The
e-Science
Think Tank
Malcolm Atkinson
Director e-Science Institute
UK e-Science Envoy
www.nesc.ac.uk
19th November 2007
Overview
• Welcome
• Today’s Goals
• What is e-Science
• Its place on the map
• Its inhabitants and trading relationships
• How should we do e-Science
• Can we achieve “production” e-Science
• Can put e-Science-enabled methods into production use
• Can we create new e-Science methods on demand
Why are we here?
• To write the e-Science “Blue Book”
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What is e-Science?
Why does it deserve attention?
What are its grand challenges?
What are the steps to address those challenges?
• Unfettered thinking
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Express your dreams
If only I had … I could …
Express your frustrations
I cannot solve … until I’ve found a way to do …
• Tease out the important points
• Detail as scaffolding to reach the insights?
• Detail as compelling motivation?
• Different points of view
• Find the reason
Our Goal
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is to Change the Path of e-Science
Rapidly produced draft of Blue Book
Stimulate debate
Provoke e-Science Institute themes
• To focus on the identified challenges
• Revise & Publish the e-Science Blue Book
• Improve e-Science Research
• Increase impact of e-Science on research in
many disciplines
We certainly need e-Science
We all need e-Science
The 21st Century
This is the century of information
PM G. Brown, University of Westminster, 25 October 2007
• We can collect it
• We can generate it
• We can move it
• We can store it
• But can we use it?
What is e-Science?
Computer Science
Evidence
Methods
Models
&
challenges
Algorithms
Models
Notations
Methods
Technology
Applied Scientist
e-Scientist
e-Science
Te
s
De ts Us
Ev ploys es
a
Ad luate
ap
ts s
s
ort
pp
Su
es
ng
alle
Ch
Ch
Ide allen
Mo as ges
de
ls
Researcher communities
using e-Science Methods
Challenges & supports
Infrastructure Provision
Operational data
and Support
Adoption
Infrastructure
Development
e-Infrastructure
Slide from John Darlington with modifications
Scope of e-Science?
Computer Science
Evidence
Methods
Models
&
challenges
Algorithms
Models
Notations
Methods
Technology
Applied Scientist
Te
s
De ts Us
Ev ploys es
a
Ad luate
ap
ts s
s
ort
pp
Su
es
ng
alle
Ch
Ch
Ide allen
Mo as ges
de
ls
Researcher communities
using e-Science Methods
Challenges & supports
Infrastructure Provision
Operational data
and Support
e-Scientist
e-Science
It has been happening for >50 years
Adoption
Infrastructure
Development
e-Infrastructure
It is a continuous activity
Who do we need for e-Science?
Algorithms
Models
Notations
Methods
Technology
Applied Scientist
e-Scientist
e-Science
Mathematicians
Te
s
De ts Us
Ev ploys es
a
Ad luate
ap
ts s
Ch
Ide allen
Mo as ges
de
ls
ne
gi
En
Computer Science
Evidence
Methods
Models
&
challenges
s
ort
pp
Su
es
ng
alle
Ch
er
s
Researcher communities
s
n
a
i
c
i
using e-Science Methods
t
i
t
s
i
t
a
St
Challenges & supports
Infrastructure Provision
Operational data
and Support
Adoption
Infrastructure
Development
e-Infrastructure
What is e-Science
• The invention of computationally enabled
methods of conducting research
• Methods for data organisation, algorithms,
architectures, …
• Improving the usability of methods
• Composition and integration methods
• Collaboration methods
• Technology to support methods
• Evaluating, refining and evaluating methods
• Improving the ways we do the above
We need more e-Science
• How can we get more done?
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Start with higher-level research platforms
Use better tools
Attract more people
Let others do the work
Use smarter methods
Empower researchers to do more
• Cue David De Roure
• Back after the break
Why do a “Blue Book”
• Researchers just do e-Science
• Easily enough? Fast enough? Well enough?
• Do enough researchers do e-Science
• Do all research who want to use e-Science methods
• Have them to hand
• Use them fluently
• Demonstrable successes of e-Science
• But one-off “cottage industry”
• Not scaling for the Information Century
• An e-Science Road map is needed
• Address the important goals
• Research the critical issues
What do we need to do?
Computer Science
Evidence
Methods
Models
&
challenges
Algorithms
Models
Notations
Methods
Technology
Applied Scientist
e-Scientist
e-Science
Te
s
De ts Us
Ev ploys es
a
Ad luate
ap
ts s
s
ort
pp
Su
es
ng
alle
Ch
Ch
Ide allen
Mo as ges
de
ls
Researcher communities
using e-Science Methods
Engage the right mix
of Researchers
Challenges & supports
Infrastructure Provision
Operational data
and Support
Adoption
Infrastructure
Development
e-Infrastructure
What do we need to do?
