ArticlePLoS computational biology2020
Ten simple rules for writing Dockerfiles for reproducible data science.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Orchestrating neuroimaging data processing using the 'Snakemake' workflow manager.Brain structure & function · 2026Article
- Reproducible-by-design: Romics Processor, a FAIR ecosystem for multi-omics and spatial-omics analysis.bioRxiv : the preprint server for biology · 2026Article
- Ten quick tips to SNIFF out sustainable and secure scientific software.PLoS computational biology · 2026Article
- Nine quick tips for software containerization.PLoS computational biology · 2026Article
- Review
- Using Open Science Tools to Teach Environmental Sciences.Ecology and evolution · 2025Article
- Article
- ENCORE: a practical implementation to improve reproducibility and transparency of computational research.Nature communications · 2024Article
- Ten simple rules for building and maintaining a responsible data science workflow.PLoS computational biology · 2024Article
- CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications.BMC bioinformatics · 2024Article
- Article
- The five pillars of computational reproducibility: bioinformatics and beyond.Briefings in bioinformatics · 2023Article
- Ten quick tips for building FAIR workflows.PLoS computational biology · 2023Article
- Alternative splicing analysis benchmark with DICAST.NAR genomics and bioinformatics · 2023Article
- Ethoscopy and ethoscope-lab: a framework for behavioural analysis to lower entrance barrier and aid reproducibility.Bioinformatics advances · 2023Article
- EGAsubmitter: A software to automate submission of nucleic acid sequencing data to the European Genome-phenome Archive.Frontiers in bioinformatics · 2023Article
- Open-source Tools for Training Resources - OTTR.Journal of statistics and data science education : an official journal of the of the American Statistical Association · 2023Article
- Ten quick tips for sequence-based prediction of protein properties using machine learning.PLoS computational biology · 2022Article
- Toward practical transparent verifiable and long-term reproducible research using Guix.Scientific data · 2022Article
- Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.