Intelligent research design for data intensive social science
Who we serve D-Lab helps UC Berkeley undergraduate students, graduate students, faculty, and staff move forward with world-class research in data intensive social science and humanities.
What we do D-Lab assists the Berkeley community with the full range of research development, research design and data acquisition. We offer guidance in statistical methods and results to data visualization and communication.
Who we are D-Lab is comprised of scholars who create a learning community that teaches workshops and offers consultations. Join us!
by Taesoo Song. Many American cities continue to face severe rental burdens. However, we rarely examine rental affordability through the lens of quantitative data. In this blog post, I demonstrate how to download and visualize rental affordability data for the San Francisco Bay Area using R...Read more about Exploring Rental Affordability in the San Francisco Bay Area Neighborhoods with R
by Victoria Hollingshead. In honor of the 2024 International Day of Care and Support, Victoria Hollingshead shares her recent work with the Center for Migrant Advocacy’s Direct Assistance Program and their innovative approach to supporting Overseas Filipino Workers (OFWs) using generative AI. OFWs...Read more about Human-Centered Design for Migrant Rights
by Christian Caballero. We live in an interconnected world, more so now than ever. Social Network Analysis (SNA) provides a toolkit to study the influence of this interconnectivity. This blog post introduces some key theoretical concepts behind SNA, as well as a family of metrics for measuring...Read more about Concepts and Measurements in Social Network Analysis
by Mango Jane Angar. As we celebrate Disability Awareness Month at the D-Lab alongside the UC Berkeley scholarly community, how can we, as social scientists, individually promote accessibility and inclusion? To advance accessibility, we should focus on addressing the barriers faced by individuals...Read more about The Case for Including Disability in Social Science Demographics
by Nanqin Ying. This study analyzes over 50 million judicial decisions from China’s Supreme People’s Court to examine disparities in legal representation and their impact on sentencing across provinces. Focusing on 290 000 drug-related cases, it employs large language models to differentiate...Read more about Leveraging Large Language Models for Analyzing Judicial Disparities in China
