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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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Sequencing for Genetic Electronic Assurance: Counterfeit Detection for a Global Supply Chain
SBC: LUNA INNOVATIONS INCORPORATED Topic: DMEA201001Securing the electronic components of the DOD is a complex challenge. In a global supply chain with thousands of distributed contractors creating high performance systems, it is critically important to ensure counterfeit components are not entering use. Chain of custody of electronic components from the time of manufacture to when the DOD takes delivery of the completed product can involve a multi ...
SBIR Phase I 2020 Department of DefenseDefense Microelectronics Activity -
Developing Regional GIC Hazard Tools for Power Utility Planning and Operations
SBC: COMPUTATIONAL PHYSICS, INC. Topic: 9302Geomagnetically induced current (GIC) hazard is a recognized threat to the United States power system. Federal models to support hazard specification and prediction for critical infrastructure are at an advanced state, including models of space weather, the Earth’s conductive structure, and regional geoelectric field. We propose to use existing NOAA Space Weather Prediction Center (NOAA-SWPC) mo ...
SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration -
Machine Learning Applied to Counterfeit Detection
SBC: GRAF RESEARCH CORPORATION Topic: DMEA192002The machine learning for counterfeit detection research program commences with a study evaluating the feasibility of applying machine learning to detect FPGA counterfeits. The underlying process will be broadly applicable throughout the supply chain. Using a proprietary non-destructive means of gathering data from FPGA devices, we will then make use of the data in a variety of machine learning alg ...
SBIR Phase I 2020 Department of DefenseDefense Microelectronics Activity