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Enabling FAIR data stewardship in complex international multi-site studies: Data Operations for the Accelerating

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This study details a data governance ecosystem for sensitive health data in schizophrenia research. It emphasizes FAIR principles and real-time quality control for enhanced data sharing and research collaboration.

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Data Science

Background:

  • Publicly funded research mandates data sharing, posing challenges for sensitive health information.
  • Balancing participant privacy with research community needs is crucial for complex studies.

Purpose of the Study:

  • To describe the data operation ecosystem for the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) project.
  • To align data management with FAIR principles while addressing ethical and regulatory requirements.

Main Methods:

  • Implementing robust data capture, dictionaries, and organizational principles.
  • Utilizing automated pipelines for data flow, security, and quality control (QA/QC).
  • Leveraging the NIMH Data Archive for data dissemination.

Main Results:

  • A seamless data flow was designed, adhering to FAIR principles.
  • Close-to-real-time QA/QC enabled immediate error correction and data quality enhancement.
  • The system facilitated enhanced collaboration in multisite research.

Conclusions:

  • The developed data operation ecosystem effectively manages sensitive mental health data.
  • This approach advances research practices for multisite studies involving high-risk individuals.
  • Real-time QA/QC is vital for maintaining data integrity and research efficiency.