List of References

Abele-Brehm, A. E., Gollwitzer, M., Steinberg, U., & Schönbrodt, F. D. (2019). Attitudes toward open science and public data sharing. Social Psychology, 50, 252–260. https://doi.org/10.1027/1864-9335/a000384

Accelerator, P. S. (n.d.). Psychological Science Accelerator. https://psysciacc.org/

Aczel, B., Szaszi, B., Nilsonne, G., Van den Akker, O., Albers, C. J., van Assen, M. A. L. M., ..., & Wagenmakers, E. (2021). Guidance for Multi-Analyst Studies. https://doi.org/10.31222/osf.io/5ecnh

Aczel, B., Szaszi, B., Sarafoglou, A., Kekecs, Z., Kucharský, Š., Benjamin, D., ..., & Wagenmakers, E. J. (2020). A consensus-based transparency checklist. Nature Human Behaviour, 4(1), 4–6. https://doi.org/10.1038/s41562-019-0772-6

Aire, O. (2020). High accuracy Data anonymisation. Amnesia. https://amnesia.openaire.eu/

Aksnes, D. W. (2003). A macro study of self-citation. Scientometrics, 56(2), 235–246. https://doi.org/10.1023/a:1021919228368

Albayrak, N. (2018). Academics’ role on the future of higher education: Important but unrecognised. Retrieved from https://lsepgcertcitl.wordpress.com/2018/11/29/academics-role-on-the-future-of-higher-education-important-but-unrecognised/

Albayrak, N. (2018). Diversity helps but decolonisation is the key to equality in higher education. Retrieved from https://lsepgcertcitl.wordpress.com/2018/04/16/diversity-helps-but-decolonisation-is-the-key-to-equality-in-higher-education/

Albayrak, N., & Okoroji, C. (2019). Facing the challenges of postgraduate study as a minority student. A Guide for Psychology Postgraduates, 63.

Albayrak-Aydemir, N. (2020). The hidden costs of being a scholar from the global south. Retrieved from https://blogs.lse.ac.uk/highereducation/2020/02/20/the-hidden-costs-of-being-a-scholar-from-the-global-south/

Ali, M. J. (2021). Understanding the Altmetrics. Seminars in Ophthalmology. https://doi.org/10.1080/08820538.2021.1930806

ALLEA - All European Academies. (2017). The European Code of Conduct for Research Integrity (Revised Edition). ALLEA - All European Academies. https://allea.org/wp-content/uploads/2017/05/ALLEA-European-Code-of-Conduct-for-Research-Integrity-2017.pdf

ALLEA - All European Academies. (2023). The European Code of Conduct for Research Integrity (Revised Edition 2023). ALLEA - All European Academies. https://doi.org/10.26356/ECoC

Allen, L., & McGonagle-O’Connell, A. (n.d.). CRediT – Contributor Roles Taxonomy. Retrieved from https://credit.niso.org/

American Psychological Association, Task Force on Socioeconomic Status. (2007). Report of the APA Task Force on Socioeconomic Status. American Psychological Association.

Anderson, A. A., Scheufele, D. A., Brossard, D., & Corley, E. A. (2012). The Role of Media and Deference to Scientific Authority in Cultivating Trust in Sources of Information about Emerging Technologies. International Journal of Public Opinion Research, 24(2), 225–237. https://doi.org/10.1093/ijpor/edr032

Anderson, M. S., Ronning, E. A., Devries, R., & Martinson, B. C. (2010). Extending the Mertonian norms: Scientists’ subscription to norms of research. Journal of Higher Education, 81(3), 366–393. https://doi.org/10.1353/jhe.0.0095

Andersson, N. (2018). Participatory research—a modernizing science for primary health care. Journal of General and Family Medicine, 19(5), 154–159. https://doi.org/10.1002/jgf2.187

Angrist, J. D., & Pischke, J. S. (2010). The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives, 24, 3–30. https://doi.org/10.1257/jep.24.2.3

Anon. (2006). Correction or retraction? In Nature (Vol. 444, pp. 123–124). https://doi.org/10.1038/444123b

Anon. (2021). What is impact?. Retrieved from https://esrc.ukri.org/research/impact-toolkit/what-is-impact/

Anon. (n.d.). About arxiv. Retrieved from https://info.arxiv.org/about/index.html

Anon. (n.d.). About CC Licenses. Retrieved from https://creativecommons.org/about/cclicenses/

Anon. (n.d.). Ckan. Retrieved from https://ckan.org/

Anon. (n.d.). Data availability statements. Retrieved from https://www.springernature.com/gp/authors/research-data-policy/data-availability-statements

Anon. (n.d.). DataCite Metadata Schema. Retrieved from https://schema.datacite.org/

Anon. (n.d.). Domov | SKRN (Slovak Reproducibility network). Retrieved from https://www.skrn.sk/

Anon. (n.d.). Home | re3data.org. Retrieved from https://www.re3data.org/

Anon. (n.d.). INVOLVE – INVOLVE Supporting public involvement in NHS, public health and social care research. Retrieved from https://www.invo.org.uk/

Anon. (n.d.). Licenses & Standards | Open Source Initiative. Retrieved from https://opensource.org/licenses

Anon. (n.d.). Open Source in Open Science | FOSTER. Retrieved from https://web.archive.org/web/20221011101535/https://www.fosteropenscience.eu/foster-taxonomy/open-source-open-science

Anon. (n.d.). Welcome to Sherpa Romeo - v2.sherpa. Retrieved from https://v2.sherpa.ac.uk/romeo/

Anon. (n.d.). What is a codebook?. Retrieved from https://www.icpsr.umich.edu/icpsrweb/content/shared/ICPSR/faqs/what-is-a-codebook.html

Anon. (n.d.). What is a digital object identifier, or DOI?. Retrieved from https://apastyle.apa.org/learn/faqs/what-is-doi

Anon. (n.d.). What is a reporting guideline?. Retrieved from https://www.equator-network.org/about-us/what-is-a-reporting-guideline/

Anon. (n.d.). What is open education?. Retrieved from https://opensource.com/resources/what-open-education

Anon. (n.d.). What is plagiarism?. Retrieved from https://www.scribbr.co.uk/category/preventing-plagiarism/

Arksey, H., & O’Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. https://doi.org/10.1080/1364557032000119616

Arslan, R. C. (2019). How to Automatically Document Data With the codebook Package to Facilitate Data Reuse. Advances in Methods and Practices in Psychological Science, 2(2), 169–187. https://doi.org/10.1177/2515245919838783

Arts and Humanities Research Council. (n.d.). Definition of eligibility for funding. Retrieved from https://ahrc.ukri.org/skills/earlycareerresearchers/definitionofeligibility/

Asai, A., He, J., Shao, R., Shi, W., Singh, A., Chang, J. C., Lo, K., Soldaini, L., Feldman, S., D’Arcy, M., Wadden, D., Latzke, M., Sparks, J., Hwang, J. D., Kishore, V., Tian, M., Ji, P., Liu, S., Tong, H., … Hajishirzi, H. (2026). Synthesizing scientific literature with retrieval-augmented language models. Nature, 650(8103), 857–863. https://doi.org/10.1038/s41586-025-10072-4

Aspers, P., & Corte, U. (2019). What is qualitative in qualitative research. Qualitative Sociology, 42(2), 139–160. https://doi.org/10.1007/s11133-019-9413-7

AusRN. (n.d.). Australian Reproducibility Network. Retrieved from https://www.aus-rn.org/

Azevedo, F. (2021). Ideology May Help Explain Anti-Scientific Attitudes | Psychology Today. https://www.psychologytoday.com/us/blog/social-justice-pacifists/202107/ideology-may-help-explain-anti-scientific-attitudes

Azevedo, F., & Jost, J. T. (2021). The ideological basis of antiscientific attitudes: Effects of authoritarianism, conservatism, religiosity, social dominance, and system justification. Group Processes & Intergroup Relations, 24(4), 518–549. https://doi.org/10.1177/1368430221990104

Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412. https://doi.org/10.1016/j.jml.2007.12.005

Bacharach, S. B. (1989). Organizational theories: Some criteria for evaluation. Academy of Management Review, 14(4), 496–515. https://doi.org/10.5465/amr.1989.4308374

Bahlai, C., Bartlett, L. J., Burgio, K. R., Fournier, A., Keiser, C., Poisot, T., & Whitney, K. (2019). Open science isn’t always open to all scientists. American Scientist, 107(2), 78.

Bak, H.-J. (2001). Education and Public Attitudes toward Science: Implications for the ‘Deficit Model’ of Education and Support for Science and Technology. Social Science Quarterly, 82(4), 779–795. https://www.jstor.org/stable/42955760

Banks, G. C., Rogelberg, S. G., Woznyj, H. M., Landis, R. S., & Rupp, D. E. (2016). Editorial: Evidence on questionable research practices: The good, the bad, and the ugly. Journal of Business and Psychology, 31(3), 323–338. https://doi.org/10.1007/s10869-016-9456-7

Barba, L. A. (2018). Terminologies for reproducible research. arXiv Preprint arXiv:1802.03311.

Bardsley, N. (2018). What lessons does the “replication crisis” in psychology hold for experimental economics? In Handbook of Psychology and Economic Behaviour, 2nd edition. Cambridge University Press. Retrieved from http://centaur.reading.ac.uk/69874/

Barnes, R. M., Johnston, H. M., MacKenzie, N., Tobin, S. J., & Taglang, C. M. (2018). The effect of ad hominem attacks on the evaluation of claims promoted by scientists. PloS One, 13(1), e0192025. https://doi.org/10.1371/journal.pone.0192025

Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278. https://doi.org/10.1016/j.jml.2012.11.001

Bartoš, F., & Schimmack, U. (2020). Z-Curve 2.0: Estimating replication rates and discovery rates. https://doi.org/10.31234/osf.io/urgtn

Bateman, I., Kahneman, D., Munro, A., Starmer, C., & Sugden, R. (2005). Testing competing models of loss aversion: An adversarial collaboration. Journal of Public Economics, 89(8), 1561–1580. https://doi.org/10.1016/j.jpubeco.2004.06.013

Baturay, M. H. (2015). An overview of the world of MOOCs. Procedia-Social and Behavioral Sciences, 174, 427–433. https://doi.org/10.1016/j.sbspro.2015.01.685

Bazeley, P. (2003). Defining “Early Career” in Research. Higher Education, 45, 257–279. https://doi.org/10.1023/A:1022698529612

Bazi, T. (2020). Peer review: single-blind, double-blind, or all the way-blind? International Urogynecology Journal, 31, 481–483. https://doi.org/10.1007/s00192-019-04187-2

Beffara Bret, B., Beffara Bret, A., & Nalborczyk, L. (2021). A fully automated, transparent, reproducible, and blind protocol for sequential analyses. Meta-Psychology, 5. https://doi.org/10.15626/MP.2018.869

Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2(2), 131–160. https://doi.org/10.1037/1082-989X.2.2.131

Beller, S., & Bender, A. (2017). Theory, the final frontier? A corpus-based analysis of the role of theory in psychological articles. Frontiers in Psychology, 8, 951. https://doi.org/10.3389/fpsyg.2017.00951

Benjamini, Y., & Braun, H. (2002). John W. Tukey’s contributions to multiple comparisons. The Annals of Statistics, 30(6), 1576–1594. https://doi.org/10.1214/aos/1043351247

Benoit, K., Conway, D., Lauderdale, B. E., Laver, M., & Mikhaylov, S. (2016). Crowd-sourced text analysis: Reproducible and agile production of political data. American Political Science Review, 110(2), 278–295. https://doi.org/10.1017/S0003055416000058

Bhopal, R., Rankin, J., McColl, E., Thomas, L., Kaner, E., Stacy, R., Pearson, P., Vernon, B., & Rodgers, H. (1997). The vexed question of authorship: views of researchers in a British medical faculty. BMJ, 314, 1009–1012. https://doi.org/10.1136/bmj.314.7086.1009

BIDS. (2020). About BIDS. Retrieved from https://bids.neuroimaging.io

BIDS. (n.d.). Modality agnostic files. Retrieved from https://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html

Bik, E. M., Casadevall, A., & Fang, F. C. (2016). The prevalence of inappropriate image duplication in biomedical research publications. MBio, 7(3), e00809-16.

Bilder, G. (2013). DOIs unambiguously and persistently identify published, trustworthy, citable online scholarly literature. Right? https://www.crossref.org/blog/dois-unambiguously-and-persistently-identify-published-trustworthy-citable-online-scholarly-literature-right/

Bishop, D. V. (2020). The psychology of experimental psychologists: Overcoming cognitive constraints to improve research: The 47th Sir Frederic Bartlett Lecture. Quarterly Journal of Experimental Psychology, 73(1), 1–19. https://doi.org/10.1177/1747021819886519

Bjørneborn, L., & Ingwersen, P. (2004). Toward a basic framework for webometrics. Journal of the American Society for Information Science and Technology, 55(14), 1216–1227. https://doi.org/10.1002/asi.20077

Blohowiak, B. B., Cohoon, J., de Wit, L., Eich, E., Farach, F. J., Hasselman, F., et al. (2020). Badges to Acknowledge Open Practices. Retrieved from https://osf.io/tvyxz

BMJ. (2015). Introducing ‘How to write and publish a Study Protocol’ using BMJ’s new eLearning programme: Research to Publication. Retrieved from https://blogs.bmj.com/bmjopen/2015/09/22/introducing-how-to-write-and-publish-a-study-protocol-using-bmjs-new-elearning-programme-research-to-publication/

Boivin, A., Richards, T., Forsythe, L., Gregoire, A., L’Esperance, A., Abelson, J., & Carman, K. L. (2018). Evaluating the patient and public involvement in research. British Medical Journal, 363, k5147. https://doi.org/10.1136/bmj.k5147

Bol, T., de Vaan, M., & van de Rijt, A. (2018). The Matthew effect in science funding. Proceedings of the National Academy of Sciences, 115(19), 4887–4890. https://doi.org/10.1073/pnas.1719557115

Bollen, K. A. (1989). Structural Equations with Latent Variables (pp. 179–225). John Wiley & Sons.

Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2011). Introduction to meta-analysis. John Wiley & Sons.

Bornmann, L., Ganser, C., Tekles, A., & Leydesdorff, L. (2019). Does the h_α index reinforce the Matthew effect in science? Agent-based simulations using Stata and R. arXiv preprint https://arxiv.org/abs/1905.11052.

Borsboom, D., Mellenbergh, G. J., & Van Heerden, J. (2004). The concept of validity. Psychological Review, 111(4), 1061. https://doi.org/10.1037/0033-295X.111.4.1061

Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). The Concept of Validity. Psychological Review, 111(4), 1061–1071. https://doi.org/10.1037/0033-295X.111.4.1061

Borsboom, D., van der Maas, H., Dalege, J., Kievit, R., & Haig, B. (2020). Theory Construction Methodology: A practical framework for theory formation in psychology. https://doi.org/10.31234/osf.io/w5tp8

Bortoli, S. (2021). NIHR Guidance on Co-Producing a Research Project. Learning For Involvement. https://www.learningforinvolvement.org.uk/?opportunity=nihr-guidance-on-co-producing-a-research-project

Bos, J. (2020). Confidentiality. In Research Ethics for Students in the Social Sciences (pp. 149–173). Springer International Publishing. https://doi.org/10.1007/978-3-030-48415-6_7

Boudry, M. (2013). The hypothesis that saves the day. Ad hoc reasoning in pseudoscience. Logique et Analyse, 245–258.

Bourne, P. E., Polka, J. K., Vale, R. D., & Kiley, R. (2017). Ten simple rules to consider regarding preprint submission. PLoS Computational Biology, 13(5), e1005473. https://doi.org/10.1371/journal.pcbi.1005473

Bouvy, J. C., & Mujoomdar, M. (2019). All-Male Panels and Gender Diversity of Issue Panels and Plenary Sessions at ISPOR Europe. PharmacoEconomics-Open, 3(3), 419–422. https://doi.org/10.1007/s41669-019-0153-0

Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71(356), 791–799.

Bramoullé, Y., & Saint-Paul, G. (2010). Research cycles. In Journal of Economic Theory (Vol. 145, pp. 1890–1920). https://doi.org/10.2139/ssrn.965816

Brand, A., Allen, L., Altman, M., Hlava, M., & Scott, J. (2015). Beyond authorship: attribution, contribution, collaboration, and credit. Learned Publishing, 28(2), 151–155. https://doi.org/10.1087/20150211

Brandt, M. J., IJzerman, H., Dijksterhuis, A., Farach, F. J., Geller, J., Giner-Sorolla, R., et al. (2014). The replication recipe: What makes for a convincing replication? Journal of Experimental Social Psychology, 50, 217–224. https://doi.org/10.1016/j.jesp.2013.10.005

Braun, V., & Clarke, V. (2013). Successful Qualitative Research. SAGE Publications.

