Background Since 2018, World Duchenne Organization, Dutch Duchenne Parent Project, and Duchenne Data Foundation have been championing efforts to make Duchenne-related data reusable in combination with data contained in other registries. Transforming human language into a coded language that machines can understand (“FAIRification”; FAIR, Findable, Accessible, Interoperable, Reusable) offers a solution. Purpose To recount and reflect on the process and challenges encountered during the FAIRification of a patient-registry, the Duchenne Data Platform. Methods The FAIRification plan was developed by a multidisciplinary team that was coordinated by a FAIR project manager. It focused on FAIRifying common data elements for rare disease registrations and patient-related outcome data. Protecting patient privacy and autonomy were at the forefront throughout the process. FAIR data transformation was accomplished through a combination of open-source and custom-written software. Data access for federated exploration was enabled through a privacy-preserving “data-visiting” approach. Results The plan consisted of 10 main steps and addressed social, legal, ethical, and technical issues. Proof-of-concept testing for interoperability between the Duchenne Data Platform and four other registries demonstrated that FAIR data discovery and reuse was possible. Misconceptions about FAIR data persist, which act as barriers to scaling-up community-level FAIR efforts. Suggestions for overcoming these barriers are provided. Conclusions Data visiting and federated analyses between registries is possible. Actions to help mitigate hesitation to implement FAIR in practice include seeking out existing FAIR training opportunities, addressing misconceptions as needed, contacting FAIR experts for advice and using the open-source resources that we have shared.
Lalout et al. (2025) studied this question.