Towards FAIR @ WUR
Within Wageningen University & Research FAIR is the main underlying value of the data policy. FAIR is a suitable and pleasant acronym, which stands for Findable, Accessible, Interoperable and Re-Usable. I can easily argue that everyone wants to be fair. WUR Rector prof. Arthur Mol declared in an opening speech of the FAIR Data symposium in December 2018 that WUR wants to be FAIR in 5 years’ time. This blog post is about FAIR (not about fair), and about FAIR maturity. And, although the scope of FAIR is a Data Object, I will argue that also an organization can become FAIR. Not to mention that both are at the heart of Data Quality where it is fundamental to assess its fitness for use!
What is FAIR?
So let’s start with FAIR in the ‘naked’ sense. FAIR is a set of 15 principles with the main goal to facilitate discovery of scientific data. The 15 principles were first published in 2016 by Wilkinson et al. All principles have a clear focus on making the data outcome of research accessible and understandable for both humans and machines. Let’s look a bit deeper into the principles.
Findable: The F principles define that a Data Object, or at least the metadata of the data, should be findable by a globally and eternally unique identifier (e.g. a DOI). F further defines the metadata to contain at least basic (machine readable) metadata allowing to distinguish it from another Data Object.
Accessible: The Data Object is accessible when it can be obtained by humans and machines with the right access credentials. This means that there must be a defined standard authorization protocol in place.
Interoperable: Interoperable is the principle which is most focused on machines. The key word in I is shared vocabularies or ontologies. It is the most difficult principle to tackle over the different scientific domains.
Re-useable: The re-usable principle defines (a.o) that metadata should include a clear and accessible usage license for both human and machine interaction (like e.g. the creative commons or GNU license).
The working group FAIR Data maturity of the global Research Data Alliance (RDA) is developing criteria to evaluate the state of FAIR-ness of a Data Object. For each criteria defined, a yes/no evaluation can be provided. The maturity model is still under discussion, but the idea is simple: the more yesses, the more FAIR mature.
FAIR @ WUR
Like I said in the introduction, the main value behind the WUR data policy is FAIR. The policy prescribes how researchers, research groups and PhD students within WUR have to handle their data. If all data were handled according to the policy, then WUR would be on the right track to become FAIR in 5 years’ time. The FAR principles (no, this is not a typo, the I was left out on purpose) would score a yes on approximately 75% of the indicators in the RDA model. Some major improvement could be made on the machine readability aspects, but all in all the WUR data policy is only a small step away from FAR. With the installation of the Wageningen Data Competence Center (WDCC), many research project were initiated that focus on elements of the I-principle. So let’s assume the outcome of these projects can be projected to the larger WUR research community. Not bad, one would conclude.
Of course there is always a “but” when presenting such an optimistic conclusion. It is the difficulty researchers experience in adopting the policy. Although there is wide awareness that data is a valuable outcome and that both funders and society want researchers to share data in a FAIR manner, researchers are still hesitant to do so. And this is where the FAIR data organization comes in. Beyond the ‘naked’ sense of FAIR, the principles are more than just data and metadata, infrastructure.
Making data FAIR takes time. WUR realizes that already much investment is asked from researchers when it comes to delivering output. Therefor WUR is in the process of installing a Data Stewardship network, in which domain experts get dedicated data support tasks.
Sharing data is sometimes seen as ‘giving away results to someone else’, who can either not understand the data or gain profit from the investment made by the creator of the data. The latter can feel as false competition. The good thing about FAIR is that there is no O(pen) in FAIR. In the context of FAIR data can be closed, shared and open. All shades are possible. The Data Stewards can help creating more FAIR-awareness and assist researchers in creating FAIR Data Objects, without need to fear to ‘give away’ data.
WUR is serious about data
The WUR data policy, the installment of the WDCC and the Data Stewardship network @ WUR show that WUR is serious about data and full speed in becoming FAIR in 5 years’ time (also read a 2016 blog post on becoming FAIR with a less optimistic conclusion). With a FAIR data policy and organization WUR can securely share it’s research output with designated private companies, society and other scientists, thus creating more impact and opportunities for new collaborations. It seems fair to be FAIR and WUR wants to be FAIR.