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FAIRness and Usability for Open-access Omics Data Systems.

Daniel C Berrios1,2, Afshin Beheshti3,2, Sylvain V Costes4,2

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Summary
This summary is machine-generated.

Omics data sharing systems were evaluated for FAIRness, with NASA's GeneLab Data Systems (GLDS) scoring well but showing interoperability challenges. Recommendations are provided to improve data accessibility for big data systems.

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Data science

Background:

  • Omics data sharing is vital for biological research.
  • Numerous collaborative systems and databases have emerged for omics and systems biology data.
  • Assessing the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles is key for data utility.

Purpose of the Study:

  • To evaluate the FAIRness of NASA's GeneLab Data Systems (GLDS) and similar omics data systems.
  • To identify strengths and weaknesses in current omics data system FAIRness.
  • To propose improvements for Big Data system accessibility.

Main Methods:

  • Utilized 14 FAIRness metrics to assess five omics data systems.
  • Quantified system performance across FAIR principles.
  • Examined experiences with semantic integration for federated querying.

Main Results:

  • Overall FAIRness scores ranged from 6-12 out of 14 (average 10.1).
  • Systems excelled in findability and accessibility but struggled with interoperability.
  • Metadata reusability was often inadequately supported.

Conclusions:

  • Current omics data systems show variable FAIRness, with significant room for improvement in interoperability.
  • Federated querying and retrieval are challenged by data interoperability issues.
  • Two new principles are proposed to enhance Big Data system accessibility.