Brembs, B., Button, K., & Munafò, M. (2013). Deep impact: unintended consequences of journal rank. Frontiers in Human Neuroscience, 7, 291. https://doi.org/10.3389/fnhum.2013.00291

Brewer, P. R., & Ley, B. L. (2013). Whose Science Do You Believe? Explaining Trust in Sources of Scientific Information About the Environment. Science Communication, 35(1), 115–137. https://doi.org/10.1177/1075547012441691

Breznau, N. (2021). I saw you in the crowd: Credibility, reproducibility, and meta-utility. PS: Political Science & Politics, 54(2), 309–313. https://doi.org/10.1017/S1049096520000980

Breznau, N., Rinke, E. M., Wuttke, A., Nguyen, H. H. V., Adem, M., Adriaans, J., Akdeniz, E., Alvarez-Benjumea, A., Andersen, H. K., Auer, D., Azevedo, F., Bahnsen, O., Bai, L., Balzer, D., Bauer, G., Bauer, P., Baumann, M., Baute, S., Benoit, V., & Żółtak, T. (2021). How Many Replicators Does It Take to Achieve Reliability? Investigating Researcher Variability in a Crowdsourced Replication. SocArXiv. https://doi.org/10.31235/osf.io/j7qta

Brod, M., Tesler, L., & Christensen, T. (2009). Qualitative research and content validity: Developing best practices based on science and experience. Quality of Life Research, 18(9), 1263–1278. https://doi.org/10.1007/s11136-009-9540-9

Brodie, S., Frainer, A., Pennino, M. G., Jiang, S., Kaikkonen, L., Lopez, J., Ortega-Cisneros, K., Peters, C. A., Selim, S. A., & Vaidianu, N. (2021). Equity in science: advocating for a triple-blind review system. Trends in Ecology & Evolution, 36(11), 957–959. https://doi.org/10.1016/j.tree.2021.07.011

Brooks, T. A. (1985). Private acts and public objects: An investigation of citer motivations. Journal of the American Society for Information Science, 36(4), 223–229. https://doi.org/10.1002/asi.4630360402

Brown, J. (2010). An Introduction to Overlay Journals (pp. 1–6) [Repositories Support Project]. University College London.

Brown, J. (2010). An Introduction to Overlay Journals [Techreport]. Repositories Support Project: UK. https://discovery.ucl.ac.uk/id/eprint/19081/

Brown, N. J., & Heathers, J. A. (2017). The grim test: A simple technique detects numerous anomalies in the reporting of results in psychology. Social Psychological and Personality Science, 8(4), 363–369.

Brown, N., Thompson, P., & Leigh, J. S. (2018). Making academia more accessible. Journal of Perspectives in Applied Academic Practice, 6(2), 82–90. https://doi.org/10.14297/jpaap.v6i2.348

Brule, J., & Blount, A. (1989). Knowledge acquisition. McGraw-Hill.

Brunner, J., & Schimmack, U. (2020). Estimating population mean power under conditions of heterogeneity and selection for significance. Meta-Psychology, 4, MP.2018.874. https://doi.org/10.15626/MP.2018.874

Bruns, S. B., & Ioannidis, J. P. (2016). P-curve and p-hacking in observational research. PLoS ONE, 11(2), e0149144. https://doi.org/10.1371/journal.pone.0149144

Bryan, C. J., Yeager, D. S., & O’Brien, J. M. (2019). Replicator degrees of freedom allow publication of misleading failures to replicate. Proceedings of the National Academy of Sciences, 116(51), 25535–25545. https://doi.org/10.1073/pnas.1910951116

Budapest Open Access Initiative. (2002). Read the Budapest open access initiative. Retrieved from https://www.budapestopenaccessinitiative.org/read

Burnette, M., Williams, S., & Imker, H. (2016). From Plan to Action: Successful Data Management Plan Implementation in a Multidisciplinary Project. Journal of eScience Librarianship, 5(1), e1101. https://doi.org/10.7191/jeslib.2016.1101

Busse, C., Kach, A. P., & Wagner, S. M. (2017). Boundary Conditions: What They Are, How to Explore Them, Why We Need Them, and When to Consider Them. Organizational Research Methods, 20(4), 574–609. https://doi.org/10.1177/1094428116641191

Button, K. S., Chambers, C. D., Lawrence, N., & Munafò, M. R. (2020). Grassroots training for reproducible science: a consortium-based approach to the empirical dissertation. Psychology Learning & Teaching, 19(1), 77–90. https://doi.org/10.1177/1475725719857659

Button, K. S., Lawrence, N. S., Chambers, C. D., & Munafò, M. R. (2016). Instilling scientific rigour at the grassroots. Psychologist, 29(3), 158–159.

Byrne, J. A., & Christopher, J. (2020). Digital magic, or the dark arts of the 21st century—how can journals and peer reviewers detect manuscripts and publications from paper mills? FEBS Letters, 594(4), 583–589. https://doi.org/10.1002/1873-3468.13747

Calvert, D. (2019). How to Make Inclusivity More Than Just an Office Buzzword. Retrieved from https://insight.kellogg.northwestern.edu/article/how-to-make-inclusivity-more-than-just-an-office-buzzword

Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings. Psychological Bulletin, 54(4), 297–312. https://doi.org/10.1037/h0040950

Campbell, D. T. (1979). Assessing the impact of planned social change. Evaluation and Program Planning, 2(1), 67–90. https://doi.org/10.1016/0149-7189(79)90048-X

Campbell, D. T., & Stanley, J. C. (1966). Experimental and Quasi Experimental Designs. Rand McNally.

Campbell, D. T., & Stanley, J. C. (2011). Experimental and quasi-experimental designs for research. Wadsworth.

Carney, D. R., & Banaji, M. R. (2012). First is best. PLoS ONE, 7(6), e35088. https://doi.org/10.1371/journal.pone.0035088

Carp, J. (2012). On the plurality of (methodological) worlds: estimating the analytic flexibility of FMRI experiments. Frontiers in Neuroscience, 6, 149. https://doi.org/10.3389/fnins.2012.00149

Carsey, T. M. (2014). Making DA-RT a reality. PS: Political Science & Politics, 47(1), 72–77. https://doi.org/10.1017/S1049096513001753

Carter, A., Tilling, K., & Munafo, M. R. (2021). Considerations of sample size and power calculations given a range of analytical scenarios. https://doi.org/10.31234/osf.io/tcqrn

Carter, E. C., Schönbrodt, F. D., Gervais, W. M., & Hilgard, J. (2019). Correcting for Bias in Psychology: A Comparison of Meta-Analytic Methods. Advances in Methods and Practices in Psychological Science, 2(2), 115–144. https://doi.org/10.1177/2515245919847196

Case, C. M. (1928). Scholarship in Sociology. Sociology and Social Research, 12, 323–340. http://www.sudoc.fr/036493414

Cassidy, S. A., Dimova, R., Giguère, B., Spence, J. R., & Stanley, D. J. (2019). Failing grade: 89% of introduction-to-psychology textbooks that define or explain statistical significance do so incorrectly. Advances in Methods and Practices in Psychological Science, 2(3), 233–239. https://doi.org/10.1177/2515245919858072

Center for Open Science. (2025). Registered Reports Initiative. https://www.cos.io/initiatives/registered-reports

Centre for Evaluation. (n.d.). Evidence Synthesis. https://www.lshtm.ac.uk/research/centres/centre-evaluation/evidence-synthesis

Centre for Open Science. (2011–2021). Open Science Framework. https://osf.io/

Centre for Open Science. (n.d.). Show Your Work. Share Your Work. Advance Science. That’s Open Science. https://www.cos.io/

CESSDA Training Team. (2017–2020). CESSDA Data Management Expert Guide. CESSDA ERIC. https://www.cessda.eu/DMGuide

CESSDA. (n.d.). Data Management Expert Guide—CESSDA TRAINING. https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide

Chambers, C. D. (2013). Registered reports: a new publishing initiative at Cortex. Cortex, 49(3), 609–610. https://doi.org/10.1016/j.cortex.2012.12.016

Chambers, C. D., & Tzavella, L. (2020). Registered Reports: Past, Present and Future. https://doi.org/10.31222/osf.io/43298

Chambers, C. D., Dienes, Z., McIntosh, R. D., Rotshtein, P., & Willmes, K. (2015). Registered reports: realigning incentives in scientific publishing. Cortex, 66, A1–A2. https://doi.org/10.1016/j.cortex.2015.03.022

Chartier, C. R., Riegelman, A., & McCarthy, R. J. (2018). StudySwap: A platform for interlab replication, collaboration, and resource exchange. Advances in Methods and Practices in Psychological Science, 1(4), 574–579. https://doi.org/10.1177/2515245918808767

Chuard, P. J. C., Vrtilek, M., Head, M. L., & Jennions, M. D. (2019). Evidence that non-significant results are sometimes preferred: Reverse P-hacking or selective reporting? PLoS Biol, 17(1), e3000127. https://doi.org/10.1371/journal.pbio.3000127

Claerbout, J. F., & Karrenbach, M. (1992). Electronic documents give reproducible research a new meaning. SEG Technical Program Expanded Abstracts 1992, 601–604. http://sepwww.stanford.edu/doku.php?id=sep:research:reproducible:seg92

Clark, H., Elsherif, M. M., & Leavens, D. A. (2019). Ontogeny vs. phylogeny in primate/canid comparisons: a meta-analysis of the object choice task. Neuroscience & Biobehavioral Reviews, 105, 178–189. https://doi.org/10.1016/j.neubiorev.2019.06.001

Co-Production Collective. (n.d.). Our Approach. Co-Production Collective. https://www.coproductioncollective.co.uk/championing-co-production/what-does-co-production-mean-to-us

cOAlition S. (2025). cOAlition S – Making Full and Immediate Open Access a Reality. https://www.coalition-s.org/

Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. The Journal of Abnormal and Social Psychology, 65(3), 145–153. https://doi.org/10.1037/h0045186

Cohen, J. (1969). Statistical power analysis for the behavioral sciences. Academic Press.

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.

Cohn, J. P. (2008). Citizen science: Can volunteers do real research? BioScience, 58(3), 192–197. https://doi.org/10.1641/B580303

Coles, N. A., Tiokhin, L., Arslan, R., Forscher, P., Scheel, A., & Lakens, D. (2020). Red Team Challenge. http://daniellakens.blogspot.com/2020/05/red-team-challenge.html

Collective, C. (2021). What Co-production means to us. https://www.coproductioncollective.co.uk/championing-co-production/what-does-co-production-mean-to-us

Committee on Reproducibility and Replicability in Science. (2019). Reproducibility and Replicability in Science (p. 25303). National Academies Press. https://doi.org/10.17226/25303

Confederation of Open Access Repositories. (2020). COAR Community Framework for Best Practices in Repositories (Version 1). Zenodo. https://doi.org/10.5281/zenodo.4110829

Cook, T. D., & Campbell, D. T. (1979). Quasi-Experimentation. Rand McNally.

Corley, K. G., & Gioia, D. A. (2011). Building theory about theory building: what constitutes a theoretical contribution? Academy of Management Review, 36(1), 12–32. https://doi.org/10.5465/amr.2009.0486

Cornell University. (2020). Measuring your research impact: i10 index. Cornell University Library. https://guides.library.cornell.edu/impact/author-impact-10

Cornwall, A., & Jewkes, R. (1995). What is participatory research? Social Science & Medicine, 41(12), 1667–1676. https://doi.org/10.1016/0277-9536(95)00127-S

Correction or retraction? (2006). Nature, 444(7116), 123–124. https://doi.org/10.1038/444123b

Corti, L., Van den Eynden, V., Bishop, L., & Woollard, M. (2019). Managing and sharing research data: a guide to good practice. Sage.

Cowan, N., Belletier, C., Doherty, J. M., Jaroslawska, A. J., Rhodes, S., Forsberg, A., & Logie, R. H. (2020). How do scientific views change? Notes from an extended adversarial collaboration. Perspectives on Psychological Science, 15(4), 1011–1025. https://doi.org/10.1177/1745691620906415

Crenshaw, K. W. (1989). Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine. University of Chicago Legal Forum, 1989(8), 139–168.

Crettaz von Roten, F. (2004). Gender differences in attitudes toward science in Switzerland. Public Understanding of Science, 13(2), 191–199. https://doi.org/10.1177/0963662504043870

Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. https://doi.org/10.1037/h0040957

Cronin, B. (2001). Hyperauthorship: A postmodern perversion or evidence of a structural shift in scholarly communication practices? Journal of the American Society for Information Science and Technology, 52(7), 558–569. https://doi.org/10.1002/asi.1097

Crosetto, P. (2021). Is MDPI a predatory publisher? https://paolocrosetto.wordpress.com/2021/04/12/is-mdpi-a-predatory-publisher/

Crutzen, R., Ygram Peters, G. J., & Mondschein, C. (2019). Why and how we should care about the General Data Protection Regulation. Psychology & Health, 34(11), 1347–1357. https://doi.org/10.1080/08870446.2019.1606222

Crüwell, S., van Doorn, J., Etz, A., Makel, M. C., Moshontz, H., Niebaum, J. C., Orben, A., Parsons, S., & Schulte-Mecklenbeck, M. (2019). Seven Easy Steps to Open Science: An Annotated Reading List. Zeitschrift Für Psychologie, 227(4), 237–248. https://doi.org/10.1027/2151-2604/a000387

Curran, P. J. (2009). The seemingly quixotic pursuit of a cumulative psychological science: Introduction to the special issue. Psychological Methods, 14(2), 77–80. https://doi.org/10.1037/a0015972

Curry, S. (2012). Sick of impact factors. http://occamstypewriter.org/scurry/2012/08/13/sick-of-impact-factors/

DataCite Schema. (n.d.). DataCite Metadata Schema. https://schema.datacite.org/

Davies, G. M., & Gray, A. (2015). Don’t let spurious accusations of pseudoreplication limit our ability to learn from natural experiments (and other messy kinds of ecological monitoring). Ecology and Evolution, 5(22), 5295–5304. https://doi.org/10.1002/ece3.1782

Day, S., Rennie, S., Luo, D., & Tucker, J. D. (2020). Open to the public: Paywalls and the public rationale for open access medical research publishing. Research Involvement and Engagement, 6(1), 8. https://doi.org/10.1186/s40900-020-0182-y

Definition, T. O. S. (n.d.). The Open Source Definition. Open Source Initiative. https://opensource.org/osd

Del Giudice, M., & Gangestad, S. W. (2021). A traveler’s guide to the multiverse: Promises, pitfalls, and a framework for the evaluation of analytic decisions. Advances in Methods and Practices in Psychological Science, 4(1), 2515245920954925. https://doi.org/10.1177/2515245920954925

Dellavalle, R. P., Banks, M. A., & Ellis, J. I. (2007). Frequently Asked Questions Regarding Self-Plagiarism: How to Avoid Recycling Fraud. Journal of the American Academy of Dermatology, 57(3), 527. https://doi.org/10.1016/j.jaad.2007.05.018

Der Kiureghian, A., & Ditlevsen, O. (2009). Aleatory or epistemic? Does it matter? Structural Safety, 31(2), 105–112. https://doi.org/10.1016/j.strusafe.2008.06.020

Deutsche Forschungsgemeinschaft. (2019). Guidelines for Safeguarding Good Research Practice. Code of Conduct. https://doi.org/10.5281/ZENODO.3923602

DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Sage.

Devezer, B., Navarro, D. J., Vandekerckhove, J., & Buzbas, E. O. (2021). The case for formal methodology in scientific reform. Royal Society Open Science, 8(3), 200805. https://doi.org/10.1098/rsos.200805

Devito, N., & Goldacre, B. (2019). Publication Bias. Catalogue of Bias. https://catalogofbias.org/biases/publication-bias/

Dickersin, K., & Min, Y. (1993). Publication Bias: The Problem That Won’t Go Away. Annals of the New York Academy of Sciences, 703(1), 135–148. https://doi.org/10.1111/j.1749-6632.1993.tb26343.x

Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. Macmillan International Higher Education.

