Data‐ and code‐archiving in the British Ecological Society journals: present status and recommendations for future improvements

Data- and code-archiving are important components of open science, as both make research more transparent, reproducible, accountable and credible, allowing future researchers to build on previous work. Despite progress in implementing data- and code-archiving policies in journals publishing ecology and evolution research, issues remain. To be more useful to future researchers, archived data and code must not only be archived but also meet good practice standards. We collected data from 1861 papers published between 2017 and 2024 in the seven British Ecological Society (BES) journals, during a hackathon event. We systematically checked associated data and/or code, metadata, help files and annotations to assess archiving practices. We determined if and where data and code files were archived, whether they could be located, downloaded and opened, and whether they had associated READMEs, digital object identifiers (DOI) and licences. We also recorded the file extensions used to save data/code files, and which programming languages code was written in. 93% of the 1861 papers we examined used data and ~90% used code. While 97% of the 1735 papers that used data also archived it, only 35% of the 1670 papers that used code also archived code. Over 85% of archived data and code could be located, downloaded and opened. Reusability, however, was more limited; around a third of papers did not have a README or similar to explain their data/code files, and the quality of READMEs varied substantially. We recommend that researchers archive their code and that archived code be explicitly mentioned in the Data (or Code) Availability statement. We also encourage researchers to provide more accessible and informative READMEs for data and code. To help achieve these recommendations, we advocate that journals employ Data/Code editors to review data and code quality, research institutions deliver more training in open science practices, and funding bodies set clear expectations on open data and code practices