Computer Science
Evidence
Methods
Models
&
challenges
Algorithms
Models
Notations
Methods
Technology
Applied Scientist
e-Scientist
e-Science
Te
s
De ts Us
Ev ploys es
a
Ad luate
ap
ts s
s
ort
pp
Su
es
ng
alle
Ch
Ch
Ide allen
Mo as ges
de
ls
Researcher communities
using e-Science Methods
Enable them to be
Creative & Productive
Challenges & supports
Infrastructure Provision
Operational data
and Support
Adoption
Infrastructure
Development
e-Infrastructure
What do we need to do?
Computer Science
Evidence
Methods
Models
&
challenges
Algorithms
Models
Notations
Methods
Technology
Applied Scientist
e-Scientist
e-Science
Te
s
De ts Us
Ev ploys es
a
Ad luate
ap
ts s
Measure
And
Improve
s
ort
pp
Su
es
ng
alle
Ch
Ch
Ide allen
Mo as ges
de
ls
Researcher communities
using e-Science Methods
Challenges & supports
Infrastructure Provision
Operational data
and Support
Adoption
Infrastructure
Development
e-Infrastructure
Parametric spread of e-Science
User population
• Data intensive, Complexity intensive,
Computation intensive, Real-time response,
Mobility, Number of researchers, Geographic
distribution, Ubiquity, …
• For every parameter that describes e-Science
• Some community will want to be as far out on the
curve as they can be - to do their research
• The majority of requirements are met by the most
economic and easily used means
Any parameter describing the Application Research requirements
Provision for Diversity
• Deliver to the majority
• easily used, economic computational tools
• Well supported, easy to use, …
• And accommodate heroic pioneers
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To extend what can be done
They have to share the pain
They have to share the funding challenge
They may change the norms
Ideal demography of e-Science
• 10,000 using packages tailored to their work
• Set up for them
• Using their familiar interface - matlab, excel, …
• Secured by their normal working practices
• 100 tailoring packages
• Understanding an application discipline and a “configurable”
technology
• Recognising a requirement - speaking the application
language - commissioned by sub-communities
• Delivering the technology wrapped and ready
• 10 Generating new methods and technologies
• New tools, new services, new computational methods, …
• Technology intercept & integration of fundamental research
Maturity of e-Science
• Maturity of relationship
• Access to resources
 Diverse resources
• Learning to communicate
 What to talk about
 Understanding those topics
• Sharing a challenge
 Working together
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Developing automation
Collaboration supported
Joint research
Expanding participation
• Experience of
Application community
• Adept
 Scientific data curation
 Computational modelling
• Aware
 Use computational tools
• Beginners
 Recognised opportunity
• Experience of CS folk
• Adept
 Have engaged in joint work
• Beginners
There will be those who are getting on OK and don’t want any change
What do we need to do?
Computer Science
Evidence
Methods
Models
&
challenges
Algorithms
Models
Notations
Methods
Technology
Applied Scientist
e-Scientist
e-Science
Te
s
De ts Us
Ev ploys es
a
Ad luate
ap
ts s
Different Research
Applications need
different e-Science?
s
ort
pp
Su
es
ng
alle
Ch
Ch
Ide allen
Mo as ges
de
ls
Researcher communities
using e-Science Methods
Challenges & supports
Infrastructure Provision
Operational data
and Support
Adoption
Infrastructure
Development
e-Infrastructure
The Breakout Groups
• They are yours - so work as you wish
• We can change the topics now
• Democratising e-Science
(Rob Procter, Newhaven, Jano van Hemert)
 Easier to use - used by more people - widely understood
• Extreme e-Science (Peter Coveney, Cramond, Iain Coleman)
 Pushing the limits to meet the hardest research challenges
• Command e-Science
(Freddie Moran, Chapterhouse, Dave Berry)
 Predetermining the methods to support large research communities
• Operation of a Group
• Room & reporter allocated
• Chair to speak at 14:00
Expected results from groups?
• To write the e-Science “Blue Book”
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•
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What are e-Science’s grand challenges?
What are the steps to address those challenges?
How do we need to change to meet these challenges?
What is the road map to get e-Science ready for 21st Century challenges
• Unfettered thinking
•
•
•
•
Express your dreams
If only I had … I could …
Express your frustrations
I cannot solve … until I’ve found a way to do …
• Tease out the important points
• Detail as scaffolding to reach the insights?
• Detail as compelling motivation?
• Different points of view
• Find the reason