Dienes, Z. (2011). Bayesian versus orthodox statistics: Which side are you on? Perspectives on Psychological Science, 6(3), 274–290. https://doi.org/10.1177/1745691611406920

Dienes, Z. (2014). Using Bayes to get the most out of non-significant results. Frontiers in Psychology, 5, 781. https://doi.org/10.3389/fpsyg.2014.00781

Dienes, Z. (2016). How Bayes factors change scientific practice. Journal of Mathematical Psychology, 72, 78–89. https://doi.org/10.1016/j.jmp.2015.10.003

Dismukes, R. K. (2010). Understanding and analyzing human error in real-world operations. In E. Salas & D. Maurino (Eds.), Human factors in aviation (2nd ed., pp. 335–374). Academic Press.

DOI Foundation. (2019). The DOI Handbook. https://www.doi.org/doi-handbook/HTML/index.html

Doll, R., & Hill, A. B. (1954). The mortality of doctors in relation to their smoking habits; a preliminary report. British Medical Journal, 1(4877), 1451–1455. https://doi.org/10.1136/bmj.1.4877.1451

Drost, E. A. (2011). Validity and reliability in social science research. Education Research and Perspectives, 38(1), 105–123.

Du Bois, W. E. B. (1968). The souls of black folk; essays and sketches. Johnson Reprint Corp.

Dubin, R. (1969). Theory building. The Free Press.

Duval, S., & Tweedie, R. (2000). A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association, 95, 89–98. https://doi.org/10.2307/2669529

Duval, S., & Tweedie, R. (2000). Trim and fill: A simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56, 455–463. https://doi.org/10.1111/j.0006-341x.2000.00455.x

Duyx, B., Swaen, G. M., Urlings, M. J., Bouter, L. M., & Zeegers, M. P. (2019). The strong focus on positive results in abstracts may cause bias in systematic reviews: A case study on abstract reporting bias. Systematic Reviews, 8(1), 1–8.

d’Espagnat, B. (2008). Is science cumulative? A physicist viewpoint. In Rethinking Scientific Change and Theory Comparison (pp. 145–151). Springer. https://doi.org/10.1007/978-1-4020-6279-7_10

Eagly, A. H., & Riger, S. (2014). Feminism and psychology: Critiques of methods and epistemology. American Psychologist, 69(7), 685–702. https://doi.org/10.1037/a0037372

Easterbrook, S. M. (2014). Open code for open science? Nature Geoscience, 7(11), 779–781. https://doi.org/10.1038/ngeo2283

Ebersole, C. R., Atherton, O. E., Belanger, A. L., Skulborstad, H. M., Allen, J. M., Banks, J. B., & Nosek, B. A. (2016). Many Labs 3: Evaluating participant pool quality across the academic semester via replication. Journal of Experimental Social Psychology, 67, 68–82. https://doi.org/10.1016/j.jesp.2015.10.012

Economic and Social Research Council. (n.d.). Defining impact. UK Research and Innovation. https://www.ukri.org/councils/esrc/impact-toolkit-for-economic-and-social-sciences/defining-impact/

Edyburn, D. L. (2010). Would you recognize universal design for learning if you saw it? Ten propositions for new directions for the second decade of UDL. Learning Disability Quarterly, 33(1), 33–41. https://doi.org/10.1177/073194871003300103

Eldermire, E. (n.d.). LibGuides: Measuring your research impact: i10-Index. https://guides.library.cornell.edu/impact/author-impact-10

Eley, A. R. (2012). Becoming a successful early career researcher. Routledge. Retrieved from http://www.worldcat.org/oclc/934369360

Ellemers, N. (2021). Science as collaborative knowledge generation. British Journal of Social Psychology, 60(1), 1–28. https://doi.org/10.1111/bjso.12430

Elliott, K. C., & Resnik, D. B. (2019). Making open science work for science and society. Environmental Health Perspectives, 127(7). https://doi.org/10.1289/EHP4808

Elm, E. von, Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007). Strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies. BMJ, 335(7624), 806–808. https://doi.org/10.1136/bmj.39335.541782.AD

Elman, C., Gerring, J., & Mahoney, J. (Eds.). (2020). The production of knowledge: Enhancing progress in social science. Cambridge University Press.

Elmore, S. A. (2018). Preprints: What Role Do These Have in Communicating Scientific Results? Toxicologic Pathology, 46(4), 364–365. https://doi.org/10.1177/0192623318767322

Elsherif, M., Middleton, S., Phan, J. M., Azevedo, F., Iley, B., Grose-Hodge, M., Tyler, S., Kapp, S. K., Gourdon-Kanhukamwe, A., Grafton-Clarke, D., Yeung, S. K., Shaw, J. J., Hartmann, H., & Dokovova, M. (2022). Bridging Neurodiversity and Open Scholarship: How Shared Values Can Guide Best Practices for Research Integrity, Social Justice, and Principled Education. MetaArXiv. https://doi.org/10.31222/osf.io/k7a9p

Epskamp, S., & Nuijten, M. B. (2016). statcheck: Extract statistics from articles and recompute p values. Retrieved from http://CRAN.R-project.org/package=statcheck

Esterling, K., Brady, D., & Schwitzgebel, E. (2021). The Necessity of Construct and External Validity for Generalized Causal Claims. Retrieved from https://doi.org/10.31219/osf.io/2s8w5.

Etz, A., Gronau, Q. F., Dablander, F., Edelsbrunner, P. A., & Baribault, B. (2018). How to become a Bayesian in eight easy steps: An annotated reading list. Psychonomic Bulletin & Review, 25, 219–234. https://doi.org/10.3758/s13423-017-1317-5

European Commission. (2021). Responsible research & innovation. Retrieved from https://web.archive.org/web/20220218023041/https://ec.europa.eu/programmes/horizon2020/en/h2020-section/responsible-research-innovation

European Commission. (n.d.). Data Protection. https://ec.europa.eu/info/law/law-topic/data-protection_en

Evans, G., & Durant, J. (1995). The relationship between knowledge and attitudes in the public understanding of science in Britain. Public Understanding of Science, 4(1), 57–74. https://doi.org/10.1088/0963-6625/4/1/004

Evans, O., & Rubin, M. (2021). In a Class on Their Own: Investigating the Role of Social Integration in the Association Between Social Class and Mental Well-Being. Personality and Social Psychology Bulletin, 014616722110211. https://doi.org/10.1177/01461672211021190

Everitt, B. S., & Skrondall, A. (2010). The Cambridge Dictionary of Statistics - Fourth Edition. Cambridge University Press.

Fanelli, D. (2010). Do Pressures to Publish Increase Scientists’ Bias? An Empirical Support from US States Data. PLOS ONE, 5(4), e10271. https://doi.org/10.1371/journal.pone.0010271

Fanelli, D. (2018). Opinion: Is science really facing a reproducibility crisis, and do we need it to? Proceedings of the National Academy of Sciences, 115(11), 2628–2631. https://doi.org/10.1073/pnas.1708272114

Farrow, R. (2017). Open Education and Critical Pedagogy. Learning, Media and Technology, 42(2), 130–146. https://doi.org/10.1080/17439884.2016.1113991

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. https://doi.org/10.3758/BRM.41.4.1149

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. https://doi.org/10.3758/BF03193146

Ferguson, C. J. (2021). Providing a lower-bound estimate for psychology’s “crud factor”: The case of aggression. Professional Psychology: Research and Practice.

Ferson, S., Joslyn, C. A., Helton, J. C., Oberkampf, W. L., & Sentz, K. (2004). Summary from the epistemic uncertainty workshop: consensus amid diversity. Reliability Engineering & System Safety, 85(1–3), 355–369. https://doi.org/10.1016/j.ress.2004.03.023

Fiedler, K., & Schwarz, N. (2016). Questionable research practices revisited. Social Psychological and Personality Science, 7(1), 45–52. https://doi.org/10.1177/1948550615612150

Fiedler, K., Kutzner, F., & Krueger, J. I. (2012). The long way from α-error control to validity proper: Problems with a short-sighted false-positive debate. Perspectives on Psychological Science, 7(6), 661–669. https://doi.org/10.1177/1745691612462587

Filipe, A., Renedo, A., & Marston, C. (2017). The co-production of what? Knowledge, values, and social relations in health care. PLoS Biology, 15(5), e2001403. https://doi.org/10.1371/journal.pbio.2001403

Fillon, A. A., Feldman, G., Yeung, S. K., Protzko, J., Elsherif, M. M., Xiao, Q., Nanakdewa, K., & Brick, C. (n.d.). Correlational Meta-Analysis Registered Report Template.

Findley, M. G., Jensen, N. M., Malesky, E. J., & Pepinsky, T. B. (2016). Can results-free review reduce publication bias? The results and implications of a pilot study. Comparative Political Studies, 49(13), 1667–1703. https://doi.org/10.1177/0010414016655539

Finlay, L., & Gough, B. (2008). Reflexivity: A practical guide for researchers in health and social sciences. John Wiley & Sons.

Flake, J. K., & Fried, E. I. (2020). Measurement schmeasurement: Questionable measurement practices and how to avoid them. Advances in Methods and Practices in Psychological Science, 3(4), 456–465. https://doi.org/10.1177/2515245920952393

Fletcher-Watson, S., Adams, J., Brook, K., Charman, T., Crane, L., Cusack, J., Leekam, S., Milton, D., Parr, J. R., & Pellicano, E. (2019). Making the Future Together: Shaping Autism Research Through Meaningful Participation. Autism, 23(4), 943–953.

Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. (2013). emcee: The MCMC Hammer. Publications of the Astronomical Society of the Pacific, 125(925), 306–312. https://doi.org/10.1086/670067

Forrt. (2019). Introducing a Framework for Open and Reproducible Research Training (FORRT). Open Science Framework. https://doi.org/10.31219/osf.io/bnh7p

FORRT. (2021). Welcome to FORRT. Framework for Open and Reproducible Research Training. https://forrt.org

Forscher, P. S., Wagenmakers, E.-J., Coles, N. A., Silan, M. A., Dutra, N., Basnight-Brown, D., & IJzerman, H. (2022). The Benefits, Barriers, and Risks of Big-Team Science. Perspectives on Psychological Science, 0(0). https://doi.org/10.1177/17456916221082970

Foster, E. D., & Deardorff, A. (2017). Open science framework (OSF). Journal of the Medical Library Association, 105(2), 203. https://doi.org/10.5195/jmla.2017.88

Franco, A., Malhotra, N., & Simonovits, G. (2014). Publication bias in the social sciences: Unlocking the file drawer. Science, 345(6203), 1502–1505. https://doi.org/10.1126/science.1255484

Frank, M. C., Bergelson, E., Bergmann, C., Cristia, A., Floccia, C., Gervain, J., Hamlin, J. K., Hannon, E. E., Kline, M., Levelt, C., Lew-Williams, C., Nazzi, T., Panneton, R., Rabagliati, H., Soderstrom, M., Sullivan, J., Waxman, S., & Yurovsky, D. (2017). A Collaborative Approach to Infant Research: Promoting Reproducibility, Best Practices, and Theory-Building. Infancy, 22, 421–435. https://doi.org/10.1111/infa.12182

Franzoni, C., & Sauermann, H. (2014). Crowd science: The organization of scientific research in open collaborative projects. Research Policy, 43(1), 1–20. https://doi.org/10.1016/j.respol.2013.07.005

Fraser, H., Bush, M., Wintle, B., Mody, F., Smith, E., Hanea, A., et al. (2021). Predicting reliability through structured expert elicitation with repliCATS (Collaborative Assessments for Trustworthy Science).

Free Our Knowledge. (2020). Preregistration Pledge. https://freeourknowledge.org/2020-12-03-preregistration-pledge/

Free Our Knowledge. (n.d.). About. Retrieved from https://freeourknowledge.org/about/

Frigg, R., & Hartmann, S. (2020). Models in Science. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Spring 2020). Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/spr2020/entries/models-science/

Frith, U. (2020). Fast lane to slow science. Trends in Cognitive Sciences, 24(1), 1–2. https://doi.org/10.1016/j.tics.2019.10.007

Galligan, F., & Dyas-Correia, S. (2013). Altmetrics: rethinking the way we measure. Serials Review, 39(1), 56–61. https://doi.org/10.1016/j.serrev.2013.01.003

Garson, G. D. (2012). Testing Statistical Assumptions (2012th ed.). North Carolina State University.

Gelman, A., & Carlin, J. (2014). Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. Perspectives on Psychological Science, 9(6), 641–651. https://doi.org/10.1177/1745691614551642

Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf

Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician, 60(4), 328–331. https://doi.org/10.1198/000313006X152649

Generalizability. (2018). Generalizability. In B. B. Frey (Ed.), The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. SAGE Publications, Inc. https://doi.org/10.4135/9781506326139.n284

Gentleman, R. (2005). Reproducible Research: A Bioinformatics Case Study. Statistical Applications in Genetics and Molecular Biology, 4, 1034. https://doi.org/10.2202/1544-6115.1034

German Research Foundation. (2019). Guidelines for Safeguarding Good Research Practice. Code of Conduct.

Geyer, C. J. (2003). Maximum Likelihood in R (pp. 1–9). Open Science Framework.

Geyer, C. J. (2007). Stat 5102 Notes: Maximum Likelihood (pp. 1–8). Open Science Framework.

Gilroy, P. (1993). The black Atlantic: Modernity and double consciousness. Harvard University Press.

Giner-Sorolla, R., Aberson, C. L., Bostyn, D. H., Carpenter, T., Conrique, B. G., Lewis, N. A., & Soderberg, C. (2019). Power to detect what? Considerations for planning and evaluating sample size. Retrieved from https://osf.io/jnmya/

Ginsparg, P. (1997). Winners and losers in the global research village. The Serials Librarian, 30(3–4), 83–95. https://doi.org/10.1300/J123v30n03_13

Ginsparg, P. (2001). Creating a global knowledge network. In Second Joint ICSU Press-UNESCO Expert Conference on Electronic Publishing in Science (pp. 19–23).

Gioia, D. A., & Pitre, E. (1990). Multiparadigm perspectives on theory building. Academy of Management Review, 15(4), 584–602. https://doi.org/10.5465/amr.1990.4310758

Git. (n.d.). Git—About Version Control. https://git-scm.com/book/en/v2/Getting-Started-About-Version-Control

git/git. (n.d.). Initial revision of ‘git’, the information manager from hell. GitHub. https://github.com/git/git/commit/e83c5163316f89bfbde7d9ab23ca2e25604af290

Glass, D. J., & Hall, N. (2008). A brief history of the hypothesis. Cell, 134(3), 378–381. https://doi.org/10.1016/j.cell.2008.07.033

GO FAIR Initiative. (2025). FAIR Principles. https://www.go-fair.org/fair-principles/

Goertzen, M. J. (2017). Introduction to Quantitative Research and Data. Library Technology Reports, 53(4), 12–18.

Gollwitzer, M., Abele-Brehm, A., Fiebach, C., Ramthun, R., Scheel, A. M., Schönbrodt, F. D., & Steinberg, U. (2020). Data Management and Data Sharing in Psychological Science: Revision of the DGPs Recommendations.

Goodman, S. N., Fanelli, D., & Ioannidis, J. P. A. (2016). What does research reproducibility mean? Science Translational Medicine, 8(341), 341ps12-341ps12. https://doi.org/10.1126/scitranslmed.aaf5027

Goodman, S. W., & Pepinsky, T. B. (2019). Gender Representation and Strategies for Panel Diversity: Lessons from the APSA Annual Meeting. PS: Political Science & Politics, 52(4), 669–676. https://doi.org/10.1017/S1049096519000908

Gorgolewski, K., Auer, T., Calhoun, V., et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3, 160044. https://doi.org/10.1038/sdata.2016.44

Graham, I. D., McCutcheon, C., & Kothari, A. (2019). Exploring the frontiers of research co-production: the Integrated Knowledge Translation Research Network concept papers. Health Research Policy and Systems, 17, 88. https://doi.org/10.1186/s12961-019-0501-7

GRN · German Reproducibility Network. (n.d.). A German Reproducibility Network. Retrieved from https://reproducibilitynetwork.de/

Grossmann, A., & Brembs, B. (2021). Current market rates for scholarly publishing services. F1000Research, 10(20), 20. https://doi.org/10.12688/f1000research.27468.1

Grzanka, P. R. (2020). From buzzword to critical psychology: An invitation to take intersectionality seriously. Women & Therapy, 43(3–4), 244–261.

