Premises, promises and challenges of interoperability standards

1. Going FAIR:
highlights from the life sciences
Susanna-Assunta Sansone, PhD
@SusannaASansone
ORCiD: 0000-0001-5306-5690
Consultant,
Founding Academic Editor
Associate Director,
Principal Investigator
RDA Europe Science Workshop, Wellcome Trust, London, 25-26 April 2017

2. Interoperability standards:
premises, promises and challenges

3. A set of principles, for those
wishing to enhance
the value of their
data holdings
Designed and endorsed by a diverse
set of stakeholders - representing
academia, industry, funding agencies,
and scholarly publishers.

4. Wider adoption by policies, e.g.

5. Wider adoption by research and infrastructure
programmes, e.g.

6. Wider adoption by pharmas, e.g.
The world's biggest public-private partnership
in the life sciences, a partnership between the European Commission and the
European pharmaceutical industry.
Funds research and infrastructure projects to improve health
by speeding up the development of, and patient access to, innovative medicines.

7. NOTE:
The Principles are high-level; do not suggest any specific
technology, standard, or implementation-solution
Beyond the nice acronym….
Principles put emphasis on enhancing the ability of machines to automatically find
and use the data, in addition to supporting its reuse by individuals

8. Interoperability standards – invisible machinery
• Identifiers and metadata to be implemented by technical
experts in tools, registries, catalogues, databases, services
§ to find, store, manage (e.g., mint, track provenance, version) and
aggregate (e.g., interlink and map etc.) digital objects
• It is essential to make standards ‘invisible’ to lay users, who
often have little or no familiarity with them

9. Metadata standards – fundamentals
• Descriptors for a digital object that help to understand what it
is, where to find it, how to access it etc.
• The type of metadata depends also on the type of digital
object (e.g. software, dataset)
• The depth and breadth of metadata varies according to
their purpose
§ e.g. reproducibility requires richer metadata then citation

10. • Domain-level descriptors that are essential for interpretation,
verification and reproducibility of datasets
• The depth and breadth of descriptors vary according to the
domain broadly covering the what, who, when, how and why
Content standards – deeper metadata for datasets

11. • Domain-level descriptors that are essential for interpretation,
verification and reproducibility of datasets
• The depth and breadth of descriptors vary according to the
domain broadly covering the what, who, when, how and why
allowing:
§ experimental components (e.g., design, conditions, parameters),
§ fundamental biological entities (e.g., samples, genes, cells),
§ complex concepts (such as bioprocesses, tissues and diseases),
§ analytical process and the mathematical models, and
§ their instantiation in computational simulations (from the molecular
level through to whole populations of individuals)
to be harmonized with respect to structure, format and
annotation
Content standards – deeper metadata for datasets

12. Formats Terminologies Guidelines
Content standards in the life/biomedical sciences
220+
115+
548+
source source
source
miame
MIRIAM
MIQAS
MIX
MIGEN
ARRIVE
MIAPE
MIASE
MIQE
MISFISHIE….
REMARK
CONSORT
SRAxml
SOFT FASTA
DICOM
MzML
SBRML
SEDML…
GELML
ISA
CML
MITAB
AAO
CHEBIOBI
PATO ENVO
MOD
BTO
IDO…
TEDDY
PRO
XAO
DO
VO
882 -> ~1000

13. de jure de facto
grass-roots
groups
standard
organizations
Nanotechnology Working Group
Variety of community efforts, just few examples:
• Formal authorities
§ openess to participations varies
§ standards are sold or licenced (at a
costs or no cost)
§ charges apply to advanced training or
programmatic access
• Bottom-up communities
§ open to interested varies
§ standards are free for use
§ volunteering efforts
§ minimal or little funds for carry out
the work, let alone provide training
Formats Terminologies Guidelines

14. • Perspective and focus vary, ranging:
§ from standards with a specific biological or clinical domain of study
(e.g. neuroscience) or significance (e.g. model processes)
§ to the technology used (e.g. imaging modality)
• Motivation is different, spanning:
§ creation of new standards (to fill a gap)
§ mapping and harmonization of complementary or contrasting efforts
§ extensions and repurposing of existing standards
• Stakeholders are diverse, including those:
§ involved in managing, serving, curating, preserving, publishing or
regulating data and/or other digital objects
§ academia, industry, governmental sectors, and funding agencies
§ producers but also also consumers of the standards, as domain (and
not just technical) expertise is a must
A complex landscape

15. Technologically-delineated
views of the world
Biologically-delineated
views of the world
Generic features (‘common core’)
- description of source biomaterial
- experimental design components
Arrays
Scanning Arrays &
Scanning
Columns
Gels
MS MS
FTIR
NMR
Columns
transcriptomics
proteomics
metabolomics
plant biology
epidemiology
microbiology
Fragmentation of content standards

17. Working in/across multiple domains is challenging
• Requires
§ Mapping between/among heterogeneous representations
§ Conceptual modelling framework to encompass the
domain specific content standards
§ Tools to handle customizable annotation, multiple
conversions and validation

19. Map of the landscape, monitoring development and evolution of
data and metadata standards, their use in databases and the
adoption of both in data policies

20. is also a WG of the

21. Data deposition:
ENA, EGA, PDBe, EuropePMC, …
Bioinformatics tools:
Bio.tools
Data Interoperability:
BioSharing, identifiers.org, OLS
Compute:
Secure data transfer, cloud
computing, AAI
Industry:
Innovation and SME programme
Bespoke collaborations
Training:
TeSS, Data Carpentry, eLearning
Data management:
Genome annotation
Data management plans
Added value data:
UniProt, Ensembl, OrphaNet, …
is part of the services

22. Standard developing groups:Journal, publishers:
Cross-links, data exchange:
Societies and organisations: Institutional RDM services:
Projects, programmes:

23. • Pain points include:
§ Fragmentation
§ Coordination, harmonization, extensions
§ Credit, incentives for contributors
§ Governance, ownership
§ Funding streams
§ Indicators and evaluation methods
§ Implementations: infrastructures, tools, services
§ Outreach and engagement with all stakeholders
§ Synergies between basic and clinical/medical areas
§ Education, documentation and training
§ Business models for sustainability
Interoperability standards - technical & social engineering

24. “As Data Science culture grows,
digital research outputs (such as
data, computational analysis and
software) are being established as
first-class citizens.
This cultural shift is required to go
one step further: to recognize
interoperability standards as digital
objects in their own right, with their
associated research, development
and educational activities”.