Why you should care about Fair data

 Data in journal publishing is currently big news. In 2016, an influential manifesto coined the term ‘Fair’ (findable, accessible, interoperable and re-usable, to which I would also add provenance) to describe the properties digital data should have to benefit the academic community, and therefore support discovery and innovation.¹ The manifesto has already prompted many librarians reinvent themselves as research data managers. But Fair data is arguably better illustrated by working examples rather than aspirations – and by the benefits it has given to computational chemistry, crystallography and spectroscopy over the past two decades.