Overview of ELIXIR and focus on FAIRsharing and the FAIR Cookbook for the USA AR-BIC 2022 8th Annual Conference.

1. FAIRification is a Team Sport
Susanna-Assunta Sansone, PhD
AR-BIC 2022 8th Annual Conference, 10-11 March 2022
Slides: https://www.slideshare.net/SusannaSansone
Group: datareadiness.eng.ox.ac.uk
ORCiD: 0000-0001-5306-5690
Twitter: @SusannaASansone
Professor of Data Readiness
Associate Director, Oxford e-Research Centre
ELIXIR
Interoperability Platform ExCo
elixir-europe.org
Founding
Academic Editor
nature.com/sdata

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
Discoverability and reuse of data at scale

4. Globally unique,
resolvable, and
persistent identifiers
To retrieve and
connect data
Community-defined
descriptive metadata
To enhance
discoverability and
interpretability
Community-defined
terminologies
To use the same term
and mean the
same thing
Detailed provenance
and workflows
To contextualize the
data and facilitate use in
applications
Terms of access “as
open as possible, as
closed a necessary”
To understand how data
can be accessed
Terms of use and clear
licenses
To enable innovation
and reuse, ensuring
credit as needed
Findable Accessible Interoperable Reusable

5. 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

6. FAIR-driven digital transformation by pharmas
• 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
• Requirements
▪ financial, technical, training
• Challenges
▪ change the culture, show business value, achieve
the ‘FAIR enough’ on an enterprise scale

7. Findable
Accessible
Interoperable
Reusable
Providing for a continuum of features,
attributes and behaviours
FAIR Principles: aspirational guidance

8. Making FAIR a reality in the research ecosystem
doi.org/10.2777/1524

9. An intergovernmental organisation that brings
together life science resources
from across Europe, to coordinate them so that
they form a single infrastructure

10. ELIXIR - a sustainable infrastructure for
biological data

11. ELIXIR Nodes:
connecting national data infrastructures
ELIXIR Nodes are the permanent structure, funded
mainly by national roadmap funding, competitive
grants and industry collaborations, and:
• Act as national coordinating entities
• Bring together national experts
• Provide services, databases, tools and resources

12. What services do ELIXIR offer and
in which domains?
Databases and Data Resources
Interoperability Resources
Bioinformatics Tools
Compute Capabilities
Bioinformatics Training Opportunities
Food & Nutrition
+ Toxicology
Domain experts, who are also service providers and/or users,
drive the developments in the Platforms

13. Focus on the interoperability platform
Databases and Data Resources
Interoperability Resources
Bioinformatics Tools
Compute Capabilities
Bioinformatics Training Opportunities

14. FAIR service framework
Ontologies,
formats, reporting
guidelines,
Identifier
Authorities
Metadata
Annotation
& Validation
Citation
Harvesting,
Indexing,
Search
Ontology
Mapping
Identifier
Mapping
Ontology
Lookup
Identifier
resolution
Ontology
Management
Identifier
minting
Standards &
databases
Ontologies Tools Workflows
Identifiers
Registries
Type specific
Mapping &
resolution
Extract-
Transform-Load
APIs
Standards
Services Type specific KBs,
integration &
aggregation
Knowledge

15. IMI2 project guidelines for
open access to publications
and research data
Recommended by
European funders
FAIR service framework: focus on two resources

17. An informative and educational resource, and a service
FAIRsharing provides curated descriptions and relationship graphs of
standards, databases and policies in all disciplines
COMMUNITY STANDARDS
POLICIES
by funders, journals
and other organizations
DATABASES
including repositories
and knowledgebases
Identifiers
Terminologies Guidelines
Formats

18. Researchers Developers and
curators
Journal
publishers
Societies and
Alliances
Librarians and
Trainers
Funders
FAIRsharing: working with and for all stakeholders

19. Guides consumers to discover, select and use these resources with confidence
Helps producers to make their resources more visible, more widely adopted and cited
Total of
over 3595
resources
(March 2022)
repositories
standards
policies
Promoting repositories, standards, policies

20. Search by subject
Powered by our Subject Ontology of 436 terms
https://fairsharing.org/browse/subject
https://github.com/FAIRsharing/subject-ontology
https://www.ebi.ac.uk/ols/ontologies/srao

21. Identifiers
Terminologies Guidelines
Formats
Conceptual model, conceptual
schema, exchange formats
to represent, contain and
move information
Controlled vocabularies,
thesauri, ontologies
to disambiguate terms and
enable semantic
relationships
Minimum information
reporting requirements,
or checklists
to report the same core,
essential information
Unambiguous, persistent and
context-independent schema
to identify data
and metadata elements
Interoperability standards are the pillars of FAIR
Source:

22. Identifiers
Terminologies Guidelines
Formats
Natural, engineering, humanities & social sciences
816
486
192
19
More than 1500 data and metadata standards
Source:

