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The TADA-Wiki: Best practice for sharing analytical code

13 References

Sources cited in this wiki, together with the key works the three source papers draw on. Bibliographic details follow the reference lists of those papers; where a source gives an abbreviated form, that form is reproduced here rather than completed by guesswork.

13.1 The source papers

Ivimey-Cook, E. R., Culina, A., Dimri, S., Grainger, M. J., Kar, F., Lagisz, M., Moran, N. P., Nakagawa, S., Roche, D. G., Tattan, S., Sánchez-Tójar, A., Windecker, S. M., & Pick, J. L. (2025). TADA! Simple guidelines to improve analytical code sharing for transparency and reproducibility. EcoEvoRxiv. https://doi.org/10.32942/X2D93K (Preprint, posted 2025-08-02; version 3 posted 2026-09-15; not yet peer reviewed.) Companion site: https://eivimeycook.github.io/TADA/

Ivimey-Cook, E. R., Pick, J. L., Bairos-Novak, K. R., Culina, A., Gould, E., Grainger, M., Marshall, B. M., Moreau, D., Paquet, M., Royauté, R., Sánchez-Tójar, A., Silva, I., & Windecker, S. M. (2023). Implementing code review in the scientific workflow: Insights from ecology and evolutionary biology. Journal of Evolutionary Biology 36: 1347–1356. https://doi.org/10.1111/jeb.14230

Pick, J. L., Allen, B. J., Bachelot, B., Bairos-Novak, K. R., Brand, J. A., Class, B., Dallas, T., D’Amelio, P. B., Fenollosa, E., Fernández-Juricic, E., Gomes, D. G. E., Grainger, M. J., Guillemaud, T., John, C., Krasnow, R., Lagisz, M., Lequime, S., Maynard, D. S., Nakagawa, S., O’Dea, R. E., Paquet, M., Petitjean, Q., Sánchez-Tójar, A., van Dis, N. E., Wilson, L. A. B., & Ivimey-Cook, E. R. (2026). The SORTEE guidelines for data and code quality control in ecology and evolutionary biology. Peer Community Journal 6: e20. https://doi.org/10.24072/pcjournal.687 (Published 2026-03-13; Peer reviewed and recommended by PCI Ecology, https://doi.org/10.24072/pci.ecology.100857.)

13.2 All references

Abdill, R. J., Talarico, E., & Grieneisen, L. (2024). A how-to guide for code sharing in biology. PLOS Biology 22: e3002815. https://doi.org/10.1371/journal.pbio.3002815

Allen, C., & Mehler, D. M. A. (2019). Open science challenges, benefits and tips in early career and beyond. PLOS Biology 17: e3000246. https://doi.org/10.1371/journal.pbio.3000246

Alston, J. M., & Rick, J. A. (2021). A beginner’s guide to conducting reproducible research. Bulletin of the Ecological Society of America 102: e01801. https://doi.org/10.1002/bes2.1801

Archmiller, A. A., Johnson, A. D., Nolan, J., Edwards, M., Elliott, L. H., Ferguson, J. M., Iannarilli, F., Vélez, J., Vitense, K., Johnson, D. H., & Fieberg, J. (2020). Computational reproducibility in The Wildlife Society’s flagship journals. The Journal of Wildlife Management 84: 1012–1017. https://doi.org/10.1002/jwmg.21855

Apptainer (n.d.). Apptainer (formerly Singularity). Accessed 3 October 2026. https://apptainer.org/

autopep8 contributors (n.d.). autopep8: A tool that automatically formats Python code to conform to the PEP 8 style guide. GitHub. Accessed 3 October 2026. https://github.com/hhatto/autopep8

Bakker, T. C. M., & Traniello, J. F. A. (2020). Ensuring data access, transparency, and preservation: Mandatory data deposition for Behavioral Ecology and Sociobiology. Behavioral Ecology and Sociobiology 74: 132. https://doi.org/10.1007/s00265-020-02910-4

Barker, M., Chue Hong, N. P., Katz, D. S., Lamprecht, A.-L., Martinez-Ortiz, C., Psomopoulos, F., et al. (2022). Introducing the FAIR Principles for research software. Scientific Data 9: 622. https://doi.org/10.1038/s41597-022-01710-x

