An extensible laboratory information management system for data harmonization across research centers: The ICTS-Dashboard

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 project-specific data models. The Institute for Clinical and Translational Science (ICTS) at the University of California, Irvine (UCI) ICTS-Dashboard fills this need by providing a general purpose research information management system. Methods: We describe the ICTS-Dashboard, built as an open-source, schema-driven platform in which database structure, server-side validation, representational state transfer application programming interfaces (REST APIs), web-based forms, and reproducible exports are all generated from a single versioned java script 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 2,558 participants, 1,237 families, 5,517 biobank entries, 2,466 sequenced biospecimens, and 289 genetic findings, and 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: 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. The Dashboard's architecture is not intrinsic to a data model or to rare disease; 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 reproducible (FAIR)-aligned accessibility.