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Collaborating with our community to increase code sharing

2021; Public Library of Science; Volume: 17; Issue: 3 Linguagem: Inglês

10.1371/journal.pcbi.1008867

1553-7358

Lauren Cadwallader, Jason A. Papin, Feilim Mac Gabhann, Rebecca Kirk,

Data Quality and Management

Biology was launched in 2005 as a journal driven by the computational biology research community and with the principle that "open access ensures not only that everything we publish is immediately freely available to anyone, anywhere in the world, but also that the contents of this journal can be redistributed and reused in ways that increase their value."[1] Delivering this important open vision has relied on the enthusiasm and integrity of the broader computational biology community as editors, as reviewers, and as dedicated authors submitting excellent work to the journal.This vision of openness, redistribution, and reuse is so essential because most scientific progress builds on prior efforts.This progress is possible because as scientists we share not just our research results, but also our methods and protocols and key research tools.Open sharing allows others to check and reproduce our observations, and to build on our work, giving it even more impact over time.This value of sharing is not just true in experimental work, where a key antibody or plasmid must be shared to enable scrutiny and further research; it is also true in computational biology, where computational code is a key reagent.Given how essential newly developed code can be to computational biology research we have been collaborating with the Editorial Board of PLOS Computational Biology and consulting with computational biology researchers to develop a new more-rigorous code policy that is intended to increase code sharing on publication of articles.Code sharing is not new to many of our authors, and in 2019 over 40% of research articles published in the journal reported sharing some code.[2] Assuming this percentage to be a baseline of code sharing behaviour, and after consulting with individual researchers about their code sharing practices, we surveyed our authors and others working in the computational biology field.The objective of this survey was to determine what proportion of articles have code associated with them, and what proportion of this code has not been openly shared in the past.We also aimed to better understand challenges researchers have to overcome to share their code, and how a stronger code sharing policy might affect their opinion of the journal.[3] The results indicated that around 70% of articles have code associated with them based on the cohort who completed the survey.However, for about a third of these articles the authors have not shared the code in the past.The reasons given for this discrepancy range from practical issues, such as not having enough time, to legal and ethical reasons, which is supported by previous research into code sharing.[4] Analysis of the survey data suggested around 5% of articles could not share code for legal and ethical reasons, and therefore more articles published in PLOS Computational Biology could share code than have to date.While there are legitimate restrictions on the sharing of some code, we know that availability of code assists with the reproducibility of research, and journal policies are effective