The five pillars of computational reproducibility: bioinformatics and beyond

Abstract

Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.

Towards more reproducibility in vegetation research

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Computational reproducibility is the ability to obtain consistent results using the same input data, computational steps, methods, and code from a previous study (Goodman et al, 2016;Ziemann et al, 2023). It plays an important role in science because it: (1) ensures the credibility of scientific results; (2) improves the understanding of complex analytical workflows; (3) promotes knowledge sharing; and (4) allows the saving of research funds (Alston & Rick, 2021).

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