Guenther, E. A., & Rodriguez, J. K. (2020). What’s wrong with ‘manels’ and what can we do about them. The Conversation. http://theconversation.com/whats-wrong-with-manels-and-what-can-we-do-about-them-148068

Guest, O. [@o_guest]. (2017). Thanks! Hopefully this thread & many other similar discussions & blogs will help make it less Bropen Science and more Open Science. *hides* . Twitter. Retrieved from https://twitter.com/o_guest/status/871675631062458368

Guest, O., & Martin, A. E. (2020). How computational modeling can force theory building in psychological science. Perspectives on Psychological Science. https://doi.org/10.1177/1745691620970585

Haak, L. L., Fenner, M., Paglione, L., Pentz, E., & Ratner, H. (2012). ORCID: A system to uniquely identify researchers. Learned Publishing, 25(4), 259–264. https://doi.org/10.1087/20120404

Hackett, R., & Kelly, S. (2020). Publishing ethics in the era of paper mills. Biology Open, 9(10), bio056556. https://doi.org/10.1242/bio.056556

Hahn, G. J., & Meeker, W. Q. (1993). Assumptions for Statistical Inference. The American Statistician, 47(1), 1–11. https://doi.org/10.1080/00031305.1993.10475924

Hamel, C., Michaud, A., Thuku, M., Skidmore, B., Stevens, A., Nussbaumer-Streit, B., & Garritty, C. (2021). Defining rapid reviews: A systematic scoping review and thematic analysis of definitions and defining characteristics of rapid reviews. Journal of Clinical Epidemiology, 129, 74–85. https://doi.org/10.1016/j.jclinepi.2020.09.041

Handbook, O. D. (n.d.). What is Open Data? Retrieved 9 July 2021. https://opendatahandbook.org/guide/en/what-is-open-data/

Hardwicke, T. E., Bohn, M., MacDonald, K., Hembacher, E., Nuijten, M. B., Peloquin, B. N., deMayo, B. E., Long, B., Yoon, E. J., & Frank, M. C. (2020). Analytic reproducibility in articles receiving open data badges at the journal Psychological Science: an observational study. Royal Society Open Science, 8(1), 201494. https://doi.org/10.1098/rsos.201494

Hardwicke, T. E., Jameel, L., Jones, M., Walczak, E. J., & Weinberg, L. M. (2014). Only human: Scientists, systems, and suspect statistics. Opticon1826, 16, 25. https://doi.org/10.5334/OPT.CH

Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., & Gérard-Marchant, P. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2

Hart, D. D., & Silka, L. (2020). Rebuilding the Ivory Tower: A Bottom-Up Experiment in Aligning Research with Societal Needs. Issues in Science and Technology, 79–85. Retrieved from https://issues.org/aligning-research-with-societal-needs/

Hartgerink, C. H., Wicherts, J. M., & Van Assen, M. A. L. M. (2017). Too good to be false: Nonsignificant results revisited. Collabra: Psychology, 3(1). https://doi.org/10.1525/collabra.71

Haven, T. L., & Van Grootel, L. (2019). Preregistering qualitative research. Accountability in Research, 26(3), 229–244. https://doi.org/10.1080/08989621.2019.1580147

Hayes, B. C., & Tariq, V. N. (2000). Gender differences in scientific knowledge and attitudes toward science: A comparative study of four Anglo-American nations. Public Understanding of Science, 9(4), 433–447. https://doi.org/10.1088/0963-6625/9/4/306

Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247. https://doi.org/10.1037/1040-3590.7.3.238

Health Research Board. (n.d.). Declaration on Research Assessment. Retrieved from https://www.hrb.ie/funding/funding-schemes/before-you-apply/how-we-assess-applications/declaration-on-research-assessment/

Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press.

Heathers, J. A., Anaya, J., van der Zee, T., & Brown, N. J. (2018). Recovering data from summary statistics: Sample Parameter Reconstruction via Iterative TEchniques (SPRITE). PeerJ Preprints, 6, e26968v1. https://doi.org/10.7287/peerj.preprints.26968v1

Hendriks, F., Kienhues, D., & Bromme, R. (2016). Trust in science and the science of trust. Trust and Communication in a Digitized World, 143–159.

Henrich, J. (2020). The weirdest people in the world: How the west became psychologically peculiar and particularly prosperous. Farrar, Straus and Giroux.

Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–83. https://doi.org/10.1017/S0140525X0999152X

Herrmannova, D., & Knoth, P. (n.d.). Semantometrics Towards Full text-based Research Evaluation. Retrieved from https://arxiv.org/pdf/1605.04180.pdf

Heyman, T., Moors, P., & Rabagliati, H. (2020). The benefits of adversarial collaboration for commentaries. Nature Human Behavior, 4, 1217. https://doi.org/10.1038/s41562-020-00978-6

Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions. 2nd Edition. John Wiley & Sons.

Higher Education Funding Council for England. (n.d.). About the REF. https://ref.ac.uk/about-the-ref/what-is-the-ref/

Himmelstein, D. S., Rubinetti, V., Slochower, D. R., Hu, D., Malladi, V. S., Greene, C. S., & Gitter, A. (2019). Open collaborative writing with Manubot. PLOS Computational Biology, 15(6), e1007128. https://doi.org/10.1371/journal.pcbi.1007128

Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102

Hitchcock, C., Meyer, A., Rose, D., & Jackson, R. (2002). Providing new access to the general curriculum: Universal design for learning. Teaching Exceptional Children, 35(2), 8–17. https://www.proquest.com/scholarly-journals/providing-new-access-general-curriculum/docview/201139970/se-2?accountid=8630

Hoekstra, R., Kiers, H., & Johnson, A. (2012). Are assumptions of well-known statistical techniques checked, and why (not)? Frontiers in Psychology, 3(137), 1–9. https://doi.org/10.3389/fpsyg.2012.00137

Hogg, D., Bovy, J., & Lang, D. (2010). Data analysis recipes: Fitting a model to data. arXiv:1008.4686 [astro-ph.IM].

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

Holcombe, A. O. (2019). Contributorship, not authorship: Use CRediT to indicate who did what. Publications, 7(3), 48. https://doi.org/10.3390/publications7030048

Holcombe, A. O., Kovacs, M., Aust, F., & Aczel, B. (2020). Documenting contributions to scholarly articles using CRediT and tenzing. Plos One, 15(12), e0244611.

Holden, R. B. (2010). Face Validity. In I. B. Weiner & W. E. Craighead (Eds.), The Corsini Encyclopedia of Psychology (4th ed.). Wiley. http://dx.doi.org/10.1002/9780470479216.corpsy0341

Holm, S. (1979). A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian Journal of Statistics, 6(2), 65–70. http://www.jstor.org/stable/4615733

Houtkoop, B. L., Chambers, C., Macleod, M., Bishop, D. V. M., Nichols, T. E., & Wagenmekers, E.-J. (2018). Data sharing in psychology: A survey on barriers and preconditions. Advances in Methods and Practices in Psychological Science, 1(1), 70.85. https://doi.org/10.1177/2515245917751886

Huber, B., Barnidge, M., Gil de Zúñiga, H., & Liu, J. (2019). Fostering public trust in science: The role of social media. Public Understanding of Science, 28(7), 759–777. https://doi.org/10.1177/0963662519869097

Huber, C. (2016). Introduction to Bayesian statistics, part 2: MCMC and the Metropolis–Hastings algorithm. In The Stata Blog. https://blog.stata.com/2016/11/15/introduction-to-bayesian-statistics-part-2-mcmc-and-the-metropolis-hastings-algorithm/

Huber, C. (2016). The Stata Blog: Introduction to Bayesian statistics, part 1: The basic concepts. In The Stata Blog. https://blog.stata.com/2016/11/01/introduction-to-bayesian-statistics-part-1-the-basic-concepts/

Huelin, R., Iheanacho, I., Payne, K., & Sandman, K. (2015). What’s in a name? Systematic and non-systematic literature reviews, and why the distinction matters. The Evidence Forum, 34–37. Retrieved from https://www.evidera.com/wp-content/uploads/2015/06/Whats-in-a-Name-Systematic-and-Non-Systematic-Literature-Reviews-and-Why-the-Distinction-Matters.pdf

Hultsch, D. F., MacDonald, S. W., & Dixon, R. A. (2002). Variability in reaction time performance of younger and older adults. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 57(2), P101–P115. https://doi.org/10.1093/geronb/57.2.P101

Hunsley, J., & Meyer, G. J. (2003). The incremental validity of psychological testing and assessment: Conceptual, methodological, and statistical issues. Psychological Assessment, 15(4), 446–455. https://doi.org/10.1037/1040-3590.15.4.446

Hunter, J. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55

Hunter, J. (2012). Post-publication peer review: Opening up scientific conversation. Frontiers in Computational Neuroscience, 6, 63. https://doi.org/10.3389/fncom.2012.00063

Hunter, J. E., & Schmidt, F. L. (2015). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (Third). SAGE.

Hurlbert, S. H. (1984). Pseudoreplication and the Design of Ecological Field Experiments. Ecological Monographs, 54(2), 187–211. https://doi.org/10.2307/1942661

Hüffmeier, J., Mazei, J., & Schultze, T. (2016). Reconceptualizing replication as a sequence of different studies: A replication typology. Journal of Experimental Social Psychology, 66, 81–92. https://doi.org/10.1016/j.jesp.2015.09.009

BIAS | Definition of BIAS by Oxford Dictionary on Lexico.com also meaning of BIAS. (n.d.). Lexico Dictionaries | English. https://web.archive.org/web/20220423220738/https://www.lexico.com/definition/bias

Bias—definition of bias in English. (2017). https://en.oxforddictionaries.com/definition/bias

CKAN - The open source data management system. (n.d.). Retrieved 9 July 2021, from https://ckan.org/.

Closed access. (n.d.). https://web.archive.org/web/20220221165721/https://casrai.org/term/closed-access/

Collaborative Assessment for Trustworthy Science | The repliCATS project. (n.d.). University of Melbourne.

CRediT - Contributor Roles Taxonomy. (n.d.). Casrai. https://credit.niso.org/

Data management plans. (n.d.). https://sdr.library.stanford.edu/documentation/data-management-plans

Declaration on Research Assessment. (n.d.). Health Research Board. https://www.hrb.ie/funding/funding-schemes/before-you-apply/how-we-assess-applications/declaration-on-research-assessment/

Digital Object Identifier System Handbook. (n.d.). DOI. https://www.doi.org/hb.html

Directory of Open Access Journals. (n.d.). https://doaj.org/apply/transparency/

Domov | SKRN (Slovak Reproducibility Network). (n.d.). SKRN. https://www.skrn.sk/

Download JASP. (n.d.). JASP - Free. https://jasp-stats.org/download/

Embargo (academic publishing). (2021). https://en.wikipedia.org/w/index.php?title=Embargo_(academic_publishing)&oldid=1016895567

Evidence Synthesis. (n.d.). LSHTM. https://www.lshtm.ac.uk/research/centres/centre-evaluation/evidence-synthesis

Measuring your research impact: i10-Index. (n.d.). https://guides.library.cornell.edu/impact/author-impact-10

NIHR Guidance on Co-Producing a Research Project. (2021). https://www.learningforinvolvement.org.uk/?opportunity=nihr-guidance-on-co-producing-a-research-project

Open Science MOOC. (n.d.). https://opensciencemooc.eu/

OSF | StudySwap: A platform for interlab replication, collaboration, and research resource exchange. (2021). https://osf.io/meetings/StudySwap/

PCI IN A FEW LINES. (n.d.). https://peercommunityin.org/

Recommended Data Repositories. (n.d.). Scientific Data. https://www.nature.com/sdata/policies/repositories

Reflexivity. (2017). http://www.oxfordreference.com/view/10.1093/acref/9780199599868.001.0001/acref-9780199599868-e-1530

ReproducibiliTea. (n.d.). https://reproducibilitea.org/

RIOT Science Club. (2021). https://web.archive.org/web/20260317091517/https://www.riotscience.co.uk/

San Francisco Declaration on Research Assessment (DORA). (n.d.). https://sfdora.org/

SORTEE. (n.d.). Society for Open, Reliable and Transparent Ecology and Evolutionary Biology. https://www.sortee.org/

Swiss Reproducibility Network. (n.d.). https://www.swissrn.org/

The Society for the Improvement of Psychological Science. (2021). https://improvingpsych.org/

ICPSR. (n.d.). What is a Codebook? Retrieved 9 July 2021. https://www.icpsr.umich.edu/icpsrweb/content/shared/ICPSR/faqs/what-is-a-codebook.html

Ikeda, A., Xu, H., Fuji, N., Zhu, S., & Yamada, Y. (2019). Questionable research practices following pre-registration. Japanese Psychological Review, 62, 281–295. https://doi.org/10.24602/sjpr.62.3_281

Improving Psychology. (n.d.). Improving Psychology. https://improvingpsych.org/

Information Commissioner’s Office. (2021). Guide to the UK General Data Protection Regulation (UK GDPR). ICO. https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/

Inter-university Consortium for Political and Social Research. (2025). What is a Codebook? https://www.icpsr.umich.edu/web/ICPSR/cms/1983

International Committee of Medical Journal Editors. (2019). Recommendations for the conduct, reporting, eduting, and publication of scholarly work in medical journals. http://www.icmje.org/icmje-recommendations.pdf

International Committee of Medical Journal Editors. (2025). Author Responsibilities—Conflicts of Interest. https://www.icmje.org/recommendations/browse/roles-and-responsibilities/author-responsibilities--conflicts-of-interest.html

INVOLVE. (n.d.). INVOLVE – Supporting public involvement in NHS, public health and social care research. https://www.invo.org.uk/

Ioannidis, J. P. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124

Ioannidis, J. P. (2018). Meta-research: Why research on research matters. PLoS Biology, 16(3). https://doi.org/10.1371/journal.pbio.2005468

Ioannidis, J. P. (2023). Systematic reviews for basic scientists: a different beast. Physiological Reviews, 103(1), 1–5. https://doi.org/10.1152/physrev.00028.2022

Ioannidis, J. P., Fanelli, D., Dunne, D. D., & Goodman, S. N. (2015). Meta-research: Eevaluation and improvement of research methods and practices. PLoS Biology, 13(10), e1002264. https://doi.org/10.1371/journal.pbio.1002264

ISO. (1993). Guide to the Expression of Uncertainty in Measurement (1st ed.). International Organization for Standardization.

JabRef Development Team. (2021). JabRef - An open-source, cross-platform citation and reference management software. https://www.jabref.org

Jacobson, D., & Mustafa, N. (2019). Social Identity Map: A Reflexivity Tool for Practicing Explicit Positionality in Critical Qualitative Research. International Journal of Qualitative Methods, 18, 1609406919870075. https://doi.org/10.1177/1609406919870075

Jafar, A. J. N. (2018). What is positionality and should it be expressed in quantitative studies? Emergency Medicine Journal, 35(5), 323–324. https://doi.org/10.1136/emermed-2017-207158

James, K. L., Randall, N. P., & Haddaway, N. R. (2016). A methodology for systematic mapping in environmental sciences. Environmental Evidence, 5(1), 1–13. https://doi.org/10.1186/s13750-016-0059-6

Jamovi. (n.d.). Jamovi—Stats. Open. Now. Jamovi. https://www.jamovi.org/

Jannot, A. S., Agoritsas, T., Gayet-Ageron, A., & Perneger, T. V. (2013). Citation bias favoring statistically significant studies was present in medical research. Journal of Clinical Epidemiology, 66(3), 296–301. https://doi.org/10.1016/j.jclinepi.2012.09.015

JASP Team. (2020). JASP (Version 0.14.1) [Computer software].

JMIR Publications. (2021). What is a group author (collaborative author) and does it need an ORCID? https://support.jmir.org/hc/en-us/articles/115001449591-What-is-a-group-author-collaborative-author-and-does-it-need-an-ORCID-

John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23(5), 524–532. https://doi.org/10.1177/0956797611430953

Jones, A., Dr, J., Duckworth, & Christiansen, P. (2020). May I have your attention, please? Methodological and Analytical Flexibility in the Addiction Stroop. https://doi.org/10.31234/osf.io/ws8xp

Joseph, T. D., & Hirshfield, L. E. (2011). `Why don’t you get somebody new to do it?’ Race and cultural taxation in the academy. Ethnic and Racial Studies, 34(1), 121–141. https://doi.org/10.1080/01419870.2010.496489

Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: a new and comprehensive solution to a pervasive but largely ignored problem. Journal of Personality and Social Psychology, 103(1), 54–69. https://doi.org/10.1037/a0028347

Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D. M., & Damian, D. (2014). The promises and perils of mining github. Proceedings of the 11th Working Conference on Mining Software Repositories, 92–101.