23. Standard organizations, e.g.: Grass-roots groups, e.g.:
Life and biomedical sciences
Identifiers
Terminologies Guidelines
Formats
486
275
151
8
More than 900 data and metadata standards
Source:

24. Standard organizations, e.g.: Grass-roots groups, e.g.:
• Industry-level standards
• Mostly regulators-driven
• Participation is often regulated
• Standards are sold or licenced
• Formal development process, often
less flexible, could be lengthy
• Charges apply to advanced training or
programmatic access
• Mostly research-level standards
• Open to any interested party
• Volunteering efforts
• Standards are free for use
• Development process varies, more
flexible and adaptable to changes
• Minimal or little funds for carry out the
work, let alone provide training
Understanding their life cycle and landscape
Source:
Identifiers
Terminologies Guidelines
Formats
Formulation
Development
Maintenance

25. URL:
https://committee.iso.org
/standard/68848.html
URL: fairsharing.org/collection/ISOCD20691CollectionDRAFT

26. Translational Medicine
Clinical Developments
URL: beta.fairsharing.org/PistoiaAllianceFIPs
(work in progress!)
A collaboration with their FAIR Implementation WG
Disclaimer: These profiles speak for a limited community and do not represent any company standards
Building and comparing
“FAIR profiles”

27. Clinical Developments
Disclaimer: These profiles speak for a limited community and do not represent any company standards
Snapshot of the semantic and
syntactic standards used

28. Findability
Sitemap.xml, JSON
Markup with Schema.org for
search indexes
DOI unique persistent
identifiers for each record
ORCID for author credit and
authentication
Accessibility
read/write REST API
read OAI-PMH
Interoperability
JSON markup
Standardized semantics
Cross-links to or import from
records in other registries
ROR for organizations (ongoing)
FundRef for funders (ongoing)
Reusability
CC BY 4.0 license
JSON export
The FAIRness of the FAIRsharing

30. Beyond the hype
Large body of generic FAIR
guidance
Motivations
Non-specific guidance for
the life sciences
Ambitions
Target specific situations to deliver a guide with
applied examples
Join academia and industry forces to make the
case for FAIR data management
Build capacity for high quality data
management in the private and public sectors

31. FAIR Cookbook:
turning knowledge into recipes
What is it?
An online, ‘live’ resource
for the life sciences
A collection of recipes
that cover the operation
steps of FAIR data
management
Who is it for?
Who developed it?
Researchers and data
managers professionals
in the life sciences, from
academia and industry
Including ELIXIR
members
faircookbook.elixir-europe.org

32. FAIR Cookbook: learning objectives
Learn how to improve the FAIRness with exemplar datasets
Understand the levels and indicators of FAIRness
Discover open source technologies, tools and services
Find out the required skills
Acknowledge the challenges
faircookbook.elixir-europe.org

33. Recipes that cover all aspects of FAIRness
Recipe summary card, examples:

34. https://doi.org/10.1038/s41597-019-0286-0
Applied examples: these are key!

35. Step by step process
Guidelines, process, description
References
What should I read next?
Ingredients
An idea of tools/skills needed
Examples
Practical
elements, code
snippets
#Python3
#zooma-annotator-script.py
file
def
get_annotations(propertyTy
pe, propertyValues, filters =
""): "””
Get Zooma annotations for
the values of a given
property of a given type.
""”
import requests
annotations = []
no_annotations = []
Where is the value?

36. ● 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
The FAIRification process

37. 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

38. 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.

39. 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
A dedicated recipe
will help to move
from Repeatable
to Defined level

40. +50 life sciences professionals, researchers and data managers
FARIplus
partners
Industry
+
Academia
FAIR Cookbook: creators and contributors
ELIXIR
Nodes
represented

41. A live ever-growing resource:
become part of a community of FAIR experts!
1Identify a chapter and a topic
Findability Accessibility Interoperability Reusability
Infrastructure Applied examples Assessment
2 Choose a way of contributing and see our guidelines
Google Docs
HackMD
Git
Markdown cheat sheet
Get recipe template
Tips and tricks
Submit an
outline
3
You can
discuss it
with the
Editorial
Board

43. Findability
Sitemap.xml, JSON-LD
Markup with Schema.org,
Bioschemas
w3id.org unique persistent
identifiers for each recipe
ORCID for authors
Accessibility
HTTPS protocol
Interoperability
JSON-LD markup
Cross-links to objects in other
registries
incl. Biotools (tools)
FAIRsharing (repositories, standards)
CreDiT attribution ontology
Reusability
CC BY 4.0 license for all
content
The FAIRness of the FAIR Cookbook

44. Watch the webinar for more information,
or watch out for the new one!
Scheduled: 1 June 2022
datascience.nih.gov/nih-data-sharing-and-reuse-
seminar-series
elixir-europe.org/events/fairplus-webinar-
discovering-fair-cookbook
May 2021
faircookbook.elixir-europe.org

45. FAIRification is a team sport,
it takes a village,
but it is no longer optional.
Because better data means
better science!