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Software is increasingly important to the scientific enterprise, and science-funding agencies are increasingly funding software work. Accordingly, many different participants need insight into how to understand the relationship between software, its development, its use, and its scientific impact. In this article, we draw on interviews and participant observation to describe the information needs of domain scientists, software component producers, infrastructure providers, and ecosystem stewards, including science funders. We provide a framework by which to categorize different types of measures and their relationships as they reach around from funding, development, scientific use, and through to scientific impact. We use this framework to organize a presentation of existing measures and techniques, and to identify areas in which techniques are either not widespread, or are entirely missing. We conclude with policy recommendations designed to improve insight into the scientific software ecosystem, make it more understandable, and thereby contribute to the progress of science.
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This content will become publicly available on June 19, 2026
Scientific Open-Source Software Is Less Likely to Become Abandoned Than One Might Think! Lessons from Curating a Catalog of Maintained Scientific Software
Scientific software is essential to scientific innovation and in many ways it is distinct from other types of software. Abandoned (or unmaintained), buggy, and hard to use software, a perception often associated with scientific software can hinder scientific progress, yet, in contrast to other types of software, its longevity is poorly understood. Existing data curation efforts are fragmented by science domain and/or are small in scale and lack key attributes. We use large language models to classify public software repositories in World of Code into distinct scientific domains and layers of the software stack, curating a large and diverse collection of over 18,000 scientific software projects. Using this data, we estimate survival models to understand how the domain, infrastructural layer, and other attributes of scientific software affect its longevity. We further obtain a matched sample of non-scientific software repositories and investigate the differences. We find that infrastructural layers, downstream dependencies, mentions of publications, and participants from government are associated with a longer lifespan, while newer projects with participants from academia had shorter lifespan. Against common expectations, scientific projects have a longer lifetime than matched non-scientific open-source software projects. We expect our curated attribute-rich collection to support future research on scientific software and provide insights that may help extend longevity of both scientific and other projects.
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- PAR ID:
- 10652546
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Software Engineering
- Volume:
- 2
- Issue:
- FSE
- ISSN:
- 2994-970X
- Page Range / eLocation ID:
- 2216 to 2239
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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