Research Design

A Recipe for Reliable Discoveries: Ensuring Stability Throughout Your Data Work

November 19, 2024
Jaewon Saw
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: achieving acceptable consistency in outcomes relative to reasonable perturbations of conditions and methods. In this blog post, I reflect on my research journey and share why grounding data work in stability is essential for reproducibility, adaptability, and trust in the final results.

LLMs for Exploratory Research

December 10, 2024, 1:00pm
In a fast evolving artificial intelligence landscape, LLMs such as GPT have become a common buzzword. In the research community, their advantages and pitfalls are hotly debated. In this workshop, we will explore different chatbots powered by LLMs, beyond just ChatGPT. Our main goal will be to understand how LLMs can be used by researchers to conduct early-stage (or exploratory) research. Throughout the workshop, we will discuss best practices for prompt engineering and heuristics to evaluate the suitability of an LLM's output for our research purposes. Though the workshop primarily focuses on early-stage research, we will briefly discuss the use cases of LLMs in later stages of research, such as data analysis and writing.

Claudia von Vacano, Ph.D.

Founding Executive Director, P.I., Research Director, FSRDC

Dr. Claudia von Vacano is the Founding Executive Director and Senior Research Associate of D-Lab and Digital Humanities at Berkeley and is on the boards of the Social Science Matrix and Berkeley Center for New Media. She has worked in policy and educational administration since 2000, and at the UC Office of the President and UC Berkeley since 2008. She received a Master’s degree from Stanford University in Learning, Design, and Technology. Her doctorate is in Policy, Organizations, Measurement, and Evaluation from UC Berkeley. Her expertise is in organizational theory and...

The Case for Including Disability in Social Science Demographics

October 15, 2024
Jane (Mango) Angar
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 with disabilities, using our research to provide insights for effective policy recommendations. Although most of us do not focus on disability-related issues, including disability as a demographic characteristic in our data collection can greatly enhance our understanding of diverse populations and improve the comprehensiveness of our analyses. This small step can contribute to broader efforts toward inclusion and social equity.

Institutional Review Board (IRB) Fundamentals

October 17, 2024, 3:00pm
Are you starting a research project at UC Berkeley that involves human subjects? If so, one of the first steps you will need to take is getting IRB approval.

Tom van Nuenen, Ph.D.

Data/Research Scientist, Senior Consultant, and Senior Instructor
D-Lab
Social Sciences
Digital Humanities

I work as a Lecturer, Data Scientist, and Senior Consultant at UC Berkeley's D-Lab. I lead the curriculum design for D-Lab’s data science workshop portfolio, as well as the Digital Humanities Summer Program at Berkeley.

Former research projects include a Research Associate position in the ‘Discovering and Attesting Digital Discrimination’ project at King’s College London (2019-2022) and a researcher-in-residence role for the UK’s National Research Centre on Privacy, Harm Reduction, and Adversarial Influence Online (2022). My research uses Natural Language Processing methods to...

Anusha Bishop

Availability: By appointment only

Consulting Areas: Python, R, Cloud & HPC Computing, Data Sources, Data Visualization, Geospatial Data, Maps & Analysis, Machine Learning, Research Design, Cluster analysis, Experimental design, Hierarchical Models, High dimensional statistics, Means Tests, Nonparametric methods, Regression Analysis, Software Output Interpretation, Spatial statistics, Bash or Command Line, Excel, Git or Github, RStudio

Elaine (Hua) Luo

Data Science Fellow 2024-2025
Graduate School of Education

Elaine (Hua) Luo is a PhD candidate in the Graduate School of Education, School Psychology PhD program. Her research interests focus on adolescents’ identity development and well-being under the transactional influence of entities in their socio-ecological systems. In her research, Elaine has utilized not only quantitative but also qualitative and mixed methods to study her research topics of interest. Before coming to Berkeley, Elaine earned her Master’s in Human Development and Psychology from Harvard Graduate School of Education and her Bachelor of Art in Education Sciences from...

Survey Fundamentals

February 21, 2024, 1:00pm
This two-hour workshop offers a comprehensive introduction to designing and conducting survey studies. Tailored for beginners, it provides clear, step-by-step guidance complemented by concise examples, practical considerations, and useful support materials. Participants will learn the entire process, from formulating a research question to creating, administering, and analyzing surveys, as well as interpreting results and communicating their findings.

Institutional Review Board (IRB) Fundamentals

February 16, 2024, 9:00am
Are you starting a research project at UC Berkeley that involves human subjects? If so, one of the first steps you will need to take is getting IRB approval.