STRONG FOA Cycle_7_Final.pdf

UNCLASSIFIED/Approved for Public Release/Distribution Unlimited

ARL Strengthening Teamwork for Robust Operations in Novel Groups (STRONG)

W911NF-19-S-0001

Amendment #10

The purpose of this modification is to issue Cycle 7 of the ARL STRONG Program. This modification includes the topic updates as well as administrative changes to the FOA. Please be sure to read the modification to its entirety. Proposals are due in grants.gov by 3:00pm (local time in North Carolina, USA) on 31 January 2025. An email receipt will be provided to each Applicant for each Proposal submission received. Applications submitted after the closing date and time will not be considered or evaluated by the Government

FUNDING OPPORTUNITY OVERVIEW 3

A. PROGRAM DESCRIPTION 4

1. Background 4

2. Cycle Structure 5

3. Deliverables 6

4. Cycle 7 Updates 7

B. FEDERAL AWARD INFORMATION 11

C. ELIGIBILITY INFORMATION 13

D. APPLICATION AND SUBMISSION INFORMATION 13

E. APPLICATION REVIEW/EVALUATION INFORMATION 25

F. AWARD ADMINISTRATION INFORMATION 29

G. AGENCY CONTACTS 29

FUNDING OPPORTUNITY OVERVIEW

1. Federal Awarding Agency Name U.S. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD 20783-1197

Issuing Acquisition Office U.S. Army Contracting Command – Aberdeen Proving Ground, Research Triangle Park (RTP) Division, 800 Park Office Drive, Suite #4229, Research Triangle Park, NC 27709

2. Research Opportunity Title

Strengthening Teamwork for Robust Operations in Novel Groups (STRONG)

3. Announcement Type Amendment: Cycle 7 updates - SEE SECTION A.4

4. Funding Opportunity Number

W911NF-19-S-0001 (The same FOA number from the first cycle will be used throughout all cycles of the program, but will be amended at the beginning of each cycle to topic area descriptions and other relevant program updates.)

5. Catalog of Federal Domestic Assistance (CFDA) Number(s)

12.630 - "Basic, Applied, and Advanced Research in Science and Engineering"

6. STRONG Website: https://www.arl.army.mil/strong

7. Key Dates:

The following is a summary of the events and dates associated with this Cycle under the overall STRONG Funding Opportunity Announcement (FOA):

EVENT ESTIMATED DATE/TIMEFRAME

Opportunity released December 2024 Opportunity Webinar 8 January 2025 Deadline for Questions on Funding Opportunity 15 January 2025 Proposals due for Cycle 7 31 January 2025 Cycle 7 Awards April 2025 (Expected) Cycle 7 Innovation Summit July 2025 Cycle 7 Follow-On Presentations January 2026 http://www.arl.army.mil/strong

Callahan- Flintoft, Chloe A CIV USARMY DEVCOM ARL (USA) Need to change all these

Fitzhugh, Sean M CIV USARMY DEVCOM ARL (USA) How about:�release: Nov�opportunity webinar: early Jan�question deadline: mid Jan�Proposals due: end of Jan�Awards: Feb�Summit: July�Follow-on presentations: September?

A. PROGRAM DESCRIPTION

Overview: The 10-year STRONG program has a specific long-term goal to identify and implement the fundamental research necessary to develop individualized, adaptive technologies that promote effective teamwork in novel groups of humans and intelligent agents. Success in future militarily scenarios will require faster than human ideation and decision-making within increasingly complex, dynamic, and rapidly evolving sociotechnical contexts. The STRONG Cycle #6 program's research initiatives center on enhancing human-AI synergy within intricate military contexts. The focus areas include developing scalable human-guided machine learning, emphasizing the integration of human intuition into long-term planning algorithms, advancing AI's capability to navigate varied information hierarchies and abstraction levels, and fostering mutual understanding and trust between humans and AI. Collectively, these efforts aim to create a cohesive, efficient, and adaptable collaborative framework to address the evolving challenges of modern military scenarios.

1. Background

The future vision for the U.S. Army includes teams of humans and intelligent agents working together to accomplish missions. The U.S. Army Research Laboratory (ARL) has established a 10-year (FY19- FY29) collaborative program, Strengthening Teamwork for Robust Operations in Novel Groups (STRONG), structured under eight annual program cycles (i.e., Cycles 1-8; FY19-FY26) with the overall goal of developing the foundation for enhanced teamwork within heterogeneous human-intelligent agent teams. This collaborative venture will bring together diverse, multidisciplinary expertise to support scientific breakthroughs relevant to specific and critical scientific questions that must be addressed to enable this future vision.

STRONG focuses directly on coordination and cooperation in human-agent teams via individualized and adaptive systems. The 10-year program has a specific long-term goal to identify and implement the fundamental research necessary to develop individualized, adaptive technologies that promote effective teamwork in novel groups of humans and intelligent agents. Prior to submitting, Applicants should review Decostanza et al. (2018) Enhancing Human-Agent Teaming with Individualized, Adaptive Technologies:

A Discussion of Critical Scientific Questions (https://brain.ieee.org/brain-storm/enhancing-human-agent-teaming/), which provides the initial vision and discussion of the scientific questions underlying the STRONG program, as well as Metcalf et. al. (2021) Systemic Oversimplification Limits the Potential for Human-AI Partnership which describes re-envisioning human machine teaming paradigms to enable success in problem spaces that are un-accessible to either humans or machine intelligence alone (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9425540).

