A day with the FAIRplus project: Implementing FAIR data principles

A few of us at SciBite recently attended the FAIRplus SME and Innovation Forum, held at the Wellcome Genome Campus, home to our Head Office. It was a great opportunity for us to get an overview of the work being completed by the FAIRplus project a year after it’s inception. The project aims to address the issue of the ever-growing volume and complexity of life science data, which is often inaccessible to researchers, by developing guidelines and tools to make data FAIR (Findable, Accessible, Interoperable, Reusable).

Overview of the FAIRplus project, FAIR ingredients and cookbooks

The day kicked off with a fantastic introduction to the project from Janssens’s Herman van Vlijmen. Herman gave an overview of the aims of the FAIRplus project, setting the scene for the days presentations and discussions, introducing the 4 IMI projects that have been used as the exemplar datasets during the projects development (Resolute, Oncotrack, eTOX and ND4BB). This was followed up by Oya Beyan from Fraunhofer who took us through the FAIR CMMI (Capability and Maturity Model Integration) and all of the important components to be considered.

Next up was Phillippe Rocca-Serra from Oxford University who described the concept of FAIR ingredients and cookbooks, that is a set of instructions and protocols that can be used to FAIRify datasets. At this moment there are cookbooks available for the 4 IMI datasets previously described. Phillippe also described how the FAIRness of data may be evaluated, either using manual or automated methods, with the next speaker, Tony Burdett from EMBL-EBI showing that the project had managed to produce a FAIRness score of 82% for the Resolute dataset, up from a score of 58% before the project commenced.

Tony stressed the importance of understanding how FAIR is FAIR enough? Commenting on the fact that no dataset will likely achieve a FAIRness rating of 100%, rather the rating should be driven by well-defined competency questions prior to a task in FAIRification and that some aspects may not be relevant to your requirements.

The final talk of the first session, contrary to the advertised session, was given by Andrew Pippow from Fraunhofer. Andrew was introducing the FAIRplus fellowship programme and started by presenting a fantastic video summarising the programme. The first session closed with a Q&A, where the ability to quantify the cost-benefit of implementing FAIR was discussed and it was stated that the cost should be measured by the “cost of losing that data”. Watch the Pistoia Alliance debates webinar on the benefits and costs of FAIR implementation for the Life Sciences industry with our CTO James Malone.

Deep dive into the work of the FAIRplus project

The afternoon session dived into the work done so far on the FAIRplus project, beginning with Phil Gribbon from Fraunhofer Institute talking about how datasets were prioritised for FAIRification. This was followed by Vassilios Ioannidis from SIB and Jolanda Strubel from The Hyve exploring the various user journeys in the FAIRplus work: the process of getting from a set of excel spreadsheets with raw data to FAIRified data.

Two panel discussions followed: first, FAIRplus sustainability and added value for SMEs, followed by a broader discussion of the FAIR Landscape with audience participation via Menti. Interoperability was determined to be the most important of the four pillars of FAIR.

The day ended with a keynote from the wonderful Carole Goble from the University of Manchester on FAIR History and the Future. She invoked Monty Python to make the point that “FAIR is not gospel, rather than one size fits all it’s a spectrum of different implementations”. Another key point made by Carole was that FAIR data are not the same as open data, and there may be many good reasons not to open your data. To learn more, you can watch her full talk.

Overall the FAIRplus Forum was a fantastic day for us to attend. There are many points at which SciBite can fit into the FAIRification process. We believe that the combination of ontologies, deep learning and FAIR data provide an unparalleled technology set that can directly impact challenges in pharma, agri-science and consumer goods.

To learn more watch our recent webinar on scaling the data mountain with ontologies, deep learning and FAIR, or get in touch with the SciBite team.

Watch the FAIR webinar