Get in the Cloud: Why Thousands of Researchers are Joining ScHARe Think-a-Thons

Posted November 1, 2023

By Deborah Guadalupe Duran, Ph.D.
Senior Advisory- Data Science, Analytics and Systems, National Institute on Minority Health and Health Disparities

Luca Calzoni, M.D., MS, Ph.D. Cand.
Physician and Data Scientist, National Institute on Minority Health and Health Disparities

Since February 17, 2023, thousands of researchers, educators, students, and community members have gathered at ScHARe Think-a-Thons to join a paradigm shift in health disparities and health outcomes research.

These Think-a-Thons are part of the Science Collaborative for Health disparities and Artificial intelligence bias Reduction (ScHARe)—a new NIH resource designed to expand access to large health disparities and health outcomes datasets and the data science skills required to analyze them. ScHARe Think-a-Thons offer free training that enables participants to link cloud-based datasets, access federated data, begin learning the Python programming language, and more.

And we’re just getting started. As we prepare for exciting new phases in the Think-a-Thon series, we want to highlight what participants have gained so far and preview the many opportunities ahead.

Training Think-a-Thons: Successfully Upskilling to Advance Careers

People without access to data science tools have gained access for the first time through ScHARe’s Training Think-a-Thons. More than two-thirds of participants report having little to no prior experience in cloud computing or programming languages, and many belong to populations that are historically underrepresented in these fields. In participant polls, nearly all respondents agree that our Training Think-a-Thons have taught them how to access and work with the large datasets hosted on ScHARe and on Terra, the web platform where ScHARe is housed. With these new skills and membership in the ScHARe learning community, participants are poised to make significant novel contributions to health disparities and health outcomes research.

(Anyone can benefit from Training Think-a-Thons: recordings are posted online two weeks after sessions conclude.)

Training Think-a-Thons: Asking New Questions to Get Better Answers

Think-a-Thon participants are enthusiastic about a paradigm shift in health disparities and health outcomes research. More than 90 percent report that they want to learn to use AI tools and cloud computing to conduct new Big Data-driven research in these areas. As one Think-a-Thon participant noted, researchers can use ScHARe’s advanced computing tools and large datasets to figure out “the basis of disparities”—what mechanisms really drive them—and thus yield powerful new approaches to persistent public health challenges.

Research Collaboration Think-a-Thons: People from Many Disciplines and Career Levels Are Joining to Advance Health Disparities & Health Outcomes Research

The ScHARe community includes people from many backgrounds and career levels—Python programmers, social scientists, community health workers, and more. Two-thirds have expressed interest in forming cross-disciplinary, multi-level collaborations to generate publishable research using cloud computing and AI tools. Starting in 2024, ScHARe Think-a-Thons will begin directly supporting these research collaborations, with dedicated space for participants to form new research teams or to introduce their existing research teams to ScHARe.

The first research collaboration Think-a-Thons will focus on Individual Social Determinants of Health (SDOH), structural SDOH, and health outcomes. Think-a-Thon participants have a wide range of research interests—the intersection of the gastrointestinal microbiome, culture, and cognition; demographic impacts on gene expression; improving LGBTQ research through linked datasets; using informatics to improve community-based care; and more. All projects are welcome!

Coming in 2024: AI Bias Mitigation

Many Think-a-Thon participants want to tackle an important challenge: bias mitigation and ethical Artificial Intelligence strategies. Several ScHARe Think-a-Thons will focus on this goal.

AI is a key tool for analyzing large datasets and building new health-related systems, such as algorithms that assess patient risk and guide care. However, its use can also reproduce and amplify existing biases in data. For example, several populations are underrepresented in biomedical and demographic datasets, and when these datasets are biased, AI can also reflect these biases, ultimately leading to incomplete research, missed or delayed diagnoses, and worse health outcomes.

In 2024, we’ll launch Think-a-Thons to address this challenge. By tapping the diverse expertise of members of the growing ScHARe community, we’ll be able not only to share best practice tools and workflows, but also develop innovative solutions.

Opportunities for Tailored Think-a-Thons

We’ve heard participants ask how to use ScHARe in unique settings, such as college-level research methods courses. In May 2023, we responded by launching special tailored Think-a-Thons.

At our first tailored Think-a-Thon, with more than 60 educators, we outlined how ScHARe’s free tools could help low-resourced colleges and community colleges teach data science and connect their students to this important field. This August, we offered a second tailored Think-a-Thon highlighting how ScHARe can be useful to Tribal Colleges and Universities and Native American-serving institutions. All Think-a-Thons are free and open to the public.

We welcome suggestions—let us know if there’s a tailored Think-a-Thon you would like to see.

Join Us!

In just their first few months, ScHARe Think-a-Thons have become a space for a diverse community of individuals interested in health disparities, health outcomes, and AI bias research. Regardless of your knowledge of data science or cloud computing, you can join Think-a-Thons to build new skills and meet new collaborators. Women and members of groups traditionally underrepresented in data science and health research are especially encouraged to participate. Join us in making data science work for everyone!