Kamraro. (2014). Responsible research & innovation. Horizon 2020 - European Commission. https://web.archive.org/web/20220218023041/https://ec.europa.eu/programmes/horizon2020/en/h2020-section/responsible-research-innovation

Kang, E., & Hwang, H. J. (2020). The consequences of data fabrication and falsification among researchers. Journal of Research and Publication Ethics, 1(2), 7–10.

Kapp, S. K. (2013). Interactions between theoretical models and practical stakeholders: the basis for an integrative, collaborative approach to disabilities. In E. Ashkenazy & M. Latimer (Eds.), Empowering Leadership: A Systems Change Guide for Autistic College Students and Those with Other Disabilities (pp. 104–113). Autistic Self Advocacy Network (ASAN).

Kathawalla, U., Silverstein, P., & Syed, M. (2020). Easing into Open Science: A Guide for Graduate Students and Their Advisors. Collabra: Psychology. https://doi.org/10.31234/osf.io/vzjdp Retrieved from https://psyarxiv.com/vzjdp

Kelley, T. L. (1927). Interpretation of educational measurements. Macmillan.

Kellogg Insight. (n.d.). How to Make Inclusivity More Than Just an Office Buzzword. https://insight.kellogg.northwestern.edu/article/how-to-make-inclusivity-more-than-just-an-office-buzzword

Kerr, J. R., & Wilson, M. S. (2021). Right-wing authoritarianism and social dominance orientation predict rejection of science and scientists. Group Processes & Intergroup Relations, 24(4), 550–567. https://doi.org/10.1177/1368430221992126

Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2(3), 196–217. https://doi.org/10.1207/s15327957pspr0203_4

Kerr, N. L., Ao, X., Hogg, M. A., & Zhang, J. (2018). Addressing replicability concerns via adversarial collaboration: Discovering hidden moderators of the minimal intergroup discrimination effect. Journal of Experimental Social Psychology, 78, 66–76. https://doi.org/10.1016/j.jesp.2018.05.001

Kidwell, M. C., Lazarević, L. B., Baranski, E., Hardwicke, T. E., Piechowski, S., Falkenberg, L. S., & Nosek, B. A. (2016). Badges to acknowledge open practices: A simple, low-cost, effective method for increasing transparency. PLoS Biology, 14(5), e1002456. https://doi.org/10.1371/journal.pbio.1002456

Kienzler, H., & Fontanesi, C. (2017). Learning through inquiry: A global health hackathon. Teaching in Higher Education, 22(2), 129–142. https://doi.org/10.1080/13562517.2016.1221805

Kiernan, C. (1999). Participation in research by people with learning disability: Origins and issues. British Journal of Learning Disabilities, 27(2), 43–47. https://doi.org/10.1111/j.1468-3156.1999.tb00084.x

King, G. (1995). Replication, replication. PS: Political Science & Politics, 28(3), 444–452. https://doi.org/10.2307/420301

Kitzes, J., Turek, D., & Deniz, F. (2017). The practice of reproducible research: Case studies and lessons from the data-intensive sciences. University of California Press.

Kiureghian, A. D., & Ditlevsen, O. (2009). Aleatory or epistemic? Does it matter? Structural Safety, 31(2), 105–112. https://doi.org/10.1016/j.strusafe.2008.06.020

Klein, O., Hardwicke, T. E., Aust, F., Breuer, J., Danielsson, H., Mohr, A. H., IJzerman, H., Nilsonne, G., Vanpaemel, W., & Frank, M. C. (2018). A practical guide for transparency in psychological science. Collabra: Psychology, 4(1), 20. https://doi.org/10.1525/collabra.158

Klein, R. A., Ratliff, K. A., Vianello, M., Adams, R. B., Bahník, Š., Bernstein, M. J., & et al. (2014). Investigating variation in replicability: A “many labs” replication project. Social Psychology, 45, 142–152. https://doi.org/10.1027/1864-9335/a000178

Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Alper, S., & … Nosek, B. A. (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

Kleinberg, B., Mozes, M., van der Toolen, Y., & Verschuere, B. (2017). NETANOS - Named entity-based Text Anonymization for Open Science. https://osf.io/w9nhb/

Knoth, P., & Herrmannova, D. (2014). Towards semantometrics: A new semantic similarity based measure for assessing a research publication’s contribution. D-Lib Magazine, 20(11), 8. https://doi.org/10.1045/november2014-knoth

Koole, S. L., & Lakens, D. (2012). Rewarding replications: A sure and simple way to improve psychological science. Perspectives on Psychological Science, 7(6), 608–614. https://doi.org/10.1177/1745691612462586

Kreuter, F. (Ed.). (2013). Improving Surveys with Paradata. https://doi.org/10.1002/9781118596869

Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2nd ed.). Academic Press.

Kuhn, T. (1962). The Structure of Scientific Revolutions. University of Chicago Press.

Kukull, W. A., & Ganguli, M. (2012). Generalizability: The trees, the forest, and the low-hanging fruit. Neurology, 78(23), 1886–1891. https://doi.org/10.1212/WNL.0b013e318258f812

Laakso, M., & Björk, B. C. (2013). Delayed open access: An overlooked high‐impact category of openly available scientific literature. Journal of the American Society for Information Science and Technology, 64(7), 1323–1329.

Laine, H. (2017). Afraid of scooping – Case study on researcher strategies against fear of scooping in the context of open science. In Data Science Journal (Vol. 16, pp. 1–14). https://doi.org/10.5334/dsj-2017-029

Lakatos, I. (1978). The Methodology of Scientific Research Programs: Vol. I. Cambridge University Press.

Lakens, D. (2014). Performing high-powered studies efficiently with sequential analyses. European Journal of Social Psychology, 44(7), 701–710. https://doi.org/10.1002/ejsp.2023

Lakens, D. (2020). The 20% Statistician: Red Team Challenge. The 20% Statistician. http://daniellakens.blogspot.com/2020/05/red-team-challenge.html

Lakens, D. (2020). Pandemic researchers — recruit your own best critics. Nature, 581, 121. https://doi.org/10.1038/d41586-020-01392-8

Lakens, D. (2021). Sample Size Justification. https://doi.org/10.31234/osf.io/9d3yf

Lakens, D. (2021). The Practical Alternative to the p Value Is the Correctly Used p Value. Perspectives on Psychological Science, 16(3), 639–648. https://doi.org/10.1177/1745691620958012

Lakens, D. (2024). When and how to deviate from a preregistration. Collabra: Psychology, 10(1), Article 117094. https://doi.org/10.1525/collabra.117094

Lakens, D., McLatchie, N., Isager, P. M., Scheel, A. M., & Dienes, Z. (2020). Improving inferences about null effects with Bayes factors and equivalence tests. The Journals of Gerontology: Series B, 75(1), 45–57. https://doi.org/10.1093/geronb/gby065

Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963

Largent, E. A., & Snodgrass, R. T. (2016). Blind peer review by academic journals. In C. T. Robertson & A. S. Kesselheim (Eds.), Blinding as a solution to bias: Strengthening biomedical science, forensic science, and law (pp. 75–95). Academic Press. https://doi.org/10.1016/B978-0-12-802460-7.00005-X

Larivière, V., Desrochers, N., Macaluso, B., Mongeon, P., Paul-Hus, A., & Sugimoto, C. R. (2016). Contributorship and division of labor in knowledge production. Social Studies of Science, 46(3), 417–435. https://doi.org/10.1177/0306312716650046

Lazic, S. E. (2019). Genuine replication and pseudoreplication: What’s the difference? In BMJ Open Science. https://blogs.bmj.com/openscience/2019/09/16/genuine-replication-and-pseudoreplication-whats-the-difference/

Leavens, D. A., Bard, K. A., & Hopkins, W. D. (2010). BIZARRE chimpanzees do not represent “the chimpanzee.” Behavioral and Brain Sciences, 33(2–3), 100–101. https://doi.org/10.1017/S0140525X10000166

Leavy, P. (2017). Research Design: Quantitative, Qualitative, Mixed Methods, Arts-Based, and Community-Based Participatory Research Approaches. The Guilford Press.

LeBel, E. P., McCarthy, R. J., Earp, B. D., Elson, M., & Vanpaemel, W. (2018). A unified framework to quantify the credibility of scientific findings. Advances in Methods and Practices in Psychological Science, 1(3), 389–402. https://doi.org/10.1177/2515245918787489

LeBel, E. P., Vanpaemel, W., Cheung, I., & Campbell, L. (2017). A brief guide to evaluate replications. Meta-Psychology, 3. https://doi.org/10.15626/MP.2018.843

Ledgerwood, A., Hudson, S. T. J., Lewis, Jr., N. A., Maddox, K. B., Pickett, C., Remedios, J. D., & Wilkins, C. L. (2021). The Pandemic as a Portal: Reimagining Psychological Science as Truly Open and Inclusive. https://doi.org/10.31234/osf.io/gdzue

Lee, R. M. (1993). Doing research on sensitive topics. Sage.

Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation Science, 5(1), 69. https://doi.org/10.1186/1748-5908-5-69

Levitt, H. M., Motulsky, S. L., Wertz, F. J., Morrow, S. L., & Ponterotto, J. G. (2017). Recommendations for designing and reviewing qualitative research in psychology: Promoting methodological integrity. Qualitative Psychology, 4(1), 2. https://doi.org/10.1037/qup0000082

Lewandowsky, S., & Bishop, D. (2016). Research integrity: Don’t let transparency damage science. Nature News, 529(7587), 459. https://doi.org/10.1038/529459a

Lewandowsky, S., & Oberauer, K. (2021). Worldview-motivated rejection of science and the norms of science. Cognition, 215, 104820. https://doi.org/10.1016/j.cognition.2021.104820

Lieberman, E. (2020). Research Cycles. In C. Elman, J. Gerring, & J. Mahoney (Eds.), The Production of Knowledge: Enhancing Progress in Social Science (pp. 42–70). Cambridge University Press. https://doi.org/10.1017/9781108762519.003

Lin, D., Crabtree, J., Dillo, I., Downs, R. R., Edmunds, R., Giaretta, D., De Giusti, M., L’Hours, H., Hugo, W., Jenkyns, R., Khodiyar, V., Martone, M. E., Mokrane, M., Navale, V., Petters, J., Sierman, B., Sokolova, D. V., Stockhause, M., & Westbrook, J. (2020). The TRUST Principles for digital repositories. Scientific Data, 7(1), 144. https://doi.org/10.1038/s41597-020-0486-7

Lind, F., Gruber, M., & Boomgaarden, H. G. (2017). Content analysis by the crowd: Assessing the usability of crowdsourcing for coding latent constructs. Communication Methods and Measures, 11(3), 191–209. https://doi.org/10.1080/19312458.2017.1317338

Lindsay, D. S. (2015). Replication in Psychological Science . Psychological Science, 26(12), 1827–1832. https://doi.org/10.1177/0956797615616374

Lindsay, D. S. (2020). Seven steps toward transparency and replicability in psychological science. Canadian Psychology/Psychologie Canadienne, 61(4), 310–317. https://doi.org/10.1037/cap0000222

Lintott, C. J., Schawinski, K., Slosar, A., Land, K., Bamford, S., Thomas, D., & Vandenberg, J. (2008). Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey. Monthly Notices of the Royal Astronomical Society, 389(3), 1179–1189. https://doi.org/10.1111/j.1365-2966.2008.13689.x

Liu, H., & Priest, S. (2009). Understanding public support for stem cell research: Media communication, interpersonal communication and trust in key actors. Public Understanding of Science, 18(6), 704–718. https://doi.org/10.1177/0963662508097625

Liu, Y., Gordon, M., Wang, J., Bishop, M., Chen, Y., Pfeiffer, T., Twardy, C., & Viganola, D. (2020). Replication Markets: Results, Lessons, Challenges and Opportunities in AI Replication. ArXiv:2005.04543 . http://arxiv.org/abs/2005.04543

Longino, H. E. (1990). Science as Social Knowledge: Values and Objectivity in Scientific Inquiry. Princeton University Press.

Longino, H. E. (1992). Taking gender seriously in philosophy of science. PSA, 2, 333–340.

Lu, J., Qiu, Y., & Deng, A. (2018). A note on Type S/M errors in hypothesis testing. British Journal of Mathematical and Statistical Psychology, 72(1), 1–17. https://doi.org/10.1111/bmsp.12132

Lutz, M. (2001). Programming Python. O’Reilly Media, Inc.

Lynch, J. G., Jr. (1982). On the External Validity of Experiments in Consumer Research. Journal of Consumer Research, 9(3), 225. https://doi.org/10.1086/208919

Lyon, L. (2016). Transparency: The Emerging Third Dimension of Open Science and Open Data. LIBER Quarterly, 25(4), 153–171. http://doi.org/10.18352/lq.10113

Lüdtke, O., Ulitzsch, E., & Robitzsch, A. (2020). A Comparison of Penalized Maximum Likelihood Estimation and Markov Chain Monte Carlo Techniques for Estimating Confirmatory Factor Analysis Models with Small Sample Sizes . https://doi.org/10.31234/osf.io/u3qag

Maass, W., Parsons, J., Purao, S., Storey, V. C., & Woo, C. (2018). Data-driven meets theory-driven research in the era of big data: Opportunities and challenges for information systems research. Journal of the Association for Information Systems, 19(12), 1253–1273. http://doi.org/10.17705/1jais.00526

Macfarlane, B., & Cheng, M. (2008). Communism, Universalism and Disinterestedness: Re-examining Contemporary Support among Academics for Merton’s Scientific Norms. Journal of Academic Ethics, 6(1), 67–78. https://doi.org/10.1007/s10805-008-9055-y

Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., & Lüdecke, D. (2019). Indices of Effect Existence and Significance in the Bayesian Framework. https://doi.org/10.3389/fpsyg.2019.02767

Manalili, M. A. R., Pearson, A., Sulik, J., Creechan, L., Elsherif, M. M., Murkumbi, I., Azevedo, F., Bonnen, K. L., Kim, J. S., Kording, K., Lee, J. J., Obscura, Kapp, S. K., Roer, J. P., & Morstead, T. (2022). From Puzzle to Progress: How engaging with Neurodiversity can improve Cognitive Science.

Marks, D. (1997). Models of disability. Disability and Rehabilitation, 19(3), 85–91. https://doi.org/10.3109/09638289709166831

Martinez-Acosta, V. G., & Favero, C. B. (2018). A discussion of diversity and inclusivity at the institutional level: The need for a strategic plan. Journal of Undergraduate Neuroscience Education, 16(3), A252.

Marwick, B., Boettiger, C., & Mullen, L. (2018). Packaging data analytical work reproducibly using R (and friends). The American Statistician, 72(1), 80–88. https://doi.org/10.1080/00031305.2017.1375986

Masur, P. K. (2020). Understanding the Effects of Analytical Choices on Finding the Privacy Paradox: A Specification Curve Analysis of Large-Scale Survey Data. Preprint. https://osf.io/m72gb/

Mayo, D. G., & Cox, D. R. (2006). Frequentist statistics as a theory of inductive inference. In Optimality: The second Erich L. Lehmann symposium (pp. 77–97). https://www.jstor.org/stable/i397717

McDaniel, G., & Corporation, I. B. M. (1994). IBM dictionary of computing. McGraw-Hill. http://archive.org/details/isbn_9780070314894

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan (2nd ed.). Taylor.

McGee, M. (2012). Neurodiversity. Contexts, 11(3), 12–13. https://doi.org/10.1177/1536504212456175

McNutt, M. K., Bradford, M., Drazen, J. M., Hanson, B., Howard, B., Jamieson, K. H., Kiermer, V., Marcus, E., Pope, B. K., Schekman, R., Swaminathan, S., Stang, P. J., & Verma, I. M. (2018). Transparency in authors’ contributions and responsibilities to promote integrity in scientific publication. Proceedings of the National Academy of Sciences of the United States of America, 115(11), 2557–2560. https://doi.org/10.1073/pnas.1715374115

Medical Research Council. (2019). Identifiability, anonymisation and pseudonymisation. Medical Research Council. https://mrc.ukri.org/documents/pdf/gdpr-guidance-note-5-identifiability-anonymisation-and-pseudonymisation/

Medin, D. L. (2012). Rigor without rigor mortis: The APS Board discusses research integrity. APS Observer, 25(5–9), 27–28. https://www.psychologicalscience.org/observer/scientific-rigor

Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66, 195–244. https://meehl.umn.edu/sites/meehl.umn.edu/files/files/144whysummaries.pdf

Mellers, B., Hertwig, R., & Kahneman, D. (2001). Do frequency representations eliminate conjunction effects? An exercise in adversarial collaboration. Psychological Science, 12(4), 269–275. https://doi.org/10.1111/1467-9280.00350

Menke, C. (2015). A Note on Science and Democracy? Robert K. Merton’s Ethos of Science. In R. Klausnitzer, C. Spoerhase, & D. Werle (Eds.), Ethos und Pathos der Geisteswissenschaften. DE GRUYTER. https://doi.org/10.1515/9783110375008-013

Mertens, G., & Krypotos, A. M. (2019). Preregistration of analyses of preexisting data. Psychologica Belgica, 59(1), 338.

