FAIR Data in Medical Research: Incorporating the FAIR Principles in the Research Data Life Cycle

The FAIR Principles, stating that research data and metadata should be Findable, Accessible, Interoperable, and Reusable for both humans and machines, are experiencing a vast uptake in acceptance and implementation by researchers, research institutes, funders, and government bodies. However, many researchers are currently unaware of the FAIR Principles, their implications, or how they can be applied to their research. Furthermore, existing workflows to make data FAIR are designed to be executed after research projects have been conducted and data have been collected, rather than throughout the life cycle of research projects.
The work presented in this thesis provides insight into researchers' and research support staff’s knowledge and perspectives on the implementation of the FAIR Principles in practice (Part I), determines the role of Natural Language Processing in making data more FAIR (Part II), and develops a process for making data FAIR from the beginning of a research project and at the source (Part III). The presented work contributes to a future in which FAIR research data are the default, and the process of making data FAIR is optimized, to add maximum value to patient care with minimal cost, effort, and delay.