Research data is/are getting a lot of airtime at the moment. 2020 is the STM Association’s ‘Research Data Year’. The upcoming Peer Review Week focuses on ‘Trust’, which for articles must often involve open data. There’s also been a flurry of action (or calls for action) from stakeholders, including CODATA’s Beijing Declaration on Research Data and global research institutions’ Sorbonne Declaration.

These declarations and initiatives largely focus on ensuring that research data are FAIR: Findable, Accessible, Interoperable, and Reusable. The FAIR data principles are the current goalposts for promoting open research data, and efforts are thus focused on a) ensuring that individual datasets have comprehensive, machine-readable metadata (a link to the protocol used to collect the data, details of the instruments used, the license under which the data were released), and b) developing a network of FAIR compliant repositories to host all these datasets.

The FAIR principles are the community consensus answer to the ‘How’ question of data sharing, in that they describe best practice for how to share a particular dataset. Community consensus about anything is very welcome, but by themselves, the FAIR principles don’t have the leverage to bring more data into the public sphere and thereby achieve the manifold benefits of an open research data ecosystem.

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