Collaboration: STRONG addresses a critical objective within a broader Army goal to enable effective integration of Artificial Intelligence / Machine Learning (AI/ML) on the battlefield. This program has been developed in coordination with other related ARL-funded collaborative efforts (see descriptions of ARL collaborative alliances at https://www.arl.army.mil/www/default.cfm?page=93) and shares a common vision of highly collaborative academia-industry-government partnerships. However, with the rapid pace of technological change and the scientific community’s nascent understanding of and empirical evidence for human-agent teaming, STRONG will implement a novel, synergistic approach/structure. Annual Cycles will be explicitly structured to foster the greatest degree of collaboration and knowledge sharing to optimally leverage or combine findings from each effort awarded under each cycle and directly tie with internal ARL research programs. This structure is intended to more effectively and efficiently achieve the overall goals of the STRONG program by preventing unnecessary duplication or silos of information, findings, or other relevant outcomes from ARL investments (internal and external). In other words, the success of STRONG requires a https://brain.ieee.org/brain-storm/enhancing-human-agent-teaming/ https://brain.ieee.org/brain-storm/enhancing-human-agent-teaming/ https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9425540 http://www.arl.army.mil/www/default.cfm?page=93) http://www.arl.army.mil/www/default.cfm?page=93) http://www.arl.army.mil/www/default.cfm?page=93) multidisciplinary, collaborative effort that synergistically views every new funded effort as a network of research or “building block” to grow and support an ecosystem of high-quality, innovative researchers actively sharing knowledge and collaboratively addressing scientific gaps critical to the Department of Defense. Given this novel approach, Government researchers and program managers will be taking a more active role to support rapid integration of findings and cohesion within the research ecosystem.

Support Infrastructure: Each Cycle will require Recipient participation in an annual Innovation Summit Series to foster active collaboration, as detailed below. In addition to general updates, a dedicated STRONG website (https://www.arl.army.mil/strong) will maintain a regularly updated knowledge bank of ARL research priorities, programs and contacts; previously funded research summaries, products and contacts; and other relevant information specific to STRONG. Additional information about the annual Innovation Summit Series is available upon request via email to strong-cra@army.mil .

2. Cycle Structure

STRONG will be executed through a series of eight annual program cycles (i.e., Cycles 1-8; FY19- FY26) with the following format: (1) 12-month seedling awards structured as individual cooperative agreements to each awardee; (2) required awardee participation in the annual Innovation Summit Series to foster novel collaborations; and (3) potential 3-year extension of a seedling award executed as an Option under the original cooperative agreement.

Enhanced Research Program funding from ARL or Other Government Agencies (OGAs) may become available during a cycle which provides a mechanism for growth and enhancement within STRONG. A proposal should not include any discussion of the Enhanced Research Program. Recipients receiving a CA will be notified and provided details if the opportunity for Enhanced Research Program funding becomes available during their award period of performance.

a) Annual Cycle Seedling Announcements:

• The FOA will be amended annually with “Cycle updates” to identify a new problem statement, topic, and other necessary changes for that specific Cycle.

o SEE SECTION A.4.a FOR THIS YEAR’S CYCLE TOPIC

• Each annual Cycle will begin with a call for “seedling” proposals with funding provided to those Recipients selected under a cooperative agreement (CA).

• Total number of seedling Recipients and funding per Recipient may vary from year to year at the discretion of the Government based on annual Cycle changes or requirements.

• Each Cycle topic will be focused on addressing a different scientific area within the scope of the broad research aims of STRONG, with all Cycles designed to ultimately converge on the specific long-term program goal noted above.

• Cycle topics will be carefully chosen based on both program achievements from the previous year and on scientific advancements by the broader research community.

• Prior to the final submission date of each Cycle (see Event Timeline above), ARL will host an opportunity webinar on to provide detailed information and question-answer opportunities. A link to the annual webinar will be posted on the STRONG Program website at https://www.arl.army.mil/strong and can also be obtained via email to strong-cra@army.mil.

• Publications and seedling abstracts from each awardee under previously completed Cycles will be shared on https://www.arl.army.mil/strong for all future applicants to review.

• Proposals from junior investigators (e.g., students, research fellows, and early-career researchers with less than 5 years past reception of their PhD or less than 5 years’ experience within the primary field of their organization) are appropriate under this opportunity.

mailto:usarmy.apg.devcom-arl.list.STRONG-CRA@army.mil http://www.arl.army.mil/strong http://www.arl.army.mil/strong mailto:usarmy.apg.devcom-arl.list.STRONG-CRA@army.mil mailto:usarmy.apg.devcom-arl.list.STRONG-CRA@army.mil

b) Innovation Summit Series:

• Seedling Recipient participation by at least one team member in each meeting of the

Innovation Summit Series under the Recipient’s awarded Cycle will be REQUIRED.

o SEE SECTION A.4.b FOR THIS YEAR’S TENTATIVE AGENDA

• Seedling presentation: Recipients will be expected to demonstrate their scientific principles and approach to the community, providing evidence of progress toward scientific breakthroughs that support the criticality of the particular concept or approach to the specific topic.

