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Analysis of .Gov Web Archive Data
Project Leads: Emily Gade (Political Science) eScience Liaison: Andrew Whitaker Data are revolutionizing all fields of science including political science. Managing unstructured data (particularly text) is a non-trivial challenge for social scientists, especially at a large scale. An example is the .gov dataset curated by the Internet Archive (IA). The IA curates web crawls from 1996 to…
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Simulating Competition in the U.S. Airline Industry
Project Lead: Charlie Manzanares (Economics) eScience Liaisons: Andrew Whitaker, Daniel Halperin Since 2005, the U.S. airline industry has experienced the most dramatic merger activity in its history, which has reduced the number of major carriers in the U.S. from eight to four. My project seeks to provide novel estimates of changes in consumer and producer welfare in the…
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Students’ Sleep and Academic Performance
Project Lead: Ângela M. Katsuyama, UW Biology Advisor: Horacio O. de la Iglesia, UW Biology eScience Liaisons: Bill Howe, Daniel Halperin This project investigates the impact of sleep in college academic performance. We hypothesize that poor academic performance in college students correlates with poor sleep behaviors. To address this hypothesis, we collected data from 72 senior students…
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Kernel-Based Moving Object Detection
Project Lead: Andrew Becker, UW Astronomy eScience Liaison: Daniel Halperin With assistance from: Andrew Whitaker, Bill Howe Kernel-Based Moving Object Detection (KBMOD) describes a new technique to discover faint moving objects in time-series imaging data. The essence of the technique is to filter each image with its own point-spread-function (PSF), and normalize by the image noise, yielding a likelihood…
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Undercurrents at the DSE Summit
Blog post by Brittany Fiore-Silfvast The Data Science Environment (DSE) Summit took place in beautiful Monterey, CA at the Asilomar Conference Center. The Summit brought together over a hundred participants across three universities (UW, UC Berkeley and NYU) involved in the Moore and Sloan Foundations’ Data Science Environment grant. As a data science ethnographer, I…
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UW “Trend in Engineering” Features Data Science
from UW CSE News: SeaFlow, a research instrument developed in the lab of UW School of Oceanography director Ginger Armbrust, analyzes 15,000 marine microorganisms per second, generating up to 15 gigabytes of data every single day of a typical multi-week-long oceanographic research cruise. UW professor of astronomy Andy Connolly is preparing for the unveiling of…
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UW CSE’s Jeff Heer is one of 14 Moore Foundation “Data-Driven Discovery Investigators”
from UW CSE News: The Gordon and Betty Moore Foundation joined last year with the Alfred P. Sloan Foundation in a process that ultimately selected the University of Washington, UC Berkeley, and New York University as partners in a 5-year, $38.7 million collaborative effort to advance data-intensive discovery. The Moore Foundation has just announced the…
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TED Talk: What’s the Next Window into Our Universe?
Big Data is everywhere — even the skies. In an informative talk, astronomer Andrew Connolly shows how large amounts of data are being collected about our universe, recording it in its ever-changing moods. Just how do scientists capture so many images at scale? It starts with a giant telescope … What’s The Next Window Into…
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ASPASIA: Adult Service Providers and Some Incidental Addenda
Project Lead: Sam Henly, a PhD student in the UW Department of Economics eScience Liaison: Andrew Whitaker, Data Scientist, eScience Institute Most prostitution in the United States is organized through Internet media. This presents an opportunity for research into a market that, historically, has proved impenetrable to systematic investigation. APSASIA is an effort to collect all of…
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Scalable Manifold Learning for Large Astronomical Survey Data
Project lead: Marina Meila, UW Department of Statistics eScience Liaison: Jake VanderPlas, Director of Research – Physical Sciences, UW eScience Institute Manifold Learning (ML), also known as Non-linear dimension reduction, finds a non-linear representation of high-dimensional data with a small number of parameters. ML is data intensive; it has been shown statistically that the estimation accuracy depends…