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Award Data
The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.
Download all SBIR.gov award data either with award abstracts (290MB)
or without award abstracts (65MB).
A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.
The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.
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A novel water quality measurement system as a teaching aid for environmental education
SBC: GAIAXUS LLC Topic: 91990023R0016Not available
SBIR Phase II 2023 Department of EducationInstitute of Education Sciences -
Nuclear Scintillation Mitigation by Matched Channel Filtering
SBC: NORTHWEST RESEARCH ASSOCIATES, INC. Topic: DTRA202005The ongoing development of new, improved technology for both hardware and software leads to the almost constant upgrade of existing strategic sensor and communications systems and the development and deployment of new systems. This work addresses warfighter concerns raised in the area of satellite communications and radar, where burst-produced ionospheric disturbances can cause scintillation of ...
SBIR Phase II 2022 Department of DefenseDefense Threat Reduction Agency -
Laser-based real-time Temperature Measurements in Detonation Environments
SBC: EXMAT RESEARCH INC Topic: DTRA19B001To support DTRA’s agent-defeat efforts, we will refine a laser-based Optical Thermocouple (OTC) technology and build and deliver to DTRA a hardened OTC prototype. This OTC system will be capable of quantifying the localized temporal evolution of temperature inside a detonation environment. The OTC technology is based on two-color fluorescence thermometry to determine the temperature. After laser ...
STTR Phase II 2022 Department of DefenseDefense Threat Reduction Agency -
A Deep Learning Approach for Enhanced Identification of Nuclear Explosions
SBC: Array Information Technology, Inc Topic: DTRA182005ML is a subset of AI. Discrimination based on SI signals is a module in monitoring systems(NDC & IDC. We will provde a prototype that will incorporate elements of the discrimination procedures found
SBIR Phase II 2022 Department of DefenseDefense Threat Reduction Agency -
Pegasus(TM) Mini
SBC: ROBOTIC RESEARCH OPCO LLC Topic: DTRA182002Improvements will be made to the platform architecture and electronics to enhance system capability. Integration of Pegasus Mini into FoS allows Pegasus Mini to leverage capabilities FoS already uses.
SBIR Phase II 2021 Department of DefenseDefense Threat Reduction Agency -
vCoder and AI Assisted Learning
SBC: BEACH DAY STUDIOS LLC Topic: 91990021R0003Not available
SBIR Phase II 2021 Department of EducationInstitute of Education Sciences -
Pictoword School: Combining AI (Machine Learning) and Game-Based Learning to Support English Learners
SBC: KOOAPPS LLC Topic: 91990021R0003Not available
SBIR Phase II 2021 Department of EducationInstitute of Education Sciences -
A Music Creation Engine to Improve Algebra Readiness
SBC: Muzology, LLC Topic: 91990021R0003Not available
SBIR Phase II 2021 Department of EducationInstitute of Education Sciences -
A Scalable and Automated Tool to Analyze and Identify Dual Use Research of Concern from Scientific Publications: SAT-DURC
SBC: Intelligent Automation, Inc. Topic: DTRA172004Intelligent Automation, Inc. (IAI), along with our collaborators, has successfully conducted a proof-of-concept study for a Scalable and Automated Tool to Analyze and Identify Dual Use Research of Concern from Scientific Publications: SAT-DURC. The key innovation of our proposed approach is a novel DURC identification system that combines the strength of natural language processing with machine le ...
SBIR Phase II 2020 Department of DefenseDefense Threat Reduction Agency -
DOEYK: Detecting Objects with Enhanced YOLOv3 and Knowledge Graph
SBC: Intelligent Automation, Inc. Topic: DTRA19B002Current state-of-the-art object detection algorithms are almost exclusively based on Deep Convolutional Neural Network (DCNN). These algorithms all require a large number of labeled examples for each of the object categories they can recognize. These algorithms will fail for novel objects that only very few or even no prior examples are available. These algorithms are also far less accurate when c ...
STTR Phase II 2021 Department of DefenseDefense Threat Reduction Agency