Presentation to the EC Workshop on Maximizing investments in health research: FAIR data for a coordinate COVID-19 response. Workshop I, October 11, 2021.

1. Susanna-Assunta Sansone, PhD
Associate Professor, Associate Director, Oxford e-Research Centre, University of Oxford, UK
ORCiD: 0000-0001-5306-5690 | Twitter: @SusannaASansone
Group: datareadiness.eng.ox.ac.uk
Maximizing investments in health research: FAIR data for a coordinate COVID-19 response. Workshop I, October 11, 2021
Slides: https://www.slideshare.net/SusannaSansone
FAIR Principle and FAIRness – focus on standards:
brief overview of relevant activities

2. Globally unique and
persistent identifiers
Community defined
descriptive metadata
Community defined
terminologies
Detailed
provenance
Terms of access
Terms of
use
Metadata make data count
DOI: 10.1038/sdata.2016.18

3. 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 to report
metadata and data
Source:

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

5. 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:

6. 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

7. Standards interlinked to data resources and policies
Guides consumers to discover, select and use these resources with confidence
Helps producers to make their resources more visible, more widely adopted and cited
Over 3500
resources

8. URL: https://committee.iso.org/standard/68848.html
ISO (draft) specification encompassing
formal and grass-roots standards
URL: fairsharing.org/collection/ISOCD20691CollectionDRAFT

9. Interoperability standards: known pain points
DOI: 10.6084/m9.figshare.4055496.v1
Technical and social challenges, including:
• Fragmentation, harmonization and extensions
• Governance and ownership
• Indicators and evaluation methods
• Implementations, tools and services
• Synergies between basic and clinical/medical areas
• Credit and incentives for contributors
• Education, documentation and training
• Funding streams to support the ‘standard life cycle’
• Business models for sustainability

10. Resource Relevant projects Recommended by
IMI2 project guidelines for
open access to publications
and research data
Included in the next version

11. 11
Different contexts mandate different standardization strategies
Molecular data
Clinical (observation based)
data
Clinical trial (event based) data
FAIRification paths: one size does not fit all

12. Molecular data
Selecting a ‘standard stacks’ for the FAIRification
Terminologies
Guidelines
Formats

13. : from knowledge to recipes
URL: fairplus.github.io/the-fair-cookbook
Authors:
…..and open to other contributions,
because FAIR is a ‘team sport’!
fairplus-cookbook@elixir-europe.org
Examples:
WEBINAR: elixir-europe.org/events/fairplus-webinar-discovering-fair-cookbook

14. FAIR indicators and capability maturity model
How to measures the FAIRness level of data?
DRAFT
How to measures capability and performance
of an organization for FAIR data generation
and management?

15. Indexing outbreak data across disciplines
BY-COVID WP3, focussing on services for the
discovery, integration and citation of data, will:
• deliver a flexible, tiered metadata discovery system
across different domains, metadata standards, and
maturity/robustness levels of data sources
• leverage also on the EOSC-Life groundwork
FAIRsharing, the catalogue of data sources in EOSC-Life, in BY-COVID will :
• assist with the selection, documentation and visualisation of standards, and
• contribute the data discovery, profiling the databases’ access methodologies
• connect to EOSC metadata aggregators, via the FAIRsharing-openAIRE collaboration

16. We need sustained investments to:
• maximize the implementation and use of standards
• create synergies between basic and clinical/medical areas
• support standard-driven curation
We should build on existing investments:
• FAIR Cookbook is for ‘FAIR doers’ in the life sciences: it will continue to grow in the ELIXIR
ecosystem, and it is open to contributions from other projects
• FAIRsharing works across disciplines: beside its educative role, it is now contributing to
data discovery and knowledge graphs for research
Summary