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Biomedical open source software: Crucial packages and hidden heroes.
Eva Maxfield Brown1, Stephan Druskat2, Laurent Hébert-Dufresne3
1Information School, University of Washington, Seattle, Washington, United States of America.
Plos Computational Biology
|May 13, 2026
Summary
Scientific software is crucial but often unrewarded. This study identifies critical foundational libraries in biomedical research ecosystems using dependency network analysis, highlighting essential software components.
Area of Science:
- Computer Science
- Bioinformatics
- Software Engineering
Background:
- Scientific software is vital for research reproducibility and advancement.
- Foundational software libraries are often unrecognized and unrewarded, despite their critical role.
- Understanding research software dependencies is essential for stakeholders like funders and infrastructure providers.
Purpose of the Study:
- To map the upstream dependencies of software used in biomedical research papers.
- To identify critical packages within scientific software ecosystems.
- To propose and apply centrality metrics for analyzing software dependency networks.
Main Methods:
- Utilized the CZ Software Mentions Dataset to analyze software dependencies.
- Developed and applied network centrality metrics to quantify package importance.
- Examined three major software ecosystems: PyPi, CRAN, and Bioconductor.
Main Results:
- Identified key foundational software packages critical to biomedical research.
- Quantified the centrality of software packages within different ecosystems.
- Revealed the hidden network of software dependencies underpinning scientific publications.
Conclusions:
- Formal recognition and reward systems for scientific software are needed.
- Centrality metrics can effectively highlight essential software components in research.
- This work provides a framework for understanding and valuing critical research software infrastructure.
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