2.3. Why make data FAIR?

Upholding integrity and reproducibility is key to any good research, and best practice in RDM is an essential part of efforts to accomplish this. Open Research and, in particular, the FAIR principles are a set of guidelines that could be viewed as a gold standard for RDM. When considering why adoption of the FAIR principles should be encouraged and embraced, there are many reasons extending beyond those of research integrity and reproducibility. Irrespective of whether they own or produce the data, or reuse data provided by others, researchers will find their lives much easier if they are able to find, retrieve and reuse data, while also increasing the value of the data due to their enhanced visibility. In addition, FAIR data enable easier data integration within and across disciplines, supporting worldwide, multi- and interdisciplinary research endeavours that address global challenges such as climate change, health emergencies or the realisation of sustainable development goals. When considering the financial implications, especially for publicly funded research, a reduction of double efforts and increasing reuse of existing data are key motivators, with studies underlining the implications of data management that is not FAIR-compliant, e.g. EC 2019. To this end, the FAIR principles go a considerable way in addressing this problem. Many funders and institutions, including the UN, WHO, OECD and others, have explicitly referenced the FAIR principles, providing a policy framework to support and sustain their growing importance. Funders' mandates mean that researchers will have to meet obligations to make their data FAIR-compliant. Meanwhile, data management plans (DMPs) are also becoming increasingly important and mandatory, with many templates explicitly providing guidance for the components of the FAIR principles, such as templates and guidelines provided until recently by Horizon 2020, and by Horizon Europe from 2021. Practical guidelines on how to comply with funding requirements and RDM policies were also developed by Science Europe (Science Europe 2021). Researchers can use these tools to identify the different considerations that need to be made for their project that correspond to each of the principles and which can be documented, such as file formats, standards and licences.

Although the FAIR principles do not necessitate data being open, the ambition is to increase alignment of the two concepts where possible, with the notable exception of data which cannot be made open for reasons such as their ethical sensitivity, copyright, cultural protocols, or commercial licensing. However, even in the case of such data, metadata should be made available for discoverability purposes which can then be requested and shared in a safe manner through access control mechanisms as and where appropriate. Not only does this aid data reuse, it also increases public trust and accountability, which is essential when considering publicly funded research.

The FAIR principles are complemented by other principles that focus on long-term governance, integrity and curation, such as the CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, and Ethics; Carroll et al. 2020) which address ethical considerations, and the TRUST Principles for digital repositories (Transparency, Responsibility, User focus, Sustainability, Technology; Lin et al. 2020). Therefore, it is important to remember that applying the FAIR principles only covers part of best practice in RDM and Open Research, e.g. data curation practices, data services, and data visualisation.

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