Barnes, N. (2010). Publish your computer code: it is good enough. Nature 467: 753. https://doi.org/10.1038/467753a

Blischak, J. D., Carbonetto, P., & Stephens, M. (2019). Creating and sharing reproducible research code the workflowr way. F1000Research 8: 1749. https://doi.org/10.12688/f1000research.20843.1

Boettiger, C. (2015). An introduction to Docker for reproducible research. ACM SIGOPS Operating Systems Review 49: 71–79. https://doi.org/10.1145/2723872.2723882

Boettiger, C. (2017). Generating CodeMeta metadata for R packages. The Journal of Open Source Software 2: 454. https://doi.org/10.21105/joss.00454

Bolnick, D. I., & Paull, J. S. (2009). Morphological and dietary differences between individuals are weakly but positively correlated within a population of threespine stickleback. Evolutionary Ecology Research 11: 1217–1233. https://bolnicklab.wordpress.com/wp-content/uploads/2015/10/bolnick-and-paull-2009-eer.pdf

Bolnick, D. (2022). EIC update: American Naturalist policy on data and code archiving. http://comments.amnat.org/2022/09/eic-update-american-naturalist-policy.html

Braga, P. H. P., Hébert, K., Hudgins, E. J., Scott, E. R., Edwards, B. P. M., Sánchez Reyes, L. L., et al. (2023). Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution. Methods in Ecology and Evolution 14: 1364–1380. https://doi.org/10.1111/2041-210X.14108

Buckley, Y. M., Bardgett, R., Gordon, R., Iler, A., Mariotte, P., Ponton, S., & Hector, A. (2025). Using dynamic documents to mend cracks in the reproducible research pipeline. Journal of Ecology 113: 270–274. https://doi.org/10.1111/1365-2745.14454

Budd, J. M., Sievert, M. E., & Schultz, T. R. (1998). Phenomena of retraction: Reasons for retraction and citations to the publications. JAMA 280: 296–297. https://doi.org/10.1001/jama.280.3.296

Cadwallader, L., & Hrynaszkiewicz, I. (2022). A survey of researchers’ code sharing and code reuse practices, and assessment of interactive notebook prototypes. PeerJ 10: e13933. https://doi.org/10.7717/peerj.13933

Campbell, T., Dixon, K. W., & Handcock, R. N. (2023). Restoration and replication: a case study on the value of computational reproducibility assessment. Restoration Ecology 31: e13968. https://doi.org/10.1111/rec.13968

Cao, H., Dodge, J., Lo, K., McFarland, D. A., & Wang, L. L. (2023). The rise of open science: Tracking the evolution and perceived value of data and methods link-sharing practices. arXiv. https://arxiv.org/abs/2310.03193

Chen, D. (2023). pyprojroot: Project-oriented workflow in Python. https://pypi.org/project/pyprojroot/

Chue Hong, N. P., Katz, D. S., Barker, M., Lamprecht, A.-L., Martinez, C., Psomopoulos, F. E., et al. (2022). FAIR Principles for Research Software (FAIR4RS Principles). Zenodo. https://doi.org/10.15497/RDA00068

choosealicense.com (n.d.). Licenses. GitHub. Accessed 3 October 2026. https://choosealicense.com/licenses/

Chure, G. (2022). gchure/reproducible_research: A template repository for how I structure my scientific research. GitHub. https://github.com/gchure/reproducible_research

conda contributors (2026). conda: A system-level, binary package and environment manager running on all major operating systems and platforms. Zenodo. https://doi.org/10.5281/zenodo.20723337

Cooper, N., & Hsing, P.-Y. (2017). A Guide to Reproducible Code in Ecology and Evolution. British Ecological Society. https://www.britishecologicalsociety.org/wp-content/uploads/2017/12/guide-to-reproducible-code.pdf

Cooper, N., & Hsing, P.-Y. (2025). Guide to Reproducible Code. British Ecological Society. Second edition. Zenodo. https://doi.org/10.5281/zenodo.16421733

Cooper, N., & the BES Data and Code Hackathon Group (2026). Data- and code-archiving in the British Ecological Society journals: Present status and recommendations for future improvements. Methods in Ecology and Evolution 17: 1954–1966. https://doi.org/10.1111/2041-210X.70338

