Understanding the ‘R’ in the FAIR Principles

Dr Robin Burgess1

1The University of New South Wales, Sydney, Australia, r.burgess@unsw.edu.au


Poster Summary

In 2015 the FAIR (Findable, Accessible, Interoperable and Reusable) principles were drafted. They are internationally recognised principles that are discipline-independent and support effective research data management. Benefits for researchers, as stated by the Australian National Data Service (ANDS) [1], are gaining maximum potential from a dataset, increasing the visibility and reproducibility of research, aligning with standards and attracting new research partnerships.

The focus of investigations of FAIR principles has been on understanding ‘F’ and ‘A’ and currently knowledge and skills in these areas are strong within Australian research infrastructure communities. Work on ‘I’ of the FAIR principles has been performed by the Australasian Repository Interoperability Working Group, that consisted of staff from Australian and New Zealand Universities, alongside members from ANDS and the Australasian Open Access Strategy Group (AOASG). A key recommendation from the group’s work was the need for common interoperability principles to be applied to repositories to help clarify the meaning of the FAIR principles [2]. Little attention, however, has been given to the ‘R’ in FAIR, understanding how to reuse data. Literature shows that consideration has been given predominantly to the sciences when it comes to reuse of data [3][4], with little attention to conditions and requirements for reusing data in the social sciences, arts and non-traditional research (NTROs).

This poster highlights the importance of reuse of data and what needs to be considered and understood for data to be effectively reused. Focus will be on R1 of the FORCE11 fair data principles that specifies ‘meta(data) have a plurality of accurate and relevant attributes’ [5].

Adoption of FAIR

Application of the FAIR principles is a robust approach towards the standardisation of data management. Importance lies in supporting institutions and researchers to understand and be able to apply the principles appropriately. For example, through the ANDS Self-Assessment Tool [6] and the current work of CAUL [7] which is looking at further understanding and applying the FAIR principles.

Planning for Data Reuse at UNSW

At UNSW, in support of reuse of data, the Research Data Management Planning (RDMP) tool [8] has a free text field asking the researcher about plans for reuse of their data. This is a descriptive approach with limited guidance for the researcher.  Not all researchers use the field and it appears that few have given much thought towards the reuse of data when commencing a research project. Planning for reuse of research data, using a tool such as an RDMP, could be a good starting point for application of the FAIR principles. An RDMP can include machine readable rights statements and provenance details, as well as guided opportunities for researchers to provide rich descriptive information which will better enable reproducibility of the research and reuse of data.

What’s next

A key component when considering reuse of data is associated with licensing, to clearly define conditions under which someone else can use the data (e.g. Creative Commons). However, there are other areas that need to be considered. These are related to the metadata associated with the data, particularly provenance information. A consideration for understanding data reuse is distinguishing between “use” and “reuse”. Knowledge needs to be shared between the producer or initial user of the data and researchers potentially reusing the data, to ensure a shared understanding of all facets of the data, including methods for generating and analysing the data, and the conditions under which these activities occurred. To be able to reuse research data, to answer new questions or to reproduce initial results, the researchers require richly described metadata that gives sufficient context about the research.


  1. ANDS ‘FAIR Data Principles’: (https://www.ands.org.au/working-with-data/fairdata)
  2. Link to a presentation given at the Repository Community Day, Brisbane (2017) about the working group. (http://www.caul.edu.au/sites/default/files/documents/cairss/repositoryevent2017ginny-natasha.pdf)
  3. Pasquetto, I.V., Randles, B.M. & Borgman, C.L., (2017). On the Reuse of Scientific Data. Data Science Journal. 16, p.8. DOI: http://doi.org/10.5334/dsj-2017-008
  4. Borgman C (2010). Research Data: Who will share what, with whom, when, and why?. Fifth China – North America library Conference. https://www.ratswd.de/download/RatSWD_WP_2010/RatSWD_WP_161.pdf
  5. The Future of Research Communication and e-Scholarship – The Data Fair Principles: https://www.force11.org/group/fairgroup/fairprinciples
  6. Fair Self-Assessment Tool: https://www.ands-nectar-rds.org.au/fair-tool
  7. CAUL Programs and Projects: https://www.caul.edu.au/programs-projects/fair-affordable-open-access-knowledge
  8. ResData, the tool for creating research data management plans at UNSW. https://resdata.unsw.edu.au
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