Making data Findable, Accessible, Interoperable, and Reusable (FAIR) is increasingly recognized as an essential part of responsible data stewardship. But how can the FAIR principles actually be put into practice?

This book provides a practical, step-by-step guide to FAIR data stewardship and the FAIRification of data. Using a project-based learning approach, it presents a realistic use case that is progressively FAIRified throughout the book. Before each step, the reader is introduced to the knowledge and techniques needed to carry it out. Additional examples illustrate how the same principles can be applied to situations beyond the running use case.

The book draws on ten years of experience teaching the FAIR principles and FAIRification processes to hundreds of data stewards, researchers, data scientists, and students across Europe, North America, and Brazil. This includes training delivered through the Nordic e-Infrastructure Collaboration (NeIC), the University of California San Diego, the University of Twente, and Brazilian universities, research communities, and governmental agencies.

Designed for both academic and industrial settings, this book is aimed at data stewards, data scientists, researchers, and other professionals involved in making data FAIR. Its structure mirrors the FAIRification process, providing the knowledge and practical guidance needed at each stage.

Whether you are beginning your FAIR journey or looking for a systematic approach to FAIRifying existing data, this book provides a hands-on introduction to the concepts, methods, and practices needed to make data FAIR(er).

Luiz Olavo Bonino da Silva Santos is an Associate Professor in the Semantics, Cybersecurity and Services group of the University of Twente, and in the BioSemantics group of the Leiden University Medical Center. For the last 10 years, Luiz has been involved in the FAIR movement, having participated in the seminal Lorentz Center workshop where the principles emerged in 2014 and is co-author of the seminal article defining the FAIR principles. His research focus has been in the design and development of technologies, workflows, methodologies and guidelines to support the realisation of the FAIR principles, including the FAIR Digital Object Framework, the FAIR Data Point and the FAIR Data Train.