The goal: investigate huge amounts of research data in new ways. The pool for teams: neuroscientists, data scientists, and software developers. The result: answering questions we didn’t even know we had.
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In Stanley Kubrick’s famous film based on Arthur C. Clark’s book, 2001: A Space Odyssey, a mysterious black monolith appears on Earth millions of years before modern humans. It’s the classic “black box.” We don’t know who made it, what’s in it, or how it works, but it’s miraculous and powerful and somehow results in jumpstarting the entire evolution of humankind.
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I am constantly amazed by the energy and momentum around data science. Only a few years ago, I would be met with a blank stare when I told someone I planned on going to grad school for machine learning. Today, there is no need for my “it's like computer science, linear algebra, and statistics had a combined love child” analogy as most people instantly respond with “Oh, like AI!”
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James Verdier: Hi, I'm James Verdier and welcome to the American Institute of Biological Sciences’ BioScience Talks which is a forum for integrating the life sciences. On the second Wednesday of each month we discuss the latest bioscience publications. And as a reminder if you'd like to read more point your browser to academic.oup.com/bioscience.
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The rise of Big Data created the need for data applications to be able to consume data residing in disparate databases, of wildly differing schema. The traditional approach to performing analytics on this sort of data has been to warehouse it; to move all the data into one place under a common schema so it can be analyzed.
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