H., et al. (2025): Postsynaptic spiking determines anti-Hebbian LTD in visual cortex basket cells. Frontiers in Synaptic Neuroscience 17. https://doi.org/10.3389/fnsyn.2025.1548563
  4. Arthur D., Albers E., Tatsuno M. (2025): Influence of STDP rule choice and network connectivity on polychronous groups and cell ensembles in spiking neural networks. bioRxiv. https://doi.org/10.1101/2025.10.25.684525
  5. Ahokainen I., Linne M. (2025): A unified model of short- and long-term plasticity: Effects on network connectivity and information capacity. bioRxiv. https://doi.org/10.1101/2025.11.07.687160
  6. Rademacher J., Grent-‘t-Jong T., Rivolta D., Sauer A., Scheller B., Gonzalez-Burgos G., et al. (2025): Computational modeling of ketamine-induced changes in gamma-band oscillations: The contribution of parvalbumin and somatostatin interneurons. PLOS Computational Biology 21(6):e1013118. https://doi.org/10.1371/journal.pcbi.1013118
  7. Knociková J. (2025): GABAergic Interneurons in the Brain – Their Role and Detection by Wavelet Transform. Proceedings of the 12th International Conference on Bioinformatics Research and Applications. https://doi.org/10.1145/3774976.3774997
  8. McFarlan A., Gomez I., Chou C., Alcolado A., Costa R., Sjöström P. (2024): The short-term plasticity of VIP interneurons in motor cortex. Frontiers in Synaptic Neuroscience 16. https://doi.org/10.3389/fnsyn.2024.1433977
  9. Antonov D., Batuev B., Sukhov S. (2024): Spiking Neural Networks Training with Combined Hebbian Rules. 2024 X International Conference on Information Technology and Nanotechnology (ITNT). https://doi.org/10.1109/ITNT60778.2024.10582358
  10. Zendrikov D., Paraskevov A. (2024): The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks. bioRxiv. https://doi.org/10.1101/2024.01.05.574396
  11. Castro J., Buyatti B., Mercado D., Di Donato A., Quintero M., Tortarolo M. (2024): Spike-timing-dependent-plasticity learning in a planar magnetic domain wall artificial synapse. Journal of Physics D: Applied Physics 58(12):125002. https://doi.org/10.1088/1361-6463/adab01
  12. McFarlan A., Guo C., Gomez I., Weinerman C., Liang T., Sjöström P. (2024): The spike-timing-dependent plasticity of VIP interneurons in motor cortex. Frontiers in Cellular Neuroscience 18. https://doi.org/10.3389/fncel.2024.1389094
  13. Debanne D., Inglebert Y. (2023): Spike timing-dependent plasticity and memory. Current Opinion in Neurobiology 80:102707. https://doi.org/10.1016/j.conb.2023.102707
  14. Hong I., Kim J., Hainmueller T., Kim D., Keijser J., Johnson R., et al. (2023): Calcium-permeable AMPA receptors govern PV neuron feature selectivity. Nature 635(8038):398-405. https://doi.org/10.1038/s41586-024-08027-2
  15. Quast K., Reh R., Caiati M., Kopell N., McCarthy M., Hensch T. (2023): Rapid synaptic and gamma rhythm signature of mouse critical period plasticity. Proceedings of the National Academy of Sciences 120(2). https://doi.org/10.1073/pnas.2123182120
  16. Saponati M., Vinck M. (2023): Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule. Nature Communications 14(1). https://doi.org/10.1038/s41467-023-40651-w
  17. Kourosh-Arami M., Komaki A., Gholami M., Marashi S., Hejazi S. (2023): Heterosynaptic plasticity-induced modulation of synapses. The Journal of Physiological Sciences 73(1):33. https://doi.org/10.1186/s12576-023-00893-1
  18. McFarlan A., Chou C., Watanabe A., Cherepacha N., Haddad M., Owens H., et al. (2022): The plasticitome of cortical interneurons. Nature Reviews Neuroscience 24(2):80-97. https://doi.org/10.1038/s41583-022-00663-9
  19. Kupferschmidt D., Cummings K., Joffe M., MacAskill A., Malik R., Sánchez-Bellot C., et al. (2022): Prefrontal Interneurons: Populations, Pathways, and Plasticity Supporting Typical and Disordered Cognition in Rodent Models. The Journal of Neuroscience 42(45):8468-8476. https://doi.org/10.1523/JNEUROSCI.1136-22.2022
  20. Rupert D., Shea S. (2022): Parvalbumin-Positive Interneurons Regulate Cortical Sensory Plasticity in Adulthood and Development Through Shared Mechanisms. Frontiers in Neural Circuits 16. https://doi.org/10.3389/fncir.2022.886629
  21. Martínez-Gallego I., Rodríguez-Moreno A., Andrade-Talavera Y. (2022): Role of Group I Metabotropic Glutamate Receptors in Spike Timing-Dependent Plasticity. International Journal of Molecular Sciences 23(14):7807. https://doi.org/10.3390/ijms23147807
  22. Schmalz J., Quinarez R., Kothare M., Kumar G. (2022): Controlling neocortical epileptic seizures using forced temporal spike-time stimulation: an in silico computational study. Frontiers in Computational Neuroscience 17. https://doi.org/10.3389/fncom.2023.1084080
  23. Schumm S., Gabrieli D., Meaney D. (2022): Plasticity impairment exposes CA3 vulnerability in a hippocampal network model of mild traumatic brain injury. Hippocampus 32(3):231-250. https://doi.org/10.1002/hipo.23402
  24. Kuramoto E., Tanaka Y., Hioki H., Goto T., Kaneko T. (2021): Local Connections of Pyramidal Neurons to Parvalbumin-Producing Interneurons in Motor-Associated Cortical Areas of Mice. eneuro 9(1):ENEURO.0567-20.2021. https://doi.org/10.1523/ENEURO.0567-20.2021
  25. Kourosh-Arami M., Hosseini N., Komaki A. (2021): Brain is modulated by neuronal plasticity during postnatal development. The Journal of Physiological Sciences 71(1):34. https://doi.org/10.1186/s12576-021-00819-9
  26. Saponati M., Vinck M. (2021): Sequence anticipation and spike-time-dependent-plasticity emerge from a predictive learning rule. bioRxiv. https://doi.org/10.1101/2021.10.31.466667
  27. Froudist-Walsh S., Bliss D., Ding X., Rapan L., Niu M., Knoblauch K., et al. (2021): A dopamine gradient controls access to distributed working memory in the large-scale monkey cortex. Neuron 109(21):3500-3520.e13. https://doi.org/10.1016/j.neuron.2021.08.024
  28. Cheng Y., Huang J., Yeh C., Pei Y. (2021): Alternation of Neuronal Feature Selectivity Induced by Paired Optogenetic-Mechanical Stimulation in the Barrel Cortex. Frontiers in Neural Circuits 15. https://doi.org/10.3389/fncir.2021.708459
  29. Rao Z., Wang R., Li S., Shi Y., Mo L., Han S., et al. (2021): Molecular Mechanisms Underlying Ascl1-Mediated Astrocyte-to-Neuron Conversion. Stem Cell Reports 16(3):534-547. https://doi.org/10.1016/j.stemcr.2021.01.006
  30. Houben A., Keil M. (2020): A calcium-influx-dependent plasticity model exhibiting multiple STDP curves. Journal of Computational Neuroscience 48(1):65-84. https://doi.org/10.1007/s10827-019-00737-1
  31. Dickey C., Sargsyan A., Madsen J., Eskandar E., Cash S., Halgren E. (2020): Travelling spindles create necessary conditions for spike-timing-dependent plasticity in humans. Nature Communications 12(1). https://doi.org/10.1038/s41467-021-21298-x
  32. Huang C., Zeldenrust F., Celikel T. (2020): Cortical Representation of Touch in Silico. Neuroinformatics 20(4):1013-1039. https://doi.org/10.1007/s12021-022-09576-5
  33. Gabrieli D., Schumm S., Vigilante N., Parvesse B., Meaney D. (2020): Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks. PLOS ONE 15(9):e0234749. https://doi.org/10.1371/journal.pone.0234749
  34. Kromer J., Tass P. (2020): Long-lasting desynchronization by decoupling stimulation. Physical Review Research 2(3). https://doi.org/10.1103/physrevresearch.2.033101
  35. Zhang K., Yu F., Zhu J., Han S., Chen J., Wu X., et al. (2020): Imbalance of Excitatory/Inhibitory Neuron Differentiation in Neurodevelopmental Disorders with an NR2F1 Point Mutation. Cell Reports 31(3):107521. https://doi.org/10.1016/j.celrep.2020.03.085
  36. Joffe M., Winder D., Conn P. (2020): Contrasting sex-dependent adaptations to synaptic physiology and membrane properties of prefrontal cortex interneuron subtypes in a mouse model of binge drinking. Neuropharmacology 178:108126. https://doi.org/10.1016/j.neuropharm.2020.108126
  37. Joffe M., Winder D., Jeffrey Conn P. (2020): Contrasting adaptations to synaptic physiology of prefrontal cortex interneuron subtypes in a mouse model of binge drinking. bioRxiv. https://doi.org/10.1101/2020.01.05.895169
  38. Bannon N., Chistiakova M., Volgushev M. (2020): Synaptic Plasticity in Cortical Inhibitory Neurons: What Mechanisms May Help to Balance Synaptic Weight Changes?. Frontiers in Cellular Neuroscience 14. https://doi.org/10.3389/fncel.2020.00204
  39. Froudist-Walsh S., Bliss D., Ding X., Jankovic-Rapan L., Niu M., Knoblauch K., et al. (2020): A dopamine gradient controls access to distributed working memory in monkey cortex. bioRxiv. https://doi.org/10.1101/2020.09.07.286500
  40. Schumm S., Gabrieli D., Meaney D. (2020): Plasticity impairment alters community structure but permits successful pattern separation in a hippocampal network model. Frontiers in Cellular Neuroscience 16. https://doi.org/10.3389/fncel.2022.977769
  41. Asabuki T., Fukai T. (2020): Somatodendritic consistency check for temporal feature segmentation. Nature Communications 11(1). https://doi.org/10.1038/s41467-020-15367-w
  42. Kimura F., Itami C. (2019): A Hypothetical Model Concerning How Spike-Timing-Dependent Plasticity Contributes to Neural Circuit Formation and Initiation of the Critical Period in Barrel Cortex. The Journal of Neuroscience 39(20):3784-3791. https://doi.org/10.1523/JNEUROSCI.1684-18.2019
  43. Jean-Marie C. Bouteiller (2019): Influence of Inter- and Intra-Synaptic Factors on Information Processing in the Brain. Frontiers Research Topics. https://doi.org/10.3389/978-2-88963-073-8
  44. K. M. Riashad Foysal (2019): Motor system plasticity induced by non-invasive stimuli. https://www.semanticscholar.org/paper/816c5d9e5f5c0649e10446fad7a9a473f62e9c00
  45. Chistiakova M., Ilin V., Roshchin M., Bannon N., Malyshev A., Kisvárday Z., et al. (2019): Distinct Heterosynaptic Plasticity in Fast Spiking and Non-Fast-Spiking Inhibitory Neurons in Rat Visual Cortex. The Journal of Neuroscience 39(35):6865-6878. https://doi.org/10.1523/JNEUROSCI.3039-18.2019
  46. McKenzie S., Huszár R., English D., Kim K., Yoon E., Buzsáki G. (2019): Preexisting hippocampal network dynamics constrain optogenetically induced place fields. bioRxiv. https://doi.org/10.1101/803577
  47. Asabuki T., Fukai T. (2019): Artificial Dendritic Neurons Enable Self-Supervised Temporal Feature Extraction. bioRxiv. https://doi.org/10.1101/517888
  48. Foncelle A., Mendes A., Jędrzejewska-Szmek J., Valtcheva S., Berry H., Blackwell K., et al. (2018): Modulation of Spike-Timing Dependent Plasticity: Towards the Inclusion of a Third Factor in Computational Models. Frontiers in Computational Neuroscience 12. https://doi.org/10.3389/fncom.2018.00049
  49. A. Foncelle (2018): Data-driven computational modelling for some of the implications of dopamine in the brain : From subcellular signalling to area networks. https://www.semanticscholar.org/paper/94af15a9c9c450e2aa977c4d8284cf7b80bd2a57
  50. Kerkhofs A., Canas P., Timmerman A., Heistek T., Real J., Xavier C., et al. (2018): Adenosine A2A Receptors Control Glutamatergic Synaptic Plasticity in Fast Spiking Interneurons of the Prefrontal Cortex. Frontiers in Pharmacology 9. https://doi.org/10.3389/fphar.2018.00133
  51. Mitani A., Dong M., Komiyama T. (2018): Brain-Computer Interface with Inhibitory Neurons Reveals Subtype-Specific Strategies. Current Biology 28(1):77-83.e4. https://doi.org/10.1016/j.cub.2017.11.035
  52. Ferrer C., Hsieh H., Wollmuth L. (2018): Input-specific maturation of NMDAR-mediated transmission onto parvalbumin-expressing interneurons in layers 2/3 of the visual cortex. Journal of Neurophysiology 120(6):3063-3076. https://doi.org/10.1152/jn.00495.2018
  53. Jazmati D., Neubacher U., Funke K. (2018): Neuropeptide Y as a possible homeostatic element for changes in cortical excitability induced by repetitive transcranial magnetic stimulation. Brain Stimulation 11(4):797-805. https://doi.org/10.1016/j.brs.2018.02.017
  54. Pafundo D., Miyamae T., Lewis D., Gonzalez-Burgos G. (2018): Presynaptic Effects of N-Methyl-D-Aspartate Receptors Enhance Parvalbumin Cell-Mediated Inhibition of Pyramidal Cells in Mouse Prefrontal Cortex. Biological Psychiatry 84(6):460-470. https://doi.org/10.1016/j.biopsych.2018.01.018
  55. Vignoud G., Venance L., Touboul J. (2018): Interplay of multiple pathways and activity-dependent rules in STDP. PLOS Computational Biology 14(8):e1006184. https://doi.org/10.1371/journal.pcbi.1006184
  56. Cabessa J., Villa A. (2018): Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters. Chaos: An Interdisciplinary Journal of Nonlinear Science 28(10). https://doi.org/10.1063/1.5042312
  57. Yamamoto K., Kobayashi M. (2018): Opposite Roles in Short-Term Plasticity for N-Type and P/Q-Type Voltage-Dependent Calcium Channels in GABAergic Neuronal Connections in the Rat Cerebral Cortex. The Journal of Neuroscience 38(46):9814-9828. https://doi.org/10.1523/JNEUROSCI.0337-18.2018
  58. Miska N., Richter L., Cary B., Gjorgjieva J., Turrigiano G. (2018): Sensory experience inversely regulates feedforward and feedback excitation-inhibition ratio in rodent visual cortex. eLife 7. https://doi.org/10.7554/eLife.38846
  59. Miska N., Richter L., Cary B., Gjorgjieva J., Turrigiano G. (2018): Sensory Deprivation Independently Regulates Neocortical Feedforward and Feedback Excitation-Inhibition Ratio. bioRxiv. https://doi.org/10.1101/283853
  60. Sammons R., Clopath C., Barnes S. (2018): Size-Dependent Axonal Bouton Dynamics following Visual Deprivation In Vivo. Cell Reports 22(3):576-584. https://doi.org/10.1016/j.celrep.2017.12.065
  61. Li Y., Fang Q., Zhang L., Tao H. (2018): Spatial Asymmetry and Short-Term Suppression Underlie Direction Selectivity of Synaptic Excitation in the Mouse Visual Cortex. Cerebral Cortex 28(6):2059-2070. https://doi.org/10.1093/cercor/bhx111
  62. Zhang Y., Magnus G., Han V. (2018): Cell type-specific plasticity at parallel fiber synapses onto Purkinje cells in the posterior caudal lobe of the mormyrid fish cerebellum. Journal of Neurophysiology 120(2):644-661. https://doi.org/10.1152/jn.00175.2018
  63. Alexandre F. Mendes (2017): Homo- et hétérosynaptique spike-timing-dependent plasticity aux synapses cortico- et thalamo-striatales. https://www.semanticscholar.org/paper/8b145a2e5a12d3da46f1fb77d48e3673bca64e21
  64. Krystal J., Anticevic A., Yang G., Dragoi G., Driesen N., Wang X., et al. (2017): Impaired Tuning of Neural Ensembles and the Pathophysiology of Schizophrenia: A Translational and Computational Neuroscience Perspective. Biological Psychiatry 81(10):874-885. https://doi.org/10.1016/j.biopsych.2017.01.004
  65. Pelkey K., Chittajallu R., Craig M., Tricoire L., Wester J., McBain C. (2017): Hippocampal GABAergic Inhibitory Interneurons. Physiological Reviews 97(4):1619-1747. https://doi.org/10.1152/physrev.00007.2017
  66. Lu M., Chen J., Chen C., Duann J., Ziemann U., Tsai C. (2017): Impaired Cerebellum to Primary Motor Cortex Associative Plasticity in Parkinson’s Disease and Spinocerebellar Ataxia Type 3. Frontiers in Neurology 8. https://doi.org/10.3389/fneur.2017.00445
  67. Rubin R., Abbott L., Sompolinsky H. (2017): Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity. Proceedings of the National Academy of Sciences 114(44). https://doi.org/10.1073/pnas.1705841114
  68. Duarte R., Morrison A. (2017): Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits. bioRxiv. https://doi.org/10.1101/230821
  69. Seeman S., Mogen B., Fetz E., Perlmutter S. (2017): Paired Stimulation for Spike-Timing-Dependent Plasticity in Primate Sensorimotor Cortex. The Journal of Neuroscience 37(7):1935-1949. https://doi.org/10.1523/JNEUROSCI.2046-16.2017
  70. Andrási T., Veres J., Rovira-Esteban L., Kozma R., Vikór A., Gregori E., et al. (2017): Differential excitatory control of 2 parallel basket cell networks in amygdala microcircuits. PLOS Biology 15(5):e2001421. https://doi.org/10.1371/journal.pbio.2001421
  71. D. Datta (2016): PYRAMIDAL CELLS: ROLE IN PRIMATE PREFRONTAL CORTEX CIRCUITRY DURING POSTNATAL DEVELOPMENT AND SCHIZOPHRENIA. https://www.semanticscholar.org/paper/f7d64f63c0d77e946c773c06c1490b163e54c17d
  72. Lewis D., Glausier J. (2016): Alterations in Prefrontal Cortical Circuitry and Cognitive Dysfunction in Schizophrenia. Nebraska Symposium on Motivation. https://doi.org/10.1007/978-3-319-30596-7_3
  73. Yang G., Murray J., Wang X. (2016): A dendritic disinhibitory circuit mechanism for pathway-specific gating. Nature Communications 7(1). https://doi.org/10.1038/ncomms12815
  74. He H., Shen W., Hiramoto M., Cline H. (2016): Experience-Dependent Bimodal Plasticity of Inhibitory Neurons in Early Development. Neuron 90(6):1203-1214. https://doi.org/10.1016/j.neuron.2016.04.044
  75. Lebida K., Mozrzymas J. (2016): Spike Timing-Dependent Plasticity in the Mouse Barrel Cortex Is Strongly Modulated by Sensory Learning and Depends on Activity of Matrix Metalloproteinase 9. Molecular Neurobiology 54(9):6723-6736. https://doi.org/10.1007/s12035-016-0174-y
  76. Novák O., Zelenka O., Hromádka T., Syka J. (2016): Immediate manifestation of acoustic trauma in the auditory cortex is layer specific and cell type dependent. Journal of Neurophysiology 115(4):1860-1874. https://doi.org/10.1152/jn.00810.2015
  77. Miao Q., Yao L., Rasch M., Ye Q., Li X., Zhang X. (2016): Selective Maturation of Temporal Dynamics of Intracortical Excitatory Transmission at the Critical Period Onset. Cell Reports 16(6):1677-1689. https://doi.org/10.1016/j.celrep.2016.07.013
  78. Mishra R., Kim S., Guzman S., Jonas P. (2016): Symmetric spike timing-dependent plasticity at CA3–CA3 synapses optimizes storage and recall in autoassociative networks. Nature Communications 7(1). https://doi.org/10.1038/ncomms11552
  79. R. Mishra, Roberto Calasso (2016): SYNAPTIC PLASTICITY RULES AT CA 3 ─ CA 3 RECURRENT SYNAPSES IN HIPPOCAMPUS. https://www.semanticscholar.org/paper/c723572c15fc5eec7ace3ef36a1d68c9fb7d16a5
  80. Stephanie C. Seeman (2016): Spike-timing dependent plasticity and connectivity in primate sensorimotor cortex. https://www.semanticscholar.org/paper/1d6205ef108f9d97df052b9a3287f5a7298264db
  81. Szegedi V., Paizs M., Csakvari E., Molnar G., Barzo P., Tamas G., et al. (2016): Plasticity in Single Axon Glutamatergic Connection to GABAergic Interneurons Regulates Complex Events in the Human Neocortex. PLOS Biology 14(11):e2000237. https://doi.org/10.1371/journal.pbio.2000237
  82. Gonzalez-Burgos G., Cho R., Lewis D. (2015): Alterations in cortical network oscillations and parvalbumin neurons in schizophrenia. Biological Psychiatry 77(12):1031-1040. https://doi.org/10.1016/j.biopsych.2015.03.010
  83. J. Oyrer (2015): Calcium-permeable AMPA receptors in layer 5 interneurons of the mouse visual cortex. https://www.semanticscholar.org/paper/5026e54158c5c5ec8d3df64b3dfbbafb81b678c7
  84. Martin Dalefield (2015): Temporal Dynamics of Cortical Adaptation. https://doi.org/10.25911/5D76327CE7F53
  85. Hiratani N., Fukai T. (2015): Mixed Signal Learning by Spike Correlation Propagation in Feedback Inhibitory Circuits. PLOS Computational Biology 11(4):e1004227. https://doi.org/10.1371/journal.pcbi.1004227
  86. Froemke R. (2015): Plasticity of cortical excitatory-inhibitory balance. Annual Review of Neuroscience 38(1):195-219. https://doi.org/10.1146/annurev-neuro-071714-034002
  87. Goudar V., Buonomano D. (2015): A model of order-selectivity based on dynamic changes in the balance of excitation and inhibition produced by short-term synaptic plasticity. Journal of Neurophysiology 113(2):509-523. https://doi.org/10.1152/jn.00568.2014
  88. Sun W., Wang L., Li S., Tie X., Jiang B. (2015): Layer‐specific endocannabinoid‐mediated long‐term depression of GABAergic neurotransmission onto principal neurons in mouse visual cortex. European Journal of Neuroscience 42(3):1952-1965. https://doi.org/10.1111/ejn.12958
  89. Li Y., Liu B., Chou X., Zhang L., Tao H. (2015): Strengthening of Direction Selectivity by Broadly Tuned and Spatiotemporally Slightly Offset Inhibition in Mouse Visual Cortex. Cerebral Cortex 25(9):2466-2477. https://doi.org/10.1093/cercor/bhu049
  90. Liu Y., Miao Q., Yuan J., Han S., Zhang P., Li S., et al. (2015): Ascl1 Converts Dorsal Midbrain Astrocytes into Functional Neurons In Vivo. Journal of Neuroscience 35(25):9336-9355. https://doi.org/10.1523/JNEUROSCI.3975-14.2015
  91. Clopath C. (2014): Long-Term Plasticity, Biophysical Models. Encyclopedia of Computational Neuroscience. https://doi.org/10.1007/978-1-4614-7320-6_351-1
  92. Ruan H., Saur T., Yao W. (2014): Dopamine-enabled anti-Hebbian timing-dependent plasticity in prefrontal circuitry. Frontiers in Neural Circuits 8. https://doi.org/10.3389/fncir.2014.00038
  93. I. Pot, PabloMéndez, A. Bacci (2014): Assortment of GABAergic Plasticity in the Cortical. https://www.semanticscholar.org/paper/bb3b011406a96ea06e3f46a341ddeec38295194a
  94. Lu J., Tucciarone J., Lin Y., Huang Z. (2014): Input-specific maturation of synaptic dynamics of parvalbumin interneurons in primary visual cortex. Proceedings of the National Academy of Sciences 111(47):16895-16900. https://doi.org/10.1073/pnas.1400694111
  95. Widloski J., Fiete I. (2014): A model of grid cell development through spatial exploration and spike time-dependent plasticity. Neuron 83(2):481-495. https://doi.org/10.1016/j.neuron.2014.06.018
  96. He L., Liu N., Cheng T., Chen X., Li Y., Shu Y., et al. (2014): Conditional deletion of Mecp2 in parvalbumin-expressing GABAergic cells results in the absence of critical period plasticity. Nature Communications 5(1). https://doi.org/10.1038/ncomms6036
  97. Duarte R., Morrison A. (2014): Dynamic stability of sequential stimulus representations in adapting neuronal networks. Frontiers in Computational Neuroscience 8. https://doi.org/10.3389/fncom.2014.00124
  98. Moreau A., Kullmann D. (2013): NMDA receptor-dependent function and plasticity in inhibitory circuits. Neuropharmacology 74:23-31. https://doi.org/10.1016/j.neuropharm.2013.03.004
  99. Blackman A., Abrahamsson T., Costa R., Lalanne T., Sjöström P. (2013): Target-cell-specific short-term plasticity in local circuits. Frontiers in Synaptic Neuroscience 5. https://doi.org/10.3389/fnsyn.2013.00011
  100. Scholl B., Tan A., Priebe N. (2013): Strabismus Disrupts Binocular Synaptic Integration in Primary Visual Cortex. The Journal of Neuroscience 33(43):17108-17122. https://doi.org/10.1523/JNEUROSCI.1831-13.2013
  101. C. Meunier (2013): Etude de la neuromodulation des réseaux neuronaux du cortex. https://www.semanticscholar.org/paper/db454f68827280b43be1a4cf901e9d38f35aa30a
  102. Shulz D., Feldman D. (2013): Spike Timing-Dependent Plasticity. Neural Circuit Development and Function in the Brain. https://doi.org/10.1016/B978-0-12-397267-5.00029-7
  103. G. Albieri (2013): The role of fast-spiking interneurons in cortical map plasticity. https://www.semanticscholar.org/paper/1c289475ae77d130f6842613b9b46ba0a0bc4f68
  104. Guillaume Hennequin (2013): Stability and amplification in plastic cortical circuits. https://doi.org/10.5075/EPFL-THESIS-5585
  105. K. Quast (2013): Functional Development and Plasticity of Parvalbumin Cells in Visual Cortex: Role of Thalamocortical Input. https://www.semanticscholar.org/paper/c9843c6bcc5e6c686f0f7a339c75443a02b44626
  106. O. Papp (2013): Excitatory synaptic inputs onto parvalbumin- positive perisomatic region-targeting interneurons in the hippocampus. https://www.semanticscholar.org/paper/3a9f99a4986dbfa034548a9b3c06ae852c8815f7
  107. Rylan S. Larsen (2013): Subtype-specific roles for presynaptic NMDA receptors in experience-dependent plasticity and visual cortical development. https://www.semanticscholar.org/paper/9d0d52473729cb6ced901da667574b908471e7c2
  108. Aton S., Broussard C., Dumoulin M., Seibt J., Watson A., Coleman T., et al. (2013): Visual experience and subsequent sleep induce sequential plastic changes in putative inhibitory and excitatory cortical neurons. Proceedings of the National Academy of Sciences 110(8):3101-3106. https://doi.org/10.1073/pnas.1208093110
  109. Huang S., Huganir R., Kirkwood A. (2013): Adrenergic Gating of Hebbian Spike-Timing-Dependent Plasticity in Cortical Interneurons. Journal of Neuroscience 33(32):13171-13178. https://doi.org/10.1523/JNEUROSCI.5741-12.2013
  110. Shao Y., Isett B., Miyashita T., Chung J., Pourzia O., Gasperini R., et al. (2013): Plasticity of recurrent l2/3 inhibition and gamma oscillations by whisker experience. Neuron 80(1):210-222. https://doi.org/10.1016/j.neuron.2013.07.026
  111. Knoblauch A., Hauser F., Gewaltig M., Körner E., Palm G. (2012): Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony?. Frontiers in Computational Neuroscience 6. https://doi.org/10.3389/fncom.2012.00055
  112. Lee C., Stoelzel C., Chistiakova M., Volgushev M. (2012): Heterosynaptic plasticity induced by intracellular tetanization in layer 2/3 pyramidal neurons in rat auditory cortex. The Journal of Physiology 590(10):2253-2271. https://doi.org/10.1113/jphysiol.2012.228247
  113. Feldman D. (2012): The spike-timing dependence of plasticity. Neuron 75(4):556-571. https://doi.org/10.1016/j.neuron.2012.08.001
  114. Kullmann D., Moreau A., Bakiri Y., Nicholson E. (2012): Plasticity of inhibition. Neuron 75(6):951-962. https://doi.org/10.1016/j.neuron.2012.07.030
  115. Rotaru D., Lewis D., Gonzalez-Burgos G. (2012): The role of glutamatergic inputs onto parvalbumin-positive interneurons: relevance for schizophrenia. Reviews in the Neurosciences 23(1). https://doi.org/10.1515/revneuro-2011-0059
  116. Phoka E., Wildie M., Schultz S., Barahona M. (2012): Sensory experience modifies spontaneous state dynamics in a large-scale barrel cortical model. Journal of Computational Neuroscience 33(2):323-339. https://doi.org/10.1007/s10827-012-0388-6
  117. Kameda H., Hioki H., Tanaka Y., Tanaka T., Sohn J., Sonomura T., et al. (2012): Parvalbumin‐producing cortical interneurons receive inhibitory inputs on proximal portions and cortical excitatory inputs on distal dendrites. European Journal of Neuroscience 35(6):838-854. https://doi.org/10.1111/j.1460-9568.2012.08027.x
  118. Muntean I., Joldos M. (2012): Grid Framework for Parallel Investigations of Spiking Neural Microcircuits. 2012 11th International Symposium on Parallel and Distributed Computing. https://doi.org/10.1109/ISPDC.2012.37
  119. Eto K., Ishibashi H., Yoshimura T., Watanabe M., Miyamoto A., Ikenaka K., et al. (2012): Enhanced GABAergic Activity in the Mouse Primary Somatosensory Cortex Is Insufficient to Alleviate Chronic Pain Behavior with Reduced Expression of Neuronal Potassium–Chloride Cotransporter. The Journal of Neuroscience 32(47):16552-16559. https://doi.org/10.1523/JNEUROSCI.2104-12.2012
  120. Gentet L. (2012): Functional diversity of supragranular GABAergic neurons in the barrel cortex. Frontiers in Neural Circuits 6. https://doi.org/10.3389/fncir.2012.00052
  121. Mayadevi M., Archana G., R. R., Omkumar R. (2012): Molecular Mechanisms in Synaptic Plasticity. Neuroscience – Dealing With Frontiers. https://doi.org/10.5772/36928
  122. Lu M., Tsai C., Ziemann U. (2012): Cerebellum to motor cortex paired associative stimulation induces bidirectional STDP-like plasticity in human motor cortex. Frontiers in Human Neuroscience 6. https://doi.org/10.3389/fnhum.2012.00260
  123. Povysheva N., Johnson J. (2012): Tonic NMDA receptor-mediated current in prefrontal cortical pyramidal cells and fast-spiking interneurons. Journal of Neurophysiology 107(8):2232-2243. https://doi.org/10.1152/jn.01017.2011
  124. Castillo P. (2012): Presynaptic LTP and LTD of excitatory and inhibitory synapses. Cold Spring Harbor Perspectives in Biology 4(2):a005728-a005728. https://doi.org/10.1101/cshperspect.a005728
  125. Fu Y., Wu X., Lu J., Huang Z. (2012): Presynaptic GABAB Receptor Regulates Activity-Dependent Maturation and Patterning of Inhibitory Synapses through Dynamic Allocation of Synaptic Vesicles. Frontiers in Cellular Neuroscience 6. https://doi.org/10.3389/fncel.2012.00057
  126. Péterfi Z., Urbán G., Papp O., Németh B., Monyer H., Szabó G., et al. (2012): Endocannabinoid-Mediated Long-Term Depression of Afferent Excitatory Synapses in Hippocampal Pyramidal Cells and GABAergic Interneurons. The Journal of Neuroscience 32(41):14448-14463. https://doi.org/10.1523/JNEUROSCI.1676-12.2012
  127. Benali A., Trippe J., Weiler E., Mix A., Petrasch-Parwez E., Girzalsky W., et al. (2011): Theta-Burst Transcranial Magnetic Stimulation Alters Cortical Inhibition. The Journal of Neuroscience 31(4):1193-1203. https://doi.org/10.1523/JNEUROSCI.1379-10.2011
  128. Coulon A., Beslon G., Soula H. (2011): Enhanced Stimulus Encoding Capabilities with Spectral Selectivity in Inhibitory Circuits by STDP. Neural Computation 23(4):882-908. https://doi.org/10.1162/NECO_a_00100
  129. Pikwer A. (2011): Depersonalization disorder may be related to glutamate receptor activation imbalance. Medical Hypotheses 77(4):593-594. https://doi.org/10.1016/j.mehy.2011.06.041
  130. Kullmann D., Lamsa K. (2011): LTP and LTD in cortical GABAergic interneurons: emerging rules and roles. Neuropharmacology 60(5):712-719. https://doi.org/10.1016/j.neuropharm.2010.12.020
  131. Rotaru D., Yoshino H., Lewis D., Ermentrout G., Gonzalez-Burgos G. (2011): Glutamate Receptor Subtypes Mediating Synaptic Activation of Prefrontal Cortex Neurons: Relevance for Schizophrenia. The Journal of Neuroscience 31(1):142-156. https://doi.org/10.1523/JNEUROSCI.1970-10.2011
  132. Tanaka D., Toriumi K., Kubo K., Nabeshima T., Nakajima K. (2011): GABAergic Precursor Transplantation into the Prefrontal Cortex Prevents Phencyclidine-Induced Cognitive Deficits. The Journal of Neuroscience 31(40):14116-14125. https://doi.org/10.1523/JNEUROSCI.2786-11.2011
  133. House D., Elstrott J., Koh E., Chung J., Feldman D. (2011): Parallel regulation of feedforward inhibition and excitation during whisker map plasticity. Neuron 72(5):819-831. https://doi.org/10.1016/j.neuron.2011.09.008
  134. Bender K., Trussell L. (2011): Synaptic plasticity in inhibitory neurons of the auditory brainstem. Neuropharmacology 60(5):774-779. https://doi.org/10.1016/j.neuropharm.2010.12.021
  135. O. Shor (2011): Target-cell-specific bidirectional synaptic plasticity at the CA1-subiculum synapse. https://www.semanticscholar.org/paper/7081117a3abd15c4c4cfc05a679474cc7c980bc9
  136. Méndez P., Bacci A. (2011): Assortment of GABAergic Plasticity in the Cortical Interneuron Melting Pot. Neural Plasticity 2011:1-14. https://doi.org/10.1155/2011/976856
  137. Sun Q., Zhang Z. (2011): Whisker experience modulates long‐term depression in neocortical γ‐aminobutyric acidergic interneurons in barrel cortex. Journal of Neuroscience Research 89(1):73-85. https://doi.org/10.1002/jnr.22530
  138. Srikanth Ramaswamy (2011): Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column. https://doi.org/10.5075/EPFL-THESIS-5208
  139. Gonzalez-Burgos G., Hashimoto T., Lewis D. (2010): Alterations of Cortical GABA Neurons and Network Oscillations in Schizophrenia. Current Psychiatry Reports 12(4):335-344. https://doi.org/10.1007/s11920-010-0124-8
  140. Gordon B Smith (2010): Requirement for AMPA receptor endocytosis and long-term depression in ocular dominance plasticity. https://www.semanticscholar.org/paper/adccab633b3ef1c21c5837d3d4c2935175e50c95
  141. Hennequin G., Gerstner W., Pfister J. (2010): STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission. Frontiers in Computational Neuroscience 4. https://doi.org/10.3389/fncom.2010.00143
  142. Wang H., Gao W. (2010): Development of calcium‐permeable AMPA receptors and their correlation with NMDA receptors in fast‐spiking interneurons of rat prefrontal cortex. The Journal of Physiology 588(15):2823-2838. https://doi.org/10.1113/jphysiol.2010.187591
  143. Carunchio I., Curcio L., Pieri M., Pica F., Caioli S., Viscomi M., et al. (2010): Increased levels of p70S6 phosphorylation in the G93A mouse model of Amyotrophic Lateral Sclerosis and in valine-exposed cortical neurons in culture. Experimental Neurology 226(1):218-230. https://doi.org/10.1016/j.expneurol.2010.08.033
  144. Schulz (2010): Cortico-Striatal Spike-Timing Dependent Plasticity After Activation of Subcortical Pathways. Frontiers in Synaptic Neuroscience. https://doi.org/10.3389/fnsyn.2010.00023
  145. Cui J., Wang F., Wang K., Xiang H. (2010): GABAergic Signaling Increases Through the Postnatal Development to Provide the Potent Inhibitory Capability for the Maturing Demands of the Prefrontal Cortex. Cellular and Molecular Neurobiology 30(4):543-555. https://doi.org/10.1007/s10571-009-9478-z
  146. Lamsa K. (2010): Spike-Timing Dependent Plasticity in Inhibitory Circuits. Frontiers in Synaptic Neuroscience. https://doi.org/10.3389/fnsyn.2010.00008
  147. Larsen R. (2010): STDP in the Developing Sensory Neocortex. Frontiers in Synaptic Neuroscience. https://doi.org/10.3389/fnsyn.2010.00009
  148. Zhang X., Poo M. (2010): Progress in neural plasticity. Science China Life Sciences 53(3):322-329. https://doi.org/10.1007/s11427-010-0062-z
  149. Xiaohui Zhang, Poo Mu-Ming (2010): Progress in neural plasticity. https://www.semanticscholar.org/paper/2435f68d513d7ccbeb7f675fe11be07e5e17c1af
  150. Frégnac Y., Pananceau M., René A., Huguet N., Marre O., Levy M., et al. (2010): A Re-Examination of Hebbian-Covariance Rules and Spike Timing-Dependent Plasticity in Cat Visual Cortex in vivo. Frontiers in Synaptic Neuroscience 2. https://doi.org/10.3389/fnsyn.2010.00147
  151. Claudia Clopath (2009): Synaptic plasticity across different time scales and its functional implications. https://doi.org/10.5075/EPFL-THESIS-4498
  152. Wang H., Gao W. (2009): Cell-type Specific Development of NMDA Receptors in the Interneurons of Rat Prefrontal Cortex. Neuropsychopharmacology 34(8):2028-2040. https://doi.org/10.1038/npp.2009.20
  153. Chen H., Jiang M., Akakin D., Roper S. (2009): Long-term potentiation of excitatory synapses on neocortical somatostatin-expressing interneurons. Journal of Neurophysiology 102(6):3251-3259. https://doi.org/10.1152/jn.00641.2009
  154. Behr J., Wozny C., Fidzinski P., Schmitz D. (2009): Synaptic plasticity in the subiculum. Progress in Neurobiology 89(4):334-342. https://doi.org/10.1016/j.pneurobio.2009.09.002
  155. Wu L., Li X., Chen T., Ren M., Zhuo M. (2009): Characterization of intracortical synaptic connections in the mouse anterior cingulate cortex using dual patch clamp recording. Molecular Brain 2(1). https://doi.org/10.1186/1756-6606-2-32
  156. Chistiakova M., Volgushev M. (2009): Heterosynaptic plasticity in the neocortex. Experimental Brain Research 199(3-4):377-390. https://doi.org/10.1007/s00221-009-1859-5
  157. Liguz‐Lecznar M., Waleszczyk W., Zakrzewska R., Skangiel‐Kramska J., Kossut M. (2009): Associative pairing involving monocular stimulation selectively mobilizes a subclass of GABAergic interneurons in the mouse visual cortex. Journal of Comparative Neurology 516(6):482-492. https://doi.org/10.1002/cne.22129
  158. Zhang S., Xu M., Miao Q., Poo M., Zhang X. (2009): Endocannabinoid-Dependent Homeostatic Regulation of Inhibitory Synapses by Miniature Excitatory Synaptic Activities. The Journal of Neuroscience 29(42):13222-13231. https://doi.org/10.1523/JNEUROSCI.1710-09.2009
  159. T. Ellender (2009): Perisomatic-targeting interneurons control the initiation of hippocampal population bursts. https://www.semanticscholar.org/paper/8dd3e5e1e8d5303f64bb76685b769dd1059076a3
  160. Yazaki-Sugiyama Y., Kang S., Câteau H., Fukai T., Hensch T. (2009): Bidirectional plasticity in fast-spiking GABA circuits by visual experience. Nature 462(7270):218-221. https://doi.org/10.1038/nature08485
  161. Freudenburg Z., Ghosh B., Ulinski P. (2009): Synaptic Adaptation and Sustained Generation of Waves in a Model of Turtle Visual Cortex. IEEE Transactions on Biomedical Engineering 56(5):1277-1286. https://doi.org/10.1109/TBME.2008.2010134
  162. Morrison A., Diesmann M., Gerstner W. (2008): Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics 98(6):459-478. https://doi.org/10.1007/s00422-008-0233-1
  163. Gonzalez-Burgos G., Lewis D. (2008): GABA neurons and the mechanisms of network oscillations: implications for understanding cortical dysfunction in schizophrenia. Schizophrenia Bulletin 34(5):944-961. https://doi.org/10.1093/schbul/sbn070
  164. Herrn Dipl, Phys Henning Sprekeler, Münster Westfalen, H C Christoph Markschies, Limberg Christian, Gutachter, et al. (2008): Slowness Learning: Mathematical Approaches and Synaptic Mechanisms. https://www.semanticscholar.org/paper/99450b32d07938ebcd62b5e2225bf921070f0f22
  165. Sjöström P., Rancz E., Roth A., Häusser M. (2008): Dendritic excitability and synaptic plasticity. Physiological Reviews 88(2):769-840. https://doi.org/10.1152/physrev.00016.2007
  166. Fidzinski P., Shor O., Behr J. (2008): Target‐cell‐specific bidirectional synaptic plasticity at hippocampal output synapses. European Journal of Neuroscience 27(5):1111-1118. https://doi.org/10.1111/j.1460-9568.2008.06089.x
  167. R. C. Gerkin (2008): Synaptic plasticity and Hebbian cell assemblies. https://www.semanticscholar.org/paper/f84df510c3448ef9e50322907b75870c74595ee2
  168. Ma Y. (2008): Properties and function of somatostatin-containing inhibitory interneurons in the somatosensory cortex of the mouse. https://doi.org/10.33915/ETD.4400
  169. E. By (): Dopamine-enabled anti-Hebbian timing-dependent plasticity in prefrontal circuitry. https://www.semanticscholar.org/paper/992ef0caeeb1ed6defe4f271e2d077d8b17dc742
  170. Gordon B Smith, M. F. Bear, Jay Gibson (): Frontiers in Cellular Neuroscience Cellular Neuroscience. https://www.semanticscholar.org/paper/71883b56da539e8cb3a6c5ff03bce4327a54427f
  171. Guillaume Hennequin, W. Gerstner, Jean-Pascal Pfister (): STDP in adaptive neurons gives close-to-optimal information transmission. https://www.semanticscholar.org/paper/bc45992dcf526961c63e0b9060a79daac15ea29d
  172. Norman Lu, R. Peña (): A Proposed Cause, Cure, and Mechanism for Focal Task-Specific Dystonia: A Theoretical-Empirical Approach. https://www.semanticscholar.org/paper/1e6b6bc705ac7f64e84b624767d51fdcdbd6c1a8

Menzel R, Brembs B, Giurfa M. (2007): Cognition in Invertebrates. In Evolution of Nervous Systems pp. 403–442. Elsevier.

