[CAnet - news] Information technology issues involved in achieving scientific goals- IEEE Computer

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[From Ian Foster's blog--BSA]

http://ianfoster.typepad.com/blog/2006/11/systemlevel_sci.html

This month's issue of IEEE Computer includes four articles on system-level
science: the integration of diverse sources of knowledge about the
constituent parts of a complex system with the goal of obtaining an
understanding of the system's properties as a whole. This being IEEE
Computer, they focus in particular on information technology (IT) issues
involved in achieving scientific goals:

    [S]ystem-level science integrates not only different disciplines but
also, typically, software systems, data, computing resources, and people.
System-level science is usually a team pursuit. Data comes from different
sources, different groups develop component models, team members provide
specialized expertise, and the often substantial computing and data
resources required for success are themselves diverse and distributed. Thus,
system-level science itself requires the creation of yet another sort of
system that may combine large numbers of both physical and human components.

The four articles are as follows:

   1. Scaling System-Level Science: Scientific Exploration and IT
Implications by myself and Carl Kesselman, introduces the special issue. We
lead off with a quote from Robert Calderbank:

          Sometimes through heroism you can make something work. However,
understanding why it worked, abstracting it, making it a primitive is the
key to getting to the next order of magnitude of scale.

      Then, we discuss IT issues that must be addressed to increase the
scale at which we tackle system-level science problems.


   2. From Molecule to Man: Decision Support in Individualized E-Health
(PDF) by Peter Sloot and colleagues:

          Computer science provides the language needed to study and
understand complex multiscale, multiscience systems. ViroLab, a grid-based
decision-support system, demonstrates how researchers can now study diseases
from the DNA level all the way up to medical responses to treatment.

   3. Multiscale Modeling: Physiome Project Standards, Tools, and Databases,
by Peter Hunter and colleagues:

          The Physiome Project's markup languages and associated tools
leverage the CellML and FieldML model databases published in peer-reviewed
journals. As these tools mature, researchers can check models for
conformance to underlying physics laws, using them to develop complex
physiological models from separately validated components.

   4. CASA and LEAD: Adaptive Cyberinfrastructure for Real-Time Multiscale
Weather Forecasting (PDF), by Beth Plale and colleagues:

          Two closely linked projects aim to dramatically improve storm
forecasting speed and accuracy. CASA is creating a distributed,
collaborative, adaptive sensor network of low-power, high-resolution radars
that respond to user needs. LEAD offers dynamic workflow orchestration and
data management in a Web services framework designed to support on-demand,
real-time, dynamically adaptive systems.


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