Data for Health

Data for Health topic

In Silico Approach to Mining Viral Sequences from Bulk RNA-Seq Data

October 28, 2025
Carly Karrick
by Carly Karrick. Viruses play important roles in evolution and influence ecosystems and host health. However, isolating and studying them can be difficult. In lieu of using resource-intensive methods to concentrate viruses into a “virome,” bulk sequencing methods include data from all biological entities present in a sample. In this tutorial, we explore an approach to mine viral sequences from publicly available bulk RNA-Seq data. The output from this analysis paves the way for future statistical analyses comparing viral communities in different contexts. This approach can be applied to other datasets, including studies of human health.

Finding Health Statistics and Data

March 15, 2023, 12:00pm
Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more.

Finding Health Statistics and Data

November 2, 2022, 1:00pm
Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more.

Finding Health Statistics and Data

February 12, 2024, 12:00pm
Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more.

Finding Health Statistics and Data

October 3, 2023, 1:00pm
Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more.

Forecasting Social Outcomes with Deep Neural Networks

October 7, 2025
Paige Park
by Paige Park. Our capacity to accurately predict social outcomes is increasing. Deep neural networks and artificial intelligence are crucial technologies pushing this progress along. As these tools reshape how social prediction is done, social scientists should feel comfortable engaging with them and meaningfully contributing to the conversation. But many social scientists are still unfamiliar with and sometimes even skeptical of deep learning. This tutorial is designed to help close that knowledge gap. We’ll walk step-by-step through training a simple neural network for a social prediction task: forecasting population-level mortality rates.

Marina Blum

Data Science Fellow 2021-2022
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.

Monica Donegan

Data Science Fellow 2022-2023
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.

Elaine (Hua) Luo

Data Science Fellow 2024-2025
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...

Looking Ahead: How Adolescents’ Consideration of Future Consequences Shapes Their Developmental Outcomes

March 25, 2025
Elaine Luo
by Elaine Luo. Adolescents constantly balance immediate impulses with long-term goals. Our research explored how adolescents differ in their tendency to think about immediate versus future consequences, and how these differences relate to academic performance, stress, and perceived life chances. Using Latent Profile Analysis, we identified three distinct groups: Indifferent (low consideration overall), Future-Focused (prioritizing future outcomes), and Dual-Focused (high consideration of both immediate and future outcomes). Results indicated the Dual-Focused adolescents had higher academic achievement, whereas the Future-Focused group perceived the most positive life prospects. A discussion on practical implications and future research direction for supporting balanced decision-making among adolescents is also provided.