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Principles for data analysis workflows

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  • Sara Stoudt
  • Váleri N Vásquez
  • Ciera C Martinez

Abstract

A systematic and reproducible “workflow”—the process that moves a scientific investigation from raw data to coherent research question to insightful contribution—should be a fundamental part of academic data-intensive research practice. In this paper, we elaborate basic principles of a reproducible data analysis workflow by defining 3 phases: the Explore, Refine, and Produce Phases. Each phase is roughly centered around the audience to whom research decisions, methodologies, and results are being immediately communicated. Importantly, each phase can also give rise to a number of research products beyond traditional academic publications. Where relevant, we draw analogies between design principles and established practice in software development. The guidance provided here is not intended to be a strict rulebook; rather, the suggestions for practices and tools to advance reproducible, sound data-intensive analysis may furnish support for both students new to research and current researchers who are new to data-intensive work.

Suggested Citation

  • Sara Stoudt & Váleri N Vásquez & Ciera C Martinez, 2021. "Principles for data analysis workflows," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-26, March.
    • Handle: RePEc:plo:pcbi00:1008770
    DOI: 10.1371/journal.pcbi.1008770
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    1. Victoria Stodden & Jennifer Seiler & Zhaokun Ma, 2018. "An empirical analysis of journal policy effectiveness for computational reproducibility," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(11), pages 2584-2589, March.
    2. Santiago Schnell, 2015. "Ten Simple Rules for a Computational Biologist’s Laboratory Notebook," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-5, September.
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