Most efforts implementing the FAIR data principles focus on the F(Findable) and A(Accessible), whilst I(Interoperable) and R(Reusable) languish: of all, achieving Reusability of data is proving the hardest. Ensuring reusability is fundamental, particularly for longitudinal studies where data have to be preserved over decades and survive multiple technological changes. The increased trend towards transdisciplinary research requires data reuse beyond their original purpose, whilst the rise of Artificial Intelligence and Deep Learning require data to be machine-readable and reusable in compute-intensive environments. Reusability is also critical for maximising impact and thus ensuring sustainability of data infrastructures.

This session will explore the many facets of reusability including:
Development and governance of domain or data type specific (meta)data standards that enable reuse in transdisciplinary research;
Combining multiple data surveys into online analysis-ready reference collections;
Insulating data against evolving technology;
Moving data out of drawers and off hard-drives so it can be reused online.