Frequently Asked Questions (FAQs)

The goal of the Data Science Incubator is to enable new research discoveries by bringing together data scientists and domain scientists to work on focused, intensive, collaborative projects. Our team of data scientists can provide expertise in state-of-the-art technology and methods in large-scale data manipulation and analytics, cloud and cluster computing, statistics and machine learning, and visualization to help researchers extract knowledge from large, complex, and noisy datasets. Projects frequently, but not exclusively, involve a non-trivial software engineering component.

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, we require a short project proposal describing the science goals, the relevant datasets, and the expected technical challenges. The ideal proposal will clearly identify both the datasets involved and the questions to be answered, and 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. We find that 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.

In reviewing the proposals, we will be looking for high-risk, high-reward research projects that this program can help push in a new direction.  In addition, we hope to select a set of projects with shared requirements; we find that participants are most successful when they interact with each other as well as with our group.

How to Get Started

If you have any questions about submitting a project for an Incubator, please consult one of our Data Scientists during their Office Hours for guidance:

Click HERE for information on how to submit a proposal and due dates!

Examples of Past Incubator Projects

Is the Incubator Program right for you? Check out some of our past projects to get an idea of the work we do. Our team has a strong track record of building systems that get real use. Below are listed some of our previous collaborations.

Ione Fine and Goeff Boynton, faculty, Psychology – “Models for retinal prosthetics”

Andrew Becker, research faculty, Astronomy – “Kernel-Based Moving Object Detection

Ângela Katsuyama, graduate student, Biology – “Students’ Sleep and Academic Performance

Carlos A. Manzanares, graduate student, Economics- “Simulating Competition in the U.S. Airline Industry

Emily Gade, graduate student, Political Science – “Analysis of .gov Web Archive Data

Sophie Clayton, postdoc, Oceanography – “Analysis of Large-Scale Patterns in Phytoplankton Diversity

Alicia Hotovec-Ellis, graduate student, Earth and Space Sciences – “Automated Detection and Analysis of Repeating Earthquakes

Benjamin Brooks, graduate student, Institute for Health Metrics and Evaluation – “Using social media data to identify geographic clustering of anti-vaccination sentiments

Ian Kelley, research consultant, Information School – “Efficient Computation on Large Spatiotemporal Network Data

Marina Meilǎ, faculty, Statistics – “Scalable Manifold Learning for Large Astronomical Survey Data

Or view all of our Past Projects.