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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.

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  1. Development of a Rapid Monitoring System for Detection of Pathogens in Marine Aquaculture Operations

    SBC: INNOVAPREP LLC            Topic: 836

    TECHNICAL ABSTRACT: InnovaPrep proposes to develop a rapid sample-to-answer monitoring system for detecting pathogens in marine aquaculture waters. An assessment of industry needs will include a kickoff meeting with NOAA and a visit to Toby Island Bay Oyster Farm. Following the assessment and report, a TRL-4 breadboard prototype system will be developed that will include the combination of sample ...

    SBIR Phase I 2018 Department of CommerceNational Oceanic and Atmospheric Administration

  2. Deep Sea Vision AI: Deep Learning-Based Real-Time Marine Animal Detection for Entanglement Mitigation

    SBC: SYNTHETIK APPLIED TECHNOLOGIES LLC            Topic: 832

    TECHNICAL ABSTRACT: The objective of this project is to develop and implement a real-time detection and warning system to mitigate marine entanglement events in offshore aquaculture operations. In order to achieve this, we leverage the latest advances in computer vision, deep learning, and real-time object detection to develop a real-time marine life detection, classification and tracking pipeline ...

    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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