Unit 1 – Introduction: Open science, Definitions and Contents
Tennant, J., Francuzik, W., Dunleavy, D. J., Fecher, B., Gonzalez-Marquez, M., & Steiner, T. (2020). Open Scholarship as a mechanism for the United Nations Sustainable Development Goals. https://osf.io/preprints/socarxiv/8yk62/
Banks, G. C., Field, J. G., Oswald, F. L., O’Boyle, E. H., Landis, R. S., Rupp, D. E., & Rogelberg, S. G. (2019). Answers to 18 questions about open science practices. Journal of Business and Psychology, 34(3), 257-270.
https://ocsdnet.org/manifesto/open-science-manifesto/ for OCSD Net principles
OS Rainbow: https://zenodo.org/record/1147025#.YLZRlTZKjyg
Whitaker, K., & Guest, O. (2020). # bropenscience is broken science: Kirstie Whitaker and Olivia Guest ask how open ‘open science’really is. The Psychologist, 33, 34-37.
https://thepsychologist.bps.org.uk/volume-33/november-2020/bropenscience-broken-science
Birhane, A., & Guest, O. (2020). Towards decolonising computational sciences. arXiv preprint arXiv:2009.14258. https://arxiv.org/abs/2009.14258
Unit 2 – Reproducibility, Replicability and Other Challenges in Science
Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 1226-1227.
Goodman, S. N., Fanelli, D., & Ioannidis, J. P. (2016). What does research reproducibility mean?. Science translational medicine, 8(341), 341ps12-341ps12.
Cassey, P., & Blackburn, T. M. (2006). Reproducibility and repeatability in ecology. BioScience, 56(12), 958-959.
Plesser, H. E. (2018). Reproducibility vs. replicability: a brief history of a confused terminology. Frontiers in neuroinformatics, 11, 76.
Zwaan, R. A., Etz, A., Lucas, R. E., & Donnellan, M. B. (2018). Making replication mainstream. Behavioral and Brain Sciences, 41.
Penders, B., Holbrook, J. B., & De Rijcke, S. (2019). Rinse and repeat: Understanding the value of replication across different ways of knowing. Publications, 7(3), 52.
Ulrich, R., & Miller, J. (2020). Meta-Research: Questionable research practices may have little effect on replicability. Elife, 9, e58237.
Fraser, H., Parker, T., Nakagawa, S., Barnett, A., & Fidler, F. (2018). Questionable research practices in ecology and evolution. PloS one, 13(7), e0200303
Rubin, M. (2020). The costs of HARKing. The British Journal for the Philosophy of Science.
Hoffmann, S., Schönbrodt, F., Elsas, R., Wilson, R., Strasser, U., & Boulesteix, A. L. (2020). The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines.
O’Boyle Jr, E. H., Banks, G. C., & Gonzalez-Mulé, E. (2017). The chrysalis effect: How ugly initial results metamorphosize into beautiful articles. Journal of Management, 43(2), 376-399.
Stroebe, W. (2019). What Can We Learn from Many Labs Replications?. Basic and Applied Social Psychology, 41(2), 91-103.
Fiedler, K., & Prager, J. (2018). The regression trap and other pitfalls of replication science—illustrated by the report of the Open Science Collaboration. Basic and Applied Social Psychology, 40(3), 115-124.
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.
Shiffrin, R. M., Börner, K., & Stigler, S. M. (2018). Scientific progress despite irreproducibility: A seeming paradox. Proceedings of the National Academy of Sciences, 115(11), 2632-2639.
Tiokhin, L., Yan, M., & Morgan, T. J. (2021). Competition for priority harms the reliability of science, but reforms can help. Nature human behaviour, 1-11.
Unit 3 – Pre-registration
Rubin, M. (2020). Does preregistration improve the credibility of research findings?. arXiv preprint arXiv:2010.10513.
Navarro, D. (2020). Paths in strange spaces: A comment on preregistration. https://psyarxiv.com/wxn58
Lin, W., & Green, D. P. (2016). Standard operating procedures: A safety net for pre-analysis plans. PS: Political Science & Politics, 49(3), 495-500.
Crüwell, S., & Evans, N. J. (2019). Preregistration in complex contexts: A preregistration template for the application of cognitive models.
MacEachern, S. N., & Van Zandt, T. (2019). Preregistration of modeling exercises may not be useful. Computational Brain & Behavior, 2(3), 179-182.
Unit 4 – Open Methodology: Methods, Materials, and Code
Wilson, G., Aruliah, D. A., Brown, C. T., Hong, N. P. C., Davis, M., Guy, R. T., ... & Wilson, P. (2014). Best practices for scientific computing. PLoS Biol, 12(1), e1001745.
Stodden, V., & Miguez, S. (2013). Best practices for computational science: Software infrastructure and environments for reproducible and extensible research. Available at SSRN 2322276.
Walters, W. P. (2020). Code Sharing in the Open Science Era. Journal of Chemical Information and Modeling, 60(10), 4417-4420.
