Exploring the FAIR Principles in Practice—from metadata to meaning.
I came to Larnaca to learn more about data. What I didn’t expect was to reflect so deeply on what it means to share knowledge—not just store it. In the world of hematology and research, we’re generating more data than ever. But what good is data if no one else can use it?
That was the heart of the HemaFAIR—HELIOS FAIR Principles training—a beautifully organized, international gathering in Larnaca, Cyprus, where we explored not just the acronym, but the mindset behind making data truly Findable, Accessible, Interoperable, and Reusable.
Over three days, around 30 participants from diverse backgrounds—clinicians, biologists, geneticists, IT specialists, and data experts—came together to tackle one of science’s most underrated challenges: making data meaningful and usable beyond the systems that create it.
Thoughtfully timed breaks helped us stay engaged (yes, coffee timing does matter), and the flow of sessions made the entire experience feel purposeful. Through guided, hands-on training sessions, real-time tools, ethical considerations, and a bit of humorous AI storytelling (yes, a panda, a bear, and a broken login), the experience was both illuminating and refreshingly human.
From Practice to Perspective: Group 5 Gets to Work
The training opened with energy and immediacy. Right from the start, César Bernabé and Martijn Kersloot led us into hands-on FAIRness—no passive listening, no long-winded theory. Just keyboards, synthetic datasets, and a challenge: make this data FAIR.
Our group—Group 5—quickly found its rhythm. We worked with metadata, applied models like FHIR, OMOP, and openEHR, and navigated concepts like triplets (subject–predicate–object). It wasn’t always smooth sailing—but it was engaging, interactive, and human. We were clinicians, geneticists, biologists, and IT specialists thrown into the same sandbox. And it worked.
“It takes a village to make data FAIR.”
We didn’t just hear that—we lived it.
By the time we reached Marco Roos’s lectures, we already had dirt under our fingernails—and his clear, structured approach gave language and logic to what we had been intuitively building.
From Practice to Principles: With Marco Roos
After diving headfirst into data modelling and structure with César Bernabé and Martijn Kersloot, we shifted gears with the insightful lectures of Marco Roos.
Marco’s sessions brought clarity to the theory underpinning everything we’d been doing. He walked us through the foundations of conceptual modelling, metadata design, and the importance of ontologies—controlled vocabularies that give meaning and consistency to health data across systems. Terms like DCAT, LOD, RDF, OWL, and JSON-LD became less intimidating and more purposeful, especially when placed in the context of collaborative, machine-readable science.
One of his most resonant points? That modeling isn’t just technical—it’s representational. It’s about shaping reality in a way that reflects purpose. And in the realm of FAIR data, that purpose is to make knowledge understandable and usable beyond the individual project.
It was a powerful reminder: “Any standard is better than no standard at all.” A phrase that echoed more than once across the room.
FAIR Starts with R (and a Panda Who Forgot the Password)
Perhaps the most surprising takeaway was this: That FAIR data doesn’t really start with “Findable”—it starts with Reusable.
As Prof. Ronald Cornet said during one of our sessions, “FAIR starts with R—Reusable.”
That simple sentence reframed everything. FAIR isn’t about storing data—it’s about making it usable, even long after we’re gone.
To drive the point home, Ronald shared a brilliant (and hilarious) animated video: a bear tries to access data shared by a panda. But the file requires a specific program… which no longer exists. The bear finds a way to get it, opens the file, and hits a final wall: “You need a password,” says the panda. “Can I have it?” the bear asks. “I’ve… forgotten it,” replies the panda.
We laughed—but we also got the message: data without structure, clarity, and forethought isn’t FAIR. It’s just frustrating.
I arrived in Larnaca to learn about data. I left with new tools, new friendships, and a new understanding: that FAIR is not just a principle. It’s a practice—and a promise to those who will use our work tomorrow.
On my last evening, watching the sea in its quiet rhythm, I knew this wasn’t just the end of a training. Cyprus didn’t just host the experience—it became part of its meaning. Until next time Cyprus. I hope to return someday.