Culina, A., van den Berg, I., Evans, S., & Sánchez-Tójar, A. (2020). Low availability of code in ecology: A call for urgent action. PLOS Biology 18: e3000763. https://doi.org/10.1371/journal.pbio.3000763

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology 35: 316–319. https://doi.org/10.1038/nbt.3820

Eglen, S. J., Marwick, B., Halchenko, Y. O., Hanke, M., Sufi, S., Gleeson, P., et al. (2017). Toward standard practices for sharing computer code and programs in neuroscience. Nature Neuroscience 20: 770–773. https://doi.org/10.1038/nn.4550

The Dataverse Project (n.d.). Dataset + File Management. Dataverse User Guide, v6.12. Accessed 3 October 2026. https://guides.dataverse.org/en/latest/user/dataset-management.html

Eustace, S. (2023). Poetry: Python dependency management and packaging made easy. https://python-poetry.org/

Feldroy, A. (2022). Cookiecutter: A command-line utility that creates projects from project templates. https://github.com/cookiecutter/cookiecutter

Fernández-Juricic, E. (2021). Why sharing data and code during peer review can enhance behavioral ecology research. Behavioral Ecology and Sociobiology 75: 103. https://doi.org/10.1007/s00265-021-03036-x

Fidler, F., Chee, Y. E., Wintle, B. C., Burgman, M. A., McCarthy, M. A., & Gordon, A. (2017). Metaresearch for evaluating reproducibility in ecology and evolution. BioScience 67: 282–289. https://doi.org/10.1093/biosci/biw159

Filazzola, A., & Lortie, C. (2022). A call for clean code to effectively communicate science. Methods in Ecology and Evolution 13: 2119–2128. https://doi.org/10.1111/2041-210X.13961

Fox, C. W., Meyer, J., & Aimé, E. (2023). Double-blind peer review affects reviewer ratings and editor decisions at an ecology journal. Functional Ecology 37: 1144–1157. https://doi.org/10.1111/1365-2435.14259

Gao, M., Ye, Y., Zheng, Y., & Lai, J. (2025). A comprehensive analysis of R’s application in ecological research from 2008 to 2023. Journal of Plant Ecology 18: rtaf010. https://doi.org/10.1093/jpe/rtaf010

GitHub Docs (n.d.). Licensing a repository. Accessed 3 October 2026. https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/licensing-a-repository

Goldacre, B., Morton, C. E., & DeVito, N. J. (2019). Why researchers should share their analytic code. BMJ 367: l6365. https://doi.org/10.1136/bmj.l6365

Gomes, D. G. E., Pottier, P., Crystal-Ornelas, R., Hudgins, E. J., Foroughirad, V., Sánchez-Reyes, L. L., et al. (2022). Why don’t we share data and code? Perceived barriers and benefits to public archiving practices. Proceedings of the Royal Society B 289: 20221113. https://doi.org/10.1098/rspb.2022.1113

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

Hardwicke, T. E., Bohn, M., MacDonald, K., Hembacher, E., Nuijten, M. B., Peloquin, B. N., de Mayo, B. E., Long, B., Yoon, E. J., & Frank, M. C. (2021). Analytic reproducibility in articles receiving open data badges at the journal Psychological Science: An observational study. Royal Society Open Science 8: 201494. https://doi.org/10.1098/rsos.201494

Harvard Longwood Medical Area Research Data Management Working Group (2023). Harvard Biomedical Repository Matrix. Zenodo. https://doi.org/10.5281/zenodo.10651775

Hillemann, F., Burant, J. B., Culina, A., & Vriend, S. J. G. (2025). Code review in practice: A checklist for computational reproducibility and collaborative research in ecology and evolution. EcoEvoRxiv. https://doi.org/10.32942/X26S6P

Huijgen, R., Boekholdt, S. M., Arsenault, B. J., Bao, W., Davaine, J.-M., Tabet, F., Petrides, F., Rye, K. A., DeMicco, D. A., Barter, P. J., Kastelein, J. J., & Lambert, G. (2012). Plasma PCSK9 levels and clinical outcomes in the TNT (treating to new targets) trial: A nested case-control study. Journal of the American College of Cardiology 59: 1778–1784. https://doi.org/10.1016/j.jacc.2011.12.043

