Biomedical Open Source Software: Crucial Packages and Hidden Heroes

TL;DR

This paper maps the dependency networks of biomedical research software, identifying crucial packages and analyzing their roles across major ecosystems using centrality metrics.

Contribution

It introduces a method to analyze the dependency networks of biomedical software and identifies key packages in major ecosystems.

Findings

Identified critical biomedical software packages using centrality metrics.

Mapped upstream dependencies of software in biomedical research papers.

Analyzed three major ecosystems: PyPi, CRAN, and Bioconductor.

Abstract

Despite the importance of scientific software for research, it is often not formally recognized and rewarded. This is especially true for foundational libraries, which are hidden below packages visible to the users (and thus doubly hidden, since even the packages directly used in research are frequently not visible in the paper). Research stakeholders like funders, infrastructure providers, and other organizations need to understand the complex network of computer programs that contemporary research relies upon. In this work, we use the CZ Software Mentions Dataset to map the upstream dependencies of software used in biomedical papers and find the packages critical to scientific software ecosystems. We propose centrality metrics for the network of software dependencies, analyze three ecosystems (PyPi, CRAN, Bioconductor), and determine the packages with the highest centrality.