1. The digital revolution has led to vast amounts of data from diverse sources that can be stored, computed on, and instantly communicated globally at low cost. This enables revealing patterns in nature and society that were previously undetectable.
2. Open data and integrated modeling across disciplines are important for addressing global challenges related to health, cities, oceans, and more. However, data integration across different types and sources of data remains a challenge.
3. Open science involves engaging scientists and non-scientists collaboratively to create solutions through networks, beyond just open data and publishing. It presents both opportunities and challenges for science, economies, and society.
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The Digital Revolution and Open Science for the Future/Geoffrey Boulton
1. The Digital Revolution, and Open Science for the Future
Geoffrey Boulton
Committee on Data for Science and Technology (CODATA)
African Open Science Platform Workshop
Pretoria, September 2018
4. Johannes Gutenberg
1400-1468
Another Gutenberg Revolution
• vast data streams
• vast source diversity
• vast computational capacity
• learning algorithms
• instantaneous communication
• access anywhere
anytime
• low cost
A NETWORKED EARTH
The Digital Revolution
TRANSFORMATION
OF SOCIETY & OF
THE HUMAN?
The technologies by which knowledge is
acquired, stored and communicated always been
essential drivers of human material and social
progress
6. Big
Data
Biggish
Data
Small
Data
Broad
Data
Monothematic
Axis
Polythematic/
Interdisciplinary
Axis
Complex pa erns
In nature & society
Patterns in space & time
Low volumes
Batch velocities
Structured varieties
Petabyte volumes
High velocities
Multistructured
Global byte/year
Yo abytes1024
Zettabytes1021 Now
Exabytes 1018
Petabytes 1015
Terabytes1012
Gigabytes109
Megabytes106
Kilobytes 103
Why Open?
Exploiting the Data Universe for interdisciplinary science
Problem: harmonising & extracting meaningful
features from a variety of data streams
High throughput
instruments
Mixed
sources
7. Big/Broad Data reveals patterns in nature and society
have been beyond resolution
Example: North Atlantic Ocean Circulation
11. Simulating system dynamics Mapping a complex state
Image of brain cells in a rat
Emergent behaviour of a specific
6-component coupled system
Complexity: system state & dynamic evolution
Most global challenges are embedded in “complex” systems
BUT: though though integrated modelling is well-established
data integration is problematicv
12. Data streams relevant to
vector-borne infectious
disease
• Agent biology
• Population genetics
• Water supply
• Food
• Travel & transport
• Social Habits
• Hospital infection
• Sanitation
• Climate
• Population dynamics
• Faunal & floral dynamics
• Atmospheric dynamics
• Microbiotic dynamics
Data Integration for Sustainable Development Goals
Goal 3: Good Health & Wellbeing
The Great Global Challenges are Interdisciplinary
“Despite incredible progress, more than 6 million
children per year still die before their fifth birthday.
16,000 chidren die each day from preventable
diseases. In many rural areas, only 56% of births are
attended by professionals. AIDS is now the leading
cause of death amongst teenagers in Africa. As
antibiotic resistance growths, the threat of
increased infection multiplies”. UN SDG 3
13. The human future is an urban future
Today's growing cities are often islands of stability
and good governance in oceans of uncertainty,
partly because they are better able to adapt to
changing realities than entire countries. They can
serve as role models, if not vanguards, for the
new political-economic models the world needs.
Understanding the “city organism” is a vital
priority for science.
