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NIJ FY 2026 Research and Evaluation on School Safety

This is a notice of funding opportunity (NOFO) for NIJ FY 2026 Research and Evaluation on School Safety.

FromNational Institute of Justice
Who can applystate governments, counties, cities and townships, special districts (water, fire, parks, etc.), school districts, public colleges and universities and 8 more types
How muchup to $5 million per award; about 6 awards; $5 million total
DeadlineMon, Nov 23, 2026 (31 days left)
CategoryScience, Technology and Research
TypeCooperative agreement
Opportunity #O-NIJ-2026-172796
Assistance listing16.560
PostedFri, Oct 9, 2026

Open the official posting

From the notice

This is a notice of funding opportunity (NOFO) for NIJ FY 2026 Research and Evaluation on School Safety. This opportunity will support rigorous research and evaluation projects to fill key gaps regarding K-12 safety, specifically focusing on the development, deployment, and operation of: • Anonymous reporting systems for threats of school violence, including mobile telephone applications, hotlines, and internet websites • School threat assessment and intervention teams that include coordination with law enforcement agencies and school personnel This NOFO seeks rigorous outcome/impact evaluations paired with process/implementation evaluations to assess the effectiveness of activities supported by STOP1 School Violence Act (SVA) projects administered by the Bureau of Justice Assistance (BJA) and the Office of Community Oriented Policing Services (COPS Office) School Violence Prevention Program (SVPP) or similar programs from other sources that align with STOP SVA's goals. To develop practical recommendations for K-12 school safety, applicants are strongly encouraged to work closely with educators, school stakeholders, and communities to develop questions, identify solutions, collect data, and interpret findings. Applicants should: • Clearly describe complex factors and how they will be measured. • Include letters of support confirming secondary data access and explain how datasets will be matched to support the analyses proposed. • Describe data limitations, how they will be ad

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