  1. Kuklovsky V., Avarguès-Weber A., Giurfa M., Scheiner R. (2026): Visual learning performance in free-flying honey bees is independent of sucrose and light responsiveness and depends on training context. Scientific Reports 16(1). https://doi.org/10.1038/s41598-025-34900-9
  2. Fenli E., Mert Ö., Aksoy V. (2025): The Role of Associative Learning in Ant Learning and Memory. Journal of Insect Behavior 38(1). https://doi.org/10.1007/s10905-025-09871-4
  3. Notar J., Go M., Johnsen S. (2023): Learning without a brain: classical conditioning in the ophiuroid Ophiocoma echinata. Behavioral Ecology and Sociobiology 77(11). https://doi.org/10.1007/s00265-023-03402-x
  4. Menzel R. (2023): Navigation and dance communication in honeybees: a cognitive perspective. Journal of Comparative Physiology A 209(4):515-527. https://doi.org/10.1007/s00359-023-01619-9
  5. Winsor A., Pagoti G., Daye D., Cheries E., Cave K., Jakob E. (2021): What gaze direction can tell us about cognitive processes in invertebrates. Biochemical and Biophysical Research Communications 564:43-54. https://doi.org/10.1016/j.bbrc.2020.12.001
  6. Warwick C., Steedman C. (2021): Wildlife-pet markets in a one-health context. International Journal of One Health 7(1):42-64. https://doi.org/10.14202/IJOH.2021.42-64
  7. Romano D., Rossetti G., Stefanini C. (2021): Learning on a chip: Towards the development of trainable biohybrid sensors by investigating cognitive processes in non-marine Ostracoda via a miniaturised analytical system. Biosystems Engineering 213:162-174. https://doi.org/10.1016/j.biosystemseng.2021.11.004
  8. Yael Ariena, Arnon Dagb, Shlomi Zarchina, Tania Mascia, Sharoni Shafira (2021): Correction for Arien et al., Omega-3 deficiency impairs honey bee learning. Proceedings of the National Academy of Sciences 118(35). https://doi.org/10.1073/pnas.2112851118
  9. M. Giurfa (2020): The Mechanisms of Insect Cognition. Frontiers Research Topics. https://doi.org/10.3389/978-2-88963-490-3
  10. Jin N., Paffhausen B., Duer A., Menzel R. (2020): Mushroom Body Extrinsic Neurons in Walking Bumblebees Correlate With Behavioral States but Not With Spatial Parameters During Exploratory Behavior. Frontiers in Behavioral Neuroscience 14. https://doi.org/10.3389/fnbeh.2020.590999
  11. Menzel R. (2020): A short history of studies on intelligence and brain in honeybees. Apidologie 52(1):23-34. https://doi.org/10.1007/s13592-020-00794-x
  12. Anton S., Rössler W. (2020): Plasticity and modulation of olfactory circuits in insects. Cell and Tissue Research 383(1):149-164. https://doi.org/10.1007/s00441-020-03329-z
  13. Schilling M., Cruse H. (2019): Decentralized control of insect walking: A simple neural network explains a wide range of behavioral and neurophysiological results. PLOS Computational Biology 16(4):e1007804. https://doi.org/10.1371/journal.pcbi.1007804
  14. Schilling M. (2019): Setup of a Recurrent Neural Network as a Body Model for Solving Inverse and Forward Kinematics as well as Dynamics for a Redundant Manipulator. 2019 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN.2019.8851783
  15. Gorostiza E. (2018): Does Cognition Have a Role in Plasticity of “Innate Behavior”? A Perspective From Drosophila. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.01502
  16. Haenicke J., Yamagata N., Zwaka H., Nawrot M., Menzel R. (2018): Neural Correlates of Odor Learning in the Presynaptic Microglomerular Circuitry in the Honeybee Mushroom Body Calyx. eneuro 5(3):ENEURO.0128-18.2018. https://doi.org/10.1523/ENEURO.0128-18.2018
  17. Carvell G., Jackson R., Cross F. (2017): Ontogenetic shift in plant-related cognitive specialization by a mosquito-eating predator. Behavioural Processes 138:105-122. https://doi.org/10.1016/j.beproc.2017.02.022
  18. Domjan M., Krause M. (2017): Generality of the Laws of Learning: From Biological Constraints to Ecological Perspectives. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/B978-0-12-809324-5.21012-2
  19. Schilling M., Cruse H. (2017): ReaCog, a Minimal Cognitive Controller Based on Recruitment of Reactive Systems. Frontiers in Neurorobotics 11. https://doi.org/10.3389/fnbot.2017.00003
  20. Menzel R. (2017): Navigation and Communication in Insects. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/B978-0-12-809324-5.21018-3
  21. Falibene A., Roces F., Rössler W. (2015): Long-term avoidance memory formation is associated with a transient increase in mushroom body synaptic complexes in leaf-cutting ants. Frontiers in Behavioral Neuroscience 9. https://doi.org/10.3389/fnbeh.2015.00084
  22. Service E., Plowright C. (2015): Food restriction and threat of predation affect visual pattern choices by flower-naïve bumblebees. Learning and Motivation 50:3-10. https://doi.org/10.1016/J.LMOT.2014.10.006
  23. Tew E., Adamson A., Hesselberg T. (2015): The web repair behaviour of an orb spider. Animal Behaviour 103:137-146. https://doi.org/10.1016/J.ANBEHAV.2015.02.016
  24. Roth G. (2015): Convergent evolution of complex brains and high intelligence. Philosophical Transactions of the Royal Society B: Biological Sciences 370(1684):20150049. https://doi.org/10.1098/rstb.2015.0049
  25. Filla I., Menzel R. (2015): Mushroom body extrinsic neurons in the honeybee (Apis mellifera) brain integrate context and cue values upon attentional stimulus selection. Journal of Neurophysiology 114(3):2005-2014. https://doi.org/10.1152/jn.00776.2014
  26. M. Tricot, R. Cammaerts (2015): Are ants (Hymenoptera, Formicidae) capable of self recognition ?. https://www.semanticscholar.org/paper/8faa120324a9b0c9bec479502e876a189fbc282a
  27. Arien Y., Dag A., Zarchin S., Masci T., Shafir S. (2015): Omega-3 deficiency impairs honey bee learning. Proceedings of the National Academy of Sciences 112(51):15761-15766. https://doi.org/10.1073/pnas.1517375112
  28. H. Cruse, M. Schilling (2014): Mental States as Emergent Properties: From Walking to Consciousness. https://doi.org/10.15502/9783958570436
  29. Cheeseman J., Millar C., Greggers U., Lehmann K., Pawley M., Gallistel C., et al. (2014): Way-finding in displaced clock-shifted bees proves bees use a cognitive map. Proceedings of the National Academy of Sciences 111(24):8949-8954. https://doi.org/10.1073/pnas.1408039111
  30. E. Pamir (2013): From behavioral plasticity to neuronal computation: An investigation of associative learning in the honeybee brain. https://doi.org/10.17169/REFUBIUM-16879
  31. Cruse H., Schilling M. (2013): How and to what end may consciousness contribute to action? Attributing properties of consciousness to an embodied, minimally cognitive artificial neural network. Frontiers in Psychology 4. https://doi.org/10.3389/fpsyg.2013.00324
  32. Eckstein M., Mack S., Liston D., Bogush L., Menzel R., Krauzlis R. (2013): Rethinking human visual attention: spatial cueing effects and optimality of decisions by honeybees, monkeys and humans. Vision Research 85:5-19. https://doi.org/10.1016/j.visres.2012.12.011
  33. Giurfa M., Menzel R. (2013): Cognitive Components of Insect Behavior. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/B978-0-12-415823-8.00003-4
  34. Hussaini S., Menzel R. (2013): Mushroom Body Extrinsic Neurons in the Honeybee Brain Encode Cues and Contexts Differently. The Journal of Neuroscience 33(17):7154-7164. https://doi.org/10.1523/JNEUROSCI.1331-12.2013
  35. Matsumoto C., Matsumoto Y., Watanabe H., Nishino H., Mizunami M. (2012): Context-dependent olfactory learning monitored by activities of salivary neurons in cockroaches. Neurobiology of Learning and Memory 97(1):30-36. https://doi.org/10.1016/j.nlm.2011.08.010
  36. M. Duijn (2012): The biocognitive spectrum : biological cognition as variations on sensorimotor coordination. https://www.semanticscholar.org/paper/23f00a741951e2e958e963bd81af35fb42af3d22
  37. Giurfa M., Sandoz J. (2012): Invertebrate learning and memory: Fifty years of olfactory conditioning of the proboscis extension response in honeybees. Learning & Memory 19(2):54-66. https://doi.org/10.1101/lm.024711.111
  38. Carazo P., Fernández-Perea R., Font E. (2012): Quantity Estimation Based on Numerical Cues in the Mealworm Beetle (Tenebrio molitor). Frontiers in Psychology 3. https://doi.org/10.3389/fpsyg.2012.00502
  39. Jeanson R., Dussutour A., Fourcassié V. (2012): Key Factors for the Emergence of Collective Decision in Invertebrates. Frontiers in Neuroscience 6. https://doi.org/10.3389/fnins.2012.00121
  40. Tomina Y., Takahata M. (2012): Discrimination learning with light stimuli in restrained American lobster. Behavioural Brain Research 229(1):91-105. https://doi.org/10.1016/j.bbr.2011.12.044
  41. Vinauger C., Buratti L., Lazzari C. (2011): Learning the way to blood: first evidence of dual olfactory conditioning in a blood-sucking insect, Rhodnius prolixus. II. Aversive learning. Journal of Experimental Biology 214(18):3039-3045. https://doi.org/10.1242/jeb.057075
  42. Vinauger C., Buratti L., Lazzari C. (2011): Learning the way to blood: first evidence of dual olfactory conditioning in a blood-sucking insect, Rhodnius prolixus. I. Appetitive learning. Journal of Experimental Biology 214(18):3032-3038. https://doi.org/10.1242/jeb.056697
  43. Pamir E., Chakroborty N., Stollhoff N., Gehring K., Antemann V., Morgenstern L., et al. (2011): Average group behavior does not represent individual behavior in classical conditioning of the honeybee. Learning & Memory 18(11):733-741. https://doi.org/10.1101/lm.2232711
  44. E. Pamir, N. Chakroborty, N. Stollhoff, K. B. Gehring, Victoria Antemann, Laura Morgenstern, et al. (2011): classical conditioning of the honeybee Average group behavior does not represent individual behavior in. https://www.semanticscholar.org/paper/eab8744e2ccf92194914f46025e9d112a2b4ee37
  45. Cruse H., Wehner R. (2011): An Insect-Inspired, Decentralized Memory for Robot Navigation. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-25489-5_7
  46. Cruse H., Wehner R. (2011): No Need for a Cognitive Map: Decentralized Memory for Insect Navigation. PLoS Computational Biology 7(3):e1002009. https://doi.org/10.1371/journal.pcbi.1002009
  47. H. Cruse, M. Schilling (2011): From egocentric systems to systems allowing for Theory of Mind and mutualism. European Conference on Artificial Life. https://www.semanticscholar.org/paper/fc898848f2942f26a1acc098067d13d1d371bab0
  48. Bar-Shai N., Keasar T., Shmida A. (2011): The use of numerical information by bees in foraging tasks. Behavioral Ecology 22(2):317-325. https://doi.org/10.1093/BEHECO/ARQ206
  49. Abramson C., Nolf S., Mixson T., Wells H. (2010): Can Honey Bees Learn the Removal of a Stimulus as a Conditioning Cue. Ethology 116(9):843-854. https://doi.org/10.1111/J.1439-0310.2010.01796.X
  50. Roussel (2010): Searching for Learning-Dependent Changes in the Antennal Lobe: Simultaneous Recording of Neural Activity and Aversive Olfactory Learning in Honeybees. Frontiers in Behavioral Neuroscience. https://doi.org/10.3389/fnbeh.2010.00155
  51. Edith Roussel, J. Sandoz, Martin Giurfa (2010): Searching for learning-dependent changes in the antennal lobe : simultaneous recording of neural activity and aversive olfactory learning in honeybees. https://www.semanticscholar.org/paper/5b339fc6c65173a3e7cc8260e85b23fc9e4e7a49
  52. Giurfa M., Avarguès-Weber A., Menzel R. (2010): Non-Elemental Learning in Invertebrates. Encyclopedia of Animal Behavior. https://doi.org/10.1016/B978-0-08-045337-8.00095-4
  53. Gal R., Libersat F. (2010): On predatory wasps and zombie cockroaches. Communicative & Integrative Biology 3(5):458-461. https://doi.org/10.4161/cib.3.5.12472
  54. Tomina Y., Takahata M. (2010): A behavioral analysis of force-controlled operant tasks in American lobster. Physiology & Behavior 101(1):108-116. https://doi.org/10.1016/j.physbeh.2010.04.023
  55. Bozkurt A., Gilmour R., Sinha A., Stern D., Lal A. (2009): Insect–Machine Interface Based Neurocybernetics. IEEE Transactions on Biomedical Engineering 56(6):1727-1733. https://doi.org/10.1109/TBME.2009.2015460
  56. Abramson C. (2009): A study in inspiration: Charles Henry Turner (1867–1923) and the investigation of insect behavior. Annual Review of Entomology 54(1):343-359. https://doi.org/10.1146/annurev.ento.54.110807.090502
  57. van den Berg M. (2009): Tailor-made memory: natural differences in associative olfactory learning in two closely related wasp species. https://doi.org/10.18174/7158
  58. Garzón P., Keijzer F. (2009): Cognition in Plants. Signaling and Communication in Plants. https://doi.org/10.1007/978-3-540-89230-4_13
  59. Josens R., Eschbach C., Giurfa M. (2009): Differential conditioning and long-term olfactory memory in individual Camponotus fellah ants. Journal of Experimental Biology 212(12):1904-1911. https://doi.org/10.1242/jeb.030080
  60. Menzel R. (2009): Serial Position Learning in Honeybees. PLoS ONE 4(3):e4694. https://doi.org/10.1371/journal.pone.0004694
  61. R. Wehner (2009): The architecture of the desert ant’s navigational toolkit (Hymenoptera: Formicidae). https://www.semanticscholar.org/paper/646f56fa27768eed23de390c069a0ea6a10a3077
  62. Unknown authors (2008): Author ‘ s personal copy Insect Minds For Human Minds.
  63. A. Filip (2008): Protein translation processes during long-term memory formation in the honeybee (Apis mellifera L.). https://www.semanticscholar.org/paper/c9b47cbae1104edae1b70d5ad525d02e24e63924
  64. M. Platt, Rapporteur Peter Dayan, S. Dehaene, K. McCabe, R. Menzel, E. Phelps, et al. (2008): 6 Neuronal Correlates of Decision Making. https://www.semanticscholar.org/paper/f04d66d88168f72ef5cddb51e52fc204e067fd1c
  65. Platt M., Dayan P., Dehaene S., McCabe K., Menzel R., Phelps E., et al. (2008): Neuronal Correlates of Decision Making. Better Than Conscious?. https://doi.org/10.7551/MITPRESS/9780262195805.003.0006
  66. Carazo P., Font E., Forteza-Behrendt E., Desfilis E. (2008): Quantity discrimination in Tenebrio molitor: evidence of numerosity discrimination in an invertebrate?. Animal Cognition 12(3):463-470. https://doi.org/10.1007/s10071-008-0207-7
  67. De Marco R., Menzel R. (2008): 1.25 – Learning and Memory in Communication and Navigation in Insects. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/B978-012370509-9.00085-1
  68. Menzel R. (2008): Insect Minds For Human Minds. Advances in Psychology. https://doi.org/10.1016/S0166-4115(08)10022-X
  69. Okada R., Rybak J., Manz G., Menzel R. (2007): Learning-Related Plasticity in PE1 and Other Mushroom Body-Extrinsic Neurons in the Honeybee Brain. The Journal of Neuroscience 27(43):11736-11747. https://doi.org/10.1523/JNEUROSCI.2216-07.2007
  70. van Duijn M., Keijzer F., Franken D. (2006): Principles of Minimal Cognition: Casting Cognition as Sensorimotor Coordination. Adaptive Behavior 14(2):157-170. https://doi.org/10.1177/105971230601400207

Brembs B, Wiener J. (2006): Context generalization and occasion setting in Drosophila visual learning. Learn. Mem. 13:618–628.

  1. van Swinderen B. (2025): Alcohol use: Passing out has long-term effects on sleep. Current Biology 35(5):R191-R193. https://doi.org/10.1016/j.cub.2025.01.051
  2. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  3. Lafon G., Geng H., Avarguès-Weber A., Buatois A., Massou I., Giurfa M. (2022): The Neural Signature of Visual Learning Under Restrictive Virtual-Reality Conditions. Frontiers in Behavioral Neuroscience 16. https://doi.org/10.3389/fnbeh.2022.846076
  4. Rohrsen C., Kumpf A., Semiz K., Aydin F., deBivort B., Brembs B. (2021): Pain is so close to pleasure: the same dopamine neurons can mediate approach and avoidance in Drosophila. bioRxiv. https://doi.org/10.1101/2021.10.04.463010
  5. Mesich J., Reynolds A., Liu M., Laberge F. (2021): Recovery-from-extinction effects in an anuran amphibian: renewal effect, but no reinstatement. Animal Cognition 25(2):359-368. https://doi.org/10.1007/s10071-021-01558-5
  6. B. Brembs, Ottavia Palazzo (2020): Molecular and behavioral study of the FoxP locus in Drosophila melanogaster. https://www.semanticscholar.org/paper/bf0849db3033b893abc7420598124e0ecbd2f881
  7. Eschbach C., Fushiki A., Winding M., Schneider-Mizell C., Shao M., Arruda R., et al. (2020): Recurrent architecture for adaptive regulation of learning in the insect brain. Nature Neuroscience 23(4):544-555. https://doi.org/10.1038/s41593-020-0607-9
  8. Grabowska M., Jeans R., Steeves J., van Swinderen B. (2020): Oscillations in the central brain of Drosophila are phase locked to attended visual features. Proceedings of the National Academy of Sciences 117(47):29925-29936. https://doi.org/10.1073/pnas.2010749117
  9. Durrieu M., Wystrach A., Arrufat P., Giurfa M., Isabel G. (2020): Fruit flies can learn non-elemental olfactory discriminations. Proceedings of the Royal Society B: Biological Sciences 287(1938):20201234. https://doi.org/10.1098/rspb.2020.1234
  10. Czaczkes T., Kumar P. (2020): Very rapid multi-odour discrimination learning in the ant Lasius niger. Insectes Sociaux 67(4):541-545. https://doi.org/10.1007/s00040-020-00787-0
  11. Eschbach C., Fushiki A., Winding M., Schneider-Mizell C., Shao M., Arruda R., et al. (2019): Multilevel feedback architecture for adaptive regulation of learning in the insect brain. bioRxiv. https://doi.org/10.1101/649731
  12. Merritt D., Melkis J., Kwok B., Tran C., van der Kooy D. (2019): Analysis of Mutants Suggests Kamin Blocking in C. elegans is Due to Interference with Memory Recall Rather than Storage. Scientific Reports 9(1). https://doi.org/10.1038/s41598-019-38939-3
  13. Fraser K., Holland P. (2019): Occasion Setting. Behavioral Neuroscience 133(2):145-175. https://doi.org/10.1037/bne0000306
  14. A. Buatois (2018): Etudes comportementales et neurobiologiques de l’apprentissage visuel chez l’abeille (Apis mellifera) en réalité virtuelle. https://www.semanticscholar.org/paper/2cfc95f35267912cbd2050ec4e7001004b8d9b70
  15. Alexis Buatois (2018): Behavioral and neurobiological studies of visual learning in honey bees (Apis mellifera) in virtual reality. https://www.semanticscholar.org/paper/176fb2176c8703d70bf8e61849abb8d256ed1668
  16. Grabowska M., Steeves J., Alpay J., van de Poll M., Ertekin D., van Swinderen B. (2018): Innate visual preferences and behavioral flexibility in Drosophila. Journal of Experimental Biology. https://doi.org/10.1242/jeb.185918
  17. Santos-Pata D., Escuredo A., Mathews Z., Verschure P. (2017): Insect behavioral evidence of spatial memories during environmental reconfiguration. bioRxiv. https://doi.org/10.1101/118687
  18. Kirkerud N., Schlegel U., Giovanni Galizia C. (2017): Aversive Learning of Colored Lights in Walking Honeybees. Frontiers in Behavioral Neuroscience 11. https://doi.org/10.3389/fnbeh.2017.00094
  19. Schultheiss P., Buatois A., Avarguès-Weber A., Giurfa M. (2017): Using virtual reality to study visual performances of honeybees. Current Opinion in Insect Science 24:43-50. https://doi.org/10.1016/j.cois.2017.08.003
  20. Brembs B. (2016): Operant Behavior in Model Systems. bioRxiv. https://doi.org/10.1101/058719
  21. Liu Q., Yang X., Tian J., Gao Z., Wang M., Li Y., et al. (2016): Gap junction networks in mushroom bodies participate in visual learning and memory in Drosophila. eLife 5. https://doi.org/10.7554/eLife.13238
  22. Farris S. (2016): Insect societies and the social brain. Current Opinion in Insect Science 15:1-8. https://doi.org/10.1016/j.cois.2016.01.010
  23. Ravi S., Garcia J., Wang C., Dyer A. (2016): The answer is blowing in the wind: free-flying honeybees can integrate visual and mechano-sensory inputs for making complex foraging decisions. Journal of Experimental Biology. https://doi.org/10.1242/jeb.142679
  24. Avarguès-Weber A., Lihoreau M., Isabel G., Giurfa M. (2015): Information transfer beyond the waggle dance: observational learning in bees and flies. Frontiers in Ecology and Evolution 3. https://doi.org/10.3389/fevo.2015.00024
  25. Heisenberg M. (2015): Outcome learning, outcome expectations, and intentionality in Drosophila. Learning & Memory 22(6):294-298. https://doi.org/10.1101/lm.037481.114
  26. Van De Poll M., Zajaczkowski E., Taylor G., Srinivasan M., van Swinderen B. (2015): Using an abstract geometry in virtual reality to explore choice behaviour: visual flicker preferences in honeybees. Journal of Experimental Biology. https://doi.org/10.1242/jeb.125138
  27. Farris S., Van Dyke J. (2015): Evolution and function of the insect mushroom bodies: contributions from comparative and model systems studies. Current Opinion in Insect Science 12:19-25. https://doi.org/10.1016/J.COIS.2015.08.006
  28. Kottler B., van Swinderen B. (2014): Taking a new look at how flies learn. eLife 3. https://doi.org/10.7554/eLife.03978
  29. Benjamin Kottler, B. Swinderen, C. Schnaitmann, S. Knapek (2014): involve distinct parts of the brain, as was previously thought. https://www.semanticscholar.org/paper/601309a2588b445830a1303206e74cb9514d1b7c
  30. C. Schnaitmann (2014): Neural circuits underlying colour vision and visual memory in Drosophila melanogaster. https://www.semanticscholar.org/paper/a1fe1f36cf811f1380fcae7125f0057ccf83ab19
  31. Vogt K., Schnaitmann C., Dylla K., Knapek S., Aso Y., Rubin G., et al. (2014): Shared mushroom body circuits underlie visual and olfactory memories in Drosophila. eLife 3. https://doi.org/10.7554/eLife.02395
  32. Guo A., Lu H., Zhang K., Ren Q., Chiang Wong Y. (2013): Visual Learning and Decision Making in Drosophila melanogaster. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/B978-0-12-415823-8.00028-9
  33. Perry C., Barron A., Cheng K. (2013): Invertebrate learning and cognition: relating phenomena to neural substrate. WIREs Cognitive Science 4(5):561-582. https://doi.org/10.1002/wcs.1248
  34. Zhang X., Ren Q., Guo A. (2013): Parallel Pathways for Cross-Modal Memory Retrieval in Drosophila. The Journal of Neuroscience 33(20):8784-8793. https://doi.org/10.1523/JNEUROSCI.4631-12.2013
  35. Diogo Pata (2012): InsectArcade : A hybrid mixed reality insect-robot system for the study of insect multi-modal navigation. https://www.semanticscholar.org/paper/15987a4939d1505d612a8f751c1cd3a484c12f49
  36. Fustiñana M., Carbó Tano M., Romano A., Pedreira M. (2012): Contextual Pavlovian conditioning in the crab Chasmagnathus. Animal Cognition 16(2):255-272. https://doi.org/10.1007/s10071-012-0570-2
  37. Ren Q., Li H., Wu Y., Ren J., Guo A. (2012): A GABAergic Inhibitory Neural Circuit Regulates Visual Reversal Learning in Drosophila. The Journal of Neuroscience 32(34):11524-11538. https://doi.org/10.1523/JNEUROSCI.0827-12.2012
  38. Tomina Y., Takahata M. (2012): Discrimination learning with light stimuli in restrained American lobster. Behavioural Brain Research 229(1):91-105. https://doi.org/10.1016/j.bbr.2011.12.044
  39. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  40. van Swinderen B. (2011): Attention in Drosophila. International Review of Neurobiology. https://doi.org/10.1016/B978-0-12-387003-2.00003-3
  41. Wessnitzer J., Young J., Armstrong J., Webb B. (2011): A model of non-elemental olfactory learning in Drosophila. Journal of Computational Neuroscience 32(2):197-212. https://doi.org/10.1007/s10827-011-0348-6
  42. Young J., Wessnitzer J., Armstrong J., Webb B. (2011): Elemental and non-elemental olfactory learning in Drosophila. Neurobiology of Learning and Memory 96(2):339-352. https://doi.org/10.1016/j.nlm.2011.06.009
  43. Farris S. (2011): Are mushroom bodies cerebellum-like structures?. Arthropod Structure & Development 40(4):368-379. https://doi.org/10.1016/j.asd.2011.02.004
  44. Farris S., Schulmeister S. (2011): Parasitoidism, not sociality, is associated with the evolution of elaborate mushroom bodies in the brains of hymenopteran insects. Proceedings of the Royal Society B: Biological Sciences 278(1707):940-951. https://doi.org/10.1098/rspb.2010.2161
  45. Mota T., Giurfa M., Sandoz J. (2011): Color modulates olfactory learning in honeybees by an occasion-setting mechanism. Learning & Memory 18(3):144-155. https://doi.org/10.1101/lm.2073511
  46. H. L. Lau (2010): Chemosensory context conditioning in Caenorhabditis elegans. https://doi.org/10.14288/1.0071000
  47. Foucaud J., Burns J., Mery F. (2010): Use of Spatial Information and Search Strategies in a Water Maze Analog in Drosophila melanogaster. PLoS ONE 5(12):e15231. https://doi.org/10.1371/journal.pone.0015231
  48. Aimee Sue Dunlap-Lehtila (2009): Change and reliability in the evolution of learning and memory. https://www.semanticscholar.org/paper/6da5c8af1624a6c82f060367c9bfd6a231700cb8
  49. B. Röttger-Rössler, H. Markowitsch (2009): Emotions as bio-cultural processes. https://doi.org/10.1007/978-0-387-09546-2
  50. van Swinderen B., McCartney A., Kauffman S., Flores K., Agrawal K., Wagner J., et al. (2009): Shared Visual Attention and Memory Systems in the Drosophila Brain. PLoS ONE 4(6):e5989. https://doi.org/10.1371/journal.pone.0005989
  51. Seugnet L., Suzuki Y., Stidd R., Shaw P. (2009): Aversive phototaxic suppression: evaluation of a short‐term memory assay in Drosophila melanogaster. Genes, Brain and Behavior 8(4):377-389. https://doi.org/10.1111/j.1601-183X.2009.00483.x
  52. Casimir M. (2009): On the Origin and Evolution of Affective Capacities in Lower Vertebrates. Emotions as Bio-cultural Processes. https://doi.org/10.1007/978-0-387-09546-2_4
  53. Brembs B. (2008): Mushroom bodies regulate habit formation in Drosophila. Current Biology 19(16):1351-1355. https://doi.org/10.1016/j.cub.2009.06.014
  54. Bueno J., Holland P. (2008): Occasion setting in Pavlovian ambiguous target discriminations. Behavioural Processes 79(3):132-147. https://doi.org/10.1016/j.beproc.2008.07.001
  55. Tanaka N., Tanimoto H., Ito K. (2008): Neuronal assemblies of the Drosophila mushroom body. Journal of Comparative Neurology 508(5):711-755. https://doi.org/10.1002/cne.21692
  56. Maye A., Hsieh C., Sugihara G., Brembs B. (2007): Order in Spontaneous Behavior. PLoS ONE 2(5):e443. https://doi.org/10.1371/journal.pone.0000443
  57. van Swinderen B. (2007): Attention-like processes in Drosophila require short-term memory genes. Science 315(5818):1590-1593. https://doi.org/10.1126/SCIENCE.1137931
  58. M. Giurfa (2007): 12 Invertebrate Cognition: Nonelemental Learning beyond Simple Conditioning. https://doi.org/10.1101/087969819.49.281
  59. Peng Y., Xi W., Zhang W., Zhang K., Guo A. (2007): Experience Improves Feature Extraction in Drosophila. The Journal of Neuroscience 27(19):5139-5145. https://doi.org/10.1523/JNEUROSCI.0472-07.2007
  60. Young Jm, Armstrong Jd, Webb B (): Title : Elemental and non-elemental olfactory learning in Drosophila Running title : Olfactory learning in Drosophila. https://www.semanticscholar.org/paper/1a5d809950104ae9cda8ccfd608952940d793738

Brembs B, Hempel de Ibarra N. (2006): Different parameters support discrimination and generalization in Drosophila at the flight simulator. Learn. Mem. 13:629–637.

  1. Ehweiner A., Duch C., Brembs B. (2024): Wings of Change: aPKC/FoxP-dependent plasticity in steering motor neurons underlies operant self-learning in Drosophila. F1000Research 13:116. https://doi.org/10.12688/f1000research.146347.2
  2. Manoonpong P., Patanè L., Xiong X., Brodoline I., Dupeyroux J., Viollet S., et al. (2021): Insect-Inspired Robots: Bridging Biological and Artificial Systems. Sensors 21(22):7609. https://doi.org/10.3390/s21227609
  3. Whitehead M., Gaskett A., Johnson S. (2018): Floral community predicts pollinators’ color preference: implications for Batesian floral mimicry. Behavioral Ecology 30(1):213-222. https://doi.org/10.1093/beheco/ary138
  4. Marilia Fernandes Erickson, Daniel Marques de Almeida, Carlos Roberto Sorensen, D. D. Fonseca, M. Fernandes, Felipe Gawryszewski (2018): Coloração de flores na visão de polinizadores. https://www.semanticscholar.org/paper/a5ab6eecfe2254843689c3fe9d9edcc13fd0bd5d
  5. Marcus M., Burnham T., Stephens D., Dunlap A. (2018): Experimental evolution of color preference for oviposition in Drosophila melanogaster. Journal of Bioeconomics 20(1):125-140. https://doi.org/10.1007/S10818-017-9261-Z
  6. Ajuria-Ibarra H., Tapia-McClung H., Rao D. (2017): Mapping the variation in spider body colouration from an insect perspective. Evolutionary Ecology 31(5):663-681. https://doi.org/10.1007/s10682-017-9904-5
  7. Robledo-Ospina L., Escobar-Sarria F., Troscianko J., Rao D. (2017): Two ways to hide: predator and prey perspectives of disruptive coloration and background matching in jumping spiders. Biological Journal of the Linnean Society 122(4):752-764. https://doi.org/10.1093/BIOLINNEAN/BLX108
  8. Marcus M., Burnham T., Stephens D., Dunlap A. (2017): Experimental evolution of color preference for oviposition in Drosophila melanogaster. Journal of Bioeconomics 20(1):125-140. https://doi.org/10.1007/s10818-017-9261-z
  9. White T., Dalrymple R., Herberstein M., Kemp D. (2017): The perceptual similarity of orb-spider prey lures and flower colours. Evolutionary Ecology 31(1):1-20. https://doi.org/10.1007/s10682-016-9876-x
  10. Germain M., Blanchet S., Loyau A., Danchin É. (2016): Mate-choice copying in Drosophila melanogaster: Impact of demonstration conditions and male-male competition. Behavioural Processes 125:76-84. https://doi.org/10.1016/j.beproc.2016.02.002
  11. Cohen S., Benjamini Y., Golani I. (2015): Coping with Space Neophobia in Drosophila melanogaster: The Asymmetric Dynamics of Crossing a Doorway to the Untrodden. PLOS ONE 10(12):e0140207. https://doi.org/10.1371/journal.pone.0140207
  12. Mendoza E., Colomb J., Rybak J., Pflüger H., Zars T., Scharff C., et al. (2014): Drosophila FoxP Mutants Are Deficient in Operant Self-Learning. PLoS ONE 9(6):e100648. https://doi.org/10.1371/journal.pone.0100648
  13. Renoult J., Valido A., Jordano P., Schaefer H. (2014): Adaptation of flower and fruit colours to multiple, distinct mutualists. New Phytologist 201(2):678-686. https://doi.org/10.1111/nph.12539
  14. Kelly M., Gaskett A. (2014): UV reflectance but no evidence for colour mimicry in a putative brood-deceptive orchid Corybas cheesemanii. Current Zoology 60(1):104-113. https://doi.org/10.1093/CZOOLO/60.1.104
  15. Giurfa M., Menzel R. (2013): Cognitive Components of Insect Behavior. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/B978-0-12-415823-8.00003-4
  16. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  17. van Swinderen B. (2011): Attention in Drosophila. International Review of Neurobiology. https://doi.org/10.1016/B978-0-12-387003-2.00003-3
  18. B. Röttger-Rössler, H. Markowitsch (2009): Emotions as bio-cultural processes. https://doi.org/10.1007/978-0-387-09546-2
  19. Björn Brembs (2009): Mushroom Bodies Regulate Habit Formation in Drosophila. https://www.semanticscholar.org/paper/6fc194f9840e722e0b9e5c75fa6d864edc4c4d82
  20. Casimir M. (2009): On the Origin and Evolution of Affective Capacities in Lower Vertebrates. Emotions as Bio-cultural Processes. https://doi.org/10.1007/978-0-387-09546-2_4
  21. Brembs B. (2008): Mushroom bodies regulate habit formation in Drosophila. Current Biology 19(16):1351-1355. https://doi.org/10.1016/j.cub.2009.06.014
  22. Brembs B., Plendl W. (2008): Double dissociation of PKC and AC manipulations on operant and classical learning in Drosophila. Current Biology 18(15):1168-1171. https://doi.org/10.1016/j.cub.2008.07.041
  23. B. Brembs (2008): The neurobiology of operant learning: biophysical and molecular mechanisms in a hierarchical organization of multiple memory systems. https://www.semanticscholar.org/paper/e093d2a5cc2d2f3f0ffe7588f0b80e5e3993d330
  24. Brembs B. (2008): Operant Learning of Drosophila at the Torque Meter. Journal of Visualized Experiments. https://doi.org/10.3791/731
  25. Maye A., Hsieh C., Sugihara G., Brembs B. (2007): Order in Spontaneous Behavior. PLoS ONE 2(5):e443. https://doi.org/10.1371/journal.pone.0000443
  26. Peng Y., Xi W., Zhang W., Zhang K., Guo A. (2007): Experience Improves Feature Extraction in Drosophila. The Journal of Neuroscience 27(19):5139-5145. https://doi.org/10.1523/JNEUROSCI.0472-07.2007
  27. Brembs B., Wiener J. (2006): Context and occasion setting in Drosophila visual learning. Learning & Memory 13(5):618-628. https://doi.org/10.1101/LM.318606

Phillips AM, Smart R, Strauss R, Brembs B, Kelly LE. (2005): The Drosophila black enigma: the molecular and behavioural characterisation of the black1 mutant allele. Gene 351:131–142.