• Active collaboration: Recipients will be expected to coordinate their future research plans with complimentary ongoing or planned efforts executed by other Recipients and government researchers within their designated Cycle. Regular communication between seedling Recipients and government researchers to develop awareness of relevant ARL-internal research programs is encouraged prior to, during, and after the Innovation Summit Series.

Connections to broader ARL programs, other service labs or other Government agencies is also encouraged. Lastly, novel connections to and collaborations with the commercial sector is strongly encouraged.

• Multi-year Extension Option: Recipients that wish to apply for up to a 3-year Extension Option will be expected to present their proposed project to an evaluation panel at a virtual/in-person hybrid event that will be scheduled in January 2026.

c) Multi-year Extension Option:

• Recipients of a seedling CA under the annual Cycle are eligible for consideration to receive funding (see Table 1) for an optional extension of up to 3 years at the conclusion of the seedling award. The period of the performance of the option will be based on the research and available funding.

• In addition to active collaboration during the Innovation Summit Series as noted above, explicit reference to any connection, extension potential, or refute of prior Cycle efforts is expected, where applicable. In other words, demonstrable knowledge of prior and current STRONG efforts is expected.

3. Deliverables / Research Products

Given the cyclical and collaborative nature of STRONG, awardees are encouraged to share research products (e.g., databases and algorithms/code) whenever possible with ARL and fellow awardees in order to contribute to the collective S&T advancement of the STRONG program and the human-agent teaming research community and ecosystem. Creative ideas to enable enduring and multiplicative benefits (e.g., government and commercial) of the research conducted under STRONG is highly encouraged. Additionally, the intent is to leverage past work to promote greater return on investment (ROI) for government investments under each award. ARL will maintain a central repository for all such STRONG deliverables, and as appropriate, these deliverables will be shared on the STRONG website.

a) Required seedling deliverables:

• Prior to 1st Summit meeting: Seedling Research Summary/Plan (publicly releasable, 1/2pg target)

• End of seedling award: Final Seedling Research Report (publicly releasable, 1-2pg max)

b) Required extension deliverables:

• 1 month post modification notification: Proposed Research Summary/Abstract (public releasable, 1/2pg target)

• Annually for extension period of performance: Annual Research Report & Summit presentation (public releasable, 1-2pg max)

c) Additional project-specific deliverable examples are below and will be determined at award time:

• Annotated databases

• Algorithms developed/used

• Testbed description, including access if possible

• Any other relevant research product

Cycle 7: Bridging theory-driven and data-driven approaches to human behavior

Overview: As the Army advances toward future Human-Machine Integration capabilities, operational success will increasingly depend on the effectiveness of Soldier interactions with intelligent agents, whether one-on-one or within hybrid teams. Soldier behaviors will vary due to multiple factors relating to their unique skills, experience, training, and physical and mental states, all influenced by the demands of specific operational environments. Future intelligent agents will need new capabilities to adapt within this highly multivariate context. While predicting behavior based on statistical patterns will be useful, a deeper understanding of why specific behaviors emerge in specific contexts would provide transformational capabilities, enabling more effective Soldier-agent interactions tailored to meet individual Soldier needs.

This is an ambitious, long-term objective, well-aligned with the STRONG program’s goal to “identify and implement the fundamental research necessary to develop individualized, adaptive technologies that prompt effective teamwork in novel groups of humans and intelligent agents.” Its achievement will ultimately contribute to the development of adaptive, intelligent agents capable of dynamically responding to the evolving needs of Soldiers in complex, high-stakes operational environments. To get there, cutting-edge theory and methodological advances are needed now, rather than immediate technology solutions.

Therefore, the central aim of this program is to deliver the scientifically rigorous approaches necessary to develop models that offer both explanatory depth and predictive accuracy through causal understandings of human behavior.

Background:

Specific to its stated aim, this program seeks innovative approaches that can enable Theory-Driven Big Data (TDBD) models with two fundamental properties: 1) they represent the multivariate causal structure of human behavior, and 2) they are grounded in very large datasets that capture the range and variation of human behavior in real-world conditions, both within individuals and across populations. The first property requires drawing upon our extensive knowledge of the causal factors and mechanisms typically explored in theory-driven laboratory research. The second reflects the Big Data1 methods characteristic of data-driven

1 The definition of “Big Data” remains a contentious and evolving issue (see. Favaretto et al., 2020) and the term is commonly used in reference to several different concepts. In this Call for Proposals, Big Data refers to large, complex datasets that are often difficult to manage and analyze using traditional data processing and analytic tools. Specifically, two of the so-called “3Vs” of Big Data (Laney, 2001) - volume (the quantity of data) and variety (the diversity of data types) - are emphasized, as they are closely tied to the large-scale sampling necessary to adequately capture the range and variation of human behavior in real-world conditions. While no specific thresholds for data volume or variety are provided, proposals should justify whether the datasets for planned research efforts should be considered as “Big Data” in relation to these criteria. However, given the evolving nature of the term, consideration of additional factors, such as data approaches aimed at predicting behavior.

