Funder template: National Science Foundation · Opportunity: PD-26-370Y · closes no deadline stated

How reviewers read this: NSF merit review asks two questions of every proposal: Intellectual Merit (the potential to advance knowledge) and Broader Impacts (the potential to benefit society). Both are weighed through five elements: potential to advance knowledge/benefit society; creativity and originality; soundness of the plan and mechanism to assess success; qualifications of the team; adequacy of resources.

Verify: Conventions per NSF PAPPG as of mid-2026. Always confirm against the current PAPPG edition and the solicitation, which may override.

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Project Summary

Page limit: 1 page (three separate text boxes in Research.gov) — verify against the NOFO.

Overview

Frame the project as a fundamental engineering study of environmental sensing and decision-support for early warning of harmful algal blooms in reservoirs, with direct relevance to safeguarding water quality and public health. State the vision and activities in the first two sentences, and make clear how low-cost distributed sensors and reporting protocols will be developed, tested, and evaluated in Oklahoma reservoirs.

• What is the core technical vision: a new sensing architecture, a new reporting protocol, or both?
• How will the work advance rapid detection and response to toxins in water in a way that aligns with EER’s emphasis on safeguarding the natural environment and human health?
• Why are Oklahoma reservoirs an important testbed for the engineering problem, without making the project primarily about earth systems?

Intellectual Merit

Explain the fundamental knowledge gap in environmental sensing, sensor-network design, and decision-relevant information delivery for harmful algal bloom detection. Emphasize the project’s contribution to basic research on distributed sensing, data interpretation, and engineered systems for water monitoring.

• What new scientific understanding will be generated about low-cost sensor performance, spatial coverage, false alarms, or detection thresholds?
• How will the project advance knowledge of how engineered sensing systems can detect pathogens/toxins or harmful events in water more accurately and quickly?
• What is novel about the decision-support logic for translating sensor signals into actionable information?

Broader Impacts

Describe specific benefits to lake managers, rural water utilities, and communities that rely on reservoir water quality, with assessable outcomes such as earlier warning, improved response timing, or reduced treatment risk. Keep the focus on engineering and public benefit, not on human behavior or social response as the main research target.

• Which user groups will receive the early-warning information, and what concrete actions will the system enable?
• How will you measure broader impacts, such as reduction in detection delay, improved alert reliability, or usability by water operators?
• What community or utility engagement activities will you conduct to ensure the reporting protocol is operationally useful?

Project Description

Page limit: 15 pages (solicitation may differ) — verify against the NOFO.

Introduction and Objectives

State the research questions and hypotheses crisply on page one, centered on low-cost distributed sensing for early harmful algal bloom detection and on how engineered reporting protocols support utility decision-making. Make explicit that the work is an engineering/environmental resiliency project, not primarily an earth systems or social-behavior project.

• What are the central hypotheses about sensor placement, signal quality, and early detection performance?
• What specific objectives will the project pursue in sensor design, network deployment, and reporting protocol development?
• How will you define success for the engineering system in EER terms?

Background and Motivation

Identify the gap in current knowledge: existing monitoring may be too costly, sparse, or slow for early warning in reservoirs used by rural water utilities. Situate the work against the state of the art in low-cost sensing, harmful algal bloom monitoring, and water-quality alerting.

• What limitations of current reservoir monitoring motivate a new distributed sensing approach?
• What is missing from existing engineering methods for rapid response to toxins in water?
• Why is now the right time to integrate low-cost sensors with decision-oriented reporting?

Research Plan

Organize the plan by objective or aim, with methods, data collection, analysis, timeline, and contingencies. Include a clear mechanism for assessing success, such as detection accuracy, lead time over conventional monitoring, robustness, or operator usability.

• How will sensors be designed, calibrated, deployed, and validated in Oklahoma reservoirs?
• What data streams will be collected, and how will they be analyzed to detect blooms or toxin risk?
• How will the reporting protocol be tested with lake managers and rural water utilities, and what contingency plans address sensor failure, sparse coverage, or noisy signals?

Broader Impacts

Provide a distinctly labeled section that names the beneficiaries, activities, and evaluation metrics. Focus on operationally useful outcomes for water managers and utilities, and on potential reuse of the sensing framework in other reservoir systems.

• What concrete products will be delivered to lake managers and rural water utilities?
• How will you evaluate whether the system improves early warning and response readiness?
• What training, dissemination, or transfer activities will make the approach reusable beyond the test sites?

Results from Prior NSF Support

If any PI or co-PI has NSF funding from the past five years, summarize the prior award’s Intellectual Merit and Broader Impacts separately and succinctly. If there is no prior NSF support, state that clearly per NSF practice or check the NOFO for preferred language.

• Is any investigator supported by an NSF award within the last five years?
• If so, what were the prior project’s key findings and broader impacts relevant to this proposal?
• How does the proposed work build on, but differ from, that prior support?

Facilities, Equipment and Other Resources

Page limit: no page limit — verify against the NOFO.

Facilities, Equipment and Other Resources

Describe only the facilities, equipment, field sites, computational resources, and institutional support that are available to carry out the project; do not provide dollar values. Trim boilerplate to the resources actually relevant to sensor prototyping, reservoir deployment, data analysis, and stakeholder interaction.

• What laboratories, fabrication tools, field equipment, or computing resources are available for sensor development and analysis?
• What access do you have to Oklahoma reservoirs and to collaborators such as lake managers or rural water utilities?
• What institutional support will enable deployment, data management, and testing?

Institutional facilities & resources (boilerplate — trim to relevance)

Maintain approved paragraphs here, one block per unit/resource. Shells
append this file to facilities documents; the researcher deletes blocks
that don’t apply. Replace the placeholders with ORS-approved language.

University of Oklahoma (general). [[FILL IN: one approved paragraph on
OU as an R1 institution, research infrastructure, computing, and libraries]]

IPPRA. [[FILL IN: approved paragraph — survey research infrastructure,
M-SISNet panel, secure data enclave (Prometheus 42), staff expertise]]

OU Supercomputing Center for Education & Research (OSCER). [[FILL IN:
approved paragraph if applicable]]

[[Additional units as needed]]

Data Management and Sharing Plan

Page limit: 2 pages — verify against the NOFO.

Data Management and Sharing Plan

Describe the data types expected from sensors, field observations, calibration experiments, and any community reporting inputs; specify formats, metadata standards, sharing timelines, repositories, and protections. If any human-subjects or survey data are collected, include IRB-related and de-identification language as appropriate.

• What data will be produced, in what formats, and with what metadata/documentation?
• Where will data, code, and protocols be archived, and when will they be made available?
• Will any reporting inputs or interviews involve identifiable people, and if so, how will privacy, consent, and de-identification be handled?