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Award Data

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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. Pathogen Classification Tool (PaCT)

    SBC: STOTTLER HENKE ASSOCIATES, INC            Topic: ST18C002

    Stottler Henke proposes PaCT, leveraging our related past work in computer vision and machine learning. Drawing from techniques used in ExPATSS, a Phase II SBIR effort slated for transition to the Naval fleet, PaCT will perform bacterial characterization using features derived from the phenotype of the bacteria. PaCT will predict bacterial characteristics such as pathogenicity, antibiotic resistan ...

    STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency
  2. Visual Tools and Progressive Automation for Complex Knowledge Management and Decision Support

    SBC: STOTTLER HENKE ASSOCIATES, INC            Topic: N17AT004

    We propose to adapt and automate the processes and technologies associated with evidence-based decision support to the Navy—providing a tool that can synthesize current cognitive and learning science knowledge and inform decisions so as to maximize the value gained for each training expenditure. We will develop a plug-play architecture that will allow us to make the best use of emerging technolo ...

    STTR Phase II 2019 Department of DefenseNavy
  3. Virtual Reality for Multi-INT Deep Learning (VR-MDL)

    SBC: INFORMATION SYSTEMS LABORATORIES INC            Topic: AF19AT010

    Recent advances and successes of deep learning neural networks (DLNN) techniques and architectures have been well publicized over the last several years. Voluminous, high-quality and annotated training data, or trial and error in a realistic environment, is required to achieve the promised performance potential of DLNNs. Unfortunately for DoD and/or Intelligence Community (IC) applications of mult ...

    STTR Phase I 2019 Department of DefenseAir Force
  4. Low-Energy Adiabatic Circuits for Space Applications

    SBC: SIGNAL SOLUTIONS LLC            Topic: AF18BT013

    Adiabatic logic-based energy-conserving circuits have potential to significantly improve energy efficiency. Adiabatic circuits recycle charge stored in load capacitance resulting in lower power dissipation as compared to conventional CMOS. However, these circuits have only targeted low-frequency operations. Research is needed to develop adiabatic logic circuits for high performance applications wi ...

    STTR Phase I 2019 Department of DefenseAir Force
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