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Research Data Management

What is FAIR Data?

According to the FAIR Data Principles, data should be: Findable, Accessible, Interoperable and Reusable. The FAIR Data Principles were developed and endorsed by researchers, publishers, funding agencies and industry partners in 2016, from a Nature Scientific Data publication, and are designed to enhance the value of all digital resources. 

  • Findable – It should be possible for others, both humans and computer systems, to discover your data. Rich metadata should be available online in a machine readable format, and the data should be assigned a persistent identifier.
  • Accessible – It should be possible for humans and machines to gain access to your data, under specific conditions or restrictions where appropriate. FAIR does not mean that data need to be open, however metadata should be present, even if the data aren’t accessible.
  • Interoperable – Data and metadata should conform to recognised formats and standards that allow them to be combined and exchanged, by both humans and computer systems. 
  • Reusable – Data and metadata should be sufficiently well described to allow data to be re-used by others. The data should conform to community norms and be accompanied by an accessible data usage license to explaining what kinds of reuse are permitted.

The benefits of FAIR data to the the broader research community are immeasurable and following the lead of the European Commission and Horizon 2020, Irish funders, such as the Health Research Board (HRB) and Irish Research Council (IRC) are now asking researchers how they will make their data FAIR, via a Data Management Plan (DMP). 

How FAIR are your Data?

A Checklist produced for use at the EUDAT summer school to discuss how FAIR the participant's research data were and what measures could be taken to improve FAIRness. Taken from Jones & Grootveld (2017)

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FAIR Data Self Assessment Tool

Australian Research Data Commons’ FAIR data self assessment tool.

Using this tool you will be able to assess the ‘FAIRness’ of you data and determine how to enhance its FAIRness (where applicable).

FAIR Data Resources