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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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Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.
SBC: ARCTOS Technology Solutions, LLC Topic: DLA18A001This Phase II project aims to assemble the key set of sensor modalities that are needed to reliably view the key process anomalies and properties of laser powder bed fusion. The research team will down-select from the Phase I sensors investigated and integrate the sensors into a sensor fusion software package that facilitates data collection and synchronization, and eventually feedback control of ...
STTR Phase II 2019 Department of DefenseDefense Logistics Agency -
Wavelength-Agile Real Time Tabletop X-ray Nanoscope based on High Harmonic Beams
SBC: Kapteyn-Murnane Laboratories, Inc. Topic: ST15C001Nanoscale, material sensitive, imaging techniques are critical for progress in many disciplines as we learn to master science and technology at the smallest dimensions — on the nanometer to atomic-scale. However, progress in both science and technology is becoming increasingly limited by the constraints of current imaging techniques and metrologies. Fortunately, by combining coherent extreme UV ...
STTR Phase II 2019 Department of DefenseDefense Advanced Research Projects Agency -
Brain-Based Prediction of Influence Message Effectiveness (BB-PRIME)
SBC: CHARLES RIVER ANALYTICS, INC. Topic: A12AT009Behavior change is a common objective across the Defense community. Recent studies suggest that neuroimaging can improve our ability to predict what messages are effective in changing behavior in individuals and at scale (Falk & Scholz, 2018). However, current neuroscience results have not established definitive causal relationships between specific types of messages and behavior change. In BB-PRI ...
STTR Phase II 2019 Department of DefenseDefense Advanced Research Projects Agency -
SWIFT ARROW STTR Phase 2
SBC: SCIENTIFIC SYSTEMS CO INC Topic: OSD12AU3In Dense Urban Environments (DUEs), a new and evolving battlefield, the ability to detect and respond quickly to multiple threats in complex environments (e.g., snipers on rooftops or in windows) is critical and is extremely important to both civilian and military tactical needs. Response latency in the critical moments after the beginning of an attack can significantly increase casualties. SWIFT ...
STTR Phase II 2019 Department of DefenseDefense Advanced Research Projects Agency -
Station-keeping using Perception and Relative Image-based Navigation and Tracking (SPRINT)
SBC: SCIENTIFIC SYSTEMS CO INC Topic: ST18C006SSCI and MIT propose to perform initial design and testing of an innovative tightly-coupled vison and GNC system for follower vehicles to achieve safe approach and station-keeping with the lead vehicle within some range tolerance and inside a 60 degree cone, under leader maneuvers and vehicle capability constraints. The resulting system is referred to as the SPRINT (Station-keeping using Perceptio ...
STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency -
Visual Algorithms for Navigation and Guidance of UAVs with Autonomous Relational Decisions (VANGUARD)
SBC: CHARLES RIVER ANALYTICS, INC. Topic: ST18C006Unmanned systems play a critical role in military operations across a wide range of missions. The DoD’s Unmanned Systems Integrated Roadmap identifies leader-follower tactics, swarming capabilities, sensor advancements, collision avoidance, and GPS-denied solutions as key technologies to support autonomy. Advances in these areas are needed to support coordinated multi-aircraft maneuvers and swar ...
STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency -
SBDUV APD/GPD detector arrays
SBC: RADIATION MONITORING DEVICES, INC. Topic: ST18C003The goal of the research is to provide a solar-blind, deep-UV photo-detector array that can be used in instruments detecting chemical and biological agents, such as TAC-BIO II, using UV Raman and Fluorescence measurements. The overall approach is to develop a solid-state detector array that achieves the performance goals for QE (>70%), gain (>1E6), dark current (
STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency -
SWAT- Scalable W(R)ubber through Advanced Technology
SBC: EnergyEne Inc. Topic: ST18C001Opportunity: Guayule, a US native plant, is the only alternate rubber crop with an established, mechanized, agronomic system. Problem: Low rubber yields and lack of effective resin and bagasse coproduct valorization, have prevented widespread adoption by American farmers and processors. Rubber is only made when the cytoplasmic monomer pool (isopentenyl-pyrophosphate; IPP) is larger than that requi ...
STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency -
Machine Learning and Data Fusion platform for Phenotype-based Pathogen Identification
SBC: TRITON SYSTEMS, INC. Topic: ST18C002Conventional methods for detecting pathogens, which are based on culturing the microorganism, are time-consuming and laborious. Machine learning provides an alternative path to identify pathogens using supervised learning algorithms. Most current computational tools utilize genomic or protein data to identify bacteria. These methods look for features in the whole genome that correlate to pathogeni ...
STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency -
PathEngine: A Platform To Automate the Integration of Data To Predict Pathogenic Potential
SBC: NETRIAS, LLC Topic: ST18C002New pathogens, both naturally occurring and adversary-engineered, are increasingly likely to emerge and represent a significant and growing risk to global health and security. These new threats often have limited genetic similarity to prior known pathogens and cannot be identified through standard genetic tests. The application of machine learning algorithms to phenotypic tests to predict pathogen ...
STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency