https://w3id.org/np/RAdklAxYmmwOgU3gY61sqcOh0leevHUdNhW3afziDHKFU
has assertion: assertion
has provenance: provenance
has publication info: pubinfo
type: Nanopublication
created: 2026-06-08T08:57:14Z
creator: wizard
license: https://creativecommons.org/publicdomain/zero/1.0/
introduces: FAIR Mind: Ontology-driven INtegration of CLinical and Behavoural Data for Anomaly Detection

FAIR Mind: Ontology-driven INtegration of CLinical and Behavoural Data for Anomaly Detection
creator: FMind
type: Domain-Specific-FAIR-Supporting-Resource
type: FAIR-Enabling-Resource
type: FAIR-Specification
type: FAIR-Supporting-Resource-to-be-Developed
type: Semantic-model
comment: FAIR Mind is Ontology-driven Integration of Clinical and Behavooral Data for Anomaly Detection. We are building a 'semantic bridge' called FAIR-Mind that allows different types of mental health data to speak the same language. First we created and Application Profile by mapping 22 specific variables-such as depression scores and heart rate metrics-to eight established global ontologies like SNOMED CR and CMO. We then represent this 'blueprint' as RDF, which transforms tradiitional database rows into a web of interconnected concepts that a computer can logically understand. To make this data searchable, we use Competence Questions. and tranlsate them into SPARQL queries, which act as a specialized search engine for linked data. This creates a powerful discovery layer where researchers can instantly find and integrate complex clinical and physiological data that was previously 'dark' or hidden in silos, making mental health research significantly more interoperable and reproducible.
in-scope-of: topic 3342
in-scope-of: OMIT_0001094

sig
has algorithm: RSA