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FAIRly big: A framework for computationally reproducible processing of large-scale data.

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Summary
This summary is machine-generated.

This study introduces a DataLad-based framework for reproducible data processing, enhancing data sharing and scalability for large scientific datasets while adhering to open science principles.

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Area of Science:

  • Computational science
  • Data science
  • Neuroscience

Background:

  • Large-scale datasets offer scientific opportunities but pose challenges for FAIR data principles (findability, accessibility, interoperability, reusability).
  • Infrastructure limitations, data usage constraints, and software licenses hinder data sharing and reproducibility.

Purpose of the Study:

  • To introduce a domain-agnostic framework for reproducible data processing.
  • To ensure compliance with open science mandates and overcome infrastructure limitations.
  • To capture machine-actionable computational provenance for verification and re-execution.

Main Methods:

  • Development of a DataLad-based framework.
  • Minimizing platform idiosyncrasies and performance complexities.
  • Capturing computational provenance for retraceability and re-execution.

Main Results:

  • Demonstrated framework performance on the studyforrest.org dataset for data sharing and transparency.
  • Showcased framework scalability using the UK Biobank dataset, the largest public brain imaging dataset.
  • Enabled re-execution of research outcomes independent of original computing infrastructure.

Conclusions:

  • The framework facilitates reproducible data processing for large-scale datasets.
  • It supports open science mandates by enhancing data sharing, transparency, and reusability.
  • The approach addresses challenges in managing and processing extensive scientific data.