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Ten simple rules for writing Dockerfiles for reproducible data science.

Daniel Nüst1, Vanessa Sochat2, Ben Marwick3

  • 1Institute for Geoinformatics, University of Münster, Münster, Germany.

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|November 10, 2020
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Summary
This summary is machine-generated.

Researchers can improve computational science workflows by writing understandable Dockerfiles. Following these rules enhances container reproducibility and facilitates sharing for scientific communication and personal use.

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Area of Science:

  • Computational science
  • Data science workflows

Background:

  • Containers enhance computational science by packaging software and data dependencies.
  • Reproducibility in scholarly workflows is crucial and influenced by container build choices.

Discussion:

  • Dockerfile instructions are commonly used for building container images.
  • This article presents rules for writing understandable Dockerfiles for data science workflows.

Key Insights:

  • Adopting these rules aids researchers in creating reproducible containers.
  • Understandable Dockerfiles improve transparency and support reproducibility.

Outlook:

  • Containers built with these rules are suitable for sharing among scientists.
  • They support scholarly communication, including education and scientific publications.
  • The rules promote effective and sustainable personal research workflows.