Part 1. Overview Information

Key Dates

All applications are due by 5:00 PM local time of applicant organization. 

Applicants are encouraged to apply early to allow adequate time to make any corrections to errors found in the application during the submission process by the due date.

No late applications will be accepted for this Notice of Funding Opportunity (NOFO).

It is critical that applicants follow the instructions in the Research (R) Instructions in the How to Apply - Application Guide, except where instructed to do otherwise (in this NOFO or in a Notice from NIH Guide for Grants and Contracts).

Conformance to all requirements (both in the Application Guide and the NOFO) is required and strictly enforced. Applicants must read and follow all application instructions in the Application Guide as well as any program-specific instructions noted in Section IV. When the program-specific instructions deviate from those in the Application Guide, follow the program-specific instructions.

Applications that do not comply with these instructions may be delayed or not accepted for review.

There are several options available to submit your application through Grants.gov to NIH and Department of Health and Human Services partners. You must use one of these submission options to access the application forms for this opportunity.

1. Use the NIH ASSIST system to prepare, submit and track your application online.
2. Use an institutional system-to-system (S2S) solution to prepare and submit your application to Grants.gov and eRA Commons to track your application. Check with your institutional officials regarding availability.
   
   
3. Use Grants.gov Workspace to prepare and submit your application and eRA Commons to track your application.



Table of Contents
Part 1. Overview Information
Key Dates
Part 2. Full Text of Announcement
Section I. Notice of Funding Opportunity Description
Section II. Award Information
Section III. Eligibility Information
Section IV. Application and Submission Information
Section V. Application Review Information
Section VI. Award Administration Information
Section VII. Agency Contacts
Section VIII. Other Information

Part 2. Full Text of Announcement

Section I. Notice of Funding Opportunity Description

Background

Novel, data-driven approaches will be necessary to fulfill the Ending the HIV Epidemic in the U.S. (EHE) key strategies to diagnose, prevent, and treat, and respond. The use of transformative technologies in fields such as artificial intelligence (AI) can accelerate scientific discovery, leading to new breakthroughs and innovative strategies for ending the HIV epidemic. Opportunities for using AI in making predictions, real-time monitoring, or to improve decision-making to address HIV prevention, care, and treatment needs abound but have yet to be fully realized. Recent advances in highly flexible, multimodal AI models have generated much interest and discussion about the promise of AI to transform research, clinical care, and public health. This NOFO seeks to leverage these advances by encouraging the use of cutting-edge multimodal AI models to expand our capacity to address the dynamic, complex, and evolving HIV epidemic.

Multimodal AI is an advanced machine learning model that integrates multiple modalities of data often encountered in clinical practice (e.g., text, images, and audio) to improve predictive accuracy beyond what simpler, unimodal models (i.e., using a single modality of data such as text or images) can achieve. These multimodal models, however, face several technical challenges including quantification of multimodal learning (i.e., quantifying dimension of heterogeneity, identifying interconnections, and optimizing learning processes). While multimodal AI has emerged as a technique that shows great promise in transforming healthcare, current challenges will require the integration of innovations for expanding capabilities to enable greater understanding and the model-based and domain-related explainability of the model outputs.

Several methods have been developed to enhance model-based explainability such as Shapley Additive Explanations (SHAP) and Deep Learning Important FeaTures (DeepLIFT) but are limited in domain explainability. One innovation to significantly increase domain interpretability and explainability is through the use of knowledge graphs – structured data models representing the complex, multi-modal relationships between HIV symptoms, treatments, medications, behaviors, social determinants of health (SDoH), and so forth – and their dimensions – providing a more nuanced understanding of HIV which allows for better targeting of prevention efforts, optimizing treatment decisions, and improving patient experience. Furthermore, embedding experimentally testable causality principles into knowledge graphs are essential for further enhancing domain explainability for high stakes uses such as in healthcare. 

To address the evolving complexities in HIV prevention, treatment, and care, integrating advanced technologies such as knowledge graphs  into multimodal AI models offers a promising new approach. Enhancing the explainability of multimodal AI models with causal knowledge graphs has the potential for more accurate, explainable, and interpretable systems that can be used in a wide range of data-driven applications including comprehensive patient histories, early HIV detection, personalized HIV prevention or treatment plans, remote patient monitoring, real-time patient support, epidemic surveillance, and more to address HIV prevention, treatment, and care needs. Human-centered AI approaches (i.e., designing, developing, and co-creating AI with end-users for a deeper understanding human needs) are also critical for ensuring ethical considerations and stakeholder needs drive the design at all stages of model development: data selection and preparation, model building, training, and assessment, and evaluation and implementation. As such, this initiative provides an opportunity for, multidisciplinary research teams to develop explainable, transparent, and trustworthy human-centered multimodal AI models to improve HIV research, clinical care, and public health.

Research Objectives and Scope

The overarching objective is to support data-driven, technological approaches to accelerate HIV diagnosis, prevention, and treatment efforts, which is closely aligned with the priorities of the NIMH Division of AIDS Research, the NIH Office of AIDS Research, and the U.S. Department of Health and Human Services. As such, this initiative supports the development of multimodal AI models to expand our capacity to address the dynamic, complex, and evolving HIV epidemic with three primary objectives: (1) development, adaptation, and evaluation of accurate, safe, efficient, and unbiased multimodal AI models to support a diverse range of HIV applications; (2) creation of knowledge graphs to enhance the interpretability and explainability for applications in HIV research, clinical care, and public health; and (3) exploration of synergistic integration of model-based explainability methods, knowledge graphs, and multimodal AI models for more precise model outputs and increased usability in HIV clinical care with a human-centered approach. Cross-disciplinary teams such as end-users, domain experts, patients/clients, community members, bioethicists, data scientists, software developers, engineers, and policymakers are encouraged. Teams are expected to use best practices and meaningful stakeholder engagement to ensure data privacy, security, and consent. Inherent in this initiative is the need for the use of a human-centered AI approach in the design, development, and application to ensure that the system is fair, ethical, and unbiased for a more transformative, meaningful, and sustained impact on HIV clinical care.

