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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. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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