1.
S a r a h J o n e s
A s s o c i a t e D i r e c t o r , D i g i t a l C u r a t i o n C e n t r e
R a p p o r t e u r o f F A I R d a t a e x p e r t g r o u p
s a r a h . j o n e s @ g l a s g o w . a c . u k
T w i t t e r : @ s j D C C
The FAIR data concept

2.
What is FAIR?
A set of principles to ensure that data are
shared in a way that enables & enhances reuse,
by humans and machines
Image CC-BY-SA by SangyaPundir

3.
Origins of FAIR
• Emerged from a workshop held in Leiden in 2014
• Come from life sciences but intended for all data
• Issued by FORCE11 community
• Echo previous principles on open data & curation
OECD Principles and
Guidelines for Access to
Research Data from
Public Funding (2007)
Science as an Open Enterprise (2012)
notion of ‘intelligent openness’ where
data are accessible, intelligible,
assessable and useable G8 Science Ministers
Statement (2013)

4.
Why has FAIR gained traction?
 Meaningful and memorable articulation of concepts
 Natural desire to want to be ‘fair’
 Easy to understand at a high-level, but…
“We understand the basic principle of FAIR, but the terminology is often difficult
to grasp immediately. Things could be explained better in plain language”
“The term interoperable is quite confusing sometimes and mixed with re-use.”
OpenAIRE & FAIR data Expert Group H2020 DMP survey
https://doi.org/10.5281/zenodo.1120245

5.
What FAIR means: 15 principles
Findable
F1. (meta)data are assigned a globally unique and
eternally persistent identifier.
F2. data are described with rich metadata.
F3. (meta)data are registered or indexed in a
searchable resource.
F4. metadata specify the data identifier.
Interoperable
I1. (meta)data use a formal, accessible, shared, and
broadly applicable language for knowledge
representation.
I2. (meta)data use vocabularies that follow FAIR
principles.
I3. (meta)data include qualified references to other
(meta)data.
Accessible
A1 (meta)data are retrievable by their
identifier using a standardized communications
protocol.
A1.1 the protocol is open, free, and universally
implementable.
A1.2 the protocol allows for an authentication and
authorization procedure, where necessary.
A2 metadata are accessible, even when the data are
no longer available.
Reusable
R1. meta(data) have a plurality of accurate and
relevant attributes.
R1.1. (meta)data are released with a clear and
accessible data usage license.
R1.2. (meta)data are associated with
their provenance.
R1.3. (meta)data meet domain-relevant community
standards.
Slide CC-BY by Erik Schultes, Leiden UMC
doi: 10.1038/sdata.2016.18

6.
The FAIR data principles explained
• Clarifications from the Dutch
Techcentre for Life Sciences
• Each principle is a link to
further clarification,
examples and context
https://www.dtls.nl/fair-
data/fair-principles-explained
R1. Meta(data) are richly described with a plurality of accurate and
relevant attributes
• By giving data many ‘labels’, it will be much easier to find and reuse the data.
• Provide not just metadata that allows discovery, but also metadata that
richly describes the context under which that data was generated
• “plurality” indicates that metadata should be as generous as possible, even to
the point of providing information that may seem irrelevant.

7.
FAIR data checklist
 Findable
- Persistent ID
- Metadata online
 Accessible
- Data online
- Restrictions where needed
 Interoperable
- Use standards, controlled vocabs
- Common (open) formats
 Reusable
- Rich documentation
- Clear usage licence
https://doi.org/10.5281/zenodo.1065991

8.
FAIR vs open data
• FAIR data does not have to be open
• Data can be shared under restrictions & still be FAIR
• Making data FAIR ensures it can be found,
understood and reused
• Open data is a subset of all the data shared
"As open as possible, as closed as necessary"

9.
Implementing FAIR
• The principles do not specify particular
technologies or implementations e.g. semantic web
• FAIR is not a standard to be followed or strict
criteria – it’s a spectrum / continuum
• Supporting FAIR will require investment in
infrastructure, coordination across initiatives and
engagement with research communities

10.
Context is king
 Practices varies across research communities
 particle physics mostly shares its data inside the large
consortia attached to its experiments
 social scientists typically provide lots of documentation and
methodological information supporting reuse
 Communities need to self-organise and define what
FAIR data means in their context

11.
Emerging initiatives
Pilot project and several
infrastructure investments
https://eoscpilot.eu
http://tiny.cc/EOSC-projects
https://www.go-fair.org
https://www.rd-alliance.org

12.
Remit of the FAIR data expert group
1. To develop recommendations on what needs to be done to turn each
component of the FAIR data principles into reality
2. To propose indicators to measure progress on each of the FAIR
components
3. To provide input to the proposed European Open Science Cloud
(EOSC) action plan on how to make data FAIR
4. To contribute to the evaluation of the Horizon 2020 Data
Management Plan (DMP) template and development of associated
sector / discipline-specific guidance
5. To provide input on the issue of costing and financing data
management activities
http://tinyurl.com/FAIR-EG

13.
FAIR EG progress and plans
 Most of the report text
is already written
 Preliminary
recommendations
 Working meeting to
initiate FAIR Data
Action plan next week
 Draft due end May
 Public consultation
around EOSC Summit
in June
 Final Report and Action
Plan due end
September
 Announce / launch at
Austrian Presidency
events in October

14.
Recommendations to facilitate change
 Define what actions are required by different stakeholders
(EC, member states, disciplines, international) and when
 Structure and prioritise actions across key topics: policy,
skills, standards, infrastructure, costs, rewards…
 Use these recommendations to develop
a core FAIR Data Action Plan
 Support the definition of more detailed
FAIR Data Action Plans at domain and
member state level

15.
Thanks - questions?
Sarah Jones
sarah.jones@glasgow.ac.uk
Twitter: @sjDCC
www.dcc.ac.uk
http://tinyurl.com/FAIR-EG
www.force11.org/group/fairgroup/fairprinciples