Development, Adaptation, and Evaluation of Accurate, Safe, Efficient and Unbiased Multimodal AI Models for HIV

Applicants are expected to propose innovative projects focused on the development, adaptation, and evaluation of multimodal AI models that can support a wide range of data-driven applications. These applications may include but are not limited to early HIV detection, personalized HIV prevention or treatment plans, and real-time patient support, epidemic monitoring and surveillance, and clinical trials research with the final model demonstrating the potential for a significant impact for the specified HIV-related application. Multimodal AI models must integrate at least two different modalities of data (e.g., text, audio, images, videos) and draw from at least two data sources (e.g., medical records, claims data, pharmacy records, wearable devices, patient surveys to identify SDoH needs). Applications should describe the entire model development process, including training and architectural design (e.g., data modalities and sources, representation learning, methods for model fusion), as well as how stakeholder perspectives will be integrated, and potential challenges (e.g., handling missing or unbalanced data, managing data dimensionality). Considerations regarding equity and biases must also be included to ensure fair and unbiased model performance. In addition, applicants may consider adapting an existing multimodal AI model when appropriate for the specific HIV application and context. This can be achieved by connecting existing models through the use of application programming interfaces (APIs), enabling new functionalities (e.g., to improve model reasoning capabilities).

Creation of Knowledge Graphs to Enhance Interpretability and Explainability for Application in HIV Research, Clinical Care, and Public Health

Combining multiple modalities increases model complexity and challenges the interpretability and explainability of multimodal AI models, making it difficult for users to understand how or why a model, for example, predicted a cluster of HIV cases or recommended a specific HIV treatment regimen. A promising novel solution to this issue is the integration of domain-specific knowledge using knowledge graphs. Knowledge graphs are designed to integrate complex, diverse data to capture nodes (representing real-world entities such as HIV symptoms, biomarkers, treatments, behaviors, SDoH) and edges (representing different relationships between nodes such as symptoms-treatment) in a structured manner. Generating a domain-specific knowledge graph, however, is challenging due to the heterogeneity of health data and the need to ensure that the knowledge graph represents the complex causal relationships and is context-specific, dynamic, and evolving. Applications should describe the process for developing the domain-specific knowledge graph to represent HIV knowledge relevant to the specific HIV application. Research teams may propose to develop or adapt an existing HIV-specific knowledge graph, but must address how they plan to assess, maintain, update, and expand the knowledge graph through automated or other means. 

Exploring the Synergistic Integration of Knowledge Graphs and Multimodal AI Models for More Precise Model Outputs and Increased Usability in HIV Diagnosis, Prevention, and Treatment

Frameworks for integrating domain-specific knowledge graphs into large language models (LLMs) have been proposed to enhance model performance and interpretability on a number of tasks (e.g., enhanced understanding and interpretation, encoding data, knowledge representation and reasoning). Novel methods using synergistic approaches that allow for bidirectional reasoning between a LLM and knowledge graph is a new and exciting area that can lead to diverse applications in multimodal AI reasoning capabilities. Applicants are expected to explore the synergistic integration of the multimodal AI model with an HIV-specific knowledge graph for enhancing knowledge representation or reasoning. 

Use of Best Practices in the Design, Development, and Use of AI Models

In collaboration with community and implementing partners, several technical, ethical, and regulatory considerations are expected to be addressed in the application in order to build user trust in the multimodal AI model. These include, but are not limited to, using secure approaches designed to protect data privacy, training models using balanced data, identifying and mitigating model biases, adhering to relevant data privacy regulations, monitoring data security and compliance with regulations, maintaining detailed documentation to enhance model transparency and accountability, such as by using model cards to describe the model, features, and intended use, and establishing a data governance framework for managing and protected the data including model use limitations. Research teams are also expected to develop clear consent documentation and procedures with their community and implementing partners that will enable transparent, clear, and open communication with users. Additional considerations when designing, developing, and using automated systems that have the potential to meaningfully impact access to critical resources or services such as healthcare are identified in the Blueprint for an AI Bill of Rights put forward by the U.S. Office of Science and Technology Policy. The National Institute of Standards and Technology (NIST)’s Artificial Intelligence Risk Management Framework also provides additional guidance to better manage AI risks for individuals, organizations, and societies.  

Expected Outcomes

Research applications should demonstrate enhanced explainability of the multimodal AI model infused with HIV-specific knowledge relevant to the selected HIV application with pre-defined metrics and measures to demonstrate success/impact.   

Applications for this funding opportunity must address the following:

• Development, adaptation, and evaluation of an accurate, safe, efficient, and an unbiased multimodal AI model that supports at least one HIV application as a demonstration of proof of concept but with the ability to support a diverse range of HIV applications in the future;
• Creating a new, or adaptation of an existing knowledge graph to strengthen the interpretability and explainability of the model outputs for application in HIV research, clinical care, and public health;
• Explore the synergistic integration of knowledge graphs and multimodal AI models for more precise model outputs and increased usability in HIV diagnosis, prevention, and treatment;
• Details ethical principles to ensure data privacy, security, transparency and consent and incorporates a human-centered approach (i.e., human-in-the-loop, with consideration of factors such as acceptability, appropriateness, feasibility);
• Meaningful engagement of key stakeholders to enable critical analysis, support fair and ethical use, and inform decisions impacting care;
• Formation of multi-disciplinary teams including, for example, end-users, data scientists, behavioral and social scientists, domain experts, patients/clients, community members, ethicists, software developers, engineers, policy makers, and so forth as appropriate for the proposed project.

As applicable, applicants are also encouraged to seek feedback and guidance from the Food and Drug Administration on potential future medical device submissions (Pre-Submission through the Q-Submission Program).

