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Research Data Management

Introducing FAIR data

The FAIR Data Principles were established to overcome data discovery and reuse obstacles by developing a minimal set of community-agreed guiding principles and practices. In 2016, Wilkinson et al. wrote in Nature Scientific Data publication:  

What constitutes ‘good data management’ is largely undefined, and is generally left as a decision for the data or repository owner. Therefore, bringing some clarity around the goals and desiderata of good data management and stewardship, and defining simple guideposts to inform those who publish and/or preserve scholarly data, would be of great utility​

Even though the Internet is a ‘data rich environment’, both humans and machines often face distinct barriers when attempting to find and process data. Machines can process massive amounts of data but can’t make a semantic judgement on whether data is relevant or appropriate. Humans can interpret whether data is useful or relevant but can’t process massive amounts of data at speed. Humans also need to know a bit about the data to judge whether it’s relevant to their research question. Using the FAIR data principles to manage your data can accelerate the impact of your work as more researchers can find and reuse your data.

The FAIR Data Principles were developed and endorsed by researchers, publishers, funding agencies and industry partners in 2016, facilitate the discovery and reuse of research data. ​FAIR stands for Findable, Accessible, Interoperable and Reusable.

  • Findable: The data is described with rich metadata, and a unique and persistent identifier (PID) has been attached to the data. The metadata has been included in an online, searchable catalogue. 
  • Accessible: The data can be retrieved by humans and machines through a standardised communication protocol. Where data cannot be shared as open data (access to it must be restricted due to sensitivity) the conditions governing access and reuse are clearly described in the metadata. 
  • Interoperable: The data and metadata use languages, file formats and controlled vocabularies that are commonly used and understood by that research community. 
  • Reusable: The metadata provides clear data provenance and usage information, including a clear machine readable licence

It's important to note that FAIR data does not equate to Open Data, the principles contain the clause ‘as Open as possible but as closed as necessary.’ This allows for the reality that not all research data can be shared as Open Data.

​​​​​​FAIR data resources

Make your data FAIR - the FAIR data principles

How FAIR are your data?

The following Checklist by Jones & Grootveld (2017) can be a useful starting point to think about how you can produce data that is FAIR.


Another useful tool is the Australian Research Data Commons’ FAIR data self assessment tool which can help you to assess the FAIRness of a dataset and determine how to enhance its FAIRness (where applicable). To use this tool, you will answer questions related to the principles underpinning Findable, Accessible, Interoperable and Reusable (FAIR) and once you’ve answered all the questions in each section, you are 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.