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nuest committed May 11, 2020
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title: "Ten Simple Rules for Writing Dockerfiles for Reproducible Data Science"
shorttitle: "Ten Simple Rules for Dockerfiles"
author:
- name: Daniel Nüst
# https://github.com/nuest
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The build for the container image is often created based on the instructions in the `Dockerfile` format.
The rules presented here help researchers to write understandable `Dockerfile`s for typical data science workflows.
By following the rules in this article researchers can create containers suitable for sharing with fellow scientists, for inclusion in scholarly communication such as education or scientific papers, and for an effective and sustainable personal workflow.
#author_summary: |
# TBD
author_summary: |
Computers and algorithms are ubiquitous in research.
Therefore the definition of the computing environment, i.e., the body of all software used directly or indirectly by a researcher, is important, because it allows other researchers to recreate the environment to understand, inspect, and reproduce an analysis.
A helpful abstraction for capturing the computing environment are called _containers_.
A container is created from a set of instructions in a recipe.
For the most common containerisation software, Docker, this recipe is called Dockerfile.
We think that for scientific research, one should follow specific practices to write a Dockerfile.
This practice is sometimes different from what generic software developers would do and focuses on transparency and understandability over performance considerations.
The rules are intended to help researchers, especially newcomers to containerisation, to leverage containers for open and effective scholarly communication and collaboration, while avoiding pitfalls especially irksome in a research lifecycle.
The recommendations cover a deliberate approach to starting a first Dockerfile, formatting and style, documentation, and habits for using containers.
bibliography: bibliography.bib
output:
rticles::plos_article
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