The FAIR principles, first coined in 2016, have rapidly become a hot-topic in the world of pharma and beyond. The principles are straightforward enough, but their implementation is far from one-size-fits-all. So how do we move from theory to practice with some degree of consistency? Over the last two weeks, we attended a couple events to find out more.
Stop 1: Breaking down the data silos
Our first stop was the Pharma Documentation Ring conference held in London on May 21 - 22: “FAIRification of External Data: Breaking down the Data Silos”. Here, speakers presented perspectives from all three attending sectors – academia, pharma, and vendors – on the extent of FAIR in their respective fields.
A recurring topic during the conference was the role of ontologies in the world of FAIR. By using an ontology you are defining rules and relationships between data elements. For example, a study subject is a person who participates in a clinical study. These relationships not only help other users interpret your data, but also make the data machine readable. Leveraging existing ontologies could be a way to make data more interoperable and reusable. You could even argue that ontologies and linked data help improve findability, since an ontology often follows a clear hierarchy.
The conference gave us a better understanding of the status of FAIR across industries but we were still missing the practical approach; for this we traveled to Niel, Belgium.
Stop 2: FAIR data stewardship
In Niel, near Antwerp, we attended the hands-on Helis Academy course on FAIR data stewardship organized by the Dutch Techcenter for Life Sciences’ (DTL) . With nine other students and the help of some excellent instructors, we explored the topics of persistent identifiers (PIDs), semantic web, and data management plans (DMP).
Mateusz Kuzak led an interesting session on Data Carpentry. A perhaps often overlooked aspect of reusability is the need for clean and organised data. As Kuzak evidenced during this session, messy data is extremely challenging to interpret, let alone to reuse. We were with provided a test dataset and were taught some quick and easy ways to clean up the data in Excel. The session ended with a dive into OpenRefine, an extremely powerful tool when it comes to data cleaning.
Where does this leave us?
There is still much to be learned about FAIR, but every discussion had, course taken and tool learned over the past weeks have brought us ever closer to what FAIR data really is, and how we can help others achieve it. So if you’re interested in learning more about FAIR, stay posted, because I’m fairly certain you’ll be hearing from us about FAIR very soon.
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