In an era rife with costly retractions, scientists have an increasing burden to be more transparent in order to maintain their credibility. One such reason has been the lack of detailed access to underlying data and statistical code used for analysis, which can provide opportunities for others to verify findings. While repeating expensive studies to validate findings is often difficult, a whole host of other reasons have contributed to the problem of reproducibility. Advances over the years have resulted in the development of complex methodologies that allow us to collect ever increasing amounts of data. Reproducibility also allows others to build upon existing work and use it to test new ideas and develop methods. Reproducible science provides the critical standard by which published results are judged and central findings are either validated or refuted. In this paper I provide an overview of Git along with use-cases that highlight how this tool can be leveraged to make science more reproducible and transparent, foster new collaborations, and support novel uses. For larger collaborative efforts, Git and Git hosting services make it possible for everyone to work asynchronously and merge their contributions at any time, all the while maintaining a complete authorship trail.
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For individual researchers, Git provides a powerful way to track and compare versions, retrace errors, explore new approaches in a structured manner, while maintaining a full audit trail. One such open source VCS, Git, provides a lightweight yet robust framework that is ideal for managing the full suite of research outputs such as datasets, statistical code, figures, lab notes, and manuscripts.
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Version control systems (VCS), which have long been used to maintain code repositories in the software industry, are now finding new applications in science. Despite this increase in transparency, it is still difficult to reproduce or build upon the findings of most scientific publications without access to a more complete workflow.
Sharing data and computer code associated with publications is becoming increasingly common, motivated partly in response to data deposition requirements from journals and mandates from funders. Maintaining a high degree of transparency in scientific reporting is essential not just for gaining trust and credibility within the scientific community but also for facilitating the development of new ideas. Reproducibility is the hallmark of good science.
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