Neither approach yet provides both explanatory depth and predictive accuracy. Theory-driven approaches enable explanation but are typically based on simple, low dimensional models tested in highly constrained laboratory conditions with small sample sizes. As a result, they lose predictive accuracy in the translation from laboratory to real-world conditions where multiple internal and external factors are simultaneously at play. Data-driven approaches provide predictive accuracy of real-world behavior. However, their focus on prediction leads to models with enormous numbers of parameters that are prone to overfitting their training data and do not support causal inference.

By bridging theory-driven and data-driven perspectives in novel TDBD approaches, this program will lay the foundations for future adaptive, intelligent agent capabilities. It should be noted, however, that TDBD is not alone in identifying the critical need for causal understanding in adaptive, intelligent agents. Ongoing research in Explainable AI (XAI) is focused on post-hoc interpretations of black-box models to improve transparency and trust, without revealing underlying causal mechanisms. The emerging field of Causal AI, like TDBD, focuses on identifying causal relationships, typically through data-driven discovery rather than theory-driven insight. While TDBD emphasizes the integration of theory-driven and data-driven approaches, comparisons with Causal AI's complementary data-driven methods may also be of interest in this program, particularly when such comparisons can clarify the strengths and limitations of each approach for causal understanding in real-world contexts.

Cycle 7 Goals and Questions

Successful proposals will provide strong evidence of the potential to significantly advance the foundational knowledge and technical innovations necessary to develop scientifically rigorous TDBD approaches.

Proposed approaches should combine and build upon the strengths of theory-driven and data-driven approaches to enable the future development of TDBD models, as described above, capable of achieving predictive accuracy and explanatory depth in real-world contexts. It is acknowledged that proposals may not have access to very large datasets, particularly very large datasets that are directly reflective of Army operational contexts. Proposals should thoughtfully address the scalability of their approaches based on the nature of the data identified for use (see footnote #1). Rather than focusing on the immediate applicability of proof-of-concept models developed, emphasis should be placed on potential scalability to handle the multivariate complexity of real-world contexts, including future Army operational environments.

The following Technical Goals have been identified as key focus areas for systematic and coherent programs of research that can advance TDBD approaches. In assessing submitted proposals, priority will be placed on scientific quality, depth, and the coherence of the research plan in addressing its Technical Goals, over comprehensiveness in addressing all Technical Goals. High-risk/high-reward proposals are encouraged. In evaluating risk, reviewers will consider the potential for scientific impact, justification of risk relative to theoretical or technical challenges, proposed mitigation strategies, and the potential reward if the research is successful.

1. Innovative Approaches to Theory-Driven Big Data Models of Human Performance

Explore novel approaches for developing TDBD models of human behavior and performance in real-world environments. Approaches should prioritize developing models with both explanatory depth and predictive accuracy, enabling future models to support agent capabilities that not only forecast behaviors velocity (i.e., the speed at which the data is generated; Laney, 2001) or the computing requirements and infrastructure needed, as well as other factors may be used further support the classification of Big Data with sufficient justification. Proposals will be evaluated on how well their provided descriptions and rationale align with the criteria described here or otherwise justify the use of the data proposed.

Garcia, Javier O. (Civ) This reads a bit redundant. I would just close this segment with a sentence summary ‘cause I think most important part are the distinctions you lay out in the previous paragraph.

but explain why they occur. Proposals are encouraged that emphasize accounting for the full distribution of behavior, including rare or extreme cases, to reveal causal relationships that may be overlooked by conventional methods focused on central tendencies alone. The primary focus is on approaches that are scalable to develop models that can handle the multivariate complexity of future Army operational environments, though proof-of-concept models in this program may be developed, tested, and validated in the absence of real-world operational data.

Key Scientific Questions:

• What novel TDBD frameworks and approaches can be developed to rigorously explore and uncover the multivariate causal structure of human behavior by leveraging the strengths of theory- and data-driven approaches?

• To what extent can multivariate TDBD models that move beyond predictive accuracy to providing explanatory depth through causal understanding outperform models developed through theory-driven or data-driven approaches alone?

• Can approaches focused on rare or extreme behaviors in a distribution enable the discovery of causal relationships that are missed by conventional approaches?

• What foundational features of TDBD models can be explored now to inform future adaptive capabilities for intelligent agents, focusing on the identification of candidate factors or mechanisms that may be critical for future applications?