  1. Hopkins B., Barmina O., Wang X., Situ M., Bolanos H., Nie S., et al. (2026): Morphological innovation without gene co-option: the Drosophila sex comb evolved via changes in developmental tempo and energy metabolism. bioRxiv. https://doi.org/10.64898/2026.02.13.705815
  2. Samano A., Musat M., Junaghare M., Ahmad A., Ali M., Alves S., et al. (2025): Structural variants are enriched in deleterious visible phenotypes in Drosophila. bioRxiv. https://doi.org/10.1101/2025.08.15.670616
  3. Paulo D., Nguyen T., Ward C., Corpuz R., Kauwe A., Rendon P., et al. (2025): Functional genomics implicates ebony in the black pupae phenotype of tephritid fruit flies. Communications Biology 8(1). https://doi.org/10.1038/s42003-025-07489-y
  4. Paulo D., Nguyen T., Ward C., Corpuz R., Kauwe A., Rendon P., et al. (2024): The genetic basis of the black pupae phenotype in tephritid fruit flies. bioRxiv. https://doi.org/10.1101/2024.06.07.597636
  5. Gong L., Ma Y., Zhang M., Feng H., Zhou Y., Zhao Y., et al. (2024): The melanin pigment gene black mediates body pigmentation and courtship behaviour in the German cockroach Blattella germanica. Bulletin of Entomological Research 114(2):271-280. https://doi.org/10.1017/S0007485324000166
  6. Hopkins B., Barmina O., Kopp A. (2023): A single-cell atlas of the sexually dimorphic Drosophila foreleg and its sensory organs during development. PLOS Biology 21(6):e3002148. https://doi.org/10.1371/journal.pbio.3002148
  7. Hopkins B., Barmina O., Kopp A. (2022): Charting the development of Drosophila leg sensory organs at single-cell resolution. bioRxiv. https://doi.org/10.1101/2022.10.07.511357
  8. Brent C., Heu C., Gross R., Fan B., Langhorst D., Hull J. (2022): RNAi-Mediated Manipulation of Cuticle Coloration Genes in Lygus hesperus Knight (Hemiptera: Miridae). Insects 13(11):986. https://doi.org/10.3390/insects13110986
  9. Dean D., Deitcher D., Paster C., Xu M., Loehlin D. (2022): “A fly appeared”: sable, a classic Drosophila mutation, maps to Yippee, a gene affecting body color, wings, and bristles. G3 Genes|Genomes|Genetics 12(5). https://doi.org/10.1093/g3journal/jkac058
  10. Ze L., Jin L., Li G. (2022): Silencing of Adc and Ebony Causes Abnormal Darkening of Cuticle in Henosepilachna vigintioctopunctata. Frontiers in Physiology 13. https://doi.org/10.3389/fphys.2022.829675
  11. Hu Z., Tian Y., Yang J., Zhu Y., Zhou H., Zheng Y., et al. (2022): Research progress of l-aspartate-α-decarboxylase and its isoenzyme in the β-alanine synthesis. World Journal of Microbiology and Biotechnology 39(2). https://doi.org/10.1007/s11274-022-03483-2
  12. Alexandrov I., Alexandrova M. (2021): The dose-, LET-, and gene-dependent patterns of DNA changes underlying the point mutations in spermatozoa of Drosophila melanogaster. I. Autosomal gene black. Mutation Research – Fundamental and Molecular Mechanisms of Mutagenesis 823:111755. https://doi.org/10.1016/j.mrfmmm.2021.111755
  13. Chen J., Li W., Lyu J., Hu Y., Huang G., Zhang W. (2021): CRISPR/Cas9-mediated knockout of the NlCSAD gene results in darker cuticle pigmentation and a reduction in female fecundity in Nilaparvata lugens (Hemiptera: Delphacidae). Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 256:110921. https://doi.org/10.1016/j.cbpa.2021.110921
  14. Takahashi M., Okude G., Futahashi R., Takahashi Y., Kawata M. (2021): The effect of the doublesex gene in body colour masculinization of the damselfly Ischnura senegalensis. Biology Letters 17(6):20200761. https://doi.org/10.1098/rsbl.2020.0761
  15. Han R., Wei T., Tseng S., Lo C. (2021): Characterizing approach behavior of Drosophila melanogaster in Buridan’s paradigm. PLOS ONE 16(1):e0245990. https://doi.org/10.1371/journal.pone.0245990
  16. Unknown authors (2020): Sub-strains of CantonS differ markedly in their Drosophila locomotor behavior. https://www.semanticscholar.org/paper/4ca6d29238943bf8e700439ae9b5a7d685bdf720
  17. J. Colomb (2020): Sub-strains of CantonS differ markedly in Drosophila their locomotor behavior [ version 2 ; peer review : 3 approved ]. https://www.semanticscholar.org/paper/18576a13cad2b699502ef4acaa532e7b88dc6ef3
  18. Oppert B., Perkin L. (2019): RNAiSeq: How to See the Big Picture. Frontiers in Microbiology 10. https://doi.org/10.3389/fmicb.2019.02570
  19. Yen H., Han R., Lo C. (2019): Quantification of Visual Fixation Behavior and Spatial Orientation Memory in Drosophila melanogaster. Frontiers in Behavioral Neuroscience 13. https://doi.org/10.3389/fnbeh.2019.00215
  20. Mun S., Noh M., Kramer K., Muthukrishnan S., Arakane Y. (2019): Gene functions in adult cuticle pigmentation of the yellow mealworm, Tenebrio molitor. Insect Biochemistry and Molecular Biology 117:103291. https://doi.org/10.1016/j.ibmb.2019.103291
  21. Christie A., Stanhope M., Gandler H., Lameyer T., Pascual M., Shea D., et al. (2018): Molecular characterization of putative neuropeptide, amine, diffusible gas and small molecule transmitter biosynthetic enzymes in the eyestalk ganglia of the American lobster, Homarus americanus. Invertebrate Neuroscience 18(4). https://doi.org/10.1007/s10158-018-0216-4
  22. M. Qin, LI You-Ran, Shi Gui-yang (2018): Advances in molecular mechanism and modification of bacterial L-aspartate alpha-decarboxylase. https://www.semanticscholar.org/paper/bec083ef3a13351ad7cfb3300dbbb13499c8a9af
  23. Takahashi M., Takahashi Y., Kawata M. (2018): Candidate genes associated with color morphs of female-limited polymorphisms of the damselfly Ischnura senegalensis. Heredity 122(1):81-92. https://doi.org/10.1038/s41437-018-0076-z
  24. Deshmukh R., Baral S., Gandhimathi A., Kuwalekar M., Kunte K. (2018): Mimicry in butterflies: co‐option and a bag of magnificent developmental genetic tricks. WIREs Developmental Biology 7(1). https://doi.org/10.1002/wdev.291
  25. Perkin L., Gerken A., Oppert B. (2017): RNA-Seq Validation of RNAi Identifies Additional Gene Connectivity in Tribolium castaneum (Coleoptera: Tenebrionidae). Journal of Insect Science 17(2). https://doi.org/10.1093/jisesa/iex026
  26. Han Y., Xiong L., Xu Y., Tian T., Wang T. (2017): The β-alanine transporter BalaT is required for visual neurotransmission in Drosophila. eLife 6. https://doi.org/10.7554/eLife.29146
  27. Connahs H., Rhen T., Simmons R. (2016): Transcriptome analysis of the painted lady butterfly, Vanessa cardui during wing color pattern development. BMC Genomics 17(1). https://doi.org/10.1186/s12864-016-2586-5
  28. Noh M., Koo B., Kramer K., Muthukrishnan S., Arakane Y. (2016): Arylalkylamine N-acetyltransferase 1 gene (TcAANAT1) is required for cuticle morphology and pigmentation of the adult red flour beetle, Tribolium castaneum. Insect Biochemistry and Molecular Biology 79:119-129. https://doi.org/10.1016/j.ibmb.2016.10.013
  29. Noh M., Muthukrishnan S., Kramer K., Arakane Y. (2016): Cuticle formation and pigmentation in beetles. Current Opinion in Insect Science 17:1-9. https://doi.org/10.1016/j.cois.2016.05.004
  30. Sugumaran M., Barek H. (2016): Critical Analysis of the Melanogenic Pathway in Insects and Higher Animals. International Journal of Molecular Sciences 17(10):1753. https://doi.org/10.3390/ijms17101753
  31. R. Chaturvedi, K. Reddig, Hong-Sheng Li (2016): Long-distance mechanism of neurotransmitter recycling mediated by glial network facilitates visual function in Drosophila. https://www.semanticscholar.org/paper/f103f4e6bae0e4c1d0d648ee79b5020531c25e76
  32. Minami R., Sato C., Yamahama Y., Kubo H., Hariyama T., Kimura K. (2016): An RNAi Screen for Genes Involved in Nanoscale Protrusion Formation on Corneal Lens in Drosophila melanogaster. Zoological Science 33(6):583. https://doi.org/10.2108/zs160105
  33. Beasley V., Dowse H. (2016): Suppression of Tryptophan 2,3-Dioxygenase Produces a Slow Heartbeat Phenotype in Drosophila melanogaster. Journal of Experimental Zoology Part A: Ecological Genetics and Physiology 325(10):651-664. https://doi.org/10.1002/jez.2057
  34. Arakane Y., Noh M., Asano T., Kramer K. (2016): Tyrosine Metabolism for Insect Cuticle Pigmentation and Sclerotization. Extracellular Composite Matrices in Arthropods. https://doi.org/10.1007/978-3-319-40740-1_6
  35. Lei Y., Wang Y., Ahola V., Luo S., Xu C., Wang R. (2016): RNA sequencing reveals differential thermal regulation mechanisms between sexes of Glanville fritillary butterfly in the Tianshan Mountains, China. Molecular Biology Reports 43(12):1423-1433. https://doi.org/10.1007/s11033-016-4076-x
  36. Dai F., Qiao L., Cao C., Liu X., Tong X., He S., et al. (2015): Aspartate Decarboxylase is Required for a Normal Pupa Pigmentation Pattern in the Silkworm, Bombyx mori. Scientific Reports 5(1). https://doi.org/10.1038/srep10885
  37. J. Colomb, B. Brembs (2015): Sub-strains of CantonS differ markedly in Drosophila their locomotor behavior. https://www.semanticscholar.org/paper/025fad54587d1c88bfdca7f2283ed639309987f9
  38. Unknown authors (2014): Sub-strains of CantonS differ markedly in their Drosophila locomotor behavior.
  39. Colomb J., Brembs B. (2014): Sub-strains of Drosophila Canton-S differ markedly in their locomotor behavior. F1000Research 3:176. https://doi.org/10.12688/f1000research.4263.2
  40. Chaturvedi R., Reddig K., Li H. (2014): Long-distance mechanism of neurotransmitter recycling mediated by glial network facilitates visual function in Drosophila. Proceedings of the National Academy of Sciences 111(7):2812-2817. https://doi.org/10.1073/pnas.1323714111
  41. Ziegler A., Brüsselbach F., Hovemann B. (2013): Activity and coexpression of Drosophila black with ebony in fly optic lobes reveals putative cooperative tasks in vision that evade electroretinographic detection. Journal of Comparative Neurology 521(6):1207-1224. https://doi.org/10.1002/cne.23247
  42. Müller R., Jenny A., Stanley P. (2013): The EGF Repeat-Specific O-GlcNAc-Transferase Eogt Interacts with Notch Signaling and Pyrimidine Metabolism Pathways in Drosophila. PLoS ONE 8(5):e62835. https://doi.org/10.1371/journal.pone.0062835
  43. Ilg T., Berger M., Noack S., Rohwer A., Gaßel M. (2013): Glutamate decarboxylase of the parasitic arthropods Ctenocephalides felis and Rhipicephalus microplus: gene identification, cloning, expression, assay development, identification of inhibitors by high throughput screening and comparison with the orthologs from Drosophila melanogaster and mouse. Insect Biochemistry and Molecular Biology 43(2):162-177. https://doi.org/10.1016/j.ibmb.2012.11.001
  44. Borycz J., Borycz J., Edwards T., Boulianne G., Meinertzhagen I. (2012): The metabolism of histamine in the Drosophila optic lobe involves an ommatidial pathway: β-alanine recycles through the retina. Journal of Experimental Biology 215(8):1399-1411. https://doi.org/10.1242/jeb.060699
  45. Colomb J., Reiter L., Blaszkiewicz J., Wessnitzer J., Brembs B. (2012): Open Source Tracking and Analysis of Adult Drosophila Locomotion in Buridan’s Paradigm with and without Visual Targets. PLoS ONE 7(8):e42247. https://doi.org/10.1371/journal.pone.0042247
  46. S. Pulver, J. Berni (2012): The Fundamentals of Flying: Simple and Inexpensive Strategies for Employing Drosophila Genetics in Neuroscience Teaching Laboratories. Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience. https://www.semanticscholar.org/paper/533a048d416dc712cbceb3cf02165523a267f2b8
  47. Saenko S., Jerónimo M., Beldade P. (2012): Genetic basis of stage-specific melanism: a putative role for a cysteine sulfinic acid decarboxylase in insect pigmentation. Heredity 108(6):594-601. https://doi.org/10.1038/hdy.2011.127
  48. Wardill T., List O., Li X., Dongre S., McCulloch M., Ting C., et al. (2012): Multiple Spectral Inputs Improve Motion Discrimination in the Drosophila Visual System. Science 336(6083):925-931. https://doi.org/10.1126/science.1215317
  49. T. Wardill, Olivier List, Xiaofeng Li, Sidhartha Dongre, M. Juusola (2012): Wardill Visual System Drosophila Multiple Spectral Inputs Improve Motion Discrimination in the. https://www.semanticscholar.org/paper/71b04d9fc266f027ccc50a60adc913441ba5c13f
  50. Rund S., Hou T., Ward S., Collins F., Duffield G. (2011): Genome-wide profiling of diel and circadian gene expression in the malaria vector Anopheles gambiae. Proceedings of the National Academy of Sciences 108(32). https://doi.org/10.1073/pnas.1100584108
  51. Gallot A., Rispe C., Leterme N., Gauthier J., Jaubert-Possamai S., Tagu D. (2010): Cuticular proteins and seasonal photoperiodism in aphids. Insect Biochemistry and Molecular Biology 40(3):235-240. https://doi.org/10.1016/j.ibmb.2009.12.001
  52. Richardson G., Ding H., Rocheleau T., Mayhew G., Reddy E., Han Q., et al. (2010): An examination of aspartate decarboxylase and glutamate decarboxylase activity in mosquitoes. Molecular Biology Reports 37(7):3199-3205. https://doi.org/10.1007/s11033-009-9902-y
  53. Le Trionnaire G., Francis F., Jaubert-Possamai S., Bonhomme J., De Pauw E., Gauthier J., et al. (2009): Transcriptomic and proteomic analyses of seasonal photoperiodism in the pea aphid. BMC Genomics 10(1). https://doi.org/10.1186/1471-2164-10-456
  54. Arakane Y., Lomakin J., Beeman R., Muthukrishnan S., Gehrke S., Kanost M., et al. (2009): Molecular and Functional Analyses of Amino Acid Decarboxylases Involved in Cuticle Tanning in Tribolium castaneum*. Journal of Biological Chemistry 284(24):16584-16594. https://doi.org/10.1074/jbc.M901629200
  55. Davis M., Primrose D., Hodgetts R. (2008): A Member of the p38 Mitogen-Activated Protein Kinase Family Is Responsible for Transcriptional Induction of Dopa decarboxylase in the Epidermis of Drosophila melanogaster during the Innate Immune Response. Molecular and Cellular Biology 28(15):4883-4895. https://doi.org/10.1128/MCB.02074-07
  56. Stuart A., Borycz J., Meinertzhagen I. (2007): The dynamics of signaling at the histaminergic photoreceptor synapse of arthropods. Progress in Neurobiology 82(4):202-227. https://doi.org/10.1016/J.PNEUROBIO.2007.03.006
  57. Richard Smiley, Jean Charchaflieh, Suny Downstate, Ivan Velickovic, J. Eloy (): Open Peer Review Invited Referee Responses. https://www.semanticscholar.org/paper/51bcae1b36f0c3cfae954c67c39510501c1b0b7c

Brembs B, Baxter DA, Byrne JH. (2004): Extending in vitro conditioning in Aplysia to analyze operant and classical processes in the same preparation. Learn. Mem. 11:412–420.

  1. Novati A., Nguyen H., Schulze-Hentrich J. (2022): Environmental stimulation in Huntington disease patients and animal models. Neurobiology of Disease 171:105725. https://doi.org/10.1016/j.nbd.2022.105725
  2. Sara Daisy Casoni, Alessia Romanelli, M. Checchi, Serena Truocchio, M. Ferretti, C. Palumbo, et al. (2022): Expression and Localization of Phosphoinositide-Specific Phospholipases C in Cultured, Differentiating and Stimulated Human Osteoblasts. Journal of Cellular Signaling 3(1). https://doi.org/10.33696/signaling.3.067
  3. Kourosh-Arami M., Komaki A., Joghataei M., Mohsenzadegan M. (2020): Phospholipase Cβ3 in the hippocampus may mediate impairment of memory by long-term blockade of orexin 1 receptors assessed by the Morris water maze. Life Sciences 257:118046. https://doi.org/10.1016/j.lfs.2020.118046
  4. Huang W., Carr A., Hajicek N., Sokolovski M., Siraliev-Perez E., Hardy P., et al. (2020): A High-Throughput Assay to Identify Allosteric Inhibitors of the PLC-γ Isozymes Operating at Membranes. Biochemistry 59(41):4029-4038. https://doi.org/10.1021/acs.biochem.0c00511
  5. Finley J. (2018): Facilitation of hippocampal long-term potentiation and reactivation of latent HIV-1 via AMPK activation: Common mechanism of action linking learning, memory, and the potential eradication of HIV-1. Medical Hypotheses 116:61-73. https://doi.org/10.1016/j.mehy.2018.04.018
  6. Hylin M., Zhao J., Tangavelou K., Rozas N., Hood K., MacGowan J., et al. (2018): A role for autophagy in long‐term spatial memory formation in male rodents. Journal of Neuroscience Research 96(3):416-426. https://doi.org/10.1002/jnr.24121
  7. Yan J., Liu X., Han M., Wang Y., Sun X., Yu N., et al. (2015): Blockage of GSK3β-mediated Drp1 phosphorylation provides neuroprotection in neuronal and mouse models of Alzheimer’s disease. Neurobiology of Aging 36(1):211-227. https://doi.org/10.1016/j.neurobiolaging.2014.08.005
  8. Rozas N., Redell J., Pita-Almenar J., Mckenna J., Moore A., Gambello M., et al. (2015): Intrahippocampal glutamine administration inhibits mTORC1 signaling and impairs long-term memory. Learning & Memory 22(5):239-246. https://doi.org/10.1101/lm.038265.115
  9. S. Gellatly (2015): Cloning and characterisation of phospholipase C X-domain containing proteins (PLCXDs). https://www.semanticscholar.org/paper/9493864f82444b90b2137579f2ef39e1aac4e102
  10. Paula-Lima A., Adasme T., Hidalgo C. (2014): Contribution of Ca2+ Release Channels to Hippocampal Synaptic Plasticity and Spatial Memory: Potential Redox Modulation. Antioxidants & Redox Signaling 21(6):892-914. https://doi.org/10.1089/ars.2013.5796
  11. Davoli E., Sclip A., Cecchi M., Cimini S., Carrà A., Salmona M., et al. (2014): Determination of tissue levels of a neuroprotectant drug: the cell permeable JNK inhibitor peptide. Journal of Pharmacological and Toxicological Methods 70(1):55-61. https://doi.org/10.1016/j.vascn.2014.04.001
  12. Fukami K., Nakamura Y. (2014): The Role of Phospholipase C Isozymes in Cellular Homeostasis. Phospholipases in Health and Disease. https://doi.org/10.1007/978-1-4939-0464-8_12
  13. Li L., Csaszar E., Szodorai E., Patil S., Pollak A., Lubec G. (2014): The differential hippocampal phosphoproteome of Apodemus sylvaticus paralleling spatial memory retrieval in the Barnes maze. Behavioural Brain Research 264:126-134. https://doi.org/10.1016/j.bbr.2014.01.047
  14. Baker K., Edwards T., Rickard N. (2013): The role of intracellular calcium stores in synaptic plasticity and memory consolidation. Neuroscience & Biobehavioral Reviews 37(7):1211-1239. https://doi.org/10.1016/j.neubiorev.2013.04.011
  15. Kwapis J., Jarome T., Gilmartin M., Helmstetter F. (2012): Intra-amygdala infusion of the protein kinase Mzeta inhibitor ZIP disrupts foreground context fear memory. Neurobiology of Learning and Memory 98(2):148-153. https://doi.org/10.1016/j.nlm.2012.05.003
  16. Suh P., Park J., Manzoli L., Cocco L., Peak J., Katan M., et al. (2008): Multiple roles of phosphoinositide-specific phospholipase C isozymes. BMB Reports 41(6):415-434. https://doi.org/10.5483/BMBREP.2008.41.6.415
  17. McCusker C., Wang Y., Shan J., Kinyanjui M., Villeneuve A., Michael H., et al. (2007): Inhibition of Experimental Allergic Airways Disease by Local Application of a Cell-Penetrating Dominant-Negative STAT-6 Peptide1. The Journal of Immunology 179(4):2556-2564. https://doi.org/10.4049/jimmunol.179.4.2556
  18. Wolf D., Nestler E., Russell D. (2007): REGULATION OF NEURONAL PLCγ BY CHRONIC MORPHINE. Brain Research 1156:9-20. https://doi.org/10.1016/J.BRAINRES.2007.04.059
  19. E. Fixman, W. Kinyanjui, A. Villeneuve, H. Michael, C. Mccusker, Yufa Wang, et al. (2007): Peptide Cell-Penetrating Dominant-Negative STAT-6 Disease by Local Application of a Inhibition of Experimental Allergic Airways. https://www.semanticscholar.org/paper/53597f123ae15487a925110c726b6bc4644d2303
  20. E. Fixman, W. Kinyanjui, A. Villeneuve, H. Michael, C. Mccusker, Yufa Wang, et al. (2007): Inhibition of Experimental Allergic Airways. https://www.semanticscholar.org/paper/af3a5bace1e490d9bd62f397935efbab2d7213e5
  21. Dietz G., Bähr M. (2007): Synthesis of cell-penetrating peptides and their application in neurobiology. Methods in Molecular Biology. https://doi.org/10.1007/978-1-59745-504-6_13
  22. Dash P., Orsi S., Moore A. (2006): Spatial Memory Formation and Memory-Enhancing Effect of Glucose Involves Activation of the Tuberous Sclerosis Complex–Mammalian Target of Rapamycin Pathway. The Journal of Neuroscience 26(31):8048-8056. https://doi.org/10.1523/JNEUROSCI.0671-06.2006
  23. Snyder E., Dowdy S. (2005): Recent advances in the use of protein transduction domains for the delivery of peptides, proteins and nucleic acids invivo. Expert Opinion on Drug Delivery 2(1):43-51. https://doi.org/10.1517/17425247.2.1.43
  24. Dietz G., Bähr M. (2005): Peptide-enhanced cellular internalization of proteins in neuroscience. Brain Research Bulletin 68(1-2):103-114. https://doi.org/10.1016/J.BRAINRESBULL.2005.08.015
  25. Dash P., Mach S., Moody M., Moore A. (2004): Performance in long‐term memory tasks is augmented by a phosphorylated growth factor receptor fragment. Journal of Neuroscience Research 77(2):205-216. https://doi.org/10.1002/jnr.20174

Brembs B. (2003): Operant conditioning in invertebrates. Curr. Opin. Neurobiol. 13:710–717.

  1. Dadarwal R. (2022): Multi-contrast Magnetic Resonance Imaging of Myelin and Iron in the Brain. https://doi.org/10.53846/goediss-9436
  2. Misquitta K., Rosen H., Tartaglia M. (2016): Neuroimaging in dementia. The Behavioral Neurology of Dementia. https://doi.org/10.1017/9781139924771.004
  3. Zhang X., Li C. (2016): Arterial spin labeling perfusion magnetic resonance imaging of non-human primates. Quantitative Imaging in Medicine and Surgery 6(5):573-581. https://doi.org/10.21037/qims.2016.10.05
  4. Bartels A., Goense J., Logothetis N. (2012): Functional magnetic resonance imaging. Handbook of Neural Activity Measurement. https://doi.org/10.1017/cbo9780511979958.011
  5. Tao L., Lauderdale J., Sornborger A. (2011): Mapping Functional Connectivity between Neuronal Ensembles with Larval Zebrafish Transgenic for a Ratiometric Calcium Indicator. Frontiers in Neural Circuits 5. https://doi.org/10.3389/fncir.2011.00002
  6. Koch C. (2008): Neurobiologia na tropie świadomości. https://doi.org/10.31338/uw.9788323527107
  7. Sato J. (2007): Modelo autoregressivo vetorial com coeficientes variantes no tempo e aplicações em RMf. https://doi.org/10.11606/t.45.2007.tde-22042013-151911
  8. Otte A., Halsband U. (2006): Brain imaging tools in neurosciences. Journal of Physiology-Paris 99(4-6):281-292. https://doi.org/10.1016/j.jphysparis.2006.03.011

Brembs B. (2003): Operant reward learning in Aplysia. Curr. Dir. Psychol. Sci. 12:218–221.

  1. Robin Tan (2017): The Effect of Androstenone as a Mating Prime on Drinking and Approach Behavior. https://www.semanticscholar.org/paper/330d2ed94f0cc53e72a333897b87be6845d2b1bc
  2. Cai Z., Neveu C., Baxter D., Byrne J., Aazhang B. (2017): Inferring neuronal network functional connectivity with directed information. Journal of Neurophysiology 118(2):1055-1069. https://doi.org/10.1152/jn.00086.2017
  3. J. Stanley (2014): Operant/Classical Conditioning: Comparisons, Intersections and Interactions The 2014 Winter Conference on Animal Learning and Behavior Focus and Research Seminar Sessions. https://www.semanticscholar.org/paper/4f45f4727e5e1051a32989ce2b514c7e98854736
  4. Perry C., Barron A., Cheng K. (2013): Invertebrate learning and cognition: relating phenomena to neural substrate. WIREs Cognitive Science 4(5):561-582. https://doi.org/10.1002/wcs.1248
  5. Zilio D. (2013): Behavioral Unit of Selection and the Operant-Respondent Distinction: The role of Neurophysiological Events in Controlling the Verbal Behavior of Theorizing about Behavior. The Psychological Record 63(4):895-918. https://doi.org/10.11133/J.TPR.2013.63.4.011
  6. Mary C. Petrosko, R. Calin-Jageman (2012): Learning and non-learning effects of Ginkgo biloba extract EGb 761 in Aplysia californica. https://www.semanticscholar.org/paper/cb407b17a87699a827f7a86a13d5512e0a69d219
  7. Brembs B. (2010): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences 278(1707):930-939. https://doi.org/10.1098/rspb.2010.2325
  8. Dorian S. Houser, J. Finneran, Sam H. Ridgway (2010): eScholarship International Journal of Comparative Psychology. https://www.semanticscholar.org/paper/9a61905f00a0775475b2f404dc73a8a6ebba3759
  9. Brenner G. (2010): Marketing und Sucht im neuronalen Belohnungssystem – ein gemeinsamer Fokus zweier Forschungsfelder. der markt 49(3-4):201-210. https://doi.org/10.1007/S12642-010-0045-4
  10. B. Röttger-Rössler, H. Markowitsch (2009): Emotions as bio-cultural processes. https://doi.org/10.1007/978-0-387-09546-2
  11. Casimir M. (2009): On the Origin and Evolution of Affective Capacities in Lower Vertebrates. Emotions as Bio-cultural Processes. https://doi.org/10.1007/978-0-387-09546-2_4
  12. Menzel R., Brembs B., Giurfa M. (2007): Cognition in Invertebrates. Evolution of Nervous Systems. https://doi.org/10.1016/B0-12-370878-8/00183-X
  13. Baxter D., Byrne J. (2006): Feeding behavior of Aplysia: a model system for comparing cellular mechanisms of classical and operant conditioning. Learning & Memory 13(6):669-680. https://doi.org/10.1101/LM.339206
  14. D. A. Baxter, J. Byrne (2006): cellular mechanisms of classical and operant conditioning A model system for comparing Aplysia : Feeding behavior of. https://www.semanticscholar.org/paper/5110ddecdb3f369e32459cd28f32d017c4699e6d
  15. R. Menzel, B. Brembs (2006): 1 . 26 Cognition in Invertebrates. https://www.semanticscholar.org/paper/cd86c7c05307517d2a90871fd20ad02736ac6100
  16. Reich R., Noll J., Goldman M. (2005): Cue patterns and alcohol expectancies: how slight differences in stimuli can measurably change cognition. Experimental and Clinical Psychopharmacology 13(1):65-71. https://doi.org/10.1037/1064-1297.13.1.65
  17. Kelley A. (2004): Memory and addiction: shared neural circuitry and molecular mechanisms. Neuron 44(1):161-179. https://doi.org/10.1016/J.NEURON.2004.09.016
  18. Jones N., Kemenes I., Kemenes G., Benjamin P. (2003): A persistent cellular change in a single modulatory neuron contributes to associative long-term memory. Current Biology 13(12):1064-1069. https://doi.org/10.1016/S0960-9822(03)00380-4

Brembs B, Lorenzetti FD, Reyes FD, Baxter DA, Byrne JH. (2002): Operant reward learning in Aplysia: neuronal correlates and mechanisms. Science 296:1706–1709.