For NIMH applications involving clinical trials:

Applications proposing to pilot an AI-generated treatment intervention model must comply with the NIMH experimental therapeutics approach which requires studies not only test the intervention effects on outcomes of interest in real world settings but also informs the understanding of the intervention's mechanism of action. As such, the scope of work must specify the intervention target(s) and mechanism(s) and include the assessment and analysis of intervention-induced changes in the presumed target(s) and mechanism(s), that are hypothesized to explain the intervention outcomes.

The NIMH has published updated policies and guidance for investigators regarding human research protection and clinical research data and safety monitoring (NOT-MH-19-027). The application’s PHS Human Subjects and Clinical Trials Information, including the Data and Safety Monitoring Plan, should reflect the policies and guidance in this notice. Plans for the protection of research participants and data and safety monitoring will be reviewed by the NIMH for consistency with NIMH and NIH policies and federal regulations.

Pre-application Webinar

NIMH and participating institutes will host a webinar for all prospective applicants to provide an opportunity to ask questions related to the scientific scope of this NOFO and technical details for applying. Prospective applicants are encouraged to submit their questions regarding the NOFO in advance of the webinar. Further details on where and when to submit the questions will be provided once the webinar has been scheduled. Participants must register for the event, but participation in the webinar is optional. Please visit the NIMH meeting and events website for pre-application information and further details regarding the webinar for this specific NOFO.

Applications Not Responsive to this NOFO

An application will be considered non-responsive to this NOFO and will not be reviewed or considered for funding if the application:  

• Does not propose the development, adaptation, or evaluation of a multimodal AI model using two or more different modalities of data (e.g., text, audio, images, videos) from two or more data sources (e.g., medical records, claims data, wearable devices).
• Does not propose the development or adaptation of an HIV-specific knowledge graph.
• Does not propose enhancing the explainability of a model by synergistically integrating an HIV-specific knowledge graph including appropriate metrics and measures to demonstrate model performance and enhanced explainability.
• Does not describe methods for ensuring data privacy, security, transparency and consent.
• Does not involve a collaborative, multidisciplinary team with relevant scientific or technical expertise. 
• Does not include a plan for meaningfully engaging community stakeholders and/or implementing partners.
• Focuses primarily on: 
  • Data collection or generation without significant effort toward model development, adaptation, or evaluation to inform HIV diagnosis, prevention, or treatment;
  • Model evaluation without significant efforts toward model enhancement using knowledge graphs;
  • Using model explainability methods in the absence of domain-specific knowledge graphs.
  • For NIMH applications only, does not follow the NIMH Experimental Therapeutic Approach. 

In recognition of the many considerations and requirements above, applicants are strongly encouraged to read the Institute-specific research priorities below and consult with the Scientific/Research Contact(s) listed in Section VII: Agency Contacts when developing plans for an application. 

National Institute on Drug Abuse

Areas of specific interest to NIDA include developing methods for advancing the use of multimodal AI in epidemiology, prevention, and treatment specific to individuals who use drugs and are at risk for or have co-occurring HIV with the goal of improving clinical care. NIDA will consider applications that include, but are not limited to, the following types of approaches:

• Leveraging comprehensive data to integrate substance use-related information into knowledge graphs to inform HIV clinical care, using sources such as surveillance data, observational cohort data, clinical databases, data warehouses, clinical trial or observational data, prescription drug monitoring data, and policy databases that offer insights into relations between substance use and HIV.  
• Integrating experts into multidisciplinary teams described in this NOFO that represent perspectives on substance use disorder epidemiology, prevention, and treatment as well as persons with lived and living experience with substance use and HIV and providers that serve such clients and patients.
• Improving the identification of people who are at high risk of SUD and HIV and/or who may be candidates for PrEP and substance use treatment in healthcare settings.
• Developing methods to better identify clinical opportunities to improve durable viral suppression among persons with SUD who have been prescribed antiretroviral medication.
• Developing and evaluating novel approaches to better identify people who appear to be at highest risk for acquiring HIV and suffer the consequences of drug use and the settings or clinical contexts where they can be reached and offered services more optimally.
• Developing, validating, and deploying predictive and generative models to better characterize prevention and treatment practice patterns and provide findings to inform clinical decision-making. These models should demonstrate ways to optimize delivery of HIV therapeutic and preventive modalities and associated clinical outcomes among people who use drugs and optimize reduction in drug use and associated harms. Areas of interest also include, but are not limited to, studies that investigate methods for changing provider behavior, improving patient communication and retention in care, addressing social bias and stigma, and better addressing HIV viral non-suppression among people with OUD. 

Investigators proposing NIH-defined clinical trials may refer to the Research Methods Resources website for information about developing statistical methods and study designs.

See Section VIII. Other Information for award authorities and regulations.

Section II. Award Information

NIH grants policies as described in the NIH Grants Policy Statement will apply to the applications submitted and awards made from this NOFO.

Section III. Eligibility Information

1. Eligible Applicants

Higher Education Institutions

• Public/State Controlled Institutions of Higher Education
• Private Institutions of Higher Education

Nonprofits Other Than Institutions of Higher Education

• Nonprofits with 501(c)(3) IRS Status (Other than Institutions of Higher Education)
• Nonprofits without 501(c)(3) IRS Status (Other than Institutions of Higher Education)

For-Profit Organizations

• Small Businesses
• For-Profit Organizations (Other than Small Businesses)

Local Governments

• State Governments
• County Governments
• City or Township Governments
• Special District Governments
• Indian/Native American Tribal Governments (Federally Recognized)
• Indian/Native American Tribal Governments (Other than Federally Recognized).

Federal Governments

• Eligible Agencies of the Federal Government
• U.S. Territory or Possession

Other

• Independent School Districts
• Public Housing Authorities/Indian Housing Authorities
• Native American Tribal Organizations (other than Federally recognized tribal governments)
• Faith-based or Community-based Organizations
• Regional Organizations

Non-domestic (non-U.S.) Entities (Foreign Organizations) are not eligible to apply.

