Opportunity Information: Apply for 10 540

  • The National Science Foundation in the science and technology and other research and development sector is offering a public funding opportunity titled "Algorithms for Threat Detection" and is now available to receive applicants.
  • Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 47.049 Mathematical and Physical Sciences.
  • This funding opportunity was created on Feb 10, 2011 and posted on Feb 24, 2010.
  • Applicants must submit their applications by Jan 10, 2012 Full Proposal Deadline(s) January 10, Annually. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
  • The funding agency has allocated a total of $6,000,000.00 to eligible and selected applicants.
  • The number of recipients for this funding is limited to 30 candidate(s).
  • Eligible applicants include: Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled Additional Information on Eligibility.
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Opportunity Summary:

The National Science Foundation (NSF), through its Division of Mathematical Sciences (DMS), partnered with the Defense Threat Reduction Agency (DTRA) to fund research aimed at improving how chemical and biological threats are detected. The core goal of the opportunity is to push forward the next generation of mathematical and statistical algorithms that can identify, characterize, or provide early warning of potential threats, with an emphasis on methods that can handle complex, high-volume, and noisy real-world data. In practice, this call is positioned for the mathematical sciences community, encouraging foundational and applied work that advances the underlying theory as well as the algorithmic tools needed for threat-detection applications.

The solicitation highlights two main research thrusts. The first is mathematical and statistical techniques for genomics, which generally points to methods for analyzing genetic or genomic data relevant to identifying or tracking biological agents, distinguishing natural outbreaks from engineered or unusual patterns, and making reliable inferences from sequencing or other molecular measurements. The second thrust is mathematical and statistical techniques for analyzing data from sensor systems, which broadly covers algorithms for extracting signals from sensor streams that may be sparse, uncertain, or contaminated by environmental and operational noise. This can include statistical detection and classification, signal processing, data fusion across multiple sensors, anomaly detection, uncertainty quantification, and methods designed to reduce false alarms while maintaining sensitivity to true threats.

Administratively, this is a discretionary grant opportunity in the Science and Technology and other Research and Development category, with an estimated total funding level of $6,000,000 and an expectation of around 30 awards. The CFDA listing is 47.049 (Mathematical and Physical Sciences). Cost sharing or matching is not required, which typically makes participation simpler for applicants because the project budget does not need to include committed institutional match.

Eligibility is listed as unrestricted, meaning it is broadly open to applicant types (subject to any clarifications contained in the full announcement). Key dates in the record show the opportunity posted on February 24, 2010, with multiple deadline cycles, including an original closing date of May 20, 2010 and later annual full proposal deadlines of January 10 (with a current closing date shown as January 10, 2012 in the provided data). The full details, including proposal preparation requirements and any specific restrictions or review criteria, are referenced as NSF Publication 10-540. For access issues, the notice directs applicants to NSF Grants.gov support at grantsgovsupport@nsf.gov.

Frequently Asked Questions (FAQs)

What is the main goal of this NSF opportunity?

The opportunity supports research that advances the next generation of mathematical and statistical algorithms to detect, characterize, or provide early warning of potential chemical and biological threats, especially in settings with complex, high-volume, and noisy real-world data.

Which NSF division is involved, and who is the partner agency?

The program is run through the National Science Foundation (NSF) Division of Mathematical Sciences (DMS) and is partnered with the Defense Threat Reduction Agency (DTRA).

What kinds of research is this solicitation trying to encourage?

It is aimed at the mathematical sciences community and encourages both foundational and applied work, including advances in underlying mathematical/statistical theory as well as development of algorithmic tools relevant to threat-detection applications.

What are the two main research thrusts described in the solicitation?

The solicitation highlights two thrusts: (1) mathematical and statistical techniques for genomics, and (2) mathematical and statistical techniques for analyzing data from sensor systems.

What does the genomics thrust generally cover?

The genomics thrust points to methods for analyzing genetic or genomic data relevant to identifying or tracking biological agents. It includes approaches to distinguish natural outbreaks from engineered or unusual patterns and to make reliable inferences from sequencing or other molecular measurements.

What does the sensor-systems thrust generally cover?

The sensor-systems thrust broadly covers algorithms for extracting signals from sensor data streams that may be sparse, uncertain, or contaminated by environmental and operational noise.

What specific method areas are mentioned for sensor-system data analysis?

Examples named include statistical detection and classification, signal processing, data fusion across multiple sensors, anomaly detection, uncertainty quantification, and methods designed to reduce false alarms while maintaining sensitivity to true threats.

Is this a discretionary grant?

Yes. The record describes it as a discretionary grant opportunity.

What is the assistance listing (CFDA) number for this opportunity?

The CFDA listing shown is 47.049, Mathematical and Physical Sciences.

What is the estimated total funding level for the program?

The estimated total funding level is $6,000,000.

About how many awards does NSF expect to make?

The opportunity indicates an expectation of around 30 awards.

Is cost sharing or matching required?

No. The opportunity states that cost sharing or matching is not required.

Who is eligible to apply?

Eligibility is listed as unrestricted, meaning it is broadly open to applicant types, subject to any clarifications in the full announcement.

When was this opportunity posted?

The record shows it was posted on February 24, 2010.

What deadlines are listed for proposal submission?

The information provided indicates multiple deadline cycles, including an original closing date of May 20, 2010, and later annual full proposal deadlines of January 10. The current closing date shown in the provided data is January 10, 2012.

Where can applicants find the full solicitation details and requirements?

The notice references NSF Publication 10-540 for full details, including proposal preparation requirements and any specific restrictions or review criteria.

What kind of data challenges is the program particularly interested in?

The solicitation emphasizes methods that can handle complex, high-volume, and noisy real-world data, including uncertainty and conditions that can produce false alarms.

What should applicants do if they have trouble accessing the opportunity through Grants.gov?

The notice directs applicants with access issues to NSF Grants.gov support at grantsgovsupport@nsf.gov.

What is the category of this grant opportunity?

It is listed under the Science and Technology and other Research and Development category.

Is the focus limited to chemical threats, biological threats, or both?

The opportunity is framed around improving detection of chemical and biological threats, with research aimed at threat identification, characterization, and early warning.

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