Intelligent research design for data intensive social science
Who we serve D-Lab helps Berkeley 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!
One of the frequent ways people can run into random numbers is through their research. We often hear the term “random sample,” or a “randomized” assignment to control. Or, sometimes, we can randomly select a certain number of rows or columns from data to perform an analysis on a...Read more about Is your Random Sample Really Random?
Have you ever found yourself in the midst of an analysis when suddenly, out of nowhere, it happens. That tiny, dreaded pinwheel appears indicating an error has occurred. Yes, that's right, they call it the spinning wheel of death. Your application freezes....Read more about Big datasets, small code chunks, and why I use Google Earth Engine
We often read about the many new advancements being made in the field of Natural Language Processing (NLP). Each month, leading organizations release new models that seem like magic to us, such as models that can write it’s own code based on user prompts...Read more about Working with State-of-the-Art NLP Models: A Friendly Introduction to Hugging Face
Resisting our Data Doppelgangers: A Proposal for Unpacking the Dangers of Data-Driven Fertility Advertising With Data Science Tools
When Janet Vertasi, a sociology professor of technology at Princeton, learned of her pregnancy, she decided to conduct a personal experiment. She hid her pregnancy from the internet for nine months. This meant only sharing her pregnancy...Read more about Resisting our Data Doppelgangers: A Proposal for Unpacking the Dangers of Data-Driven Fertility Advertising With Data Science Tools
I have always been interested in working with spatial networks. My first introduction to spatial network modeling was in Prof. John Radke’s Geographic Information Systems class when I learned about building and analyzing spatial networks using the ...Read more about Working with spatial networks using NetworkX