  1. Winters-Bostwick G., Bostwick C., Crook R. (2025): Neurochemically-evoked activity in slice preparations of the octopus arm nerve cord. https://doi.org/10.1101/2025.11.29.691307
  2. Vanaki S., Gonzalez N., Neveu C., Momohara Y., Aazhang B., Byrne J. (2025): Low-dimensional signatures of neuronal activity associated with long-term operant conditioning in Aplysia. Communications Biology 9(1). https://doi.org/10.1038/s42003-025-09357-1
  3. Waddell S., Park A. (2025): Cognitive primitives of the insect brain. Trends in Cognitive Sciences 30(3):194-196. https://doi.org/10.1016/j.tics.2025.11.013
  4. Norekian T., Moroz L. (2025): Dopaminergic Central Neurons and Peripheral Sensory Systems in Pteropod and Nudibranch Molluscs. Journal of Comparative Neurology 533(5). https://doi.org/10.1002/cne.70054
  5. Winters-Bostwick G., Giancola-Detmering S., Bostwick C., Crook R. (2024): Three-dimensional molecular atlas highlights spatial and neurochemical complexity in the axial nerve cord of octopus arms. Current Biology 34(20):4756-4766.e6. https://doi.org/10.1016/j.cub.2024.08.049
  6. Winters-Bostwick G., Giancola-Detmering S., Bostwick C., Crook R. (2024): Three-Dimensional Molecular Atlas of Octopus Arm Neuroanatomy Highlights Spatial and Functional Complexity. https://doi.org/10.1101/2024.04.14.589438
  7. Hurwitz I., Tam S., Jing J., Chiel H., Susswein A. (2024): Repeated stimulation of feeding mechanoafferents inAplysiagenerates responses consistent with the release of food. Learning & Memory 31(6):a053880. https://doi.org/10.1101/lm.053880.123
  8. Byrne J., Hochner B., Shomrat T., Kemenes G. (2024): Cellular and molecular mechanisms of memory in molluscs. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-443-15754-7.00031-6
  9. Birch J. (2024): The Edge of Sentience. https://doi.org/10.1093/9780191966729.001.0001
  10. Birch J. (2024): The Concept of Sentience. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0003
  11. Birch J. (2024): Fetuses and Embryos. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0011
  12. Birch J. (2024): Debating Proportionality. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0009
  13. Birch J. (2024): Stepping Back. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0019
  14. Birch J. (2024): The Mind-Body Problem. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0004
  15. Birch J. (2024): Personal Acknowledgements. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0025
  16. Birch J. (2024): Ethics and Religion. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0005
  17. Birch J. (2024): Involving the Public. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0008
  18. Birch J. (2024): List of Illustrations. The Edge of Sentience. https://doi.org/10.1093/9780191966729.002.0006
  19. Birch J. (2024): Publisher Acknowledgements. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0027
  20. Birch J. (2024): Neural Organoids. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0012
  21. Birch J. (2024): Converging on Precautions. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0007
  22. Birch J. (2024): People with Disorders of Consciousness. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0010
  23. Birch J. (2024): The Run-Ahead Principle. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0018
  24. Birch J. (2024): Summary of the Framework and Proposals. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0001
  25. Birch J. (2024): Against Complacency. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0016
  26. Birch J. (2024): The Clearest Candidates. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0013
  27. Birch J. (2024): A Walk along the Edge. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0002
  28. Birch J. (2024): The Science of Consciousness and Emotion. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0006
  29. Birch J. (2024): Pushing the Boundaries. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0014
  30. Birch J. (2024): Large Language Models and the Gaming Problem. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0017
  31. Birch J. (2024): Frontiers of Proportionality. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0015
  32. Birch J. (2024): Funding Acknowledgement. The Edge of Sentience. https://doi.org/10.1093/9780191966729.003.0026
  33. Mozzachiodi R., Byrne J. (2024): Plasticity of intrinsic excitability as a mechanism for memory storage. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-443-15754-7.00041-9
  34. Mao R., Guo S., Zhang G., Li Y., Xu J., Wang H., et al. (2024): Two C-terminal isoforms of Aplysia tachykinin–related peptide receptors exhibit phosphorylation-dependent and phosphorylation-independent desensitization mechanisms. Journal of Biological Chemistry 300(8):107556. https://doi.org/10.1016/j.jbc.2024.107556
  35. Norekian T., Liu Y., Gribkova E., Cui J., Gillette R. (2024): A peripheral subepithelial network for chemotactile processing in the predatory sea slug Pleurobranchaea californica. PLOS ONE 19(2):e0296872. https://doi.org/10.1371/journal.pone.0296872
  36. Norekian T., Moroz L. (2024): The distribution and evolutionary dynamics of dopaminergic neurons in molluscs. https://doi.org/10.1101/2024.06.26.600886
  37. Silva W. (2024): Neurobiologia do condicionamento pavloviano e condicionamento operante – Revisão narrativa. Research, Society and Development 13(11):e147131147523. https://doi.org/10.33448/rsd-v13i11.47523
  38. Neveu C., Smolen P., Baxter D., Byrne J. (2023): Voltage- and Calcium-Gated Membrane Currents Tune the Plateau Potential Properties of Multiple Neuron Types. The Journal of Neuroscience 43(45):7601-7615. https://doi.org/10.1523/jneurosci.0789-23.2023
  39. Bielecki J., Dam Nielsen S., Nachman G., Garm A. (2023): Associative learning in the box jellyfish Tripedalia cystophora. Current Biology 33(19):4150-4159.e5. https://doi.org/10.1016/j.cub.2023.08.056
  40. Crossley M., Benjamin P., Kemenes G., Staras K., Kemenes I. (2023): A circuit mechanism linking past and future learning through shifts in perception. Science Advances 9(12). https://doi.org/10.1126/sciadv.add3403
  41. Bui M., Krishen A., Kemp E. (2023): It’s a force of habit: influences of emotional eating on indulgent tendencies. Journal of Consumer Marketing 40(4):445-457. https://doi.org/10.1108/jcm-01-2022-5146
  42. Fu P., Mei Y., Liu W., Chen P., Jin Q., Guo S., et al. (2023): Identification of three elevenin receptors and roles of elevenin disulfide bond and residues in receptor activation in Aplysia californica. Scientific Reports 13(1). https://doi.org/10.1038/s41598-023-34596-9
  43. Dresp-Langley B. (2022): From Biological Synapses to “Intelligent” Robots. Electronics 11(5):707. https://doi.org/10.3390/electronics11050707
  44. Zhang G., Guo S., Yin S., Yuan W., Chen P., Kim J., et al. (2022): Functional characterization of a neuropeptide receptor exogenously expressed in Aplysia neurons. https://doi.org/10.1101/2022.02.14.480444
  45. Jiang H., Yang Z., Xue Y., Wang H., Guo S., Xu J., et al. (2022): Identification of an allatostatin C signaling system in mollusc Aplysia. Scientific Reports 12(1). https://doi.org/10.1038/s41598-022-05071-8
  46. Raza M., Wang T., Li Z., Nie H., Giurfa M., Husain A., et al. (2022): Biogenic amines mediate learning success in appetitive odor conditioning in honeybees. Journal of King Saud University – Science 34(4):101928. https://doi.org/10.1016/j.jksus.2022.101928
  47. Costa R., Baxter D., Byrne J. (2022): Neuronal population activity dynamics reveal a low-dimensional signature of operant learning in Aplysia. Communications Biology 5(1). https://doi.org/10.1038/s42003-022-03044-1
  48. Williams W. (2022): Instrumental Learning. Encyclopedia of Animal Cognition and Behavior. https://doi.org/10.1007/978-3-319-55065-7_1114
  49. Momohara Y., Neveu C., Chen H., Baxter D., Byrne J. (2022): Specific Plasticity Loci and Their Synergism Mediate Operant Conditioning. The Journal of Neuroscience 42(7):1211-1223. https://doi.org/10.1523/jneurosci.1722-21.2021
  50. Bédécarrats A., Puygrenier L., Castro O’Byrne J., Lade Q., Simmers J., Nargeot R. (2021): Organelle calcium-derived voltage oscillations in pacemaker neurons drive the motor program for food-seeking behavior in Aplysia. eLife 10. https://doi.org/10.7554/elife.68651
  51. Bédécarrats A., Puygrenier L., O’Byrne J., Lade Q., Simmers J., Nargeot R. (2021): Organelle calcium-derived voltage oscillations in pacemaker neurons drive food-seeking behavior in Aplysia. https://doi.org/10.1101/2021.03.30.437701
  52. Lv J., Jiang N., Wang H., Huang H., Bao Y., Chen Y., et al. (2021): Simulated weightlessness induces cognitive changes in rats illustrated by performance in operant conditioning tasks. Life Sciences in Space Research 29:63-71. https://doi.org/10.1016/j.lssr.2021.03.004
  53. Thiede K., Born J., Vorster A. (2021): Sleep and conditioning of the siphon withdrawal reflex in Aplysia. Journal of Experimental Biology 224(16). https://doi.org/10.1242/jeb.242431
  54. Klein K., Croteau-Chonka E., Narayan L., Winding M., Masson J., Zlatic M. (2021): Serotonergic Neurons Mediate Operant Conditioning in Drosophila Larvae. https://doi.org/10.1101/2021.06.14.448341
  55. Yamagata N., Ezaki T., Takahashi T., Wu H., Tanimoto H. (2021): Presynaptic inhibition of dopamine neurons controls optimistic bias. eLife 10. https://doi.org/10.7554/elife.64907
  56. Kappeler P. (2021): Development and Control of Behaviour. Animal Behaviour. https://doi.org/10.1007/978-3-030-82879-0_12
  57. Costa R., Baxter D., Byrne J. (2021): Neuronal population activity dynamics reveal a low-dimensional signature of operant learning. https://doi.org/10.1101/2021.12.06.471434
  58. Okada S., Hirano N., Abe T., Nagayama T. (2021): Aversive operant conditioning alters the phototactic orientation of the marbled crayfish. Journal of Experimental Biology 224(6). https://doi.org/10.1242/jeb.242180
  59. Ginsburg S., Jablonka E. (2021): Evolutionary transitions in learning and cognition. Philosophical Transactions of the Royal Society B 376(1821). https://doi.org/10.1098/rstb.2019.0766
  60. Momohara Y., Neveu C., Chen H., Baxter D., Byrne J. (2021): Specific plasticity loci and their synergism mediate operant conditioning. https://doi.org/10.1101/2021.12.02.470828
  61. Brembs B. (2020): The brain as a dynamically active organ. Biochemical and Biophysical Research Communications 564:55-69. https://doi.org/10.1016/j.bbrc.2020.12.011
  62. Rueckert D., Pico K., Kim D., Calero Sánchez X. (2020): Gamifying the foreign language classroom for brain‐friendly learning. Foreign Language Annals 53(4):686-703. https://doi.org/10.1111/flan.12490
  63. Gérard Manière, Gérard Coureaud (2020): From Stimulus to Behavioral Decision-Making. Frontiers Research Topics. https://doi.org/10.3389/978-2-88963-463-7
  64. Zhang H., Zeng H., Priimagi A., Ikkala O. (2020): Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning. Advanced Materials 32(20). https://doi.org/10.1002/adma.201906619
  65. Gill J., Chiel H. (2020): Rapid Adaptation to Changing Mechanical Load by Ordered Recruitment of Identified Motor Neurons. eneuro 7(3):ENEURO.0016-20.2020. https://doi.org/10.1523/eneuro.0016-20.2020
  66. Miller M. (2020): Dopamine as a Multifunctional Neurotransmitter in Gastropod Molluscs: An Evolutionary Hypothesis. The Biological Bulletin 239(3):189-208. https://doi.org/10.1086/711293
  67. Kappeler P. (2020): Entwicklung und Kontrolle des Verhaltens. Verhaltensbiologie. https://doi.org/10.1007/978-3-662-60546-2_12
  68. Wolf R., Heisenberg M., Brembs B., Waddell S., Mishra A., Kehrer A., et al. (2020): Memory, anticipation, action – working with Troy D. Zars. Journal of Neurogenetics 34(1):9-20. https://doi.org/10.1080/01677063.2020.1715976
  69. Costa R., Baxter D., Byrne J. (2020): Computational model of the distributed representation of operant reward memory: combinatoric engagement of intrinsic and synaptic plasticity mechanisms. Learning & Memory 27(6):236-249. https://doi.org/10.1101/lm.051367.120
  70. Wee C., Song E., Johnson R., Ailani D., Randlett O., Kim J., et al. (2019): A bidirectional network for appetite control in larval zebrafish. eLife 8. https://doi.org/10.7554/elife.43775
  71. Luque D., Molinero S., Watson P., López F., Le Pelley M. (2019): Measuring habit formation through goal-directed response switching. Journal of Experimental Psychology: General 149(8):1449-1459. https://doi.org/10.1037/xge0000722
  72. Luque D., Molinero S., Watson P., López F., Le Pelley M. (2019): Measuring habit formation through goal-directed response switching. https://doi.org/10.31234/osf.io/xnmfc
  73. Zwaka H., Bartels R., Lehfeldt S., Jusyte M., Hantke S., Menzel S., et al. (2019): Learning and Its Neural Correlates in a Virtual Environment for Honeybees. Frontiers in Behavioral Neuroscience 12. https://doi.org/10.3389/fnbeh.2018.00279
  74. McManus J., Chiel H., Susswein A. (2019): Successful and unsuccessful attempts to swallow in a reduced Aplysia preparation regulate feeding responses and produce memory at different neural sites. Learning & Memory 26(5):151-165. https://doi.org/10.1101/lm.048983.118
  75. Hawkins R. (2019): The contributions and mechanisms of changes in excitability during simple forms of learning in Aplysia. Neurobiology of Learning and Memory 164:107049. https://doi.org/10.1016/j.nlm.2019.107049
  76. Nargeot R., Puygrenier L. (2019): Operant Learning in Invertebrates. Reference Module in Life Sciences. https://doi.org/10.1016/b978-0-12-809633-8.90788-x
  77. Yang W., Meng Y., Li D., Wen Q. (2019): Visual Contrast Modulates Operant Learning Responses in Larval Zebrafish. Frontiers in Behavioral Neuroscience 13. https://doi.org/10.3389/fnbeh.2019.00004
  78. Fox A. (2018): The future is upon us. Behavior Analysis: Research and Practice 18(2):144-150. https://doi.org/10.1037/bar0000106
  79. Goldner A., Farruggella J., Wainwright M., Mozzachiodi R. (2018): cGMP mediates short- and long-term modulation of excitability in a decision-making neuron in Aplysia. Neuroscience Letters 683:111-118. https://doi.org/10.1016/j.neulet.2018.06.046
  80. Ehsan Abolhasani (2018): Operant Conditioning of Human Upper-Limb Stretch Reflexes. Scholarship@Western (Western University).
  81. Brown J., Schaub B., Klusas B., Tran A., Duman A., Haney S., et al. (2018): A role for dopamine in the peripheral sensory processing of a gastropod mollusc. PLOS ONE 13(12):e0208891. https://doi.org/10.1371/journal.pone.0208891
  82. Farruggella J., Acebo J., Lloyd L., Wainwright M., Mozzachiodi R. (2018): Role of nitric oxide in the induction of the behavioral and cellular changes produced by a common aversive stimulus in Aplysia. Behavioural Brain Research 360:341-353. https://doi.org/10.1016/j.bbr.2018.12.010
  83. Leod K., Seas A., Wainwright M., Mozzachiodi R. (2018): Effects of internal and external factors on the budgeting between defensive and non-defensive responses in Aplysia. Behavioural Brain Research 349:177-185. https://doi.org/10.1016/j.bbr.2018.04.040
  84. Unknown authors (2017): References. Microscopic Magnetic Resonance Imaging. https://doi.org/10.1201/9781315107325-17
  85. Unknown authors (2017): Front Matter of Volume 1. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-805159-7.01001-9
  86. Monteiro Ribeiro A., Gonçalves C. (2017): No Ritmo de um Silêncio: a Música como Produtora de Processos Psicológicos. Pensando Psicología 13(22):61-75. https://doi.org/10.16925/pe.v13i22.1989
  87. Cyr A., Avarguès-Weber A., Thériault F. (2017): Sameness/difference spiking neural circuit as a relational concept precursor model: A bio-inspired robotic implementation. Biologically Inspired Cognitive Architectures 21:59-66. https://doi.org/10.1016/j.bica.2017.05.001
  88. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  89. Curtis L. Neveu, Curtis L. Neveu (2017): MODIFICATION OF APLYSIA FEEDING NETWORK BY L-DOPA AND DOPAMINE-DEPENDENT LEARNING. Digital Commons-TMC (Texas Medical Center).
  90. Weisz H., Wainwright M., Mozzachiodi R. (2017): A novel in vitro analog expressing learning-induced cellular correlates in distinct neural circuits. Learning & Memory 24(8):331-340. https://doi.org/10.1101/lm.045229.117
  91. Mather J., Dickel L. (2017): Cephalopod complex cognition. Current Opinion in Behavioral Sciences 16:131-137. https://doi.org/10.1016/j.cobeha.2017.06.008
  92. Byrne J., Hochner B., Kemenes G. (2017): Cellular and Molecular Mechanisms of Memory in Mollusks. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21097-3
  93. Hernandez J., Wainwright M., Mozzachiodi R. (2017): Long-term sensitization training in Aplysia decreases the excitability of a decision-making neuron through a sodium-dependent mechanism. Learning & Memory 24(6):257-261. https://doi.org/10.1101/lm.044883.116
  94. Simmers J., Sillar K. (2017): Plasticity and Learning in Motor Control Networks. Neurobiology of Motor Control. https://doi.org/10.1002/9781118873397.ch13
  95. Silva J., Ferreira P., Coimbra J., Menezes I. (2017): Theater and Psychological Development: Assessing Socio-Cognitive Complexity in the Domain of Theater. Creativity Research Journal 29(2):157-166. https://doi.org/10.1080/10400419.2017.1302778
  96. Wong-Lin K., Wang D., Moustafa A., Cohen J., Nakamura K. (2017): Toward a multiscale modeling framework for understanding serotonergic function. Journal of Psychopharmacology 31(9):1121-1136. https://doi.org/10.1177/0269881117699612
  97. Casado-Aranda L., Martínez-Fiestas M., Sánchez-Fernández J. (2017): Neural effects of environmental advertising: An fMRI analysis of voice age and temporal framing. Journal of Environmental Management 206:664-675. https://doi.org/10.1016/j.jenvman.2017.10.006
  98. Mizunami M., Matsumoto Y. (2017): Roles of Octopamine and Dopamine Neurons for Mediating Appetitive and Aversive Signals in Pavlovian Conditioning in Crickets. Frontiers in Physiology 8. https://doi.org/10.3389/fphys.2017.01027
  99. Siddique N., Dhakan P., Rano I., Merrick K. (2017): A Review of the Relationship between Novelty, Intrinsic Motivation and Reinforcement Learning. Paladyn, Journal of Behavioral Robotics 8(1):58-69. https://doi.org/10.1515/pjbr-2017-0004
  100. Xu P., Wang K., Lu C., Dong L., Chen Y., Wang Q., et al. (2017): Effects of the chronic restraint stress induced depression on reward-related learning in rats. Behavioural Brain Research 321:185-192. https://doi.org/10.1016/j.bbr.2016.12.045
  101. Butlin P. (2017): Why Hunger is not a Desire. Review of Philosophy and Psychology 8(3):617-635. https://doi.org/10.1007/s13164-017-0332-9
  102. Benjamin P., Kemenes G. (2017): Behavioral and Circuit Analysis of Learning and Memory in Mollusks ☆. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21023-7
  103. Menzel R. (2017): Learning Theory and Behavior: Introduction and Overview ☆. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21002-x
  104. Holca-Lamarre R., Lücke J., Obermayer K. (2017): Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations. Frontiers in Computational Neuroscience 11. https://doi.org/10.3389/fncom.2017.00054
  105. Mozzachiodi R., Byrne J. (2017): Plasticity of Intrinsic Excitability as a Mechanism for Memory Storage ☆. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21114-0
  106. Nargeot R., Bédécarrats A. (2017): Electrical Synapses and Learning–Induced Plasticity in Motor Rhythmogenesis. Network Functions and Plasticity. https://doi.org/10.1016/b978-0-12-803471-2.00006-0
  107. Williams W. (2017): Instrumental Learning. Encyclopedia of Animal Cognition and Behavior. https://doi.org/10.1007/978-3-319-47829-6_1114-1
  108. Brembs B. (2016): Operant Behavior in Model Systems. https://doi.org/10.1101/058719
  109. Yang C., Yu K., Wang Y., Chen S., Liu D., Wang Z., et al. (2016): Aplysia Locomotion: Network and Behavioral Actions of GdFFD, a D-Amino Acid-Containing Neuropeptide. PLOS ONE 11(1):e0147335. https://doi.org/10.1371/journal.pone.0147335
  110. Lu C., Shi Z., Sun X., Pan R., Chen S., Li Y., et al. (2016): Kai Xin San aqueous extract improves Aβ 1-40 -induced cognitive deficits on adaptive behavior learning by enhancing memory-related molecules expression in the hippocampus. Journal of Ethnopharmacology 201:73-81. https://doi.org/10.1016/j.jep.2016.10.002
  111. Awata H., Wakuda R., Ishimaru Y., Matsuoka Y., Terao K., Katata S., et al. (2016): Roles of OA1 octopamine receptor and Dop1 dopamine receptor in mediating appetitive and aversive reinforcement revealed by RNAi studies. Scientific Reports 6(1). https://doi.org/10.1038/srep29696
  112. Kemenes I., Kemenes G. (2016): PACAP and Learning in Invertebrates. Current Topics in Neurotoxicity. https://doi.org/10.1007/978-3-319-35135-3_4
  113. de Araujo I. (2016): High fat takes the low road to the brain’s reinforcement system. Current Opinion in Behavioral Sciences 9:158-162. https://doi.org/10.1016/j.cobeha.2016.04.013
  114. Huang J., Ruan X., Yu N., Fan Q., Li J., Cai J. (2016): A Cognitive Model Based on Neuromodulated Plasticity. Computational Intelligence and Neuroscience 2016:1-15. https://doi.org/10.1155/2016/4296356
  115. Colomb J., Brembs B. (2016): PKC in motorneurons underlies self-learning, a form of motor learning in Drosophila. PeerJ 4:e1971. https://doi.org/10.7717/peerj.1971
  116. Silverman K., Jarvis B., Jessel J., Lopez A. (2016): Incentives and motivation. Translational Issues in Psychological Science 2(2):97-100. https://doi.org/10.1037/tps0000073
  117. Wolfe K., Wainwright M., Smee D., Mozzachiodi R. (2016): Eat or be eaten? Modifications of Aplysia californica feeding behaviour in response to natural aversive stimuli. Animal Behaviour 120:123-133. https://doi.org/10.1016/j.anbehav.2016.07.030
  118. Lakin M., Stefanovic D. (2016): Supervised Learning in Adaptive DNA Strand Displacement Networks. ACS Synthetic Biology 5(8):885-897. https://doi.org/10.1021/acssynbio.6b00009
  119. Kappeler P. (2016): Entwicklung und Kontrolle des Verhaltens. Verhaltensbiologie. https://doi.org/10.1007/978-3-662-53145-7_11
  120. Kool V., Agrawal R. (2016): Technology, Psychology, and Evolution. Psychology of Technology. https://doi.org/10.1007/978-3-319-45333-0_2
  121. Bronfman Z., Ginsburg S., Jablonka E. (2016): The Transition to Minimal Consciousness through the Evolution of Associative Learning. Frontiers in Psychology 7. https://doi.org/10.3389/fpsyg.2016.01954
  122. Sakurai A., Katz P. (2015): Phylogenetic and individual variation in gastropod central pattern generators. Journal of Comparative Physiology A 201(9):829-839. https://doi.org/10.1007/s00359-015-1007-6
  123. Cyr A., Thériault F. (2015): Action Selection and Operant Conditioning: A Neurorobotic Implementation. Journal of Robotics 2015:1-10. https://doi.org/10.1155/2015/643869
  124. Carballo F., Freidin E., Bentosela M. (2015): Estudios Sobre Cooperación en Perros Domésticos: una Revisión Crítica. Revista Colombiana de Psicología 24(1):145-163. https://doi.org/10.15446/rcp.v24n1.41221
  125. Newquist G., Gardner R. (2015): Reconsidering Food Reward, Brain Stimulation, and Dopamine: Incentives Act Forward. The American Journal of Psychology 128(4):431-444. https://doi.org/10.5406/amerjpsyc.128.4.0431
  126. Awata H., Watanabe T., Hamanaka Y., Mito T., Noji S., Mizunami M. (2015): Knockout crickets for the study of learning and memory: Dopamine receptor Dop1 mediates aversive but not appetitive reinforcement in crickets. Scientific Reports 5(1). https://doi.org/10.1038/srep15885
  127. Hollis K., Guillette L. (2015): What Associative Learning in Insects Tells Us about the Evolution of Learned and Fixed Behavior. International Journal of Comparative Psychology 28. https://doi.org/10.46867/ijcp.2015.28.01.07
  128. Kurt Heininger (2015): Duality of stochasticity and natural selection shape the ecology-driven pattern of social interactions: the fall of Hamilton’s rule. https://doi.org/10.13140/rg.2.2.26412.95368
  129. Kirszenblat L., van Swinderen B. (2015): The Yin and Yang of Sleep and Attention. Trends in Neurosciences 38(12):776-786. https://doi.org/10.1016/j.tins.2015.10.001
  130. Michiel M. Dorenbosch (2015): The Idea of Will. Journal of consciousness exploration & research.
  131. Gillette R., Brown J. (2015): The Sea Slug,Pleurobranchaea californica: A Signpost Species in the Evolution of Complex Nervous Systems and Behavior. Integrative and Comparative Biology. https://doi.org/10.1093/icb/icv081
  132. Hawkins R., Byrne J. (2015): Associative Learning in Invertebrates. Cold Spring Harbor Perspectives in Biology 7(5):a021709. https://doi.org/10.1101/cshperspect.a021709
  133. Bhimani R., Huber R. (2015): Operant avoidance learning in crayfish, Orconectes rusticus: Computational ethology and the development of an automated learning paradigm. Learning & Behavior 44(3):239-249. https://doi.org/10.3758/s13420-015-0205-y
  134. Buchta W., Riegel A. (2015): Chronic cocaine disrupts mesocortical learning mechanisms. Brain Research 1628:88-103. https://doi.org/10.1016/j.brainres.2015.02.003
  135. Shi Z., Lu C., Sun X., Wang Q., Chen S., Li Y., et al. (2015): Tong Luo Jiu Nao ameliorates Aβ1–40-induced cognitive impairment on adaptive behavior learning by modulating ERK/CaMKII/CREB signaling in the hippocampus. BMC Complementary and Alternative Medicine 15(1). https://doi.org/10.1186/s12906-015-0584-9
  136. Cyr A., Boukadoum M., Thériault F. (2014): Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot’s controller. Frontiers in Neurorobotics 8. https://doi.org/10.3389/fnbot.2014.00021
  137. Soltoggio A. (2014): Short-term plasticity as cause–effect hypothesis testing in distal reward learning. Biological Cybernetics 109(1):75-94. https://doi.org/10.1007/s00422-014-0628-0
  138. Debanne D., Campanac E. (2014): Memory (Mechanisms Other than LTP ). Encyclopedia of Life Sciences. https://doi.org/10.1002/9780470015902.a0021398.pub2
  139. Kandel E., Dudai Y., Mayford M. (2014): The Molecular and Systems Biology of Memory. Cell 157(1):163-186. https://doi.org/10.1016/j.cell.2014.03.001
  140. Sieling F., Bédécarrats A., Simmers J., Prinz A., Nargeot R. (2014): Differential Roles of Nonsynaptic and Synaptic Plasticity in Operant Reward Learning-Induced Compulsive Behavior. Current Biology 24(9):941-950. https://doi.org/10.1016/j.cub.2014.03.004
  141. Radecki G., Nargeot R., Jelescu I., Le Bihan D., Ciobanu L. (2014): Functional magnetic resonance microscopy at single-cell resolution in Aplysia californica. Proceedings of the National Academy of Sciences 111(23):8667-8672. https://doi.org/10.1073/pnas.1403739111
  142. Knierim J. (2014): Information Processing in Neural Networks. From Molecules to Networks. https://doi.org/10.1016/b978-0-12-397179-1.00019-1
  143. Byrne J., LaBar K., LeDoux J., Schafe G., Thompson R. (2014): Learning and Memory. From Molecules to Networks. https://doi.org/10.1016/b978-0-12-397179-1.00020-8
  144. Byrne J., Fioravante D., Antzoulatos E. (2014): Cellular and molecular mechanisms of associative and nonassociative learning. Textbook of Neural Repair and Rehabilitation. https://doi.org/10.1017/cbo9780511995583.007
  145. Dickinson K., Wainwright M., Mozzachiodi R. (2014): Change in excitability of a putative decision-making neuron in Aplysia serves as a mechanism in the decision not to feed following food satiation. Behavioural Brain Research 281:131-136. https://doi.org/10.1016/j.bbr.2014.12.022
  146. Lakin M., Minnich A., Lane T., Stefanovic D. (2014): Design of a biochemical circuit motif for learning linear functions. Journal of The Royal Society Interface 11(101):20140902. https://doi.org/10.1098/rsif.2014.0902
  147. Michael Selzer, Stéphanie Clarke, Leonardo G. Cohen, Gert Kwakkel, Robert H. Miller (2014): Neural plasticity: cellular and molecular mechanisms of neural plasticity. Textbook of Neural Repair and Rehabilitation. https://doi.org/10.1017/cbo9780511995583.003
  148. Le Pelley M. (2014): Primate polemic: Commentary on Smith, Couchman, and Beran (2014). Journal of Comparative Psychology 128(2):132-134. https://doi.org/10.1037/a0034227
  149. Jain P., Bhalla U. (2014): Transcription Control Pathways Decode Patterned Synaptic Inputs into Diverse mRNA Expression Profiles. PLoS ONE 9(5):e95154. https://doi.org/10.1371/journal.pone.0095154
  150. Rohan V. Bhimani (2014): Operant Place Aversion in the Rusty Crayfish, Orconectes rusticus. OhioLink ETD Center (Ohio Library and Information Network).
  151. Dasgupta S., Wörgötter F., Manoonpong P. (2014): Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control. Frontiers in Neural Circuits 8. https://doi.org/10.3389/fncir.2014.00126
  152. Schacher S., Hu J. (2014): The less things change, the more they are different: contributions of long-term synaptic plasticity and homeostasis to memory. Learning & Memory 21(3):128-134. https://doi.org/10.1101/lm.027326.112
  153. Weiss S., Rosales-Ruiz J. (2014): Operant/Classical Conditioning: Comparisons,Intersections and Interactions The 2014 Winter Conference on Animal Learning and Behavior Focus and Research Seminar Sessions. International Journal of Comparative Psychology 27(4). https://doi.org/10.46867/ijcp.2014.27.04.07
  154. Bédécarrats A., Cornet C., Simmers J., Nargeot R. (2013): Implication of dopaminergic modulation in operant reward learning and the induction of compulsive-like feeding behavior inAplysia. Learning & Memory 20(6):318-327. https://doi.org/10.1101/lm.029140.112
  155. Amit Ophir, Alon Korngreen, J. Koester, Abraham J. Susswein (2013): B31/B32 of Aplysia Currents Contributing to Decision Making in Neurons.
  156. Soltoggio A., Lemme A., Reinhart F., Steil J. (2013): Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances. Frontiers in Neurorobotics 7. https://doi.org/10.3389/fnbot.2013.00006
  157. Perry C., Barron A., Cheng K. (2013): Invertebrate learning and cognition: relating phenomena to neural substrate. WIREs Cognitive Science 4(5):561-582. https://doi.org/10.1002/wcs.1248
  158. Weatherill D., Dunn T., McCamphill P., Sossin W. (2013): Exploring Mechanisms of Synaptic Plasticity Using Exogenous Expression of Proteins at the Sensory-to-Motor Neuron Synapse of Aplysia. Neuromethods. https://doi.org/10.1007/978-1-62703-517-0_3
  159. Zilio D. (2013): Behavioral Unit of Selection and the Operant-Respondent Distinction: The Role of Neurophysiological Events in Controlling the Verbal Behavior of Theorizing About Behavior. The Psychological Record 63(4):895-918. https://doi.org/10.11133/j.tpr.2013.63.4.011
  160. Kemenes G. (2013): Molecular and Cellular Mechanisms of Classical Conditioning in the Feeding System of Lymnaea. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00020-4
  161. Hastings M., Farah C., Sossin W. (2013): Roles of Protein Kinase C and Protein Kinase M in Aplysia Learning. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00018-6
  162. Daw N., O’Doherty J. (2013): Multiple Systems for Value Learning. Neuroeconomics. https://doi.org/10.1016/b978-0-12-416008-8.00021-8
  163. Benjamin P. (2013): A Systems Analysis of Neural Networks Underlying Gastropod Learning and Memory. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00014-9
  164. Mozzachiodi R., Baxter D., Byrne J. (2013): Comparison of Operant and Classical Conditioning of Feeding Behavior in Aplysia. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00015-0
  165. Shi Z., Chen L., Li S., Chen S., Sun X., Sun L., et al. (2013): Chronic scopolamine-injection-induced cognitive deficit on reward-directed instrumental learning in rat is associated with CREB signaling activity in the cerebral cortex and dorsal hippocampus. Psychopharmacology 230(2):245-260. https://doi.org/10.1007/s00213-013-3149-y
  166. Susswein A., Chiel H. (2012): Nitric oxide as a regulator of behavior: New ideas from Aplysia feeding. Progress in Neurobiology 97(3):304-317. https://doi.org/10.1016/j.pneurobio.2012.03.004
  167. Acheampong A., Kelly K., Shields-Johnson M., Hajovsky J., Wainwright M., Mozzachiodi R. (2012): Rapid and persistent suppression of feeding behavior induced by sensitization training in Aplysia. Learning & Memory 19(4):159-163. https://doi.org/10.1101/lm.024638.111
  168. Soltoggio A., Stanley K. (2012): From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation. Neural Networks 34:28-41. https://doi.org/10.1016/j.neunet.2012.06.005
  169. Harris C., Buckley C., Nowotny T., Passaro P., Seth A., Kemenes G., et al. (2012): Multi-Neuronal Refractory Period Adapts Centrally Generated Behaviour to Reward. PLoS ONE 7(7):e42493. https://doi.org/10.1371/journal.pone.0042493
  170. Gantt E., Melling B., Reber J. (2012): Mechanisms or metaphors? The emptiness of evolutionary psychological explanations. Theory & Psychology 22(6):823-841. https://doi.org/10.1177/0959354311434071
  171. Eng Yeow Cheu, Chai Quek, See Kiong Ng (2012): ARPOP: An Appetitive Reward-Based Pseudo-Outer-Product Neural Fuzzy Inference System Inspired From the Operant Conditioning of Feeding Behavior in Aplysia. IEEE Transactions on Neural Networks and Learning Systems 23(2):317-329. https://doi.org/10.1109/tnnls.2011.2178529
  172. Hirayama K., Catanho M., Brown J., Gillette R. (2012): A Core Circuit Module for Cost/Benefit Decision. Frontiers in Neuroscience 6. https://doi.org/10.3389/fnins.2012.00123
  173. Shields-Johnson M., Hernandez J., Torno C., Adams K., Wainwright M., Mozzachiodi R. (2012): Effects of aversive stimuli beyond defensive neural circuits: Reduced excitability in an identified neuron critical for feeding in Aplysia. Learning & Memory 20(1):1-5. https://doi.org/10.1101/lm.028084.112
  174. Nargeot R., Simmers J. (2012): Functional Organization and Adaptability of a Decision-Making Network in Aplysia. Frontiers in Neuroscience 6. https://doi.org/10.3389/fnins.2012.00113
  175. Schultz W. (2012): Updating dopamine reward signals. Current Opinion in Neurobiology 23(2):229-238. https://doi.org/10.1016/j.conb.2012.11.012
  176. Dudai Y. (2012): The Restless Engram: Consolidations Never End. Annual Review of Neuroscience 35(1):227-247. https://doi.org/10.1146/annurev-neuro-062111-150500
  177. Tomina Y., Takahata M. (2012): Discrimination learning with light stimuli in restrained American lobster. Behavioural Brain Research 229(1):91-105. https://doi.org/10.1016/j.bbr.2011.12.044
  178. Shi Z., Sun X., Liu X., Chen S., Chang Q., Chen L., et al. (2012): Evaluation of an Aβ1–40-induced cognitive deficit in rat using a reward-directed instrumental learning task. Behavioural Brain Research 234(2):323-333. https://doi.org/10.1016/j.bbr.2012.07.006
  179. Unknown authors (2011): What Can Different Brains Do with Reward?. Neurobiology of Sensation and Reward. https://doi.org/10.1201/b10776-10
  180. Unknown authors (2011): Copyright Page. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.002.0003
  181. Unknown authors (2011): Author’s Preface. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.002.0006
  182. Unknown authors (2011): List of abbreviations. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.002.0009
  183. Unknown authors (2011): Dedication. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.002.0004
  184. Unknown authors (2011): Epigraph. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.002.0007
  185. Unknown authors (2011): Foreword. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.002.0005
  186. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  187. Claire Eschbach (2011): Klassiches und operantes Lernen bei Larven der Drosophila.
  188. Murray E., Wise S., Rhodes S. (2011): What Can Different Brains Do with Reward?. Frontiers in Neuroscience. https://doi.org/10.1201/b10776-6
  189. Ponulak,2 F., Kasinski A. (2011): Introduction to spiking neural networks: Information processing, learning and applications. Acta Neurobiologiae Experimentalis 71(4):409-433. https://doi.org/10.55782/ane-2011-1862
  190. Lorenzetti F., Baxter D., Byrne J. (2011): Classical Conditioning Analog Enhanced Acetylcholine Responses But Reduced Excitability of an Identified Neuron. The Journal of Neuroscience 31(41):14789-14793. https://doi.org/10.1523/jneurosci.1256-11.2011
  191. Auletta G. (2011): Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.001.0001
  192. Auletta G. (2011): 22 Development and Culture. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0023
  193. Auletta G. (2011): 2 Quantum and Classical Information and Entropy. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0003
  194. Auletta G. (2011): 20 Intentionality and Conceptualization. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0021
  195. Auletta G. (2011): 13 The Brain as an Information‐Control System. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0014
  196. Auletta G. (2011): 5 Dealing with Target Motion and Our Own Movement. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0006
  197. Auletta G. (2011): 3 The Brain: An Outlook. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0004
  198. Auletta G. (2011): 19 What Symbols Are. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0020
  199. Auletta G. (2011): 6 Complexity: A Necessary Condition. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0007
  200. Auletta G. (2011): 1 Quantum Mechanics as a General Framework. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0002
  201. Auletta G. (2011): 24 Mind and Brain (Body). Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0025
  202. Auletta G. (2011): 8 The Organism as a Semiotic and Cybernetic System. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0009
  203. Auletta G. (2011): 7 General Features of Life. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0008
  204. Auletta G. (2011): 14 Decisional, Emotional, and Cognitive Systems. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0015
  205. Auletta G. (2011): 25 Final Philosophical Remarks. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0026
  206. Auletta G. (2011): 18 The Basic Symbolic Systems. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0019
  207. Auletta G. (2011): 15 Behavior. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0016
  208. Auletta G. (2011): 11 Epigeny. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0012
  209. Auletta G. (2011): 17 Memory. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0018
  210. Auletta G. (2011): Introduction. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0001
  211. Auletta G. (2011): 9 Phylogeny. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0010
  212. Auletta G. (2011): 23 Language. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0024
  213. Auletta G. (2011): 16 Learning. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0017
  214. Auletta G. (2011): 21 Consciousness. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0022
  215. Auletta G. (2011): 4 Vision. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0005
  216. Auletta G. (2011): 12 Representational Semiotics. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0013
  217. Auletta G. (2011): 10 Ontogeny. Cognitive Biology. https://doi.org/10.1093/acprof:oso/9780199608485.003.0011
  218. Miller N., Saada R., Markovich S., Hurwitz I., Susswein A. (2011): l-arginine via nitric oxide is an inhibitory feedback modulator ofAplysiafeeding. Journal of Neurophysiology 105(4):1642-1650. https://doi.org/10.1152/jn.00827.2010
  219. Kappeler P. (2011): Entwicklung und Kontrolle des Verhaltens. Springer-Lehrbuch. https://doi.org/10.1007/978-3-642-20653-5_11
  220. MacPhail R., Hunter D., Irons T., Padilla S. (2011): Locomotion and Behavioral Toxicity in Larval Zebrafish: Background, Methods, and Data. Zebrafish. https://doi.org/10.1002/9781118102138.ch12
  221. Barron A., Søvik E., Cornish J. (2010): The Roles of Dopamine and Related Compounds in Reward-Seeking Behavior Across Animal Phyla. Frontiers in Behavioral Neuroscience 4. https://doi.org/10.3389/fnbeh.2010.00163
  222. Brembs B. (2010): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences 278(1707):930-939. https://doi.org/10.1098/rspb.2010.2325
  223. DoraReglo ̋ di (2010): AHomologoftheVertebratePituitaryAdenylateCyclase- ActivatingPolypeptideIsBothNecessaryandInstructiveforthe RapidFormationofAssociativeMemoryinanInvertebrate.
  224. Cheu E., Quek C., Ng S. (2010): Time series forecasting with appetitive reward-based pseudo-outer-product fuzzy neural network. The 2010 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2010.5596738
  225. Evgueni Jitsev (2010): On the self-organization of a hierarchical memory for compositional object representation in the visual cortex.
  226. Krasne F., Kemenes G., Glanzman D. (2010): Analysis of Learning in Invertebrates. Encyclopedia of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-08-045396-5.00011-7
  227. Wu J., Vilim F., Hatcher N., Due M., Sweedler J., Weiss K., et al. (2010): Composite Modulatory Feedforward Loop Contributes to the Establishment of a Network State. Journal of Neurophysiology 103(4):2174-2184. https://doi.org/10.1152/jn.01054.2009
  228. Guerra L., Silva M. (2010): Learning processes and the neural analysis of conditioning. Psychology & Neuroscience 3(2):195-208. https://doi.org/10.3922/j.psns.2010.2.009
  229. Nargeot R., Simmers J. (2010): Neural mechanisms of operant conditioning and learning-induced behavioral plasticity in Aplysia. Cellular and Molecular Life Sciences 68(5):803-816. https://doi.org/10.1007/s00018-010-0570-9
  230. Danek R. (2010): A funny thing happened on the way to the maze. https://doi.org/10.17077/etd.srii9jji
  231. Pirger Z., László Z., Kemenes I., Tóth G., Reglődi D., Kemenes G. (2010): A Homolog of the Vertebrate Pituitary Adenylate Cyclase-Activating Polypeptide Is Both Necessary and Instructive for the Rapid Formation of Associative Memory in an Invertebrate. The Journal of Neuroscience 30(41):13766-13773. https://doi.org/10.1523/jneurosci.2577-10.2010
  232. Khan A., Spencer G. (2009): Novel neural correlates of operant conditioning in normal and differentially reared Lymnaea. Journal of Experimental Biology 212(7):922-933. https://doi.org/10.1242/jeb.023069
  233. Claridge-Chang A., Roorda R., Vrontou E., Sjulson L., Li H., Hirsh J., et al. (2009): Writing Memories with Light-Addressable Reinforcement Circuitry. Cell 139(2):405-415. https://doi.org/10.1016/j.cell.2009.08.034
  234. Katzoff A., Miller N., Susswein A. (2009): Nitric oxide and histamine signal attempts to swallow: A component of learning that food is inedible inAplysia. Learning & Memory 17(1):50-62. https://doi.org/10.1101/lm.1624610
  235. Martínez‐Rubio C., Serrano G., Miller M. (2009): Localization of biogenic amines in the foregut of Aplysia californica: Catecholaminergic and serotonergic innervation. Journal of Comparative Neurology 514(4):329-342. https://doi.org/10.1002/cne.21991
  236. Debanne D., Campanac E. (2009): Memory: Mechanisms Other than LTP. Encyclopedia of Life Sciences. https://doi.org/10.1002/9780470015902.a0021398
  237. Kemenes G. (2009): Learning and Memory: How Sea Slug Behaviors Become Compulsive. Current Biology 19(13):R515-R517. https://doi.org/10.1016/j.cub.2009.05.052
  238. Byrne J., Antzoulatos E., Fioravante D. (2009): Learning and Memory in Invertebrates: Aplysia. Encyclopedia of Neuroscience. https://doi.org/10.1016/b978-008045046-9.00797-x
  239. Dayan P., Huys Q. (2009): Serotonin in Affective Control. Annual Review of Neuroscience 32(1):95-126. https://doi.org/10.1146/annurev.neuro.051508.135607
  240. Mozzachiodi R., Byrne J. (2009): More than synaptic plasticity: role of nonsynaptic plasticity in learning and memory. Trends in Neurosciences 33(1):17-26. https://doi.org/10.1016/j.tins.2009.10.001
  241. Nargeot R., Le Bon-Jego M., Simmers J. (2009): Cellular and Network Mechanisms of Operant Learning-Induced Compulsive Behavior in Aplysia. Current Biology 19(12):975-984. https://doi.org/10.1016/j.cub.2009.05.030
  242. Unknown authors (2008): Elterliche Fürsorge. Springer-Lehrbuch. https://doi.org/10.1007/978-3-540-68792-4_10
  243. Proekt A., Wong J., Zhurov Y., Kozlova N., Weiss K., Brezina V. (2008): Predicting Adaptive Behavior in the Environment from Central Nervous System Dynamics. PLoS ONE 3(11):e3678. https://doi.org/10.1371/journal.pone.0003678
  244. Andrea Soltoggio, John A. Bullinaria, Claudio Mattiussi, Peter Dürr, Dario Floreano (2008): Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios.
  245. Andrea Soltoggio (2008): Neuromodulation Increases Decision Speed in Dynamic Environments.
  246. Brembs B., Plendl W. (2008): Double Dissociation of PKC and AC Manipulations on Operant and Classical Learning in Drosophila. Current Biology 18(15):1168-1171. https://doi.org/10.1016/j.cub.2008.07.041
  247. Glanzman D. (2008): The Cell Biology of Learning and Memory in Aplysia. Advances in Psychology. https://doi.org/10.1016/s0166-4115(08)10021-8
  248. Levitan D., Lyons L., Perelman A., Green C., Motro B., Eskin A., et al. (2008): Training with inedible food in Aplysia causes expression of C/EBP in the buccal but not cerebral ganglion. Learning & Memory 15(6):412-416. https://doi.org/10.1101/lm.970408
  249. Franco Bertolucci (2008): Operant and classical learning in Drosophila melanogaster: the ignorant gene (ign). Online Publication Service of Würzburg University (Würzburg University).
  250. Lorenzetti F., Baxter D., Byrne J. (2008): Molecular Mechanisms Underlying a Cellular Analog of Operant Reward Learning. Neuron 59(5):815-828. https://doi.org/10.1016/j.neuron.2008.07.019
  251. Lorenzetti F., Byrne J. (2008): Cellular Mechanisms of Associative Learning in Aplysia. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-012370509-9.00012-7
  252. Isabel G., Preat T. (2008): Molecular and System Analysis of Olfactory Memory in Drosophila. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-012370509-9.00009-7
  253. Kisch J., Haupt S. (2008): Side-specific operant conditioning of antennal movements in the honey bee. Behavioural Brain Research 196(1):131-133. https://doi.org/10.1016/j.bbr.2008.07.002
  254. Casimir M. (2008): On the Origin and Evolution of Affective Capacities in Lower Vertebrates. Emotions as Bio-cultural Processes. https://doi.org/10.1007/978-0-387-09546-2_4
  255. Benjamin P., Kemenes G. (2008): Behavioral and Circuit Analysis of Learning and Memory in Mollusks. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-012370509-9.00068-1
  256. Benjamin P. (2008): Non-synaptic neuronal mechanisms of learning and memory in gastropod molluscs. Frontiers in Bioscience Volume(13):4051. https://doi.org/10.2741/2993
  257. Menzel R. (2008): Introduction and Overview. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-012370509-9.00046-2
  258. Mozzachiodi R., Lorenzetti F., Baxter D., Byrne J. (2008): Changes in neuronal excitability serve as a mechanism of long-term memory for operant conditioning. Nature Neuroscience 11(10):1146-1148. https://doi.org/10.1038/nn.2184
  259. Mozzachiodi R., Byrne J. (2008): Plasticity of Intrinsic Excitability as a Mechanism for Memory Storage. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-012370509-9.00041-3
  260. Barandiaran X., Moreno A. (2008): Adaptivity: From Metabolism to Behavior. Adaptive Behavior 16(5):325-344. https://doi.org/10.1177/1059712308093868
  261. Lee Y., Bailey C., Kandel E., Kaang B. (2008): Transcriptional regulation of long-term memory in the marine snail Aplysia. Molecular Brain 1(1). https://doi.org/10.1186/1756-6606-1-3
  262. Maye A., Hsieh C., Sugihara G., Brembs B. (2007): Order in Spontaneous Behavior. PLoS ONE 2(5):e443. https://doi.org/10.1371/journal.pone.0000443
  263. Alcaro A., Huber R., Panksepp J. (2007): Behavioral functions of the mesolimbic dopaminergic system: An affective neuroethological perspective. Brain Research Reviews 56(2):283-321. https://doi.org/10.1016/j.brainresrev.2007.07.014
  264. David L. Glanzman (2007): 14 Simple Minds: The Neurobiology of Invertebrate Learning and Memory. Cold Spring Harbor Monograph Archive. https://doi.org/10.1101/087969819.49.347
  265. Rumbaugh D., King J., Beran M., Washburn D., Gould K. (2007): A Salience Theory of Learning and Behavior: With Perspectives on Neurobiology and Cognition. International Journal of Primatology 28(5):973-996. https://doi.org/10.1007/s10764-007-9179-8
  266. Daucé E. (2007): Learning and control with large dynamic neural networks. The European Physical Journal Special Topics 142(1):123-161. https://doi.org/10.1140/epjst/e2007-00060-8
  267. Martin G., Oakes C., Tousignant H., Crabtree H., Yamakawa R. (2007): Structure and function of haemocytes in two marine gastropods, Megathura crenulata and Aplysia californica. Journal of Molluscan Studies 73(4):355-365. https://doi.org/10.1093/mollus/eym032
  268. Philips G., Tzvetkova E., Carew T. (2007): Transient Mitogen-Activated Protein Kinase Activation Is Confined to a Narrow Temporal Window Required for the Induction of Two-Trial Long-Term Memory inAplysia. The Journal of Neuroscience 27(50):13701-13705. https://doi.org/10.1523/jneurosci.4262-07.2007
  269. Serrano G., Martínez-Rubio C., Miller M. (2007): Endogenous Motor Neuron Properties Contribute to a Program-Specific Phase of Activity in the Multifunctional Feeding Central Pattern Generator ofAplysia. Journal of Neurophysiology 98(1):29-42. https://doi.org/10.1152/jn.01062.2006
  270. Hurwitz I., Ophir A., Korngreen A., Koester J., Susswein A. (2007): Currents Contributing to Decision Making in Neurons B31/B32 ofAplysia. Journal of Neurophysiology 99(2):814-830. https://doi.org/10.1152/jn.00972.2007
  271. Mukhtar S., Mukhtar S., Rana W. (2007): Brain-Friendly Strategies for the Inclusion Classroom. Asia Pacific Journal of Public Health 34(4):439-442. https://doi.org/10.1177/10105395211072500
  272. Silva M., Gonçalves F., Garcia-Mijares M. (2007): Neural events in the reinforcement contingency. The Behavior Analyst 30(1):17-30. https://doi.org/10.1007/bf03392140
  273. Menzel R., Brembs B., Giurfa M. (2007): Cognition in Invertebrates. Evolution of Nervous Systems. https://doi.org/10.1016/b0-12-370878-8/00183-x
  274. Nargeot R., Petrissans C., Simmers J. (2007): Behavioral andIn VitroCorrelates of Compulsive-Like Food Seeking Induced by Operant Conditioning inAplysia. The Journal of Neuroscience 27(30):8059-8070. https://doi.org/10.1523/jneurosci.1950-07.2007
  275. Barandiaran X., Moreno A. (2007): On the nature of neural information: A critique of the received view 50 years later. Neurocomputing 71(4-6):681-692. https://doi.org/10.1016/j.neucom.2007.09.014
  276. Nikitin E., Korshunova T., Zakharov I., Balaban P. (2007): Olfactory experience modifies the effect of odour on feeding behaviour in a goal-related manner. Journal of Comparative Physiology A 194(1):19-26. https://doi.org/10.1007/s00359-007-0272-4
  277. Unknown authors (2006): Elterliche Fürsorge. Springer-Lehrbuch. https://doi.org/10.1007/3-540-29977-7_10
  278. Katzoff A., Ben-Gedalya T., Hurwitz I., Miller N., Susswein Y., Susswein A. (2006): Nitric Oxide Signals ThatAplysiaHave Attempted to Eat, a Necessary Component of Memory Formation After Learning That Food Is Inedible. Journal of Neurophysiology 96(3):1247-1257. https://doi.org/10.1152/jn.00056.2006
  279. Brembs B., Wiener J. (2006): Context and occasion setting in Drosophila visual learning. Learning & Memory 13(5):618-628. https://doi.org/10.1101/lm.318606
  280. Baxter D., Byrne J. (2006): Feeding behavior of Aplysia: A model system for comparing cellular mechanisms of classical and operant conditioning. Learning & Memory 13(6):669-680. https://doi.org/10.1101/lm.339206
  281. Cataldo E., Byrne J., Baxter D. (2006): Computational Model of a Central Pattern Generator. Lecture Notes in Computer Science. https://doi.org/10.1007/11885191_17
  282. Kemenes I., Straub V., Nikitin E., Staras K., O’Shea M., Kemenes G., et al. (2006): Role of Delayed Nonsynaptic Neuronal Plasticity in Long-Term Associative Memory. Current Biology 16(13):1269-1279. https://doi.org/10.1016/j.cub.2006.05.049
  283. Cheng J., Feenstra M. (2006): Individual differences in dopamine efflux in nucleus accumbens shell and core during instrumental learning. Learning & Memory 13(2):168-177. https://doi.org/10.1101/lm.1806
  284. Luigi Rossini, P Rossini (2006): PHARMACOTHERAPEUTIC RECEPTOR SPECIFICITIES AND SELECTIVITY CLASSES, AND PLACEBO EFFECTS: A PERSPECTIVE.
  285. Guerra L. (2006): Princípios de condicionamento à luz da análise neural do estímulo antecedente. https://doi.org/10.11606/t.47.2006.tde-28052006-213453
  286. Díaz-Ríos M., Miller M. (2006): Target-Specific Regulation of Synaptic Efficacy in the Feeding Central Pattern Generator ofAplysia:Potential Substrates for Behavioral Plasticity?. The Biological Bulletin 210(3):215-229. https://doi.org/10.2307/4134559
  287. Giurfa M. (2006): Associative Learning: The Instructive Function of Biogenic Amines. Current Biology 16(20):R892-R895. https://doi.org/10.1016/j.cub.2006.09.021
  288. Lowe M., Spencer G. (2006): Perturbation of the activity of a single identified neuron affects long-term memory formation in a molluscan semi-intact preparation. Journal of Experimental Biology 209(4):711-721. https://doi.org/10.1242/jeb.02047
  289. Hawkins R., Clark G., Kandel E. (2006): Operant Conditioning of Gill Withdrawal inAplysia. The Journal of Neuroscience 26(9):2443-2448. https://doi.org/10.1523/jneurosci.3294-05.2006
  290. Zhang T., Dong X., Chen M. (2006): Recognition of LXXLL by Ligand Binding Domain of the Farnesoid X Receptor in Molecular Dynamics Simulation. Journal of Chemical Information and Modeling 46(6):2623-2630. https://doi.org/10.1021/ci060112v
  291. Frick A., Johnston D. (2005): Plasticity of dendritic excitability. Journal of Neurobiology 64(1):100-115. https://doi.org/10.1002/neu.20148
  292. McComb C., Rosenegger D., Varshney N., Kwok H., Lukowiak K. (2005): Operant conditioning of an in vitro CNS-pneumostome preparation of Lymnaea. Neurobiology of Learning and Memory 84(1):9-24. https://doi.org/10.1016/j.nlm.2005.02.002
  293. Shirinyan D., Teshiba T., Taylor K., O’Neill P., Lee S., Krasne F. (2005): Rostral Ganglia Are Required for Induction But Not Expression of Crayfish Escape Reflex Habituation: Role of Higher Centers in Reprogramming Low-Level Circuits. Journal of Neurophysiology 95(4):2721-2724. https://doi.org/10.1152/jn.00914.2005
  294. Barbas D., Zappulla J., Angers S., Bouvier M., Mohamed H., Byrne J., et al. (2005): An aplysia dopamine1‐like receptor: molecular and functional characterization. Journal of Neurochemistry 96(2):414-427. https://doi.org/10.1111/j.1471-4159.2005.03561.x
  295. Fioravante D., Smolen P., Byrne J. (2005): The 5-HT- and FMRFa-activated signaling pathways interact at the level of the Erk MAPK cascade: Potential inhibitory constraints on memory formation. Neuroscience Letters 396(3):235-240. https://doi.org/10.1016/j.neulet.2005.11.036
  296. Lorenzetti F., Mozzachiodi R., Baxter D., Byrne J. (2005): Classical and operant conditioning differentially modify the intrinsic properties of an identified neuron. Nature Neuroscience 9(1):17-19. https://doi.org/10.1038/nn1593
  297. Reyes F., Mozzachiodi R., Baxter D., Byrne J. (2005): Reinforcement in an in vitro analog of appetitive classical conditioningof feeding behavior in Aplysia: Blockade by a dopamineantagonist. Learning & Memory 12(3):216-220. https://doi.org/10.1101/lm.92905
  298. Tobler P., O’Doherty J., Dolan R., Schultz W. (2005): Human Neural Learning Depends on Reward Prediction Errors in the Blocking Paradigm. Journal of Neurophysiology 95(1):301-310. https://doi.org/10.1152/jn.00762.2005
  299. Pellicciari R., Costantino G., Fiorucci S. (2005): Farnesoid X Receptor:  From Structure to Potential Clinical Applications. Journal of Medicinal Chemistry 48(17):5383-5403. https://doi.org/10.1021/jm0582221
  300. Douglas S., Dawson-Scully K., Sokolowski M. (2005): The neurogenetics and evolution of food-related behaviour. Trends in Neurosciences 28(12):644-652. https://doi.org/10.1016/j.tins.2005.09.006
  301. Stephan Shuichi Haupt (2005): Das gustatorische System und antennales Lernen in der Honigbiene (Apis mellifera L.). DepositOnce. https://doi.org/10.14279/depositonce-1239
  302. Meyer U., Costantino G., Macchiarulo A., Pellicciari R. (2005): Is Antagonism of E/Z-Guggulsterone at the Farnesoid X Receptor Mediated by a Noncanonical Binding Site? A Molecular Modeling Study. Journal of Medicinal Chemistry 48(22):6948-6955. https://doi.org/10.1021/jm0505056
  303. Smith W., Starck S., Roberts R., Schuman E. (2005): Dopaminergic Stimulation of Local Protein Synthesis Enhances Surface Expression of GluR1 and Synaptic Transmission in Hippocampal Neurons. Neuron 45(5):765-779. https://doi.org/10.1016/j.neuron.2005.01.015
  304. Kristan W., Calabrese R., Friesen W. (2005): Neuronal control of leech behavior. Progress in Neurobiology 76(5):279-327. https://doi.org/10.1016/j.pneurobio.2005.09.004
  305. Kelley A. (2004): Memory and Addiction. Neuron 44(1):161-179. https://doi.org/10.1016/j.neuron.2004.09.016
  306. Brembs B., Baxter D., Byrne J. (2004): Extending In Vitro Conditioning in Aplysia to Analyze Operant and Classical Processes in the Same Preparation. Learning & Memory 11(4):412-420. https://doi.org/10.1101/lm.74404
  307. Claudia Pahl‐Wostl, Sonia Schmidt, Andrea Emilio Rizzoli, Anthony J. Jakeman (2004): Complexity and Integrated Resources Management.
  308. Cropper E., Evans C., Hurwitz I., Jing J., Proekt A., Romero A., et al. (2004): Feeding Neural Networks in the Mollusc <i>Aplysia</i>. Neurosignals 13(1-2):70-86. https://doi.org/10.1159/000076159
  309. Leonard J., Edstrom J. (2004): Parallel processing in an identified neural circuit: the Aplysia californica gill‐withdrawal response model system. Biological Reviews 79(1):1-59. https://doi.org/10.1017/s1464793103006183
  310. Panksepp J., Huber R. (2004): Ethological analyses of crayfish behavior: a new invertebrate system for measuring the rewarding properties of psychostimulants. Behavioural Brain Research 153(1):171-180. https://doi.org/10.1016/j.bbr.2003.11.014
  311. Díaz-Ríos M., Miller M. (2004): Rapid Dopaminergic Signaling by Interneurons That Contain Markers for Catecholamines and GABA in the Feeding Circuitry of Aplysia. Journal of Neurophysiology 93(4):2142-2156. https://doi.org/10.1152/jn.00003.2004
  312. Friedel R. (2004): Dopamine Dysfunction in Borderline Personality Disorder: A Hypothesis. Neuropsychopharmacology 29(6):1029-1039. https://doi.org/10.1038/sj.npp.1300424
  313. Roland W. Scholz, Claudia R. Binder (2004): Principles of Human-Environment Systems (HES) Research. ScholarsArchive (Brigham Young University).
  314. Cooper S. (2004): Donald O. Hebb’s synapse and learning rule: a history and commentary. Neuroscience & Biobehavioral Reviews 28(8):851-874. https://doi.org/10.1016/j.neubiorev.2004.09.009
  315. Brown T., Byrne J., LaBar K., LeDoux J., Lindquist D., Thompson R., et al. (2004): Learning and Memory: Basic Mechanisms. From Molecules to Networks. https://doi.org/10.1016/b978-012148660-0/50019-6
  316. Roberts A., Glanzman D. (2003): Learning in Aplysia: looking at synaptic plasticity from both sides. Trends in Neurosciences 26(12):662-670. https://doi.org/10.1016/j.tins.2003.09.014
  317. Brembs B. (2003): Operant conditioning in invertebrates. Current Opinion in Neurobiology 13(6):710-717. https://doi.org/10.1016/j.conb.2003.10.002
  318. Brembs B. (2003): Operant Reward Learning in Aplysia. Current Directions in Psychological Science 12(6):218-221. https://doi.org/10.1046/j.0963-7214.2003.01265.x
  319. Barbas D., DesGroseillers L., Castellucci V., Carew T., Marinesco S. (2003): Multiple Serotonergic Mechanisms Contributing to Sensitization inAplysia: Evidence of Diverse Serotonin Receptor Subtypes. Learning & Memory 10(5):373-386. https://doi.org/10.1101/lm.66103
  320. Daoudal G., Debanne D. (2003): Long-Term Plasticity of Intrinsic Excitability: Learning Rules and Mechanisms. Learning & Memory 10(6):456-465. https://doi.org/10.1101/lm.64103
  321. Wickens J., Reynolds J., Hyland B. (2003): Neural mechanisms of reward-related motor learning. Current Opinion in Neurobiology 13(6):685-690. https://doi.org/10.1016/j.conb.2003.10.013
  322. Jones N., Kemenes I., Kemenes G., Benjamin P. (2003): A Persistent Cellular Change in a Single Modulatory Neuron Contributes to Associative Long-Term Memory. Current Biology 13(12):1064-1069. https://doi.org/10.1016/s0960-9822(03)00380-4
  323. Dembrow N., Jing J., Proekt A., Romero A., Vilim F., Cropper E., et al. (2003): A Newly Identified Buccal Interneuron Initiates and Modulates Feeding Motor Programs inAplysia. Journal of Neurophysiology 90(4):2190-2204. https://doi.org/10.1152/jn.00173.2003
  324. Mozzachiodi R., Lechner H., Baxter D., Byrne J. (2003): In Vitro Analog of Classical Conditioning of Feeding Behavior in Aplysia. Learning & Memory 10(6):478-494. https://doi.org/10.1101/lm.65303
  325. Roland W. Scholz, Claudia R. Binder (2003): The paradigm of human-environment systems. Repository for Publications and Research Data (ETH Zurich). https://doi.org/10.3929/ethz-a-004520890
  326. Nargeot R. (2003): Voltage-Dependent Switching of Sensorimotor Integration by a Lobster Central Pattern Generator. The Journal of Neuroscience 23(12):4803-4808. https://doi.org/10.1523/jneurosci.23-12-04803.2003
  327. Shaik S. (2003): Chemistry—A Central Pillar of Human Culture. Angewandte Chemie International Edition 42(28):3208-3215. https://doi.org/10.1002/anie.200330038
  328. Shaik S. (2003): Die Chemie – eine zentrale Säule der menschlichen Kultur. Angewandte Chemie 115(28):3326-3333. https://doi.org/10.1002/ange.200230038
  329. Unknown authors (2002): Incentives and motivation. Politics of Bureaucracy. https://doi.org/10.4324/9780203455494-12
  330. Katzoff A., Ben-Gedalya T., Susswein A. (2002): Nitric Oxide Is Necessary for Multiple Memory Processes after Learning That a Food Is Inedible inAplysia. The Journal of Neuroscience 22(21):9581-9594. https://doi.org/10.1523/jneurosci.22-21-09581.2002
  331. Rankin C. (2002): A Bite to Remember. Science 296(5573):1624-1625. https://doi.org/10.1126/science.1072683
  332. Scheindlin J. (2002): Proliferative Diabetic Retinopathy: Current Treatment Strategies for Progression. Diabetic Renal-Retinal Syndrome. https://doi.org/10.1007/978-94-010-0614-9_9
  333. Carew T. (2002): Understanding the consequences. Nature 417(6891):803-805. https://doi.org/10.1038/417803a
  334. Schultz W. (2002): Getting Formal with Dopamine and Reward. Neuron 36(2):241-263. https://doi.org/10.1016/s0896-6273(02)00967-4

