NIH grant: Bridging the Terminology Gap: Building User-Friendly Tools for FAIR Data Creation (1R50LM015244-01)

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NIH grant 1R50LM015244-01: Bridging the Terminology Gap: Building User-Friendly Tools for FAIR Data Creation
Institute: {'code': 'LM', 'abbreviation': 'NLM', 'name': 'National Library of Medicine', 'admin_org_id': '12500048', 'admin_funding_url': ''}. PI: Martin  O'Connor. Award: $231343.
Start: 2026-08-01T00:00:00.
Project Summary
The NIH Strategic Plan for Data Science emphasizes the need for scalable, standards-driven infrastructure to
support effective biomedical data sharing. A central component of that infrastructure is high-quality metadata—
structured, semantically consistent annotations that make data findable, accessible, interoperable, and
reusable (FAIR). While tools like the CEDAR Workbench and BioPortal have made it easier to use ontologies
in metadata, a critical gap remains: the lack of robust infrastructure to manage the lifecycle of value sets, the
curated collections of terms that underpin standardized metadata. This project will address that gap by building
a general-purpose, modular infrastructure for value-set lifecycle management. The system will support value-
set discovery, collaborative editing, validation, version tracking, and integration with ontology repositories.
Designed for use across diverse NIH initiatives, the infrastructure will help ensure that metadata are consistent
with domain standards, support reuse, and evolve reliably over time. By enabling researchers, curators, and
informaticians to jointly create and manage reusable vocabularies, the new software will improve metadata
quality and alignment with NIH data-sharing mandates. For metadata authors, it will streamline the process of
defining terms that are semantically valid and contextually relevant. For repository users, it will enhance
dataset comparability and enable better semantic search. For researchers seeking to reuse public data, it will
reduce the need for manual harmonization and facilitate meaningful cross-study analysis. This work directly
supports NIH’s vision of a connected, standards-based data ecosystem and advances the infrastructure
needed to make that vision operational across the biomedical research landscape.

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