Opportunity Information: Apply for USGS18FA0158
The funding opportunity titled "Machine learning methods application for analyzing hydro-acoustic telemetry data" (Funding Opportunity Number USGS18FA0158) is a discretionary research award offered by the U.S. Department of the Interior, U.S. Geological Survey (USGS). It is structured as a cooperative agreement, which typically means the agency expects to have substantial involvement in the project as it is carried out, rather than simply providing funding with minimal interaction. The opportunity sits within the Science and Technology and other Research and Development activity category and is listed under CFDA 15.808.
The core purpose of the project is to speed up and modernize the way scientists handle hydro-acoustic telemetry datasets by applying machine learning. Hydro-acoustic telemetry often produces large volumes of detection and tracking data, and conventional processing workflows can be slow and labor-intensive. This grant is aimed at identifying suitable machine learning approaches for these kinds of datasets and then turning those approaches into practical, usable software so that scientific teams can process telemetry information much more quickly than they can with typical methods.
A central deliverable described in the opportunity is a Python software package that implements the selected machine learning methods. The emphasis is not only on experimenting with algorithms but on producing a tool that is usable in real scientific workflows. The description also highlights high-throughput computing, with Brigham Young University (BYU) proposing to use computational techniques designed to handle large datasets efficiently. In practical terms, this suggests the software is expected to be built with performance and scalability in mind, so it can run analyses quickly across large telemetry datasets and potentially leverage parallelization or other high-throughput processing strategies.
The intended outcome is improved turnaround time from raw telemetry data to actionable results. The opportunity explicitly links the software to "timely delivery of results to resource managers," meaning the work is expected to support applied decision-making, such as fisheries and aquatic resource management, where faster access to reliable analyses can influence monitoring, planning, and management actions. In other words, the project is framed as an applied research and development effort that translates machine learning into an operational capability for telemetry data processing.
Administrative details indicate the opportunity was created on June 1, 2018, with an original closing date of June 15, 2018, signaling a short application window. The award ceiling is $49,296, with one expected award, suggesting a small, targeted project, likely focused on a specific development milestone or prototype-level software deliverable rather than a large multi-year research program. Eligibility is limited to private institutions of higher education, which aligns with the BYU reference in the description and indicates the award was intended for a university-led effort with relevant technical expertise.Apply for USGS18FA0158
- The Department of the Interior, U. S. Geological Survey in the science and technology and other research and development sector is offering a public funding opportunity titled "Machine learning methods application for analyzing hydro-acoustic telemetry data" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.808.
- This funding opportunity was created on Jun 01, 2018.
- Applicants must submit their applications by Jun 15, 2018. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $49,296.00 in funding.
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: Private institutions of higher education.
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Frequently Asked Questions (FAQs)
What is the title of this funding opportunity?
The funding opportunity is titled "Machine learning methods application for analyzing hydro-acoustic telemetry data."
What is the Funding Opportunity Number (FON)?
The Funding Opportunity Number is USGS18FA0158.
Which federal agency is offering this award?
This is a discretionary research award offered by the U.S. Department of the Interior, U.S. Geological Survey (USGS).
What type of award is being offered?
The opportunity is structured as a cooperative agreement.
What does it mean that this is a cooperative agreement?
A cooperative agreement typically indicates the agency expects to have substantial involvement in the project as it is carried out, rather than simply providing funding with minimal interaction.
What is the activity category for this opportunity?
The opportunity sits within the Science and Technology and other Research and Development activity category.
What CFDA number is associated with this opportunity?
The opportunity is listed under CFDA 15.808.
What is the main goal of the project?
The core purpose is to speed up and modernize the way scientists handle hydro-acoustic telemetry datasets by applying machine learning methods.
Why is machine learning being emphasized for hydro-acoustic telemetry data?
Hydro-acoustic telemetry can produce large volumes of detection and tracking data, and conventional processing workflows can be slow and labor-intensive. The project is intended to identify suitable machine learning approaches and translate them into practical tools that make processing faster.
What kinds of telemetry data challenges is this award trying to address?
It targets large-scale detection and tracking datasets produced by hydro-acoustic telemetry, where traditional methods may struggle with throughput and require substantial manual effort.
What deliverable is specifically highlighted in the description?
A central deliverable is a Python software package that implements the selected machine learning methods.
Is the focus only on research, or also on building usable software?
The emphasis is not only on experimenting with algorithms. The opportunity highlights producing a tool that is usable in real scientific workflows.
What programming language is expected for the key software deliverable?
The description calls out a Python software package as a central deliverable.
How important are performance and scalability for the expected software?
Performance and scalability are emphasized. The description highlights high-throughput computing and suggests the software should be built to handle large datasets efficiently, potentially using parallelization or other high-throughput strategies.
What does "high-throughput computing" imply in this context?
In practical terms, it suggests computational techniques intended to process large telemetry datasets efficiently and quickly, rather than relying on slow, manual, or sequential workflows.
What outcomes are expected if the project is successful?
The intended outcome is improved turnaround time from raw telemetry data to actionable results, enabling faster analysis delivery.
Who is expected to benefit from the faster turnaround time?
The opportunity links the work to the timely delivery of results to resource managers, supporting applied decision-making such as fisheries and aquatic resource management.
How is this project positioned: basic research or applied R&D?
It is framed as an applied research and development effort that translates machine learning into an operational capability for telemetry data processing.
When was the opportunity created?
The administrative details indicate the opportunity was created on June 1, 2018.
What was the original closing date?
The original closing date was June 15, 2018.
How long was the application window based on the dates provided?
Based on the creation date (June 1, 2018) and the closing date (June 15, 2018), the application window was short.
What is the maximum award amount (award ceiling)?
The award ceiling is $49,296.
How many awards were expected?
One award was expected.
What does a single expected award suggest about the scope of this project?
With one expected award and a $49,296 ceiling, it suggests a small, targeted project likely focused on a specific development milestone or prototype-level software deliverable rather than a large multi-year research program.
Who is eligible to apply?
Eligibility is limited to private institutions of higher education.
Does the opportunity description reference any specific institution?
Yes. The description references Brigham Young University (BYU) in relation to proposing high-throughput computing approaches.
How does the BYU reference relate to the purpose of the grant?
It aligns with the stated emphasis on high-throughput computing and efficient processing of large hydro-acoustic telemetry datasets, and it is consistent with the eligibility limitation to private institutions of higher education.
What is the practical end use for the software package described?
The software is intended to help scientific teams process hydro-acoustic telemetry information much more quickly than typical methods, supporting timely delivery of analysis results for management and decision-making contexts.
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