Baier A, Wittek B, Brembs B. (2002): Drosophila as a new model organism for the neurobiology of aggression? J. exp. Biol. 205:1233–1240.

  1. Saxena S., Salunke A., Pandya N. (2026): Elucidating the role of neurotransmitters in the behavioural plasticity of Camponotus compressus (Hymenoptera: Formicidae). https://doi.org/10.21203/rs.3.rs-8751427/v1
  2. Asahina K. (2025): Neuromodulation of conflicts and hierarchy in insects. Comprehensive Molecular Insect Science. https://doi.org/10.1016/b978-0-323-95424-2.00045-5
  3. Goubault M., Roux A., Bussy M., Tibbetts E. (2025): Neuroendocrine control of insect aggression: do environmental stressors modulate aggressive behavior?. Current Opinion in Insect Science 71:101407. https://doi.org/10.1016/j.cois.2025.101407
  4. Huang Y., Wang M., Chang X., Ke Y., Li Z. (2025): Comparison Between Worker and Soldier Transcriptomes of Termite Neotermes binovatus Reveals Caste Specialization of Host–Flagellate Symbiotic System. Insects 16(3):325. https://doi.org/10.3390/insects16030325
  5. Gálvez Salido A., de la Herrán R., Robles F., Ruiz Rejón C., Navajas-Pérez R. (2024): Automatic counting and identification of two Drosophila melanogaster (Diptera: Drosophilidae) morphs with image-recognition artificial intelligence. The Canadian Entomologist 156. https://doi.org/10.4039/tce.2024.36
  6. Jiang L., Li W., Liu X., Li C., Sun Z., Wu F., et al. (2024): Integrative techniques for insect behavior analysis using micro‐CT and Blender. Insect Science 32(4):1466-1472. https://doi.org/10.1111/1744-7917.13458
  7. Mezheritskiy M., Vorontsov D., Dyakonova V., Zakharov I. (2024): Behavioral Functions of Octopamine in Adult Insects under Stressful Conditions. Biology Bulletin Reviews 14(5):535-547. https://doi.org/10.1134/s2079086424700014
  8. Mezheritskiy M., Vorontsov D., Dyakonova V., Zakharov I. (2024): Behavioral functions of octopamine in adult insects under stressful conditions. Журнал общей биологии 85(1):3-16. https://doi.org/10.31857/s0044459624010015
  9. Liang Q., Liu D., Zhu B., Wang F. (2024): NMDAR-CaMKII Pathway as a Central Regulator of Aggressiveness: Evidence from Transcriptomic and Metabolomic Analysis in Swimming Crabs Portunus trituberculatus. International Journal of Molecular Sciences 25(23):12560. https://doi.org/10.3390/ijms252312560
  10. Park A. (2023): Neurobiology of Alcohol-Induced Aggression. Handbook of Anger, Aggression, and Violence. https://doi.org/10.1007/978-3-031-31547-3_88
  11. Park A. (2023): Neurobiology of Alcohol-Induced Aggression. Handbook of Anger, Aggression, and Violence. https://doi.org/10.1007/978-3-030-98711-4_88-1
  12. Wyszkowska J., Kobak J., Aonuma H. (2023): Electromagnetic field exposure affects the calling song, phonotaxis, and level of biogenic amines in crickets. Environmental Science and Pollution Research 30(40):93255-93268. https://doi.org/10.1007/s11356-023-28981-0
  13. Wyszkowska J., Kobak J., Aonuma H. (2023): Electromagnetic field exposure affects the calling song, phonotaxis, and level of biogenic amines in crickets. https://doi.org/10.21203/rs.3.rs-2957977/v1
  14. Rosikon K., Bone M., Lawal H. (2023): Regulation and modulation of biogenic amine neurotransmission in Drosophila and Caenorhabditis elegans. Frontiers in Physiology 14. https://doi.org/10.3389/fphys.2023.970405
  15. Godfrey R., Alsop E., Bjork R., Chauhan B., Ruvalcaba H., Antone J., et al. (2023): Modelling TDP-43 proteinopathy in Drosophila uncovers shared and neuron-specific targets across ALS and FTD relevant circuits. Acta Neuropathologica Communications 11(1). https://doi.org/10.1186/s40478-023-01656-0
  16. Sun X., Yu X., Li K. (2023): Anger and aggression research: A bibliometric analysis from 2012 to 2022. Medicine 102(36):e35132. https://doi.org/10.1097/md.0000000000035132
  17. Sheardown E., Mech A., Petrazzini M., Leggieri A., Gidziela A., Hosseinian S., et al. (2022): Translational relevance of forward genetic screens in animal models for the study of psychiatric disease. Neuroscience & Biobehavioral Reviews 135:104559. https://doi.org/10.1016/j.neubiorev.2022.104559
  18. Han G., Zeng Y., Zhu D. (2022): Effect of a Nitric Oxide Synthase Inhibitor on Fighting Behavior of Male Crickets Velarifictorus aspersus (Orthoptera: Gryllidae) under Different Resource Conditions. Journal of Entomological Science 57(2):288-296. https://doi.org/10.18474/jes21-59
  19. Klowden M., Palli S. (2022): Behavioral systems. Physiological Systems in Insects. https://doi.org/10.1016/b978-0-12-820359-0.00004-9
  20. Wang R., Ma B., Shi K., Wu F., Zhou C. (2022): Effects of lithium on aggression in Drosophila. Neuropsychopharmacology 48(5):754-763. https://doi.org/10.1038/s41386-022-01475-2
  21. Carvajal-Oliveros A., Campusano J. (2021): Studying the Contribution of Serotonin to Neurodevelopmental Disorders. Can This Fly?. Frontiers in Behavioral Neuroscience 14. https://doi.org/10.3389/fnbeh.2020.601449
  22. Damrau C., Colomb J., Brembs B. (2021): Sensitivity to expression levels underlies differential dominance of a putative null allele of the Drosophila tβh gene in behavioral phenotypes. PLOS Biology 19(5):e3001228. https://doi.org/10.1371/journal.pbio.3001228
  23. Sasaki K., Okada Y., Shimoji H., Aonuma H., Miura T., Tsuji K. (2021): Social Evolution With Decoupling of Multiple Roles of Biogenic Amines Into Different Phenotypes in Hymenoptera. Frontiers in Ecology and Evolution 9. https://doi.org/10.3389/fevo.2021.659160
  24. Monyak R., Golbari N., Chan Y., Pranevicius A., Tang G., Fernández M., et al. (2021): Masculinized Drosophila females adapt their fighting strategies to their opponent. Journal of Experimental Biology 224(6). https://doi.org/10.1242/jeb.238006
  25. Troy Takemori (2021): Exploring the Genetic Underpinnings of Aggression in Drosophila melanogaster. PubMed.
  26. Simard C., Touaibia M., Allain E., Hebert-Chatelain E., Pichaud N. (2020): Role of the Mitochondrial Pyruvate Carrier in the Occurrence of Metabolic Inflexibility in Drosophila melanogaster Exposed to Dietary Sucrose. Metabolites 10(10):411. https://doi.org/10.3390/metabo10100411
  27. Bilz F., Gilles M., Schatton A., Pflüger H., Schubert M. (2020): Intensity coded octopaminergic modulation of aversive crawling behavior in Drosophila melanogaster larvae. https://doi.org/10.1101/2020.09.04.281022
  28. Simon J., Heberlein U. (2020): Social hierarchy is established and maintained with distinct acts of aggression in male Drosophila. Journal of Experimental Biology. https://doi.org/10.1242/jeb.232439
  29. Simon J., Heberlein U. (2020): Social hierarchy is established and maintained with distinct acts of aggression in male Drosophila. https://doi.org/10.1101/2020.05.12.091553
  30. Sherer L., Catudio Garrett E., Morgan H., Brewer E., Sirrs L., Shearin H., et al. (2020): Octopamine neuron dependent aggression requires dVGLUT from dual-transmitting neurons. PLOS Genetics 16(2):e1008609. https://doi.org/10.1371/journal.pgen.1008609
  31. Belenioti M., Chaniotakis N. (2020): Aggressive Behaviour of Drosophila suzukii in Relation to Environmental and Social Factors. Scientific Reports 10(1). https://doi.org/10.1038/s41598-020-64941-1
  32. Agrawal P., Kao D., Chung P., Looger L. (2020): The neuropeptide Drosulfakinin regulates social isolation-induced aggression in Drosophila. Journal of Experimental Biology. https://doi.org/10.1242/jeb.207407
  33. Smith A., Simons M., Bazarko V., Harach J., Seid M. (2019): Queen–worker aggression in the facultatively eusocial bee Megalopta genalis. Insectes Sociaux 66(3):479-490. https://doi.org/10.1007/s00040-019-00712-0
  34. Palavicino-Maggio C., Chan Y., McKellar C., Kravitz E. (2019): A small number of cholinergic neurons mediate hyperaggression in female Drosophila. Proceedings of the National Academy of Sciences 116(34):17029-17038. https://doi.org/10.1073/pnas.1907042116
  35. Agrawal P., Kao D., Chung P., Looger L. (2019): The neuropeptide Drosulfakinin regulates social isolation-induced aggression in Drosophila. https://doi.org/10.1101/646232
  36. Sahu S., Dhar G., Mishra M. (2019): Methods to Detect the Complex Behaviours in Drosophila. Springer Protocols Handbooks. https://doi.org/10.1007/978-1-4939-9756-5_19
  37. Damrau C., Toshima N., Tanimura T., Brembs B., Colomb J. (2018): Octopamine and Tyramine Contribute Separately to the Counter-Regulatory Response to Sugar Deficit in Drosophila. Frontiers in Systems Neuroscience 11. https://doi.org/10.3389/fnsys.2017.00100
  38. Damrau C., Colomb J., Brembs B. (2018): Differential dominance of an allele of the Drosophila tßh gene challenges standard genetic techniques. https://doi.org/10.1101/504332
  39. Hong K., Park Y., Suh H. (2018): Two combined amino acids promote sleep activity in caffeine-induced sleepless model systems. Nutrition Research and Practice 12(3):208. https://doi.org/10.4162/nrp.2018.12.3.208
  40. Kononenko N., Hartfil S., Willer J., Ferch J., Wolfenberg H., Pflüger H. (2018): A population of descending tyraminergic/octopaminergic projection neurons of the insect deutocerebrum. Journal of Comparative Neurology 527(6):1027-1038. https://doi.org/10.1002/cne.24583
  41. Kang W., Zeng Y., Zhu D. (2018): Effects of physical and social experiences and octopamine receptor agonist on fighting behavior of male crickets Velarifictorus aspersus (Orthoptera: Gryllidae). Journal of Asia-Pacific Entomology 21(2):445-450. https://doi.org/10.1016/j.aspen.2018.02.008
  42. Golden C., Zachar R., Lowry B., Tran V. (2017): Role of Neurobiological Factors. Handbook of Behavioral Criminology. https://doi.org/10.1007/978-3-319-61625-4_3
  43. Shimoji H., Aonuma H., Miura T., Tsuji K., Sasaki K., Okada Y. (2017): Queen contact and among-worker interactions dually suppress worker brain dopamine as a potential regulator of reproduction in an ant. Behavioral Ecology and Sociobiology 71(2). https://doi.org/10.1007/s00265-016-2263-3
  44. Bosco J., Riechert S., O’Meara B. (2017): The ontogeny of personality traits in the desert funnel‐web spider, Agelenopsis lisa (Araneae: Agelenidae). Ethology 123(9):648-658. https://doi.org/10.1111/eth.12639
  45. Asahina K. (2017): Neuromodulation and Strategic Action Choice in Drosophila Aggression. Annual Review of Neuroscience 40(1):51-75. https://doi.org/10.1146/annurev-neuro-072116-031240
  46. Watanabe K., Chiu H., Pfeiffer B., Wong A., Hoopfer E., Rubin G., et al. (2017): A Circuit Node that Integrates Convergent Input from Neuromodulatory and Social Behavior-Promoting Neurons to Control Aggression in Drosophila. Neuron 95(5):1112-1128.e7. https://doi.org/10.1016/j.neuron.2017.08.017
  47. Pons M., Soulard C., Soustelle L., Parmentier M., Grau Y., Layalle S. (2017): A New Behavioral Test and Associated Genetic Tools Highlight the Function of Ventral Abdominal Muscles in Adult Drosophila. Frontiers in Cellular Neuroscience 11. https://doi.org/10.3389/fncel.2017.00371
  48. Stevenson P., Rillich J. (2017): Neuromodulators and the Control of Aggression in Crickets. The Cricket as a Model Organism. https://doi.org/10.1007/978-4-431-56478-2_12
  49. Davis S., Thomas A., Liu L., Campbell I., Dierick H. (2017): Isolation of Aggressive Behavior Mutants in Drosophila Using a Screen for Wing Damage. Genetics 208(1):273-282. https://doi.org/10.1534/genetics.117.300292
  50. Chapman T., Wolfner M. (2017): Reproductive behaviour: Make love, then war. Nature Ecology & Evolution 1(6). https://doi.org/10.1038/s41559-017-0174
  51. Buhl C., Rogers S. (2016): Mechanisms underpinning aggregation and collective movement by insect groups. Current Opinion in Insect Science 15:125-130. https://doi.org/10.1016/j.cois.2016.04.011
  52. Anderson D. (2016): Circuit modules linking internal states and social behaviour in flies and mice. Nature Reviews Neuroscience 17(11):692-704. https://doi.org/10.1038/nrn.2016.125
  53. Hoopfer E. (2016): Neural control of aggression in Drosophila. Current Opinion in Neurobiology 38:109-118. https://doi.org/10.1016/j.conb.2016.04.007
  54. Schneider J., Atallah J., Levine J. (2016): Social structure and indirect genetic effects: genetics of social behaviour. Biological Reviews 92(2):1027-1038. https://doi.org/10.1111/brv.12267
  55. Hong K., Park Y., Suh H. (2016): Sleep-promoting effects of a GABA/5-HTP mixture: Behavioral changes and neuromodulation in an invertebrate model. Life Sciences 150:42-49. https://doi.org/10.1016/j.lfs.2016.02.086
  56. Trannoy S., Kravitz E. (2016): Strategy changes in subsequent fights as consequences of winning and losing in fruit fly fights. Fly 11(2):129-138. https://doi.org/10.1080/19336934.2016.1259041
  57. Kim Y. (2016): A Drosophila Model for Aggression. Animal Models of Behavior Genetics. https://doi.org/10.1007/978-1-4939-3777-6_2
  58. Anders Vesterberg (2015): Gene-Environment Interplay on Oviposition Site Selection in Drosophila melanogaster. TSpace.
  59. Bubak A., Rieger N., Watt M., Renner K., Swallow J. (2015): David vs. Goliath: Serotonin modulates opponent perception between smaller and larger rivals. Behavioural Brain Research 292:521-527. https://doi.org/10.1016/j.bbr.2015.07.028
  60. Maximino C., Silva R., da Silva S., Rodrigues L., Barbosa H., de Carvalho T., et al. (2015): Non-mammalian models in behavioral neuroscience: consequences for biological psychiatry. Frontiers in Behavioral Neuroscience 9. https://doi.org/10.3389/fnbeh.2015.00233
  61. Rohrscheib C., Bondy E., Josh P., Riegler M., Eyles D., van Swinderen B., et al. (2015): Wolbachia Influences the Production of Octopamine and Affects Drosophila Male Aggression. Applied and Environmental Microbiology 81(14):4573-4580. https://doi.org/10.1128/aem.00573-15
  62. Christine Damrau (2015): Aminergic control of Drosophila behavior. Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver. https://doi.org/10.17169/refubium-17177
  63. Kravitz E., Fernandez M. (2015): Aggression in Drosophila. Behavioral Neuroscience 129(5):549-563. https://doi.org/10.1037/bne0000089
  64. Elizabeth K. Peterson, P E Cheryl Carrico (2015): Laboratory Exercise in Behavioral Genetics Using Team-based Learning Strategies. Bioscene: The Journal Of College Biology Teaching.
  65. Hoopfer E., Jung Y., Inagaki H., Rubin G., Anderson D. (2015): P1 interneurons promote a persistent internal state that enhances inter-male aggression in Drosophila. eLife 4. https://doi.org/10.7554/elife.11346
  66. Benelli G. (2015): Should I fight or should I flight? How studying insect aggression can help integrated pest management. Pest Management Science 71(7):885-892. https://doi.org/10.1002/ps.3974
  67. Rillich J., Stevenson P. (2015): Releasing stimuli and aggression in crickets: octopamine promotes escalation and maintenance but not initiation. Frontiers in Behavioral Neuroscience 9. https://doi.org/10.3389/fnbeh.2015.00095
  68. Shorter J., Couch C., Huang W., Carbone M., Peiffer J., Anholt R., et al. (2015): Genetic architecture of natural variation in Drosophila melanogaster aggressive behavior. Proceedings of the National Academy of Sciences 112(27). https://doi.org/10.1073/pnas.1510104112
  69. Zwarts L., Vanden Broeck L., Cappuyns E., Ayroles J., Magwire M., Vulsteke V., et al. (2015): The genetic basis of natural variation in mushroom body size in Drosophila melanogaster. Nature Communications 6(1). https://doi.org/10.1038/ncomms10115
  70. Haynes P., Christmann B., Griffith L. (2015): A single pair of neurons links sleep to memory consolidation in Drosophila melanogaster. eLife 4. https://doi.org/10.7554/elife.03868
  71. Newland P., Al Ghamdi M., Sharkh S., Aonuma H., Jackson C. (2015): Exposure to static electric fields leads to changes in biogenic amine levels in the brains ofDrosophila. Proceedings of the Royal Society B: Biological Sciences 282(1812):20151198. https://doi.org/10.1098/rspb.2015.1198
  72. Bubak A., Grace J., Watt M., Renner K., Swallow J. (2014): Neurochemistry as a bridge between morphology and behavior: Perspectives on aggression in insects. Current Zoology 60(6):778-790. https://doi.org/10.1093/czoolo/60.6.778
  73. Christopher M. Clark (2014): Neural Orchestration of the C. elegans Escape Response: A Dissertation. https://doi.org/10.13028/m24s4t
  74. Penick C., Brent C., Dolezal K., Liebig J. (2014): Neurohormonal changes associated with ritualized combat and the formation of a reproductive hierarchy in the antHarpegnathos saltator. Journal of Experimental Biology. https://doi.org/10.1242/jeb.098301
  75. Moore D., Paquette C., Shropshire J., Seier E., Joplin K. (2014): Extensive Reorganization of Behavior Accompanies Ontogeny of Aggression in Male Flesh Flies. PLoS ONE 9(4):e93196. https://doi.org/10.1371/journal.pone.0093196
  76. Luo J., Lushchak O., Goergen P., Williams M., Nässel D. (2014): Drosophila Insulin-Producing Cells Are Differentially Modulated by Serotonin and Octopamine Receptors and Affect Social Behavior. PLoS ONE 9(6):e99732. https://doi.org/10.1371/journal.pone.0099732
  77. Asahina K., Watanabe K., Duistermars B., Hoopfer E., González C., Eyjólfsdóttir E., et al. (2014): Tachykinin-Expressing Neurons Control Male-Specific Aggressive Arousal in Drosophila. Cell 156(1-2):221-235. https://doi.org/10.1016/j.cell.2013.11.045
  78. Ruiz-Rubio M., Calahorro F., Gámez-del-Estal M. (2014): Invertebrate Models of Synaptic Transmission in Autism Spectrum Disorders. Neuromethods. https://doi.org/10.1007/978-1-4939-2250-5_6
  79. Selcho M., Pauls D., Huser A., Stocker R., Thum A. (2014): Characterization of the octopaminergic and tyraminergic neurons in the central brain of Drosophila larvae. Journal of Comparative Neurology 522(15):3485-3500. https://doi.org/10.1002/cne.23616
  80. Yapici N., Zimmer M., Domingos A. (2014): Cellular and molecular basis of decision‐making. EMBO reports 15(10):1023-1035. https://doi.org/10.15252/embr.201438993
  81. Philip Goergen (2014): The Molecular Mechanism of Aggression and Feeding Behaviour in Drosophila melanogaster. KTH Publication Database DiVA (KTH Royal Institute of Technology).
  82. Raghavendra Magalam (2014): Interaction between dopamine and octopamine in Drosophila melanogaster brain. Epsilon Archive for Student Projects (University of Southampton).
  83. Jezzini S., Reyes-Colón D., Sosa M. (2014): Characterization of a Prawn OA/TA Receptor in Xenopus Oocytes Suggests Functional Selectivity between Octopamine and Tyramine. PLoS ONE 9(10):e111314. https://doi.org/10.1371/journal.pone.0111314
  84. Bubak A., Swallow J., Renner K. (2013): Whole brain monoamine detection and manipulation in a stalk-eyed fly. Journal of Neuroscience Methods 219(1):124-130. https://doi.org/10.1016/j.jneumeth.2013.07.006
  85. Szczuka A., Korczynska J., Wnuk A., Symonowicz B., Szwacka A., Mazurkiewicz P., et al. (2013): The effects of serotonin, dopamine, octopamine and tyramine on behavior of workers of the ant Formica polyctena during dyadic aggression tests. Acta Neurobiologiae Experimentalis 73(4):495-520. https://doi.org/10.55782/ane-2013-1955
  86. Kamhi J., Traniello J. (2013): Biogenic Amines and Collective Organization in a Superorganism: Neuromodulation of Social Behavior in Ants. Brain, Behavior and Evolution 82(4):220-236. https://doi.org/10.1159/000356091
  87. Donnelly J., Clark C., Leifer A., Pirri J., Haburcak M., Francis M., et al. (2013): Monoaminergic Orchestration of Motor Programs in a Complex C. elegans Behavior. PLoS Biology 11(4):e1001529. https://doi.org/10.1371/journal.pbio.1001529
  88. Julia Riedl (2013): Identification of neurons controlling orientation behavior in the Drosophila melanogaster larva. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas).
  89. Klowden M. (2013): Behavioral Systems. Physiological Systems in Insects. https://doi.org/10.1016/b978-0-12-415819-1.00005-2
  90. Briffa M., Hardy I., Mowles S. (2013): Prospects for animal contests. Animal Contests. https://doi.org/10.1017/cbo9781139051248.018
  91. Williams M., Goergen P., Rajendran J., Klockars A., Kasagiannis A., Fredriksson R., et al. (2013): Regulation of Aggression by Obesity-Linked GenesTfAP-2andTwzThrough Octopamine Signaling inDrosophila. Genetics 196(1):349-362. https://doi.org/10.1534/genetics.113.158402
  92. Stevenson P., Schildberger K. (2013): Mechanisms of experience dependent control of aggression in crickets. Current Opinion in Neurobiology 23(3):318-323. https://doi.org/10.1016/j.conb.2013.03.002
  93. Strauss R. (2013): Neurobiological Models of the Central Complex and the Mushroom Bodies. Cognitive Systems Monographs. https://doi.org/10.1007/978-3-319-02362-5_1
  94. Georgia-Martha Gkotsi, Lazare Benaroyo (2012): Neuroscience and the Treatment of Mentally Ill. Criminal Offenders: Some Ethical Issues. IRIS.
  95. Zwarts L., Versteven M., Callaerts P. (2012): Genetics and neurobiology of aggression in Drosophila. Fly 6(1):35-48. https://doi.org/10.4161/fly.19249
  96. Zwarts L., Clements J., Callaerts P. (2012): Deciphering the Adult Brain: From Neuroanatomy to Behavior. Neuromethods. https://doi.org/10.1007/978-1-61779-830-6_1
  97. Selcho M., Pauls D., el Jundi B., Stocker R., Thum A. (2012): The Role of octopamine and tyramine in Drosophila larval locomotion. Journal of Comparative Neurology 520(16):3764-3785. https://doi.org/10.1002/cne.23152
  98. Stevenson P., Rillich J. (2012): The Decision to Fight or Flee – Insights into Underlying Mechanism in Crickets. Frontiers in Neuroscience 6. https://doi.org/10.3389/fnins.2012.00118
  99. Huang Q., Sun P., Zhou X., Lei C. (2012): Characterization of Head Transcriptome and Analysis of Gene Expression Involved in Caste Differentiation and Aggression in Odontotermes formosanus (Shiraki). PLoS ONE 7(11):e50383. https://doi.org/10.1371/journal.pone.0050383
  100. Cossío-Bayúgar R., Miranda-Miranda E., Narváez Padilla V., Olvera-Valencia F., Reynaud E. (2012): Perturbation of tyraminergic/octopaminergic function inhibits oviposition in the cattle tick Rhipicephalus (Boophilus) microplus. Journal of Insect Physiology 58(5):628-633. https://doi.org/10.1016/j.jinsphys.2012.01.006
  101. Veronica L. Fregoso (2012): Biogenic Amine Levels Correlate with Time of Day, Age, Light Cycle, and Aggressive State in the Flesh Fly, Sarcophaga crassipalpis. Digital Commons – East Tennessee State University (East Tennessee State University).
  102. Neckameyer W., Argue K. (2012): Comparative approaches to the study of physiology:Drosophilaas a physiological tool. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 304(3):R177-R188. https://doi.org/10.1152/ajpregu.00084.2012
  103. van Alphen B., van Swinderen B. (2011): Drosophila strategies to study psychiatric disorders. Brain Research Bulletin 92:1-11. https://doi.org/10.1016/j.brainresbull.2011.09.007
  104. Brooks E., Greer C., Romero-Calderón R., Serway C., Grygoruk A., Haimovitz J., et al. (2011): A Putative Vesicular Transporter Expressed in Drosophila Mushroom Bodies that Mediates Sexual Behavior May Define a Neurotransmitter System. Neuron 72(2):316-329. https://doi.org/10.1016/j.neuron.2011.08.032
  105. Sharon G., Segal D., Zilber-Rosenberg I., Rosenberg E. (2011): Symbiotic bacteria are responsible for diet-induced mating preference inDrosophila melanogaster, providing support for the hologenome concept of evolution. Gut Microbes 2(3):190-192. https://doi.org/10.4161/gmic.2.3.16103
  106. Pflüger H., Duch C. (2011): Dynamic Neural Control of Insect Muscle Metabolism Related to Motor Behavior. Physiology 26(4):293-303. https://doi.org/10.1152/physiol.00002.2011
  107. Zwarts L., Magwire M., Carbone M., Versteven M., Herteleer L., Anholt R., et al. (2011): Complex genetic architecture of Drosophila aggressive behavior. Proceedings of the National Academy of Sciences 108(41):17070-17075. https://doi.org/10.1073/pnas.1113877108
  108. Jonsson T., Kravitz E., Heinrich R. (2011): Sound production during agonistic behavior of maleDrosophila melanogaster. Fly 5(1):29-38. https://doi.org/10.4161/fly.5.1.13713
  109. Nässel D., Winther Å. (2010): Drosophila neuropeptides in regulation of physiology and behavior. Progress in Neurobiology 92(1):42-104. https://doi.org/10.1016/j.pneurobio.2010.04.010
  110. Bolduc F., Valente D., Nguyen A., Mitra P., Tully T. (2010): An assay for social interaction in Drosophila fragile X mutants. Fly 4(3):216-225. https://doi.org/10.4161/fly.4.3.12280
  111. Verlinden H., Vleugels R., Marchal E., Badisco L., Pflüger H., Blenau W., et al. (2010): The role of octopamine in locusts and other arthropods. Journal of Insect Physiology 56(8):854-867. https://doi.org/10.1016/j.jinsphys.2010.05.018
  112. Boerner J., Duch C. (2010): Average shape standard atlas for the adult Drosophila ventral nerve cord. Journal of Comparative Neurology 518(13):2437-2455. https://doi.org/10.1002/cne.22346
  113. Jennifer B. Price (2010): Neurochemical Levels Correlate with Population Level Differences in Social Structure and Individual Behavior in the Polyphenic Spider, Anelosimus studiosus .
  114. Merav Tauber (2010): Molecular Genetics of Aggressive Behaviour in Drosophila melanogaster. Figshare.
  115. Strauss R., Berg C. (2010): The central control of oriented locomotion in insects – towards a neurobiological model. The 2010 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2010.5596461
  116. Avanesian A., Semnani S., Jafari M. (2009): Can Drosophila melanogaster represent a model system for the detection of reproductive adverse drug reactions?. Drug Discovery Today 14(15-16):761-766. https://doi.org/10.1016/j.drudis.2009.05.010
  117. Edwards A., Zwarts L., Yamamoto A., Callaerts P., Mackay T. (2009): Mutations in many genes affect aggressive behavior in Drosophila melanogaster. BMC Biology 7(1). https://doi.org/10.1186/1741-7007-7-29
  118. Edwards A., Ayroles J., Stone E., Carbone M., Lyman R., Mackay T. (2009): A transcriptional network associated with natural variation in Drosophilaaggressive behavior. Genome Biology 10(7). https://doi.org/10.1186/gb-2009-10-7-r76
  119. Edwards A., Mackay T. (2009): Quantitative Trait Loci for Aggressive Behavior inDrosophila melanogaster. Genetics 182(3):889-897. https://doi.org/10.1534/genetics.109.101691
  120. Ueda A., Wu C. (2009): Effects of Social Isolation on Neuromuscular Excitability and Aggressive Behaviors in Drosophila : Altered Responses by Hk and gsts1 , Two Mutations Implicated in Redox Regulation. Journal of Neurogenetics 23(4):378-394. https://doi.org/10.3109/01677060903063026
  121. Brembs B. (2009): Mushroom Bodies Regulate Habit Formation in Drosophila. Current Biology 19(16):1351-1355. https://doi.org/10.1016/j.cub.2009.06.014
  122. Serway C., Kaufman R., Serway C., Kaufman R., Strauss R., Steven de Belle J. (2009): Mushroom Bodies Enhance Initial Motor Activity indrosophila. Journal of Neurogenetics 23(1-2):173-184. https://doi.org/10.1080/01677060802572895
  123. Danielle Boisvert (2009): Rethinking Gottfredson and Hirschi’s General Theory of Crime: A Behavioral Genetic Approach. OhioLink ETD Center (Ohio Library and Information Network).
  124. Boudko D. (2009): Molecular Ontology of Amino Acid Transport. Epithelial Transport Physiology. https://doi.org/10.1007/978-1-60327-229-2_16
  125. Kotsyuba E. (2009): Effects of temperature stress on NO-synthase and tyrosine hydroxylase activities in the central nervous system of bivalve molluscs. Journal of Evolutionary Biochemistry and Physiology 45(1):138-146. https://doi.org/10.1134/s0022093009010141
  126. Cholewa J., Pflüger H. (2009): Descending unpaired median neurons with bilaterally symmetrical axons in the suboesophageal ganglion of Manduca sexta larvae. Zoology 112(4):251-262. https://doi.org/10.1016/j.zool.2008.10.004
  127. Iliadi K. (2009): The Genetic Basis of Emotional Behavior: Has the Time Come for a Drosophila Model?. Journal of Neurogenetics 23(1-2):136-146. https://doi.org/10.1080/01677060802471650
  128. Wang L., Anderson D. (2009): Identification of an aggression-promoting pheromone and its receptor neurons in Drosophila. Nature 463(7278):227-231. https://doi.org/10.1038/nature08678
  129. Vierk R., Pflueger H., Duch C. (2009): Differential effects of octopamine and tyramine on the central pattern generator for Manduca flight. Journal of Comparative Physiology A 195(3):265-277. https://doi.org/10.1007/s00359-008-0404-5
  130. Pain S. (2009): Signs of Anger: Representation of Agonistic Behaviour in Invertebrate Cognition. Biosemiotics 2(2):181-191. https://doi.org/10.1007/s12304-009-9043-7
  131. Kim Y. (2009): Sexual Selection and Aggressive Behavior in Drosophila. Handbook of Behavior Genetics. https://doi.org/10.1007/978-0-387-76727-7_22
  132. PAQUETTE C., JOPLIN K., SEIER E., PEYTON J., MOORE D. (2008): Sex‐specific differences in spatial behaviour in the flesh fly Sarcophaga crassipalpis. Physiological Entomology 33(4):382-388. https://doi.org/10.1111/j.1365-3032.2008.00646.x
  133. Caleb Joseph Paquette (2008): Gender-Specific Differences in Spatial Behavior of the Flesh Fly, Sarcophaga crassipalpis .
  134. Potter C., Luo L. (2008): Octopamine fuels fighting flies. Nature Neuroscience 11(9):989-990. https://doi.org/10.1038/nn0908-989
  135. Zhou C., Rao Y., Rao Y. (2008): A subset of octopaminergic neurons are important for Drosophila aggression. Nature Neuroscience 11(9):1059-1067. https://doi.org/10.1038/nn.2164
  136. Dierick H. (2008): Fly Fighting: Octopamine Modulates Aggression. Current Biology 18(4):R161-R163. https://doi.org/10.1016/j.cub.2007.12.026
  137. Nishimura K., Kitamura Y., Inoue T., Umesono Y., Yoshimoto K., Taniguchi T., et al. (2008): Characterization of tyramine β-hydroxylase in planarian Dugesia japonica: Cloning and expression. Neurochemistry International 53(6-8):184-192. https://doi.org/10.1016/j.neuint.2008.09.006
  138. Cabral L., Foley B., Nuzhdin S. (2008): Does Sex Trade with Violence among Genotypes in Drosophila melanogaster?. PLoS ONE 3(4):e1986. https://doi.org/10.1371/journal.pone.0001986
  139. Wang L., Dankert H., Perona P., Anderson D. (2008): A common genetic target for environmental and heritable influences on aggressiveness in Drosophila. Proceedings of the National Academy of Sciences 105(15):5657-5663. https://doi.org/10.1073/pnas.0801327105
  140. Klowden M. (2008): Behavioral Systems. Physiological Systems in Insects. https://doi.org/10.1016/b978-012369493-5.50006-7
  141. Johnson O., Becnel J., Nichols C. (2008): Serotonin 5-HT2 and 5-HT1A-like receptors differentially modulate aggressive behaviors in Drosophila melanogaster. Neuroscience 158(4):1292-1300. https://doi.org/10.1016/j.neuroscience.2008.10.055
  142. Hoyer S., Eckart A., Herrel A., Zars T., Fischer S., Hardie S., et al. (2008): Octopamine in Male Aggression of Drosophila. Current Biology 18(3):159-167. https://doi.org/10.1016/j.cub.2007.12.052
  143. Susanne Hoyer, Andreas Eckart, Anthony Herrel, Troy Zars, Susanne Fischer, Shannon L. Hardie, et al. (2008): Article Octopamine in Male Aggression of Drosophila.
  144. Simon A., Krantz D. (2007): Road rage and fruit flies. Nature Genetics 39(5):581-582. https://doi.org/10.1038/ng0507-581
  145. Brembs B., Christiansen F., Pflüger H., Duch C. (2007): Flight Initiation and Maintenance Deficits in Flies with Genetically Altered Biogenic Amine Levels. The Journal of Neuroscience 27(41):11122-11131. https://doi.org/10.1523/jneurosci.2704-07.2007
  146. Dierick H., Greenspan R. (2007): Serotonin and neuropeptide F have opposite modulatory effects on fly aggression. Nature Genetics 39(5):678-682. https://doi.org/10.1038/ng2029
  147. Dierick H. (2007): A method for quantifying aggression in male Drosophila melanogaster. Nature Protocols 2(11):2712-2718. https://doi.org/10.1038/nprot.2007.404
  148. Kaun K., Hendel T., Gerber B., Sokolowski M. (2007): Natural variation in Drosophila larval reward learning and memory due to a cGMP-dependent protein kinase. Learning & Memory 14(5):342-349. https://doi.org/10.1101/lm.505807
  149. Sameera Dasari (2007): INFLUENCE OF THE SEROTONERGIC SYSTEM ON PHYSIOLOGY, DEVELOPMENT, AND BEHAVIOR OF DROSOPHILA MELANOGASTER. UKnowledge (University of Kentucky).
  150. Sameera Dasari (2007): ABSTRACT OF DISSERTATION.
  151. Certel S., Savella M., Schlegel D., Kravitz E. (2007): Modulation of Drosophila male behavioral choice. Proceedings of the National Academy of Sciences 104(11):4706-4711. https://doi.org/10.1073/pnas.0700328104
  152. Chan Y., Kravitz E. (2007): Specific subgroups of Fru M neurons control sexually dimorphic patterns of aggression in Drosophila melanogaster. Proceedings of the National Academy of Sciences 104(49):19577-19582. https://doi.org/10.1073/pnas.0709803104
  153. Edwards A., Rollmann S., Morgan T., Mackay T. (2006): Quantitative Genomics of Aggressive Behavior in Drosophila melanogaster. PLoS Genetics 2(9):e154. https://doi.org/10.1371/journal.pgen.0020154
  154. Brembs B., Wiener J. (2006): Context and occasion setting in Drosophila visual learning. Learning & Memory 13(5):618-628. https://doi.org/10.1101/lm.318606
  155. Robin C., Daborn P., Hoffmann A. (2006): Fighting fly genes. Trends in Genetics 23(2):51-54. https://doi.org/10.1016/j.tig.2006.12.005
  156. GONZALES E., HAMRICK J., SMOUSE P., DYER R. (2006): Pollen‐mediated gene dispersal within continuous and fragmented populations of a forest understorey species, Trillium cuneatum. Molecular Ecology 15(8):2047-2058. https://doi.org/10.1111/j.1365-294x.2006.02913.x
  157. Pflüger H., Büschges A. (2006): Neuromodulation of Microcircuits in Motor Systems with Special Reference to Invertebrates. Microcircuits. https://doi.org/10.7551/mitpress/4596.003.0006
  158. Dierick H., Greenspan R. (2006): Molecular analysis of flies selected for aggressive behavior. Nature Genetics 38(9):1023-1031. https://doi.org/10.1038/ng1864
  159. Kevin M. Beaver (2006): The Intersection of Genes, the Environment, and Crime and Delinquency: A Longitudinal Study of Offending. OhioLink ETD Center (Ohio Library and Information Network).
  160. Fox L., Soll D., Wu C. (2006): Coordination and Modulation of Locomotion Pattern Generators inDrosophilaLarvae: Effects of Altered Biogenic Amine Levels by the Tyramine β Hydroxlyase Mutation. The Journal of Neuroscience 26(5):1486-1498. https://doi.org/10.1523/jneurosci.4749-05.2006
  161. Scott M. (2006): The role of juvenile hormone in competition and cooperation by burying beetles. Journal of Insect Physiology 52(10):1005-1011. https://doi.org/10.1016/j.jinsphys.2006.04.006
  162. Yuan Q., Joiner W., Sehgal A. (2006): A Sleep-Promoting Role for the Drosophila Serotonin Receptor 1A. Current Biology 16(11):1051-1062. https://doi.org/10.1016/j.cub.2006.04.032
  163. Boudko D., Donly B., Stevens B., Harvey W. (2005): Amino Acid and Neurotransmitter Transporters. Comprehensive Molecular Insect Science. https://doi.org/10.1016/b0-44-451924-6/00071-5
  164. FOX L., UEDA A., BERKE B., PENG I., WU C. (2005): Movement Disorders in Drosophila Mutants of Potassium Channels and Biogenic Amine Pathways. Animal Models of Movement Disorders. https://doi.org/10.1016/b978-012088382-0/50045-1
  165. Carbone M., Llopart A., deAngelis M., Coyne J., Mackay T. (2005): Quantitative Trait Loci Affecting the Difference in Pigmentation BetweenDrosophila yakubaandD. santomea. Genetics 171(1):211-225. https://doi.org/10.1534/genetics.105.044412
  166. Svetec N., Ferveur J. (2005): Social experience and pheromonal perception can change male–male interactions inDrosophila melanogaster. Journal of Experimental Biology 208(5):891-898. https://doi.org/10.1242/jeb.01454
  167. Stevenson P., Dyakonova V., Rillich J., Schildberger K. (2005): Octopamine and Experience-Dependent Modulation of Aggression in Crickets. The Journal of Neuroscience 25(6):1431-1441. https://doi.org/10.1523/jneurosci.4258-04.2005
  168. Sinakevitch I., Strausfeld N. (2005): Comparison of octopamine-like immunoreactivity in the brains of the fruit fly and blow fly. The Journal of Comparative Neurology 494(3):460-475. https://doi.org/10.1002/cne.20799
  169. Hoyer S., Liebig J., Rössler W. (2005): Biogenic amines in the ponerine ant Harpegnathos saltator: serotonin and dopamine immunoreactivity in the brain. Arthropod Structure & Development 34(4):429-440. https://doi.org/10.1016/j.asd.2005.03.003
  170. Blenau W., Baumann A. (2005): Molecular characterization of the ebony gene from the American cockroach, Periplaneta americana. Archives of Insect Biochemistry and Physiology 59(3):184-195. https://doi.org/10.1002/arch.20064
  171. Zhang B., Lu H., Xi W., Zhou X., Xu S., Zhang K., et al. (2004): Exposure to hypomagnetic field space for multiple generations causes amnesia in Drosophila melanogaster. Neuroscience Letters 371(2-3):190-195. https://doi.org/10.1016/j.neulet.2004.08.072
  172. Park D., Han M., Kim Y., Han K., Taghert P. (2004): Ap-let neurons—a peptidergic circuit potentially controlling ecdysial behavior in Drosophila. Developmental Biology 269(1):95-108. https://doi.org/10.1016/j.ydbio.2004.01.015
  173. LIBERSAT F., PFLUEGER H. (2004): Monoamines and the Orchestration of Behavior. BioScience 54(1):17. https://doi.org/10.1641/0006-3568(2004)054[0017:matoob]2.0.co;2
  174. Knaden M., Wehner R. (2004): Path Integration in Desert Ants Controls Aggressiveness. Science 305(5680):60-60. https://doi.org/10.1126/science.1097165
  175. Pagé M., Cooper R. (2004): Novelty stress and reproductive state alters responsiveness to sensory stimuli and 5-HT neuromodulation in crayfish. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 139(2):149-158. https://doi.org/10.1016/j.cbpb.2004.08.003
  176. Banerjee S., Lee J., Venkatesh K., Wu C., Hasan G. (2004): Loss of Flight and Associated Neuronal Rhythmicity in Inositol 1,4,5-Trisphosphate Receptor Mutants ofDrosophila. The Journal of Neuroscience 24(36):7869-7878. https://doi.org/10.1523/jneurosci.0656-04.2004
  177. Ye Y., Xi W., Peng Y., Wang Y., Guo A. (2004): Long‐term but not short‐term blockade of dopamine release in Drosophila impairs orientation during flight in a visual attention paradigm. European Journal of Neuroscience 20(4):1001-1007. https://doi.org/10.1111/j.1460-9568.2004.03575.x
  178. Kravitz E., Huber R. (2003): Aggression in invertebrates. Current Opinion in Neurobiology 13(6):736-743. https://doi.org/10.1016/j.conb.2003.10.003
  179. Manev H., Dimitrijevic N., Dzitoyeva S. (2003): Techniques: fruit flies as models for neuropharmacological research. Trends in Pharmacological Sciences 24(1):41-43. https://doi.org/10.1016/s0165-6147(02)00004-4
  180. Monastirioti M. (2003): Distinct octopamine cell population residing in the CNS abdominal ganglion controls ovulation in Drosophila melanogaster. Developmental Biology 264(1):38-49. https://doi.org/10.1016/j.ydbio.2003.07.019
  181. Dasari S., Cooper R. (2003): Modulation of sensory–CNS–motor circuits by serotonin, octopamine, and dopamine in semi-intact Drosophila larva. Neuroscience Research 48(2):221-227. https://doi.org/10.1016/j.neures.2003.10.005
  182. Saraswati S., Fox L., Soll D., Wu C. (2003): Tyramine and octopamine have opposite effects on the locomotion of Drosophila larvae. Journal of Neurobiology 58(4):425-441. https://doi.org/10.1002/neu.10298
  183. Chen S., Lee A., Bowens N., Huber R., Kravitz E. (2002): Fighting fruit flies: A model system for the study of aggression. Proceedings of the National Academy of Sciences 99(8):5664-5668. https://doi.org/10.1073/pnas.082102599

