Implementation & Adoption Stories

A team from Radboud University’s Research Data Repository used FDMM and F-UJI to assess the FAIRness of their service and as a starting point to create an action plan to improve it.

We selected two open access datasets from the RDR’s collection for the assessment. We prioritised open access datasets as we consider it is more important to make those FAIR.
We got a moderate score for both datasets in F-UJI (62%). Findable (full score) and Accessible (moderate score) were good, Interoperable and Reusable appeared as less well developed, which makes sense because we are a generalist repository, and it is difficult to focus on the interoperability and reusability aspects. We got some feedback from the mentor on how to improve those aspects. Some points highlighted were that we need to complete the information in our metadata export via schema.org3 in the html header of our dataset’s landing page, and in our DataCite4 metadata export, which we do by registering the DOI via DataCite.

Supported applicant: Didi Lamer | Radboud University

FAIR-IMPACT Support: Clara Linés | Digital Curation Centre (DCC), Samantha Willemsen |DANS

The University of Novi Sad software research infrastructure development team implemented FAIR Signposting level 1 to improve the machine readability of their in-house developed platform for research outputs and information.

We wanted to make the landing pages of research entities in our platform more machine readable by implementing FAIR Signposting links in the HTTP responses of the landing pages. The idea was to have some publications linked to author profile pages by using the DOI, and to also link to institution profile pages.

Supported applicant: Dragan Ivanovic | University of Novi Sad

FAIR-IMPACT Support: Clara Linés | Digital Curation Centre (DCC)

With the aim to improve the machine actionability of their Environmental Data Platform, a team from Eurac Research implemented signposting on their catalogue of geospatial datasets using a link set document.

We are part of an informal FAIR team, with other people across Eurac institutes and the Research Support Office. We did a FAIR self-assessment of the institute last year, trying to evaluate whether we are compliant with each of the principles. Even if we had already spent a year reading about FAIR, the support action has really made a difference for all of us. We have learned a lot and now we are much clearer on what FAIRness means, especially for machines. We were always thinking in terms of the HTML page, for humans, but had a less concrete idea of what had to be done for machines and we never really got to investigate that. There are always many things to do and that never got top priority, until the support action brought the chance to prioritise it.

Supported applicant: Piero Campalani | Climate and Disaster Risk group - Center for Climate Change and Transformation - Eurac Research, Simone Tritini | Center for Sensing Solutions - Eurac Research

FAIR-IMPACT Support: Clara Linés | Digital Curation Centre (DCC)

A team involved in the Generations and Gender Programme tested F-UJI, FDMM and SHARC to assess the FAIRness of their longitudinal panel survey dataset..

"We tried a tool, F-UJI, and two methods: FDMM and SHARC. Using the F-UJI tool, we initially obtained a very low level of FAIRness (4%). We hypothesized three possible reasons for this low score: the F-UJI tool's sensitivity to the software we use to develop our catalogue (Colectica), the lack of persistent identifiers such as DOI for our datasets and metadata files, and the type of DDI version we use (DDI version 3.3)."

FAIR-IMPACT Support: Clara Linés | Digital Curation Centre (DCC)