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