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ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability.

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The Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) and Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platforms improve FAIR data principles for clinical and neuroimaging research. These tools facilitate decentralized analyses and data sharing, enhancing data accessibility and reusability.

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Area of Science:

  • Neuroscience
  • Data Science
  • Biomedical Informatics

Background:

  • The FAIR principles (Findable, Accessible, Interoperable, Reusable) are crucial for maximizing the utility of clinical and neuroimaging data.
  • The Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium and the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform aim to address these principles.

Purpose of the Study:

  • To demonstrate how the ENIGMA and COINSTAC platforms support the FAIR principles for research data.
  • To showcase the integration of these platforms in a decentralized meta-analysis.
  • To outline future plans for advancing FAIR principles within the ENIGMA+COINSTAC framework.

Main Methods:

  • Utilizing the COINSTAC platform for decentralized analyses within the ENIGMA consortium.
  • Implementing machine-actionable data objects to enhance data interoperability.
  • Conducting a decentralized meta-analysis focusing on sex differences in negative symptom severity in schizophrenia.

Main Results:

  • ENIGMA and COINSTAC currently facilitate improved Access, Interoperability, and Reusability of neuroimaging data.
  • The integration supports decentralized meta-analyses, enabling large-scale collaborative research.
  • The study highlights specific areas for further enhancement to achieve full FAIR compliance.

Conclusions:

  • The ENIGMA+COINSTAC integration represents a significant step towards FAIR data practices in neuroimaging research.
  • Continued development is necessary to enhance data Findability and further improve Access, Interoperability, and Reusability.
  • Future efforts will focus on expanding connections to external resources to broaden data discoverability.