Key Dates

Solicitation Release Date: April 25th, 2023

Application Due Date: June 26th, 2023

Program Start Date: September 1st, 2023

Issued by

Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD)

Website: https://aim-ahead.net

Purpose 

This Call for Proposals aims to solicit applications to develop the workforce capacity and opportunities for minority-serving institutions to participate in the development and operations of AI and data infrastructure. The program uses a two-phased approach to develop institutional capacity in the data and infrastructure and become partners with the AIM-AHEAD Coordinating Center that will support and supplement the activities of the Data and Research Core and the Infrastructure Core. In planning Phase I of this program, we solicit planning grant applications that propose to develop and demonstrate institutional capacity in data and infrastructure through needs assessment and development of partnerships and workforce capacity to be competitive for Phase II of implementation of the Data and Infrastructure Programs and become partners of the AIM-AHEAD Coordinating Center.

We are planning a two-phased approach. During Phase 1, successful applicants will carry out activities needed to prepare competitive Phase 2 applications. We anticipate that Phase 1 activities will include:  introspective assessments of current infrastructure and workforce capabilities and gaps, partnerships with leading institutions to share expertise and create vibrant learning communities, outward assessments, and partnerships with the ACC to understand the needs of Phase 2.

Phase I planning awards are expected to include the following activities, but participants are encouraged to propose additional planning activities if they contribute to the development of a robust DICB application:

• Development of multi-disciplinary and/or multi-institutional partnerships between relevant organizations, including other minority-serving institutions, research-intensive institutions, institutions/departments with robust AI/ML and data repository, harmonization and curation capabilities, professional societies, and community-serving organizations, to establish a single new partner in one of the four focus areas of DICB described below. We anticipate team partnerships between faculty, data scientists, IT departments, ethical/legal experts, and low-resource and high-resource institutions toward the common goals of this program.
• Detailed assessment of both A) workforce AI/ML training and development needs and B) existing AI/ML data and infrastructure capacity of institutions and/or organizations within the proposed application and an analysis of how successful activities may be effectively extended through the Phase II award.
• Identification of implementation strategy and/or development of open-source AI/ML tools, data sources, governance models, data and infrastructure security processes, data curation and harmonization processes will be included in the Phase II application.
• Demonstration of institutional capacity, including identification, and inclusion of various expertise needed for accomplishing one or more goals of the four focus areas of DICB, including assessment of data and infrastructure capacity and development of partnerships.
• Development of the workforce capacity through training in the development and operation of data and infrastructure.

Phase II (implementation phase) will follow Phase I. Phase I planning award awardees will be invited to submit applications with a detailed plan for Phase II activities in the focus area of their Phase I award. The plan will be reviewed for scientific and programmatic alignment with the AIM-AHEAD coordinating center. We anticipate one team award per focus area in Phase II. Phase II applicants will be selected based on Phase I performance, engagement with A-CC cores and activities, scientific and programmatic alignment with A-CC goals, and the robustness of their plan for Phase II.

Phase II awardees (total 4, one in each focus area) are expected to contribute to the following activities as AIM-AHEAD consortium partners:

• Integrate their activities with the other partners in the Data and Research Core (DRC) and Infrastructure Core (IC) of the AIM-AHEAD Coordinating Center.
• Provide data and infrastructure concierge services/support, workforce development, and training for the future stakeholders of the AIM-AHEAD Consortium.
• Strategize and establish best practices for data sharing, harmonization, and AI/ML infrastructure needs of AIM-AHEAD Consortium partners and trainees.
• Integrate access to data, infrastructure, and training content for consortium stakeholders using the AIM-AHEAD Connect Platform.

In planning Phase I of this program, we solicit planning award applications that propose to develop and demonstrate institutional capacity in data and infrastructure through needs assessment and development of partnerships and workforce capacity to be competitive for Phase II of implementation of the Data and Infrastructure Programs and become partners of the AIM-AHEAD Coordinating Center. Each phase is anticipated to be 12 months in duration. It is anticipated that 8 awards (two in each of the focus areas) will be made in phase I, and 4 awards (one in each of the focus areas) will be made in phase II.

DICB Focus Areas and Expected Outcomes

Planning award applications in Phase I should be developed in one of the following focus areas. Each application will select one focus area for its institutional capacity building.  Successful Phase I awardees will be invited to submit a Phase II application to implement their focus area for the AIM-AHEAD Coordinating Center (ACC). A key component for success in AIM-AHEAD is the ability to work collaboratively across teams. In addition to addressing a specific focus area, applications should include how they plan to work collaboratively with the other DICB funded projects.

