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Development of a Virtual Reality Assessment of Everyday Living Skills
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FAIR assessment tools: evaluating use and performance.

N A Krans1, A Ammar2, P Nymark3

  • 1National Institute for Public Health and the Environment (RIVM), Centre for Safety of Substances and Products, Bilthoven, the Netherlands.

Nanoimpact
|June 19, 2022
PubMed
Summary
This summary is machine-generated.

This study reviewed ten FAIR assessment tools for research data, finding online self-assessment tools useful for quick dataset checks and semi-automated tools for databases. Offline tools were unreliable, and most tools provided varied results without actionable recommendations for improving data FAIRness.

Keywords:
AssessmentDecision supportFAIRFAIR dataNano-informaticsTools

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Area of Science:

  • Environmental Science
  • Materials Science
  • Data Science

Background:

  • Research data publishing requires a findable, accessible, interoperable, and reusable (FAIR) approach for innovation, especially in predicting (eco)toxicological risks of nanomaterials and microplastics.
  • Efficient use of existing data is crucial for developing advanced risk assessment methods.
  • Tools assessing data FAIRness are essential for improving data reusability.

Purpose of the Study:

  • To review and characterize ten FAIR assessment tools.
  • To evaluate tool performance using nanomaterial and microplastic risk assessment datasets.
  • To compare the usability and outcomes of different categories of FAIR assessment tools.

Main Methods:

  • Reviewed ten FAIR assessment tools, categorizing them into online/offline self-assessment, (semi-)automated, and other types.
  • Evaluated tools using two datasets from the nanomaterials and microplastics risk assessment domain.
  • Assessed tool usability, output similarity, and recommendation provision.

Main Results:

  • Online self-assessment tools offer quick dataset scans with ease of use.
  • (Semi-)automated tools are more practical for assessing entire databases.
  • Offline tools were found to be limited and unreliable; most tools yielded varied results due to differing FAIR principle implementations, with only one tool offering improvement recommendations.

Conclusions:

  • The choice of FAIR assessment tool depends on whether datasets or full databases are being evaluated.
  • There is a need for standardized FAIR assessment methodologies and tools that provide actionable recommendations for data improvement.
  • This review provides guidance for users and developers of FAIR assessment tools in research data management.