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Award Data
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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SBC: ACCUDYNE SYSTEMS, INC. Topic: N/AN/A
SBIR Phase I 2000 National Aeronautics and Space Administration -
N/A
SBC: E BEAM INC Topic: N/AN/A
SBIR Phase I 2000 National Aeronautics and Space Administration -
N/A
SBC: E BEAM INC Topic: N/AN/A
SBIR Phase I 2000 National Aeronautics and Space Administration -
N/A
SBC: E BEAM INC Topic: N/AN/A
SBIR Phase II 2000 National Aeronautics and Space Administration -
N/A
SBC: HIGH PERFORMANCE MATERIALS GROUP, LLC Topic: N/AN/A
SBIR Phase I 2000 Department of DefenseSpecial Operations Command -
N/A
SBC: UMPQUA RESEARCH COMPANY Topic: N/AN/A
SBIR Phase I 2000 National Aeronautics and Space Administration -
N/A
SBC: UMPQUA RESEARCH COMPANY Topic: N/AN/A
SBIR Phase I 2000 National Aeronautics and Space Administration -
N/A
SBC: UMPQUA RESEARCH COMPANY Topic: N/AN/A
SBIR Phase II 2000 National Aeronautics and Space Administration -
A Verifier for Multicore C11 or C++11 Code
SBC: The Formula Factory Topic: 9030177RGalois will build a practical, efficient, modular, deductive code verifier and verification methodology for multithread C11 software. The verifier will take code written to the C11 standard, suitably annotated with function contracts, assertions, program/data invariants, ghost data/code, and any platform-specific assumptions beyond those guaranteed by the standard, and will prove that the code mee ...
SBIR Phase I 2015 Department of CommerceNational Institute of Standards and Technology -
Auto-Suggest Capability via Machine Learning in SMART NAS
SBC: THE INNOVATION LABORATORY, INC. Topic: A302We build machine learning capabilities that enables the Shadow Mode Assessment using Realistic Technologies for the NAS (SMART NAS) system to synthesize, optimize, and "auto-suggest" optimized Traffic Management Initiatives (TMIs). Multi Level Multi View (MLMV) machine learning is used to identify similar historical situations (days, scenarios, or airport conditions) in the NAS. TMIs used in his ...
SBIR Phase I 2015 National Aeronautics and Space Administration