The Data Intelligence published a special issue on emerging FAIR implementation choices and challenges, a compilation devoted to community efforts around FAIR practices which is co-edited by Barend Mons, Erik Schultes and Annika Jacobsen.

It was published on the Data Intelligence Journal webpage at the beginning of December 2019 and has now been published by the MIT Press. All articles are open-access.

“FAIR enough”?… A question asked on a daily basis in the rapidly evolving field of open science and the underpinning data stewardship profession. After the publication of the FAIR principles in 2016, they have sparked theoretical debates, but some communities have already begun to implement FAIR-guided data and services. No-one really argues against the idea that data, as well as the accompanying workflows and services should be findable, accessible under well-defined conditions, interoperable without data munging, and thus optimally reusable. […]

In this issue the original conception of the FAIR principles and what they are intended to cover is explained in detail. In an attempt to narrow down to the essence of what the original composers of the FAIR guiding principles had in mind, we would like to introduce an even higher level of abstraction than the principles themselves: the trigger for so much international attention for better data stewardship and Open Science is likely correlated to the data explosion we have created through ever increasing automation and instrumentation advances. It follows that we need “machines”, both as creators of data and as analytical assistants, all the time: we better make them as efficient and collaborative as possible. So at their very core, the FAIR guiding principles should lead us to ensure that “Machines know what it means“. […]

(Excerpt from the introduction to this special issue)

For the full introduction and all articles visit MIT press.

The Data Intelligence journal is a publication jointly launched by the MIT Press and Chinese Academy of Sciences. It is an open-access, metadata-centric journal intended for data creators, curators, stewards, policymakers, and domain scientists as well as communities interested in sharing data.