eScience:
Driving advances in all fields of science
We are at the dawn of a revolutionary new era that will transform the process of discovery in all fields of science and engineering -- the era of eScience.
For centuries, there have been three principal approaches to discovery: theory, experiment, and observation. These were mutually reinforcing: for example, observations might suggest theories, which could be tested by experiments.
Over the past 50 years, we have augmented the traditional “three legs of the stool” with an incredibly powerful new tool: high-speed computation.
In traditional “computational science,” we use simulation to conduct “virtual experiments” – experiments that can’t be conducted in the lab, for various reasons. For example: understanding the interaction of highly structured proteins to amplify the forces in striated muscles; or computing the properties and interactions of nuclei using quantum chromodynamics; or understanding dark matter – fundamental to our understanding of the origins of the Universe; or calculating protein structure – a key to modern biotechnology.
eScience, like simulation, relies on the extraordinary power of the digital computer. But in eScience, the focus is data rather than computation. The data can come from simulation models, but now and more importantly it is coming from new generations of broadband and narrow-band sensors and instruments – sensors deployed on the sea floor, sensors embedded in buildings and roadways, sensors distributed in forests and crop lands, sensors in telescopes, sensors in gene sequencers, sensors in high-resolution feature recognition instruments, sensors for seismic tomography, sensors in volcanic vents, or sensors in living organisms (ranging from migrating salmon to humans). The volume of data is overwhelming, and the challenge is to capture, transport, store, organize, access, mine, visualize, and interpret these data in order to extract knowledge. This “computational knowledge extraction lies at the heart of 21st century discovery.
The Regional Scale Node (RSN) of the NSF Ocean Observatories Initiative, illustrated here, is an excellent example. Understanding the processes at work in the world’s oceans is essential to the stewardship of our planet. Oceanography, though, has always been a “data-poor” science – oceanographers go to sea in ships and collect data that is very sparse in both space and time. The RSN will place thousands of physical, chemical, geological and biological sensors on 2000 kilometers of fiber optic cable on the sea floor off the coast of Washington State, continuously streaming enormous volumes of data back to shore for analysis. This will transform oceanography from a data-poor to a data-rich science. It will help unlock secrets about the ocean’s ability to absorb greenhouse gases, and about how stresses on the seafloor cause earthquakes and tsunamis along Pacific coastlines. It will help improve weather forecasting, and the management of valuable fish stocks such as salmon.
In this new world of eScience, discovery involves the collection, management, and exploration of enormous volumes of data: sensors, networking, databases, data mining, machine learning, visualization. All fields of science and engineering will rely upon these capabilities – all institutions must excel at these capabilities to remain at the forefront. Rapid advances in information technology are essential.
Example projects
For further reading
