An extensible laboratory information management system for data harmonization across research centers: The ICTS Dashboard
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Background
Collaborative research programs increasingly require infrastructure capable of integrating heterogeneous participant, sample, and experimental data while meeting evolving research needs. Existing tools, including clinical EHRs, REDCap, generic research information management systems, and bespoke database builds, were not designed to operationalize externally governed, versioned consortium data models across database structure, APIs, validation, web forms, and reproducible exports. The ICTS Dashboard, developed at the University of California, Irvine Institute for Clinical and Translational Science, fills this need by providing a general purpose research information management system.
Methods
The ICTS Dashboard was built as an open-source, schema-driven platform in which database structure, server-side validation, REST APIs, web-based forms, and reproducible exports are derived from a versioned JavaScript Object Notation (JSON) Schema set. The backend is implemented in Django, Django REST Framework, and PostgreSQL; the frontend in React. We instantiate the platform with the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Data Model and extend it with two case studies: a locally developed biobank table for biospecimen logistics, and an embedded adaptation of the RAG-HPO retrieval-augmented phenotype curation tool.
Results
The ICTS Dashboard deployed at the UCI-GREGoR site supports 37 schema-derived tables and 250 documented API endpoints. It holds metadata for 2,563 participants, 1,238 families, 5,517 biobank entries, 2,593 sequenced experiments, and 289 genetic findings. It supports quarterly external data submissions regenerated directly from the database. The biobank extension adds entities the consortium does not standardize while preserving foreign-key linkage to rare disease records; the RAG-HPO module adds curator-mediated phenotype normalization against 19,389 indexed HPO terms. Both were integrated without modifying the GREGoR Data Model.
Conclusion
The Dashboard’s architecture is not limited to GREGoR or to rare disease research. A version-controlled, machine-readable data model can serve not only as a data sharing standard but as the operational backbone of a research program when paired with schema-governed tooling. Any collaborative research program with a structured, versioned model can adopt the same pattern to reduce implementation overhead and improve reproducibility, harmonization, and findable, accessible, interoperable, and reusable (FAIR)-aligned data management.
