Abstract
Abstract
The new millennium, now almost 20 years old, has been characterised by a recognition within the research community of the importance of the free flow of research data; not simply in the ability to access the data, but also in the understanding that this valuable resource needs to be reused and built upon. We believe there have been at least two main drivers for this. First, those who pay for the research want to know it is leading to useful outcomes with impact–the transparency and accountability agenda. Second is an appreciation that the major global concerns (food, health, climate, economy) are extraordinarily complex (‘wicked’) problems, [1] whose solution requires interdisciplinary teams able to exchange data, information, and knowledge across domains. Moreover, ensuring data are understandable by other researchers, a hard-enough proposition in its own right, is no longer sufficient. The scale of modern data-intensive research is now only possible using computational techniques that require data to also be understandable by machines. There is a broad consensus across expert groups and scientific organisations that mutually-agreed data standards are essential to achieving these aims. [2, 3, 4]
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