Biomedical Open Source Software: Crucial Packages and Hidden Heroes
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arXiv
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| Format: | Preprint |
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2024
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| author | Brown, Eva Maxfield Druskat, Stephan Hébert-Dufresne, Laurent Howison, James Mietchen, Daniel Nesbitt, Andrew Pimentel, João Felipe Veytsman, Boris |
| author_facet | Brown, Eva Maxfield Druskat, Stephan Hébert-Dufresne, Laurent Howison, James Mietchen, Daniel Nesbitt, Andrew Pimentel, João Felipe Veytsman, Boris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Biomedical Open Source Software: Crucial Packages and Hidden Heroes Brown, Eva Maxfield Druskat, Stephan Hébert-Dufresne, Laurent Howison, James Mietchen, Daniel Nesbitt, Andrew Pimentel, João Felipe Veytsman, Boris Software Engineering Computers and Society D.2; K.4; D.2.13 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. |
| title | Biomedical Open Source Software: Crucial Packages and Hidden Heroes |
| topic | Software Engineering Computers and Society D.2; K.4; D.2.13 |
| url | https://arxiv.org/abs/2404.06672 |