The FAIRplus project aims to improve data discoverability and reuse through the implementation of 'FAIR' principles: Findable, Accessible, Interoperable, and Reusable. It emphasizes the necessity for well-structured, self-described data to facilitate human-machine collaboration and improve the efficiency of data scientists, who typically spend 80% of their work on data preparation. The project also provides guidance on fairification processes and offers resources like the FAIR Cookbook to help organizations implement and sustain FAIR data practices.

1. FAIR data for humans and machines:
The FAIRplus project
Susanna-Assunta Sansone
ORCiD: 0000-0001-5306-5690 | Twitter: @SusannaASansone
datareadiness.eng.ox.ac.uk
Associate Professor, Information Engineering
Associate Director, Oxford e-Research Centre
MCBIOS & MAQC joint virtual conference, 29-30 April 2021
Slides: https://www.slideshare.net/SusannaSansone

2. Discoveries are made using shared data and this requires data that are:
• Retrievable and structured in standard format(s)
• Self-described so that third parties can make sense of it
The problem
Forbes article on 2016 Data Scientist Report
https://www.forbes.com/sites/gilpress/2016/03/23/data-
preparation-most-time-consuming-least-enjoyable-data-science-
task-survey-says/#276a35e6f637
Data preparation accounts for about 80% of the work of data scientists

3. A set of principles to enhance the
value of all digital resources and its
reuse by humans and machines
Data that is discoverable and reusable at scale

4. Findable
Accessible
Interoperable
Reusable
• Globally unique, resolvable, and persistent identifiers
▪ To retrieve and connect data
• Community defined descriptive metadata
▪ To enhance discoverability
• Common terminologies
▪ To use the same term mean the same thing
• Detailed provenance
▪ To contextualize the data and facilitate reproducibility
• Terms of access
▪ Open as possible, closed as necessary
• Terms of use
▪ Clear licences, ideally to enable innovation and reuse
The FAIR Principles in a nutshell

5. Findable
Accessible
Interoperable
Reusable
Providing for a continuum of features,
attributes and behaviours
FAIR guiding principles

6. FAIR: just principles, not practice

7. The scholarly publishing
ecosystem is changing
Data-relates mandates by
funders and institutions are
growing
Researchers need
recognition and credit
theconversation.com/how-robots-can-help-us-embrace-a-more-human-view-of-disability-76815
Human-machine collaboration is the future
o 21% pharmacology data (doi.org/10.1038/nrd3439-c1)
o 11% cancer data (doi.org/10.1038/483531a)
o unsatisfactory in ML (openreview.net/pdf?id=By4l2PbQ-)
towardsdatascience.com/scientific-data-analysis-pipelines-and-reproducibility-75ff9df5b4c5
Reproducibility of published studies is still problematic
Responding to needs and crisis

8. doi.org/10.2777/986252
www.gov.uk/government/publications/open-
research-data-task-force-final-report
www.turing.ac.uk/research/impact-
stories/changing-culture-data-science
www.fair-access.net.au
doi.org/10.1787/25186167
ark:/48223/pf0000374837
FAIR has aligned the broad community
around common guidelines
doi.org/10.7486/DRI.tq582c863

9. doi.org/10.2777/02999
The cost of not having FAIR research data
Impact on innovation

10. Credit to:
A crowded space, examples of European projects

11. Credit to:
A crowded space, examples of European projects

12. Examples:
A growing number of metrics, indicators,
certifications of FAIRness
Diversity of methods and opinions:
• Metrics and indicators
• Automated and manual

13. Define Implement Embed & Sustain
Concepts for FAIR
implementation
FAIR culture
FAIR
ecosystem
Skills for FAIR
Incentives and
metrics for FAIR data
and services
Investment in
FAIR
Economic Technical Social Political
doi.org/10.2777/1524
Making FAIR a reality in the research ecosystem

14. My fair share
of the work

15. €5.3 billion
programme
European
intergovernmental
organization
23 member
countries and over
220 research
organizations
Since 2014
1
2
3 Started in 2019
Since in 2014, several programs:
2014-2017
2017-2018
Example of FAIR-enabling programs and projects
Since 2009
Pre-competitive collaboration between over
100 global organizations, SMEs and academia

16. The way biopharma works has changed
Today & tomorrow
Proprietary
content
providers
Public
content
providers
Academic
groups
Software vendors
CROs
Service providers
Regulatory
authorities
Exemplar initiatives:
The rise of pre-competitive initiatives around data

17. • Biopharma R&D productivity can be improved
by implementing the FAIR Principles
• FAIR enables powerful new AI analytics to
access data for machine learning and prediction
Ø Requirements
§ financial, technical, training
Ø Challenges
§ change the culture, show business value,
achieve the ‘FAIR enough’ on an enterprise scale
FAIR as enabler for the digital transformation

19. ● No practical advice on how to do FAIRification
● Can’t tell how FAIR I am already
● Don’t know how to become more FAIR
● My organisation doesn’t care anyway
Deliver the FAIR Cookbook
Mature FAIRification processes & build a maturity model
Assess FAIR levels of projects and data
Change data management culture
Objectives and outputs of the FAIRplus project

20. Defined by using a number of IMI health research and innovation projects
The FAIRification process

21. 20%
identifiers
80%
metadata
https://doi.org/10.2777/1524
Two pillars of FAIR
Findable
Accessible
Interoperable
Reusable

22. 22
Different contexts mandate different strategies
Molecular data
Clinical (observation based) data Clinical trial (event based) data
FAIRification paths in the context of IMI projects

