Data wisdom is needed to make discoveries and assure the significance of the results of data-intensive research, says Bin Yu, a statistician and data scientist at the University of California, Berkeley.
She describes the best of applied statistics as an essentially "soft lens" when working with large amounts of data, similar to a powerful telescope or precision gene microarray.
Yu participated in Berkeley's "mind-reading" project in 2011, in which researchers used a type of magnetic resonance imaging (MRI) to detect indirect neuron firing at precise locations in the brain's visual processing area, and then determined the rough outlines of what experimental subjects were seeing in movie clips. Yu's team analyzed a torrent of functional MRI data to identify from thousands of movie clips the 100 frames that most likely matched a given voxel activity pattern, and then "averaged" these shapes to yield the outline of what subjects were seeing. Yu says only a powerful interlocking of science, computation, and statistics made this possible.
"In computational neuroscience, it is important to gauge how much variation there is in the signals--in this case, how much of this variation is due to the movies and how much is due to 'noise,'" she says.
From Berkeley Research
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