Research Planning

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

November 19, 2024
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.

The Case for Including Disability in Social Science Demographics

October 15, 2024
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.

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.

Institutional Review Board (IRB) Fundamentals

October 9, 2023, 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.

Institutional Review Board (IRB) Fundamentals

February 7, 2023, 10:00am
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.

Institutional Review Board (IRB) Fundamentals

November 7, 2022, 12:00pm
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.

Institutional Review Boards (IRB) Fundamentals

March 17, 2022, 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.

Enhancing Research Transparency Inspired by Grounded Theory

April 30, 2024
by Farnam Mohebi. Grounded theory, a powerful tool for qualitative analysis, can enhance data science research by improving transparency and impact. Researchers can create a vivid record of their process by meticulously documenting the entire research journey, including the decisions they make and the corresponding rationale behind them, from initial data exploration to developing and refining theories. Embracing grounded theory principles, such as iterative coding and constant comparison, can help data scientists build robust, data-driven theories while ensuring transparency throughout the research process. This approach makes research more replicable and understandable and invites others to engage with the work, fostering collaboration and constructive critique, ultimately elevating the value and reach of their findings.