2. Large-Scale and/or Multi-Modal Sampling for Real-World Contexts The reliable and efficient collection of data that capture the full range and variation of real-world human behavior across multiple dimensions and timescales will be essential for TDBD approaches. These data, whether passively collected (without active participation) or actively gathered through direct interaction, must reflect the complexity of behavior in real-world contexts. Large-scale sampling of real-world behavioral data poses significant practical challenges (e.g., reliability, quality, privacy, participant compliance). Real-time, multimodal, and dense sampling present additional challenges, particularly in real-world contexts. Proposals should address these challenges and innovations to enhance the effectiveness and efficiency of data collection to enable TDBD models scalable to the multivariate complexity of future Army operational environments are encouraged. Innovative approaches to leverage TDBD models or to integrate real-time and/or multimodal data, including physiological data, to enhance the accuracy and explanatory power of TDBD models are highly encouraged.

Key Scientific Questions:

• What are the most critical scientific challenges in collecting large-scale or and/or multimodal data for TDBD approaches, and how can innovative solutions address these challenges to ensure the representativeness of data to reflect the full range and variation of human behavior in future operational contexts?

• What are the critical technological, logistical, and ethical challenges in collecting large-scale or densely sample multimodal data for TDBD approaches, and what novel solutions can ensure that these data are reliable and secure, and that data collection approaches are scalable to the multivariate complexity of future Army operational environments?

• How can multimodal measurement approaches, including approaches for densely sampled and/or real-time physiological data, be optimized for collection and integration with the large-scale data essential to TDBD models to ensure the data supports exploration of key theoretical questions about the multivariate causal factors underlying human performance?

3. Scalable Data Infrastructure and Algorithms:

While collection of large-scale behavioral and multimodal data is essential for the success of future TDBD approaches, these data must be stored and processed efficiently and securely. Scalable data infrastructure and algorithms are critical to enable the approaches to develop TDBD models, as well as enabling real-time analysis that results in actionable insights, as data reflecting the multivariate complexity of human behavior evolves on multiple timescales in dynamic, real-world contexts.

Innovative algorithms are needed to efficiently manage and process these datasets, integrating data from diverse sources to enable TDBD models that effectively account for the causal structure underlying human behavior in and across different real-world contexts, including future Army operational environments. To enable future adaptive, intelligent agent capabilities, algorithms must provide real-time analysis to support actionable insights in dynamic, operational environments. Proposals should focus on both the development of scalable data architectures and the algorithms that will enable the reliable, secure, and high-speed processing of data for TDBD models.

Key Scientific Questions:

• What are the critical challenges in designing scalable, secure, and efficient data infrastructures that can support the collection, storage, and real-time processing of large, multi-modal datasets in real-world contexts?

• What innovations in data management are required to handle the large volumes and complexity of data required to model the multivariate causal structure of human behavior, while ensuring data fidelity and efficient computational performance?

• How can algorithms be designed to integrate and process high-dimensional data, including potential real-time processing, that reflect the multivariate causal structure of human behavior to enable actionable insights in real-world settings?

• What approaches can be developed to ensure that data infrastructures and algorithms remain robust and scalable in resource-constrained environments, such as real-time military operations?

4. Adaptive Model Refinement and Learning The long-term effectiveness of TDBD approaches relies on their ability to develop models that maintain predictive accuracy and explanatory depth, even as new data becomes available. Incorporating learning or related adaptation mechanisms will allow these models to refine their causal understandings to improve their predictions and explanations and ensure their continued effectiveness over time.

Moreover, by leveraging real-time data streams, adaptive TDBD models can respond to context-dependent and individual-specific shifts in the causal factors driving behavior, leading to more precise predictions and tailored interactions as operational conditions and Soldier states change. Proposals should explore innovative approaches for developing adaptive models that remain robust to the variability of human behavior, within and across individuals, contexts, and operational demands.

Key Scientific Questions:

• How can real-time multimodal data be integrated with large-scale data within TDBD approaches to support the real-time adaptive behavior and continuous learning of models, ensuring more accurate and contextually relevant prediction and explanation in real-world contexts?

• What adaptive learning mechanisms can be developed to refine the causal pathways of models developed through TDBD approaches to improve their prediction and explanatory accuracy as new data is collected?

• What foundational methods can be explored for adaptive model refinement to avoid potential maladaptive changes in the real-time interactions between humans and intelligent agents?

5. Validation of Approaches for Real-World Contexts:

Novel validation strategies are needed to ensure future TDBD models achieve and maintain predictive accuracy and explanatory depth in real-world applications. Proposals should focus on technical and methodological innovations that allow for robust validation of TDBD models in varied contexts.

Proposers should address the challenges of translating TDBD models across different real-world contexts, including validation of models that can adapt as the range and variation of human behavior changes with changes in operational demands and conditions, potentially altering the underlying causal structure of the behavior observed in different real-world contexts.

Key Scientific Questions:

• How can empirical validation strategies be designed to assess the predictive accuracy and reliability of models developed through TDBD approaches in varied real-world contexts?

• What gaps exist in current validation paradigms that may limit their applicability to models developed through TDBD approaches, and how can these gaps be addressed?

• What methodological innovations are needed to ensure that applications driven by models developed through TDBD approaches can maintain predictive accuracy and explanatory depth when faced with different, context-dependent real-world contexts?

• What foundational methods can be developed for the validation of adaptive TDBD models to ensure effective adjustments to new or unforeseen contextual demands?

