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A large-scale analysis of bioinformatics code on GitHub

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to...

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Autores principales: Russell, Pamela H., Johnson, Rachel L., Ananthan, Shreyas, Harnke, Benjamin, Carlson, Nichole E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209220/
https://www.ncbi.nlm.nih.gov/pubmed/30379882
http://dx.doi.org/10.1371/journal.pone.0205898
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author Russell, Pamela H.
Johnson, Rachel L.
Ananthan, Shreyas
Harnke, Benjamin
Carlson, Nichole E.
author_facet Russell, Pamela H.
Johnson, Rachel L.
Ananthan, Shreyas
Harnke, Benjamin
Carlson, Nichole E.
author_sort Russell, Pamela H.
collection PubMed
description In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.
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spelling pubmed-62092202018-11-19 A large-scale analysis of bioinformatics code on GitHub Russell, Pamela H. Johnson, Rachel L. Ananthan, Shreyas Harnke, Benjamin Carlson, Nichole E. PLoS One Research Article In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8. Public Library of Science 2018-10-31 /pmc/articles/PMC6209220/ /pubmed/30379882 http://dx.doi.org/10.1371/journal.pone.0205898 Text en © 2018 Russell et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Russell, Pamela H.
Johnson, Rachel L.
Ananthan, Shreyas
Harnke, Benjamin
Carlson, Nichole E.
A large-scale analysis of bioinformatics code on GitHub
title A large-scale analysis of bioinformatics code on GitHub
title_full A large-scale analysis of bioinformatics code on GitHub
title_fullStr A large-scale analysis of bioinformatics code on GitHub
title_full_unstemmed A large-scale analysis of bioinformatics code on GitHub
title_short A large-scale analysis of bioinformatics code on GitHub
title_sort large-scale analysis of bioinformatics code on github
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209220/
https://www.ncbi.nlm.nih.gov/pubmed/30379882
http://dx.doi.org/10.1371/journal.pone.0205898
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