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 of 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 start by introducing the concept of a data editor and data and code quality control, what is expected from data and code quality control, the relative costs and benefits to journals, authors, and readers, and then introduce and detail the SORTEE-led guidelines, ending with advice for journals and authors. We believe that by adopting these standardised guidelines, journals will help increase the consistency and transparency of the data editor process for readers, authors, and data editors.
Pick et al. (Fri,) studied this question.