Susanna Sansone's talk at the "Beyond Open" Knowledge Dialogues/Open Data Hong Kong event on research data, hosted at the Hong Kong Innocentre on Monday 20 November 2017.

1. Associate Professor, Associate Director
Susanna-Assunta Sansone, PhD
Open Data Hong Kong & Knowledge Dialogues event on FAIR data, HK, 20 Nov, 2017
@SusannaASansone

2. Open data is a mean to do better science
more efficiently
https://dx.doi.org/10.17605/OSF.IO/3EGZH

3. Open science
• Achieving research data transparency
- incentivize the shared management of data, documentation and
discoverability, as well as standards for interoperability and curation
– in partnership with universities and others across the innovation
ecosystem (business, government etc.)

4. Open science
• Achieving research data transparency
- incentivize the shared management of data, documentation and
discoverability, as well as standards for interoperability and curation
– in partnership with universities and others across the innovation
ecosystem (business, government etc.)
• Maximising the use of e-infrastructure
- develop simplified and secure access models and innovative ways
of utilising technical developments, in a distributed and scalable
manner

5. Open science
• Achieving research data transparency
- incentivize the shared management of data, documentation and
discoverability, as well as standards for interoperability and curation
– in partnership with universities and others across the innovation
ecosystem (business, government etc.)
• Maximising the use of e-infrastructure
- develop simplified and secure access models and innovative ways
of utilising technical developments, in a distributed and scalable
manner
• Connecting existing silo-ed disciplines
- build new capabilities, for example in data analytics, modelling,
algorithms, visualisation and software
- maximise investment in people, skills, methods

6. Engineering the Imagination: Disability, Prostheses and the Body
Engineering and cultural studies
Exploring Water Re-use - the nexus of politics, technology and economics
Before and After Halley: Medieval Visions of Modern Science
Astrophysics and medieval studies
The ontogeny of bone microstructure as a model of programmed transformation in 4D materials
Archaeology, anthropology and mechanical engineering
How can we improve Healthcare IT when most people are blind to its poor engineering?
ICT, medicine and engineering
People, Pollinators & Pesticides in Peri-Urban Farming
Biology, zoology, law & policy
Systemic Risk: Mathematical Modelling and Interdisciplinary Approaches
Mathematics and economics
Working across boundaries of discipline,
geography and time

7. Open science
• Achieving research data transparency
- incentivize the shared management of data, documentation and
discoverability, as well as standards for interoperability and curation
– in partnership with universities and others across the innovation
ecosystem (business, government etc.)
• Maximising the use of e-infrastructure
- develop simplified and secure access models and innovative ways
of utilising technical developments, in a distributed and scalable
manner
• Connecting existing silo-ed disciplines
- build new capabilities, for example in data analytics, modelling,
algorithms, visualisation and software
- maximise investment in people, skills, methods
• Meting ethics and public expectations, around safe usage
of data

8. Open science
• Achieving research data transparency
- incentivize the shared management of data, documentation and
discoverability, as well as standards for interoperability and curation
– in partnership with universities and others across the innovation
ecosystem (business, government etc.)
• Maximising the use of e-infrastructure
- develop simplified and secure access models and innovative ways
of utilising technical developments, in a distributed and scalable
manner
• Connecting existing silo-ed disciplines
- build new capabilities, for example in data analytics, modelling,
algorithms, visualisation and software
- maximise investment in people, skills, methods
• Meting ethics and public expectations, around safe usage
of data

10. 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.
https://www.force11.org/group/fairgroup/fairprinciples

11. These put emphasis on enhancing the
ability of machines to automatically
find and use the data, in addition to
supporting its reuse by individual

12. “….We support effort to promote voluntary knowledge diffusion and technology transfer on mutually
agreed terms and conditions. Consistent with this approach, we support appropriate efforts to promote
open science and facilitate appropriate access to publicly funded research results on findable, accessible,
interoperable and reusable (FAIR) principles….” http://europa.eu/rapid/press-release_STATEMENT-16-2967_en.htm
G20 Leaders’ Communique Hangzhou Summit

