Ten simple rules for writing Dockerfiles for reproducible data science.

Autor: Daniel Nüst, Vanessa Sochat, Ben Marwick, Stephen J Eglen, Tim Head, Tony Hirst, Benjamin D Evans
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: PLoS Computational Biology, Vol 16, Iss 11, p e1008316 (2020)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1008316
Popis: 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.
Databáze: Directory of Open Access Journals
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