Brembs B, Heisenberg M. (2001): Conditioning with compound stimuli in Drosophila at the flight simulator. J. exp. Biol. 204:2849–2859.

  1. Rachad E., Deimel S., Epple L., Gadgil Y., Jürgensen A., Springer M., et al. (2025): Functional dissection of a neuronal brain circuit mediating higher-order associative learning. Cell Reports 44(5):115593. https://doi.org/10.1016/j.celrep.2025.115593
  2. Guiraud M., Gallo V., Quinsal-Keel E., MaBouDi H. (2025): Bumble bee visual learning: simple solutions for complex stimuli. Animal Behaviour 221:123070. https://doi.org/10.1016/j.anbehav.2024.123070
  3. Dwijesha A., Eswaran A., Berry J., Phan A. (2024): Diverse memory paradigms inDrosophilareveal diverse neural mechanisms. Learning & Memory 31(5):a053810. https://doi.org/10.1101/lm.053810.123
  4. Davidson A., Hige T. (2024): Roles of feedback and feed-forward networks of dopamine subsystems: insights fromDrosophilastudies. Learning & Memory 31(5):a053807. https://doi.org/10.1101/lm.053807.123
  5. Jürgensen A., Schmitt F., Nawrot M. (2024): Minimal circuit motifs for second-order conditioning in the insect mushroom body. Frontiers in Physiology 14. https://doi.org/10.3389/fphys.2023.1326307
  6. Dan C., Hulse B., Kappagantula R., Jayaraman V., Hermundstad A. (2024): A neural circuit architecture for rapid learning in goal-directed navigation. Neuron 112(15):2581-2599.e23. https://doi.org/10.1016/j.neuron.2024.04.036
  7. Sen E., El-Keredy A., Jacob N., Mancini N., Asnaz G., Widmann A., et al. (2024): Cognitive limits of larval Drosophila : testing for conditioned inhibition, sensory preconditioning, and second-order conditioning. Learning & Memory 31(5):a053726. https://doi.org/10.1101/lm.053726.122
  8. Guiraud M., Gallo V., Quinsal-Keel E., MaBouDi H. (2024): Bumblebee visual learning: simple solutions for complex stimuli. https://doi.org/10.1101/2024.03.16.585132
  9. Deimel S. (2024): Learning-dependent plasticity of the Drosophila mushroom body: An optophysiological approach. https://doi.org/10.53846/goediss-10629
  10. Jürgensen A., Schmitt F., Nawrot M. (2023): Minimal circuit motifs for second-order conditioning in the insect mushroom body. https://doi.org/10.1101/2023.09.11.557174
  11. Yamada D., Bushey D., Li F., Hibbard K., Sammons M., Funke J., et al. (2023): Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila. eLife 12. https://doi.org/10.7554/elife.79042
  12. Sen E., El-Keredy A., Jacob N., Mancini N., Asnaz G., Widmann A., et al. (2023): Cognitive limits of larval Drosophila : Testing for conditioned inhibition, sensory preconditioning and second-order conditioning. https://doi.org/10.1101/2023.08.21.554112
  13. Rachad E. (2023): Neural circuit plasticity underlying learning and memory in Drosophila melanogaster: from synaptic connections to behavior. https://doi.org/10.53846/goediss-9845
  14. Kobayashi N., Hasegawa Y., Okada R., Sakura M. (2023): Visual learning in tethered bees modifies flight orientation and is impaired by epinastine. Journal of Comparative Physiology A 209(4):529-539. https://doi.org/10.1007/s00359-023-01623-z
  15. Yamada D., Bushey D., Feng L., Hibbard K., Sammons M., Funke J., et al. (2022): Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila. https://doi.org/10.1101/2022.03.30.486484
  16. Thiagarajan D., Sachse S. (2022): Multimodal Information Processing and Associative Learning in the Insect Brain. Insects 13(4):332. https://doi.org/10.3390/insects13040332
  17. Gostolupce D., Lay B., Maes E., Iordanova M. (2022): Understanding Associative Learning Through Higher-Order Conditioning. Frontiers in Behavioral Neuroscience 16. https://doi.org/10.3389/fnbeh.2022.845616
  18. Gkanias E., McCurdy L., Nitabach M., Webb B. (2022): An incentive circuit for memory dynamics in the mushroom body of Drosophila melanogaster. eLife 11. https://doi.org/10.7554/elife.75611
  19. Martinez-Cervantes J., Shah P., Phan A., Cervantes-Sandoval I. (2022): Higher-order unimodal olfactory sensory preconditioning in Drosophila. eLife 11. https://doi.org/10.7554/elife.79107
  20. Kobayashi N., Hasegawa Y., Okada R., Sakura M. (2022): Involvement of octopamine in conditioned visual flight orientation in honeybees. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-2171985/v1
  21. Matsumoto Y. (2022): Learning and memory in the cricket Gryllus bimaculatus. Physiological Entomology 47(3):147-161. https://doi.org/10.1111/phen.12387
  22. Phan A., Martinez-Cervantes J., Cervantes-Sandoval I. (2021): Olfactory sensory preconditioning in Drosophila : role of memory forgetting in gating S1/S2 associations. https://doi.org/10.1101/2021.11.29.470429
  23. Dan C., Hulse B., Kappagantula R., Jayaraman V., Hermundstad A. (2021): A neural circuit architecture for rapid behavioral flexibility in goal-directed navigation. https://doi.org/10.1101/2021.08.18.456004
  24. Gkanias E., McCurdy L., Nitabach M., Webb B. (2021): The incentive circuit: memory dynamics in the mushroom body of Drosophila melanogaster. https://doi.org/10.1101/2021.06.11.448104
  25. Lafon G., Howard S., Paffhausen B., Avarguès-Weber A., Giurfa M. (2021): Motion cues from the background influence associative color learning of honey bees in a virtual-reality scenario. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-00630-x
  26. MaBouDi H., Barron A., Li S., Honkanen M., Loukola O., Peng F., et al. (2021): Non-numerical strategies used by bees to solve numerical cognition tasks. Proceedings of the Royal Society B: Biological Sciences 288(1945):20202711. https://doi.org/10.1098/rspb.2020.2711
  27. Bennett J., Philippides A., Nowotny T. (2021): Learning with reinforcement prediction errors in a model of the Drosophila mushroom body. Nature Communications 12(1). https://doi.org/10.1038/s41467-021-22592-4
  28. Judd J., Smith E., Kim J., Shah V., Sanabria F., Conrad C. (2020): Chronic stress has lasting effects on improved cued discrimination early in extinction. Learning & Memory 27(8):319-327. https://doi.org/10.1101/lm.051060.119
  29. Bouchekioua Y., Blaisdell A., Kosaki Y., Tsutsui‐Kimura I., Craddock P., Mimura M., et al. (2020): Spatial inference without a cognitive map: the role of higher‐order path integration. Biological Reviews 96(1):52-65. https://doi.org/10.1111/brv.12645
  30. König C., Khalili A., Niewalda T., Gao S., Gerber B. (2019): An optogenetic analogue of second-order reinforcement inDrosophila. Biology Letters 15(7):20190084. https://doi.org/10.1098/rsbl.2019.0084
  31. Merritt D., Melkis J., Kwok B., Tran C., van der Kooy D. (2019): Analysis of Mutants Suggests Kamin Blocking in C. elegans is Due to Interference with Memory Recall Rather than Storage. Scientific Reports 9(1). https://doi.org/10.1038/s41598-019-38939-3
  32. Guo A., Gong Z., Li H., Li Y., Liu L., Liu Q., et al. (2017): Vision, Memory, and Cognition in Drosophila. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21029-8
  33. Dylla K., Raiser G., Galizia C., Szyszka P. (2017): Trace Conditioning in Drosophila Induces Associative Plasticity in Mushroom Body Kenyon Cells and Dopaminergic Neurons. Frontiers in Neural Circuits 11. https://doi.org/10.3389/fncir.2017.00042
  34. Bronfman Z., Ginsburg S., Jablonka E. (2016): The Transition to Minimal Consciousness through the Evolution of Associative Learning. Frontiers in Psychology 7. https://doi.org/10.3389/fpsyg.2016.01954
  35. Terao K., Matsumoto Y., Mizunami M. (2015): Critical evidence for the prediction error theory in associative learning. Scientific Reports 5(1). https://doi.org/10.1038/srep08929
  36. Schubert M., Sandoz J., Galizia G., Giurfa M. (2015): Odourant dominance in olfactory mixture processing: what makes a strong odourant?. Proceedings of the Royal Society B: Biological Sciences 282(1802):20142562. https://doi.org/10.1098/rspb.2014.2562
  37. Guo A., Lu H., Zhang K., Ren Q., Chiang Wong Y. (2013): Visual Learning and Decision Making in Drosophila melanogaster. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/b978-0-12-415823-8.00028-9
  38. Sanderson C., Cook P., Hill P., Orozco B., Abramson C., Wells H. (2013): Nectar quality perception by honey bees (Apis mellifera ligustica). Journal of Comparative Psychology 127(4):341-351. https://doi.org/10.1037/a0032613
  39. Zhang X., Ren Q., Guo A. (2013): Parallel Pathways for Cross-Modal Memory Retrieval inDrosophila. The Journal of Neuroscience 33(20):8784-8793. https://doi.org/10.1523/jneurosci.4631-12.2013
  40. Matsumoto Y., Hirashima D., Mizunami M. (2013): Analysis and modeling of neural processes underlying sensory preconditioning. Neurobiology of Learning and Memory 101:103-113. https://doi.org/10.1016/j.nlm.2013.01.008
  41. Paulk A., Millard S., van Swinderen B. (2012): Vision in Drosophila: Seeing the World Through a Model’s Eyes. Annual Review of Entomology 58(1):313-332. https://doi.org/10.1146/annurev-ento-120811-153715
  42. Milinkeviciute G., Gentile C., Neely G. (2012): Drosophila as a tool for studying the conserved genetics of pain. Clinical Genetics 82(4):359-366. https://doi.org/10.1111/j.1399-0004.2012.01941.x
  43. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  44. van Swinderen B. (2011): Attention in Drosophila. International Review of Neurobiology. https://doi.org/10.1016/b978-0-12-387003-2.00003-3
  45. Tabone C., de Belle J. (2011): Second-order conditioning in Drosophila. Learning & Memory 18(4):250-253. https://doi.org/10.1101/lm.2035411
  46. Young J., Wessnitzer J., Armstrong J., Webb B. (2011): Elemental and non-elemental olfactory learning in Drosophila. Neurobiology of Learning and Memory 96(2):339-352. https://doi.org/10.1016/j.nlm.2011.06.009
  47. Guo A., Zhang K., Peng Y., Xi W. (2010): Research progress on Drosophila visual cognition in China. Science China Life Sciences 53(3):374-384. https://doi.org/10.1007/s11427-010-0073-9
  48. van Swinderen B., Brembs B. (2010): Attention-Like Deficit and Hyperactivity in aDrosophilaMemory Mutant. The Journal of Neuroscience 30(3):1003-1014. https://doi.org/10.1523/jneurosci.4516-09.2010
  49. Abramson C., Nolf S., Mixson T., Wells H. (2010): Can Honey Bees Learn the Removal of a Stimulus as a Conditioning Cue?. Ethology 116(9):843-854. https://doi.org/10.1111/j.1439-0310.2010.01796.x
  50. Bolduc F., Tully T. (2009): Fruit flies and intellectual disability. Fly 3(1):91-104. https://doi.org/10.4161/fly.3.1.7812
  51. Mizunami M., Unoki S., Mori Y., Hirashima D., Hatano A., Matsumoto Y. (2009): Roles of octopaminergic and dopaminergic neurons in appetitive and aversive memory recall in an insect. BMC Biology 7(1). https://doi.org/10.1186/1741-7007-7-46
  52. Brembs B., Plendl W. (2008): Double Dissociation of PKC and AC Manipulations on Operant and Classical Learning in Drosophila. Current Biology 18(15):1168-1171. https://doi.org/10.1016/j.cub.2008.07.041
  53. Brembs B. (2008): Operant Learning of Drosophila at the Torque Meter. Journal of Visualized Experiments. https://doi.org/10.3791/731
  54. Smith D., Wessnitzer J., Webb B. (2008): A model of associative learning in the mushroom body. Biological Cybernetics 99(2):89-103. https://doi.org/10.1007/s00422-008-0241-1
  55. Hazlett B. (2007): Conditioned reinforcement in the crayfish Orconectes rusticus. Behaviour 144(7):847-859. https://doi.org/10.1163/156853907781476409
  56. van Swinderen B., Flores K. (2007): Attention‐like processes underlying optomotor performance in a Drosophila choice maze. Developmental Neurobiology 67(2):129-145. https://doi.org/10.1002/dneu.20334
  57. van Swinderen B. (2007): The Remote Roots of Consciousness in Fruit-fly Selective Attention?††“The remote roots of consciousness in fruit-fly selective attention” by Bruno van Swinderen appeared in BioEssays 27:321-330 (2005). Reprinted with permission of Wiley-Liss, Inc., a subsidiary of John Wiley & Sons, Inc. Consciousness Transitions. https://doi.org/10.1016/b978-044452977-0/50003-6
  58. Menzel R., Brembs B., Giurfa M. (2007): Cognition in Invertebrates. Evolution of Nervous Systems. https://doi.org/10.1016/b0-12-370878-8/00183-x
  59. Hussaini S., Komischke B., Menzel R., Lachnit H. (2007): Forward and backward second-order Pavlovian conditioning in honeybees. Learning & Memory 14(10):678-683. https://doi.org/10.1101/lm.471307
  60. Yarali A., Hendel T., Gerber B. (2006): Olfactory learning and behaviour are ‘insulated’ against visual processing in larval Drosophila. Journal of Comparative Physiology A 192(10):1133-1145. https://doi.org/10.1007/s00359-006-0140-7
  61. Brembs B., Wiener J. (2006): Context and occasion setting in Drosophila visual learning. Learning & Memory 13(5):618-628. https://doi.org/10.1101/lm.318606
  62. Brembs B., Hempel de Ibarra N. (2006): Different parameters support generalization and discrimination learning in Drosophila at the flight simulator. Learning & Memory 13(5):629-637. https://doi.org/10.1101/lm.319406
  63. van Swinderen B., Flores K. (2006): Attention-like processes underlying optomotor performance in aDrosophila choice maze. Journal of Neurobiology. https://doi.org/10.1002/neu.20334
  64. Goel P., Gelperin A. (2006): A neuronal network for the logic of Limax learning. Journal of Computational Neuroscience 21(3):259-270. https://doi.org/10.1007/s10827-006-8097-7
  65. Swinderen B. (2005): The remote roots of consciousness in fruit-fly selective attention?. BioEssays 27(3):321-330. https://doi.org/10.1002/bies.20195
  66. Guerrieri F., Lachnit H., Gerber B., Giurfa M. (2005): Olfactory blocking and odorant similarity in the honeybee. Learning & Memory 12(2):86-95. https://doi.org/10.1101/lm.79305
  67. Guo J., Guo A. (2005): Crossmodal Interactions Between Olfactory and Visual Learning in Drosophila. Science 309(5732):307-310. https://doi.org/10.1126/science.1111280
  68. Greenspan R., van Swinderen B. (2004): Cognitive consonance: complex brain functions in the fruit fly and its relatives. Trends in Neurosciences 27(12):707-711. https://doi.org/10.1016/j.tins.2004.10.002
  69. Siwicki K., Ladewski L. (2003): Associative learning and memory in Drosophila: beyond olfactory conditioning. Behavioural Processes 64(2):225-238. https://doi.org/10.1016/s0376-6357(03)00137-2
  70. Evripidis Gkanias, Li Yan McCurdy, Michael N. Nitabach, Barbara Webb (): . Edinburgh Research Explorer.

Heisenberg M, Wolf R, Brembs B. (2001): Flexibility in a single behavioral variable of Drosophila. Learn. Mem. 8:1–10.

