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Principles for data analysis workflows.

Sara Stoudt1,2, Váleri N Vásquez1,3, Ciera C Martinez1,4

  • 1Berkeley Institute for Data Science, University of California Berkeley, Berkeley, California, United States of America.

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|March 18, 2021
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Summary
This summary is machine-generated.

Establishing a reproducible data analysis workflow is crucial for academic research. This paper outlines a 3-phase model (Explore, Refine, Produce) to guide data-intensive investigations from raw data to insightful contributions.

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Area of Science:

  • Data Science
  • Computational Biology
  • Bioinformatics

Background:

  • Data-intensive research demands systematic and reproducible workflows for reliable scientific inquiry.
  • Traditional research practices often lack structured approaches for managing complex data analysis pipelines.
  • Reproducibility is a cornerstone of scientific integrity, especially in data-driven fields.

Purpose of the Study:

  • To define and elaborate basic principles for a reproducible data analysis workflow.
  • To introduce a 3-phase model (Explore, Refine, Produce) for organizing data analysis processes.
  • To provide guidance for researchers, particularly those new to data-intensive work, on establishing sound analytical practices.

Main Methods:

  • Conceptual framework development based on systematic workflow principles.
  • Definition of three distinct phases: Explore, Refine, and Produce, each with specific objectives and audiences.
  • Drawing analogies from software development design principles and established practices.

Main Results:

  • A structured 3-phase workflow (Explore, Refine, Produce) for data analysis is proposed.
  • Each phase is defined by its target audience and communication goals.
  • The workflow facilitates the generation of diverse research products beyond traditional publications.

Conclusions:

  • Implementing a systematic and reproducible data analysis workflow enhances the rigor of academic research.
  • The proposed 3-phase model offers a flexible yet structured approach to data-intensive investigations.
  • Guidance and tools are provided to support researchers in adopting reproducible data analysis practices.