Non-domestic (non-U.S.) components of U.S. Organizations are not eligible to apply.

Foreign components, as defined in the NIH Grants Policy Statement, are allowed.

Applicant Organizations

Applicant organizations must complete and maintain the following registrations as described in the How to Apply- Application Guide to be eligible to apply for or receive an award. All registrations must be completed prior to the application being submitted. Registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. Failure to complete registrations in advance of a due date is not a valid reason for a late submission, please reference the NIH Grants Policy Statement Section 2.3.9.2 Electronically Submitted Applications for additional information.

• System for Award Management (SAM) – Applicants must complete and maintain an active registration, which requires renewal at least annually. The renewal process may require as much time as the initial registration. SAM registration includes the assignment of a Commercial and Government Entity (CAGE) Code for domestic organizations which have not already been assigned a CAGE Code.
  • NATO Commercial and Government Entity (NCAGE) Code – Foreign organizations must obtain an NCAGE code (in lieu of a CAGE code) in order to register in SAM.
  • Unique Entity Identifier (UEI) - A UEI is issued as part of the SAM.gov registration process. The same UEI must be used for all registrations, as well as on the grant application.
• eRA Commons - Once the unique organization identifier is established, organizations can register with eRA Commons in tandem with completing their Grants.gov registrations; all registrations must be in place by time of submission. eRA Commons requires organizations to identify at least one Signing Official (SO) and at least one Program Director/Principal Investigator (PD/PI) account in order to submit an application.
• Grants.gov – Applicants must have an active SAM registration in order to complete the Grants.gov registration.

Program Directors/Principal Investigators (PD(s)/PI(s))

All PD(s)/PI(s) must have an eRA Commons account.  PD(s)/PI(s) should work with their organizational officials to either create a new account or to affiliate their existing account with the applicant organization in eRA Commons. If the PD/PI is also the organizational Signing Official, they must have two distinct eRA Commons accounts, one for each role. Obtaining an eRA Commons account can take up to 2 weeks.

Any individual(s) with the skills, knowledge, and resources necessary to carry out the proposed research as the Program Director(s)/Principal Investigator(s) (PD(s)/PI(s)) is invited to work with their organization to develop an application for support.

For institutions/organizations proposing multiple PDs/PIs, visit the Multiple Program Director/Principal Investigator Policy and submission details in the Senior/Key Person Profile (Expanded) Component of the How to Apply-Application Guide.

2. Cost Sharing

This NOFO does not require cost sharing as defined in the NIH Grants Policy Statement Section 1.2 Definition of Terms.

3. Additional Information on Eligibility

Number of Applications

Applicant organizations may submit more than one application, provided that each application is scientifically distinct.

The NIH will not accept duplicate or highly overlapping applications under review at the same time, per NIH Grants Policy Statement Section 2.3.7.4 Submission of Resubmission Application. This means that the NIH will not accept:

• A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application.
• A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.
• An application that has substantial overlap with another application pending appeal of initial peer review (see NIH Grants Policy Statement 2.3.9.4 Similar, Essentially Identical, or Identical Applications).

Section IV. Application and Submission Information

1. Requesting an Application Package

The application forms package specific to this opportunity must be accessed through ASSIST, Grants.gov Workspace or an institutional system-to-system solution. Links to apply using ASSIST or Grants.gov Workspace are available in Part 1 of this NOFO. See your administrative office for instructions if you plan to use an institutional system-to-system solution.

2. Content and Form of Application Submission

It is critical that applicants follow the instructions in the Research (R) Instructions in the How to Apply - Application Guide except where instructed in this notice of funding opportunity to do otherwise. Conformance to the requirements in the Application Guide is required and strictly enforced. Applications that are out of compliance with these instructions may be delayed or not accepted for review.

Letter of Intent

Although a letter of intent is not required, is not binding, and does not enter into the review of a subsequent application, the information that it contains allows IC staff to estimate the potential review workload and plan the review.

By the date listed in Part 1. Overview Information, prospective applicants are asked to submit a letter of intent that includes the following information:

• Descriptive title of proposed activity
• Name(s), address(es), and telephone number(s) of the PD(s)/PI(s)
• Names of other key personnel
• Participating institution(s)
• Number and title of this funding opportunity

The letter of intent should be sent to:

Email: nimhpeerreview@mail.nih.gov

Page Limitations

All page limitations described in the How to Apply- Application Guide and the Table of Page Limits must be followed.

The following section supplements the instructions found in the How to Apply- Application Guide and should be used for preparing an application to this NOFO.

SF424(R&R) Cover

All instructions in the How to Apply - Application Guide must be followed.

SF424(R&R) Project/Performance Site Locations

All instructions in the How to Apply- Application Guide must be followed.

SF424(R&R) Other Project Information

All instructions in the How to Apply- Application Guide must be followed.

This NOFO requires an attachment: A Meaningful Stakeholder Engagement Plan

Meaningful Stakeholder Engagement Plan (Required -  2 page maximum)

Include a detailed plan for meaningfully engaging community members and other implementing partners, including health system and public health leaders, clinical or public health informaticians, patients/clients, families, providers or other clinical care staff, payers, and community leaders, as appropriate to the specific goals of the study.

SF424(R&R) Senior/Key Person Profile

All instructions in the How to Apply- Application Guide must be followed.

R&R or Modular Budget

All instructions in the How to Apply- Application Guide must be followed.

R&R Subaward Budget

All instructions in the How to Apply-Application Guide must be followed.

PHS 398 Cover Page Supplement

All instructions in the How to Apply- Application Guide must be followed.

PHS 398 Research Plan

All instructions in the How to Apply- Application Guide must be followed, with the following additional instructions:

Factor 1

Significance:

• Describe how the proposed research advances the state of the science in multimodal AI for HIV research, clinical care, or public health. 
• Describe the benefits of using multimodal AI over other methods for the proposed HIV-related application.
• Describe how the proposed HIV application is likely to impact HIV research, clinical care, or public health. 