Piccolo, S. R., & Frampton, M. B. (2016). Tools and techniques for computational reproducibility. GigaScience, 5(1), s13742-016. https://www.biorxiv.org/content/10.1101/022707v3
Morin, A., Urban, J., & Sliz, P. (2012). A quick guide to software licensing for the scientist-programmer. PLoS Comput Biol, 8(7), e1002598.
Cozzarelli, N. R. (2004). UPSIDE: uniform principle for sharing integral data and materials expeditiously.
Unit 5 – Open Data
Friedlin, F. J., & McDonald, C. J. (2008). A software tool for removing patient identifying information from clinical documents. Journal of the American Medical Informatics Association, 15(5), 601-610.
Joel, S., Eastwick, P. W., & Finkel, E. J. (2018). Open sharing of data on close relationships and other sensitive social psychological topics: Challenges, tools, and future directions. Advances in Methods and Practices in Psychological Science, 1(1), 86-94.
Soderberg, C. K. (2018). Using OSF to share data: A step-by-step guide. Advances in Methods and Practices in Psychological Science, 1(1), 115-120.
Vanpaemel, W., Vermorgen, M., Deriemaecker, L., & Storms, G. (2015). Are we wasting a good crisis? The availability of psychological research data after the storm. Collabra, 1(1).
Unit 6 – Publication Practices
Peer Review:
Roediger III, H. L. (2007). Twelve tips for reviewers. APS Observer, 20(4).
Ross-Hellauer, T., & Görögh, E. (2019). Guidelines for open peer review implementation. Research integrity and peer review, 4(1), 1-12. https://researchintegrityjournal.biomedcentral.com/articles/10.1186/s41073-019-0063-9
Schmidt, B., Ross-Hellauer, T., van Edig, X., & Moylan, E. C. (2018). Ten considerations for open peer review. F1000Research, 7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6073088/
Bishop, D. V. M. (2016). Open research practices: unintended consequences and suggestions for averting them.(Commentary on the Peer Reviewers' Openness Initiative). Royal Society open science, 3(4), 160109.
Open Access:
Greshake, B. (2017). Looking into pandora's box: the content of sci-hub and its usage. F1000Research, 6.
Green, T. (2017). We've failed: Pirate black open access is trumping green and gold and we must change our approach. Learned Publishing, 30(4).
Shaw, D. M., & Elger, B. S. (2018). Unethical aspects of open access. Accountability in research, 25(7-8), 409-416.
Jacobs, N. (Ed.). (2006). Open Access: Key strategic, technical and economic aspects. Elsevier.
Momeni, F., Mayr, P., Fraser, N., & Peters, I. (2021). What happens when a journal converts to Open Access? A bibliometric analysis. Scientometrics, 1-17.
Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., Norlander, B., ... & Haustein, S. (2018). The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles. PeerJ, 6, e4375.
Unit 7 – Conclusion
(Personal) pick: better science isn’t (only) Open Science:
Devezer, B., Navarro, D. J., Vandekerckhove, J., & Buzbas, E. O. (2020). The case for formal methodology in scientific reform. biorxiv.
van Rooij, I., & Baggio, G. (2020). Theory before the test: How to build high-verisimilitude explanatory theories in psychological science. Perspectives on Psychological Science, 1745691620970604.
Vandekerckhove, J., White, C. N., Trueblood, J. S., Rouder, J. N., Matzke, D., Leite, F. P., ... & Lee, M. D. (2019). Robust diversity in cognitive science. Computational Brain & Behavior, 2(3), 271-276.
Navarro, D. J. (2019). Between the devil and the deep blue sea: Tensions between scientific judgement and statistical model selection. Computational Brain & Behavior, 2(1), 28-34.
Navarro, D. J. (2021). If mathematical psychology did not exist we might need to invent it: A comment on theory building in psychology. Perspectives on Psychological Science, 1745691620974769.
van Rooij, I., & Baggio, G. (2020). Theory development requires an epistemological sea change. Psychological Inquiry, 31(4), 321-325.
Asendorpf, J. B., Conner, M., De Fruyt, F., De Houwer, J., Denissen, J. J., Fiedler, K., ... & Wicherts, J. M. (2013). Recommendations for increasing replicability in psychology. European journal of personality, 27(2), 108-119.
Smaldino, P. E. (2020). How to build a strong theoretical foundation. Psychological Inquiry, 31(4), 297-301.
Chen, X., Dallmeier-Tiessen, S., Dasler, R., Feger, S., Fokianos, P., Gonzalez, J. B., ... & Neubert, S. (2019). Open is not enough. Nature Physics, 15(2), 113-119.
Gervais, W. M. (2020). Practical methodological reform needs good theory.
Open Science for pandemics:
Kapczynski, A. (2016). Order without intellectual property law: Open science in influenza. Cornell L. Rev., 102, 1539.
Besançon, L., Peiffer-Smadja, N., Segalas, C., Jiang, H., Masuzzo, P., Smout, C. A., ... & Leyrat, C. (2020). Open science saves lives: Lessons from the COVID-19 pandemic. BioRxiv.