GitHub Statistics as a Measure of the Impact of Open-Source Bioinformatics Software

Area of Science:

• Bioinformatics
• Computational Biology
• Software Development

Background:

• Modern research heavily relies on bioinformatics software, but traditional publication metrics like journal impact factor poorly predict software popularity.
• Identifying useful bioinformatics tools is challenging due to numerous competing software options and limitations of publication-based assessments.

Purpose of the Study:

• To evaluate GitHub statistics (stars, watchers, forks) as a reliable and unbiased measure of bioinformatics software impact.
• To compare the efficacy of GitHub statistics against traditional metrics such as journal impact factor, citation counts, and alternative metrics.

Main Methods:

• Compiled lists of impactful bioinformatics software.
• Analyzed traditional impact metrics (Journal Impact Factor, citations, Altmetrics, CiteScore) and GitHub statistics for 50 genomics-oriented bioinformatics tools.
• Compared GitHub statistics with traditional metrics to assess their ability to capture community attention.

Main Results:

• GitHub statistics (stars, watchers, forks) provide a distinct measure of community attention compared to journal impact factor, citation counts, and alternative metrics.
• Examples of community-selected bioinformatics resources demonstrated the utility of GitHub statistics in identifying widely used and impactful tools.
• GitHub statistics were found to be a more unbiased indicator of software usability and community engagement.

Conclusions:

• GitHub statistics offer a valuable, community-driven metric for assessing the impact and usability of bioinformatics software.
• Integrating GitHub statistics alongside traditional metrics can enhance the discovery of high-quality, community-endorsed bioinformatics tools.
• This approach addresses the limitations of journal-based impact factors in the rapidly evolving field of bioinformatics software.