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Research Data Management


What is Research Data Management

What are the FAIR Data Principles?



The FAIR Data Principles are a set of guiding principles in order to make data findable, accessible, interoperable and reusable (Wilkinson et al., 2016).

These four principles provide guidance for scientific data management and stewardship and are relevant to all stakeholders in the current digital ecosystem.

They directly address data producers and data publishers to promote maximum use of research data. 

FAIR Self Assessment Tool

 The Australian Research Data Commons’ FAIR data self assessment tool.

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

You will be asked questions related to the principles underpinning Findable, Accessible, Interoperable and Reusable (FAIR). Once you have answered all the questions in each section you will be given a ‘green bar’ indicator based on your answers in that section, and when all sections are completed, an overall ‘FAIRness’ indicator is provided. 

Make your Research Data FAIR

How to make your Data FAIR

The Four Basics of FAIR
 'Findable' i.e. discoverable with metadata, identifiable and locatable by means of a standard identification mechanism
'Accessible' i.e. always available and obtainable; even if the data is restricted, the metadata is open
'Interoperable' i.e. both syntactically parseable and semantically understandable, allowing data exchange and reuse between researchers, institutions, organisations or countries; and
'Reusable' i.e. sufficiently described and shared with the least restrictive licences, allowing the widest reuse possible and the least cumbersome integration with other data sources.


Useful Reports

"Turning FAIR into reality: Final Report and Action Plan from the European Commission Expert Group on FAIR Data" (2018) 

"Guidelines on FAIR Data Management in H2020" (2016)



FAIR Data explained

How FAIR is 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 (2017).