Making Data FAIR
Researchers spend considerable time, money and effort collecting and interrogating data. Making data findable, accessible, interoperable and reusable (FAIR) can accelerate your research impact, including by gaining more citations for your datasets.
What Are 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 data 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 Does It Mean to Be FAIR?
Findable
Accessible
Interoperable
Reusable
Why Are the FAIR Principles Important to Research?
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. FAIR data 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, FAIR data 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 Adopting the FAIR Principles
The ARDC offers a range of best-practice guides, tools and services for adopting the FAIR data principles.
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:
FAIR Data Self-Assessment Tool
FAIR Data Training Resources
Metadata
Community-Endorsed Data Standards
CARE Principles
Good Data Practices
FAIR Principles for Research Software (FAIR4RS)
FAIR Software Checklist
FAIR Containers for Research Software
FAIR for Jupyter Notebooks: A Practical Guide
FAIR Policy for ARDC and ARDC Co-Investment Project Materials
Further ARDC and Community Support for FAIR
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).
