Call for Projects

Submission criteria
We are accepting all types of research projects, including (but not limited to):
- Group class project
- Individual class project
- Independent research projects such as honors thesis, master’s thesis, or dissertation chapter
- Ongoing collaborative project with a faculty member

We would like to feature projects of all scopes - from evidence-based policy analysis to a full research poster. No need to submit a complete and polished paper or article, we’re interested in research at all stages. Our team will work with you to identify the best way to present your results to a general audience on our website!

Important Dates
To be considered for the research symposium during Homecoming, submit by October 10th. Projects received after this date will be included on the website but will NOT be eligible for participation in the symposium. Please contact [email protected] with any questions.

What is the DACSS Program?
The Data Analytics and Computational Social Science (DACSS) program trains graduate and undergraduate students to:
Identify sources of evidence for informed decision-making
• Design original research
• Work with large datasets
• Develop reliable and ethical data management practices
• Interpret and visualize results of their analysis, and
• Communicate about data and research to decision-makers and general audiences.
The goal of the DACSS program is to help students acquire cutting-edge training in the computational social sciences and attract a higher proportion of women and other underrepresented groups to the STEM field of social data science.

Submit your project here

Address

Bartlett Hall
Amherst, MA, 01003

About us

The Data Analytics and Computational Social Science (DACSS) is a new academic program that includes both graduate and undergraduate courses and certifications. The goal of DACSS is to help students acquire cutting-edge training in the computational social sciences and attract a higher proportion of women and underrepresented groups to the STEM field of social data science. See the DACSS website for more!

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