Paving the Way for Brazil’s First National Rare Diseases Registry: The RARAS Data Governance Model

Background: Digital governance in health research is crucial for ensuring data quality, accessibility, and interoperability, particularly in the context of rare diseases. Establishing a National Rare Diseases Registry in Brazil faces structural and technological challenges. The Brazilian Rare Disease Network (RARAS) proposes an innovative digital governance model to standardize, manage, and enhance the use of these data, aligning with the FAIR Principles (Findable, Accessible, Interoperable, and Reusable) to optimize scientific collaboration and public health decision-making. Methods: This study employed a multi-tiered governance framework integrating technological, ethical, and operational perspectives. The model prioritizes data standardization, implementation of FAIR principles, and stakeholder engagement. A structured Data Management Plan was developed using the ARGOS platform, and metadata compliance was assessed through the F-UJI tool. The network leveraged linked data technologies, ontologies, and REDCap for secure electronic data capture and management. The RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance) was applied to evaluate the model’s impact. Results: The RARAS governance model successfully improved data integration across 45 rare disease centers, enhancing interoperability and data-sharing practices. FAIR assessment using F-UJI indicated moderate compliance (50% FAIR score), with strengths in semantic interoperability but gaps in metadata completeness and accessibility. Training initiatives across Brazilian regions reached 463 participants, promoting awareness of data quality. Challenges included heterogeneous data collection practices, limited digital literacy among stakeholders, and the need for automated metadata validation. The RE-AIM analysis highlighted substantial reach and adoption but identified areas for improvement in long-term sustainability. Conclusion: This study presents a scalable digital governance model for rare disease research in Brazil, improving data standardization and integration. The findings underscore the need for continuous FAIRification efforts, enhanced metadata structures, and federated data-sharing strategies. The model provides a foundation for developing a robust national registry, supporting data-driven public health policies, and fostering international collaboration in rare disease research. Future work should focus on automation, federated learning, and expanding FAIR compliance to maximize the long-term impact of digital governance in health research.