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 Bruno Smaniotto. Trend-cycle decompositions are statistical tools that help us understand the different components of Time Series – Trend, Cycle, Seasonal, and Error. In this blog post, we will provide an introduction to these methods, focusing on the intuition behind the definition of the...Read more about What are Time Series Made of?
by Sohail Khan. Language models are becoming integral to daily life as trusted sources of advice. While their utility has expanded from simple tasks like text summarization to more complex interactions, the empathetic quality of their responses is crucial. This article explores methods to assess...Read more about Language Models in Mental Health Conversations – How Empathetic Are They Really?
by Jaewon Saw. Imagine perfecting a favorite recipe, then sharing it with others, only to find their results differ because of small changes in tools or ingredients. How do you ensure the dish still reflects your original vision? This challenge captures the principle of stability in data science:...Read more about A Recipe for Reliable Discoveries: Ensuring Stability Throughout Your Data Work
by Amber Galvano. Interested in learning how to merge data and metadata from multiple sources into a consolidated dataset? Dealing with annotated audio and want to automate your workflow? Tried Praat scripting but want something more streamlined? This blog post will walk through some key domain-...Read more about Python Data Processing Basics for Acoustic Analysis
