susanna sansone at the knowledge dialogues/odhk "beyond open"event

Associate Professor, Associate Director

Susanna-Assunta Sansone, PhD

Open Data Hong Kong & Knowledge Dialogues event on FAIR data, HK, 20 Nov, 2017

@SusannaASansone

Open data is a mean to do better science

more efficiently

https://dx.doi.org/10.17605/OSF.IO/3EGZH

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.)

Open science






• 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

Open science









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

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

Open science









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

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

These put emphasis on enhancing the

ability of machines to automatically

find and use the data, in addition to

supporting its reuse by individual

“….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

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

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”

Wider adoption by many biomedical research

infrastructure programmes in EU and USA, e.g.

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

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

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

An IMI project advancing the FAIR concept

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

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

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

• 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…

Metadata for data discovery

Databases/data

repositories

Metadata standards

Formats Terminologies Guidelines

Interlink standards among themselves and with repositories

Data policies by

funders, journals and

other organizations

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.:

• 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

• 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

Technology

Social engineering

Let’s work

together to foster a culture

in which FAIR science is the norm