Research Data Management: FAIR Data

The FAIR Data Principles, published in an article in Scientific Data in 2016, are a set of guidelines for making data more Findable, Accessible, Interoperable and Reusable.

Essentially, these guidelines are concerned with the extent to which computer systems can find digital objects, understand, alter, and use them in various ways. Naturally, this is of immense importance for researchers, who need to think about how others will discover their data and what they may wish to do with it. Central questions for the researcher will revolve around:

• the language (or metadata) used to describe the data
• where the data should be located online (a data repository)
• what kind of identifier (a unique number, usually a DOI) can be applied to the data to facilitate sharing
• which kind of licence should be attached covering permission for reuse of the material  

As a researcher, are there standard vocabularies available to describe your data? What file format(s) should you be using, and what file-naming convention will you adopt to manage folders that may soon be bursting at the seams with multiple versions being created and updated, perhaps by other members of the research team? How will you organise your dataset? Here, it may be helpful to put yourself in the position of the end-user and consider how you would like to find everything laid out if you were seeing it all for the first time. To make life as easy as possible for potential users, creation of a Readme File is essential. Finally, have you reached out for advice and started tracking down a suitable repository for the data? This is vitally important, both in terms of FAIRness and making sure your data is looked after well into the future. 

FAIR resources 

OpenAire - How to Make Your Data FAIR: https://www.openaire.eu/how-to-make-your-data-fair 

Australian Research Data Commons: https://ardc.edu.au/resource/fair-data-self-assessment-tool/