FAIR Data: history and present context

In this review, we discuss FAIR Data, why it exists, and who it applies to. We further review the principles of FAIR data, and how they are managed by research institutions. We also discuss the types of problems that researchers encounter, and what an information professional can do to assist them. In 2016, the journal Scientific Data published the 'FAIR Guiding Principles for Scientific Data Management and Stewardship'. FAIR data are data that comply with the principles of findability, accessibility, interoperability, and reusability. The principles aim to guide data producers and publishers in the 21st-century digital environment, where data is the new gold. FAIR data tunes into the open science movement, and responds to the digital revolution to maximize the added value offered by scholarly digital publications. In 2018, the European Union ratified and promoted the principles with the report 'Turning FAIR into reality', aiming to use the analytical power of machines on a large scale and ensure transparency and social utility, both of data and other digital objects produced and used for research. Researchers generate large amounts of data, which are necessary to generate knowledge and innovation. The integration and subsequent reuse of data accompanying publications were left to the discretion of the data owner. The emergence of the FAIR principles arose because there were no guidelines or standards created for the proper integration of research data into the digital ecosystem. Therefore, the creation of the principles goes beyond collection, description, and archiving. Data management also contemplates long-term preservation, the generation of machine-processable metadata that facilitates discovery, evaluation, and reuse in further research. At present, the vast majority of research centers subscribe to the principles. Furthermore, a Data Management Plan (DMPs) is required for the award of public funding, which must detail how the data will be managed, stored and preserved. But both centers and researchers face the arduous task of understanding the model, managing and implementing it. They must know data formats and standards. For a correct description and to facilitate data retrieval and interoperability, they must know about different types of metadata schemas. They must know about digital preservation and specific aspects of knowledge and information management. In addition, there are also ethical issues, intellectual property, and cultural differences. All these controversies translate into a huge extra workload for researchers, who only get a return in the form of citations. For proper data management and compliance with FAIR principles, advanced knowledge of information management is needed. The creation of a repository, developing good practices that allow data management, promoting and facilitating open publishing, training in open science and data management; are subjects and disciplines that the profile of professional librarians or information managers have. Information professionals can play a key role in the proper management of research data and contribute to the achievement of the objectives described by the principles: making data findable, accessible, interoperable, and reusable