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Related Experiment Video

Updated: Oct 12, 2025

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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An automated solution for measuring the progress toward FAIR research data.

Anusuriya Devaraju1, Robert Huber2

  • 1Terrestrial Ecosystem Research Network (TERN), The University of Queensland, Long Pocket Precinct, Level 5 Foxtail Building #1019, 80 Meiers Road, Indooroopilly, QLD 4068 Australia.

Patterns (New York, N.Y.)
|November 25, 2021
PubMed
Summary
This summary is machine-generated.

Automated assessment of research data

Keywords:
FAIR data principlesautomated assessmentdata discoverydata reusemetricsresearch objectstrustworthy digital repository

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Area of Science:

  • Data Science
  • Digital Curation
  • Research Reproducibility

Background:

  • Increasing volume of scientific datasets necessitates robust methods for assessing data quality and compliance.
  • Ensuring research data meets Findable, Accessible, Interoperable, and Reusable (FAIR) principles is crucial for scientific advancement.
  • Existing methods for FAIR compliance testing are often manual and time-consuming.

Purpose of the Study:

  • To present a programmatic solution for automated FAIR data assessment.
  • To translate FAIR data principles into measurable metrics and practical tests.
  • To develop and apply an open-source tool for evaluating FAIR compliance.

Main Methods:

  • Conceptualized and implemented practical tests for each FAIR metric based on data curation and sharing practices.
  • Developed an open-source tool to automate the FAIR assessment process.
  • Applied the developed metrics and tool to evaluate multidisciplinary datasets from trustworthy repositories.

Main Results:

  • Successfully translated FAIR principles into quantifiable metrics.
  • Demonstrated the application of an open-source tool for evaluating FAIR compliance of research data.
  • Identified areas for improvement and provided recommendations based on evaluation results.

Conclusions:

  • The developed programmatic solution and open-source tool offer an efficient approach to automated FAIR data assessment.
  • The experience and lessons learned provide valuable insights for developing similar assessment methods for digital objects and services.
  • Promoting FAIR data compliance through automated tools enhances research reproducibility and data utility.