Increasing the capacity of Nevada for wildland fire research, education and workforce development
VISION STATEMENT
The HDRFS Project Vision is to enable healthy coexistence with wildland fire and to mitigate wildfire danger to human life, infrastructure, and the landscape in Nevada and the intermountain western U.S.
MISSION STATEMENT
The HDRFS Project Mission is to increase the capacity and excellence in Nevada for wildland fire research, research infrastructure planning and coordination, education, and workforce development and to demonstrate this through Team Science in the regionally important sagebrush ecosystem.
Project Overview
The overarching goal of the RII Track-1: Harnessing the Data Revolution for Fire Science (HDRFS) project is to increase the capacity of Nevada (NV) for wildland fire research, education, and workforce development and to demonstrate this increased capacity through technology-enhanced fire science in the regionally important sagebrush ecosystem.
This project will inform and improve land and fire management by providing scaling of fire effects and impacts from smaller to larger fires in our fire science areas:
Ecology
Fire Processes
Hydrology
Fire Emissions and their Atmospheric Aging
Cyberinfrastructure Innovations
Funding Opportunities
2025-2026 Harnessing the Data Revolution for Fire Science (HDRFS) Seed Grants
To engage faculty in areas related to, but not recognized in, the current research to provide new opportunities and pursue high-risk, high-impact research.
Deadline: April 15, 2025
2025 Tuition-Covered Data Analytics Training
To provide tuition coverage for students interested in data analytics training and provides internship opportunities with NSHE faculty research mentors.
Deadline: March 24, 2025
Student Research Publication and Travel Support
To support student research publication fees and travel expenses for undergraduate and graduate students funded by the Harnessing the Data Revolution for Fire Science (HDRFS) project.
Deadline: Ongoing
Acknowledgement: This material is based upon work supported by the National Science Foundation under Grant No. OIA- 2148788.