Merton, R. K. (1938). Science and the social order. Philosophy of Science, 5(3), 321–337. https://doi.org/10.1086/286513

Merton, R. K. (1942). A note on science and democracy. Journal of Legal and Political Sociology, 1, 115–126. https://doi.org/10.1515/9783110375008-013

Merton, R. K. (1968). The Matthew Effect in Science. Science, 159(3810), 56–63. https://doi.org/10.1126/science.159.3810.56

Meslin, E. M. (2009). Achieving global justice in health through global research ethics: supplementing Macklin’s ‘top-down’ approach with one from the ‘ground up’. In R. M. Green, A. Donovan, & S. A. Jauss (Eds.), Global Bioethics: Issues of Conscience for the Twenty-First Century (pp. 163–177). University Press.

Messick, S. (1995). Standards of validity and the validity of standards in performance assessment. Educational Measurement: Issues and Practice, 14(4), 5–8. https://doi.org/10.1111/j.1745-3992.1995.tb00881.x

Michener, W. K. (2015). Ten simple rules for creating a good data management plan. PLoS Computational Biology, 11(10), e1004525. https://doi.org/10.1371/journal.pcbi.1004525

Mischel, W. (2009). Becoming a Cumulative Science. Association for Psychological Science. https://www.psychologicalscience.org/observer/becoming-a-cumulative-science

Moher, D., Bouter, L., Kleinert, S., Glasziou, P., Sham, M. H., Barbour, V., & Dirnagl, U. (2020). The Hong Kong Principles for assessing researchers: Fostering research integrity. PLoS Biology, 18(7), e3000737. https://doi.org/10.1371/journal.pbio.3000737

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097

Moher, D., Naudet, F., Cristea, I. A., Miedema, F., Ioannidis, J. P. A., & Goodman, S. N. (2018). Assessing scientists for hiring, promotion, and tenure. PLOS Biology, 16(3), e2004089. https://doi.org/10.1371/journal.pbio.2004089

Monroe, K. R. (2018). The rush to transparency: DA-RT and the potential dangers for qualitative research. Perspectives on Politics, 16(1), 141–148. https://doi.org/10.1017/S153759271700336X

Morabia, A., Have, T. T., & Landis, J. R. (1997). Interaction Fallacy. Journal of Clinical Epidemiology, 50(7), 809–812. https://doi.org/10.1016/S0895-4356(97)00053-X

Moran, H., Karlin, L., Lauchlan, E., Rappaport, S. J., Bleasdale, B., Wild, L., & Dorr, J. (2020). Understanding Research Culture: What researchers think about the culture they work in. Wellcome Open Research, 5, 201. https://doi.org/10.12688/wellcomeopenres.15832.1

Moreno, S. G., Sutton, A. J., Ades, A. E., Stanley, T. D., Abrams, K. R., Peters, J. L., & Cooper, N. J. (2009). Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study. BMC Medical Research Methodology, 9, 2. https://doi.org/10.1186/1471-2288-9-2

Moretti, M. (2020). Beyond Open-washing: Are Narratives the Future of Open Data Portals? Medium Blog. https://medium.com/nightingale/beyond-open-washing-are-stories-and-narratives-the-future-of-open-data-portals-93228d8882f3

Morey, R. D., Chambers, C. D., Etchells, P. J., Harris, C. R., Hoekstra, R., Lakens, D., Lewandowsky, S., Morey, C. C., Newman, D. P., Schönbrodt, F. D., Vanpaemel, W., Wagenmakers, E.-J., & Zwaan, R. A. (2016). The Peer Reviewers’ Openness Initiative: incentivizing open research practices through peer review. Royal Society Open Science, 3(1). https://doi.org/10.1098/rsos.150547

Morgan, C. (1998). The DOI (Digital Object Identifier). Serials, 11(1), 47–51. http://doi.org/10.1629/1147

Morse, J. M. (2010). “Cherry picking”: Writing from thin data. Qualitative Health Research, 20(1), 3–3. https://doi.org/10.1177/1049732309354285

Moshontz, H., Campbell, L., Ebersole, C. R., IJzerman, H., Urry, H. L., Forscher, P. S., & Chartier, C. R. (2018). The Psychological Science Accelerator: Advancing psychology through a distributed collaborative network. Advances in Methods and Practices in Psychological Science, 1(4), 501–515. https://doi.org/10.1177/2515245918797607

Moshontz, H., Ebersole, C. R., Weston, S. J., & Klein, R. A. (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

Mourby, M., Mackey, E., Elliot, M., Gowans, H., Wallace, S. E., Bell, J., Smith, H., Aidinlis, S., & Kaye, J. (2018). Are ‘pseudonymised’ data always personal data? Implications of the GDPR for administrative data research in the UK. Computer Law & Security Review, 34(2), 222–233. https://doi.org/10.1016/j.clsr.2018.01.002

Muller, J. Z. (2018). The Tyranny of Metrics. Princeton University Press.

Muma, J. R. (1993). The need for replication. Journal of Speech and Hearing Research, 36, 927–930. https://doi.org/10.1044/jshr.3605.927

Munn, Z., Peters, M. D., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18(1), 1–7. https://doi.org/10.1186/s12874-018-0611-x

Murphy, K. R., & Aguinis, H. (2019). HARKing: How badly can cherry-picking and question trolling produce bias in published results? Journal of Business and Psychology, 34, 1–17. https://doi.org/10.1007/s10869-017-9524-7

Muthukrishna, M., Bell, A. V., Henrich, J., Curtin, C. M., Gedranovich, A., McInerney, J., & Thue, B. (2020). Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) psychology: Measuring and mapping scales of cultural and psychological distance. Psychological Science, 31, 678–701. https://doi.org/10.1177/0956797620916782

National Academies of Sciences, Engineering, and Medicine. (2019). Reproducibility and Replicability in Science. In Understanding Reproducibility and Replicability [Techreport]. National Academies Press (US). https://www.ncbi.nlm.nih.gov/books/NBK547546/

Naudet, F., Ioannidis, J., Miedema, F., Cristea, I. A., Goodman, S. N., & Moher, D. (2018). Six principles for assessing scientists for hiring, promotion, and tenure. Impact of Social Sciences Blog.

Navarro, D. (2020). Paths in strange spaces: A comment on preregistration.

Nelson, L. D., Simmons, J. P., & Simonsohn, U. (2012). Let’s Publish Fewer Papers. Psychological Inquiry, 23(3), 291–293. https://doi.org/10.1080/1047840X.2012.705245

Neuroskeptic. (2012). The nine circles of scientific hell. Perspectives on Psychological Science, 7(6), 643–644. https://doi.org/10.1177/1745691612459519

Neyman, J., & Pearson, E. S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference: Part I. Biometrika, 20(1/2), 175–240. https://doi.org/10.2307/2331945

Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., et al. (2017). Best practices in data analysis and sharing in neuroimaging using MRI. Nature Neuroscience, 20(3), 299–303. https://doi.org/10.1038/nn.4500

Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220. https://doi.org/10.1037/1089-2680.2.2.175

Nieuwenhuis, S., Forstmann, B. U., & Wagenmakers, E. J. (2011). Erroneous analyses of interactions in neuroscience: a problem of significance. Nature Neuroscience, 14(9), 1105–1107. https://doi.org/10.1038/nn.2886

Nimon, K. F. (2012). Statistical Assumptions of Substantive Analyses Across the General Linear Model: A Mini-Review. Frontiers in Psychology, 3, 322. https://doi.org/10.3389/fpsyg.2012.00322

Nisbet, M. C., Scheufele, D. A., Shanahan, J., Moy, P., Brossard, D., & Lewenstein, B. V. (2002). Knowledge, Reservations, or Promise?: A Media Effects Model for Public Perceptions of Science and Technology. Communication Research, 29(5), 584–608. https://doi.org/10.1177/009365002236196

Nittrouer, C., Hebl, M., Ashburn-Nardo, L., Trump-Steele, R., Lane, D., & Valian, V. (2018). Gender disparities in colloquium speakers. Proceedings of the National Academy of Sciences, 115(1), 104–108. https://doi.org/10.1073/pnas.1708414115

Nogrady, B. (2023). Hyperauthorship: The publishing challenges for “big team” science. Nature News. https://doi.org/10.1038/d41586-023-00575-3 Retrieved from https://www.nature.com/articles/d41586-023-00575-3

Nosek, B. A. (2019). Strategy for Culture Change. Center for Open Science. https://www.cos.io/blog/strategy-for-culture-change

Nosek, B. A., & Bar-Anan, Y. (2012). Scientific utopia: I. Opening scientific communication. Psychological Inquiry, 23(3), 217–243. https://doi.org/10.1080/1047840X.2012.692215

Nosek, B. A., & Errington, T. M. (2020). What is replication? PLOS Biology, 18(3), e3000691. https://doi.org/10.1371/journal.pbio.3000691

Nosek, B. A., & Lakens, D. (2014). Registered reports. Social Psychology, 45, 137–141. https://doi.org/10.1027/1864-9335/a000192

Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114

Nosek, B. A., Spies, J. R., & Motyl, M. (2012). Scientific utopia: II. Restructuring incentives and practices to promote truth over publishability. Perspectives on Psychological Science, 7(6), 615–631. https://doi.org/10.1177/1745691612459058

Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. https://corais.org/sites/default/files/ontology_development_101_aguide_to_creating_your_first_ontology.pdf

Nuijten, M. B., Hartgerink, C. H., van Assen, M. A., Epskamp, S., & Wicherts, J. M. (2016). The prevalence of statistical reporting errors in psychology (1985–2013). Behavior Research Methods, 48(4), 1205–1226.

Nüst, D. C., Boettiger, C., & Marwick, B. (2018). How to Read a Research Compendium. arXiv Preprint arXiv:1806.09525. https://arxiv.org/abs/1806.09525

Obels, P., Lakens, D., Coles, N. A., Gottfried, J., & Green, S. A. (2020). Analysis of open data and computational reproducibility in registered reports in psychology. Advances in Methods and Practices in Psychological Science, 3(2), 229–237. https://doi.org/10.1177/2515245920917961

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

OER Commons. (n.d.). OER Commons. https://www.oercommons.org/

OER Commons. (n.d.). Open Scholarship Knowledge Base | OER Commons. https://www.oercommons.org/hubs/OSKB

of Open Science, T. T. E. T. D., & Data, O. (2016). Transparency: The Emerging Third Dimension of Open Science and Open Data. LIBER QUARTERLY, 25(4), 153–171. https://doi.org/10.18352/lq.10113

Oliver, M. (1983). Social Work with Disabled People. Macmillan.

Oliver, M. (2013). The social model of disability: Thirty years on. Disability & Society, 28(7), 1024–1026. https://doi.org/10.1080/09687599.2013.818773

Onie, S. (2020). Redesign open science for Asia, Africa and Latin America. Nature, 587, 35–37. https://doi.org/10.1038/d41586-020-03052-3

Open Aire. (n.d.). Amnesia Anonymization Tool—Data anonymization made easy. https://amnesia.openaire.eu/

Open Knowledge Foundation. (n.d.). The Open Definition. https://opendefinition.org/

Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716

Open Source Initiative. (n.d.). Licenses & Standards | Open Source Initiative. https://opensource.org/licenses

Open Source Initiative. (n.d.). The Open Source Definition. https://opensource.org/osd

OpenNeuro. (n.d.). A free and open platform for sharing MRI, MEG, EEG, iEEG, ECoG, ASL, and PET data—OpenNeuro. OpenNeuro. https://openneuro.org/

Opensource.Com. (n.d.). What is open education? Retrieved 9 July 2021. https://opensource.com/resources/what-open-education

Orben, A. (2019). A journal club to fix science. Nature, 573(7775), 465–466. https://doi.org/10.1038/d41586-019-02842-8

Orben, A., & Lakens, D. (2020). Crud (re)defined. Advances in Methods and Practices in Psychological Science, 3(2), 238–247. https://doi.org/10.1177/2515245920917961

ORCID. (n.d.). ORCID. https://orcid.org/

OSF. (n.d.). Open Science Framework. https://osf.io/

OSF. (n.d.). OSF | StudySwap: A platform for interlab replication, collaboration, and research resource exchange. https://osf.io/meetings/StudySwap/

Ottmann, G., Laragy, C., Allen, J., & Feldman, P. (2011). Coproduction in practice: Participatory action research to develop a model of community aged care. Systemic Practice and Action Research, 24, 413–427. https://doi.org/10.1007/s11213-011-9192-x

O’Dea, R. E., Parker, T. H., Chee, Y. E., Culina, A., Drobniak, S. M., Duncan, D. H., Fidler, F., Gould, E., Ihle, M., Kelly, C. D., Lagisz, M., Roche, D. G., Sánchez-Tójar, A., Wilkinson, D. P., Wintle, B. C., & Nakagawa, S. (2021). Towards open, reliable, and transparent ecology and evolutionary biology. BMC Biology, 19(1). https://doi.org/10.1186/s12915-021-01006-3

O’Grady, O. (2020). Psychology’s replication crisis inspires ecologists to push for more reliable research. Science. https://doi.org/10.1126/science.abg0894

O’Sullivan, L., Ma, L., & Doran, P. (2021). An overview of post-publication peer review. Scholarly Assessment Reports, 3(1), 1–11. https://doi.org/10.29024/sar.26

Padilla, A. M. (1994). Research news and comment: Ethnic minority scholars; research, and mentoring: Current and future issues. Educational Researcher, 23(4), 24–27. https://doi.org/10.3102/0013189X023004024

Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., & McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. British Medical Journal, 372. https://doi.org/10.1136/bmj.n160

Patience, G. S., Galli, F., Patience, P. A., & Boffito, D. C. (2019). Intellectual contributions meriting authorship: Survey results from the top cited authors across all science categories. PLoS One, 14(1), e0198117. https://doi.org/10.1371/journal.pone.0198117

Pautasso, M. (2013). Ten Simple Rules for Writing a Literature Review. PLoS Computational Biology, 9(7), e1003149. https://doi.org/10.1371/journal.pcbi.1003149

Pavlov, Y. G., Adamian, N., Appelhoff, S., Arvaneh, M., Benwell, C., Beste, C., & Mushtaq, F. (2020). #EEGManyLabs: Investigating the Replicability of Influential EEG Experiments. PsyArXiv Preprint. https://doi.org/10.31234/osf.io/528nr

Peer Community In Registered Reports. (2025). About PCI Registered Reports. https://rr.peercommunityin.org/about/about

Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017). Beyond the Turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70, 153–163. https://doi.org/10.1016/j.jesp.2017.01.006

Peirce, J. W. (2007). PsychoPy - Psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2), 8–13. https://doi.org/10.1016/j.jneumeth.2006.11.017

Peirce, J. W., Hirst, R. J., & MacAskill, M. R. (2022). Building Experiments in PsychoPy (2nd ed.). Sage.

