For the past year or so genetic scientists at the Albert Einstein College of Medicine in New York have been collaborating with a specialist from another universe: Daniel Kohn, a Brooklyn-based painter and conceptual artist.
Kohn has no training in computers or genetics, and he’s not there to conduct art therapy classes. His role is to help the scientists with a signature 21st-century problem: Big Data overload.
Advanced computing produces waves of abstract digital data that in many cases defy interpretation; there’s no way to discern a meaningful pattern in any intuitive way. To extract some order from this chaos, analysts need to continually reimagine the ways in which they represent their data — which is where Mr. Kohn comes in. He spent 10 years working with scientists and knows how to pose useful questions. He might ask, for instance, What if the data were turned sideways? Or upside down? Or what if you could click on a point on the plotted data and see another dimension?
“A lot of the value of his input is jolting us out of our comfort zone, and making us aware that we can and should be thinking about the representation of data in new ways,” said John Greally, director of Einstein’s Center for Epigenomics, who brought on Kohn.
“The problem today is that biological data are often abstracted into the digital domain,” Greally added, “and we need some way to capture the gestalt, to develop an instinct for what’s important.”
Read full, original article: Learning to See Data