Innovation:

• Describe the innovative methods that will be taken to ensure the model is extensible to allow for future HIV applications.

Factor 2

Approach

• Describe the extent to which the data modalities and data sources selected are appropriate for model building.
• Describe the methods used for developing, adapting, and integrating the knowledge graph in the multimodal AI.
• Describe how the proposed methods for developing the new or adapted knowledge graph strengthens the interpretability and explainability of the model outputs. In addition, describe the metrics and measures used for demonstrating model performance and enhanced domain explainability.
• Describe how ethical considerations will influence the research design including methods for enhancing data privacy, security, transparency, and consent.
• Describe how the timeline for the model building, development and integration of the knowledge graph, and model evaluation are technically feasible and realistic.

Factor 3

Investigators/Team:

• Within the Meaningful Stakeholder Engagement Plan, describe plans for how relevant community members and other implementing partners (which may include patients, physicians, healthy system, public health, and community leaders) will be engaged in model design and testing. Describe the metrics that will be used to measure the impact of the engagement.

Resource Sharing Plan: Individuals are required to comply with the instructions for the Resource Sharing Plans as provided in the How to Apply- Application Guide.

Specific to this RFA. The NIMH Division of AIDS Research embraces open science principles to promote the sharing of scientific resources with the broader community. As such, a resource sharing plan is required for this NOFO. Applicants must describe their plan for open dissemination of protocols, methods, models, software, and/or code, findings and related tools to the community such that they are readily usable and extensible, where applicable. The multimodal AI model, as well as the processes for developing and preparing the model (e.g., data curation, model training, model assessment), must be documented using appropriate methods (e.g., model card) to enhance transparency, accountability, and reproducibility. 

Other Plan(s): 

All instructions in the How to Apply-Application Guide must be followed, with the following additional instructions:

All applicants planning research (funded or conducted in whole or in part by NIH) that results in the generation of scientific data are required to comply with the instructions for the Data Management and Sharing Plan. All applications, regardless of the amount of direct costs requested for any one year, must address a Data Management and Sharing Plan.

To advance the goal of advancing research through widespread data sharing among researchers, investigators funded under this Notice of Funding Opportunity (NOFO) are expected to share those data via the National Institute of Mental Health Data Archive (NDA; see NOT-MH-23-100). Established by the NIH, NDA is a secure informatics platform for scientific collaboration and data-sharing that enables the effective communication of detailed research data, tools, and supporting documentation. NDA links data across research projects through its Global Unique Identifier (GUID) and Data Dictionary technology. Investigators funded under this NOFO are expected to use these technologies to submit data to NDA.

To accomplish this objective, it will be important to formulate a) an enrollment strategy that will obtain the information necessary to generate a GUID for each participant, and b) a budget strategy that will cover the costs of data submission. The NDA website provides two tools to help investigators develop appropriate strategies: 1) the NDA Data Submission Cost Model which offers a customizable Excel worksheet that includes tasks and hours for the Program Director/Principal Investigator and Data Manager to budget for data sharing; and 2) plain language text to be considered in your informed consent available from the NDA's Data Contribution page. Investigators are expected to certify the quality of all data generated by grants funded under this NOFO prior to submission to NDA and review their data for accuracy after submission. Submission of descriptive/raw data is expected semi-annually (every January 15 and July 15); submission of all other data is expected at the time of publication, or prior to the end of the grant, whichever occurs first (see NDA Sharing Regimen for more information); Investigators are expected to share results, positive and negative, specific to the cohorts and outcome measures studied. For more guidance on submitting data to NDA, refer to the NDA Data Management and Sharing Plan on the NDA website.   NDA staff will work with investigators to help them submit data types not yet defined in the NDA Data Dictionary. 

Specific to this RFA.  All data from the awarded studies are expected to be shared (or linked) using the NIMH Data Archives (NDA) consistent with the NIMH Data Management and Sharing Policy (NOT-MH-23-100).

Appendix: Only limited Appendix materials are allowed. Follow all instructions for the Appendix as described in the How to Apply- Application Guide.

• No publications or other material, with the exception of blank questionnaires or blank surveys, may be included in the Appendix.

PHS Human Subjects and Clinical Trials Information

When involving human subjects research, clinical research, and/or NIH-defined clinical trials (and when applicable, clinical trials research experience) follow all instructions for the PHS Human Subjects and Clinical Trials Information form in the How to Apply- Application Guide, with the following additional instructions:

If you answered “Yes” to the question “Are Human Subjects Involved?” on the R&R Other Project Information form, you must include at least one human subjects study record using the Study Record: PHS Human Subjects and Clinical Trials Information form or Delayed Onset Study record.

Study Record: PHS Human Subjects and Clinical Trials Information

All instructions in the How to Apply- Application Guide must be followed.

Delayed Onset Study

Note: Delayed onset does NOT apply to a study that can be described but will not start immediately (i.e., delayed start).All instructions in the How to Apply- Application Guide must be followed.

PHS Assignment Request Form

All instructions in the How to Apply- Application Guide must be followed.

3. Unique Entity Identifier and System for Award Management (SAM)

See Part 2. Section III.1 for information regarding the requirement for obtaining a unique entity identifier and for completing and maintaining active registrations in System for Award Management (SAM), NATO Commercial and Government Entity (NCAGE) Code (if applicable), eRA Commons, and Grants.gov

4. Submission Dates and Times

Part I. contains information about Key Dates and times. Applicants are encouraged to submit applications before the due date to ensure they have time to make any application corrections that might be necessary for successful submission. When a submission date falls on a weekend or Federal holiday, the application deadline is automatically extended to the next business day.

