What is FAIR data?


The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015. 

 

The principles have since received worldwide recognition by various organisations including FORCE11, NIH a nd the European Commission as a useful framework for thinking about sharing data in a way that will enable maximum use and reuse.

 

 

 

 

 

 

 

 

The principles are useful because they:

 

  • support knowledge discovery and innovation

  • support data and knowledge integration

  • promote sharing and reuse of data

  • are discipline independent and allow for differences in disciplines

  • move beyond high level guidance, containing detailed advice on activities that can be undertaken to make data more FAIR

  • help data and metadata to be ‘machine readable’, supporting new discoveries through the harvest and analysis of multiple datasets.

 
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 alike, 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

  • using new innovative research approaches and tools

  • achieving maximum impact from research.

 

How to make your data FAIR


Translating the FAIR principles in practice will be different for different disciplines, however the below guidelines set out the broad principles:

 

  • 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 discovery portals (local and international).

  • Accessible

    • This may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse.

  • Interoperable

    • To be interoperable the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information 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 for reuse.

 

 

Is your data FAIR?
Click on the FAIRis wheel to find out!

 Click on the PDF icon for the printable FAIR data brochure

Monash University is the lead agent of ANDS.

The University of Melbourne is the lead agent of Nectar.

The University of Queensland is the lead agent of RDS.

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This activity received grant funding from the Australian Government through the National Collaborative Research Infrastructure Strategy.