Hunter-Zinck, H., de Siqueira, A. F., Vásquez, V. N., Barnes, R., & Martinez, C. C. (2021). Ten simple rules on writing clean and reliable open-source scientific software. PLOS Computational Biology 17: e1009481. https://doi.org/10.1371/journal.pcbi.1009481

Indriasari, T. D., Luxton-Reilly, A., & Denny, P. (2020). A review of peer code review in higher education. ACM Transactions on Computing Education 20: 1–25. https://doi.org/10.1145/3403935

Ivimey-Cook, E. R., Sánchez-Tójar, A., Berberi, I., Culina, A., Roche, D. G., Almeida, R. A., et al. (2025). From policy to practice: Progress towards data- and code-sharing in ecology and evolution. Proceedings of the Royal Society B 292: 20251394. https://doi.org/10.1098/rspb.2025.1394

Jenkins, G. B., Beckerman, A. P., Bellard, C., Benítez-López, A., Ellison, A. M., Foote, C. G., et al. (2023). Reproducibility in ecology and evolution: Minimum standards for data and code. Ecology and Evolution 13: e9961. https://doi.org/10.1002/ece3.9961

Jiménez, R. C., Kuzak, M., Alhamdoosh, M., Barker, M., Batut, B., Borg, M., et al. (2017). Four simple recommendations to encourage best practices in research software. F1000Research. https://doi.org/10.12688/f1000research.11407.1

Kambouris, S., Wilkinson, D. P., Smith, E. T., & Fidler, F. (2024). Computationally reproducing results from meta-analyses in ecology and evolutionary biology using shared code and data. PLOS ONE 19: e0300333. https://doi.org/10.1371/journal.pone.0300333

Kane, A., & Amin, B. (2023). Amending the literature through version control. Biology Letters 19: 20220463. https://doi.org/10.1098/rsbl.2022.0463

Kang, D., Kang, T., & Jang, J. (2023). Papers with code or without code? Impact of GitHub repository usability on the diffusion of machine learning research. Information Processing & Management 60: 103477. https://doi.org/10.1016/j.ipm.2023.103477

Kellner, K. F., Doser, J. W., & Belant, J. L. (2025). Functional R code is rare in species distribution and abundance papers. Ecology 106: e4475. https://doi.org/10.1002/ecy.4475

Kimmel, K., Avolio, M. L., & Ferraro, P. J. (2023). Empirical evidence of widespread exaggeration bias and selective reporting in ecology. Nature Ecology & Evolution 7: 1525–1536. https://doi.org/10.1038/s41559-023-02144-3

König, L., Gärtner, A., Slack, H., Dhakal, S., Adetula, A., Dougherty, M., et al. (2025). How to bolster employability through open science. OSF. https://osf.io/preprints/osf/zgfkd_v1

Köster, J., & Rahmann, S. (2012). Snakemake — a scalable bioinformatics workflow engine. Bioinformatics 28: 2520–2522. https://doi.org/10.1093/bioinformatics/bts480

Kurtzer, G. M., Sochat, V., & Bauer, M. W. (2017). Singularity: Scientific containers for mobility of compute. PLOS ONE 12: e0177459. https://doi.org/10.1371/journal.pone.0177459

Lai, J., Lortie, C. J., Muenchen, R. A., Yang, J., & Ma, K. (2019). Evaluating the popularity of R in ecology. Ecosphere 10: e02567. https://doi.org/10.1002/ecs2.2567

Lamprecht, A.-L., Garcia, L., Kuzak, M., Martinez, C., Arcila, R., Martin Del Pico, E., et al. (2020). Towards FAIR principles for research software. Data Science 3: 37–59. https://doi.org/10.3233/DS-190026

Landau, W. M. (2021). The targets R package: A dynamic make-like function-oriented pipeline toolkit for reproducibility and high-performance computing. Journal of Open Source Software 6: 2959. https://doi.org/10.21105/joss.02959

Light, R. P., Polley, D. E., & Börner, K. (2014). Open data and open code for big science of science studies. Scientometrics 101: 1535–1551. https://doi.org/10.1007/s11192-014-1238-2

