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Reproducible big data science: A case study in continuous FAIRness.

Ravi Madduri1,2, Kyle Chard1,2, Mike D'Arcy3

  • 1Globus, University of Chicago, Chicago, Illinois, United States of America.

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Summary
This summary is machine-generated.

New tools enable researchers to capture and identify big biomedical data and code, making complex analyses reproducible and reusable. This ensures large datasets are accessible and usable for scientific discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Large-scale biomedical datasets present challenges for data capture, analysis, and sharing.
  • Ensuring data and code are findable, accessible, interoperable, and reusable (FAIR) is crucial for scientific advancement.
  • Existing methods often struggle to manage the complexity and volume of big data analyses.

Purpose of the Study:

  • To introduce and illustrate tools for capturing and assigning identifiers to data and code throughout the data lifecycle.
  • To demonstrate how these tools facilitate the FAIR principles for big biomedical data.
  • To address the challenges of usability and reproducibility in big data research.

Main Methods:

  • Development and application of tools for automated data and code capture.
  • Utilizing cloud computing and fast networks for efficient data processing.
  • Case study involving the creation of a transcription factor binding site atlas from ENCODE sequencing data.
  • User study to evaluate the effectiveness and usability of the developed tools.

Main Results:

  • The tools automate complex, multi-step analyses, capturing algorithms in reusable formats.
  • A case study successfully generated an atlas of putative transcription factor binding sites from terabytes of data.
  • Cloud-based processing enabled rapid analysis without compromising usability or reproducibility.
  • A user study showed 91% of participants could replicate a complex analysis.

Conclusions:

  • The developed tools significantly improve the FAIRness of big biomedical data.
  • Automated capture and identifier assignment enhance the reproducibility and reusability of complex analyses.
  • These tools make big data more accessible and usable for scientific discovery, overcoming previous limitations.