• How can systems detect when a significant shift in operational context has occurred, enabling timely adaptation of TDBD models to maintain performance and ensure predictive accuracy and relevance into the new context?

Together, the five Technical Goals provide a broad and overarching framework for advancing scientifically rigorous TDBD approaches. To foster the innovation necessary to achieve transformative impact, the requirements defined below for Cycle 7 one-year seedling and multi-year follow-on projects aim to provide flexibility in the design of high quality, high-risk/high-reward programs of research. With these Technical Goals as a guide, submitted proposals are expected to demonstrate how they address the Key Scientific and Technical Questions while adhering to the following submission requirements.

One-year seedling projects are expected to explore foundational elements within the identified Technical Goals. Seedling proposals must address at least three Key Scientific Questions. No limits are placed on the number of questions addressed within any single Goal, though a high priority is placed on the first three Goals. No maximum number of Key Scientific Questions is required. However, the quality, depth, and realism of the proposed research plans within the one-year period of performance, as well as potential contribution to the broader programs of research in multi-year follow-on projects, will be prioritized over the quantity of Key Scientific Questions covered.

Multi-year follow-on projects are expected to expand the scope and complexity of the initial seedling efforts within a holistic, well-integrated program of high-risk/high-reward research. Follow-on projects are expected to build upon seedling efforts to deliver TDBD approaches that can handle the multivariate complexity of real-world contexts, including future Army operational environments. Follow-on proposals must address:

• At least one question from each of the first three Goals

• At least two questions from the 4th and 5th Goals, collectively (i.e., two questions from Goal 4, two questions from Goal 5, OR one from each)

Additional questions may be addressed but are not required. Both the quality of the proposed research and the overall coherence of the proposed program of research will be weighed more heavily than the quantity of Key Scientific Questions or Goals addressed.

Notional Activity Outline occurring in the May-August 2025 timeframe:

ORIENTATION & OVERVIEWS - (PIs or representative required)

• Introduction to Army science mission, planning and strategy

• Demonstrations of initial concept and approach from current Cycle Seedling Awardees

• Presentations from prior Cycle Option Awardees as appropriate

• Industry Perspective: presentations and brainstorming on shared research challenges and gaps impacting both military and commercial sectors. Identify unique collaboration opportunities and/or multiple paths for research transition

COLLABORATION / TEAM-BUILDING & RESEARCH – (junior/student participation required with guidance by senior/PI)

• Facilitated activities to support new collaborations and new ideas for those interested in the Option effort

• Opportunity for development of junior investigators to link into the ecosystem of researchers working on questions relative to Army priorities

• Hack-a-thon focused on experimental paradigms

• Dedicated time to interact with and discuss collaboration options with ARL scientists and specific ARL research programs

• Independent break-out sessions for research planning

Notional Activity Outline occurring in the January 2026 timeframe:

PROPOSED FOLLOW-ON OPTION EFFORTS

• Presentation of proposed follow-on efforts (approximately 1 hour with questions) open to collaborators and government only.

• White papers for Follow-On efforts due.

C. References

• Favaretto, M., De Clercq, E., Schneble, C. O., & Elger, B. S. (2020). What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade. PloS one, 15(2), e0228987.

• Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety.

META group research note, 6(70), 1.

B. FEDERAL AWARD INFORMATION

Multiple CAs will be awarded from this FOA. The Applicants selected for award will be notified by the Grants Officer or his/her designee telephonically or via email. The CA award is not official until the Recipient has received the award signed by the Grants Officer.

Cooperative agreements for institutions of higher education, nonprofit organizations, foreign organizations, and foreign public entities are primarily governed by the following:

• Federal statutes

• Federal regulations

• 2 CFR Part 200

• 2 CFR Part 1104

• 32 CFR Parts 21, 22, 26, and 28

• DoD Research and Development General Terms and Conditions

• Agency-specific Research Terms and Conditions

Cooperative agreements for for-profit and nonprofit organizations exempted from Subpart E—Cost Principles of 2 CFR Part 200, are primarily governed by the following:

• Federal statutes

• Federal regulations

• 32 CFR Part 34 – Administrative Requirements for Grants and Agreements with For-Profit

Organizations

• 32 CFR Parts 21, 22, 26, and 28

• DoD Research and Development General Terms and Conditions

• Agency-specific Research Terms and Conditions

The following websites may be accessed to obtain an electronic copy of the governing regulations and guidance:

• FAR, DFARS, and AFARS: https://www.acquisition.gov/

• Code of Federal Regulations: http://www.ecfr.gov

• DoD Research and Development General Terms and Conditions September 2021

• ACC-APG-RTP Division Assistance, Research General Terms and

Conditions dated January 2022

Core Funding Cycles:

FY19

FY20

FY21

FY22

FY23

FY24

FY25

FY26

FY27

FY28

FY29

Proposal

Submissions

Cycle 1 B O O O DEC 2018

Cycle 2 B O O O SEP 2019

Cycle 3 B O O O SEP 2020

Cycle 5 B O O O SEP 2021

Cycle 5 B O O O SEP 2022

Cycle 6 B O O O SEP 2023

Cycle 7 B O O O SEP 2024

Cycle 8 B O O O SEP 2025

Note: B is for Base (“seedling”) period of Cooperative Agreement; O indicates 3-yr Option.

