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Science;|;DOI: 10.1145/1839676.1839682
Gregory;Goth
turning Data
into Knowledge
Today’s data deluge is leading to new approaches
to visualize, analyze, and catalog enormous datasets.

Image courtesy of Lsst corporatIon

The aMouNT oF data available to scientists of nearly every discipline has almost be- come a “Can you top this?” exercise in numbers.

The Sloan Digital Sky Survey (SDSS), for example, is often cited as a prime example. Since the survey’s 2.5-meter telescope first went online in 1998, more than 2,000 refereed publications have been produced, but they use just 10% of the survey’s available imaging data, according to a recent U.S. National Science Foundation workshop on data-enabled science in the mathematical and physical sciences. Once the next-generation, state-of-the-art Large Synoptic Survey Telescope (LSST) goes online in 2016, however, it is estimated to be capable of producing a SDSS-equivalent dataset every night for the next 10 years. Another of-ten-cited example is the Large Hadron Collider. It will generate two SDSS’s worth of data each day.

On the surface, then, the scientific community’s mandate seems clear: create better computational tools to visualize, analyze, and catalog these enormous datasets. And to some extent, there is wide agreement these tasks must be pursued.

the Large synoptic survey telescope will have the ability to survey the entire sky in only three nights.

 

Some leading computational research scientists believe, however, that progress in utilizing the vast expansion of data will best be attacked on a project-by-project basis rather than by a pan-disciplinary computational blueprint.

“In theory, you might think we
should all be working together, and
the reality might be that each of the
people working on their own discipline
are achieving the results they need to
scientifically,” says Dan Masys, M.D.,
chairman of biomedical informatics at
Vanderbilt University. “There’s a cost
of communication that reaches an ir-
reducible minimum when you work