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Implementing Code Review in the Scientific Workflow: Insights from Ecology and Evolutionary Biologyuse asterix (*) to get italics
Edward Ivimey-Cook, Joel Pick, Kevin Bairos-Novak, Antica Culina, Elliot Gould, Matthew Grainger, Benjamin Marshall, David Moreau, Matthieu Paquet, Raphaël Royauté, Alfredo Sanchez-Tojar, Inês Silva, Saras WindeckerPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p>Code review increases reliability and improves reproducibility of research. As such, code review is an inevitable step in software development and is common in fields such as computer science. However, despite its importance, code review is noticeably lacking in ecology and evolutionary biology. This is problematic as it facilitates the propagation of coding errors and a reduction in reproducibility and reliability of published results. To address this, we provide a detailed commentary on how to effectively review code, how to set up your project to enable this form of review and detail its possible implementation at several stages throughout the research process. This guide serves as a primer for code review, and adoption of the principles and advice here will go a long way in promoting more open, reliable, and transparent ecology and evolutionary biology.</p>
You should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
You should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
reliability, reproducibility, software development, coding errors, research process, open science, transparency.
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Meta-analyses, Statistical ecology
Esteban Fernandez- Juricic [], Ethan White [], Natalie Cooper [], Christopher J. Lortie [], David M. A. Mehler [], Dominique Roche [], Neal R. Haddaway [] No need for them to be recommenders of PCIEcology. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe []
2023-05-19 15:54:01
Corina Logan