Key linked variables for the
urban ecosystem and its
quality/resilience/governance
• Population & its fluxes
(formal/informal)
• Energy
• Water
• Sewerage
• Waste
• Food
• Retail
• Transport
• Income
• Housing
• Leisure
• Ethnicity
• Education
• Employment
• Health
• Social services
Data Integration for Sustainable Development Goals
Goal 11: Sustainable Cities and Communities
14. Resilient
Cities
Disaster
Risk
:
Infectious
Disease
1. Pilot project
2. Full project
3. High level integration
Domain Scientists
Data Scientists
Data
Providers
Collaborators
- Other programmes
- Data science groups
- Data services
Stakeholders
National & International Policymakers & Users
Projects
Time
Funders Sponsors
Stages
ISC-CODATA
PROGRAMME
Early Development
supported by CAST
Data Integration for Interdisciplinary Challenges
16. Digital Technologies
What it is not:
• open data + open publishing – that is science talking to itself, though more efficiently
• scientists as knowledge ‘producers’ with others as passive ‘users’
• citizen contributors of data to scientists’ analyses
What it is (all the above plus):
• engaging publicly on key issues
• bringing scientists and non-scientists together as knowledge partners in networks
of collaborative learning and problem-solving
• a social process of creating actionable knowledge that has scientific credibility,
practical relevance and socio-political legitimacy
• the creativity of diversity
Open Science
Open Innovation Discovery Public ActionOutcomes
Process Open Science
Infrastructure
17. The Open Science Iceberg
The Technical Challenge
The Consent Challenge
The Ecosystem Challenge
The Funding Challenge
The Support Challenge
The Skills Challenge
The Incentives Challenge
The Mindset Challenge
Processes &
Organisation
People
motivation and ethos. National/Regional Infrastructure
Technology
20. Give me a lever and
a place to stand,
and
I will move the
Earth
Archimedes
Maximising return on public investment: put the effort in the right place!
The right place:
dull, long-term
but powerful The wrong place:
Seductive, quick
but ineffectual
Human capital
Infrastructure
22. Challenges for Science
• Integrating data from diverse fields
• Standards for reproducibility
• Maintaining traceability
• Data to be Findable-Accessible-Interoperable-Reusable
• Open access publishing
Challenges for Society created by the digital revolution
• Managing the World’s data
• Machine learning results neither predictable nor explicable
• Autonomous systems & the human role
• Implants and the transformation of humanity
• The future of work
• Ubiquitous monitoring
• Another “north-south” knowledge divide?
• Science as a public enterprise – or the privatisation of knowledge?
• Education
• The “dark side”
Challenges of the Digital Revolution
23. “At last an authoritative voice has
demonstrated the corruption of science,
driven by an almost religious certainty, that
has propounded a theory that can now clearly
be seen to be false, based on unreliable and
in some cases invented evidence, ruthlessly
used to advocate damaging and unnecessary
changes in US policy, “
Public policies
Emotions trump Facts:
why is science a poor persuader on many major issues?
scientists must be both emotionally intelligent and rigorously rational
25. The Dark Side
Cyber security
• disruption
• Political manipulation
• Invasion of privacy
• Cyber-crime
• Autonomous weaponry
• Cyber-warfare
Illegality & its near neighbours Pandora’s “Post-Truth” Box
The Dark Side
The Web is indifferent to falsehood and honesty.
“The most prodigious capacity to spread lies
the world has ever seen”
The crucial need for the scientific community: to combat the dark side and defend
the value of ideas tested against reality.
27. • The digital revolution coincides with a time when:
“global society is confronted by multiple, intersecting sets of converging environmental,
socio-economic, political and cultural problems, many as consequences of complex
coupling between social and biogeophysical processes”.
UN Science & Technology Forum , 2017
• In this context, the digital revolution offers powerful new opportunities:
• to address these problems of global sustainability;
• to enhance scientific understanding and economic and social welfare.
• But the technology itself creates major new challenges.
• Many states are developing strategies to exploit the opportunities, but few are
seriously addressing the challenges.
• No responsible state can avoid or should ignore these issues, or fail to equip itself
with the technical and intellectual means to address them. Buying in solutions from
elsewhere will prove costly and ineffectual.
• Collaborative, multi-state responses offer an efficient and timely pathway.
29. EMBL-EBI services
Labs around the
world send us
their data and
we…
Archive it
Classify it
Share it with
other data
providers
Analyse, add
value and
integrate it
…provide
tools to help
researchers
use it
A collaborative
enterprise
Discipline-driven Government-driven
International Systemic Platforms/Commons
European Open Science Cloud
International Union of Crystallography
Platforms offer:
• Efficiency in planning, procurement, provision & service management
• Scaling-up through shared capacities
• Stimulating dynamism & creativity through diversity
• Amplifying impact through common purpose
