Climate+ is a new vertical offered within Duke University’s Data+ program, a full-time, ten-week summer research experience that welcomes Duke undergraduate and masters students interested in exploring new data-driven approaches to interdisciplinary challenges.
Climate+ is aligned with the Duke Climate Commitment, which unites the university’s education, research, operations and public service missions to address the climate crisis. The commitment builds on Duke’s longstanding leadership in climate, energy and sustainability to educate and deploy a generation of climate- and sustainability-fluent innovators and create just, equitable solutions for all.
In addition to experiencing all the general educational benefits of the Data+ program, Climate+ students will form a subcohort that engages regularly with climate, environment, and energy researchers and practitioners. Like the broader Data+ program, each Climate+ project team will consist of at most three undergraduates and one graduate student, who will work in a communal environment to learn how to marshal, analyze, and visualize data. Graduate students (including master’s and PhD students) typically serve as project managers, helping their teams stay on track with deliverables and timeline; their compensation may vary.
Climate+ is offered by the Rhodes Information Initiative at Duke in partnership with the Nicholas Institute for Energy, Environment & Sustainability.
Browse our Climate+ projects below.
Get Involved
Applications are now open for 2024 projects! Submit your applications here:
2024 Projects
2024 Data+ projects have been posted! See the list.
Climate+ Projects
Students will develop and use a database that links physical, chemical, and social environmental data with incidence of specific immune-related diseases within neighborhoods in North Carolina. This database will consist of both publicly-available environmental, social, and climate data as well as data specific to/generated by Duke researchers; health information will...
A team of students led by researchers in Energy Materials and Machine Learning groups, supervised by Prof. Olivier Delaire and Prof. David Carlson, will develop means to evaluate and quantity motions of atoms in novel materials for energy conversion and energy storage. This project will advance our understanding of two...
In collaboration with Duke’s River Center, a team of students will use remote sensing, in-situ water quality data, and machine learning algorithms to detect saltwater intrusion in coastal rivers. As sea levels rise, coastal waterways will become increasingly saline, threatening freshwater biodiversity and ecosystem services. Students will develop a harmonized...
Led by researchers from Duke University and Duke Kunshan University, a team of students will embark on an interdisciplinary research journey to explore the dynamic intersection of environmental science and machine learning, engaging in the recognition of wetland plant species through the analysis of satellite image time series. Students will...
A group of students led by professors of climate sciences and stochastic analysis will use climate models to improve the projected rainfall over the southeastern United States. Students will learn about the climate processes that influence precipitation, flooding, and droughts, as well as how to improve model capability to predict...
A team of students led by Duke Forest staff as well as a faculty and postdoc from Duke’s Nicholas School of the Environment will explore, organize, and create visualizations for observation data of reptiles and amphibians—collectively known as “herpetofauna”—in the Duke Forest. This data is directly collected by Duke Forest’s...
A team of students led by researchers in the Energy Access Project and the Energy Data Analytics Lab will apply machine learning techniques to high resolution aerial imagery data to identify the location of solar panels throughout Cape Town, South Africa. Currently, solar panels are being used by wealthier households...
Students will analyze data on climate risk, most likely including flood risk, perhaps a combination of public data, private data, and insurance sector data for pilot communities in North Carolina. They will integrate this data with other local data sets such as critical community infrastructure (schools, hospitals) and provide visualization...
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 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...
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...
This project helped to build a globally scalable foundation model to enable near real-time tracking of climate change causes and impacts. A foundation model is a model (usually a deep neural network) that has been trained on a large and diverse set of data, after which it can be adapted...
Researchers with the Duke River Center and the Watershed Biogeochemistry Lab investigate patterns of anoxia, or periods of little to no oxygen, in rivers. Oxygen is a necessary element for many organisms to live in rivers, but researchers know little about the timing, duration, and magnitude of low oxygen time...
A team of students led by Civil & Environmental Engineering Professor Helen Hsu-Kim developed a resource reserves database of coal ash wastes stored in hundreds of legacy disposal sites in the United States. The team extracted key information from historical datasets on coal energy production, incorporated geochemical information of coal...
A team of students led by researchers within the Saltwater Intrusion and Sea Level Rise (SWISLR) Research Coordination Network created a geospatial database summarizing the current extent of SWISLR and the current knowledge on SWISLR within the North American Coastal Plain. Students were responsible for mapping scholarly articles, news stories,...
A team of students led by researchers at the Duke Marine Lab will explore the changing distribution of krill around the Antarctic Peninsula. Krill are a key prey species in this ecosystem, supporting a number of animals including whales, seals, and penguins, but they are dependent on winter sea ice...
This project is also part of Duke’s first Climate+ cohort. A team of students led by researchers at Duke and abroad will develop and evaluate machine learning solutions to model behavioral patterns of electric use, emphasizing data privacy. Data collected in different parts of the world will be analyzed to...
This project is also part of Duke’s first Climate+ cohort. A student team working with the Energy Data Analytics Lab will work to democratize access to data relevant to climate change mitigation and adaptation planning as well as the underlying models to acquire those data. This project will work towards building the...
This project is also part of Duke’s first Climate+ cohort. A team of students led by researchers in the Hydroclimatological Lab will comprehensively quantify the wetland carbon emissions in the entire Southeast (SE) US using machine learning techniques and various climate datasets—including in situ measurements, remote sensing data, climate observations,...
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