The document outlines the FAIR data principles, which are essential for making research data findable, accessible, interoperable, and reusable. These principles enhance research transparency, facilitate knowledge discovery, and support innovation by ensuring data can be efficiently shared and repurposed. The document also provides tips for researchers on how to integrate FAIR principles into their practice, emphasizing the importance of compliance with funders' policies.

1. What are the FAIR data
principles
https://www.openaire.eu/how-to-make-your-data-fair
Judith Carr – Research Data Manager
The Open Research Team - The Library

2. AIM
At the end of this session you will know what the FAIR data
principles are, what is required and be in a position to think how
these would relate to your research practice.
Why do we have the FAIR data principles? why do they help ?
Four things to remember about FAIR data principles
The principles themselves
Tips on getting started

3. Why are the FAIR data principles needed ?
What do the FAIR data principles help with ?
• Research data is an asset and digital research data can be stored, reused,
repurposed
• Access to this data facilitates knowledge, discovery, improves research
transparency
• Sharing aids innovations/solves problems – avoids repetition
• Obligation to keep for a long time
• Provide a description of how outputs should be organised
so they can be more easily accessed, understood,
exchanged and reused.
• Funders support and FAIR data to maximise the integrity and
impact of their research investment. Adopting FAIR data
principles can help you as researcher maximise your impact/
contribution to your field.

4. Both humans and machines are
intended as digesters of data.
The FAIR principles apply to
both data and metadata.
The principles are not necessarily about open
data.
The FAIR principles are not rules
or standards.
Four thing to note about FAIR data principles

5. Photo by Markus Winkler on Unsplash
FINDABLE
Both the metadata and your datasets should be easy to find for
both humans and computers. Machine-readable metadata are
essential for automatic discovery of datasets and services.

6. Photo by Nareeta Martin on Unsplash
ACCESSIBLE
Your data and/or metadata should not only be findable, but both
humans and machines should be able to gain access. As FAIR does not
necessarily mean openly sharing, this could be under specific
conditions. However you share the data, it is still possible to make the
metadata publicly available.

7. INTEROPERABLE
As the aim is to speed up discovery and uncover new
insights, research data should be easily combined with
other datasets, applications and workflows by humans
and computer systems.

8. REUSABLE
Research data should be ready for future research and future
processing, making it clear that findings can be replicated and
the data/results can be used to build/develop new research
questions.

9. Tips on how to get started
What happens now in your discipline - are there specific repositories that are used?
Do you use secondary data? Think about the issues you have had in interpreting the data?
What could you do differently? Would thinking about FAIR help?
Pick on one thing you would like to do that relates to FAIR and investigate how to go about it.
If you are funded, find out what your funder’s policy is. Follow it and ask us if necessary.
Publishing open access, what about your data? ( Note UKRI new requirements).
FAIR data underpins Open Research, open research tools often follow FAIR principles.
Don’t panic

10. Thank you and any questions?
Useful Webpages
www.Liverpool.ac.uk/rdm – Liverpool Research Data
https://www.howtofair.dk/what-is-fair/
https://www.go-fair.org/fair-principles/fairification-process/
https://www.re3data.org/ - registry of research data repositories