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Medientyp:
E-Artikel
Titel:
Ten simple rules for writing Dockerfiles for reproducible data science
Beteiligte:
Nüst, Daniel;
Sochat, Vanessa;
Marwick, Ben;
Eglen, Stephen J.;
Head, Tim;
Hirst, Tony;
Evans, Benjamin D.
Erschienen:
Public Library of Science (PLoS), 2020
Erschienen in:PLOS Computational Biology
Sprache:
Englisch
DOI:
10.1371/journal.pcbi.1008316
ISSN:
1553-7358
Entstehung:
Anmerkungen:
Beschreibung:
<jats:p>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 <jats:monospace>Dockerfile</jats:monospace> format. In support of this approach, we present a set of rules to help researchers write understandable <jats:monospace>Dockerfiles</jats:monospace> 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.</jats:p>