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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. GoManage: A Self-Management Curriculum App for Students with Disabilities

    SBC: ATTAINMENT CO., INC.            Topic: 91990021R0003

    Not available

    SBIR Phase II 2021 Department of EducationInstitute of Education Sciences

  2. GoManage: A Self-Management Curriculum App for Students with Disabilities

    SBC: ATTAINMENT CO., INC.            Topic: 91990020R0006

    This project will develop a prototype of GoManage, a self-management app for students with disabilities to choose a target goal, track progress towards that goal, and earn virtual tokens as they go. Participants will include students from Kindergarten to grade 8 who have extensive language support needs (for example, students with autism or an intellectual disability) and less extensive language s ...

    SBIR Phase I 2020 Department of EducationInstitute of Education Sciences

  3. Go Write

    SBC: ATTAINMENT CO., INC.            Topic: 91990018R0006

    In this project, the team will develop a prototype of GoWrite, a platform designed for students with moderate to severe disabilities to guide instruction in writing. The cross-curricular intervention will have four modes including instruction, writing about academic content, journaling, and social media. At the end of Phase I, in a pilot study with 30 students (10 each in grade school, middle scho ...

    SBIR Phase I 2018 Department of EducationInstitute of Education Sciences

  4. Development of a GPU-based High Performance Community Radiative Transfer Model

    SBC: Hyper Sensing, LLC            Topic: 835D

    Computation of the radiative transfer model for a hyperspectral sounder with thousands of spectral channels is very time-consuming. Consequently, operational data assimilation systems can assimilate only a few hundred channels. The radiative transfer model is very suitable for GPU implementation to take advantage of GPU massively parallel computing capability, where radiances at various channels c ...

    SBIR Phase I 2011 Department of CommerceNational Oceanic and Atmospheric Administration
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