Organizations must submit applications to Grants.gov (the online portal to find and apply for grants across all Federal agencies). Applicants must then complete the submission process by tracking the status of the application in the eRA Commons, NIH’s electronic system for grants administration. NIH and Grants.gov systems check the application against many of the application instructions upon submission. Errors must be corrected and a changed/corrected application must be submitted to Grants.gov on or before the application due date and time.  If a Changed/Corrected application is submitted after the deadline, the application will be considered late. Applications that miss the due date and time are subjected to the NIH Grants Policy Statement Section 2.3.9.2 Electronically Submitted Applications.

Applicants are responsible for viewing their application before the due date in the eRA Commons to ensure accurate and successful submission.

Information on the submission process and a definition of on-time submission are provided in the How to Apply-Application Guide.

5. Intergovernmental Review (E.O. 12372)

This initiative is not subject to intergovernmental review.

Use of Common Data Elements in NIH-funded Research

Many NIH ICs encourage the use of common data elements (CDEs) in basic, clinical, and applied research, patient registries, and other human subject research to facilitate broader and more effective use of data and advance research across studies. CDEs are data elements that have been identified and defined for use in multiple data sets across different studies. Use of CDEs can facilitate data sharing and standardization to improve data quality and enable data integration from multiple studies and sources, including electronic health records. NIH ICs have identified CDEs for many clinical domains (e.g., neurological disease), types of studies (e.g. genome-wide association studies (GWAS)), types of outcomes (e.g., patient-reported outcomes), and patient registries (e.g., the Global Rare Diseases Patient Registry and Data Repository). NIH has established a “Common Data Element (CDE) Resource Portal" (http://cde.nih.gov/) to assist investigators in identifying NIH-supported CDEs when developing protocols, case report forms, and other instruments for data collection. The Portal provides guidance about and access to NIH-supported CDE initiatives and other tools and resources for the appropriate use of CDEs and data standards in NIH-funded research. Investigators are encouraged to consult the Portal and describe in their applications any use they will make of NIH-supported CDEs in their projects.

NIMH expects investigators for this funding announcement to collect Common Data Elements (CDEs) for HIV-related human subjects research funded by NIMH (NOT-MH-23-105). Unless NIMH stipulates otherwise during the negotiation of the terms and conditions of a grant award, this Notice applies to all grant applications involving human research participants. The necessary funds for collecting and submitting these CDEs from all research participants to the NIMH Data Archive (NDA) should be included in the requested budget. A cost estimator (https://nda.nih.gov/ndarpublicweb/Documents/NDA_Data_Submission_Costs.xlsx) is available to facilitate the calculation of these costs. NIMH may seek further information regarding CDEs prior to award. Details for HIV-funded NIMH research can be found at NOT-MH-23-105 and the NIMH webpage on Data Management and Sharing for Applicants and Awardees. The NIMH CDEs for HIV-funded research should be briefly described in the Data Management and Sharing Plan.

6. Funding Restrictions

All NIH awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement.

Pre-award costs are allowable only as described in the NIH Grants Policy Statement Section 7.9.1 Selected Items of Cost.

Applications must be submitted electronically following the instructions described in the How to Apply - Application Guide. Paper applications will not be accepted.

Applicants must complete all required registrations before the application due date. Section III. Eligibility Information contains information about registration.

For assistance with your electronic application or for more information on the electronic submission process, visit How to Apply – Application Guide. If you encounter a system issue beyond your control that threatens your ability to complete the submission process on-time, you must follow the Dealing with System Issues guidance. For assistance with application submission, contact the Application Submission Contacts in Section VII.

Important reminders:

All PD(s)/PI(s) must include their eRA Commons ID in the Credential field of the Senior/Key Person Profile form. Failure to register in the Commons and to include a valid PD/PI Commons ID in the credential field will prevent the successful submission of an electronic application to NIH. See Section III of this NOFO for information on registration requirements.

The applicant organization must ensure that the unique entity identifier provided on the application is the same identifier used in the organization’s profile in the eRA Commons and for the System for Award Management. Additional information may be found in the How to Apply - Application Guide.

See more tips for avoiding common errors.

Upon receipt, applications will be evaluated for completeness and compliance with application instructions by the Center for Scientific Review and for responsiveness by NIMH. Applications that are incomplete, non-compliant and/or nonresponsive will not be reviewed.

Recipients or subrecipients must submit any information related to violations of federal criminal law involving fraud, bribery, or gratuity violations potentially affecting the federal award. See Mandatory Disclosures, 2 CFR 200.113 and NIH Grants Policy Statement Section 4.1.35.

Send written disclosures to the NIH Chief Grants Management Officer listed on the Notice of Award for the IC that funded the award and to the HHS Office of Inspector Grant Self Disclosure Program at grantdisclosures@oig.hhs.gov.

Post Submission Materials

Applicants are required to follow the instructions for post-submission materials, as described in the policy

Section V. Application Review Information

1. Criteria

Only the review criteria described below will be considered in the review process. Applications submitted to the NIH in support of the NIH mission are evaluated for scientific and technical merit through the NIH peer review system.

Reviewers will provide an overall impact score to reflect their assessment of the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved, in consideration of the following scored review criteria and additional review criteria (as applicable for the project proposed).  An application does not need to be strong in all categories to be judged likely to have a major scientific impact.

Reviewers will consider Factors 1, 2 and 3 in the determination of scientific merit, and in providing an overall impact score. In addition, Factors 1 and 2 will each receive a separate factor score. 

As applicable for the project proposed, reviewers will consider the following additional items while determining scientific and technical merit, but will not give criterion scores for these items, and should consider them in providing an overall impact score.

As applicable for the project proposed, reviewers will consider each of the following items, but will not give scores for these items, and should not consider them in providing an overall impact score.

2. Review and Selection Process

Applications will be evaluated for scientific and technical merit by (an) appropriate Scientific Review Group(s) convened by NIMH, in accordance with NIH peer review policies and practices, using the stated review criteria. Assignment to a Scientific Review Group will be shown in the eRA Commons.

