Dozmorov Mikhail G
Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, United States.
Front Bioeng Biotechnol. 2018 Dec 18;6:198. doi: 10.3389/fbioe.2018.00198. eCollection 2018.
Modern research is increasingly data-driven and reliant on bioinformatics software. Publication is a common way of introducing new software, but not all bioinformatics tools get published. Giving there are competing tools, it is important not merely to find the appropriate software, but have a metric for judging its usefulness. Journal's impact factor has been shown to be a poor predictor of software popularity; consequently, focusing on publications in high-impact journals limits user's choices in finding useful bioinformatics tools. Free and open source software repositories on popular code sharing platforms such as GitHub provide another venue to follow the latest bioinformatics trends. The open source component of GitHub allows users to bookmark and copy repositories that are most useful to them. This Perspective aims to demonstrate the utility of GitHub "stars," "watchers," and "forks" (GitHub statistics) as a measure of software impact. We compiled lists of impactful bioinformatics software and analyzed commonly used impact metrics and GitHub statistics of 50 genomics-oriented bioinformatics tools. We present examples of community-selected best bioinformatics resources and show that GitHub statistics are distinct from the journal's impact factor (JIF), citation counts, and alternative metrics (Altmetrics, CiteScore) in capturing the level of community attention. We suggest the use of GitHub statistics as an unbiased measure of the usability of bioinformatics software complementing the traditional impact metrics.
现代研究越来越依赖数据，并依靠生物信息学软件。发表文章是介绍新软件的常见方式，但并非所有生物信息学工具都会发表。鉴于存在竞争工具，不仅要找到合适的软件，还要有一种衡量其有用性的指标，这一点很重要。期刊的影响因子已被证明不能很好地预测软件的受欢迎程度；因此，专注于在高影响因子期刊上发表文章会限制用户寻找有用生物信息学工具的选择。在GitHub等流行代码共享平台上的免费和开源软件库提供了另一种追踪最新生物信息学趋势的途径。GitHub的开源组件允许用户收藏和复制对他们最有用的软件库。本观点旨在证明GitHub上的“星标”“关注者”和“复刻”（GitHub统计数据）作为衡量软件影响力的效用。我们编制了有影响力的生物信息学软件列表，并分析了50种面向基因组学的生物信息学工具常用的影响指标和GitHub统计数据。我们列举了社区选定的最佳生物信息学资源的例子，并表明GitHub统计数据在反映社区关注程度方面与期刊影响因子（JIF）、引用次数和替代指标（替代计量学、CiteScore）不同。我们建议使用GitHub统计数据作为衡量生物信息学软件可用性的无偏指标，以补充传统的影响指标。
