FAIR Data Self Assessment Tool

Use our FAIR data self assessment tool to assess how FAIR your research dataset is and get practical tips on how to enhance its FAIRness.

This handy tool helps you assess the FAIRness of a dataset and determine how to enhance its FAIRness (where applicable).


In the tool below, you will answer questions related to the principles underpinning Findable, Accessible, Interoperable and Reusable (FAIR).

Once you’ve answered all the questions in each section, you’ll be given a ‘green bar’ indicator based on your answers in that section. When all sections are completed, it provides you with an overall ‘FAIRness’ indicator.

The tool has been designed predominantly for data librarians and IT staff, but could be used by software engineers developing FAIR data tools and services, and researchers provided they have assistance from research support staff.

FAIR guiding principles for scientific data management and stewardship was published in 2016. It provides a useful framework for thinking about sharing data in a way that will enable maximum use and reuse.

Learn more about making your data more FAIR.

Find more training resources on FAIR.

Last updated

12 May 2022




Research Topic

Total across FAIR

0/12 Answered

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12


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.

2 green check

Is the dataset identifier included in all metadata records/files describing the data?

3 green check

How is the data described with metadata?

4 green check

What type of repository or registry is the metadata record in?

Findable meter


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.

6 green check

Is the data available online without requiring specialised protocols or tools once access has been approved?

7 green check

Will the metadata record be available even if the data is no longer available?

Accessible meter


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.

8 green check

What (file) format(s) is the data available in?

Learn more about file formats for data, different types of vocabularies, ontologies and tagging schemas for data and linking metadata to other data and metadata.
Related Resources
9 green check

What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?

10 green check

How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?

Interoperable meter


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.

12 green check

How much provenance information has been captured to facilitate data reuse?

Reusable meter

Get Personalised Resources And Save Your Results

Get personalised resources that will help you level up in making your data FAIR. You’ll receive an email with the list of best resources for you based on your answers. Additionally, you’ll get a unique link to be able to access your assessment at a later time.

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Tool Disclaimer

The ARDC FAIR data self assessment tool has been developed by the ARDC. It is provided purely for educational and informational purposes. It is based on our interpretation of the FAIR Data Principles with the acknowledgement that there are other interpretations of the principles. Other tools like the CSIRO 5 star data rating tool and the DANS FAIRdat tool provided valuable inspiration in developing this tool.

The scores arising from this tool are intended for self assessment purposes only and to trigger thinking and discussion around possible ways of making data more FAIR.

The code for this tool is available for reuse on Github.