The Computing Community Consortium (CCC) Convergence of Data and Computing Task Force, led by CCC Council Members Vasant G. Honavar from Pennsylvania State University, Mark D. Hill from University of Wisconsin-Madison, and Katherine Yelick from University of California at Berkeley, has just released another community white paper called Accelerating Science: A Computing Research Agenda. This white paper seeks to articulate a research agenda for developing cognitive tools that can augment human intellect and partner with humans on the scientific process.

The recent advances in sensing, measurement, storage and communication technologies and the resulting emergence of “big data” offer unprecedented opportunities for not only accelerating scientific advances, but also enabling new modes of discovery. However, there is a huge gap between our ability to acquire, store, and process data and our ability to make effective use of the data to advance science.

Accelerating science to keep pace with the rate of data acquisition and data processing calls for focused investments in a research program that encompasses both:

• Development, analysis, integration, sharing, and simulation of algorithmic or information processing abstractions of natural processes, coupled with formal methods and tools for their analyses and simulation;
• Innovations in cognitive tools that augment and extend human intellect and partner with humans in all aspects of science. This requires:
  • The formalization, development, analysis, of algorithmic or information processing abstractions of various aspects of the scientific process;
  • The development of computational artifacts (representations, processes, software) that embody such understanding; and
  • The integration of the resulting cognitive tools into collaborative human-machine systems and infrastructure to advance science.

A research agenda focused on accelerating science can be expected to yield fundamental advances in multiple areas of computer and information sciences and cognitive tools. The resulting new cognitive tools can help realize the transformative potential of big data in many sciences, by dramatically accelerating science. Read the full white paper to learn more.