Ten Simple Rules for Making Research Software More Robust
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Abstract (arXiv)
Software produced for research, published and otherwise, suffers from a number of common problems that make it difficult or impossible to run outside the original institution, or even off the primary developer's computer. We present ten simple rules to make such software robust enough to run anywhere, and inspire confidence in your reproducibility, and thereby delight your users and collaborators.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Oct 2016.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 2.
3 papers.
- 2023A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflowscited by 1
- 2026Forensic Reproducibility Audit of a Radiology Vision-Language Model Benchmark: From Intended Protocol to Released Artifactcited by 0
- 2026Technical Report on the Turba Fertilizer Machine Learning Stack in Moroccocited by 0
- 2026Technical Report on the Turba Fertilizer Machine Learning Stack in Morocco5 Oct 2026
- 2026Forensic Reproducibility Audit of a Radiology Vision-Language Model Benchmark: From Intended Protocol to Released Artifact28 Jul 2026
- 2023A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows13 Jan 2023