Pellicano, E., & den Houting, J. (2022). Annual Research Review: Shifting from ‘normal science’ to neurodiversity in autism science. Journal of Child Psychology and Psychiatry, 63(4), 381–396. https://doi.org/10.1111/jcpp.13534

Peng, R. D. (2011). Reproducible Research in Computational Science. Science, 334(6060), 1226–1227. https://doi.org/10.1126/science.1213847

Percie du Sert, N., Hurst, V., Ahluwalia, A., Alam, S., Avey, M. T., Baker, M., Browne, W. J., Clark, A., Cuthill, I. C., Dirnagl, U., Emerson, M., Garner, P., Holgate, S. T., Howells, D. W., Karp, N. A., Lazic, S. E., Lidster, K., MacCallum, C. J., Macleod, M., et al. (2020). The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLOS Biology, 18(7), e3000410. https://doi.org/10.1371/journal.pbio.3000410

Pernet, C. R. (2015). Null hypothesis significance testing: a short tutorial. F1000Research, 4, 621. https://doi.org/10.12688/f1000research.6963.3

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., & Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6(1), 103. https://doi.org/10.1038/s41597-019-0104-8

Pernet, C., Garrido, M. I., Gramfort, A., Maurits, N., Michel, C. M., Pang, E., et al. (2020). Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nature Neuroscience, 23(12), 1473–1483. https://doi.org/10.1038/s41593-020-00709-0

Peters, M. D., Godfrey, C. M., Khalil, H., McInerney, P., Parker, D., & Soares, C. B. (2015). Guidance for conducting systematic scoping reviews. JBI Evidence Implementation, 13(3), 141–146. https://doi.org/10.1097/XEB.0000000000000050

Peterson, D., & Panofsky, A. (2020). Metascience as a scientific social movement. https://doi.org/10.31235/osf.io/4dsqa

Petre, M., & Wilson, G. (2014). Code review for and by scientists. arXiv Preprint arXiv:1407.5648. https://arxiv.org/abs/1407.5648

Poldrack, R. A., & Gorgolewski, K. J. (2014). Making big data open: Data sharing in neuroimaging. Nature Neuroscience, 17(11), 1510–1517. https://doi.org/10.1038/nn.3818

Poldrack, R. A., & Gorgolewski, K. J. (2017). OpenfMRI: Open sharing of task fMRI data. Neuroimage, 144, 259–261. https://doi.org/10.1016/j.neuroimage.2015.05.073

Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T. D., Wagner, A. D., Devlin, J. T., Cumba, C., Koyejo, O., & Milham, M. P. (2013). Toward open sharing of task-based fMRI data: The OpenfMRI project. Frontiers in Neuroinformatics, 7, 1–12. https://doi.org/10.3389/fninf.2013.00012

Pollet, I. L., & Bond, A. L. (2021). Evaluation and recommendations for greater accessibility of colour figures in ornithology. Ibis, 163, 292–295. https://doi.org/10.1111/ibi.12887

Popper, K. (1959). The logic of scientific discovery. Routledge.

Posselt, J. R. (2020). Equity in Science: Representation, Culture, and the Dynamics of Change in Graduate Education. Stanford University Press. https://books.google.de/books?id=2CjwDwAAQBAJ

Pownall, M., Talbot, C. V., Henschel, A., Lautarescu, A., Lloyd, K. E., Hartmann, H., Darda, K. M., Tang, K. T. Y., Carmichael-Murphy, P., & Siegel, J. A. (2021). Navigating Open Science as Early Career Feminist Researchers. Psychology of Women Quarterly, 45(4), 526–539. https://doi.org/10.1177/03616843211029255

Pownall, M., Talbot, C. V., Henschel, A., Lautarescu, A., Lloyd, K., Hartmann, H., et al. (2020). Navigating Open Science as Early Career Feminist Researchers. https://doi.org/10.31234/osf.io/f9m47

Preregistration Pledge. (n.d.). Preregistration Pledge. Google Docs. https://docs.google.com/forms/d/e/1FAIpQLSf8RflGizFJZamE874o8aDOhyU7UsNByR4dLmzhOtEOiu8KRQ/viewform?embedded=true&usp=embed_facebook

Press, W. (2007). Numerical recipes: the art of scientific computing, 3rd edition.

Protzko, J. (2018). Null-hacking, a lurking problem in the open science movement. https://doi.org/10.31234/osf.io/9y3mp

psyTeachR Team. (n.d.). P | Glossary. psyTeachR. https://psyteachr.github.io/glossary

Publication Bias. (2019). Publication Bias. Catalog of Bias. https://catalogofbias.org/biases/publication-bias/

PubPeer. (n.d.). PubPeer—Search publications and join the conversation. Pubpeer. https://www.pubpeer.com/

R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

R Project for Statistical Computing. (n.d.). R: The R Project for Statistical Computing. R Project. https://www.r-project.org/

Rabagliati, H., Moors, P., & Heyman, T. (2019). Can item effects explain away the evidence for unconscious sound symbolism? An adversarial commentary on Heyman, Maerten, Vankrunkelsven, Voorspoels, and Moors (2019). Psychological Science, 31(9), 1200–1204. https://doi.org/10.1177/0956797620949461

Rakow, T., Thompson, V., Ball, L., & Markovits, H. (2014). Rationale and guidelines for empirical adversarial collaboration: A Thinking & Reasoning initiative. Thinking & Reasoning, 21(2), 167–175. https://doi.org/10.1080/13546783.2015.975405

Ramagopalan, S. V., Skingsley, A. P., Handunnetthi, L., Klingel, M., Magnus, D., Pakpoor, J., & Goldacre, B. (2014). Prevalence of primary outcome changes in clinical trials registered on ClinicalTrials.gov: A cross-sectional study. F1000Research, 3, 77. https://doi.org/10.12688/f1000research.3784.1

Replication Markets. (n.d.). Replication Markets – Reliable research replicates…you can bet on it. Replication Markets. https://www.replicationmarkets.org/

RepliCATS project. (2020). Collaborative Assessment for Trustworthy Science. The University of Melbourne. https://replicats.research.unimelb.edu.au/

Research Data Alliance. (2020). Data management plan (DMP) common standard. https://github.com/RDA-DMP-Common/RDA-DMP-Common-Standard

Retraction Watch. (n.d.). Retraction Watch. Retraction Watch. https://retractionwatch.com/

Rodriguez, J. K., & Günther, E. A. (2020). What’s wrong with manels and what can we do about it. The Conversation. https://theconversation.com/whats-wrong-with-manels-and-what-can-we-do-about-them-148068

Rogers, A., Castree, N., & Kitchin, R. (2013). Reflexivity. In A Dictionary of Human Geography. Oxford University Press. https://www.oxfordreference.com/view/10.1093/acref/9780199599868.001.0001/acref-9780199599868-e-1530

Rolls, L., & Relf, M. (2006). Bracketing interviews: addressing methodological challenges in qualitative interviewing in bereavement and palliative care. Mortality, 11(3), 286–305. https://doi.org/10.1080/13576270600774893

Rose, D. (2000). Universal design for learning. Journal of Special Education Technology, 15(3), 45–49. https://doi.org/10.1177/016264340001500307

Rose, D. (2018). Participatory research: Real or imagined. Social Psychiatry and Psychiatric Epidemiology, 53, 765–771. https://doi.org/10.1007/s00127-018-1549-3

Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. In The Corsini Encyclopedia of Psychology. Association for Supervision.

Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641. https://doi.org/10.1037/0033-2909.86.3.638

Ross-Hellauer, T. (2017). What is open peer review? A systematic review [version 2; peer review: 4 approved]. F1000Research, 6, 588. https://doi.org/10.12688/f1000research.11369.2

Rossner, M., Van Epps, H., & Hill, E. (2008). Show me the data. https://doi.org/10.1083/jcb.200711140

Rothstein, H. R., Sutton, A. J., & Borenstein, M. (2005). Publication bias in meta-analysis. In Publication bias in meta-analysis: Prevention, assessment and adjustments (pp. 1–7). John Wiley & Sons, Ltd. https://doi.org/10.1002/0470870168.ch1

Rowhani-Farid, A., Aldcroft, A., & Barnett, A. G. (2020). Did awarding badges increase data sharing in BMJ Open? A randomized controlled trial. Royal Society Open Science, 7(3), 191818. https://doi.org/10.1098/rsos.191818

Rubin, M. (2021). Explaining the association between subjective social status and mental health among university students using an impact ratings approach. SN Social Sciences, 1(1), 1–21. https://doi.org/10.1007/s43545-020-00031-3

Rubin, M. (2022). Does preregistration improve the interpretability and credibility of research findings? [Powerpoint slides]. https://www.slideshare.net/MarkRubin14/does-preregistration-improve-the-interpretability-and-credibility-of-research-findings

Rubin, M., Evans, O., & McGuffog, R. (2019). Social class differences in social integration at university: Implications for academic outcomes and mental health. In J. Jetten & K. Peters (Eds.), The social psychology of inequality (pp. 87–102). Springer. https://doi.org/10.1007/978-3-030-28856-3_6

Russell, B. (2020). A priori justification and knowledge. Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/apriori/

Sagarin, B. J., Ambler, J. K., & Lee, E. M. (2014). An ethical approach to peeking at data. Perspectives on Psychological Science, 9(3), 293–304. https://doi.org/10.1177/1745691614528214

Salem, D. N., & Boumil, M. M. (2013). Conflict of Interest in Open-Access Publishing. New England Journal of Medicine, 369(5), 491–491. https://doi.org/10.1056/NEJMc1307577

Sasaki, K., & Yamada, Y. (2022). SPARKing: Sampling planning after the results are known. PsyArXiv. https://doi.org/10.31234/osf.io/ngz8k

Sato, T. (1996). Type I and Type II error in multiple comparisons. The Journal of Psychology, 130(3), 293–302. https://doi.org/10.1080/00223980.1996.9915010

Scargle, J. D. (1999). Publication bias (the “file-drawer problem”) in scientific inference. ArXiv Preprint Physics. https://doi.org/10.48550/arXiv.physics/9909033

Schafersman, S. D. (1997). An Introduction to Science. https://web.archive.org/web/20221109113137/https://www.geo.sunysb.edu/esp/files/scientific-method.html

Schmidt, F. L., & Hunter, J. E. (2014). Methods of meta-analysis: Correcting error and bias in research findings (3rd ed.). Sage.

Schmidt, R. H. (1987). A worksheet for authorship of scientific articles. The Bulletin of the Ecological Society of America, 68, 8–10. http://www.jstor.org/stable/20166549

Schneider, J., Merk, S., & Rosman, T. (2019). (Re)Building Trust? Investigating the effects of open science badges on perceived trustworthiness in journal articles. https://doi.org/10.17605/OSF.IO/VGBRS

Schuirmann, D. J. (1987). A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics, 15, 657–680. https://doi.org/10.1007/BF01068419

Schulz, K. F., & Grimes, D. A. (2005). Multiplicity in randomised trials I: endpoints and treatments. The Lancet, 365(9470), 1591–1595. https://doi.org/10.1016/S0140-6736(05)66461-6

Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Trials, 11(1), 32. https://doi.org/10.1186/1745-6215-11-32

Schwarz, N., & Strack, F. (2014). Does Merely Going Through the Same Moves Make for a “Direct” Replication?: Concepts, Contexts, and Operationalizations. Social Psychology, 45(4), 305–306.

Schönbrodt, F. (2019). Training students for the Open Science future. Nature Human Behaviour, 3(10), 1031–1031. https://doi.org/10.1038/s41562-019-0726-z

Schönbrodt, F. D., Wagenmakers, E.-J., Zehetleitner, M., & Perugini, M. (2017). Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. Psychological Methods, 22(2), 322–339. https://doi.org/10.1037/met0000061

Science, C. (n.d.). Open science badges. https://www.cos.io/initiatives/badges

Scientific Data. (n.d.). Recommended Data Repositories. Scientific Data. https://www.nature.com/sdata/policies/repositories

Scopatz, A. M., & Huff, K. D. (2015). Effective Computation in Physics: Field Guide to Research with Python (1st ed.). O’Reilly Media. http://shop.oreilly.com/product/0636920033424.do

Sechrest, L. (1963). Incremental validity: A recommendation. Educational and Psychological Measurement, 23(1), 153–158. https://doi.org/10.1177/001316446302300113

Senn, S. (2003). Bayesian, likelihood, and frequentist approaches to statistics. Applied Clinical Trials, 12(8), 35–38.

Sert, N. P. du, Hurst, V., Ahluwalia, A., Alam, S., Avey, M. T., Baker, M., Browne, W. J., Clark, A., Cuthill, I. C., Dirnagl, U., Emerson, M., Garner, P., Holgate, S. T., Howells, D. W., Karp, N. A., Lazic, S. E., Lidster, K., MacCallum, C. J., Macleod, M., et al. (2020). The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLOS Biology, 18(7), e3000410. https://doi.org/10.1371/journal.pbio.3000410

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

Shakespeare, T. (2013). The social model of disability. In L. J. Davis (Ed.), The Disability Studies Reader (4th ed., pp. 214–221). Routledge.

Sharma, M., Sarin, A., Gupta, P., Sachdeva, S., & Desai, A. (2014). Journal impact factor: its use, significance and limitations. World Journal of Nuclear Medicine, 13(2), 146. https://doi.org/10.4103/1450-1147.139151

Shepard, B. (2015). Community projects as social activism. SAGE.

Shi, L., & Lin, L. (2019). The trim-and-fill method for publication bias: Practical guidelines and recommendations based on a large database of meta-analyses. Medicine, 98(23), e15987. https://doi.org/10.1097/MD.0000000000015987

Siddaway, A. P., Wood, A. M., & Hedges, L. V. (2019). How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annual Review of Psychology, 70, 747–770. https://doi.org/10.1146/annurev-psych-010418-102803

Sijtsma, K. (2016). Playing with data—Or how to discourage questionable research practices and stimulate researchers to do things right. Psychometrika, 81(1), 1–15. https://doi.org/10.1007/s11336-015-9446-0

Silberzahn, R., Simonsohn, U., & Ulhmann, E. L. (2014). Matched-names analysis reveals no evidence of name-meaning effects: A collaborative commentary on Silberzahn and Uhlmann (2013). Psychological Science, 25(7), 1504–1505. https://doi.org/10.1177/0956797614533802

Silberzahn, R., Uhlmann, E. L., Martin, D. P., Anselmi, P., Aust, F., Awtrey, E., Bahník, Š., Bai, F., Bannard, C., Bonnier, E., et al. (2018). Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results. Advances in Methods and Practices in Psychological Science, 337–356. https://doi.org/10.1177/2515245917747646

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632

Simmons, J., Nelson, L., & Simonsohn, U. (2021). Pre‐registration: Why and how. Journal of Consumer Psychology, 31(1), 151–162. https://doi.org/10.1002/jcpy.1208

Simons, D. J., Shoda, Y., & Lindsay, D. S. (2017). Constraints on generality (COG): A proposed addition to all empirical papers. Perspectives on Psychological Science, 12(6), 1123–1128. https://doi.org/10.1177/1745691617708630

Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). P-curve and effect size: Correcting for publication bias using only significant results. Perspectives on Psychological Science, 9(6), 666–681. https://doi.org/10.1177/1745691614553988

Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). P-curve: a key to the file-drawer. Journal of Experimental Psychology: General, 143(2), 534. https://doi.org/10.1037/a0030850

Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2019). P-curve won’t do your laundry, but it will distinguish replicable from non-replicable findings in observational research: Comment on Bruns & Ioannidis (2016). PLoS ONE, 14(3), e0213454. https://doi.org/10.1371/journal.pone.0213454

Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2015). Specification curve: Descriptive and inferential statistics on all reasonable specifications. http://sticerd.lse.ac.uk/seminarpapers/psyc16022016.pdf

Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4(11), 1208–1214. https://doi.org/10.1038/s41562-020-0912-z

Slow Science Academy. (2010). The Slow Science Manifesto. Slow Science. http://slow-science.org/

Smaldino, P. E., & McElreath, R. (2016). The natural selection of bad science. Royal Society Open Science, 3(9), 160384. https://doi.org/10.1098/rsos.160384

Smela, B., Toumi, M., Świerk, K., Francois, C., Biernikiewicz, M., Clay, E., & Boyer, L. (2023). Rapid literature review: definition and methodology. Journal of Market Access & Health Policy, 11(1), Article 2241234. https://doi.org/10.1080/20016689.2023.2241234

Smith, A. C., Merz, L., Borden, J. B., Gulick, C., Kshirsagar, A. R., & Bruna, E. M. (2020). Assessing the effect of article processing charges on the geographic diversity of authors using Elsevier’s ‘Mirror Journal’ system. https://doi.org/10.31222/osf.io/s7cx4

Smith, A. J., Clutton, R. E., Lilley, E., Hansen, K. E. A., & Brattelid, T. (2018). PREPARE: Guidelines for planning animal research and testing. Laboratory Animals, 52(2), 135–141. https://doi.org/10.1177/0023677217724823

Smith, G. T. (2005). On Construct Validity: Issues of Method and Measurement. Psychological Assessment, 17(4), 396–408. https://doi.org/10.1037/1040-3590.17.4.396

Sorsa, M. A., Kiikkala, I., & Åstedt-Kurki, P. (2015). Bracketing as a skill in conducting unstructured qualitative interviews. Nursing Research, 22(4), 8–12. https://doi.org/10.7748/nr.22.4.8.e1317

Spence, J. R., & Stanley, D. J. (2018). Concise, simple, and not wrong: In search of a short-hand interpretation of statistical significance. Frontiers in Psychology, 9, 2185. https://doi.org/10.3389/fpsyg.2018.02185

Spencer, E. A., & Heneghan, C. (2018). Confirmation bias. Catalogue Of Bias. https://catalogofbias.org/biases/confirmation-bias/

Spiegelhalter, D., & Rice, K. (2009). Bayesian statistics. Scholarpedia, 4(8), 5230. http://dx.doi.org/10.4249/scholarpedia.5230

Stanford Libraries. (n.d.). Data Management Plans | Stanford Libraries. https://guides.library.stanford.edu/dmps

Stanovich, K. E., West, R. F., & Toplak, M. E. (2013). Myside bias, rational thinking, and intelligence. Current Directions in Psychological Science, 22(4), 259–264. https://doi.org/10.1177/0963721413480174

Steckler, A., & McLeroy, K. R. (2008). The Importance of External Validity. American Journal of Public Health, 98(1), 9–10. https://doi.org/10.2105/AJPH.2007.126847

Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing Transparency through a Multiverse Analysis. Perspectives on Psychological Science, 11, 702–712. https://doi.org/10.1177/1745691616658637

Steup, M., & Neta, R. (2020). Epistemology. Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/epistemology/

Steup, M., & Neta, R. (2020). Epistemology. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/fall2020/entries/epistemology/

Stewart, N., Chandler, J., & Paolacci, G. (2017). Crowdsourcing samples in cognitive science. Trends in Cognitive Sciences, 21(10), 736–748. https://doi.org/10.1016/j.tics.2017.06.007

Stodden, V. C. (2011). Trust your science? Open your data and code.

