The D-Lab is closed for Winter Break! Happy Holidays!
We will not have a virtual front desk, workshops, or any consultations, and we will return in the Spring.
D-Lab
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 Maksymilian Jasiak. Spatial time series (consecutive measurements across space and time) are often difficult to interpret, especially when there are many overlapping signals. However, have no fear! Filtering and visualizing can help better interpret and understand the spatial time series data.Read more about Filtering, Visualizing, and Interpreting Spatial Time Series Data
by Skyler Chen. This post discusses how my training in data science changed the way I think about behavioral research. I share how simply exploring everyday datasets and noticing small, unexpected patterns can spark new research questions, and how archival data and experiments each offer distinct...Read more about Seeing Behavior in Everyday Data
by Elena Stacy. Researchers today increasingly have access to a wealth of tools to streamline or automate labor-intensive data processing and generation tasks. When it comes to mapping, progress has been slower. This blog details the author's experience tackling the digitization of a historical map...Read more about Digitization of Historical Maps in the Age of AI
by Sarah Daniel. Researchers increasingly recognize that neighborhoods profoundly shape life outcomes, yet measuring them remains challenging. A common approach uses administrative boundaries, such as census tracts, as proxies for neighborhoods, but this method presents three key challenges. First...Read more about A Participant-Centered, GIS-Based Approach to Improving Contextual Measurement
