Embedding FAIR Data Practices in Shared Core Resource Facilities

Abstract

Data fragmentation, lack of standardization, and incomplete clinical context limit the utility and reuse of datasets in biomedical research. Core facilities (shared resources) generate a large portion of research data and are well positioned to implement FAIR (Findable, Accessible, Interoperable, and Reusable) data practices to align with the NIH Data Management and Sharing Policy. This article highlights strategies for incorporating FAIR data practices into core facility workflows. Core facilities generate diverse data types across multiple platforms. However, variability in data formats and metadata practices can pose challenges for interoperability and cross-study integration. A critical first step in addressing this challenge is to connect core-generated datasets derived from patient biospecimens with structured clinical metadata. This involves using standardized identifiers, integration with biobanks and electronic health records. Structured metadata capture during experimental workflows to ensure data provenance will enable meaningful downstream analysis. Equally important is the adoption of standardized data formats and metadata frameworks across technologies. Adoption of community standards for various data types, along with standard metadata schemas, improves data discoverability and reuse. Robust institutional infrastructure (e.g. laboratory information management systems, integrated data platforms) and coordinated governance frameworks can help to implement these efforts. Partnerships with institutional stakeholders and targeted workforce training further support the consistent implementation of effective data stewardship practices. These approaches position core facilities as central operational hubs for advancing FAIR data principles, enhancing reproducibility, and maximizing the long-term value of biomedical research data.