Ten Simple Rules for Taking Advantage of git and GitHub

Abstract

IntroductionBioinformatics is a broad discipline in which one common denominator is the need to produce and/or use software that can be applied to biological data in different contexts. To enable and ensure the replicability and traceability of scientific claims, it is essential that the scientific publication, the corresponding datasets, and the data analysis are made publicly available [1,2]. All software used for the analysis should be either carefully documented (e.g., for commercial software) or, better yet, openly shared and directly accessible to others [3,4]. The rise of openly available software and source code alongside concomitant collaborative development is facilitated by the existence of several code repository services such as SourceForge, Bitbucket, GitLab, and GitHub, among others. These resources are also essential for collaborative software projects because they enable the organization and sharing of programming tasks between different remote contributors. Here, we introduce the main features of GitHub, a popular webbased platform that offers a free and integrated environment for hosting the source code, documentation, and project-related web content for open-source projects. GitHub also offers paid plans for private repositories (see Box 1) for individuals and businesses as well as free plans including private repositories for research and educational use.GitHub relies, at its core, on the well-known and open-source version control system Git, originally designed by Linus Torvalds for the development of the Linux kernel and now developed and maintained by the Git community. One reason for GitHub's success is that it offers more than a simple source code hosting service [5,6]. It provides developers and researchers with a dynamic and collaborative environment, often referred to as a social coding platform, that supports peer review, commenting, and discussion [7]. A diverse range of efforts, ranging from individual to large bioinformatics projects, laboratory repositories, as well as global PLOS Computational Biology |

Developing a modern data workflow for evolving data

“…Most research data and data processing will fit comfortably within these limits (the largest file in the Portal database is currently <20 MB and it takes <15 minutes for all data checking and processing code to run), so we think this type of system will work for the majority of research projects. However, in cases where larger data files or longer run times are necessary, it is possible to adapt our general approach by using equivalent tools that can be run on local computing resources (e.g, GitLab for managing git repositories and Jenkins for continuous integration) and using tools that are designed for versioning large data (e.g., dat; Ogden, McKelvey, & Madsen, 2017;or git Large File Storage;Perez-Riverol et al 2016).…”

Executable Simulation Model of the Liver

“…To keep track of these changes, EXSIMOs are built on top of version control. In our example we take advantage of the features of git and GitHub (https://github.com/matthiaskoenig/exsimo, Figure 2H) [11]. By using version control we enable collaborative work (merging changes), managing different versions (creating branches), tracking of changes (analyzing diffs), reuse (forking the repository), and versioning (using tags) of an EXSIMO.…”

babette: BEAUti 2, BEAST2 and Tracer for R

“…babette's development takes place on GitHub, https://github.com/richelbilderbeek/babette, which accommodates collaboration (Gorgolewski & Poldrack, ). and improves transparency (Perez‐Riverol et al., ) babette's GitHub facilitates feature requests and has guidelines how to do so.…”