Strathern, M. (1997). ‘Improving ratings’: audit in the British University system. European Review, 5(3), 305–321. https://doi.org/10.1002/(SICI)1234-981X(199707)5:3%3C305::AID-EURO184%3E3.0.CO;2-4

Suber, P. (2004). The primacy of authors in achieving Open Access. In Nature. Retrieved from http://dash.harvard.edu/handle/1/4391161

Suber, P. (2015). Open Access Overview. http://legacy.earlham.edu/~peters/fos/overview.htm

Support Centre for Data Sharing. (n.d.). What is data sharing? Retrieved 11 July 2021. https://web.archive.org/web/20221130202915/https://eudatasharing.eu/what-data-sharing?page=1

Syed, M. (2019). The Open Science Movement is for all of us. PsyArXiv. https://doi.org/10.31234/osf.io/cteyb

Syed, M., & Kathawalla, U. (2020). Cultural Psychology, Diversity, and Representation in Open Science. https://doi.org/10.31234/osf.io/t7hp2

Szollosi, A., & Donkin, C. (2019). Arrested theory development: The misguided distinction between exploratory and confirmatory research. PsyArXiv. https://doi.org/10.31234/osf.io/suzej

Szomszor, M., Pendlebury, D. A., & Adams, J. (2020). How much is too much? The difference between research influence and self-citation excess. Scientometrics, 123, 1119–1147. https://doi.org/10.1007/s11192-020-03417-5

Teixeira da Silva, J. A., & Dobránszki, J. (2015). Problems with traditional science publishing and finding a wider niche for post-publication peer review. In Accountability in Research (pp. 22–40). Informa UK Limited. https://doi.org/10.1080/08989621.2014.899909

Tennant, J., Beamer, J. E., Bosman, J., Brembs, B., Chung, N. C., Clement, G., Crick, T., Dugan, J., Dunning, A., Eccles, D., Enkhbayar, A., Graziotin, D., Harding, R., Havemann, J., Katz, D. S., Khanal, K., Kjaer, J. N., Koder, T., Macklin, P., & Turner, A. (2019). Foundations for Open Scholarship Strategy Development. MetaArXiv. https://doi.org/10.31222/osf.io/b4v8p

Tennant, J., Bielczyk, N. Z., Cheplygina, V., Greshake Tzovaras, B., Hartgerink, C. H. J., Havemann, J., Masuzzo, P., & Steiner, T. (2019). Ten simple rules for researchers collaborating on Massively Open Online Papers (MOOPs). MetaArXiv. https://doi.org/10.31222/osf.io/et8ak

Tennant, J., Bielczyk, N. Z., Greshake Tzovaras, B., Masuzzo, P., & Steiner, T. (2019). Introducing Massively Open Online Papers (MOOPs). MetaArXiv. https://doi.org/10.31222/osf.io/et8ak

Tenney, S., & Abdelgawad, I. (2019). Statistical significance. In StatsPearls. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK535373/

Tenny, S., & Abdelgawad, I. (2021). Statistical Significance. In StatPearls. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK459346/

Thaler, K. M. (2017). Mixed Methods Research in the Study of Political and Social Violence and Conflict. Journal of Mixed Methods Research, 11(1), 59–76. https://doi.org/10.1177/1558689815585196

The BMJ. (n.d.). Authorship & contributorship. https://www.bmj.com/about-bmj/resources-authors/article-submission/authorship-contributorship

The Committee on Publication Ethics. (n.d.). Transparency & best practice – DOAJ. DOAJ. https://doaj.org/apply/transparency/

The CONSORT Group, Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. Trials, 11(1), 32. https://doi.org/10.1186/1745-6215-11-32

The EQUATOR Network. (n.d.). What is a reporting guideline? Retrieved 10 July 2021. https://www.equator-network.org/about-us/what-is-a-reporting-guideline/

The jamovi project. (2020). jamovi (Version 1.2) [Computer Software]. https://www.jamovi.org

The Nine Circles of Scientific Hell. (2012). In Perspectives on Psychological Science (Vol. 7, pp. 643–644). https://doi.org/10.1177/1745691612459519

The R Foundation. (n.d.). The R Project for Statistical Computing. https://www.r-project.org/

The Slow Science Academy. (2010). The Slow Science Manifesto. SLOW-SCIENCE.Org — Bear with Us, While We Think. http://slow-science.org/

Thombs, B. D., Levis, A. W., Razykov, I., Syamchandra, A., Leentjens, A. F., Levenson, J. L., & Lumley, M. A. (2015). Potentially coercive self-citation by peer reviewers: a cross-sectional study. Journal of Psychosomatic Research, 78(1), 1–6. https://doi.org/10.1016/j.jpsychores.2014.09.015

Tierney, W., Hardy III, J. H., Ebersole, C. R., Leavitt, K., Viganola, D., Clemente, E. G., et al. (2020). Creative destruction in science. Organizational Behavior and Human Decision Processes, 161, 291–309. https://doi.org/10.1016/j.obhdp.2020.07.002

Tierney, W., Hardy III, J., Ebersole, C. R., Viganola, D., Clemente, E. G., Gordon, M., et al. (2021). 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

Tiokhin, L., Yan, M., & Horgan, T. J. H. (2021). Competition for priority harms the reliability of science, but reforms can help. Nature Human Behaviour. https://doi.org/10.1038/s41562-020-01040-1

Topor, M., Pickering, J. S., Barbosa Mendes, A., Bishop, D. V. M., Büttner, F. C., Elsherif, M. M., et al. (2021). An integrative framework for planning and conducting Non-Intervention, Reproducible, and Open Systematic Reviews (NIRO-SR). https://doi.org/10.31222/osf.io/8gu5z

Tscharntke, T., Hochberg, M. E., Rand, T. A., Resh, V. H., & Krauss, J. (2007). Author sequence and credit for contributions in multiauthored publications. PLoS Biology, 5(1), e18. https://doi.org/10.1371/journal.pbio.0050018

Tufte, E. R. (1983). The visual display of quantitative information. Graphics Press.

Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.

Tvina, A., Spellecy, R., & Palatnik, A. (2019). Bias in the peer review process: can we do better? Obstetrics & Gynecology, 133(6), 1081–1083. https://doi.org/10.1097/AOG.0000000000003260

Uhlmann, E. L., Ebersole, C. R., Chartier, C. R., Errington, T. M., Kidwell, M. C., Lai, C. K., McCarthy, R. J., Riegelman, A., Silberzahn, R., & Nosek, B. A. (2019). Scientific utopia III: Crowdsourcing science. Perspectives on Psychological Science, 14(5), 711–733. https://doi.org/10.1177/1745691619850561

UK Reproducibility Network. (n.d.). UK Reproducibility Network. Retrieved 10 July 2021. https://www.ukrn.org/

UNESCO. (2017). Open Educational Resources (OER). https://en.unesco.org/themes/building-knowledge-societies/oer

University of Illinois at Urbana-Champaign, Burnette, M., Williams, S., & Imker, H. (2016). From Plan to Action: Successful Data Management Plan Implementation in a Multidisciplinary Project. Journal of EScience Librarianship, 5(1), e1101. https://doi.org/10.7191/jeslib.2016.1101

University of Oxford. (2023). Plagiarism. https://www.ox.ac.uk/students/academic/guidance/skills/plagiarism

van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1–26. https://doi.org/10.1038/s43586-020-00001-2

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

Vazire, S., Schiavone, S. R., & Bottesini, J. G. (2020). Credibility Beyond Replicability: Improving the Four Validities in Psychological Science. https://doi.org/10.31234/osf.io/bu4d3

Velázquez, A. (2021). Data analysis: Definition, types and examples. https://www.questionpro.com/blog/what-is-data-analysis/

Verma, J. P., & Verma, P. (2020). Determining Sample Size and Power in Research Studies. Springer Singapore. https://doi.org/10.1007/978-981-15-5204-5

Viechtbauer, W. (n.d.). Trim and Fill Analysis for “rma.uni” Objects (trimfill), metafor package documentation. https://wviechtb.github.io/metafor/reference/trimfill.html

Villum, C. (2016). “Open-washing” – The difference between opening your data and simply making them available. https://blog.okfn.org/2014/03/10/open-washing-the-difference-between-opening-your-data-and-simply-making-them-available/

Vlaeminck, S., & Podkrajac, F. (2017). Journals in Economic Sciences: Paying Lip Service to Reproducible Research? IASSIST Quarterly, 41(1–4), 16. https://doi.org/10.29173/iq6

Von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Annals of Internal Medicine, 147(8), 573–577. https://doi.org/10.1136/bmj.39335.541782.AD

Voracek, M., Kossmeier, M., & Tran, U. S. (2019). Which Data to Meta-Analyze, and How? Zeitschrift Für Psychologie. https://doi.org/10.1027/2151-2604/a000357

Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzlingly high correlations in fMRI studies on emotion, personality, and social cognition. Perspectives on Psychological Science, 4(3), 274–290. https://doi.org/10.1177/0956797611417632

Vuorre, M., & Curley, J. P. (2018). Curating research assets: A tutorial on the Git version control system. Advances in Methods and Practices in Psychological Science, 1(2), 219–236. https://doi.org/10.1177/2515245918754826

Wacker, J. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16(4), 361–385. https://doi.org/10.1016/s0272-6963(98)00019-9

Wagenmakers, E. J., Wetzels, R., Borsboom, D., van der Maas, H. L., & Kievit, R. A. (2012). An agenda for purely confirmatory research. Perspectives on Psychological Science, 7(6), 632–638. https://doi.org/10.1177/1745691612463078

Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., Matzke, D., Rouder, J. N., & Morey, R. D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25(1), 35–57. https://doi.org/10.3758/s13423-017-1343-3

Wagge, J. R., Baciu, C., Banas, K., Nadler, J. T., Schwarz, S., Weisberg, Y., et al. (2019). A demonstration of the collaborative replication and education project: Replication attempts of the red-romance effect. Collabra: Psychology, 5(1). https://doi.org/10.1525/collabra.177

Walker, N. (2021). Neuroqueer Heresies: Notes on the Neurodiversity Paradigm, Autistic Empowerment, and Postnormal Possibilities. Autonomous Press.

Walker, P., & Miksa, T. (2019). RDA-DMP-Common/RDA-DMP-Common-Standard. GitHub. https://github.com/RDA-DMP-Common/RDA-DMP-Common-Standard

Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology, 12(3), 129–140. https://doi.org/10.1080/17470216008416717

Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70, 129–133. https://doi.org/10.1080/00031305.2016.1154108

Webster, M. M., & Rutz, C. (2020). How STRANGE are your study animals? Nature, 582, 337–340. https://doi.org/10.1038/d41586-020-01751-5

Week, C. (2021). What is Crowdsourcing? https://crowdsourcingweek.com/what-is-crowdsourcing/

Wendl, M. C. (2007). H-index: however ranked, citations need context. Nature, 449(7161), 403–403. https://doi.org/10.1038/449403b

Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495. https://doi.org/10.5465/amr.1989.4308371

Whitaker, K., & Guest, O. (2020). #bropenscience is broken science. The Psychologist, 33, 34–37.

Wicherts, J. M., Veldkamp, C. L., Augusteijn, H. E., Bakker, M., Van Aert, R., & Van Assen, M. A. (2016). Degrees of freedom in planning, running, analyzing, and reporting psychological studies: A checklist to avoid p-hacking. Frontiers in Psychology, 7, 1832. https://doi.org/10.3389/fpsyg.2016.01832

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 1–9. https://doi.org/10.1038/sdata.2016.18

Willroth, E. C., & Atherton, O. E. (2024). Best laid plans: A guide to reporting preregistration deviations. Advances in Methods and Practices in Psychological Science, 7(1). https://doi.org/10.1177/25152459231213802

Wilson, B., & Fenner, M. (2012). Open Researcher & Contributor ID (ORCID): Solving the Name Ambiguity Problem. Educause Review - E-Content, 47(3), 54–55. https://er.educause.edu/articles/2012/5/open-researcher--contributor-id-orcid-solving-the-name-ambiguity-problem

Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. eLife, 8, e49547. https://doi.org/10.7554/eLife.49547

Wingen, T., Berkessel, J. B., & Englich, B. (2020). No Replication, No Trust? How Low Replicability Influences Trust in Psychology. Social Psychological and Personality Science, 11(4). https://doi.org/10.1177/1948550619877412

Winter, S. R., Rice, C., Capps, J., Trombley, J., Milner, M. N., Anania, E. C., Walters, N. W., & Baugh, B. S. (2020). An analysis of a pilot’s adherence to their personal weather minimums. Safety Science, 123, 104576. https://doi.org/10.1016/j.ssci.2019.104576

Woelfle, M., Olliaro, P., & Todd, M. H. (2011). Open science is a research accelerator. Nature Chemistry, 3(10), 745–748. https://doi.org/10.1038/nchem.1149

Working Group 1 of the Joint Committee for Guides in Metrology JCGM. (2008). Evaluation of measurement data—Guide to the expression of uncertainty in measurement (pp. 1–120). JCGM. https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf/cb0ef43f-baa5-11cf-3f85-4dcd86f77bd6

World Wide Web Consortium. (2021). Web Accessibility Initiative. https://www.w3.org/WAI/

Wren, J. D., Valencia, A., & Kelso, J. (2019). Reviewer-coerced citation: case report, update on journal policy and suggestions for future prevention. Bioinformatics, 35(18), 3217–3218. https://doi.org/10.1093/bioinformatics/btz071

Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039. https://doi.org/10.1126/science.1136099

Xia, J., Harmon, J. L., Connolly, K. G., Donnelly, R. M., Anderson, M. R., & Howard, H. A. (2015). Who publishes in “predatory” journals? Journal of the Association for Information Science and Technology, 66(7), 1406–1417. https://doi.org/10.1002/asi.23265

Yamada, Y. (2018). How to crack pre-registration: Toward transparent and open science. Frontiers in Psychology, 9, 1831. https://doi.org/10.3389/fpsyg.2018.01831

Yarkoni, T. (2020). The generalizability crisis. Behavioral and Brain Sciences, 1–37. https://doi.org/10.1017/S0140525X20001685

Yeung, S. K., Feldman, G., Fillon, A., Protzko, J., Elsherif, M. M., Xiao, Q., & Pickering, J. (2020). Experimental Studies Meta-Analysis Registered Report Templates. https://osf.io/ytgrp/

Zenodo. (n.d.). Zenodo—Research. Shared. https://www.zenodo.org/

Zurn, P., Bassett, D. S., & Rust, N. C. (2020). The Citation Diversity Statement: A Practice of Transparency, A Way of Life. Trends in Cognitive Sciences, 24(9), 669–672. https://doi.org/10.1016/j.tics.2020.06.009

Zwaan, R., Etz, A., Lucas, R., & Donnellan, M. (2018). Making replication mainstream. Behavioral and Brain Sciences, 41, E120. https://doi.org/10.1017/S0140525X17001972