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Summary
Software pervades every domain of science (1–3), perhaps nowhere more decisively than in modeling. In key scientific areas of great societal importance, models and the software that implement them define both how science is done and what science is done (4, 5). Across all science, this dependence has led to concerns around the need for open access to software (6, 7), centered on the reproducibility of research (1, 8–10). From fields such as high-performance computing, we learn key insights and best practices for how to develop, standardize, and implement software (11). Open and systematic approaches to the development of software are essential for all sciences. But for many scientists this is not sufficient. We describe problems with the adoption and use of scientific software.
