Data sharing in neurodegenerative disease research: challenges and learnings from the innovative medicines initiative public-private partnership model - Bradshaw, Angela; Hughes, Nigel; Vallez-Garcia, David; Chokoshvili, Davit; Owens, Andrew; Hansen, Clint; Emmert, Kirsten; Maetzler, Walter; Killin, Lewis; Barnes, Rodrigo; Brookes, Anthony J.; Visser, Pieter Jelle; et al. (Frontiers in Neurology, 2023)
Efficient data sharing is hampered by an array of organisational, ethical and technical challenges, slowing research progress and reducing the utility of data generated by clinical studies on neurodegenerative diseases. There is a particular need to address differences between public and private sector environments for research and data sharing, which have varying standards, expectations, motivations, and interests. The Neuronet data sharing Working Group was created to understand barriers to data sharing in public-private partnership projects, and provide guidance to overcome these barriers, by convening data-sharing experts from diverse IMI neurodegeneration projects. In this policy and practice review, we outline the challenges and learnings of the WG, providing the neurodegeneration community with examples of good practices and recommendations on overcoming obstacles to data-sharing. These obstacles span organisational issues linked to the unique structure of cross-sectoral, collaborative research initiatives, to technical issues that affect the storage, structure and annotations of individual datasets. We also identify sociotechnical hurdles, such as academic recognition and reward systems that disincentivise data-sharing, and legal challenges linked to heightened perceptions of data privacy risk, compounded by a lack of clear guidance on GDPR compliance mechanisms for public-private research. Focusing on real-world, neuroimaging and digital biomarker data, we highlight particular enablers for data-sharing, such as data management planning, ethical codes of conduct, and harmonisation of protocols and curation processes. Cross-cutting solutions and enablers include transparency, standardisation and co-design – from open, accessible metadata catalogues that enhance findability of data, to measures that increase visibility and trust in data reuse.