Supporting knowledge discovery and innovation
The FAIR principles are designed to support knowledge discovery and innovation both by humans and machines, support data and knowledge integration, promote sharing and reuse of data, be applied across multiple disciplines and help data and metadata to be ‘machine readable’, support new discoveries through the harvest and analysis of multiple datasets and outputs. However translating the FAIR principles into practice will vary for each discipline.
The FAIR principles are described below and can easily be applied to any discipline:
Findable: This includes assigning a persistent identifier (like a DOI or Handle), having rich metadata to describe the data and making sure it is findable through disciplinary local or international discovery portals.
Accessible: This may include making the data open using a standardised protocol. However the data does not necessarily have to be open (such as sensitive data). Examples of sensitive data include privacy concerns, national security or commercial interests. When it’s not able to be open, there should be clarity and transparency around the conditions governing access and reuse.
Interoperable: This involves using community of practice accepted languages, formats and vocabularies in the data and metadata. Metadata should reference and describe relationships to other data, metadata and information through using identifiers.
Reusable: Reusable data should maintain its initial richness. For example, it should not be diminished for the purpose of explaining the findings in one particular publication. It needs a clear machine readable licence and provenance information on how the data was formed. It should also have discipline-specific data and metadata standards to give it rich contextual information that will allow reuse.
Why make your data FAIR?
Making research data more FAIR will provide a range of benefits to researchers, research communities, research infrastructure facilities and research organisations, including:
- gaining maximum potential from data assets
- increasing the visibility and citations of research
- improving the reproducibility and reliability of research
- staying aligned with international standards and approaches
- attracting new partnerships with researchers, business, policy and broader communities
- enabling new research questions to be answered
- sing new innovative research approaches and tools achieving maximum impact from research.
Use the ARDC’s FAIR data self-assessment tool to assess the ‘FAIRness’ of a dataset. You’ll even receive tips on how to enhance its FAIRness.
Take the FAIR test
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How the ARDC supports FAIR Data
We do this in a number of ways, including supporting and driving a number of international and national initiatives as well as finding the right resources for our users to ensure they’re always using best practice methods within their research. We’ve also provided a series of webinars which explore each of the four FAIR principles in depth using practical case studies from a range of disciplines, Australian and international perspectives, and resources to support the uptake of FAIR principles.
International and national initiatives
- Australia: FAIR Access to research outputs policy statement
- Australia and international: Top 10 FAIR Data Global Sprint
- Europe: GO FAIR initiative
- US: NIH Data Commons Pilot Phase Explores Using the Cloud to Access and Share FAIR Biomedical Big Data
- US and international: Enabling FAIR Data Project
- International: FAIRmetrics working group
- FAIRsFAIR European project
Community resources
- The FAIR principles as published by FORCE11
- Nature article launching the FAIR concept
- Data Sharing and Citations: New Author Guidelines Promoting Open and FAIR Data in the Earth, Space, and Environmental Sciences
- Revisiting the FAIR principles for the European Open Science Cloud
- Explanation of the FAIR Data principles by the Dutch Centre for Life Sciences
- FAIRsharing lists standards, policies and databases related to FAIR
- Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud
- All the flavours of FAIR, fair & F.A.I.R ‘ webinar by AOASG
- Jisc report: FAIR in Practice
- SURF report: FAIR Data Advanced Use Cases
- FAIR data assessment tool
FAIR Data webinars
Findable
This webinar provides an overview of the FAIR principles: their origins, Australian FAIR initiatives, what FAIR is (and what it is not), the four findable principles which underpin the discoverability of data and the resources to support institutional awareness and uptake of findable principles to make your institutional data globally discoverable.
Watch | Read the transcript
Accessible
This webinar includes an overview of the accessible principles which underpin access and reuse of data and the resources to support institutional awareness and uptake of accessible principles.
Watch | Read the transcript
Interoperable
This webinar provides an overview of the three Interoperable principles which use vocabularies for knowledge representation, standardisation and references other metadata as well as the resources to support institutional awareness and uptake of interoperable principles.
Watch | Read the transcript
Reusable
This webinar includes an overview of the four reusable principles which bring together licensing, provenance and domain-relevant standards and resources to support institutional awareness and uptake of reusable principles.
Watch | Read the transcript
Your FAIR Data toolkit
We’ve put together a list of handy guides and tools to help you ensure you’re always up to date on best practices for making your data FAIR.
- FAIR data self assessment tool
- FAIRsharing: curated resource on data and metadata standards