ZonMw is aware that FAIR data are crucial in realising the ambitions in research and innovation to meet the challenges with respect to health and health care processes. The knowledge we need therefore must be more precise, more personalised, timely available, and it must be based on data from multiple sources, often in large amounts (i.e. big data).


Realising these ambitions depends strongly on the opportunities to use computational technology and data science on a large scale. The data that we use in research and innovation must therefore be fully understandable for the computer, in other words: data must be ‘machine readable’ or ‘machine actionable’.

FAIRification in practice

The purpose of this section is to provide background information for researchers and data stewards who are active in FAIRifying their data. With the term FAIRification we stress that the creation of FAIR data is a process, in which data gradually become more FAIR. At the end, data are optimally reusable, both by humans and -where possible- by machines, with full compliance to privacy protection regulations (if relevant). FAIRification is important for all types of data, whether they are generated through research, innovation processes, or societal activities.

Read more about these (and other) topics in the in DATA Intelligence, the Special Issue on Emerging FAIR Practices. It includes an article by ZonMw and Health Research Board (Ireland) about a FAIR funding model.

This presentation of the FAIR guiding principles (provided by Erik Schultes, GO FAIR) highlights a number of aspects that are important in the process of FAIRification. Read more in Some important aspects of FAIR data that we have to keep in mind.

How does ZonMw enable FAIRification in its projects?

ZonMw is building expertise in its research programmes to enable researchers, data stewards and other data producers to generate FAIR data. ZonMw strives to incorporate state of art FAIRification processes in its procedures, tools and services such that it becomes as easy as possible to use them. To improve interoperability of data and metadata, ZonMw stimulates researchers to choose standards (etc) that are commonly used within their research domain. In addition, ZonMw started in some programmes to organise activities for research communities to agree on such preferred domain-relevant standards and technologies.

The new FAIRification approaches that ZonMw implemented, are developed in collaboration with GO FAIR (see also GO FAIR Foundation), DTL and Health-RI, and will be further optimised.

More information

We invite you to read our other webpages about data management:

Disclaimer: this webpage will be gradually completed during September 2021

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