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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.
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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.
SBC: LUNA INNOVATIONS INCORPORATED Topic: N19BT030
The Navy is actively developing a self-sealing, water-activated fuel bladder to mitigate fuel leaks upon mechanical shock (e.g. penetration by a .50 caliber bullet). To circumvent the requirement of an external water source to activate the self-sealing, this system will require water-filled microcapsules that can be incorporated directly into the polymeric matrix of the fuel bladder. Upon mechanic ...STTR Phase I 2019 Department of DefenseNavy
SBC: DECISIVE ANALYTICS CORPORATION Topic: N13AT024
This effort is focused on the development of a system called Situational Awareness via Mixed-initiative Universal Recognition, Analysis, and Inference (SAMURAI). SAMURAI will provide a single cloud-enabled end-to-end workflow covering the full range of data analysis from data ingest to situational assessment and decision support. As part of this workflow SAMURAI will provide the ability for automa ...STTR Phase I 2013 Department of DefenseNavy
SBC: INNOVATIVE DEFENSE TECHNOLOGIES, LLC Topic: N19AT012
In order to achieve real-time monitoring, analysis, and alerting for complex systems, a unified logging architecture must exist that can support the collection and analysis of big data. Our technical objective is to develop a unified logging architecture that supports collection, aggregation, storage, and analysis of system performance and cybersecurity logs, events, and alerts produced by Naval C ...STTR Phase I 2019 Department of DefenseNavy
SBC: KITWARE INC Topic: N10AT029
Analysts are faced with the challenge of sifting through enormous quantities of documents, blog posts, communications, etc. to find deceptive behaviors. We propose novel techniques for efficiently and automatically detecting deception on large data with high accuracy by using methodologies from both stylometry and topic modeling. This combined approach will learn models of authors and will detect ...STTR Phase I 2010 Department of DefenseNavy