The Social Science Data Lab collaborates with faculty, staff, and student researchers to design and implement custom technical solutions, addressing complex challenges in their work through advanced programming and statistical methods.
SSDL support is open to all researchers, from first-time programmers to advanced statisticians, and is tailored to the bandwidth of each project, from direct implementation to meet a pressing deadline to guided instruction and collaboration.
Examples of this support have included:
- Collecting real-time vote counts using Python to scrape official state and county election websites
- Implementing OpenAI’s Whisper to transcribe podcast audio using automatic speech recognition
- Navigating the high performance computing resources available through Dartmouth Research Computing to efficiently use powerful machine learning models
- Using the OpenAI API to systematically process and code large volumes of news articles, social media posts, and other text data
- Using official APIs and software packages to retrieve data from repositories (e.g. ACLED, USPTO PatentsView), government agencies (e.g. Census Bureau, Congress), and social media platforms (e.g. YouTube, Spotify)
- General dataset transformation–variable creation, recoding, aggregating, merging, and cleaning data
- Preparing and presenting technical research findings at a conference
Inquiries about SSDL research support should be directed to Grace Aronsohn.