Data for Health

Data for Health topic

Excel Data Analysis: Charts, Pivot Tables, and VLOOKUP

June 13, 2024, 1:00pm
This three-hour workshop will cover charts in more detail, review pivot tables, and the widely-used VLOOKUP function. We recommend first taking the introductory workshop Excel Data Analysis: Introduction.

Excel Data Analysis: Charts, Pivot Tables, and VLOOKUP

April 20, 2022, 9:00am
This two-hour workshop will cover charts in more detail, review pivot tables, and the widely-used VLOOKUP function. We recommend first taking the introductory workshop Excel Data Analysis.

Excel Data Analysis: Charts, Pivot Tables, and VLOOKUP

October 18, 2023, 1:00pm
This three-hour workshop will cover charts in more detail, review pivot tables, and the widely-used VLOOKUP function. We recommend first taking the introductory workshop Excel Data Analysis: Introduction.

Using Big Data for Development Economics

March 18, 2024
by Leïla Njee Bugha. The proliferation of new sources of data emerging from 20th and 21st century technologies such as social media, internet, and mobile phones offers new opportunities for development economics research. Where such research was limited or impeded by existing data gaps or limited statistical capacity, big data can be used as a stopgap and help accurately quantify economic activity and inform policymaking in many different fields of research. Reduced cost and improved reliability are some key benefits of using big data for development economics, but as with all research designs, it requires thoughtful consideration of potential risks and harms.

How can we use big data from iNaturalist to address important questions in Entomology?

February 26, 2024
by Leah Lee. Large-scale geographic data over time on insect diversity can be used to answer important questions in Entomology. Open-source, open-access citizen science platforms like iNaturalist generate huge amounts of data on species diversity and distribution at accelerating rates. However, unstructured citizen science data contain inherent biases and need to be used with care. One of the efforts to validate big data from iNaturalist is to cross-check with systematically collected data, such as museum specimens.

Artificial Intelligence and the Mental Health Space: Current Failures and Future Directions

October 31, 2023
by María Martín López. María Martín López, a PhD student in the department of psychology whose research focuses on large language models within the context of mental illness, gives an overview of current failures and possible future directions of NLP models in the mental health space. She brings up questions that must be considered by all researchers working in this space and encourages these individuals to think creatively about the use of AI beyond direct treatment.

The Geography of Cannabis: Does California’s dual licensing program (de)criminalize cannabis and drive unnecessary anthropogenic activity in remote rural environments?

August 29, 2023
by Chevon Holmes. When California voters (de)criminalized cannabis production, the state’s dual licensure requirement forced local jurisdictions to create permitting programs or uphold prohibition. Many Counties developed ersatz zoning ordinances to regulate cannabis activities and hired staff to administer local permits. As an inspector, administrator, and project planner for Mendocino County from 2017-2021, I visited hundreds of cultivation sites and production facilities where I learned first-hand how two legal pathways impacted the ways in which operators could transition their businesses. This post details a dataset created to track, aggregate, and analyze the relationship between cannabis infrastructure and licensing.

My Summer Exploring Data Science for Social Justice: Learnings, Tensions & Recommendations

September 5, 2023
by Genevieve Smith. This summer I joined the D-Lab hosted Data Science for Social Justice workshop at UC Berkeley diving into Python – including TF-IDF, sentiment analysis, word embeddings, and more – with a lens towards leveraging data science for social justice. My team explored a Reddit channel on abortion and used computational analysis to answer key questions related to abortion access from before versus after Roe vs. Wade was overturned. Computational social science is incredibly powerful, but I continue to grapple with tensions particularly as it relates to employing machine learning and large language in international research, and end with key recommendations for CSS practitioners.

Monica Donegan

Data Science Fellow
Environmental Science, Policy, and Management

Monica is a third-year Ph.D. candidate in the Environmental Science, Policy, and Management program. She uses computational tools to study the evolution and ecology of agricultural plant pathogens. Previously, she worked on a data science team at a biotech company in Boston.

Marina Blum

Data Science Fellow
School of Public Health

Marina is a master's student in the Health and Social Behavior division of the School of Public Health. She has extensive experience in ATLAS.ti and can help you get the most out of the program. She is passionate about data visualization, and is happy to help with related questions and questions on qualitative methods.