Overview of FAIR and the IMI FAIRplus project at the UK Conference of Bioinformatics and Computational Biology 2020: https://www.earlham.ac.uk/uk-conference-bioinformatics-and-computational-biology-2020

1. The FAIR Principles and FAIRplus:
tools & guidelines for making life science data FAIR
Susanna-Assunta Sansone
ORCiD: 0000-0001-5306-5690 | Twitter: @SusannaASansone
datareadiness.eng.ox.ac.uk
Associate Professor, Engineering Science
Associate Director, Oxford e-Research Centre
UK Conference of Bioinformatics and Computational Biology 2020, 29-30 Sept 2020
Slides: https://www.slideshare.net/SusannaSansone

2. 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

3. 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

4. doi.org/10.2777/02999
doi.org/10.2777/1524www.gov.uk/government/publications/open-
research-data-task-force-final-report
doi.org/10.5281/zenodo.1245568www.turing.ac.uk/research/impact-stories/changing-
culture-data-science
FAIR has de facto become a global norm; just some examples
“…we estimate to be 10.2
billion euros per year…”
www.fair-access.net.au
doi.org/10.1787/25186167

5. 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

6. Findable
Accessible
Interoperable
Reusable
Is NOT a standard but a set of guiding
principles that provide for a continuum of
features, attributes and behaviours, via
many different implementations
The FAIR Principles are aspirational

7. Depends upon several stakeholders actively playing their parts to:
• deliver research infrastructures and tools
• use and harmonize the (meta)data standards
• address policies, education and training
• overcome technical, social and cultural challenges
• identify motivators, credit and rewards mechanisms
Making FAIR a reality in the research ecosystem

8. A crowded space, examples of FAIR evaluation tools

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

10. • 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’
Credit to:
Ian Harrow,
FAIR & OM projects
FAIR as enabler for the digital transformation

11. Funded from January 2019 to June 2022
23 participants:
• 3 SMEs
• 7 Pharmas
• 13 Academics
including ELIXIR-UK members:
o University of Oxford (SA. Sansone, P. Rocca-Serra)
o University of Manchester (G. Goble, N. Juty)
o Heriot Watt University (A. Gray)
Project Coordinator: ELIXIR
Project Leader: Janssen
www.fairplus-project.eu

12. Project types:
• completed
• ongoing
• new
Data types:
• molecular
• clinical
IMI projects:
Objectives and outputs

13. Project types:
• completed
• ongoing
• new
Data types:
• molecular
• clinical
• non-clinical
IMI projects: 1. Create the FAIR cookbook with
best practices on data
FAIRification and FAIR data
management
2. Identify FAIRification processes
and tools that work in the real
world
3. Increase FAIR levels of at least
20 IMI projects and internal
EFPIA datasets
4. Networking events for SMEs
5. FAIR Fellowship Programme -
FAIR data training
6. Change data management
culture - FAIR from the start,
with explicit plans built into IMI
projects

14. 1. Create the FAIR cookbook with
best practices on data
FAIRification and FAIR data
management
2. Identify FAIRification processes
and tools that work in the real
world
3. Increase FAIR levels of at least
20 IMI projects and internal
EFPIA datasets
4. Networking events for SMEs
5. FAIR Fellowship Programme -
FAIR data training
6. Change data management
culture - FAIR from the start,
with explicit plans built into IMI
projects
Project types:
• completed
• ongoing
• new
IMI projects:
Data types:
• molecular
• clinical
• non-clinical

15. Address questions/issues, rather then perform technical duties
Prioritization of the work based on pharma's needs
The FAIRification process

16. Address questions/issues, rather then perform technical duties
Prioritization of the work based on pharma's needs
The FAIRification process

17. Address questions/issues, rather then perform technical duties
Prioritization of the work based on pharma's needs
The FAIRification process

18. 1. Ontologies
2. Standards
3. Versioning
4. Identifiers
5. Licensing
Driver use cases:
• Update data to new ontology versions
• Capture ontology annotation provenance
• Ontologies as a service
• Ontology recommendations
• Ontology annotation recommendations
Top needs and challenges

19. ● To measures the FAIRness level of data
○ For use in the FAIRification processes to define initial/final level of data
FAIRness => leveraging on
● 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
FAIR indicators and capability maturity model

20. Publishing the FAIRified data and turning knowledge
into recipes

21. ● A comprehensive resource collating ‘recipes’ for making different
types of data FAIR
● Examples of published recipes:
● Converting Excel files to frictionless data package readable by computers
● Request terms to be added to a public ontology
What is it?

22. ● 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

23. 23
https://fairplus.github.io/cookbook-dev/intro

24. 24
The FAIR Cookbook

25. https://doi.org/10.1038/s41597-019-0286-0

26. 26
Technical infrastructure
• The Carpentries
• Galaxy Training Community
• The Alan Turing Institute’s Turing Way book
• Elixir Training Platform
Content
• The NIH Common Fund Data Ecosystem
• Pistoia Alliance’s FAIR Toolkit
• IMI EDHEN for OMOP documentation
Engaging and outreach

28. Before FAIR
The road to data management

29. Before FAIR
After FAIR
The road to data management

30. Before FAIR
After FAIR
….from chaos,
comes order?
http://blogs.nature.com/scientificdata/2019/10/22/the-layered-cake
The road to data management

31. 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