A lot of new interesting work is being done on questions of reproducibility across the sciences by diverse researchers from different disciplines. Here we share 2-3 papers every month that come from what Project Members are reading. Have a suggestion? Let us know.
Gordon, M., D. Viganola, M. Bishop, Y. Chen, A. Dreber, B. Goldfedder, F. Holzmeister, M. Johannesson, Y. Liu, C. Twardy, J. Wang, and T. Pfeiffer. 2020. Are replication rates the same across academic fields? Community forecasts from the DARPA SCORE programme. Royal Society Open Science 7(7):200566.
Heesen, R. and L.K. Bright. 2020. Is peer review a good idea? The British Journal for the Philosophy of Science: axz029.
Jin, J., N. Agarwala, P. Kundu, Y. Wang, R. Zhao, and N. Chatterjee. 2020. Transparency, reproducibility, and validation of COVID-19 projection models. COVID-19: School of Public Health Expert Insights. Johns Hopkins Bloomberg School of Public Health.
Moher, D. L. Bouter, S. Kleinert, P. Glasziou, M.H. Sham, V. Barbour, A.-M. Coriat, N. Foeger, and Ul Dirnagl. 2020. The Hong Kong Principles for assessing researchers: Fostering research integrity. PLoS Biology 18(7):e3000737.
Adda, J., C. Decker, and M. Ottaviani. 2020. P-hacking in clinical trials and how incentives shape the distribution of results across phases. Proceedings of the National Academy of Sciences 117:13386-13392.
Botvinik-Nezer, R., F. Holzmeister, C. F. Camerer, A. Dreber, J. Huber, et al. 2020. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582:84-88.
Ofosu, G.K., and D.N. Posner. 2020. Do pre-analysis plans hamper publication? AEA Papers and Proceedings 110:70-74.
Gilmore, R.O., P.M, Cole, S. Verma, M.A. Van Aken, and C.M. Worthman. 2020. Advancing scientific integrity, transparency, and openness in child development research: challenges and possible solutions. Child Development Perspectives 14:9-14.
London, A.J. and J. Kimmelman. 2020. Against pandemic research exceptionalism. Science 368:476-477.
Wilkinson, M., M. Dumontier, I. Aalbersberg, et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3:160018.
Nosek, B.A. and T.M. Errington. 2020. What is replication? PLoS Biology 18(3): e3000691.
Tiokhin, L., J. Hackman, S. Munira, K. Jesmin, and D. Hruschka. 2019. Generalizability is not optional: insights from a cross-cultural study of social discounting. Royal Society Open Science 6: 181386.
Wilson, B.M., C.R. Harris, J.T. Wixted. 2020. Science is not a signal detection problem. Proceedings of the National Academy of Sciences 117(11):5559–5567.
Laskowski, K. 2020. What to do when you don’t trust your data anymore.
Lundwall, R.A. 2019. Changing institutional incentives to foster sound scientific practices: One department. Infant Behavior and Development 55:69-76.
Koroshetz, W.J., S. Behrman, C.J. Brame, et al. 2020. Research culture: Framework for advancing rigorous research. eLife 9:e55915.
Milton, M.J.T. and A. Possolo. 2019. Trustworthy data underpin reproducible research. Nature Physics 16:117–119.
Bryan, C.J., D.S. Yeager, and J.M. O'Brien. 2019. Replicator degrees of freedom allow publication of misleading failures to replicate. Proceedings of the National Academy of Sciences 116:25535–25545.
Feest, U. 2019. Why replication is overrated. Philosophy of Science 86:895–905.
Krummel, M., C. Blish, M. Kuhns, K. Cadwell, A. Oberst, A. Goldrath, K.M. Ansel, J. Chi, R. O'Connell, E.J. Wherry, M. Pepper, and The Future Immunology Consortium. 2019. Universal principled review: a community-driven method to improve peer review. Cell 179:1441–1445