1. Research Data Repository to Host AIM-AHEAD Generated Datasets:

   The overarching goals of this focus area (in a two-phased approach) are, in Phase I,  to conduct the feasibility study, capability assessment, partnerships (between low-resource and high-resource institutions), and workforce development by the applicant awardee, and, in Phase II, to design and develop an AIM-AHEAD data repository (working closely and sharing best practices with the Infrastructure Core) to host datasets currently in development through the data and research core (DRC), and generated by other AIM-AHEAD programs and partners. The data repository will host de-identified, synthetic, and research datasets (with participant consent for data use) for research and training use by AIM-AHEAD Consortium members and trainees. This is an opportunity to foster the adoption of standardized data formats and common data models for data harmonization. The awardee (as integral part of ACC) will seek to understand the type of data and data resources with consideration for health disparities research, including EHR, synthetic data, social determinants, and other types of data. The multidisciplinary awardee will demonstrate institutional capabilities and expertise in the design and development of different data infrastructure modalities, capabilities and expertise in cybersecurity, and attaining, maintaining, and providing services in security standards (such as FISMA, HIPAA, FedRAMP, etc.) while at the same time, ensuring the data is maintained and governed to preserve privacy and autonomy. In Phase I, awardees should develop the capacity and knowledge of cybersecurity standards and compliance to meet federal security standards through needs assessment and workforce development. Awardees in Phase I will work closely with ACC partners to train and acquire knowledge and skills to comply with the NIH approval process in cybersecurity and data governance required to host AIM-AHEAD datasets. In Phase II, the awardees must demonstrate institutional capabilities and expertise in cybersecurity, attain compliance with security standards (such as FISMA, HIPAA, FedRAMP, etc.), and demonstrate the capacity to provide services in cybersecurity. Awardees in Phase II will be required to go through the NIH approval process in cybersecurity and data governance prior to hosting and managing AIM-AHEAD datasets.

2. Resource Center for Data Curation, Linkages, and Harmonization Datasets:

   The overarching goals of this focus area (in a two-phased approach) are to design and implement robust data pipelines to support data curation, linking and harmonization, and data preprocessing for AI-readiness, for the datasets that are generated by AIM-AHEAD programs to be hosted in the research data repository. In Phase I (planning phase), the awardee teams (as integral part of ACC) will work closely with its partner (research-intensive institutions with proven capacity for data curation, harmonization, and AI data readiness),  the Data and Research Core, the other research data repository awardee (planning phase), and other DICB awardees to address data linkages and curation priorities, research strategies, and best practices in data harmonization from multiple data sources including social determinants of health data. The awardee teams will acquire and demonstrate the skills and knowledge needed for:  data acquisition, data access models, participant consent, privacy protection, cybersecurity, data sharing and management, data curation, linking, and harmonization for datasets. In Phase II, the awardee team will share insights and methods with other AIM-AHEAD Consortium partners in data acquisition, data access models, cybersecurity, data sharing and management, data curation, linking, and harmonization for datasets. The goal is to support research data pipelines to form an inclusive basis for AI/ML use cases to illuminate strategies and approaches to ameliorating health disparities. An understanding of the linkages and harmonization will be used to drive the data and computing infrastructure design and associated data governance models and training offerings for the consortium stakeholders.

3. Resource Center for Data Access and Data Governance:

   The overarching goals of this focus area (in a two-phased approach) are to support the data and research core, by establishing a resource center for data access and data governance needs to support the consortium stakeholders. With this focus area we are seeking organizations with the potential to develop expertise in data access and governance policy and data sharing models as a means to AI/ML capacity building to advance health equity. In Phase I, the awardee (as integral part of ACC) will determine options and needs of AIM-AHEAD consortium stakeholders related to data sharing, data access (e.g., types of data access control), oversight, regulatory policies, and governance that facilitate AI/ML, including, as appropriate, options for distributed/federated learning and supporting the technical needs of the consortium members who range from novice to expert in AI/ML.  In Phase II, the awardee will partner with the ACC to enhance the capacity of consortium institutions and organizations to develop knowledge of data governance, DUA, regulatory policies, cybersecurity, data management issues, incidences, types of data access control, and ethical considerations in data management; when data sharing, they will give particular attention to tribal sovereignty treaties, laws, regulations, and preferences regarding their data, as well as social determinants of health data and data from vulnerable populations. In addition, they will develop skills and knowledge in identifying appropriate data infrastructure modalities (centralized Repository vs. federated and distributed) based on the type of datasets and stakeholder needs, including tribal laws).

