Ten simple rules for writing Dockerfiles for reproducible data science

Markel, Scott; Nüst, Daniel; Sochat, Vanessa; Marwick, Ben; Eglen, Stephen J.; Head, Tim; Hirst, Tony and Evans, Benjamin D. (2020). Ten simple rules for writing Dockerfiles for reproducible data science. PLOS Computational Biology, 16(11), article no. e1008316.

DOI: https://doi.org/10.1371/journal.pcbi.1008316

Abstract

Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility; in turn, a workflow’s reproducibility can be greatly affected by the choices that are made with respect to building containers. In many cases, the build process for the container’s image is created from instructions provided in a Dockerfile format. In support of this approach, we present a set of rules to help researchers write understandable Dockerfiles for typical data science workflows. By following the rules in this article, researchers can create containers suitable for sharing with fellow scientists, for including in scholarly communication such as education or scientific papers, and for effective and sustainable personal workflows.

Viewing alternatives

Look up in Google Scholar

Download history

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

CORE (COnnecting REpositories)

Recommendations