Researchers spend considerable time, money and effort collecting and interrogating data. Making your data findable, accessible, interoperable and reusable (FAIR) maximises the impact of that investment, including gaining more citations for your data sets.

Check your dataset is FAIR with our handy self-assessment tool.
Watch our FAIR data webinar playlist.
Access free online FAIR data training and resources.

Why FAIR Data is Important

Using the FAIR data principles can accelerate the impact of your work as more researchers can find and reuse your data. This can result in increased collaboration with research and industry and acknowledgement of your data in other publications. It also benefits research communities, research infrastructure facilities and research organisations.

FAIR benefits researchers and research organisations in the following ways:

  • 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
  • establishing new innovative research approaches and tools achieving maximum impact from research.

What is FAIR Data?

FAIR provides a useful framework for thinking about sharing data in a way that will enable maximum use and reuse.

The FAIR guiding principles for scientific data management and stewardship were developed by the international research community and published in 2016 to:

  • support knowledge discovery and innovation both by humans and machines
  • support data and knowledge integration
  • support new discoveries through the harvest and analysis of multiple datasets and outputs
  • promote sharing and reuse of data
  • be applied across multiple disciplines, even those that involve sensitive data
  • help data and metadata to be ‘machine readable’.

The authors produced the document to provide guidelines to improve the findability, accessibility, interoperability, and reuse of digital assets. However, translating the FAIR principles into practice varies for each discipline.

The FAIR Principles


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), having rich metadata to describe the data and making sure it is findable through disciplinary local or international discovery portals.


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. Data can be sensitive due to 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.


The associated data and metadata uses 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. Metadata should reference and describe relationships to other data, metadata and information through identifiers.


The associated metadata provides rich and accurate information, and the data comes with a  clear usage licence and detailed provenance information. 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 use discipline-specific data and metadata standards to give it rich contextual information that will allow reuse.

The above information was drawn partly from: European Commission Expert Group, Chaired by Simon Hodson, Turning FAIR into Reality (2018) https://doi.org/10.2777/1524

Check Your Data is FAIR

Use the ARDC’s FAIR data self-assessment tool to check the ‘FAIRness’ of a dataset. You’ll even receive tips on how to enhance its FAIRness.

Take the FAIR test.

How the ARDC Supports FAIR Data

We support FAIR data 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 created a series of recorded webinars on FAIR that explore each of the 4 FAIR data 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

Community resources

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Last updated

12 May 2022



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