4. Resource Center to Provide Concierge Service to Support Open-source AI/ML Tools:

   The overarching goals of this focus area (in a two-phased approach) is to bring new awardees to be new collaborators with the ACC to establish a resource center to provide real-time support and concierge service for AI/ML tools, cloud computing, open-source machine learning/deep learning tools, data analysis and preprocessing, and other support needed. In Phase I, the awardee will research, acquire, and develop skills in open-source and cloud-based AI/ML and relevant software and technologies. As an integral part of the ACC, awardees in Phase I will develop phase II activity for providing individualized technical assistance to A-CC stakeholders with AI/ML tools, training models, validation, algorithm optimization, development of AI applications, and research. Phase II awardees will provide services, including hosting regular office hours and providing real-time support using the AIM-AHEAD Connect help desk. Awardees will create knowledge articles to support A-CC stakeholders and projects to leverage open-source and cloud-based AI/ML tools and services.

   There are a wide variety of data and computing infrastructure options to facilitate AI/ML. Cloud platforms, for example, integrate data storage, compute clusters, security, and, often, analysis tools for geographically distributed users and groups. Distributed or federated learning approaches are more appropriate when data cannot be pooled. In Phase II, the awardee team will consider the needs and constraints of AIM-AHEAD Consortium partners regarding different data and computing infrastructure, tools, and governance models, including data policy and organizational models, to provide concierge service and support on these tools. The awardee will support workforce development and training in AI tools and infrastructure resources for AIM-AHEAD Consortium members, including faculty, staff, and students with varying levels of AI/ML technical skills.

Overview of AIM-AHEAD

The National Institutes of Health’s Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program has established mutually beneficial, coordinated, and trusted partnerships to enhance the participation and representation of researchers and communities currently underrepresented in the development of AI/ML models and to improve the capabilities of this emerging technology, beginning with electronic health records (EHR) and extending to other diverse data to address health disparities and inequities. The rapid increase in the volume of data generated through electronic health records (EHR) and other biomedical research presents exciting opportunities for developing data science approaches (e.g., AI/ML methods) for biomedical research and improving healthcare. Many challenges hinder more widespread use of AI/ML technologies, such as the cost, capability for widespread application, and access to appropriate infrastructure, resources, and training. Additionally, lack of diversity of both data and researchers in the AI/ML field runs the risk of creating and perpetuating harmful biases in its practice, algorithms, and outcomes, thus fostering continued health disparities and inequities. Many underrepresented and underserved communities, which are often disproportionately affected by diseases and health conditions, have the potential to contribute expertise, data, diverse recruitment strategies, and cutting-edge science, and to inform the field on the most urgent research questions, but may lack financial, infrastructural, and data science training capacity to apply AI/ML approaches to research questions of interest to them. This program seeks to enhance trust within the communities impacted by health disparities and inequities.

The National Institutes of Health is committed to leveraging the potential of AI/ML to accelerate the pace of biomedical innovation, while prioritizing and addressing health disparities and inequities. Tackling the complex drivers of health disparities and inequities requires an innovative and transdisciplinary framework that transcends scientific and organizational silos. Mutually beneficial and trusted partnerships can be established to enhance the participation and representation of researchers and communities currently underrepresented in AI/ML modeling and application, and improve the capabilities of data curation and this emerging technology.

The AIM-AHEAD Coordinating Center is a consortium of institutions and organizations that have a core mission to serve minorities and other under-represented or underserved groups impacted by health disparities.

The AIM-AHEAD Coordinating Center consists of 4 Cores:

• Administration/Leadership Core - Lead, recruit, and coordinate the AIM-AHEAD Consortium.
• Data Science Training Core - Assess, develop, and implement data science training curriculum.
• Data and Research Core - Address research priorities and needs to form an inclusive basis for AI/ML.
• Infrastructure Core - Assess data, computing, and software infrastructure to facilitate AI/ML and health disparities research.

The AIM-AHEAD Program seeks to support multi-disciplinary research projects that use artificial intelligence/machine learning (AI/ML) to develop novel algorithms and approaches to address health disparities and inequities in alignment with the AIM-AHEAD North Stars in populations that experience health disparities in the US. The AIM-AHEAD Program is interested in research projects that use new, real-world data, or synthetic and existing datasets, such as electronic health records (EHR), image data, and social determinants of health (SDOH), to develop and enhance AI/ML algorithms and AI/ML applications that have potential to reduce health disparities while improving healthcare and outcomes.

