Scientific Open-Source Software Is Less Likely to Become Abandoned Than One Might Think! Lessons from Curating a Catalog of Maintained Scientific Software

2025-04-26

AI Summary

This study challenges the prevailing assumption that scientific software is more prone to abandonment. Method: We systematically analyze the lifetimes of over 18,000 open-source scientific software projects, constructing a cross-domain, layered (e.g., infrastructure-layer) scientific software catalog and matching each with comparable non-scientific projects. We integrate LLM-based automated domain-and-stack-layer classification, Cox proportional hazards survival analysis, and matched-sample causal inference. Contribution/Results: Our large-scale empirical analysis reveals—contrary to conventional wisdom—that open-source scientific software exhibits significantly longer median lifetimes than matched non-scientific counterparts. Key longevity determinants identified include government sponsorship, high downstream dependency, scholarly citation, and infrastructure-layer positioning. This work establishes the first large-sample empirical foundation for scientific software sustainability and delivers an actionable predictive framework grounded in robust causal evidence.

Technology Category

• Application Domains: Natural Sciences
• Reasoning under Uncertainty: Causality
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Analyzing longevity factors in scientific vs non-scientific open-source software
• Curating a large dataset of 18,000+ scientific software projects
• Investigating how domain and infrastructure affect software maintenance

Innovation

• Used large language models for repository classification
• Curated 18,000+ scientific software projects collection
• Applied survival models to analyze software longevity