Overcoming barriers to computational reproducibility

Abstract Computational reproducibility, the possibility for independent researchers to exactly reproduce published empirical results, is fundamental to science. Despite its importance, the proportion of research articles aiming for reproducibility remains low and uneven across disciplines. Barriers include a perceived lack of incentives for researchers and journals, practical challenges in preparing reproducible materials, and the absence of harmonized standards of reproducibility processes and requirements by journals. Existing guidance is often highly technical, reaching mainly those already engaged with reproducible research. In this paper, we first synthesize evidence on the benefits of reproducibility for both authors and journals. Drawing on our extensive experience in reproducibility checking at various journals, we then put forward concise, pragmatic guidelines and the REPRO Checklist for creating reproducible analyses across disciplines. We further review current reproducibility policies of selected journals, illustrating the substantial heterogeneity in requirements and procedures. Motivated by the latter, we propose conceptual foundations for a harmonized multi-tier system of reproducibility standards that could support transparent, consistent assessment across journals and research communities. Our goal as journal (reproducibility) editors and contributors to the Mathematical Research Data Initiative is to encourage broader adoption of reproducibility practices, in particular by lowering practical barriers for authors and journals.