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Problems in FAIRifying Medical Datasets.

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The FAIR principles guide research data management but require domain-specific interpretation. This study addresses challenges in applying FAIR data principles to medical data, using the Leipzig Health Atlas as a case study.

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Area of Science:

  • Health Informatics
  • Data Science
  • Medical Research

Background:

  • The Findable, Accessible, Interoperable, and Reusable (FAIR) principles are crucial for modern research data management.
  • Their generic nature presents challenges for domain-specific implementation, particularly in sensitive areas like medical research.

Purpose of the Study:

  • To identify and address practical challenges in applying FAIR principles to medical research data.
  • To explore necessary future developments for effective FAIR data provisioning in the health sector.

Main Methods:

  • Analysis of practical experiences from operating a data repository.
  • Case study focusing on the Leipzig Health Atlas project for medical data research.

Main Results:

  • The generic FAIR principles require significant interpretation for medical data.
  • Specific issues were identified in the FAIR provisioning of medical data within the Leipzig Health Atlas project.

Conclusions:

  • Domain-specific guidelines are essential for the successful implementation of FAIR principles in medical research.
  • Future developments should focus on tailored approaches to ensure FAIR data in health research.