  1. Dan C., Hulse B., Kappagantula R., Jayaraman V., Hermundstad A. (2024): A neural circuit architecture for rapid learning in goal-directed navigation. Neuron 112(15):2581-2599.e23. https://doi.org/10.1016/j.neuron.2024.04.036
  2. Ronad A., Vartak R. (2023): The Larval Plate Assay: An Exercise to Enhance Undergraduate Students’ Understanding of Experimental Design in Behavioral Assays. The American Biology Teacher 85(6):343-350. https://doi.org/10.1525/abt.2023.85.6.343
  3. Kobayashi N., Hasegawa Y., Okada R., Sakura M. (2023): Visual learning in tethered bees modifies flight orientation and is impaired by epinastine. Journal of Comparative Physiology A 209(4):529-539. https://doi.org/10.1007/s00359-023-01623-z
  4. Cai J., Yan F., Shi Y., Zhang M., Guo L. (2022): Autonomous robot navigation based on a hierarchical cognitive model. Robotica 41(2):690-712. https://doi.org/10.1017/S0263574722001539
  5. Dan C., Hulse B., Kappagantula R., Jayaraman V., Hermundstad A. (2021): A neural circuit architecture for rapid behavioral flexibility in goal-directed navigation. bioRxiv. https://doi.org/10.1101/2021.08.18.456004
  6. Chuntao Dan, B. Hulse, V. Jayaraman, A. Hermundstad (2021): Flexible control of behavioral variability mediated by an internal representation of head direction. https://www.semanticscholar.org/paper/e1c7c5519e940e582532dd344a2512a2e514dcd2
  7. Rusch C., Alonso San Alberto D., Riffell J. (2021): Visuo-Motor Feedback Modulates Neural Activities in the Medulla of the Honeybee, Apis mellifera. The Journal of Neuroscience 41(14):3192-3203. https://doi.org/10.1523/JNEUROSCI.1824-20.2021
  8. Lafon G., Howard S., Paffhausen B., Avarguès-Weber A., Giurfa M. (2021): Motion cues from the background influence associative color learning of honey bees in a virtual-reality scenario. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-00630-x
  9. Gordon J., Masek P. (2021): Excessive energy expenditure due to acute physical restraint disrupts Drosophila motivational feeding response. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-03575-3
  10. Bouchekioua Y., Kosaki Y., Watanabe S., Blaisdell A. (2021): Higher-Order Conditioning in the Spatial Domain. Frontiers in Behavioral Neuroscience 15. https://doi.org/10.3389/fnbeh.2021.766767
  11. Brembs B. (2020): The brain as a dynamically active organ. Biochemical and Biophysical Research Communications – BBRC. https://doi.org/10.31234/osf.io/j37av
  12. Walters E. (2020): Evolutionary Aspects of Nociception and Pain. The Senses: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.24237-5
  13. Walters E. (2018): Nociceptive Biology of Molluscs and Arthropods: Evolutionary Clues About Functions and Mechanisms Potentially Related to Pain. Frontiers in Physiology 9. https://doi.org/10.3389/fphys.2018.01049
  14. Grabowska M., Steeves J., Alpay J., van de Poll M., Ertekin D., van Swinderen B. (2018): Innate visual preferences and behavioral flexibility in Drosophila. Journal of Experimental Biology. https://doi.org/10.1242/jeb.185918
  15. S. Talukder, T. Clandinin, Darlene Talukder, Leilah Talukder, T. Clandinin (2018): Exploring Visual Memory Formation in Drosophila melanogaster by Sabera Talukder. https://www.semanticscholar.org/paper/d10dae75140c07f83f85f58c86b70d0a35367b85
  16. Brembs B. (2016): Operant Behavior in Model Systems. bioRxiv. https://doi.org/10.1101/058719
  17. Avarguès-Weber A., Lihoreau M., Isabel G., Giurfa M. (2015): Information transfer beyond the waggle dance: observational learning in bees and flies. Frontiers in Ecology and Evolution 3. https://doi.org/10.3389/fevo.2015.00024
  18. Guo C., Du Y., Yuan D., Li M., Gong H., Gong Z., et al. (2015): A conditioned visual orientation requires the ellipsoid body in Drosophila. Learning & Memory 22(1):56-63. https://doi.org/10.1101/lm.036863.114
  19. Giurfa M. (2015): Learning and cognition in insects. WIREs Cognitive Science 6(4):383-395. https://doi.org/10.1002/wcs.1348
  20. Ghimire S., Kim M. (2015): Defensive Behavior against Noxious Heat Stimuli Is Declined with Aging Due to Decreased Pain-Associated Gene Expression in Drosophila. Biomolecules & Therapeutics 23(3):290-295. https://doi.org/10.4062/biomolther.2014.147
  21. Soibam B., Chen L., Roman G., Gunaratne G. (2014): Exploratory activity and habituation of Drosophila in confined domains. The European Physical Journal Special Topics 223(9):1787-1803. https://doi.org/10.1140/EPJST/E2014-02226-7
  22. C. Schnaitmann (2014): Neural circuits underlying colour vision and visual memory in Drosophila melanogaster. https://www.semanticscholar.org/paper/a1fe1f36cf811f1380fcae7125f0057ccf83ab19
  23. Moritz J. (2014): ANIMAL SUFFERING, EVOLUTION, AND THE ORIGINS OF EVIL: TOWARD A “FREE CREATURES” DEFENSE. Zygon: Journal of Religion and Science 49(2). https://doi.org/10.1111/ZYGO.12085
  24. Dickinson M. (2014): Death Valley, Drosophila, and the Devonian toolkit. Annual Review of Entomology 59(1):51-72. https://doi.org/10.1146/annurev-ento-011613-162041
  25. Heisenberg M. (2014): The Beauty of the Network in the Brain and the Origin of the Mind in the Control of Behavior. Journal of Neurogenetics 28(3-4):389-399. https://doi.org/10.3109/01677063.2014.912279
  26. Ros T., J. Baars B., Lanius R., Vuilleumier P. (2014): Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework. Frontiers in Human Neuroscience 8. https://doi.org/10.3389/fnhum.2014.01008
  27. Giurfa M. (2013): Cognition with few neurons: higher-order learning in insects. Trends in Neurosciences 36(5):285-294. https://doi.org/10.1016/j.tins.2012.12.011
  28. Giurfa M., Menzel R. (2013): Cognitive Components of Insect Behavior. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/B978-0-12-415823-8.00003-4
  29. Heisenberg M. (2013): Action Selection: The Brain as a Behavioral Organizer. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/B978-0-12-415823-8.00002-2
  30. Heisenberg M. (2013): The Origin of Freedom in Animal Behaviour. Is Science Compatible with Free Will?. https://doi.org/10.1007/978-1-4614-5212-6_7
  31. Valente A., Huang K., Portugues R., Engert F. (2012): Ontogeny of classical and operant learning behaviors in zebrafish. Learning & Memory 19(4):170-177. https://doi.org/10.1101/lm.025668.112
  32. Iliadi K., Knight D., Boulianne G. (2012): Healthy Aging – Insights from Drosophila. Frontiers in Physiology 3. https://doi.org/10.3389/fphys.2012.00106
  33. Kuo-Hua Huang (2012): Control of Turning Behaviors by Spinal Projection Neurons in the Larval Zebrafish. https://www.semanticscholar.org/paper/459471d8863b838533b32055e80b5f303409b67d
  34. Giurfa M. (2012): Social Learning in Insects: A Higher-Order Capacity?. Frontiers in Behaviorial Neuroscience 6. https://doi.org/10.3389/fnbeh.2012.00057
  35. Giurfa M., Sandoz J. (2012): Invertebrate learning and memory: Fifty years of olfactory conditioning of the proboscis extension response in honeybees. Learning & Memory 19(2):54-66. https://doi.org/10.1101/lm.024711.111
  36. Tomina Y., Takahata M. (2012): Discrimination learning with light stimuli in restrained American lobster. Behavioural Brain Research 229(1):91-105. https://doi.org/10.1016/j.bbr.2011.12.044
  37. Sorribes A., Armendariz B., Lopez-Pigozzi D., Murga C., de Polavieja G. (2011): The Origin of Behavioral Bursts in Decision-Making Circuitry. PLoS Computational Biology 7(6):e1002075. https://doi.org/10.1371/journal.pcbi.1002075
  38. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  39. van Swinderen B. (2011): Attention in Drosophila. International Review of Neurobiology. https://doi.org/10.1016/B978-0-12-387003-2.00003-3
  40. E. A. Kelley (2011): Lemur catta in the Region of Cap Sainte-Marie, Madagascar: Introduced cacti, xerophytic Didiereaceae-Euphorbia bush, and tombs. https://doi.org/10.7936/K708639J
  41. J. Barham (2011): Teleological realism in biology. https://www.semanticscholar.org/paper/e20a45912a185968b4d497e3e1a2138a798d76fe
  42. Kahsai L., Zars T. (2011): Learning and memory in Drosophila: behavior, genetics, and neural systems. International Review of Neurobiology. https://doi.org/10.1016/B978-0-12-387003-2.00006-9
  43. Bekinschtein T., Peeters M., Shalom D., Sigman M. (2011): Sea Slugs, Subliminal Pictures, and Vegetative State Patients: Boundaries of Consciousness in Classical Conditioning. Frontiers in Psychology 2. https://doi.org/10.3389/fpsyg.2011.00337
  44. T. Melano (2011): Insect-Machine Interfacing. https://www.semanticscholar.org/paper/02021d96f664ae017c01b608c8bb9704688cacd8
  45. Wu Z., Guo A. (2011): A model study on the circuit mechanism underlying decision-making in Drosophila. Neural Networks 24(4):333-344. https://doi.org/10.1016/j.neunet.2011.01.002
  46. Brembs B. (2010): Towards a scientific concept of free will as a biological trait: spontaneous actions and decision-making in invertebrates. Proceedings of the Royal Society B: Biological Sciences 278(1707):930-939. https://doi.org/10.1098/rspb.2010.2325
  47. Colomb J., Brembs B. (2010): The biology of psychology. Communicative & Integrative Biology 3(2):142-145. https://doi.org/10.4161/cib.3.2.10334
  48. Zars T. (2010): Short-term memories in Drosophila are governed by general and specific genetic systems. Learning & Memory 17(5):246-251. https://doi.org/10.1101/lm.1706110
  49. Unknown authors (2009): Learning in Adult Flies.
  50. Brembs B. (2009): The Importance of Being Active. Journal of Neurogenetics 23(1-2):120-126. https://doi.org/10.1080/01677060802471643
  51. Schnaitmann C. (2009): Appetitive and Aversive Visual Learning in Freely Moving Drosophila. Frontiers in Behavioral Neuroscience 4. https://doi.org/10.3389/fnbeh.2010.00010
  52. Gross H., Pahl M., Si A., Zhu H., Tautz J., Zhang S. (2009): Number-Based Visual Generalisation in the Honeybee. PLoS ONE 4(1):e4263. https://doi.org/10.1371/journal.pone.0004263
  53. Pitman J., DasGupta S., Krashes M., Leung B., Perrat P., Waddell S. (2009): There are many ways to train a fly. Fly 3(1):3-9. https://doi.org/10.4161/fly.3.1.7726
  54. Iliadi K. (2009): The Genetic Basis of Emotional Behavior: Has the Time Come for a Drosophila Model?. Journal of Neurogenetics 23(1-2):136-146. https://doi.org/10.1080/01677060802471650
  55. Menzel R. (2009): Working Memory in Bees: Also in Flies?. Journal of Neurogenetics 23(1-2):92-99. https://doi.org/10.1080/01677060802610612
  56. V. Torley (2009): The Anatomy of a Minimal Mind. https://www.semanticscholar.org/paper/580465812d0f4f83f60012ac569b57e8aba8d679
  57. Pan Y., Zhou Y., Guo C., Gong H., Gong Z., Liu L. (2009): Differential roles of the fan-shaped body and the ellipsoid body in Drosophila visual pattern memory. Learning & Memory 16(5):289-295. https://doi.org/10.1101/lm.1331809
  58. B. Brembs (2008): The neurobiology of operant learning: biophysical and molecular mechanisms in a hierarchical organization of multiple memory systems. https://www.semanticscholar.org/paper/e093d2a5cc2d2f3f0ffe7588f0b80e5e3993d330
  59. Maimon G., Straw A., Dickinson M. (2008): A simple vision-based algorithm for decision making in flying Drosophila. Current Biology 18(6):464-470. https://doi.org/10.1016/j.cub.2008.02.054
  60. Mustard J., Edgar E., Mazade R., Wu C., Lillvis J., Wright G. (2008): Acute ethanol ingestion impairs appetitive olfactory learning and odor discrimination in the honey bee. Neurobiology of Learning and Memory 90(4):633-643. https://doi.org/10.1016/j.nlm.2008.07.017
  61. Xi W., Peng Y., Guo J., Ye Y., Zhang K., Yu F., et al. (2008): Mushroom bodies modulate salience‐based selective fixation behavior in Drosophila. European Journal of Neuroscience 27(6):1441-1451. https://doi.org/10.1111/j.1460-9568.2008.06114.x
  62. Maye A., Hsieh C., Sugihara G., Brembs B. (2007): Order in Spontaneous Behavior. PLoS ONE 2(5):e443. https://doi.org/10.1371/journal.pone.0000443
  63. C. Rankin, J. Dubnau (2007): 13 Memories of Worms and Flies: From Gene to Behavior. https://doi.org/10.1101/087969819.49.309
  64. Isabel G., Comas D., Preat T. (2007): From Molecule to Memory System: Genetic Analyses in Drosophila. Research and Perspectives in Neurosciences. https://doi.org/10.1007/978-3-540-45702-2_3
  65. M. Giurfa (2007): 12 Invertebrate Cognition: Nonelemental Learning beyond Simple Conditioning. https://doi.org/10.1101/087969819.49.281
  66. Menzel R., Brembs B., Giurfa M. (2007): Cognition in Invertebrates. Evolution of Nervous Systems. https://doi.org/10.1016/B0-12-370878-8/00183-X
  67. Katsov A., Clandinin T. (2006): Insect vision: remembering the shape of things. Current Biology 16(10):R369-R371. https://doi.org/10.1016/J.CUB.2006.04.006
  68. Yurkovic A., Wang O., Basu A., Kravitz E. (2006): Learning and memory associated with aggression in Drosophila melanogaster. Proceedings of the National Academy of Sciences 103(46):17519-17524. https://doi.org/10.1073/pnas.0608211103
  69. Brembs B., Wiener J. (2006): Context and occasion setting in Drosophila visual learning. Learning & Memory 13(5):618-628. https://doi.org/10.1101/LM.318606
  70. Brembs B., Hempel de Ibarra N. (2006): Different parameters support generalization and discrimination learning in Drosophila at the flight simulator. Learning & Memory 13(5):629-637. https://doi.org/10.1101/LM.319406
  71. B. Brembs, N. H. D. Ibarra (2006): at the flight simulatorDrosophilalearning in Different parameters support generalization and discrimination. https://www.semanticscholar.org/paper/e7c99957e995cc51f6f9717c7b18bb2af80fff17
  72. Dupuy F., Sandoz J., Giurfa M., Josens R. (2006): Individual olfactory learning in Camponotus ants. Animal Behaviour 72(5):1081-1091. https://doi.org/10.1016/J.ANBEHAV.2006.03.011
  73. Zars M., Zars T. (2006): High and low temperatures have unequal reinforcing properties in Drosophila spatial learning. Journal of Comparative Physiology A 192(7):727-735. https://doi.org/10.1007/s00359-006-0109-6
  74. R. Menzel, B. Brembs (2006): 1 . 26 Cognition in Invertebrates. https://www.semanticscholar.org/paper/cd86c7c05307517d2a90871fd20ad02736ac6100
  75. Gonzalo G. de Polavieja (2005): Inteligencia en cerebros de un milímetro cúbico. https://www.semanticscholar.org/paper/1d5de5004b80aadabddde581ad507d87777710fa
  76. Manev H., Dimitrijevic N. (2005): Fruit flies for anti-pain drug discovery. Life Sciences 76(21):2403-2407. https://doi.org/10.1016/J.LFS.2004.12.007
  77. Guo J., Guo A. (2005): Crossmodal Interactions Between Olfactory and Visual Learning in Drosophila. Science 309(5732):307-310. https://doi.org/10.1126/SCIENCE.1111280
  78. Brembs B., Baxter D., Byrne J. (2004): Extending in vitro conditioning in Aplysia to analyze operant and classical processes in the same preparation. Learning & Memory 11(4):412-420. https://doi.org/10.1101/LM.74404
  79. Zhang B., Lu H., Xi W., Zhou X., Xu S., Zhang K., et al. (2004): Exposure to hypomagnetic field space for multiple generations causes amnesia in Drosophila melanogaster. Neuroscience Letters 371(2-3):190-195. https://doi.org/10.1016/J.NEULET.2004.08.072
  80. Björn Brembs, D. A. Baxter, John H. Byrne (2004): Extending in vitro conditioning in Aplysia to analyze operant and classical processes in the same preparation. Learning & memory (Cold Spring Harbor, N.Y.). https://www.semanticscholar.org/paper/8f23e25664bb2f30ca96c19868001ef93ef389b4
  81. Tang S., Wolf R., Xu S., Heisenberg M. (2004): Visual Pattern Recognition in Drosophila Is Invariant for Retinal Position. Science 305(5686):1020-1022. https://doi.org/10.1126/SCIENCE.1099839
  82. Ye Y., Xi W., Peng Y., Wang Y., Guo A. (2004): Long‐term but not short‐term blockade of dopamine release in Drosophila impairs orientation during flight in a visual attention paradigm. European Journal of Neuroscience 20(4):1001-1007. https://doi.org/10.1111/j.1460-9568.2004.03575.x
  83. Siwicki K., Ladewski L. (2003): Associative learning and memory in Drosophila: beyond olfactory conditioning. Behavioural Processes 64(2):225-238. https://doi.org/10.1016/S0376-6357(03)00137-2
  84. Heisenberg M. (2003): Mushroom body memoir: from maps to models. Nature Reviews Neuroscience 4(4):266-275. https://doi.org/10.1038/nrn1074
  85. Scherer S., Stocker R., Gerber B. (2003): Olfactory learning in individually assayed Drosophila larvae. Learning & Memory 10(3):217-225. https://doi.org/10.1101/LM.57903
  86. Wang S., Li Y., Feng C., Guo A. (2003): Dissociation of visual associative and motor learning in Drosophila at the flight simulator. Behavioural Processes 64(1):57-70. https://doi.org/10.1016/S0376-6357(03)00105-0
  87. Wang S., Tang S., Li Y., Guo A. (2003): Behavioral modification in choice process ofDrosophila. Science in China Series C Life Sciences 46(4):399-413. https://doi.org/10.1007/BF03192583
  88. Schubert M., Lachnit H., Francucci S., Giurfa M. (2002): Nonelemental visual learning in honeybees. Animal Behaviour 64(2):175-184. https://doi.org/10.1006/ANBE.2002.3055
  89. Tang S., Guo A. (2001): Choice Behavior of Drosophila Facing Contradictory Visual Cues. Science 294(5546):1543-1547. https://doi.org/10.1126/SCIENCE.1058237
  90. Björn Brembs, D. A. Baxter, John H. Byrne (): Extending in Vitro Conditioning in Aplysia to Analyze Operant and Classical Processes in the Same Preparation. https://www.semanticscholar.org/paper/0b487111facc4edee8153dc4ce4d750b1794007c
  91. C. Schnaitmann, K. Vogt, Tilman Triphan, Hiromu Tanimoto (): Appetitive and aversive visual learning in freely moving Drosophila. https://www.semanticscholar.org/paper/1eb8ee554379291e035e72d377d47209795bab7e

Brembs B. (2001): Hamilton’s Theory. In Encyclopedia of Genetics pp. 906–910. Elsevier.

  1. Barr A., Dekker M., Fafchamps M. (2014): The Formation of Community-Based Organizations: An Analysis of a Quasi-Experiment in Zimbabwe. World Development 66:131-153. https://doi.org/10.1016/j.worlddev.2014.08.003
  2. Barr A., Dekker M., Fafchamps M. (2012): Who Shares Risk with Whom under Different Enforcement Mechanisms?. Economic Development and Cultural Change 60(4):677-706. https://doi.org/10.1086/665599
  3. Tokumitsu M., Ishida Y. (2012): A systemic payoff in a self-repairing network. Artificial Life and Robotics 16(4):563-566. https://doi.org/10.1007/s10015-011-0991-z
  4. Robert Schoen (2012): The kinship web in a simple stationary population. https://doi.org/10.4402/genus-410
  5. Tokumitsu M., Ishida Y. (2011): An Adaptive Control Technique for a Connection Weight of Agents in a Self-repairing Network. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-19167-1_5
  6. Tokumitsu M., Ishida Y. (2011): Spatial Distribution of Connection Weight in Self-repairing Network. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-23866-6_34
  7. Wang R., Shi L. (2010): The evolution of cooperation in asymmetric systems. Science China Life Sciences 53(1):139-149. https://doi.org/10.1007/s11427-010-0007-6
  8. Marcel Fafchamps (2009): Household Separation and Child Well-Being. Oxford University Research Archive (ORA) (University of Oxford).
  9. Abigail Barr, Marleen Dekker, Marcel Fafchamps (2008): Risk sharing relations and enforcement mechanisms. Oxford University Research Archive (ORA) (University of Oxford).
  10. Jenneke Fokker, Huib de Ridder, Piet Westendorp, Johan Pouwelse (2007): Psychological backgrounds for inducing cooperation in peer-to-peer television. European Conference on Interactive TV. https://doi.org/10.5555/1763017.1763036
  11. Fokker J., de Ridder H., Westendorp P., Pouwelse J. (2007): Psychological Backgrounds for Inducing Cooperation in Peer-to-Peer Television. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-72559-6_15
  12. Fafchamps M., Wahba J. (2006): Child labor, urban proximity, and household composition. Journal of Development Economics 79(2):374-397. https://doi.org/10.1016/j.jdeveco.2006.01.005

Brembs B, Heisenberg M. (2000): The operant and the classical in conditioned orientation of Drosophila melanogaster at the flight simulator. Learn. Mem. 7:104–115.

  1. Corcoran J., Storks L., Wong R. (2025): Bold zebrafish (Danio rerio) learn faster in a classical associative learning task. Scientific Reports 15(1). https://doi.org/10.1038/s41598-025-00423-6
  2. Dan C., Hulse B., Kappagantula R., Jayaraman V., Hermundstad A. (2024): A neural circuit architecture for rapid learning in goal-directed navigation. Neuron 112(15):2581-2599.e23. https://doi.org/10.1016/j.neuron.2024.04.036
  3. Rozenfeld E., Parnas M. (2024): Neuronal circuit mechanisms of competitive interaction between action-based and coincidence learning. Science Advances 10(49). https://doi.org/10.1126/sciadv.adq3016
  4. Tynan M., Byrne E., Finnegan C., Istasse M., Kalgashkin I., Du H., et al. (2024): CAPTAIN CARBON: Using Gamification to Influence Students Choice for Sustainable Transport. 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). https://doi.org/10.1109/ITSC58415.2024.10919995
  5. Bielecki J., Dam Nielsen S., Nachman G., Garm A. (2023): Associative learning in the box jellyfish Tripedalia cystophora. Current Biology 33(19):4150-4159.e5. https://doi.org/10.1016/j.cub.2023.08.056
  6. Kobayashi N., Hasegawa Y., Okada R., Sakura M. (2023): Visual learning in tethered bees modifies flight orientation and is impaired by epinastine. Journal of Comparative Physiology A 209(4):529-539. https://doi.org/10.1007/s00359-023-01623-z
  7. Rajagopalan A., Darshan R., Hibbard K., Fitzgerald J., Turner G. (2022): Reward expectations direct learning and drive operant matching in Drosophila. bioRxiv. https://doi.org/10.1101/2022.05.24.493252
  8. Gatto E., Loukola O., Petrazzini M., Agrillo C., Cutini S. (2022): Illusional Perspective across Humans and Bees. Vision 6(2):28. https://doi.org/10.3390/vision6020028
  9. Sun J., Han J., Wang Y., Liu P. (2022): Memristor-Based Neural Network Circuit of Operant Conditioning Accorded With Biological Feature. IEEE Transactions on Circuits and Systems I: Regular Papers 69(11):4475-4486. https://doi.org/10.1109/TCSI.2022.3194364
  10. Giurfa M., Macri C. (2022): Neuroscience: Mechanisms for bridging stimuli in Pavlovian trace conditioning in flies. Current Biology 32(11):R532-R535. https://doi.org/10.1016/j.cub.2022.04.059
  11. Dan C., Hulse B., Kappagantula R., Jayaraman V., Hermundstad A. (2021): A neural circuit architecture for rapid behavioral flexibility in goal-directed navigation. bioRxiv. https://doi.org/10.1101/2021.08.18.456004
  12. Chuntao Dan, B. Hulse, V. Jayaraman, A. Hermundstad (2021): Flexible control of behavioral variability mediated by an internal representation of head direction. https://www.semanticscholar.org/paper/e1c7c5519e940e582532dd344a2512a2e514dcd2
  13. Lafon G., Howard S., Paffhausen B., Avarguès-Weber A., Giurfa M. (2021): Motion cues from the background influence associative color learning of honey bees in a virtual-reality scenario. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-00630-x
  14. Moreyra S., Lozada M. (2021): Spatial memory in Vespula germanica wasps: a pilot study using a Y-maze assay. Behavioural Processes 189:104439. https://doi.org/10.1016/j.beproc.2021.104439
  15. B. Brembs, Ottavia Palazzo (2020): Molecular and behavioral study of the FoxP locus in Drosophila melanogaster. https://www.semanticscholar.org/paper/bf0849db3033b893abc7420598124e0ecbd2f881
  16. Avraham G., Taylor J., Breska A., Ivry R., McDougle S. (2020): Contextual effects in sensorimotor adaptation adhere to associative learning rules. bioRxiv. https://doi.org/10.1101/2020.09.14.297143
  17. Guy Avraham, Guy Avraham, Jordan A. Taylor, R. Ivry, R. Ivry, S. McDougle (2020): An associative learning account of sensorimotor adaptation. https://www.semanticscholar.org/paper/98b9f70f13aaceb0d8c03f3ec2d024c07d72fd18
  18. Birch J., Ginsburg S., Jablonka E. (2020): Unlimited Associative Learning and the origins of consciousness: a primer and some predictions. Biology & Philosophy 35(6). https://doi.org/10.1007/s10539-020-09772-0
  19. Skora L., Yeomans M., Crombag H., Scott R. (2020): Evidence that instrumental conditioning requires conscious awareness in humans. Cognition 208:104546. https://doi.org/10.1016/j.cognition.2020.104546
  20. Dirksen N., Langbein J., Matthews L., Puppe B., Elliffe D., Schrader L. (2020): Conditionability of ‘voluntary’ and ‘reflexive-like’ behaviors, with special reference to elimination behavior in cattle. Neuroscience & Biobehavioral Reviews 115:5-12. https://doi.org/10.1016/j.neubiorev.2020.05.006
  21. Wolf R., Heisenberg M., Brembs B., Waddell S., Mishra A., Kehrer A., et al. (2020): Memory, anticipation, action – working with Troy D. Zars. Journal of Neurogenetics 34(1):9-20. https://doi.org/10.1080/01677063.2020.1715976
  22. Seidenbecher S., Sanders J., von Philipsborn A., Kvitsiani D. (2020): Reward foraging task and model-based analysis reveal how fruit flies learn value of available options. PLOS ONE 15(10):e0239616. https://doi.org/10.1371/journal.pone.0239616
  23. Moreland-Capuia A. (2019): Substances of Abuse and the Brain. Training for Change. https://doi.org/10.1007/978-3-030-19208-2_4
  24. Sitaraman D., LaFerriere H. (2019): Finding a place and leaving a mark in memory formation. Journal of Neurogenetics 34(1):21-27. https://doi.org/10.1080/01677063.2019.1706094
  25. Commons M., Miller P., Malhotra S., Wei S. (2019): Adaptive Neural Networks Accounted for by Five Instances of “Respondent-Based” Conditioning. International Journal of Comparative Psychology 32. https://doi.org/10.46867/ijcp.2019.32.03.01
  26. Shah M., Commons M. (2019): A developmental and evolutionary theory of punishment. Ethics, Medicine and Public Health 8:108-119. https://doi.org/10.1016/J.JEMEP.2019.03.001
  27. Matthew Isaacson (2019): Using new tools to study the neural mechanisms of sensation: Auditory processing in locusts and translational motion vision in flies. https://doi.org/10.17863/CAM.36002
  28. Meda N., Frighetto G., Megighian A., Zordan M. (2019): Searching for relief: Drosophila melanogaster navigation in a virtual bitter maze. bioRxiv. https://doi.org/10.1101/804054
  29. Nargeot R., Puygrenier L. (2019): Operant Learning in Invertebrates. Reference Module in Life Sciences. https://doi.org/10.1016/b978-0-12-809633-8.90788-x
  30. Seidenbecher S., Sanders J., von Philipsborn A., Kvitsiani D. (2019): Foraging fruit flies mix navigational and learning-based decision-making strategies. bioRxiv. https://doi.org/10.1101/842096
  31. A. Buatois (2018): Etudes comportementales et neurobiologiques de l’apprentissage visuel chez l’abeille (Apis mellifera) en réalité virtuelle. https://www.semanticscholar.org/paper/2cfc95f35267912cbd2050ec4e7001004b8d9b70
  32. Alexis Buatois (2018): Behavioral and neurobiological studies of visual learning in honey bees (Apis mellifera) in virtual reality. https://www.semanticscholar.org/paper/176fb2176c8703d70bf8e61849abb8d256ed1668
  33. Buatois A., Flumian C., Schultheiss P., Avarguès-Weber A., Giurfa M. (2018): Transfer of Visual Learning Between a Virtual and a Real Environment in Honey Bees: The Role of Active Vision. Frontiers in Behavioral Neuroscience 12. https://doi.org/10.3389/fnbeh.2018.00139
  34. I. Ljubičić (2018): Social Influences on Songbird Behavior: From Song Learning to Motion Coordination. https://www.semanticscholar.org/paper/049fbba64eb5b6e9fedb22061e43bf4ff93daff6
  35. Patanè L., Strauss R., Arena P. (2018): Controlling and Learning Motor Functions. SpringerBriefs in Applied Sciences and Technology. https://doi.org/10.1007/978-3-319-73347-0_4
  36. Arena E., Arena P., Strauss R., Patané L. (2017): Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System. Frontiers in Neurorobotics 11. https://doi.org/10.3389/fnbot.2017.00012
  37. Walter T., Couzin I. (2017): Revised roles of ISL1 in a hES cell-based model of human heart chamber specification. bioRxiv. https://doi.org/10.1101/2020.10.14.338996
  38. Schultheiss P., Buatois A., Avarguès-Weber A., Giurfa M. (2017): Using virtual reality to study visual performances of honeybees. Current Opinion in Insect Science 24:43-50. https://doi.org/10.1016/j.cois.2017.08.003
  39. Geissmann Q., Garcia Rodriguez L., Beckwith E., French A., Jamasb A., Gilestro G. (2017): Ethoscopes: An open platform for high-throughput ethomics. PLOS Biology 15(10):e2003026. https://doi.org/10.1371/journal.pbio.2003026
  40. Avarguès-Weber A., Mota T. (2016): Advances and limitations of visual conditioning protocols in harnessed bees. Journal of Physiology-Paris 110(3):107-118. https://doi.org/10.1016/j.jphysparis.2016.12.006
  41. Brembs B. (2016): Operant Behavior in Model Systems. bioRxiv. https://doi.org/10.1101/058719
  42. Ljubičić I., Hyland Bruno J., Tchernichovski O. (2016): Social influences on song learning. Current Opinion in Behavioral Sciences 7:101-107. https://doi.org/10.1016/J.COBEHA.2015.12.006
  43. Jooyoung Park, D. Glanzman, A. Grinnell, P. Narins, Shanping Chen (2016): and RNA expression level changes with long-term in. https://www.semanticscholar.org/paper/cf6362b408be4dbb2b5603a9b90b9b1c53aa193a
  44. Rasch M., Shi A., Ji Z. (2016): Closing the loop: tracking and perturbing behaviour of individuals in a group in real-time. bioRxiv. https://doi.org/10.1101/071308
  45. Lucon-Xiccato T., Dadda M., Gatto E., Bisazza A. (2016): Development and testing of a rapid method for measuring shoal size discrimination. Animal Cognition 20(2):149-157. https://doi.org/10.1007/s10071-016-1050-x
  46. Bronfman Z., Ginsburg S., Jablonka E. (2016): The Transition to Minimal Consciousness through the Evolution of Associative Learning. Frontiers in Psychology 7. https://doi.org/10.3389/fpsyg.2016.01954
  47. Avarguès-Weber A., Lihoreau M., Isabel G., Giurfa M. (2015): Information transfer beyond the waggle dance: observational learning in bees and flies. Frontiers in Ecology and Evolution 3. https://doi.org/10.3389/fevo.2015.00024
  48. Paulk A., Kirszenblat L., Zhou Y., van Swinderen B. (2015): Closed-Loop Behavioral Control Increases Coherence in the Fly Brain. Journal of Neuroscience 35(28):10304-10315. https://doi.org/10.1523/JNEUROSCI.0691-15.2015
  49. Taylor G., Paulk A., Pearson T., Moore R., Stacey J., Ball D., et al. (2015): Insects modify their behaviour depending on the feedback sensor used when walking on a trackball in virtual reality. Journal of Experimental Biology. https://doi.org/10.1242/jeb.125617
  50. Mirwan H., Mason G., Kevan P. (2015): Complex operant learning by worker bumblebees (Bombus impatiens): detour behaviour and use of colours as discriminative stimuli. Insectes Sociaux 62(3):365-377. https://doi.org/10.1007/s00040-015-0414-6
  51. Giurfa M. (2015): Learning and cognition in insects. WIREs Cognitive Science 6(4):383-395. https://doi.org/10.1002/wcs.1348
  52. Heisenberg M. (2015): Outcome learning, outcome expectations, and intentionality in Drosophila. Learning & Memory 22(6):294-298. https://doi.org/10.1101/lm.037481.114
  53. Schleyer M., Reid S., Pamir E., Saumweber T., Paisios E., Davies A., et al. (2015): The impact of odor–reward memory on chemotaxis in larval Drosophila. Learning & Memory 22(5):267-277. https://doi.org/10.1101/lm.037978.114
  54. Pierre Junca (2015): Bases comportementales et génétiques des apprentissages aversif et appétitif chez l’abeille, Apis mellifera. https://www.semanticscholar.org/paper/9055cb5fd359e97b7b4d33f633f093827048eb7b
  55. Peckmezian T., Taylor P. (2015): A virtual reality paradigm for the study of visually mediated behaviour and cognition in spiders. Animal Behaviour 107:87-95. https://doi.org/10.1016/J.ANBEHAV.2015.06.018
  56. Mendoza E., Colomb J., Rybak J., Pflüger H., Zars T., Scharff C., et al. (2014): Drosophila FoxP Mutants Are Deficient in Operant Self-Learning. PLoS ONE 9(6):e100648. https://doi.org/10.1371/journal.pone.0100648
  57. H. Mirwan (2014): Bumblebees’ Bombus impatiens (Cresson) Learning: An Ecological Context. https://www.semanticscholar.org/paper/cb94b29118f302b90c016856b7c4a990eba45bc9
  58. Christopher J., de Belle J. (2014): Behavioral Genetics of the Fly ( Drosophila Melanogaster ): Olfactory learning and memory assays. Behavioral Genetics of the Fly (Drosophila Melanogaster). https://doi.org/10.1017/CBO9780511920585.019
  59. P. Killeen (2014): Pavlov + Skinner = Premack. https://www.semanticscholar.org/paper/b98d29b61de0ddccb93b4477940aadbf8ff13678
  60. Dasgupta S., Wörgötter F., Manoonpong P. (2014): Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control. Frontiers in Neural Circuits 8. https://doi.org/10.3389/fncir.2014.00126
  61. Harrigan W., Commons M. (2014): The stage of development of a species predicts the number of neurons. Behavioral Development Bulletin 19(4):12-21. https://doi.org/10.1037/H0101077
  62. Guo A., Lu H., Zhang K., Ren Q., Chiang Wong Y. (2013): Visual Learning and Decision Making in Drosophila melanogaster. Handbook of Behavioral Neuroscience. https://doi.org/10.1016/B978-0-12-415823-8.00028-9
  63. Perry C., Barron A., Cheng K. (2013): Invertebrate learning and cognition: relating phenomena to neural substrate. WIREs Cognitive Science 4(5):561-582. https://doi.org/10.1002/wcs.1248
  64. Mery F. (2013): Natural variation in learning and memory. Current Opinion in Neurobiology 23(1):52-56. https://doi.org/10.1016/j.conb.2012.09.001
  65. Jennifer M. Li (2013): Identification of an Operant Learning Circuit by Whole Brain Functional Imaging in Larval Zebrafish. https://www.semanticscholar.org/paper/72bca9893fa8304a548001ef0d1bc5de1011154a
  66. Giurfa M. (2013): Cognition with few neurons: higher-order learning in insects. Trends in Neurosciences 36(5):285-294. https://doi.org/10.1016/j.tins.2012.12.011
  67. MANOONPONG P., KOLODZIEJSKI C., WÖRGÖTTER F., MORIMOTO J. (2013): Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement. Advances in Complex Systems 16(02n03):1350015. https://doi.org/10.1142/S021952591350015X
  68. Zwarts L., Clements J., Callaerts P. (2012): Deciphering the Adult Brain: From Neuroanatomy to Behavior. Neuromethods. https://doi.org/10.1007/978-1-61779-830-6_1
  69. Nuwal N., Stock P., Hiemeyer J., Schmid B., Fiala A., Buchner E. (2012): Avoidance of Heat and Attraction to Optogenetically Induced Sugar Sensation as Operant Behavior in Adult Drosophila. Journal of Neurogenetics 26(3-4):298-305. https://doi.org/10.3109/01677063.2012.700266
  70. Amber Hayden (2011): The role of learning in the feeding behavior of antlions. https://www.semanticscholar.org/paper/dcedc470d370c4f2c5239e89906dbb7ad3fe3a07
  71. Brembs B. (2011): Spontaneous decisions and operant conditioning in fruit flies. Behavioural Processes 87(1):157-164. https://doi.org/10.1016/j.beproc.2011.02.005
  72. Murray E., Wise S., Rhodes S. (2011): What Can Different Brains Do with Reward. Frontiers in Neuroscience. https://doi.org/10.1201/b10776-6
  73. V. Népoux (2011): Natural variation in learning ability in “Drosophila melanogaster”. https://www.semanticscholar.org/paper/672af67cc71b83707a769dbe4c3af35087be9672
  74. Guo A., Zhang K., Peng Y., Xi W. (2010): Research progress on Drosophila visual cognition in China. Science China Life Sciences 53(3):374-384. https://doi.org/10.1007/s11427-010-0073-9
  75. van Swinderen B., Brembs B. (2010): Attention-Like Deficit and Hyperactivity in a Drosophila Memory Mutant. The Journal of Neuroscience 30(3):1003-1014. https://doi.org/10.1523/JNEUROSCI.4516-09.2010
  76. Sokolowski M., Disma G., Abramson C. (2010): A paradigm for operant conditioning in blow flies (Phormia terrae novae Robineau-Desvoidy, 1830). Journal of the Experimental Analysis of Behavior 93(1):81-89. https://doi.org/10.1901/jeab.2010.93-81
  77. Zars T. (2010): Short-term memories in Drosophila are governed by general and specific genetic systems. Learning & Memory 17(5):246-251. https://doi.org/10.1101/lm.1706110
  78. É. Bourg, Christian Buecher (2010): Learned suppression of photopositive tendencies in Drosophila melanogaster . https://www.semanticscholar.org/paper/213d70043eb9d36eca272b0c37964fb6f1af9f2b
  79. Unknown authors (2009): Learning in Adult Flies.
  80. Claridge-Chang A., Roorda R., Vrontou E., Sjulson L., Li H., Hirsh J., et al. (2009): Writing memories with light-addressable reinforcement circuitry. Cell 139(2):405-415. https://doi.org/10.1016/j.cell.2009.08.034
  81. Björn Brembs (2009): Mushroom Bodies Regulate Habit Formation in Drosophila. https://www.semanticscholar.org/paper/6fc194f9840e722e0b9e5c75fa6d864edc4c4d82
  82. Akalal D., Davis R. (2009): Learning and Memory in Invertebrates: Drosophila. Encyclopedia of Neuroscience. https://doi.org/10.1016/B978-008045046-9.00799-3
  83. Würbel H. (2009): Ethology applied to animal ethics. Applied Animal Behaviour Science 118(3-4):118-127. https://doi.org/10.1016/J.APPLANIM.2009.02.019
  84. Pitman J., DasGupta S., Krashes M., Leung B., Perrat P., Waddell S. (2009): There are many ways to train a fly. Fly 3(1):3-9. https://doi.org/10.4161/fly.3.1.7726
  85. Seugnet L., Suzuki Y., Stidd R., Shaw P. (2009): Aversive phototaxic suppression: evaluation of a short‐term memory assay in Drosophila melanogaster. Genes, Brain and Behavior 8(4):377-389. https://doi.org/10.1111/j.1601-183X.2009.00483.x
  86. Dayan P., Huys Q. (2009): Serotonin in affective control. Annual Review of Neuroscience 32(1):95-126. https://doi.org/10.1146/annurev.neuro.051508.135607
  87. Menzel R. (2009): Working Memory in Bees: Also in Flies?. Journal of Neurogenetics 23(1-2):92-99. https://doi.org/10.1080/01677060802610612
  88. Pan Y., Zhou Y., Guo C., Gong H., Gong Z., Liu L. (2009): Differential roles of the fan-shaped body and the ellipsoid body in Drosophila visual pattern memory. Learning & Memory 16(5):289-295. https://doi.org/10.1101/lm.1331809
  89. Brembs B. (2008): Mushroom bodies regulate habit formation in Drosophila. Current Biology 19(16):1351-1355. https://doi.org/10.1016/j.cub.2009.06.014
  90. Brembs B., Plendl W. (2008): Double dissociation of PKC and AC manipulations on operant and classical learning in Drosophila. Current Biology 18(15):1168-1171. https://doi.org/10.1016/j.cub.2008.07.041
  91. B. Brembs (2008): The neurobiology of operant learning: biophysical and molecular mechanisms in a hierarchical organization of multiple memory systems. https://www.semanticscholar.org/paper/e093d2a5cc2d2f3f0ffe7588f0b80e5e3993d330
  92. Brembs B. (2008): Operant Learning of Drosophila at the Torque Meter. Journal of Visualized Experiments. https://doi.org/10.3791/731
  93. Franco Bertolucci (2008): Operant and classical learning in Drosophila melanogaster: the ignorant gene (ign). https://www.semanticscholar.org/paper/c840d1ffa502d7e2c1a7ca1138ac884c79661ea4
  94. Klowden M. (2008): Chapter 5 – Behavioral Systems. Physiological Systems in Insects. https://doi.org/10.1016/B978-0-12-415819-1.00005-2
  95. Brembs B., Plendl W. (2007): Dissecting the mechanisms of learning-by-doing in Drosophila. Nature Precedings. https://doi.org/10.1038/NPRE.2007.1354.1
  96. M. Giurfa (2007): 12 Invertebrate Cognition: Nonelemental Learning beyond Simple Conditioning. https://doi.org/10.1101/087969819.49.281
  97. Menzel R., Brembs B., Giurfa M. (2007): Cognition in Invertebrates. Evolution of Nervous Systems. https://doi.org/10.1016/B0-12-370878-8/00183-X
  98. Brembs B., Wiener J. (2006): Context and occasion setting in Drosophila visual learning. Learning & Memory 13(5):618-628. https://doi.org/10.1101/LM.318606
  99. Brembs B., Hempel de Ibarra N. (2006): Different parameters support generalization and discrimination learning in Drosophila at the flight simulator. Learning & Memory 13(5):629-637. https://doi.org/10.1101/LM.319406
  100. B. Brembs, N. H. D. Ibarra (2006): at the flight simulatorDrosophilalearning in Different parameters support generalization and discrimination. https://www.semanticscholar.org/paper/e7c99957e995cc51f6f9717c7b18bb2af80fff17
  101. Dupuy F., Sandoz J., Giurfa M., Josens R. (2006): Individual olfactory learning in Camponotus ants. Animal Behaviour 72(5):1081-1091. https://doi.org/10.1016/J.ANBEHAV.2006.03.011
  102. Liu G., Seiler H., Wen A., Zars T., Ito K., Wolf R., et al. (2006): Distinct memory traces for two visual features in the Drosophila brain. Nature 439(7076):551-556. https://doi.org/10.1038/nature04381
  103. J. Finlay, Stewart (2006): Visual-olfactory integration in free-flying Drosophila. https://www.semanticscholar.org/paper/ccb0b7ca3fb6c3c816b1cff26c59ecba3fceaf06
  104. R. Menzel, B. Brembs (2006): 1 . 26 Cognition in Invertebrates. https://www.semanticscholar.org/paper/cd86c7c05307517d2a90871fd20ad02736ac6100
  105. Brembs B., Baxter D., Byrne J. (2004): Extending in vitro conditioning in Aplysia to analyze operant and classical processes in the same preparation. Learning & Memory 11(4):412-420. https://doi.org/10.1101/LM.74404
  106. Björn Brembs, D. A. Baxter, John H. Byrne (2004): Extending in vitro conditioning in Aplysia to analyze operant and classical processes in the same preparation. Learning & memory (Cold Spring Harbor, N.Y.). https://www.semanticscholar.org/paper/8f23e25664bb2f30ca96c19868001ef93ef389b4
  107. Giurfa M., Malun D. (2004): Associative mechanosensory conditioning of the proboscis extension reflex in honeybees. Learning & Memory 11(3):294-302. https://doi.org/10.1101/LM.63604
  108. Siwicki K., Ladewski L. (2003): Associative learning and memory in Drosophila: beyond olfactory conditioning. Behavioural Processes 64(2):225-238. https://doi.org/10.1016/S0376-6357(03)00137-2
  109. Wang S., Li Y., Feng C., Guo A. (2003): Dissociation of visual associative and motor learning in Drosophila at the flight simulator. Behavioural Processes 64(1):57-70. https://doi.org/10.1016/S0376-6357(03)00105-0
  110. Wang S., Tang S., Li Y., Guo A. (2003): Behavioral modification in choice process ofDrosophila. Science in China Series C Life Sciences 46(4):399-413. https://doi.org/10.1007/BF03192583
  111. Tracey W., Wilson R., Laurent G., Benzer S. (2003): painless, a Drosophila gene essential for nociception. Cell 113(2):261-273. https://doi.org/10.1016/S0092-8674(03)00272-1
  112. Le Bourg É., Buecher C. (2002): Learned suppression of photopositive tendencies inDrosophila melanogaster. Animal Learning & Behavior 30(4):330-341. https://doi.org/10.3758/BF03195958
  113. Björn Brembs, Martin Heisenberg (2001): Conditioning with compound stimuli in Drosophila melanogaster in the flight simulator. Journal of Experimental Biology. https://www.semanticscholar.org/paper/4b8a3d97d7aa24c0e3f330670b51cffaf9b9c51f
  114. J. Kisch (2001): Verhaltens- und elektrophysiologische Untersuchungen zur operanten Konditionierung von Antennenbewegungen der Honigbiene. https://doi.org/10.14279/DEPOSITONCE-348
  115. Heisenberg M., Wolf R., Brembs B. (2001): Flexibility in a single behavioral variable of Drosophila. Learning & Memory 8(1):1-10. https://doi.org/10.1101/LM.37501
  116. B. Brembs (2000): An Analysis of Associative Learning in Drosophila at the Flight Simulator. https://www.semanticscholar.org/paper/1db6e7dcae28ab3a422794596f565aab41591bdb
  117. Björn Brembs, D. A. Baxter, John H. Byrne (): Extending in Vitro Conditioning in Aplysia to Analyze Operant and Classical Processes in the Same Preparation. https://www.semanticscholar.org/paper/0b487111facc4edee8153dc4ce4d750b1794007c
  118. S. M. Miller, Trung Thành Ngô, B. van Swinderen (): Human Neuroscience Hypothesis and Theory Article Attentional Switching in Humans and Flies: Rivalry in Large and Miniature Brains. https://www.semanticscholar.org/paper/f19f6d37316f70258a837832a3272745a73f72df

Cutts CJ, Brembs B, Metcalfe NB, Taylor AC. (1999): Prior residence, territory quality and life-history strategies in juvenile Atlantic salmon (Salmo salar L.). J. Fish. Biol. 55:784–794.

