eScience Institute: Data Science Incubation Program
Posted: 10/5/2016 (Funding)
The goal of the Data Science Incubator is to enable new science by bringing together data scientists and domain scientists to work on focused, intensive, collaborative projects. The team of data scientists provides expertise in state-of-the-art technology and methods in statistics and machine learning, data manipulation and analytics at all scales, cloud and cluster computing, software design and engineering, visualization, and other topics. It invites short proposals (1-2 pages) for one-quarter data-intensive research projects focusing on extracting insight from large, noisy, or heterogeneous datasets.
The program is open to any faculty, staff, or student whose research can be significantly advanced by intensive collaboration with a data science expert. To apply, a short project proposal describing the science goals, the relevant datasets, and the expected technical challenges is required. The ideal proposal will clearly identify both the datasets involved and the questions to be answered, and it will explain how the technical component of the project is critical to delivering exciting new findings.
Each project must include a project lead who is willing to physically co-locate with the incubator staff. Collaboration in a shared space is important for deeper technical engagement and provides opportunities for “cross-pollination” among multiple concurrent projects. The Incubator operates on Tuesdays and Thursdays out of the WRF Data Science Studio (6th floor of the Physics/Astronomy Tower). The project lead should plan to work in the Studio for several hours on these days.
Incubator projects are not “for-hire” software jobs—the project lead will work in collaboration with the data scientists and the broader eScience community. Each project lead will “own” their project (and its results) and be responsible for its successful completion, with the eScience team providing guidance on methods, technologies, and best practices as well as general software engineering.
Deadline: 11/07/2016