In recent years, the term FAIR data has gained a lot of attention in health and life sciences research. FAIR stands for Findable, Accessible, Interoperable, and Reusable. These principles guide how we manage and describe data so that it can be more easily discovered, understood, and reused, by both humans and machines.
But what do these principles really mean in practice, and why are they so important for health research within Sciensano?
An introduction to the FAIR Principles
- Findable
Data (and metadata) should be easy to find for both humans and computers. Having a searchable and machine readable metadata catalogue can support and even automate the process of finding appropriate datasets for your needs. It is important though that the metadata is rich, complete and preferably adheres to a standard. At Sciensano we use the HealthDCAT-AP standard that was specifically developed for describing the metadata of health data, in the context of the European Health Data Space. To facilitate the creation of these records a HealthDCAT-AP editor was developed, which offers a user friendly interface that guides you through the process. - Accessible
Once found, users should know how to access the data. This doesn’t mean that all health data must be open, but the conditions for gaining access should be clear. Depending on the health data access rights this process will differ. This description is one of the required elements of a dataset entry using HealthDCAT-AP. - Interoperable
Integration with other datasets should be simplified, this can be done through the use of standardized formats, vocabularies, and ontologies. - Reusable
In the end the main goal of FAIR is to make data more re-usable. This will allow for the data within Sciensano to be used to its full potential. Data should be well-described and licensed so it can be reused in future research. Clear provenance and usage rights are essential.
Why FAIR Matters in Health Research
- Accelerating discovery and policy
FAIR data enables faster identification of relevant datasets, which helps researchers and policymakers respond more quickly to public health challenges.
During the COVID-19 pandemic, rapid access to harmonized health and epidemiological datasets was crucial for monitoring cases, predicting spread, and evaluating interventions. - Ensuring responsible use of sensitive data
FAIR does not mean “open to everyone.” Instead, it ensures that even when data cannot be shared freely (e.g. due to GDPR), metadata remains visible, and clear procedures are in place for requesting access. - Supporting reproducibility and trust
Transparent, well-documented data builds confidence in scientific results and helps prevent duplication of effort. - Enabling interdisciplinary insights
Health outcomes are closely linked with environmental, demographic, and social data. FAIR practices allow integration across domains, for example, linking air quality datasets with hospital admissions to study the impact of pollution on respiratory diseases.
FAIR at Sciensano
Sciensano, as Belgium’s institute for health, is increasingly applying FAIR principles to the datasets it manages. One of the ways we aim to achieve this is through the launch of the Sciensano health data portal, where you can find a metadata catalogue of the Sciensano datasets as well as resources around FAIR principles and use.
These efforts ensure that data produced with public funding is visible, responsibly accessible, and reusable for maximum scientific and societal impact.