Funding for seedling project efforts will not exceed $100,000 per award. Award of approximately 10 seedling CAs is expected during this Cycle. Funding for a Multi-year Option beyond the seedling CA is expected to be larger in scope, with an estimated budget ranging between $350,000 and $500,000 per year, per award. Multiple seedling Recipients are encouraged to collaboratively propose an extension effort. Seedling Recipients may include new entities as subawardees that were not part of the initial seedling under a proposed 3-year Option. Any discussion about options is only applicable for those Seedling Recipients who receive an option.

Enhanced Research Program funding from ARL or Other Government Agencies (OGAs) may become available during a cycle which provides a mechanism for growth and enhancement within STRONG. A proposal should not include any discussion of the Enhanced Research Program. Recipients receiving a CA will be notified and provided details if the opportunity for Enhanced Research Program funding becomes available during their award period of performance.

https://www.acquisition.gov/ http://www.ecfr.gov/

Award Instrument: This Funding Opportunity is expected to result in the award of multiple “seedling” cooperative agreements (CA) during each Cycle as defined at 31 U.S.C. 6305 for the execution of the program. The CA is used to enter into a relationship:

a. The principal purpose of which is to transfer a thing of value to the Recipient to carry out a public purpose of support or stimulation authorized by a law or the United States, rather than to acquire property or services for the Federal Government’s direct benefit or use.

b. Substantial involvement is expected between the Federal Government and the Recipient when carrying out the activity contemplated by the CA.

c. No fee or profit is allowed

Structure of Award: The CAs will consist of an initial 12-month Base award that will be executed and considered a “seedling” CA. Each seedling CA will provide for the potential for exercising an option, adding up to three more years to the base award, as outlined in the table above. This proposed additional effort may be scoped for one to three years based on its objectives and available funding, will be collaboratively developed with input from Government scientists during the period of performance of the seedling CA, and will be presented in the January 2026 time frame for Cycle 7.

Proposal Submission: The application process consists of proposal submissions from applicants for each Cycle under this FOA. Applicants should note there are page limitations and other requirements associated with the submission process. Submissions in connection with this FOA are due by the date and time specified below. FOA amendments for future topics will include the submission requirements for those submissions. The Government’s decision to award a seedling CA will be based upon the evaluation results of the proposal submission.

Period of Performance: The CA Awards made as a result of this FOA will provide for a period of performance of one year, with the potential exercise an option period for up to three additional years at the discretion of the Government.

Place of Performance: There is no limitation on the place of performance, except an application must address planned Recipient team member participation during the Innovation Summit Series described above. The locations and other pertinent information for the summit will be posted on STRONG Program website at https://www.arl.army.mil/strong and also available upon request via email to strong-cra@army.mil.

Collaboration: Collaboration is expected as described in Section A.1.

Funding: This FOA is issued subject to the availability of funds. ARL has submitted the requisite documents to request funding for the period covered by the program. However, Applicants are reminded this request is subject to Presidential, Congressional and Departmental approval. Funding levels specified in this FOA are estimated funding levels and are for proposal preparation purposes only; actual funding levels of the CAs will be updated annually as part of the federal appropriation process.

Profit/Fee: Profit/fee is not permitted under the CA.

Cost Sharing: Cost sharing is not required under this FOA.

Opportunity Webinar: ARL will host an opportunity webinar (see Event timeline above). A link to the webinars can be requested via email to strong-cra@army.mil and will be posted on the STRONG Program website at https://www.arl.army.mil/strong.

mailto:usarmy.apg.devcom-arl.list.STRONG-CRA@army.mil mailto:usarmy.apg.devcom-arl.list.STRONG-CRA@army.mil

Contact Information: Additional information about scheduling and events related to the STRONG CRA is available upon request via email to strong-cra@army.mil. All questions or comments concerning this FOA shall be submitted to strong-cra@army.mil. Comments or questions submitted should be concise and to the point. In addition, the relevant part and paragraph of the FOA to which a comment or question pertains must be referenced. Responses to non-proprietary questions received will be answered at the STRONG opportunity webinar for the benefit of all interested parties. All clearly identified and marked proprietary questions submitted will be responded to via an individual email response, and not responded to at the opportunity webinar. Applicants are encouraged to submit questions as early as possible. The deadline for submission of questions which will be answered under this FOA is listed in Event timeline above. Any answers provided to questions do not change the requirements of this FOA. Future amendments to this FOA, including new cycle topics, will be issued via an amended FOA posted in grants.gov.

C. ELIGIBILITY INFORMATION

1. Eligible Applicants

It is our goal for the program to include a diverse group of Applicants with varied long-term interests.

Participants may be institutions of higher education, for-profit, or non-profit organization.

