Computing Community Consortium Blog

The goal of the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives. The purpose of this blog is to provide a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.

Data as a Driver for Scientific Innovation

March 4th, 2011 / in big science, research horizons / by Erwin Gianchandani

Science Magazine:  Special Section:  Dealing with Data [Courtesy AAAS/Science magazine]If you haven’t seen it already, the February 11 issue of Science magazine is worth a look.  It contains a special section — titled “Dealing with Data” — that describes the challenges and opportunities arising from the wealth of scientific data being generated.

As the staff of Science writes in the overview piece:

If we can use and reuse scientific data better, the opportunities, as indicated in many examples in this special section, are myriad. Large integrated data sets can potentially provide a much deeper understanding of both nature and society and open up many new avenues of research. And they are critical for addressing key societal problems—from improving public health and managing natural resources intelligently to designing better cities and coping with climate change.

To realize these opportunities, many of the articles in this collection speak of changing the culture of science and the practices of scientists, as well as recognizing the growing responsibility for much better data stewardship.

The perspectives in Dealing with Data cover a wide range of topics, including the climate, ecology, neuroscience, social science, global health, signal processing, and metaknowledge (i.e., about scientific discovery and innovation).  All of these describe — directly or indirectly — the need for computational approaches such as data mining, machine learning, predictive modeling, etc.

There are also some interesting results from a poll of researchers who served as peer reviewers for Science in 2010.  Of 1700 respondents:

  • About 20% regularly use or analyze data sets exceeding 100 GB
  • Nearly 40% don’t have the necessary expertise in their labs or groups to adequately analyze the data.

Check out the issue — available online — today.

(Contributed by Erwin Gianchandani, CCC Director)

Data as a Driver for Scientific Innovation

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