References

Breznau N et al. 2022. Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. Proceedings of the National Academy of Sciences. 119(44):e2203150119. https://doi.org/10.1073/pnas.2203150119

Cartwright N. 2021. Rigour versus the need for evidential diversity. Synthese. 199:13095–13119. https://doi.org/10.1007/s11229-021-03368-1

Cartwright N, Hardie J. 2012. Evidence-based policy: A practical guide to doing it better. Oxford University Press.

Gelman A, Loken E. 2014. The statistical crisis in science: Data-dependent analysis—a “garden of forking paths”—explains why many statistically significant comparisons don’t hold up. American Scientist. 102(6):460–465. https://doi.org/10.1511/2014.111.460

Goodman SN, Fanelli D, Ioannidis JPA. 2016. What does research reproducibility mean? Science Translational Medicine. 8(341):341ps12. https://doi.org/10.1126/scitranslmed.aaf5027

Gould E et al. 2025. Same data, different analysts: Variation in effect sizes due to analytical decisions in ecology and evolutionary biology. BMC Biology. 23(1):35. https://doi.org/10.1186/s12915-024-02101-x

Gould E et al. 2026. “But I can’t preregister my research”: Improving the reproducibility and transparency of ecology and conservation with adaptive preregistration for model-based research. Methods in Ecology and Evolution. 17(6):1768–1787. https://doi.org/10.1111/2041-210X.70311

Knuth DE. 1984. Literate programming. The Computer Journal. 27(2):97–111. https://doi.org/10.1093/comjnl/27.2.97

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

Munafò MR et al. 2017. A manifesto for reproducible science. Nature Human Behaviour. 1(1):0021 [accessed 2024 Apr 29]. https://doi.org/10.1038/s41562-016-0021

Sandve GK, Nekrutenko A, Taylor J, Hovig E. 2013. Ten simple rules for reproducible computational research. PLOS Computational Biology. 9(10):e1003285. https://doi.org/10.1371/journal.pcbi.1003285

Silberzahn R 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. 1(3):337–356. https://doi.org/10.1177/2515245917747646

Steegen S, Tuerlinckx F, Gelman A, Vanpaemel W. 2016. Increasing transparency through a multiverse analysis. Perspectives on Psychological Science. 11(5):702–712. https://doi.org/10.1177/1745691616658637

Wilson G et al. 2017. Good enough practices in scientific computing. PLOS Computational Biology. 13(6):e1005510. https://doi.org/10.1371/journal.pcbi.1005510