The Future of FAIR research data: 4 key points for librarians

By: Guest contributor, Tue Jun 22 2021

Author: Guest contributor

In 2021 we mark five years since publication of the FAIR data principles. The FAIR principles refer to the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention. These principles were defined in a March 2016 paper in the journal Scientific Data by a consortium of scientists and organizations. FAIR data are data, that meet the principles of findability, accessibility, interoperability, and reusability.

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According to Varsha Khodiyar, Data Curation Manager at Springer Nature, the concept of FAIR data has been instrumental in bringing open science and research data to the attention of the research community, as well as to the broader group of stakeholders involved in facilitating, managing and disseminating research. But what could the future look like?

Here are four key takeaways for librarians from a new white paper, The Future of FAIR: Highlights and reflections from the Better Research Through Better Data roundtable:

  1. Evidence of the benefits of sharing research data is growing. However, awareness of FAIR among researchers, particularly in certain disciplines and regions, remains low. In some disciplines, researchers may not even be aware that they have research data to share, for example, Art Historians’ high resolution images, or Social Scientists’ audio files are types of research data. Professionals throughout the research lifecycle have a part to play in raising awareness and sharing best practice.
  2. Supporting FAIR data requires diverse skill sets in a variety of support roles within the research ecosystem. It is not enough for organisations to hire only data experts such as data stewards. It is also necessary to equip other professionals with the knowledge to be able to support FAIR data.
  3. For FAIR data support to be effective, it is necessary not only to embed the FAIR principles in research education and practice, but also to reward and showcase FAIR data implementation to encourage others. Rather than a binary, FAIR is a spectrum.
  4. Researchers need support to understand and implement the  incremental steps they can apply in practice, to make their data as FAIR as possible. 

The Future of FAIR: Highlights and reflections from the Better Research Through Better Data roundtable brings together an international cohort of research data professionals to celebrate the real-world impact of the FAIR data principles, and consider what will be next for research data and open science. 

Download the full white paper here.


Author: Guest contributor

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