23. 23
Molecular data
Clinical (observation based) data Clinical trial (event based) data
Selecting standard stacks for the FAIRification

24. 24
Clinical (observation based) data Clinical trial (event based) data
Selecting standard stacks for the FAIRification

25. 25
Terminology
Format
Checklists
Molecular data
Selecting standard stacks for the FAIRification

26. 390+
162+
729+
~1300
13
MIAME
MIRIAM
MIQAS
MIX
MIGEN
ARRIVE
MIAPE
MIASE
MIQE
MISFISHIE
….
REMARK
CONSORT
SRAxml
SOFT FASTA
DICOM
MzML
SBRML
SEDML
…
GELML
ISA
CML
MITAB
…
AAO
CHEBI
OBI
PATO ENVO
MOD
BTO
IDO
…
TEDDY
PRO
XAO
DO
VO
EC number
URL
PURL
LSID
Handle
ORCID
RRID
InChI
…
IVOA ID
DOI
standard
organizations
grass-roots
groups
Formats Terminologies Guidelines Identifiers
ID
COMMUNITY STANDARDS
for metadata and identifiers

27. DATA & METADATA STANDARDS
REPOSITORIES
databases and
knowledgebases
DATA POLICIES
by funders, journals and
other organizations
an informative and educational resource
Guides consumers to discover, select and use these
resources with confidence
Helps producers to make their resources more visible,
more widely adopted and cited

28. DATA & METADATA STANDARDS
REPOSITORIES
databases and
knowledgebases
DATA POLICIES
by funders, journals and
other organizations
Provides curated, community-vetted
descriptions and knowledge graphs that
represent these resources and their inter-relationships
an informative and educational resource

29. … 232 standards
https://fairsharing.org/collection/ISOCD20691CollectionDRAFT
ISO/CD 20691 specification that details the requirements for
the consistent formatting and documentation of data and
metadata in the life sciences and biotechnology, including
biomedical research and non-human biological research and
development; it covers manual or computational workflows.
This FAIRsharing Collection includes the standards detailed in
the ISO/CD 20691 specification, and serves as a 'live' list to
search and discover these standards, their use by repositories,
as well as their evolution over time.

30. Publishing the FAIRified data and turning knowledge
into recipes
https://fairplus.github.io/the-fair-cookbook

31. ● A comprehensive resource collating ‘recipes’ for making different
types of data FAIR
● Work in progress: currently 50 recipes and growing….
What is it?
FAIRification processes

32. ● How to FAIRify or improve the FAIRness of exemplar datasets
● Which are the levels and indicators of FAIRness
● Which open source technologies, tools and services are available
● What skills are required
● Awareness of known challenges
Learning outcomes

33. 33

34. 34

35. 35

36. https://doi.org/10.1038/s41597-019-0286-0
Example of converting
Excel files to frictionless
data package, and then
build a semantic model to
have a linked data graphs

37. 37
The FAIR Cookbook

38. 38
The FAIR Cookbook

39. 39
Overview

40. 40
Capability Maturity
Model
FAIRsharing
cross linking
Biotools
cross linking

41. 41
Tracking authors and their
contributions with the
CRedIT vocabulary
Open license

42. 42
Executable code as Jupyter notebook

43. 43
https://fairtoolkit.pistoiaalliance.org
Partnership with

44. 1. Ontologies
2. Standards
3. Versioning
4. Identifiers
5. Licensing
Top needs and challenges
FAIRification processes

45. FAIRification processes
● How to measures the FAIRness level of data?
○ For use in the FAIRification processes to define initial/final level of data FAIRness
● How to measures capability and performance of an organization for FAIR data
generation and management?
○ For use at the strategy level to identify investment areas, monitor processes
○ E.g. ability to provide ETL capability, an ontology look-up service, or mapping services
FAIR indicators and capability maturity model

46. The capability maturity model
Which capabilities are needed to
improve data reusability?
The optimum level of FAIRness
is a trade-off between desired
data reuse level and cost to
achieve that level

47. The capability maturity model - the ontology example
Which capabilities are needed to
improve data reusability?
The optimum level of FAIRness
is a trade-off between desired
data reuse level and cost to
achieve that level
No use of
ontologies
Use of internal
ontologies
Use of
community
ontologies
+ Ontology service to
manage several
ontologies, mapping,
versioning etc.
+ Term suggestion,
automatic annotation,
terms conflict
resolution etc.

48. No use of
ontologies
Use of internal
ontologies
Use of
community
ontologies
+ Ontology service to
manage several
ontologies, mapping,
versioning etc.
+ Term suggestion,
automatic annotation,
terms conflict
resolution etc.
The capability maturity model - the ontology example
Robot recipe will
help to move
from Repeatable
to Defined level

49. ● Practical advice on how to do FAIRification
● Exemplar FAIRified data
● Process and maturity model
● My organisation cares about FAIR data
Deliver the FAIR Cookbook
Mature FAIRification
Projects and datasets
Change data management culture
FAIRplus project - summary

50. Before FAIR
After FAIR
The road to FAIRness

51. After FAIR
The road to FAIRness
….from chaos,
comes order?
Before FAIR

52. infrastructures
standards
tools
policies
education
training
cultural normalization
incentives
long term investment
It is not simple, but it is no longer optional
A FAIRY tale needs some magic
The Magic Roundabout in Swindon, England

53. datareadiness.eng.ox.ac.uk