Federally Funded Research and Development Centers (FFRDCs) are subject to applicable direct competition limitations and cannot propose to this solicitation in any capacity unless they meet the following conditions. (1) FFRDCs must clearly demonstrate that the proposed work is not otherwise available from the private sector. (2) FFRDCs must provide a letter, on official letterhead from their sponsoring organization, that (a) cites the specific authority establishing their eligibility to propose to Government solicitations and compete with industry, and (b) certifies the FFRDC’s compliance with the associated FFRDC sponsor agreement’s terms and conditions. These conditions are a requirement for FFRDCs proposing to be awardees or subawardees.

Proposals may consist of teams from any combination of organizations (e.g., prime and subawardees), but this is not a requirement for award and award will only be made to a single entity.

For each Cycle, only those Applicants awarded a seedling CA will be eligible for consideration of a 3-year option period for that Cycle as a prime during the follow-on presentations that will occur in the January 2026 timeframe; however, if desired, a Recipient of a seedling CA can include new sub-awardees in their white paper and follow-on presentation. All eligible Applicants may apply for future topics under future cycles.

2. Cost Sharing or Matching

Cost sharing is not required under this Funding Opportunity.

D. APPLICATION AND SUBMISSION INFORMATION

The application process consists of a single Proposal stage. Applicants will receive feedback regarding their proposal ONLY IF IT IS SELECTED FOR AWARD, in order to improve the proposal and ensure alignment of the proposed research with Government goals. Applicants with non-selected proposals will be notified of their non-selection, but will not receive feedback.

1. Address to Request Application Package

This Funding Opportunity may be accessed from the following: Grants.gov (www.grants.gov).

Amendments, if any, to this FOA will be posted to these websites when they occur. Interested parties are encouraged to periodically check these websites for updates and amendments.

2. Content and Format of Application Submission

The following information is for those wishing to respond to the FOA:

Grants.gov Application Submission and Receipt Procedures

This section provides the application submission and receipt instructions for DoD program applications. Please read the following instructions carefully and completely.

DoD is participating in the Grants.gov initiative to provide the grant community with a single site to find and apply for grant funding opportunities. For this funding opportunity, DoD requires applicants to submit their applications online through Grants.gov. This funding opportunity may be found on Grants.gov by going to the Grants.gov Search Grants screen and entering the funding opportunity number for this FOA, W911NF-19-S-0001, in the Funding Opportunity search box. You can also search for the CFDA Number 12.630

How to Register to Apply through Grants.gov

1. Instructions: Read the instructions below about registering to apply for DoD funds.

Applicants should read the registration instructions carefully and prepare the information requested before beginning the registration process. Reviewing and assembling the required information before beginning the registration process will alleviate last-minute searches for required information.

Organizations must have a active System for Award Management (SAM) registration and Grants.gov account to apply for grants. Creating a Grants.gov account can be completed online in minutes, but SA registrations may take additional time. Therefore, an organization's registration should be done in sufficient time to ensure it does not impact the entity's ability to meet required application submission deadlines.

Complete organization instructions can be found on Grants.gov here:

https://www.grants.gov/web/grants/applicants/organization-registration.html

a. Register with SAM: All organizations applying online through Grants.gov must register with the System for Award Management (SAM). Failure to register with SAM will prevent your organization from applying through Grants.gov. SAM registration must be renewed annually. For more detailed instructions for registering with SAM, refer to:

https://www.grants.gov/web/grants/applicants/organization-registration/step-2- register-with-sam.html

b. Create a Grants.gov Account: The next step is to register an account with Grants.gov. Follow the on-screen instructions or refer to the detailed instructions here:

https://www.grants.gov/web/grants/applicants/registration.html

c. Add a Profile to a Grants.gov Account: A profile in Grants.gov corresponds to a single applicant organization the user represents http://www.grants.gov/web/grants/applicants/organization-registration.html http://www.grants.gov/web/grants/applicants/organization-registration.html https://www.grants.gov/w http://www.grants.gov/web/grants/applicants/organization-registration/step-2-http://www.grants.gov/web/grants/applicants/organization-registration/step-2-http://www.grants.gov/web/grants/applicants/registration.html http://www.grants.gov/web/grants/applicants/registration.html

(i.e., an applicant) or an individual applicant. If you work for or consult with multiple organizations and have a profile for each, you may log in to one Grants.gov account to access all of your grant applications. To add an organizational profile to your Grants.gov account, enter the UEI Number for the organization in the UEI field while adding a profile. For more detailed instructions about creating a profile on Grants.gov, refer to:

https://www.grants.gov/web/grants/applicants/registration/add-profile.html

d. EBiz POC Authorized Profile Roles: After you register with Grants.gov and create an Organization Applicant Profile, the organization applicant's request for Grants.gov roles and access is sent to the EBiz POC. The EBiz POC will then log in to Grants.gov and authorize the appropriate roles, which may include the Authorized Organization Representative (AOR) role, thereby giving you permission to complete and submit applications on behalf of the organization. You will be able to submit your application online any time after you have been assigned the AOR role. For more detailed instructions about creating a profile on Grants.gov, refer to:

https://www.grants.gov/web/grants/applicants/registration/authori ze-roles.html

e. Track Role Status: To track your role request, refer…