As part of the scientific peer review, all applications will receive a written critique.

Applications may undergo a selection process in which only those applications deemed to have the highest scientific and technical merit (generally the top half of applications under review) will be discussed and assigned an overall impact score.

Appeals of initial peer review will not be accepted for applications submitted in response to this NOFO.

Applications will be assigned on the basis of established PHS referral guidelines to the appropriate NIH Institute or Center. Applications will compete for available funds with all other recommended applications submitted in response to this NOFO. Following initial peer review, recommended applications will receive a second level of review by the appropriate national Advisory Council or Board. The following will be considered in making funding decisions, consistent with applicable law.

• Scientific and technical merit of the proposed project as determined by scientific peer review.
• Availability of funds.
• Relevance of the proposed project to program priorities.

Please note that reviewers will not consider race, ethnicity, age, or sex of a researcher, award participant, or trainee, even in part, in providing critiques, scores, or funding recommendations. NIH will not consider such factors in making its funding decisions.

If the application is under consideration for funding, NIH will request "just-in-time" information from the applicant as described in the NIH Grants Policy Statement Section 2.5.1. Just-in-Time Procedures. This request is not a Notice of Award nor should it be construed to be an indicator of possible funding.

Prior to making an award, NIH reviews an applicant’s federal award history in SAM.gov to ensure sound business practices. An applicant can review and comment on any information in the Responsibility/Qualification records available in SAM.gov. NIH will consider any comments by the applicant in the Responsibility/Qualification records in SAM.gov to ascertain the applicant’s integrity, business ethics, and performance record of managing Federal awards per 2 CFR Part 200.206 “Federal awarding agency review of risk posed by applicants.” This provision will apply to all NIH grants and cooperative agreements except fellowships.

3. Anticipated Announcement and Award Dates

After the peer review of the application is completed, the PD/PI will be able to access his or her Summary Statement (written critique) via the eRA Commons. Refer to Part 1 for dates for peer review, advisory council review, and earliest start date.

Information regarding the disposition of applications is available in the NIH Grants Policy Statement Section 2.4.4 Disposition of Applications.

Section VI. Award Administration Information

1. Award Notices

A Notice of Award (NoA) is the official authorizing document notifying the applicant that an award has been made and that funds may be requested from the designated HHS payment system or office. The NoA is signed by the Grants Management Officer and emailed to the recipient’s business official.

In accepting the award, the recipient agrees that any activities under the award are subject to all provisions currently in effect or implemented during the period of the award, other Department regulations and policies in effect at the time of the award, and applicable statutory provisions.

Recipients must comply with any funding restrictions described in Section IV.6. Funding Restrictions. Any pre-award costs incurred before receipt of the NoA are at the applicant's own risk.  For more information on the Notice of Award, please refer to the NIH Grants Policy Statement Section 5. The Notice of Award and NIH Grants & Funding website, see Award Process.

The NIMH has published policies and guidance for investigators regarding human research protection, data and safety monitoring, Independent Safety Monitors and Data and Safety Monitoring Boards, reportable events, and participant recruitment monitoring (NOT-MH-19-027). The application’s PHS Human Subjects and Clinical Trials Information should reflect the manner in which these policies will be implemented for each study record. These plans will be reviewed by the NIMH for consistency with NIMH and NIH policies and federal regulations. The NIMH will expect clinical trials to be conducted in accordance with these policies including, but not limited to: timely registration to ClinicalTrials.gov, submission of review determinations from the clinical trial’s data and safety monitoring entity (at least annually), timely submission of reportable events as prescribed, and establishment of recruitment milestones and progress reporting

Individual awards are based on the application submitted to, and as approved by, the NIH and are subject to the IC-specific terms and conditions identified in the NoA.

ClinicalTrials.gov: If an award provides for one or more clinical trials. By law (Title VIII, Section 801 of Public Law 110-85), the "responsible party" must register and submit results information for certain “applicable clinical trials” on the ClinicalTrials.gov Protocol Registration and Results System Information Website (https://register.clinicaltrials.gov). NIH expects registration and results reporting of all trials whether required under the law or not. For more information, see https://grants.nih.gov/policy/clinical-trials/reporting/index.htm

Institutional Review Board or Independent Ethics Committee Approval: Recipient institutions must ensure that all protocols are reviewed by their IRB or IEC. To help ensure the safety of participants enrolled in NIH-funded studies, the recipient must provide NIH copies of documents related to all major changes in the status of ongoing protocols.

Data and Safety Monitoring Requirements: The NIH policy for data and safety monitoring requires oversight and monitoring of all NIH-conducted or -supported human biomedical and behavioral intervention studies (clinical trials) to ensure the safety of participants and the validity and integrity of the data. Further information concerning these requirements is found at http://grants.nih.gov/grants/policy/hs/data_safety.htm and in the application instructions (SF424 (R&R) and PHS 398).

Investigational New Drug or Investigational Device Exemption Requirements: Consistent with federal regulations, clinical research projects involving the use of investigational therapeutics, vaccines, or other medical interventions (including licensed products and devices for a purpose other than that for which they were licensed) in humans under a research protocol must be performed under a Food and Drug Administration (FDA) investigational new drug (IND) or investigational device exemption (IDE).

