=Paper=
{{Paper
|id=Vol-2038/invited2
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2038/invited2.pdf
|volume=Vol-2038
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2038/invited2.pdf</pdf>
<pre>
 A Simple Approach to Assessing the FAIRness of
      Data in Trusted Digital Repositories
                          Peter Doorn and Eleftheria Tsoupra

                Data Archiving and Networked Services (DANS), Haag,
                                   The Netherlands
            {peter.doorn, eleftheria.tsoupra}@dans.knaw.nl


The FAIR principles (short for Findable, Accessible, Interoperable, Reusable) have in
a short time become a household name in the research data world. In this paper, we
describe a simple approach to evaluate datasets in Trusted Digital Repositories
(TDR), using an assessment tool based on a brief questionnaire. It is argued that re-
positories complying with the Data Seal of Approval (or the new Core Trust Seal, the
new common requirements of DSA and World Data System) already provide a basic
level of FAIRness. In fact, the principles underlying the DSA, which were formulated
in 2006, are quite similar to the FAIR principles formulated in 2014.
   Essentially, the DSA gives quality criteria for digital repositories, whereas the
FAIR principles target individual data sets (or data objects – this is an issue of granu-
larity, but that is not fundamental for the discussion of this paper). Additionally, the
FAIR Guiding Principles explicitly express it an aim that they are valid not only for
humans, but also for machines, a demand never formulated explicitly by the DSA. In
spite of these differences in orientation, there is a remarkable resemblance of the
(four) FAIR Guiding Principles to the (five) principles underlying the DSA.
   There is a growing demand for quality criteria for research datasets. In this paper
we will argue that the DSA and FAIR principles get as close as possible to giving
quality criteria for research data. They do not do this by trying to make value judge-
ments about the content of data sets, but rather by qualifying the fitness for data reuse
in an impartial and measurable way. By bringing the ideas of the DSA and FAIR
together, we will be able to offer an operationalization that can be implemented in any
Trustworthy Digital Repository.
   In the simple DANS operationalization, it will be possible to score each data set
upon ingest in a TDR, where a data archivist will be able to do the assessment, helped
by pre- set scores on criteria that can be established automatically. After reuse, re-
searchers will be asked to give user reviews based on the same criteria. In this way,
every data set can receive a FAIR profile, of which we will provide some examples.
   Work on creating FAIR metrics is ongoing in various context. This paper will
compare our proposed approach with work going on elsewhere, especially by the
FAIR metrics group of the GO FAIR initiative, see: https://www.dtls.nl/fair-data/fair-
metrics-group/ in which we also participate. The ultimate aim is to arrive at a core set
of FAIR metrics and methods that is applicable across disciplines and continents,
although it may appear that different domains may require additional criteria and
approaches.

</pre>