13. Wider adoption by policies in UK and EU, e.g.

15. European Open Science Cloud (EOSC) Pilot
Consortium of 33 pan-European organisations & 15 third parties covering a
range of disciplines and organisations working together to develop a
European-wide governance framework for a pan-European “trusted virtual
environment with free, open and seamless services for data storage,
management, analysis, sharing and re-use, across disciplines”

16. Wider adoption by many biomedical research
infrastructure programmes in EU and USA, e.g.

17. Building a pan-European infrastructure for biological information
Categorized by European Council
as one of Europe’s three priority
new Research Infrastructures
€19 million
2015 - 2019

18. 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.
IMI phase 2 programme (2014-2020) has a 3.3 billion EURO budget

19. Big
Life
Science
Company
Yesterday Today Tomorrow
Yesterday Today Tomorrow
Innovation Model Innovation inside Searching for Innovation Heterogeneity of collaborations;
part of the wider ecosystem
IT Internal apps & data Struggling with change
security and trust
Cloud, services
Data Mostly inside In and out Distributed
Portfolio Internally driven and owned Partially shared Shared portfolio
Credit to:
Big
Life
Science
Company
Proprietary
content
provider
Public
content
provider
Academic
group
Software vendor
CRO
Service provider
Regulatory
authorities
The rise of public-private-partnerships

20. An IMI project advancing the FAIR concept

21. A recently closed call for proposals on FAIRification
Aim of the winning consortium is to FAIRify the output of
20 IMI-funded research projects,
leveraging on the work by eTRIKS and ELIXIR

22. A trans-NIH funding initiative established
in 2014 to enable biomedical research as
a digital research enterprise
• Facilitate broad use of biomedical digital assets by making them discoverable,
accessible, and citable
• Conduct research and develop the methods, software, and tools needed to
analyze biomedical Big Data
• Enhance training in the development and use of methods and tools necessary for
biomedical Big Data science
• Support a data ecosystem that accelerates discovery as part of a digital enterprise

23. New FAIR Data Commons Pilot phase
start started (2017-2020, $95.5 Million)
• Focus 9 areas:
1. FAIR Guidelines and Metrics
2. Global Unique Identifiers for FAIR Biomedical Digital Objects
3. Open Standard APIs
4. Cloud Agnostic Architecture and Frameworks
5. Workspaces for Computation
6. Research Ethics, Privacy, and Security
7. Indexing and Search
8. Scientific Use cases
9. Training, Outreach, Coordination

25. • Available in a public repository
• Findable through some sort of search facility
• Retrievable in a standard format
• Self-described so that third parties can make sense of it
• Intended to outlive the experiment for which they were collected
To do better science, more efficiently
we need data that are…

27. Metadata for data discovery

30. Databases/data
repositories
Metadata standards
Formats Terminologies Guidelines
Interlink standards among themselves and with repositories
Data policies by
funders, journals and
other organizations

31. Standard developing groups, incl:Journal, publishers, incl:
Cross-links, data exchange, incl:
Societies and organisations, incl: Institutional RDM services, incl:
Projects, programmes:
Working with and for producers and consumers, e.g.:

33. • Data has to become an integral part
of the scholarly communications
• Responsibilities lie across several
stakeholder groups: researchers,
data centers, librarians, funding
agencies and publishers
• But publishers occupy a “leverage
point” in this process
FAIR data - roles and responsibilities

34. • Incentive, credit for sharing
- Big and small data
- Unpublished data
- Long tail of data
- Curated aggregation
• Peer review of data
• Value of data vs. analysis
• Discoverability and reusability
- Complementing community
databases
FAIR data – the value of data articles/journals

35. Technology
Social engineering

36. Let’s work
together to foster a culture
in which FAIR science is the norm