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FAIR Data: Introduction
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FAIR Data
FAIR stands for Findable, Accessible, Interoperable and Reusable. The FAIR Data Principles were developed and endorsed by researchers, publishers, funding agencies and industry partners in 2016 and are designed to enhance the value of all digital resources.
Following the lead of the European Commission and Horizon 2020, Irish funders, including the Health Research Board (HRB) and Irish Research Council (IRC) are now asking Irish researchers to address, via a Data Management Plan (DMP), how they will make their data FAIR.
- Findable – It should be possible for others to discover your data. Rich metadata should be available online in a searchable resource, 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! There should be metadata, even if the data aren’t accessible.
- Interoperable – Data and metadata should conform to recognised formats and standards to allow them to be combined and exchanged.
- Reusable – Lots of documentation is needed to support data interpretation and reuse. The data should conform to community norms and be clearly licensed so others know what kinds of reuse are permitted.
If your goal is to make your data FAIR you should build this into your research plan from the start.
From:
- 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.
Jones, Sarah, & Grootveld, Marjan. (2017, November). How FAIR are your data?. Zenodo. http://doi.org/10.5281/zenodo.1065991
Make Your Research Data F.A.I.R
FAIR Data Further Resources
- Force11 - The FAIR Data PrinciplesA set of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable.
- The FAIR Guiding Principles for scientific data management and stewardshipWilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016).
- 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.
Jones, Sarah, & Grootveld, Marjan. (2017, November). How FAIR are your data?. Zenodo. http://doi.org/10.5281/zenodo.1065991 - Australian Research Data Commons’ FAIR data self assessment toolUsing this tool you will be able to assess the ‘FAIRness’ of a dataset and determine how to enhance its FAIRness (where applicable).
- GO FAIR RDM Starter KitThis page is supposed to serve as a Starter Kit for research data management (RDM). It lists resources designed to help researchers get started to organize their data.
- FAIRsharingFAIRsharing is a curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
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Help@UCD: Addressing the FAIR Data Principles in a Data Management Plan
- Addressing the FAIR Data Principles in a Data Management Plan [Information Sheet]Information sheet about the steps required to make your data FAIR
Help@UCD: RDM LibGuide
- Research Data Management LibGuideBringing together University resources and services to facilitate researchers in the production of high quality data.