Throughout this guide where information relates to the FAIR Data Principles you will see one of these icons.
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.
FAIR data resources
Force11 - The FAIR Data Principles - A set of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable.
FAIRsharing - A curated, informative and educational resource on data and metadata standards, inter-related to databases/repositories and data policies.
Addressing the FAIR Data Principles in a Data Management Plan - A useful guide produced by University College Dublin (UCD) on how to integrate the FAIR data principles into your Data Management Plan.
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.