Research Design

Antipodal Experiments: Digital Humanities & Social Science (D-HASS) in Australasia

May 19, 2025, 4:00pm
Professor Smithies will highlight recent initiatives, such as major national hubs like the HASS Digital Research Hub at ANU, and explore themes of collective well-being, pluralism, environmental sustainability, Indigenous data sovereignty, and human-centered design. This talk will challenge narratives of decline by presenting innovative, community-focused digital research strategies and the potential for renewal through antipodal, pluralistic, and inclusive thinking.

LLMs for Exploratory Research

March 20, 2025, 10:00am
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...

Field Experiments in Corporations

January 28, 2025
Yue Lin
by Yue Lin. How do social science researchers conduct field experiments with private actors? Yue Lin provides a brief overview of the recent developments in political economy and management strategy, with a focus on filing field experiments within private corporations. Unlike conventional targets like individuals and government agencies, private companies are an emergent sweet spot for scholars to test for important theories, such as sustainability, censorship, and market behavior. After comparing the strengths and weaknesses of this powerful yet nascent method, Lin brainstorms some practical solutions to improve the success rate of field experimental studies. She aims to introduce a new methodological tool in a nascent research field and shed some light on improving experimental quality while adhering to ethical standards.

Fritz_X_DargesBlue42… Who Are You?

January 14, 2025
Jonathan Pérez
by Jonathan Pérez. Reflecting on the complexities of the human experience is paramount to conducting research. Jonathan Pérez, through his exploration of a conspiracy subreddit, reflects on his experience trying to find the human behind the datum. Jonathan critiques the harmful effects of dehumanizing rhetoric and the researcher’s responsibility to navigate ethical implications. In doing so, he establishes three guiding rules to support researchers seeking to humanize their analysis: 1) a researcher must always find the story behind the data; 2) a researcher must protect themselves; 3) a researcher must still humanize participants (even those who perpetuate harmful narratives).

Navigating AI Tools in Open Source Contributions: A Guide to Authentic Development

December 17, 2024
Sahiba Chopra
by Sahiba Chopra. The rise of ChatGPT has transformed how developers approach their work - but it might be hurting your reputation in the open-source community. While AI can supercharge your productivity, knowing when not to use it is just as crucial as knowing how to use it effectively. This guide reveals the unspoken rules of AI usage in open source, helping you navigate the fine line between leveraging AI and maintaining authenticity. Learn when to embrace AI tools and when to rely on your own expertise, plus get practical tips for building trust in the open-source community.

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...

Institutional Review Board (IRB) Fundamentals

February 18, 2025, 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.

Language Models in Mental Health Conversations – How Empathetic Are They Really?

December 3, 2024
Sohail Khan
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 the emotional appropriateness of these models, using metrics such as BLEU, ROUGE, and Sentence Transformers. By analyzing models like LLaMA in mental health dialogues, we learn that while they suffer through traditional word-based metrics, LLaMA's performance in capturing empathy through semantic similarity is promising. In addition, we must advocate for continuous monitoring to ensure these models support their users' mental well-being effectively.

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.