What is FAIR Data?
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors tried to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse (FAIR) of digital assets like patient data. The principles focus on machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because people increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
Why is this necessary?
There is general agreement that the data collected from a person ‘belongs’ to that person in the sense that reuse of the data for other proposes than for which the original consent was given (if any) lies with that person. In the EU this is now also regulated by the GDPR (General Data Protection Regulation). Medical professionals and researchers are increasingly obliged to provide the data to the person from which they originated upon request, but in actual practice it is still a very cumbersome and discouraging process for individual patients to ‘recover’ their data from medical and research collections spread over many different places.
FAIR Data in Duchenne
World Duchenne Organization on March 21 and 22, 2019 organized a meeting in Amsterdam to discuss the FAIR initiative, the Personal Health Train, Duchenne Data Platform and next steps related to data visiting. The meeting was attended by representatives of registries, patient advocate leaders, clinicians, scientists, companies and regulators. A Data Declaration resulted from this meeting. A workshop report is published in Neuromuscular Disorders (Official journal of the World Muscle Society). If you want to receive a copy please send a message.