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Get Involved

Get Involved

There are many ways to get involved with the eScience Institute. Find out more about our Office Hours, Postdoctoral Fellowships, Seminars, and Incubator programs.

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Research

Research

We use data science methods for data driven research across all fields. Explore some of our many data and research science projects.

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Education

Education

We support and approach data science education across the curriculum, as well as hosting various bootcamps and tutorials.

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Upcoming Events

Working Group Leads and Faculty Meeting

04/25/2018 | 2:30 pm - 3:30 pm

Data Science Seminar: Meredith Broussard

04/25/2018 | 3:30 pm - 4:30 pm

Data Science Reading Group

04/26/2018 | 11:30 am - 1:00 pm

Community Seminar

04/26/2018 | 4:30 pm - 5:20 pm

Python in Geosciences

05/01/2018 | 3:30 pm - 4:30 pm

eScience Executives Meeting

05/02/2018 | 2:30 pm - 3:30 pm

Community Seminar: Tess Russo

05/03/2018 | 4:30 pm - 5:20 pm

Oceanhackweek

08/20/2018 - 08/24/2018 | All Day

Geohackweek

09/10/2018 - 09/14/2018 | All Day

  • Participants in the West Big Data Hub Data Carpentry workshop. Photo, Robin Brooks, eScience Institute

Extending data science training across the West

April 13th, 2018|

In partnership with the West Big Data Innovation Hub, the […]

  • Jenny Muilenberg presents at the UW Data Science Summit. Photo, Robin Brooks, eScience Institute

First UW Data Science Summit a success

April 12th, 2018|

By Robin Brooks
Over 200 students, faculty, staff, and community members […]

  • By combining artificial and human intelligence to improve mountain flood prediction, researchers hope to facilitate a better understanding of floods in coastal Washington watersheds. During a November 2015 flood of the Skagit River near Sedro-Woolley, Wash., resident Greg Platt moves bicycles to higher ground. Photo credit: Skagit Valley Herald. The modeled flood shown was the largest most recent flood caused by an atmospheric river (AR) event in 2006. Snow and ice are critical natural reservoirs of water resources. Improved understanding is expected to improve management decisions, planning, climate impact assessment, flood & drought resiliency. Before the incubator, hydrologic modeling predications were reported with unknown uncertainty estimates. The work accomplished during the Winter Incubator resulted in a standard method for ensuring ergodic, optimal model behavior required to support hypothesis testing used in scientific decision making. Given the hours of model run time required, the computing demand could not have been possible without the help of UW IT Cloud Services and Amazon Web Services.

Reflections on the 2018 Winter Incubator program

March 26th, 2018|

Nineteen project leads, eScience data scientists, and researchers participated in […]

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