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

Jessica A. Turner, Vince D. Calhoun, Paul M. Thompson, Neda Jahanshad, Christopher R.K. Ching, Sophia I. Thomopoulos, Eric Verner, Gregory P. Strauss, Anthony O. Ahmed, Matthew D. Turner, Sunitha Basodi, Judith M. Ford, Daniel H. Mathalon, Adrian Preda, Aysenil Belger, Bryon A. Mueller, Kelvin O. Lim, Theo G.M. van Erp

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products Findable, Accessible, Interoperable, and Reusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTAC support the FAIR principles, and then showcase their integration with a decentralized meta-analysis of sex differences in negative symptom severity in schizophrenia, and finally present ongoing activities and plans to advance FAIR principles in ENIGMA + COINSTAC. ENIGMA and COINSTAC currently represent efforts toward improved Access, Interoperability, and Reusability. We highlight additional improvements needed in these areas, as well as future connections to other resources for expanded Findability.

Original languageEnglish (US)
Pages (from-to)261-275
Number of pages15
JournalNeuroinformatics
Volume20
Issue number1
DOIs
StatePublished - Jan 2022

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • COINSTAC
  • Data privacy
  • Decentralized
  • ENIGMA
  • Meta-analysis

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