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Lowenberg, D. (2021). Doing it right: A better approach for software & data. Dryad blog, 8 February 2021. https://blog.datadryad.org/2021/02/08/doing-it-right-a-better-approach-for-software-amp-data/

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Maitner, B., Santos Andrade, P. E., Lei, L., Kass, J., Owens, H. L., Barbosa, G. C. G., et al. (2024). Code sharing in ecology and evolution increases citation rates but remains uncommon. Ecology and Evolution 14: e70030. https://doi.org/10.1002/ece3.70030

McKiernan, E. C., Bourne, P. E., Brown, C. T., Buck, S., Kenall, A., Lin, J., et al. (2016). How open science helps researchers succeed. eLife 5: e16800. https://doi.org/10.7554/eLife.16800

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Mislan, K. A. S., Heer, J. M., & White, E. P. (2016). Elevating the status of code in ecology. Trends in Ecology & Evolution 31: 4–7. https://doi.org/10.1016/j.tree.2015.11.006

Müller, K., & Bryan, J. (2020). here: A simpler way to find your files. R package. https://cran.r-project.org/package=here

Müller, K., & Walthert, L. (2020). styler: Non-invasive pretty printing of R code. R package version 1.3.2. https://cran.r-project.org/package=styler

Nakagawa, S., Ivimey-Cook, E. R., Grainger, M. J., O’Dea, R. E., Burke, S., Drobniak, S. M., Gould, E., Macartney, E. L., Martinig, A. R., Morrison, K., Paquet, M., et al. (2023). Method reporting with initials for transparency (MeRIT) promotes more granularity and accountability for author contributions. Nature Communications 14: 1788. https://doi.org/10.1038/s41467-023-37039-1

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Patel, B., Soundarajan, S., Ménager, H., & Hu, Z. (2023). Making biomedical research software FAIR: Actionable step-by-step guidelines with a user-support tool. Scientific Data 10: 557. https://doi.org/10.1038/s41597-023-02463-x

Peikert, A., & Brandmaier, A. M. (2021). A reproducible data analysis workflow with R Markdown, Git, Make, and Docker. Quantitative and Computational Methods in Behavioral Sciences. https://doi.org/10.5964/qcmb.3763

Petersen, A. H., & Ekstrøm, C. T. (2019). dataMaid: Your assistant for documenting supervised data quality screening in R. Journal of Statistical Software 90: 1–38. (The package is now dataReporter.) https://doi.org/10.18637/jss.v090.i06

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Sánchez-Tójar, A., Bezine, A., Purgar, M., & Culina, A. (2025). Code-sharing policies are associated with increased reproducibility potential of ecological findings. Peer Community Journal 5. https://doi.org/10.24072/pcjournal.541

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Sharma, N. K., Ayyala, R., Deshpande, D., Patel, Y., Munteanu, V., Ciorba, D., et al. (2024). Analytical code sharing practices in biomedical research. PeerJ Computer Science 10: e2066. https://doi.org/10.7717/peerj-cs.2066

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Trisovic, A., Lau, M. K., Pasquier, T., & Crosas, M. (2022). A large-scale study on research code quality and execution. Scientific Data 9: 60. https://doi.org/10.1038/s41597-022-01143-6

Ushey, K., McPherson, J., Cheng, J., Atkins, A., Allaire, J. J., & Allen, T. (2022). Packrat: A dependency management system for projects and their R package dependencies. https://cran.r-project.org/package=packrat

Ushey, K., & Wickham, H. (2023). renv: Project environments. https://cran.r-project.org/package=renv

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Vandewalle, P. (2012). Code sharing is associated with research impact in image processing. Computing in Science & Engineering 14: 42–47. https://doi.org/10.1109/MCSE.2012.63

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Williams, D., & Bürkner, P.-C. (2020). Coding errors lead to unsupported conclusions: A critique of Hofmann et al. (2015). Meta-Psychology 4: 4. https://doi.org/10.15626/MP.2018.872

13.3 About this wiki

This wiki is a synthesis and does not present original findings. Where it extends beyond the source papers — worked examples, comparison tables, checklists, reviewer scripts — that material is shown in a Wiki Tip box. Corrections are welcome; see Contribute.

The three source papers are linked by DOI above, in The source papers. The SORTEE guidelines paper is open access under CC BY 4.0.