Now accepting Project Proposals for Summer 2024!
For more information on how to submit a proposal, see our request for proposals (PDF).
Data+ is a full-time 10-week summer research experience that welcomes undergraduate and masters students interested in exploring new data-driven approaches to interdisciplinary challenges. It is suitable for students from all class years and from all majors.
Students join small project teams (at most 3 undergrads and 1 master’s student per team), working alongside other teams in a communal environment. They learn how to marshal, analyze, and visualize data, while gaining broad exposure to the modern world of data science. The projects come from an extremely diverse set of subject areas. It is our hope that students will be able to both work deeply into their specific project and get a very broad picture of most of the skills needed for modern data science.
Participants will receive a $5,000 stipend, out of which they must arrange their own housing and travel. Funding and infrastructure support are provided by a wide range of departments, schools, and initiatives from across Duke University, as well as by outside industry and community partners.
Data+ has returned to 100% in-person participation. Participants may not accept employment or take classes during the program; this requirement is strictly enforced and non-negotiable.
Due to the nature of the data involved in some of the projects, human subjects research training will be required of all participants and will be provided after admission to the program. With each project, we have attempted to list potential majors and/or interests that might be most interested in the project, but these should not be seen as requirements in any way! Quantitative STEM majors like mathematics, computer science, statistics, and electrical engineering are relevant to all.
Get Involved
Now accepting Project Proposals for Summer 2024!
For more information on how to submit a proposal, see our request for proposals (PDF).
Applications
Student applications will open in late December, stay tuned for our announcement!
Partners
Industry partners are essential to Data+. Learn how to become a partner.


From Our Data+ Students
Data+ by the Numbers
weeks during the summer
undergraduates per team
grad student mentors per team
projects sharing ideas and code
Data+ Projects
A team of students analyzed, mapped, and visualized student enrollment and demographic data in Durham County. The team collaborated closely with analytics professionals at the Durham Public Schools (DPS) operations department and provided analysis to help DPS plan and manage student enrollment and improve fairness in its student assignment policies....
A team of students led by Physics professor Dan Scolnic collaborated with Duke Dining leadership to provide an in-depth, quantitative accounting of the carbon footprint of the Duke Dining program. Students used the latest research quantifying CO2 equivalent greenhouse gas emissions for various food types, meals, and sources to produce...
A team of students led by researchers in the O-Lab for auditory neuroscience determined whether the imagination of speech and nonspeech sounds can be distinguished using on a non-invasive measurement of activation in the brain, electroencephalography (EEG). Students collected and analyzed EEG data from human participants in response to both...
A team of students led by professors Maurizio Forte, Classical Studies and AAHVS and Leonard White, Neurology, studied the embodied aesthetic experience engendered by real and virtual interactions with archeological ruins (“ruinscapes”) and virtual representations of places, spaces, and cultural artifacts associated with an ancient city. The focus will be...
A team of students led by Prof. Zuchuan Li and co-led by Prof. Nicolas Cassar developed means to estimate the amount of CO2 transferred from the ocean surface to the deep ocean through machine learning techniques applied to satellite data and automatic observations. The team identified variables that can be...
A team of students led by researchers in the Hydroclimatological Lab created a workflow/pipeline for comprehensively estimating the carbon emissions from the Southeastern (SE) United States (US) wetlands using machine learning techniques applied to multi-source data, including field measurements, remote sensing products, and biophysical model outputs. The team first applied...
Is it ethically permissible to sell, buy, and use luxury goods? What labor practices do we tolerate to make these goods available? In the late Middle Ages and Renaissance, England was faced with an ever-growing supply of new and exciting goods, made possible by new trade routes to the New...
This project looks at policy implications of the vast expansion of Brazil’s higher education system from 2004 to 2016, which sought to promote economic mobility and reduce social disparities. Through public and private university expansion, it doubled the number of Brazilians attending college. The project team analyzed 14 years of...
Students collaborated with the research team of Dr. Kathleen Cooney, including prominent partners both at Duke and other institutes, to identify genetic variants likely associated with early onset prostate cancer in African American patients identified by the Metropolitan Detroit Cancer Surveillance System (MDCSS) cancer registry. Students analyzed whole exome sequencing...
A team of students led by Dr. Otis Jennings explored and created a research paper that seeks to understand the gap between the stated diversity funding, goals and support by corporations relative to their actual level of support to diverse founders. The research required navigating corporate reporting, media, press releases,...
A team of students led by Dr. Jim Heffernan of the Nicholas School of the Environment, used remote imagery and object identification tools to determine changes in parking lot occupancy in the Research Triangle region during the Covid-19 pandemic’s acute and post-acute phases. Students used open-source geospatial data to select...
Students collaborated with CEE Professors David Carlson and Mike Bergin to model the effects of land use on the urban heat island effect using satellite imagery and ground-level temperature measurements. Students used machine learning to segment satellite images of Durham, North Carolina by land use. They then paired land use...
A team of students led by a data scientist at NetApp developed the means to evaluate technical documentation through machine learning techniques. Students identified features of language and documents that can be used to demonstrate how effective that documentation is at communicating technical specifications. Additionally, students applied machine learning methods...
Using digitized card catalogs from the David M. Rubenstein Rare Book and Manuscript Library, a team of students explored extracting structured data from over 115,000 subject cards to develop searchable and sortable descriptions of manuscript and archival collections. They prepared the digitized subject cards for online access in the Internet...
A team of students led by researchers in the BIG IDEAs Lab optimized and further developed an existing cloud-based infection detection platform that populates and translated wearable data from a variety of sources. The project involved working with existing wearable data pipelines (e.g., APIs) to collect, process, and visualize wearable...
A team of students led by researchers from the Nicholas School of the Environment developed an app to estimate the distance of animals from a camera trap. Students merged camera trap data with terrestrial lidar (3D imagery of forest around the camera trap) to locate the position of animals. By...
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