The SORTEE guidelines for data and code quality control in ecology and evolutionary biology

Open data and code are crucial to increasing transparency and reproducibility, and in building trust in scientific research. However, despite an increasing number of journals in ecology and evolutionary biology mandating for data and code to be archived alongside published articles, the amount and quality of archived data and code, and subsequent reproducibility of results, has remained worryingly low. As a result, a handful of journals have recruited dedicated data editors, whose role is to help authors increase the overall quality of archived data and code. There is, however, a general lack of consensus around what a data editor should check, how to do it, and to what level of detail, and the process is often vague and hidden from readers and authors alike. Here, with the input from multiple data editors across several journals in ecology and evolutionary biology, we establish and describe the first standardised guidelines for Data and Code Quality Control on behalf of the Society for Open, Reliable, and Transparent Ecology and Evolutionary Biology (SORTEE). We then introduce the SORTEE-led guidelines as a flexible six-stage framework that journals can implement incrementally and/or apply on a case-by-case basis, particularly when some checks (e.g., computational reproducibility) are not feasible (e.g., proprietary software). We conclude with practical advice for journals and authors, arguing that flexible adoption of these standardised guidelines will improve the consistency and transparency of the data editor process for readers, authors, data editors, and the wider scientific community.

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Publication Details

Journal
Peer Community Journal
Published
2026-03-13
DOI
https://doi.org/10.24072/pcjournal.687
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
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article

The SORTEE guidelines for data and code quality control in ecology and evolutionary biology

Natalie E. van Dis, Malgorzata Lagisz, Erola Fenollosa, Thomas Guillemaud et al.
Peer Community Journal
Scientific Computing and Data Management
article

The SORTEE guidelines for data and code quality control in ecology and evolutionary biology

Natalie E. van Dis, Malgorzata Lagisz, Erola Fenollosa, Thomas Guillemaud, Christian John, Matthieu Paquet, Tad Dallas, Joel L. Pick, Quentin Petitjean, Jack A. Brand, Daniel S. Maynard, Sébastian Lequime, Shinichi Nakagawa, Dylan Gomes, Matthew Grainger, Barbara Class, Bethany J. Allen, Ruby Krasnow, Kevin R. Bairos-Novak, Rose E. O'Dea, Pietro B D'Amelio, Laura A. B. Wilson, Benedicte Bachelot, Edward R Ivimey-Cook, Alfredo Sánchez-Tójar, Esteban Fernández-Juricic
article en

Abstract

Open data and code are crucial to increasing transparency and reproducibility, and in building trust in scientific research. However, despite an increasing number of journals in ecology and evolutionary biology mandating for data and code to be archived alongside published articles, the amount and quality of archived data and code, and subsequent reproducibility of results, has remained worryingly low. As a result, a handful of journals have recruited dedicated data editors, whose role is to help authors increase the overall quality of archived data and code. There is, however, a general lack of consensus around what a data editor should check, how to do it, and to what level of detail, and the process is often vague and hidden from readers and authors alike. Here, with the input from multiple data editors across several journals in ecology and evolutionary biology, we establish and describe the first standardised guidelines for Data and Code Quality Control on behalf of the Society for Open, Reliable, and Transparent Ecology and Evolutionary Biology (SORTEE). We then introduce the SORTEE-led guidelines as a flexible six-stage framework that journals can implement incrementally and/or apply on a case-by-case basis, particularly when some checks (e.g., computational reproducibility) are not feasible (e.g., proprietary software). We conclude with practical advice for journals and authors, arguing that flexible adoption of these standardised guidelines will improve the consistency and transparency of the data editor process for readers, authors, data editors, and the wider scientific community.

Peer Community JournalVol. 6
University of East Anglia (GB), Australian National University (AU), Centre National de la Recherche Scientifique (FR), Oregon Department of Fish and Wildlife (US), Institut National de la Recherche Agronomique (MA), Zoological Society of London (GB), Australian Institute of Marine Science (AU), University of Helsinki (FI), University of Alberta (CA), University of California, Santa Barbara (US), University of South Carolina (US), The University of Melbourne (AU), University of Groningen (NL), Bielefeld University (DE), Purdue University West Lafayette (US), Swedish University of Agricultural Sciences (SE), UNSW Sydney (AU), University of Oxford (GB), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Département agronomie et sciences de l'environnement pour les agroécosystèmes (FR), Netherlands Institute of Ecology (NL), Oklahoma State University Oklahoma City (US), GFZ Helmholtz Centre for Geosciences (DE), Institut Sophia Agrobiotech (FR), Norwegian Institute for Nature Research (NO), Station d’Écologie Théorique et Expérimentale (FR), Environmental Earth Sciences (AU), Ecosystem Sciences (AU), University College London (GB), Reed College (US), University of Coimbra (PT), University of Maine (US), University of Edinburgh (GB)
Openalex Percentile: Top 22%
Scientific Computing and Data Management
DOIOpen Access PDFOpenAlex
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