The Applied Data (AD) Fellows Program, generously supported by the Quantitative Experiential Learning Fund (QELF), provides undergraduate students with meaningful, hands-on experience in quantitative data analysis through applied research and collaborative projects.
AD Fellows work with faculty, staff, or community partners on projects that address real-world research questions and analytical challenges. Students are guided by professional and technical mentors who work in collaboration with their project partners.
Fellowships are available for semester-long, academic-year, and summer projects. Project proposals are welcome from faculty and staff across all academic divisions, departments, and administrative offices, as well as from community partners.
For Students: Become an AD Fellow
AD Fellows gain practical experience applying quantitative methods to meaningful projects while developing technical, analytical, and professional skills. AD Fellows will be paired with existing projects from across the College and Carlisle community.
As an AD Fellow, you will:
- Conduct data cleaning, analysis, visualization, and statistical modeling, as appropriate for your project
- Develop problem solving skills by troubleshooting technical and analytical challenges
- Collaborate with a project partner to understand their research questions and project needs
- Receive ongoing guidance and support through regular meetings with technical and professional mentors
- Communicate your findings and present project outcomes to your project partner.
Application Deadlines:
- November 15, 2026: For Spring 2027 Fellows
- April 4, 2027: For Summer 2027 and Fall 2027 Fellows
APPLICATIONS FOR SPRING 2027 WILL OPEN NOVEMBER 2, 2026
For Partners: Propose a Project
The AD Fellows Program connects faculty, staff, and community partners with undergraduate students who can contribute quantitative expertise to research and applied projects.
As a project partner, you will:
- Collaborate with a motivated student to advance your research or project goals
- Receive assistance with data analysis, modeling, visualization, or other quantitative tasks
- Help shape a meaningful experiential learning opportunity and contribute to a student's professional development
Criteria for an appropriate project:
- The project addresses a question or problem that can be informed by data or quantitative analysis.
- The project can likely produce meaningful results within one or two semesters.
- The necessary data already exists or can realistically be obtained, accessed, cleaned, and prepared during the project period.
- The project provides opportunities for a student to develop or apply technical, analytical, and/or quantitative skills.
- The project has a tangible and realistic outcome, such as a report, dashboard, visualization, model, dataset, or other product.
Application Deadlines:
- November 15, 2026: For Spring 2027 Projects
- April 4, 2027: For Summer 2027 and Fall 2027 Projects
SUBMIT YOUR PROJECT PROPOSAL
For Partners: Project Ideas and Examples
How might working with an Applied Data Fellow be helpful?
Analyze quantitative data
- Clean and organize data, conduct statistical analyses, identify patterns, and create visualizations.
- Example: A faculty member has five years of survey responses and wants to better understand trends in student attitudes. A Fellow could clean the data, conduct descriptive and statistical analyses, and develop visualizations for a paper or presentation.
Analyze texts and documents
- Use computational text analysis, coding, or other methods to identify patterns in a large collection of documents.
- Example: A faculty member is studying several decades of newspaper coverage but has more articles than can reasonably be reviewed manually. A Fellow could create a searchable dataset, categorize articles, analyze changes in topics or language over time, and visualize the results.
Work with geographic data
- Use GIS, mapping, and spatial analysis to investigate relationships between people, places, and events.
- Example: A historian has information about businesses, residences, or institutions in Carlisle from different periods. A Fellow could geocode the information and create interactive maps showing how the community changed over time.
Develop databases and research datasets
- Transform unstructured research materials into organized, reusable datasets.
- Example: A faculty member has archival information on hundreds of individuals but no consistent database. A Fellow could develop a data structure, enter and clean the information, and prepare it for future research.
Create data visualizations or interactive tools
- Turn research findings into accessible charts, dashboards, maps, or other visualizations.
- Example: A faculty member has already completed an analysis but needs a more effective way to communicate the findings to readers, conference audiences, students, or the public. A Fellow could develop visualizations or an interactive presentation of the results.
Automate repetitive research tasks
- Use programming or other computational tools to make time-consuming research processes more efficient and reproducible.
- Example: A faculty member regularly collects information from hundreds of online or archival records. A Fellow could develop a workflow to automate portions of the data collection, cleaning, or organization process, reducing repetitive manual work.
