NSF Grant — Submitted

CO-PI: David Ussery, Oklahoma State University

Project Summary 

Overview

Humanity faces increasingly complex health threats that span people, animals, and the environment. In recent decades, we have seen a surge in emerging infectious diseases (EIDs) and a rise in antimicrobial resistance (AMR), which undermines our ability to treat infections. Concurrently, environmental degradation and climate-driven hazards, such as deforestation, extreme heat, wildfire, and pollution, are disrupting ecosystems and fueling health risks. Food security is also at stake, as foodborne illnesses emerge from the convergence of contamination in water, animal farming, and human supply chains. These global challenges are not isolated; they intersect and amplify one another. Thus, they demand a more unified approach than ever before.

One Health has emerged as the leading paradigm to confront these intertwined challenges. It mobilizes multiple disciplines, professional sectors, and local communities to work collaboratively at all levels of society. By uniting veterinarians, epidemiologists, ecologists, microbiologists, climate scientists, and community leaders under a common approach, One Health enables a holistic understanding of health threats. Despite its clear value, implementing One Health on the ground has proven challenging due to workforce and skill gaps, technical barriers to research reproducibility and replicability, and institutional and cultural silos.

To overcome these gaps in One Health implementation, this project will: 1) establish a cross-disciplinary advisory committee for One Health curriculum development; 2) design CI4OH, a modular cyberinfrastructure for a One Health research curriculum; 3) develop reproducible and replicable One Health workflows leveraging NSF cyberinfrastructure; and 4) evaluate curriculum effectiveness through hands-on training workshops.

Intellectual Merit

First, the project develops a set of structured training modules that enable One Health researchers to work with complex multisector datasets while applying FAIR and TRUST principles. Second, the project integrates computational thinking and secure cyberinfrastructure practices into One Health data workflows, thereby promoting new ways of conducting interdisciplinary research. Third, the project produces a rigorous evaluation of curriculum design, usability, and learning outcomes through hands-on workshops and iterative refinement.

This evaluation will generate new knowledge about how trainees from different disciplinary backgrounds acquire secure data science skills and how integrated cyberinfrastructure training can accelerate scientific discovery in One Health.

Broader Impacts

This project will broaden national workforce capacity in One Health by providing accessible and modular training that prepares students, researchers, and practitioners to securely use cyberinfrastructure and multisector data. It directly advances workforce equity by recruiting learners from non-R1 institutions, community colleges, and minority-serving institutions, thereby creating a pipeline of cyberinfrastructure-proficient One Health professionals in regions with limited training resources.

At the same time, the project enhances national resilience by training participants to manage secure data pipelines for pandemic surveillance, antimicrobial resistance tracking, and environmental monitoring, strengthening public health preparedness and national security. All curriculum modules, virtual labs, and case studies will be openly disseminated through NSF cyberinfrastructure platforms, ensuring broad accessibility, long-term sustainability, and immediate scalability.

By expanding participation, improving secure data science capacity, and supporting data-driven health protection, the project contributes to societal well-being and promotes more resilient scientific and public health systems.

Keywords

Contributors and users; graduate students and early-career researchers; ecologists; epidemiologists; veterinarians; public health professionals; social scientists; One Health; FAIR data management; AI/ML for disease modeling; spatial statistical modeling; workflow development; reproducibility and replicability; interdisciplinary integration.