Making Data FAIR
The FAIR Principles
An abbreviation for “findable, accessible, interoperable and reusable”, the FAIR Principles provide a framework for sharing data in a way that maximises its use and reuse.
The FAIR Principles emerged from Wilkinson et al.’s 2016 journal article “FAIR Guiding Principles for scientific data management and stewardship”. Developed by the international research community, these principles aim to:
- support knowledge discovery and innovation both by humans and machines
- facilitate data and knowledge integration
- enable new discoveries through the analysis of multiple datasets
- promote the sharing and reuse of data
- apply across multiple disciplines, including those with sensitive data
- strive for machine-readable data and metadata.
The FAIR Principles provide guidelines to improve the findability, accessibility, interoperability and reusability of digital assets. Note that applying these principles varies by discipline.
What Being FAIR Means
The following information was sourced in part from Turning FAIR into reality, a 2018 report by the European Commission Expert Group, chaired by Simon Hodson.
The data has sufficiently rich metadata and a unique and persistent identifier to be easily discovered by others. This includes:
- assigning a persistent identifier (like a DOI or Handle)
- providing rich metadata to describe the data based on standards
ensuring it is findable through disciplinary local or international discovery portals – see our guide to choosing a data repository.
The data is retrievable by humans and machines through a standardised communication protocol with authentication and authorisation where necessary.
The data does not necessarily have to be open, given that it can be sensitive due to privacy concerns, national security or commercial interests. When data cannot be open, there should be clear and transparent conditions governing access and reuse. Consideration should also be given to the persistence of the data in the selected repository and whether metadata will remain accessible even after the data is longer available.
Data often needs to be integrated with other data, applications or workflows to facilitate analyses, storage and processing. This integration requires the associated data and metadata to use a “formal, accessible, shared, and broadly applicable language for knowledge representation”. This involves:
- using community accepted languages, formats and vocabularies in the data and metadata
- referencing and describing relationships to other data, metadata, and information through identifiers
- striving toward machine-readability.
Through the WorldFAIR Project, the Committee on Data of the International Science Council (CODATA) and the Research Data Alliance (RDA) are improving interoperability as well as reusability and FAIRness in general of digital research objects, including data. Learn more about interoperability by watching a webinar series by WorldFAIR.
Optimal reuse of data requires levels of description sufficient to allow data to be replicated and/or combined in different settings. The associated metadata needs to provide rich and accurate information, and the data must come with a clear usage licence and detailed provenance information. Reusable data should maintain its initial richness. It should not be diminished for the purpose of explaining the findings in one particular publication. It needs:
- a clear machine readable licence
- provenance information detailing how the data was formed
- use of discipline-specific data and metadata standards to give it rich contextual information that facilitates reuse.
Why FAIR Data Is Important
Adopting the FAIR Principles accelerates the impact of your work by making it easier for other researchers to find and reuse your data. This can lead to increased collaboration with both research and industry, and acknowledgement of your data in other publications. It also benefits research communities, research infrastructure facilities and research organisations.
Well-researched topics provide rich information for deeper and more complex investigations, and making data from these endeavours more FAIR provides insights into less well studied topics. Meanwhile, making data more FAIR in less studied topics can help turn understanding of important topics in health, environment and society into deeper knowledge more quickly.
Benefits of FAIR data include:
- maximising the potential of data assets
- increasing the visibility and citations of research
- improving the reproducibility and reliability of research
- aligning with international standards and approaches
- attracting new partnerships with researchers, business, policy and broader communities
- enabling new research questions to be answered
- achieving maximum impact from research.
ARDC Resources for FAIR Data and FAIR Digital Research Objects
The ARDC offers a range of best-practice guides, tools and services for making data FAIR.
Besides research data, the FAIR Principles can be useful for other digital research objects. For example, the FAIR Principles for Research Software (FAIR4RS) were published in 2022 to improve the sharing and reuse of research software. We also offer various tools and guides that help make these digital objects FAIR.
Explore our resources for making data and other digital research objects FAIR:
Further ARDC and Community Support for FAIR Data
Besides offering gued and tools that help you achieve FAIR, the ARDC supports and drives a number of international and national initiatives:
- Enabling FAIR Data Project (US and international)
- FAIRmetrics working group (international)
- FAIRsFAIR project (Europe)
- GO FAIR initiative (Europe)
- NIH Data Commons Pilot Phase, which explored using the cloud to access and share FAIR biomedical big data (US)
- Policy Statement on F.A.I.R. Access to Australia’s Research Outputs (Australia)
- Top 10 FAIR Data and Software Things, brief guides to making research data and software FAIR (Australia and international).
The ARDC has also developed a policy that applies the FAIR Principles to our own and co-invested materials. When the ARDC partners with other organisations, we ask that they follow this policy.
We’ve also curated community resources that ensure best-practice research methods:
- Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud (Mons, Neylon, Velterop et al. 2017)
- Data Sharing and Citations: New Author Guidelines Promoting Open and FAIR Data in the Earth, Space, and Environmental Sciences (Stall, Cruse, Cousijn et al. 2018)
- Enabling FAIR Data in the Earth and Space Sciences, a webinar hosted by the ARDC in October 2019 to discuss a case study involving hundreds of partners from across the geoscience community to make geoscience data more FAIR on a large scale
- FAIR Data Advanced Use Cases: from principles to practice in the Netherlands (Imming 2018)
- FAIR Data Principles as published by FORCE11
- FAIRsharing, a website that lists standards, policies and databases related to FAIR
- FAIR in Practice: Jisc report on the Findable, Accessible, Interoperable and Reusable Data Principles (Robert and David 2018)
- The FAIR Guiding Principles for scientific data management and stewardship (Wilkinson, Dumontier, Aalbersberg et al. 2016).
