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November SAB Workshop

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To refresh our vision of e-Science
What can it do?
How can this be achieved?

  • across the full breadth of research
  • helping at all scales: global collaborations to individual endeavours.

This should lead to a road map for e-Science and steer the future of eSI in facilitating that e-Science.


We are undergoing a transition in

  • the power of affordable computing,
  • the available wealth of data and
  • the capacity of digital communication.

e-Science provides leadership in interdisciplinary collaboration.
By combining these we will provide unprecedented ability to undertake research, design and diagnosis.


  1. The computational continuum - with a growth in computational power per unit cost or chip, with massive investment in clusters and a continuing investment in national and international computational facilities there is no logical boundary between the personal computer, the institutional facilities and the national facilities. E-Science should be equally relevant across this continuum.
  2. Storage and computation are energy intensive - particularly when conducted on a large scale. The remote system and application techniques enabled by grids provide mechanisms for placing data centres where energy has a low C footprint and both managing and using them remotely.
  3. Many of the advances in our ability to undertake research are built around advances in the management and sharing of data. Some research is dependent on very large volumes of data. Other research is driven by access to many diverse and evolving data resources. Often the increase in ability to use complex data is enabling geographic and interdisciplinary collaboration. The complexity of data that drives science is a major challenge. The growing ubiquity, speed and resolution of digital instruments, digital sensors and the growth of laboratory automation will increase the potential and challenges of effectively exploiting data.
  4. The growth of commercially inspired distributed computing, such as Google, Amazon, Facebook, Flickr, YouTube and the collaborative tools they offer raises expectations, provides opportunities and poses the risk of high-cost dependency. Science has always depended on community action - e.g. for life-sciences and archaeological fieldwork. Can the web-based phenomena accelerate collaborative behaviour among researchers more than it already does?
  5. Interdisciplinary and computational thinking are both key ingredients. How do we strengthen their role?

Organising the Day


  • New inputs (people) to provoke new thinking
  • As many of the SAB as can attend
  • Theme leaders
  • Few provocative talks + discussion / working groups.
  • Reporters
    • Dr Dave Berry
    • Dr Iain Coleman
    • Dr Jano van Hemert

Break-out Tracks

SUGGESTED TRACKS - The initial input to these is from Malcolm Atkinson, but they are supposed to be provocative and are open for comment and discussion (and even change).

Track A - Democratising e-Science

The majority of scientific research is undertaken by large numbers of researchers who back up high-profile leaders. Much is undertaken by altruistic contribution of their time by expert amateur field-workers. Public acceptance depends on public engagement and opportunity to re-use and review scientific arguments.

Would we gain resources, understanding and better e-Science methods by investing more intellectual effort and resources in democratisation? Would we slow the advance of e-Science's front line by using our resources on mopping up operations?

Track B - Extreme e-Science

The computational challenges of e-Science are characterised by many dimensions: complexity of research domain, range of interacting scales within phenomena studied, data volumes and rates, FLOPS, number of users, geographic distribution, requirement for rapid response, requirement for security, ... . For each dimension some research pushes the limits we can attain.

Hypothesis: by focusing on the grand research challenges that push us to the limits we will (a) generate more advances in that research domain than we could otherwise, and (b) that will also accelerate the rate of invention of e-Science methods.

Counterargument: by working on research challenges that can be met much more cost effectively as they avoid extreme engineering we can do high-throughput e-Science which will advance understanding and knowledge faster.

Where does the truth lie?

Track C - Command e-Science

The tenet of command e-Science is that by understanding a research discipline well enough we can formulate a computational infrastructure for that discipline: an established ontology, established data management, established communication and resources, and then research can make good progress because they are provisioned and have a computational lingua franca. As interdisciplinary research requires overlapping fields this could lead to a "Gaia" model of e-Science. (Ozymandias comes to mind!)

It is impossible to achieve such an overarching model for any discipline because of they are replete with inconsistencies and opposing views, and because new knowledge changes the requirements faster than anyone can assimilate it.

Is this a counsel of despair? Does it mean that no long-term shared infrastructure and conceptual framework is possible? Each project should do its own thing!

There is a middle way. How do we find it?


Date: 19 November 2007

Venue: e-Science Institute (Meeting by invitation only)

  • 10:00 - Coffee in the Chapterhouse (Basement)

Morning Session - Chair: Prof Richard Kenway (Location: Newhaven Lecture Theatre)

  • 10:30 - Introduction by Prof Malcolm Atkinson
  • 10:50 - The New e-Science by Prof David de Roure
    • Abstract: In their early years, e-Science and cyberinfrastructure were dominated by heroic science using heroic infrastructures. But now we are seeing researchers across all disciplines taking advantage of new technologies to do new research. Much of this user-centred activity is drawing on the Web as a distributed application platform, with mashups for integration, easy access to resources "in the cloud", and social networking to share the pieces and practice of digital science. As in other walks of life, the new technologies are empowering the individual, a trend set to continue with the increasing power of the multicore desktop. This evolution throws some of our existing approaches, epitomised by the Grid, into question. Adopting a user and application perspective, this talk will present a definition of the New e-Science and it will discuss how we can create a flourishing ecosystem of scientists, software developers and service providers rather than just a ‘pipeline of provision’.
  • 11:20 - Brief plenary discussion and briefing for breakouts
  • 11:50 - Three break-out tracks

(A Buffet lunch will be available during the break-out tracks.)

Afternoon Session - Chair: Prof Keith Haines (Location: Newhaven Lecture Theatre)

  • 14:00 - Plenary discussion from breakout, followed by open discussion.
  • 16:30 - Wrap-up
  • 17:00 - 18:00 Delegates can check into their hotels

Discussion Points for the SAB Meeting

Funding and reward structures

  • How can we encourage collaboration between researchers using e-Science methods, and researchers developing e-Science methods.
  • How do we make it interesting for computing scientists?
  • Do we need a journal for e-Science?

Can we teach innovation and problem solving?

  • How do we educate scientists from undergraduate level upwards?

Overcoming barriers

  • Lack of trust in data and technology when it comes to more open systems: which knowledge do you trust in an open, “democratic” system?
  • How do we break down barriers between e-Science and Grid research?

Encouraging collaboration versus developing technology

  • Should we see e-Science as a facilitator for getting computational and domain scientists to collaborate, rather than as a technology provider?
  • Should we concentrate on bringing expertise together, rather than developing technologies ahead of the collaboration?

Community structures

  • Which approaches (democratic e-science, web 2.0, community-developed standards) are applicable to which problems?

Where do we focus investment?

  • Grand challenges or everyday research?

Enabling cross-disciplinary research

  • How do you identify methods in one discipline that can work in other disciplines, and how do you migrate them?
  • What frameworks can be established across disciplines?

Post Meeting Discussion

Cloud Computing

eSI Annual Report 2006-7

The e-Science Institute Annual Report 2006-2007 is now available. A pdf version can be downloaded here (This is a 4.9MB file).

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