From Policy to Practice: Progress towards Data- and Code-Sharing in Ecology and Evolution

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Abstract

High quality research data and analytical code are essential for ensuring the credibility of scientific results, are key research outputs, and are crucial elements to facilitate reproducibility. However, in ecology and evolution (E&E) in particular, it is currently unknown how many journals have policies on data- and code-sharing for peer review purposes, or upon manuscript acceptance. Furthermore, the clarity of such policies may impact authors' compliance. Thus, we assessed the clarity, strictness, and timing of data- and code-sharing policies across 275 journals in E&E. We also analysed initial policy compliance using submission data from two journals: Proceedings of the Royal Society B and Ecology Letters. Across all 275 journals, 22.5% encouraged and 38.2% mandated data-sharing, whereas 26.6% encouraged and 26.9% mandated code-sharing. Most journals that mandated data- or code-sharing required these to be provided “during peer review” (59.0% and 77.0%). This number was reduced for journals that encouraged data- and code-sharing (40.3% and 24.7%). More journals mandated or encouraged data- (+5.7%) and code-sharing (+12.6%) since the last assessments of these percentages in 2021 and 2020. Mandatory policies were associated with higher rates of data- and code-sharing upon submission (16.9% pre-mandate to 42.6% post-mandate), even when not fully adhered to. When enforced by editorial staff, mandated policies led to very high compliance rates (e.g., 96.5%). Our results also suggest that low initial compliance may in part be explained by vague wording used in sharing policies. We provide seven specific recommendations to help journals improve policy compliance and boost data- and code-sharing in E&E.
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Abstract

High quality research data and analytical code are essential for ensuring the credibility of scientific results, are key research outputs, and are crucial elements to facilitate reproducibility. However, in ecology and evolution (E&E) in particular, it is currently unknown how many journals have policies on data- and code-sharing for peer review purposes, or upon manuscript acceptance. Furthermore, the clarity of such policies may impact authors' compliance. Thus, we assessed the clarity, strictness, and timing of data- and code-sharing policies across 275 journals in E&E. We also analysed initial policy compliance using submission data from two journals: Proceedings of the Royal Society B and Ecology Letters. Across all 275 journals, 22.5% encouraged and 38.2% mandated data-sharing, whereas 26.6% encouraged and 26.9% mandated code-sharing. Most journals that mandated data- or code-sharing required these to be provided “during peer review” (59.0% and 77.0%). This number was reduced for journals that encouraged data- and code-sharing (40.3% and 24.7%). More journals mandated or encouraged data- (+5.7%) and code-sharing (+12.6%) since the last assessments of these percentages in 2021 and 2020. Mandatory policies were associated with higher rates of data- and code-sharing upon submission (16.9% pre-mandate to 42.6% post-mandate), even when not fully adhered to. When enforced by editorial staff, mandated policies led to very high compliance rates (e.g., 96.5%). Our results also suggest that low initial compliance may in part be explained by vague wording used in sharing policies. We provide seven specific recommendations to help journals improve policy compliance and boost data- and code-sharing in E&E. DOI https://doi.org/10.32942/X2492Q Subjects Ecology and Evolutionary Biology, Life Sciences

Keywords

open science, journal policy, reproducibility, replicability, transparency, peer-review, journal policy, reproducibility, Replicability, transparency, Peer Review Dates Published: 2025-01-20 09:33 Last Updated: 2025-01-20 17:47 Older Versions License CC BY Attribution 4.0 International Additional Metadata Conflict of interest statement: None Data and Code Availability Statement: All data and code used for processing, analysis, and visualisation are available at Open Science Framework (https://osf.io/cqn3f/, DOI:10.17605/OSF.IO/CQN3F; Ivimey-Cook et al., 2024). Language: English

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License: CC-BY-4.0