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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.

  1. Development of “Permit Wizard” Software for AssistedPermit Application Completion

    SBC: TOTAL QUALITY SYSTEMS, INC.            Topic: 833

    TECHNICAL ABSTRACT: The objective of this project is to research the technical feasibility of designing a software tool “Permit Wizard” that will automate the process for aquamarine permit application submittal, review, approval/disapproval and issue (including collection of fees). Aquaculture producers are currently faced with slow, complex, and often confusing permitting processes that must ...

    SBIR Phase I 2018 Department of CommerceNational Oceanic and Atmospheric Administration
  2. Doppler Wind Temperature Sounder

    SBC: BRANDYWINE PHOTONICS LLC            Topic: 8212

    TECHNICAL ABSTRACT: We propose developing a new observational capability for measuring upper atmosphere (20-Km to 200-Km+ altitude) wind and temperature dynamics, based on Doppler imaging of Limb concentrations of NO, N2O, and CO2 called the Doppler Wind Temperature Sounder. The principle of operation is that by measuring the Doppler shift of trace gases created by the differential velocity betwee ...

    SBIR Phase I 2018 Department of CommerceNational Oceanic and Atmospheric Administration
  3. Densely Connected Neural Networks for Remote Sensing

    SBC: LYNNTECH INC.            Topic: NGA181010

    The objective of this project is to design a software architecture based on densely-connected neural network to perform automatic targetsegmentation and recognition using training datasets of limited size (low-shot). Deep learning architectures have proved to be extremelyeffective at object detection and recognition, but such capability comes at the cost of having large labeled datasets. Such data ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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