2. Administrative and National Policy Requirements

The following Federal wide and HHS-specific policy requirements apply to awards funded through NIH:

• The rules listed at 2 CFR Part 200, Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards.
• All NIH grant and cooperative agreement awards include the NIH Grants Policy Statement as part of the terms and conditions in the Notice of Award (NoA). The NoA includes the requirements of this NOFO. For these terms of award, see the NIH Grants Policy Statement Part II: Terms and Conditions of NIH Grant Awards, Subpart A: General and Part II: Terms and Conditions of NIH Grant Awards, Subpart B: Terms and Conditions for Specific Types of Grants, Recipients, and Activities.
• If a recipient receives an award, the recipient must follow all applicable nondiscrimination laws. The recipient agrees to this when registering in SAM.gov. The recipient must also submit an Assurance of Compliance (HHS-690). To learn more, see the Laws and Regulations Enforced by the HHS Office for Civil Rights website.
  • HHS recognizes that NIH research projects are often limited in scope for many reasons that are nondiscriminatory, such as the principal investigator’s scientific interest, funding limitations, recruitment requirements, and other considerations. Thus, criteria in research protocols that target or exclude certain populations are warranted where nondiscriminatory justifications establish that such criteria are appropriate with respect to the health or safety of the subjects, the scientific study design, or the purpose of the research. For additional guidance regarding how the provisions apply to NIH grant programs, please contact the Scientific/Research Contact that is identified in Section VII under Agency Contacts of this NOFO.

All federal statutes and regulations relevant to federal financial assistance, including those highlighted in NIH Grants Policy Statement Section 4 Public Policy Requirements, Objectives and Other Appropriation Mandates.

Recipients are responsible for ensuring that their activities comply with all applicable federal regulations.  NIH may terminate awards under certain circumstances.  See 2 CFR Part 200.340 Termination and NIH Grants Policy Statement Section 8.5.2 Remedies for Noncompliance or Enforcement Actions: Suspension, Termination, and Withholding of Support. 

Successful recipients under this NOFO agree that:

Where the award funding involves implementing, acquiring, or upgrading health IT for activities by any funded entity, recipients and subrecipient(s) are required to: Use health IT that meets standards and implementation specifications adopted in 45 CFR part 170, Subpart B, if such standards and implementation specifications can support the activity.  Visit https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-D/part-170/subpart-B to learn more.

Where the award funding involves implementing, acquiring, or upgrading health IT for activities by eligible clinicians in ambulatory settings, or hospitals, eligible under Sections 4101, 4102, and 4201 of the HITECH Act, use health IT certified under the ONC Health IT Certification Program if certified technology can support the activity. Visit https://www.healthit.gov/topic/certification-ehrs/certification-health-it to learn more.

Pursuant to the Cybersecurity Act of 2015, Div. N, § 405, Pub. Law 114-113, 6 USC § 1533(d), the HHS Secretary has established a common set of voluntary, consensus-based, and industry-led guidelines, best practices, methodologies, procedures, and processes.

Successful recipients under this NOFO agree that:

When recipients, subrecipients, or third-party entities have:

1. ongoing and consistent access to HHS owned or operated information or operational technology systems; and 
2. receive, maintain, transmit, store, access, exchange, process, or utilize personal identifiable information (PII) or personal health information (PHI) obtained from the awarding HHS agency for the purposes of executing the award.

Recipients shall develop plans and procedures, modeled after the NIST Cybersecurity framework, to protect HHS systems and data. Please refer to NIH Post-Award Monitoring and Reporting for additional information. 

Not Applicable

Consistent with the 2023 NIH Policy for Data Management and Sharing, when data management and sharing is applicable to the award, recipients will be required to adhere to the Data Management and Sharing requirements as outlined in the NIH Grants Policy Statement. Upon the approval of a Data Management and Sharing Plan, it is required for recipients to implement the plan as described.

4. Reporting

When multiple years are involved, recipients will be required to submit the Research Performance Progress Report (RPPR) annually and financial statements as required in the NIH Grants Policy Statement Section 8.4.1 Reporting. To learn more about post-award monitoring and reporting, see the NIH Grants & Funding website, see Post-Award Monitoring and Reporting.

A final RPPR, invention statement, and the expenditure data portion of the Federal Financial Report are required for closeout of an award, as described in the NIH Grants Policy Statement Section 8.6 Closeout. NIH NOFOs outline intended research goals and objectives. Post award, NIH will review and measure performance based on the details and outcomes that are shared within the RPPR, as described at 2 CFR Part 200.301.

Section VII. Agency Contacts

We encourage inquiries concerning this funding opportunity and welcome the opportunity to answer questions from potential applicants.

eRA Service Desk (Questions regarding ASSIST, eRA Commons, application errors and warnings, documenting system problems that threaten submission by the due date, and post-submission issues)

Finding Help Online: https://www.era.nih.gov/need-help (preferred method of contact)
Telephone: 301-402-7469 or 866-504-9552 (Toll Free)

General Grants Information (Questions regarding application instructions, application processes, and NIH grant resources)
Email: GrantsInfo@nih.gov (preferred method of contact)
Telephone: 301-480-7075

Grants.gov Customer Support (Questions regarding Grants.gov registration and Workspace)
Contact Center Telephone: 800-518-4726
Email: support@grants.gov

Lori A.J. Scott-Sheldon, Ph.D. 
National Institute of Mental Health (NIMH)
Telephone: 301-792-2309
E-mail: lori.scott-sheldon@nih.gov

Tamara Haegerich, Ph.D.
National Institute of Drug Abuse (NIDA)
Telephone: 301-443-1185
Email: Tamara.Haegerich@nih.gov

Nicholas Gaiano, Ph.D.
National Institute of Mental Health (NIMH)
Telephone: 301-827-3420
Email: nick.gaiano@nih.gov

Rita Sisco
National Institute of Mental Health (NIMH)
Telephone: 301-443-2805
Email:siscor@mail.nih.gov

Pamela G Fleming
NIDA - National Institute on Drug Abuse (NIDA)
Phone: 301-480-1159
E-mail: pfleming@mail.nih.gov

Section VIII. Other Information

Recently issued trans-NIH policy notices may affect your application submission. A full list of policy notices published by NIH is provided in the NIH Guide for Grants and Contracts. All awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement.

Awards are made under the authorization of Sections 301 and 405 of the Public Health Service Act as amended (42 USC 241 and 284) and under Federal Regulations 42 CFR Part 52 and 2 CFR Part 200.