  1. Simmons O., Robertsen G., Sundt-Hansen L. (2025): Temperature and daily, rapid flow fluctuations alter emergence timing, survival, and body size of Atlantic salmon (Salmo salar) fry. Environmental Biology of Fishes 108(12):2199-2212. https://doi.org/10.1007/s10641-025-01757-w
  2. Aykanat T., Erkinaro J. (2025): Early life exploration behavior and life-history loci are co-localized in an adaptive genomic hotspot in Atlantic salmon. bioRxiv. https://doi.org/10.1101/2025.05.28.656530
  3. Yamashita Y., Iwasaki Y., Strüssmann C. (2025): Experimental observation of the prior residence advantage in masu salmon in an experimental channel. Ecological Solutions and Evidence 6(4). https://doi.org/10.1002/2688-8319.70132
  4. Graziano M., Solberg M., Glover K., Vasudeva R., Dyrhovden L., Murray D., et al. (2023): Pre-fertilization gamete thermal environment influences reproductive success, unmasking opposing sex-specific responses in Atlantic salmon (Salmo salar). Royal Society Open Science 10(12). https://doi.org/10.1098/rsos.231427
  5. Lovén Wallerius M., Moran V., Závorka L., Höjesjö J. (2022): Asymmetric competition over space use and territory between native brown trout (Salmo trutta) and invasive brook trout (Salvelinus fontinalis). Journal of Fish Biology 100(4):1033-1043. https://doi.org/10.1111/jfb.15010
  6. Shahinur S. Islam, B. Wringe, K. Bøe, I. Bradbury, I. Fleming (2021): Early-life fitness trait variation among divergent European and North American farmed and Newfoundland wild Atlantic salmon populations. https://www.semanticscholar.org/paper/42cefa20abc46e71dc6b0bee8d62eada5e10a542
  7. Hawke T., Bino G., Kingsford R., Iervasi D., Iervasi K., Taylor M. (2021): Long-term movements and activity patterns of platypus on regulated rivers. Scientific Reports 11(1). https://doi.org/10.1038/s41598-021-81142-6
  8. Monk C., Chéret B., Czapla P., Hühn D., Klefoth T., Eschbach E., et al. (2020): Behavioural and fitness effects of translocation to a novel environment: whole-lake experiments in two aquatic top predators. Journal of Animal Ecology 89(10):2325-2344. https://doi.org/10.1111/1365-2656.13298
  9. Ames E., Gade M., Nieman C., Wright J., Tonra C., Marroquin C., et al. (2020): Striving for population-level conservation: integrating physiology across the biological hierarchy. Conservation Physiology 8(1). https://doi.org/10.1093/conphys/coaa019
  10. Xu X., Zhang Z., Guo H., Qin J., Zhang X. (2020): Changes in Aggressive Behavior, Cortisol and Brain Monoamines during the Formation of Social Hierarchy in Black Rockfish (Sebastes schlegelii). Animals 10(12):2357. https://doi.org/10.3390/ani10122357
  11. C. Chifamba (2019): The biology and impacts of Oreochromis niloticus and Limnothrissa miodon introduced in Lake Kariba. https://www.semanticscholar.org/paper/f724c775114ba6e112252f153fc91cbfa1c3084b
  12. Colella D., Paijmans K., Wong M. (2019): Size, sex and social experience: Experimental tests of multiple factors mediating contest behaviour in a rockpool fish. Ethology 125(6):369-379. https://doi.org/10.1111/ETH.12861
  13. M. Thorn (2018): Maternal Effects and the Evolution of Chinook Salmon. https://www.semanticscholar.org/paper/1fa46329f7cd5f0e9efc1853ac7a7e7ca54fa9a2
  14. Näslund J., Sandquist L., Johnsson J. (2017): Is behaviour in a novel environment associated with bodily state in brown trout Salmo trutta fry. Ecology of Freshwater Fish 26(3):462-474. https://doi.org/10.1111/EFF.12291
  15. Thorn M., Morbey Y. (2017): Egg size and the adaptive capacity of early life history traits in Chinook salmon (Oncorhynchus tshawytscha). Evolutionary Applications 11(2):205-219. https://doi.org/10.1111/eva.12531
  16. Berg O., Fleming I. (2017): Energetic Trade‐Offs Faced by Brown Trout During Ontogeny and Reproduction. Brown Trout. https://doi.org/10.1002/9781119268352.CH8
  17. Bolliet V., Bardonnet A. (2017): Impact of Embeddedness on Salmo trutta at Different Periods of their Early Ontogenesis. Brown Trout. https://doi.org/10.1002/9781119268352.CH9
  18. Moginie B. (2016): Going all the way: The implications of life history and phenotype on reproductive success of the common triplefin, Forsterygion lapillum. https://doi.org/10.26686/wgtn.17019293
  19. Chifamba P., Mauru T. (2016): Comparative aggression and dominance of Oreochromis niloticus (Linnaeus, 1758) and Oreochromis mortimeri (Trewavas, 1966) from paired contest in aquaria. Hydrobiologia 788(1):193-203. https://doi.org/10.1007/s10750-016-2997-y
  20. Winberg S., Höglund E., Øverli Ø. (2016): Variation in the Neuroendocrine Stress Response. Fish Physiology. https://doi.org/10.1016/B978-0-12-802728-8.00002-3
  21. Harding Gradil, J. Kaylá (2015): Thermal performance covaries with environmental temperature across populations of Atlantic salmon (Salmo salar). https://www.semanticscholar.org/paper/c58375de16868e9633fdaabeca1afed97c4936a0
  22. J. Näslund (2015): The Pace of Life of Juvenile Brown Trout – Inter- and Intra-individual Variation in Growth and Behaviour. https://www.semanticscholar.org/paper/b1fc2efa7f82f35dc036c20dca576d6fecbe2d6d
  23. Régnier T., Labonne J., Chat J., Yano A., Guiguen Y., Bolliet V. (2015): No early gender effects on energetic status and life history in a salmonid. Royal Society Open Science 2(12):150441. https://doi.org/10.1098/rsos.150441
  24. Moreau D., Gamperl A., Fletcher G., Fleming I. (2014): Delayed Phenotypic Expression of Growth Hormone Transgenesis during Early Ontogeny in Atlantic Salmon (Salmo salar)?. PLoS ONE 9(4):e95853. https://doi.org/10.1371/journal.pone.0095853
  25. Solberg M., Fjelldal P., Nilsen F., Glover K. (2014): Hatching Time and Alevin Growth Prior to the Onset of Exogenous Feeding in Farmed, Wild and Hybrid Norwegian Atlantic Salmon. PLoS ONE 9(12):e113697. https://doi.org/10.1371/journal.pone.0113697
  26. Sami Nerg (2014): Densities of Atlantic salmon (Salmo salar L.) fry in relation to physical habitat, spawning redd distribution and parr abundance in a small subarctic river. https://www.semanticscholar.org/paper/a9692540425e99ebc2b9c48a8f48e78dc92c75d0
  27. Warnock W., Rasmussen J. (2014): Comparing competitive ability and associated metabolic traits between a resident and migratory population of bull trout against a non-native species. Environmental Biology of Fishes 97(4):415-423. https://doi.org/10.1007/s10641-013-0161-3
  28. Sigourney D., Letcher B., Obedzinski M., Cunjak R. (2013): Interactive effects of life history and season on size-dependent growth in juvenile Atlantic salmon. Ecology of Freshwater Fish 22(4):495-507. https://doi.org/10.1111/EFF.12042
  29. J. D’silva (2013): Causes of Intra-specific Variation in Metabolic Rate in Zebrafish, Danio rerio. https://doi.org/10.20381/RUOR-2996
  30. Simmons R., Quinn T., Seeb L., Schindler D., Hilborn R. (2013): Summer emigration and resource acquisition within a shared nursery lake by sockeye salmon (Oncorhynchus nerka) from historically discrete rearing environments. Canadian Journal of Fisheries and Aquatic Sciences 70(1):57-63. https://doi.org/10.1139/CJFAS-2012-0159
  31. Ryan K. Simmons, T. Quinn, L. Seeb, D. Schindler, R. Hilborn (2013): Summer emigration and resource acquisition within a shared nursery lake by sockeye salmon ( Oncorhynchus nerka ) from historically discrete rearing environments. https://www.semanticscholar.org/paper/f841f461c4ea9f530d83576a06523066b8cb7470
  32. Mogensen S., Hutchings J. (2012): Maternal fitness consequences of interactions among agents of mortality in early life of salmonids. Canadian Journal of Fisheries and Aquatic Sciences 69(9):1539-1555. https://doi.org/10.1139/F2012-071
  33. Jonsson B., Jonsson N. (2011): Population Enhancement and Population Restoration. Ecology of Atlantic Salmon and Brown Trout. https://doi.org/10.1007/978-94-007-1189-1_11
  34. Moreau D., Fleming I., Fletcher G., Brown J. (2011): Growth hormone transgenesis does not influence territorial dominance or growth and survival of first-feeding Atlantic salmon Salmo salar in food-limited stream microcosms. Journal of Fish Biology 78(3):726-740. https://doi.org/10.1111/j.1095-8649.2010.02888.x
  35. Reid D., Armstrong J., Metcalfe N. (2011): Estimated standard metabolic rate interacts with territory quality and density to determine the growth rates of juvenile Atlantic salmon. Functional Ecology 25(6):1360-1367. https://doi.org/10.1111/J.1365-2435.2011.01894.X
  36. Kvingedal E., Einum S. (2011): Prior residency advantage for Atlantic salmon in the wild: effects of habitat quality. Behavioral Ecology and Sociobiology 65(6):1295-1303. https://doi.org/10.1007/s00265-011-1143-0
  37. Van Leeuwen T., Rosenfeld J., Richards J. (2011): Failure of physiological metrics to predict dominance in juvenile Pacific salmon (Oncorhynchus spp.): habitat effects on the allometry of growth in dominance hierarchies11Order of authors represents their contribution to the manuscript. Canadian Journal of Fisheries and Aquatic Sciences 68(10):1811-1818. https://doi.org/10.1139/F2011-099
  38. Frøydis Bolme Hamnes (2011): Size-dependent habitat use in juvenile Atlantic salmon (Salmo salar L.). https://www.semanticscholar.org/paper/76491307954c27d8baaf36fed843b31fb3839277
  39. Kim J., Grant J., Brown G. (2011): Do juvenile Atlantic salmon (Salmo salar) use chemosensory cues to detect and avoid risky habitats in the wild. Canadian Journal of Fisheries and Aquatic Sciences 68(4):655-662. https://doi.org/10.1139/F2011-011
  40. T. E. Leeuwen, J. Rosenfeld, J. Richards (2011): Failure of physiological metrics to predict dominance in juvenile Pacific salmon (Oncorhynchus spp.): habitat effects on the allometry of growth in dominance hierarchies 1. https://www.semanticscholar.org/paper/fa8f0333ba58fb1d5145f3e926c6c62ba7f70145
  41. Stephanie Mogensen (2010): Maternal Fitness Consequences of Different Causative Agents of Offspring Mortality in Early Life. https://www.semanticscholar.org/paper/a62f1cbc9f9b882b6ce2803b5e70804bd4b1404e
  42. Van Leeuwen, T. Edward (2010): Variation in metabolic rate between individuals and species : cryptic physiological tradeoffs underlying habitat partitioning and life history strategies of juvenile salmonids. https://doi.org/10.14288/1.0071309
  43. B. Jonsson (2009): Restoration and Enhancement of Salmonid Populations and Habitats with Special Reference to Atlantic Salmon. https://www.semanticscholar.org/paper/726afe563ca491f36c4fa20f25e0a7d30bb74580
  44. Montero D., Lalumera G., Izquierdo M., Caballero M., Saroglia M., Tort L. (2009): Establishment of dominance relationships in gilthead sea bream Sparus aurata juveniles during feeding: effects on feeding behaviour, feed utilization and fish health. Journal of Fish Biology 74(4):790-805. https://doi.org/10.1111/j.1095-8649.2008.02161.x
  45. J. McMillan (2009): Early maturing males in a partially migratory population of anadromous and resident rainbow trout Oncorhynchus mykiss: influences of individual condition and stream temperature. https://www.semanticscholar.org/paper/bb8374be0cfb2bc02802cb7a5c564e14df95d099
  46. J. Murua (2009): The role of social rank in the development, physiology and reproductive strategies in salmonids. https://www.semanticscholar.org/paper/8444b44b9ea7e46c52f5fa178f51c4a254ef49f4
  47. Railsback S., Harvey B., Jackson S., Lamberson R. (2009): InSTREAM: the individual-based stream trout research and environmental assessment model. https://doi.org/10.2737/PSW-GTR-218
  48. Seiler S., Keeley E. (2009): Competition between native and introduced salmonid fishes: cutthroat trout have lower growth rate in the presence of cutthroat-rainbow trout hybrids. Canadian Journal of Fisheries and Aquatic Sciences 66(1):133-141. https://doi.org/10.1139/F08-194
  49. Lelong A., Bolliet V., Bancel D., Gaudin P. (2008): Detection of elemental composition and caloric value variability at individual level in newly emerged brown trout Salmo trutta L. Journal of Fish Biology 72(10):2695-2699. https://doi.org/10.1111/J.1095-8649.2008.01883.X
  50. Martin A., Moore P. (2008): The Influence of Dominance on Shelter Preference and Eviction Rates in the Crayfish, Orconectes rusticus. Ethology 114(4):351-360. https://doi.org/10.1111/J.1439-0310.2008.01473.X
  51. Seppänen E., Kuukka H., Huuskonen H., Piironen J. (2008): Relationship between standard metabolic rate and parasite-induced cataract of juveniles in three Atlantic salmon stocks. Journal of Fish Biology 72(7):1659-1674. https://doi.org/10.1111/J.1095-8649.2008.01832.X
  52. E. Seppänen (2008): Relationships between metabolic rate, growth rate, smolting and parasite infection in salmonid fishes. https://www.semanticscholar.org/paper/d653d336560417e2ad99dbb81b76571b12c75822
  53. Neregård L., Sundt‐Hansen L., Björnsson B., Johnsson J. (2008): Growth hormone affects behaviour of wild brown trout Salmo trutta in territorial owner–intruder conflicts. Journal of Fish Biology 73(10):2341-2351. https://doi.org/10.1111/J.1095-8649.2008.02082.X
  54. Jönsson M., Skov C., Koed A., Anders Nilsson P. (2008): Temporal clumping of prey and coexistence of unequal interferers: experiments on social forager groups of brown trout feeding on invertebrate drift. Oikos 117(12):1782-1787. https://doi.org/10.1111/J.1600-0706.2008.16848.X
  55. Arthur L. Martin (2007): Underlying Mechanisms That Affect Crayfish Agonistic Interactions and Resource Acquisition. https://www.semanticscholar.org/paper/eb9acdfe9d3475aa4f905f50a27abc19832beeb9
  56. Beckman B., Gadberry B., Parkins P., Cooper K., Arkush K. (2007): State-dependent life history plasticity in Sacramento River winter-run chinook salmon (Oncorhynchus tshawytscha): interactions among photoperiod and growth modulate smolting and early male maturation. Canadian Journal of Fisheries and Aquatic Sciences 64(2):256-271. https://doi.org/10.1139/F07-001
  57. Schjolden J., Winberg S. (2007): Genetically Determined Variation in Stress Responsiveness in Rainbow Trout: Behavior and Neurobiology. Brain, Behavior and Evolution 70(4):227-238. https://doi.org/10.1159/000105486
  58. M. Wellenreuther (2007): Ecological factors associated with speciation in New Zealand triplefin fishes (Family Tripterygiidae). https://www.semanticscholar.org/paper/7fae09849448d98bc2f8888393c5da103f18ee42
  59. Jonsson B., Jonsson N. (2006): Cultured Atlantic salmon in nature: a review of their ecology and interaction with wild fish. ICES Journal of Marine Science 63(7):1162-1181. https://doi.org/10.1016/J.ICESJMS.2006.03.004
  60. Kokko H., López‐Sepulcre A., Morrell L. (2006): From Hawks and Doves to Self‐Consistent Games of Territorial Behavior. The American Naturalist 167(6):901-912. https://doi.org/10.1086/504604
  61. HOFFMAN J., TRATHAN P., AMOS W. (2006): Genetic tagging reveals extreme site fidelity in territorial male Antarctic fur seals Arctocephalus gazella. Molecular Ecology 15(12):3841-3847. https://doi.org/10.1111/j.1365-294X.2006.03053.x
  62. DiBattista J., Anisman H., Whitehead M., Gilmour K. (2005): The effects of cortisol administration on social status and brain monoaminergic activity in rainbow trout Oncorhynchus mykiss. Journal of Experimental Biology 208(14):2707-2718. https://doi.org/10.1242/jeb.01690
  63. Gilmour K. (2005): Physiological Causes and Consequences of Social Status in Salmonid Fish1. Integrative and Comparative Biology 45(2):263-273. https://doi.org/10.1093/icb/45.2.263
  64. Hedger R., Dodson J., Bergeron N., Caron F. (2005): Habitat selection by juvenile Atlantic salmon: the interaction between physical habitat and abundance. Journal of Fish Biology 67(4):1054-1071. https://doi.org/10.1111/J.0022-1112.2005.00808.X
  65. Letcher B., Dubreuil T., O’Donnell M., Obedzinski M., Griswold K., Nislow K. (2004): Long-term consequences of variation in timing and manner of fry introduction on juvenile Atlantic salmon (Salmo salar) growth, survival, and life-history expression. Canadian Journal of Fisheries and Aquatic Sciences 61(12):2288-2301. https://doi.org/10.1139/F04-214
  66. Hoffman J., Boyd I., Amos W. (2004): EXPLORING THE RELATIONSHIP BETWEEN PARENTAL RELATEDNESS AND MALE REPRODUCTIVE SUCCESS IN THE ANTARCTIC FUR SEAL ARCTOCEPHALUS GAZELLA. Evolution 58(9):2087-2099. https://doi.org/10.1111/j.0014-3820.2004.tb00492.x
  67. S. Gregory, J. McEnroe, P. Klingeman, J. Wyrick (2004): FISH PASSAGE THROUGH RETROFITTED CULVERTS Final Report. https://www.semanticscholar.org/paper/2f1843f2007330899acb2d8815aa6d633eb74959
  68. S. Gregory, J. McEnroe, P. Klingeman, J. Wyrick (2004): FISH PASSAGE THROUGH RETROFITTED CULVERTS. https://www.semanticscholar.org/paper/dda321f6d7c29744ea920ddb48b0a1f61bd1f963
  69. Harwood A., Griffiths S., Metcalfe N., Armstrong J. (2003): The relative influence of prior residency and dominance on the early feeding behaviour of juvenile Atlantic salmon. Animal Behaviour 65(6):1141-1149. https://doi.org/10.1006/ANBE.2003.2125
  70. C. A. Blann (2003): Competition in cultured and wild salmonid juveniles with emphasis on competitive interactions between farmed Atlantic salmon (Salmo salar) and wild coho salmon (Oncorhynchus kisutch) or coastal cutthroat trout (O. clarki clarki). https://doi.org/10.14288/1.0091402
  71. Lewis C., Olive P., Bentley M. (2003): Pre-emptive competition as a selective pressure for early reproduction in the polychaete Nereis virens. Marine Ecology Progress Series 254:199-211. https://doi.org/10.3354/MEPS254199
  72. Weber E., Fausch K. (2003): Interactions between hatchery and wild salmonids in streams: differences in biology and evidence for competition. Canadian Journal of Fisheries and Aquatic Sciences 60(8):1018-1036. https://doi.org/10.1139/F03-087
  73. Vøllestad L., Quinn T. (2003): Trade-off between growth rate and aggression in juvenile coho salmon, Oncorhynchus kisutch. Animal Behaviour 66(3):561-568. https://doi.org/10.1006/ANBE.2003.2237
  74. Jones M., Laurila A., Peuhkuri N., Piironen J., Seppä T. (2003): Timing an ontogenetic niche shift: responses of emerging salmon alevins to chemical cues from predators and competitors. Oikos 102(1):155-163. https://doi.org/10.1034/J.1600-0706.2003.12347.X
  75. Cutts C. (2002): Fish may fight rather than feed in a novel environment: metabolic rate and feeding motivation in juvenile Atlantic salmon. Journal of Fish Biology 61(6):1540-1548. https://doi.org/10.1006/JFBI.2002.2173
  76. Cutts C., Metcalfe N., Taylor A. (2002): Juvenile Atlantic Salmon (Salmo salar) with relatively high standard metabolic rates have small metabolic scopes. Functional Ecology 16(1):73-78. https://doi.org/10.1046/J.0269-8463.2001.00603.X
  77. H. C. Suter (2002): The effects of maternal steroids on individual variation in juvenile salmonids. https://www.semanticscholar.org/paper/624eb2ac5222cbb945b81b68d47a161f5edc1e30
  78. Johnsson J., Forser A. (2002): Residence duration influences the outcome of territorial conflicts in brown trout (Salmo trutta). Behavioral Ecology and Sociobiology 51(3):282-286. https://doi.org/10.1007/s00265-001-0430-6
  79. Sloman K., Armstrong J. (2002): Physiological effects of dominance hierarchies: laboratory artefacts or natural phenomena?. Journal of Fish Biology 61(1):1-23. https://doi.org/10.1111/J.1095-8649.2002.TB01733.X
  80. Brown C. (2001): Familiarity with the test environment improves escape responses in the crimson spotted rainbowfish, Melanotaenia duboulayi. Animal Cognition 4(2):109-113. https://doi.org/10.1007/s100710100105
  81. Mccarthy I. (2001): Competitive ability is related to metabolic asymmetry in juvenile rainbow trout. Journal of Fish Biology 59(4):1002-1014. https://doi.org/10.1111/J.1095-8649.2001.TB00167.X
  82. Volpe J., Anholt B., Glickman B. (2001): Competition among juvenile Atlantic salmon (Salmo salar) and steelhead (Oncorhynchus mykiss): relevance to invasion potential in British Columbia. Canadian Journal of Fisheries and Aquatic Sciences 58(1):197-207. https://doi.org/10.1139/F00-209
  83. Sloman K., Taylor A., Metcalfe N., Gilmour K. (2001): Effects of an environmental perturbation on the social behaviour and physiological function of brown trout. Animal Behaviour 61(2):325-333. https://doi.org/10.1006/ANBE.2000.1567
  84. Berg O., Hendry A., Svendsen B., Bech C., Arnekleiv J., Lohrmann A. (2001): Maternal provisioning of offspring and the use of those resources during ontogeny: variation within and between Atlantic Salmon families. Functional Ecology 15(1):13-23. https://doi.org/10.1046/J.1365-2435.2001.00473.X
  85. Griffiths S., Armstrong J. (2001): The benefits of genetic diversity outweigh those of kin association in a territorial animal. Proceedings of the Royal Society of London. Series B: Biological Sciences 268(1473):1293-1296. https://doi.org/10.1098/rspb.2001.1660
  86. Railsback S., Harvey B. (2001): Individual-based Model Formulation for Cutthroat Trout, Little Jones Creek, California. https://doi.org/10.2737/psw-gtr-182
  87. B. Brembs (2000): An Analysis of Associative Learning in Drosophila at the Flight Simulator. https://www.semanticscholar.org/paper/1db6e7dcae28ab3a422794596f565aab41591bdb
  88. K. Sloman (2000): Environmental and behavioural stressors : effects on physiological function in salmonid fish. https://www.semanticscholar.org/paper/65a04ba375ce0a6ba5eb1898a1924130ea67dcd7
  89. Grimm V Augusiak, F. A, F. B., G. F., Johnston Asa, C. Liu, et al. (): Trace Document. https://www.semanticscholar.org/paper/c6fc75e715ce65852049399156575b62f48dbfad

Brembs B. (1996): Chaos, cheating and cooperation: potential solutions to the Prisoner’s Dilemma. OIKOS 76:14–24.

  1. Darshan Dilip Nahar (2021): Multi-Scale Modeling of Microbial Defection in the Presence of Antibiotics. Digital Commons – USU (Utah State University). https://doi.org/10.26076/2262-3448
  2. Liu Y., Wang L., Zhang F., Wang R. (2020): Diffusion sustains cooperation via forming diverse spatial patterns in prisoner’s dilemma game. Applied Mathematics and Computation 375:125077. https://doi.org/10.1016/j.amc.2020.125077
  3. Bhaumik A., Roy S., Weber G. (2019): Hesitant interval-valued intuitionistic fuzzy-linguistic term set approach in Prisoners’ dilemma game theory using TOPSIS: a case study on Human-trafficking. Central European Journal of Operations Research 28(2):797-816. https://doi.org/10.1007/s10100-019-00638-9
  4. Brembs B. (2017): Operant Behavior in Model Systems. Learning and Memory: A Comprehensive Reference. https://doi.org/10.1016/b978-0-12-809324-5.21032-8
  5. Grinsted L., Field J. (2017): Biological markets in cooperative breeders: quantifying outside options. Proceedings of the Royal Society B: Biological Sciences 284(1856):20170904. https://doi.org/10.1098/rspb.2017.0904
  6. Muegge S. (2017): A game theory perspective on product development project charters: the project manager – project sponsor relationship as an iterated Prisoner’s Dilemma. International Journal of Project Organisation and Management 9(1):57. https://doi.org/10.1504/ijpom.2017.083115
  7. Brembs B. (2016): Operant Behavior in Model Systems. https://doi.org/10.1101/058719
  8. Kurokawa S. (2016): Evolutionary stagnation of reciprocators. Animal Behaviour 122:217-225. https://doi.org/10.1016/j.anbehav.2016.09.014
  9. Sparks A., Burleigh T., Barclay P. (2015): We can see inside: Accurate prediction of Prisoner’s Dilemma decisions in announced games following a face-to-face interaction. Evolution and Human Behavior 37(3):210-216. https://doi.org/10.1016/j.evolhumbehav.2015.11.003
  10. Kimberly A. Horndeski (2015): Deciding How to Decide: An Evaluation of Cultural Typologies on the Decision Making Structure of Watershed Organizations. OhioLink ETD Center (Ohio Library and Information Network).
  11. Mariano P., Correia L. (2015): The Give and Take Game: Analysis of a Resource Sharing Game. International Journal of Applied Mathematics and Computer Science 25(4):753-767. https://doi.org/10.1515/amcs-2015-0054
  12. van den Berg P., Weissing F. (2015): The importance of mechanisms for the evolution of cooperation. Proceedings of the Royal Society B: Biological Sciences 282(1813):20151382. https://doi.org/10.1098/rspb.2015.1382
  13. Mendoza R. (2015): A Game-Theoretic Model of Marketing Skin Whiteners. Health Marketing Quarterly 32(4):367-381. https://doi.org/10.1080/07359683.2015.1093884
  14. Brembs B. (2013): Brains as output/input systems. https://doi.org/10.59350/a7061-xkb13
  15. Brembs B. (2013): Brains as output/input systems. https://doi.org/10.59350/qx0gn-n8773
  16. Osiński J. (2013): Darwinowski algorytm. Wymiana społeczna z perspektywy psychologii ewolucyjnej. https://doi.org/10.31338/uw.9788323513919
  17. Kemper K. (2013): Tribal Sovereignty Means Competition, Broadband Access, and Economic Development for Indian Country: A Law and Economics Analysis of the Efficiency of the FCC’s Standing Rock Sioux Case. Journal of Information Policy 3:442-463. https://doi.org/10.5325/jinfopoli.3.2013.0442
  18. Kemper K. (2013): Tribal Sovereignty Means Competition, Broadband Access, and Economic Development for Indian Country: A Law and Economics Analysis of the Efficiency of the FCC’s Standing Rock Sioux Case. Journal of Information Policy 3:442-463. https://doi.org/10.5325/jinfopoli.3.1.442
  19. Aguado Franco J., De las Heras Camino D. (2012): Cooperación en los dilemas sociales. SOCIAL REVIEW. International Social Sciences Review / Revista Internacional de Ciencias Sociales 1(2). https://doi.org/10.37467/gka-revsocial.v1.1220
  20. Pereira M. (2012): Dilema do prisioneiro contínuo com agentes racionais e classificadores de cooperação. https://doi.org/10.11606/t.59.2012.tde-08012013-222525
  21. 박철, 이상혁 (2012): The Effect of Online Fund-Raising Campaign based on Competitive Altruism. Journal of Consumption Culture 15(1):21-44. https://doi.org/10.17053/jcc.2012.15.1.002
  22. Pomianek C., Palmer C., Wadley R., Coe K. (2011): Cultural Traditions and the Treatment of Freeriders. Journal of International and Global Studies 3(1). https://doi.org/10.62608/2158-0669.1061
  23. Li J., Hingston P., Kendall G. (2011): Engineering Design of Strategies for Winning Iterated Prisoner’s Dilemma Competitions. IEEE Transactions on Computational Intelligence and AI in Games 3(4):348-360. https://doi.org/10.1109/tciaig.2011.2166268
  24. Dyer M., Mohanaraj V. (2011): The Iterated Prisoner’s Dilemma on a Cycle. arXiv. https://doi.org/10.48550/arxiv.1102.3822
  25. Barclay P. (2011): Competitive helping increases with the size of biological markets and invades defection. Journal of Theoretical Biology 281(1):47-55. https://doi.org/10.1016/j.jtbi.2011.04.023
  26. Stafford R., Davies M., Williams G. (2011): Cheats in a cooperative behaviour? Behavioural differences and breakdown of cooperative behaviour in aggregating, intertidal littorinids (Mollusca). Marine Ecology 33(1):66-74. https://doi.org/10.1111/j.1439-0485.2011.00474.x
  27. Wang R., Sun B., Zheng Q., Shi L., Zhu L. (2011): Asymmetric interaction and indeterminate fitness correlation between cooperative partners in the fig–fig wasp mutualism. Journal of The Royal Society Interface 8(63):1487-1496. https://doi.org/10.1098/rsif.2011.0063
  28. Brembs B. (2009): The Importance of Being Active. Journal of Neurogenetics 23(1-2):120-126. https://doi.org/10.1080/01677060802471643
  29. Conrad Power (2009): A Spatial Agent-Based Model of N-Person Prisoner’s Dilemma Cooperation in a Socio-Geographic Community. RePEc: Research Papers in Economics.
  30. Neil Rogers, Dan Ashlock (2009): The Impact of Long-Term Memory in the Iterated Prisoner’s Dilemma. Intelligent Engineering Systems through Artificial Neural Networks. https://doi.org/10.1115/1.802953.paper31
  31. Bentley P. (2009): The game of funding. Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers. https://doi.org/10.1145/1570256.1570367
  32. Sherratt T., Wilkinson D. (2009): Big Questions in Ecology and Evolution. Oxford University Press eBooks. https://doi.org/10.1093/oso/9780199548606.001.0001
  33. Pereira M. (2008): Dilema do prisioneiro evolucionário Darwiniano e Pavloviano no autômato celular unidimensional: uma nova representação e exploração exaustiva do espaço de parâmetros. https://doi.org/10.11606/d.59.2008.tde-12052008-122340
  34. Corning P. (2008): Holistic Darwinism: The new evolutionary paradigm and some implications for political science. Politics and the Life Sciences 27(1):22-54. https://doi.org/10.2990/27_1_22
  35. Unknown authors (2007): Preface. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.002.0007
  36. Unknown authors (2007): Table of Cases. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.001.0001.002.001
  37. Unknown authors (2007): Copyright Page. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.002.0003
  38. Unknown authors (2007): General Editor’s Introduction. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.002.0006
  39. Unknown authors (2007): Dedication. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.002.0005
  40. Unknown authors (2007): Table of Legislation. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.001.0001.002.002
  41. Maye A., Hsieh C., Sugihara G., Brembs B. (2007): Order in Spontaneous Behavior. PLoS ONE 2(5):e443. https://doi.org/10.1371/journal.pone.0000443
  42. Maye A., Hsieh C., Sugihara G., Brembs B. (2007): Order in Spontaneous Behavior. SciVee. https://doi.org/10.4016/726.01
  43. Kevin Heaslip, John Collura, W. Louisell (2007): EVALUATION OF WORK ZONE DESIGN STRATEGIES: QUANTIFYING THE IMPACT OF DRIVER BEHAVIOR ON TRAFFIC FLOW AND SAFETY.
  44. Paolucci M., Conte R. (2007): Roost Size for Multilevel Selection of Altruism Among Vampire Bats. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-76539-4_6
  45. GREEN S. (2007): Stealing. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0007
  46. GREEN S. (2007): The Meaning of White-Collar Crime. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0002
  47. GREEN S. (2007): Some Generalizations About the Moral Content of White-Collar Crime. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0003
  48. GREEN S. (2007): Coercion and Exploitation. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0008
  49. GREEN S. (2007): A Three-Part Framework for Analysis. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0004
  50. GREEN S. (2007): A Final Thought on Moral Wrongfulness. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0012
  51. GREEN S. (2007): Extortion and Blackmail. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0018
  52. GREEN S. (2007): Disloyalty. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0009
  53. GREEN S. (2007): Perjury. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0013
  54. GREEN S. (2007): False Statements. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0015
  55. GREEN S. (2007): Cheating. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0005
  56. GREEN S. (2007): Introduction. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0001
  57. GREEN S. (2007): Promise-Breaking. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0010
  58. GREEN S. (2007): Bribery. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0017
  59. GREEN S. (2007): Fraud. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0014
  60. GREEN S. (2007): Conclusion. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0022
  61. GREEN S. (2007): Regulatory Offenses. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0021
  62. GREEN S. (2007): Deception. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0006
  63. GREEN S. (2007): Tax Evasion. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0020
  64. GREEN S. (2007): Insider Trading. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0019
  65. GREEN S. (2007): Disobedience. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0011
  66. GREEN S. (2007): Obstruction of Justice. Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.003.0016
  67. Green S. (2007): Lying, Cheating, and Stealing. https://doi.org/10.1093/acprof:oso/9780199225804.001.0001
  68. Gardener T., Moffat J. (2007): Changing behaviours in defence acquisition: a game theory approach. Journal of the Operational Research Society 59(2):225-230. https://doi.org/10.1057/palgrave.jors.2602476
  69. Thibert‐Plante X., Parrott L. (2007): Prisoner’s dilemma and clusters on small‐world networks. Complexity 12(6):22-36. https://doi.org/10.1002/cplx.20182
  70. Thibert-Plante X., Charbonneau P. (2007): Crossover and Evolutionary Stability in the Prisoner’s Dilemma. Evolutionary Computation 15(3):321-344. https://doi.org/10.1162/evco.2007.15.3.321
  71. Aguado Franco, Juan Carlos (2006): La cooperación en los dilemas sociales: el caso de los recursos naturales renovables.
  72. Petersen C. (2006): Sexual selection and reproductive success in hermaphroditic seabasses. Integrative and Comparative Biology 46(4):439-448. https://doi.org/10.1093/icb/icj045
  73. Garcia J., Hernandez G., Galeano J. (2006): Cooperation, Solution Concepts and Long-term Dynamics in the Iterated Prisoner’s Dilemma. 2006 IEEE International Conference on Evolutionary Computation. https://doi.org/10.1109/cec.2006.1688502
  74. Paolucci M., Conte R., Tosto G. (2006): A Model of Social Organization and the Evolution of Food Sharing in Vampire Bats. Adaptive Behavior 14(3):223-238. https://doi.org/10.1177/105971230601400305
  75. Fishman M. (2006): Involuntary defection and the evolutionary origins of empathy. Journal of Theoretical Biology 242(4):873-879. https://doi.org/10.1016/j.jtbi.2006.05.004
  76. Anthes N., Putz A., Michiels N. (2006): Sex role preferences, gender conflict and sperm trading in simultaneous hermaphrodites: a new framework. Animal Behaviour 72(1):1-12. https://doi.org/10.1016/j.anbehav.2005.09.017
  77. Conte R., Paolucci M., Di Tosto G. (2006): Vampire Bats & The Micro-Macro Link. Contributions to Economics. https://doi.org/10.1007/3-7908-1721-x_9
  78. Kun Á., Boza G., Scheuring I. (2006): Asynchronous snowdrift game with synergistic effect as a model of cooperation. Behavioral Ecology 17(4):633-641. https://doi.org/10.1093/beheco/ark009
  79. Waisel D. (2005): Developing Social Capital in the Operating Room. Anesthesiology 103(6):1305-1310. https://doi.org/10.1097/00000542-200512000-00026
  80. Waisel D. (2005): Developing Social Capital in the Operating Room. Anesthesiology 103(6):1305-1310. https://doi.org/10.1097/00000542-200512010-00026
  81. Doebeli M., Hauert C. (2005): Models of cooperation based on the Prisoner’s Dilemma and the Snowdrift game. Ecology Letters 8(7):748-766. https://doi.org/10.1111/j.1461-0248.2005.00773.x
  82. Michael Doebeli, Christoph Hauert (2005): SYNTHESES Models of cooperation based on the Prisoner’s Dilemma and the Snowdrift game.
  83. Daniel Jang, Peter A. Whigham, Grant Dick (2004): On evolving fixed pattern strategies for Iterated Prisoner’s Dilemma.
  84. Allen D., Griffiths L., Lyne P. (2004): Understanding complex trajectories in health and social care provision. Sociology of Health & Illness 26(7):1008-1030. https://doi.org/10.1111/j.0141-9889.2004.00426.x
  85. Gutnisky D., Zanutto B. (2004): Cooperation in the Iterated Prisoner’s Dilemma Is Learned by Operant Conditioning Mechanisms. Artificial Life 10(4):433-461. https://doi.org/10.1162/1064546041766479
  86. Greig D., Travisano M. (2004): The Prisoner’s Dilemma and polymorphism in yeast SUC genes. Proceedings of the Royal Society of London. Series B: Biological Sciences 271(suppl_3). https://doi.org/10.1098/rsbl.2003.0083
  87. Kreft J. (2004): Conflicts of interest in biofilms. Biofilms 1(4):265-276. https://doi.org/10.1017/s1479050504001486
  88. McNamara J., Barta Z., Houston A. (2004): Variation in behaviour promotes cooperation in the Prisoner’s Dilemma game. Nature 428(6984):745-748. https://doi.org/10.1038/nature02432
  89. K. R. F Oster (2004): Diminishing returns in social evolution: the not-so-tragic commons.
  90. Foster K. (2004): Diminishing returns in social evolution: the not-so-tragic commons. Journal of Evolutionary Biology 17(5):1058-1072. https://doi.org/10.1111/j.1420-9101.2004.00747.x
  91. Lotem A., Fishman M., Stone L. (2003): From reciprocity to unconditional altruism through signalling benefits. Proceedings of the Royal Society of London. Series B: Biological Sciences 270(1511):199-205. https://doi.org/10.1098/rspb.2002.2225
  92. Dubois F., Giraldeau L. (2003): The Forager’s Dilemma: Food Sharing and Food Defense as Risk‐Sensitive Foraging Options. The American Naturalist 162(6):768-779. https://doi.org/10.1086/379202
  93. Mariano P., Correia L. (2003): A Resource Sharing Model to Study Social Behaviours. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-24580-3_16
  94. Thomas D. Gamble, Li, X (2003): Emergence of Cooperation in the IPD Game using Spatial Interactions. RMIT Research Repository (RMIT University Library).
  95. W. Louisell (2003): A Framework and Analytical Methods for Evaluation of Preferential Treatment for Emergency and Transit Vehicles at Signalized Intersections. VTechWorks (Virginia Tech).
  96. Simms E. (2002): Partner Choice in Nitrogen-Fixation Mutualisms of Legumes and Rhizobia. Integrative and Comparative Biology 42(2):369-380. https://doi.org/10.1093/icb/42.2.369
  97. Pedro Mariano, Luís Correia (2002): The effect of agreements in a game with multiple strategies for cooperation.
  98. Bliege Bird R., Bird D., Smith E., Kushnick G. (2002): Risk and reciprocity in Meriam food sharing. Evolution and Human Behavior 23(4):297-321. https://doi.org/10.1016/s1090-5138(02)00098-3
  99. Brembs B. (2001): Hamilton’s Theory. Encyclopedia of Genetics. https://doi.org/10.1006/rwgn.2001.0581
  100. Wilkinson D., Sherratt T. (2001): Horizontally acquired mutualisms, an unsolved problem in ecology?. Oikos 92(2):377-384. https://doi.org/10.1034/j.1600-0706.2001.920222.x
  101. Hare J., Atkins B. (2001): The squirrel that cried wolf: reliability detection by juvenile Richardson’s ground squirrels ( Spermophilus richardsonii ). Behavioral Ecology and Sociobiology 51(1):108-112. https://doi.org/10.1007/s002650100414
  102. Wakano J., Yamamura N. (2001): A simple learning strategy that realizes robust cooperation better than Pavlov in Iterated Prisoners’ Dilemma. Journal of Ethology 19(1):1-8. https://doi.org/10.1007/s101640170010
  103. Bronstein J. (2001): The exploitation of mutualisms. Ecology Letters 4(3):277-287. https://doi.org/10.1046/j.1461-0248.2001.00218.x
  104. FISHMAN M., LOTEM A., STONE L. (2001): Heterogeneity Stabilizes Reciprocal Altruism Interactions. Journal of Theoretical Biology 209(1):87-95. https://doi.org/10.1006/jtbi.2000.2248
  105. Agrawal A., Fordyce J. (2000): Induced indirect defence in a lycaenid-ant association: the regulation of a resource in a mutualism. Proceedings of the Royal Society of London. Series B: Biological Sciences 267(1455):1857-1861. https://doi.org/10.1098/rspb.2000.1221
  106. Björn Brembs (2000): An Analysis of Associative Learning in Drosophila at the Flight Simulator. University of Regensburg Publication Server (University of Regensburg). https://doi.org/10.5283/epub.28568
  107. Bazzan A., Bordini R., Campbell J. (1999): Moral Sentiments in Multi-agent Systems. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-49057-4_8
  108. Wilkinson (1999): Bacterial ecology, antibiotics and selection for virulence. Ecology Letters 2(4):207-209. https://doi.org/10.1046/j.1461-0248.1999.00079.x
  109. SHERRATT T., ROBERTS G. (1999): The Evolution of Quantitatively Responsive Cooperative Trade. Journal of Theoretical Biology 200(4):419-426. https://doi.org/10.1006/jtbi.1999.1005
  110. Roberts G. (1998): Competitive altruism: from reciprocity to the handicap principle. Proceedings of the Royal Society of London. Series B: Biological Sciences 265(1394):427-431. https://doi.org/10.1098/rspb.1998.0312
  111. Doebeli M., Knowlton N. (1998): The evolution of interspecific mutualisms. Proceedings of the National Academy of Sciences 95(15):8676-8680. https://doi.org/10.1073/pnas.95.15.8676