AIM-AHEAD North Stars 

• North Star (I)     Develop a diverse, equitable, and inclusive AI/ML workforce.
• North Star (II)    Increase knowledge, awareness, and national-scale community engagement/empowerment in AI/ML.
• North Star (III)   Use AI/ML to address disparities and minority health in behavioral health, cardiometabolic health, and cancer. ​
• North Star (IV)   Build community capacity and infrastructure in AI/ML to address community-centric health disparities and minority health.

Eligibility

Applications to this solicitation may propose partnerships with AI-intensive institutions. However, a key review criterion will be that the proposed plan feasibly leads to sustainable capacity-building in minority serving institutions which meet the eligibility criteria below.  

Eligible Organizations 

Consistent with the goals of the AIM-AHEAD Coordinating Center, the following types of higher education and other institutions/organizations are highly encouraged to apply for support:

Higher Education Institutions:

• Public/State Controlled Institutions of Higher Education

• Private Institutions of Higher Education

The following types of Higher Education Institutions are always encouraged to apply for NIH support as Public or Private Institutions of Higher Education:

• Hispanic Serving Institutions (HSIs)

• Historically Black Colleges and Universities (HBCUs)

• Tribally Controlled Colleges and Universities (TCCUs)

• Alaska Native and Native Hawaiian Serving Institutions (ANNHSIs)

• Asian American Native American Pacific Islander Serving Institutions (AANAPISIs)

To be eligible for this FOA, the applicant institution must be a domestic institution located in the United States and its territories which:

• Has received an average of less than $50 million per year of NIH support for the past three fiscal years;

• Awards degrees in the health professions or the sciences related to health; and

• Has a historical and current mission to educate students from any of the populations that have been identified as underrepresented in biomedical research as defined by the National Science Foundation NSF (see http://www.nsf.gov/statistics/wmpd/) (i.e., African Americans or Blacks, Hispanic or Latino Americans, American Indians, Alaska Natives, Native Hawaiians, U.S. Pacific Islanders, and persons with disabilities), or has a documented record of: (1) recruiting, training and/or educating, and graduating underrepresented students as defined by NSF (see above), which has resulted in increasing the institution's contribution to the national pool of graduates from underrepresented backgrounds who pursue biomedical research careers and, (2) for institutions that deliver health care services, providing clinical services to medically underserved communities.

Eligible Applicants

1. Individuals from racial and ethnic groups that have been shown by the National Science Foundation to be underrepresented in health-related sciences on a national basis (see data at http://www.nsf.gov/statistics/showpub.cfm?TopID=2&SubID=27 and especially the report Women, Minorities, and Persons with Disabilities in Science and Engineering). The following racial and ethnic groups have been shown to be underrepresented in biomedical research: Blacks or African Americans, Hispanics or Latinos, American Indians or Alaska Natives, Native Hawaiians and other Pacific Islanders. In addition, it is recognized that underrepresentation can vary from setting to setting; individuals from racial or ethnic groups that can be demonstrated convincingly to be underrepresented by the awardee institution should be encouraged to participate in NIH programs to enhance diversity. For more information on racial and ethnic categories and definitions, see the OMB Revisions to the Standards for Classification of Federal Data on Race and Ethnicity (https://www.govinfo.gov/content/pkg/FR-1997-10-30/html/97-28653.htm).
2. Individuals with disabilities, who are defined as those with a physical or mental impairment that substantially limits one or more major life activities, as described in the Americans with Disabilities Act of 1990, as amended. See NSF data at, https://www.nsf.gov/statistics/2017/nsf17310/static/data/tab7-5.pdf.
3. Individuals from disadvantaged backgrounds, defined as those who meet two or more of the following criteria:
   1. Were or currently are homeless, as defined by the McKinney-Vento Homeless Assistance Act (Definition: https://nche.ed.gov/mckinney-vento/);
   2. Were or currently are in the foster care system, as defined by the Administration for Children and Families (Definition: https://www.acf.hhs.gov/cb/focus-areas/foster-care);
   3. Were eligible for the Federal Free and Reduced Lunch Program for two or more years (Definition: https://www.fns.usda.gov/school-meals/income-eligibility-guidelines);
   4. Have/had no parents or legal guardians who completed a bachelor’s degree (see https://nces.ed.gov/pubs2018/2018009.pdf);
   5. Were or currently are eligible for Federal Pell grants (Definition: https://www2.ed.gov/programs/fpg/eligibility.html);
   6. Received support from the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) as a parent or child (Definition: https://www.fns.usda.gov/wic/wic-eligibility-requirements);
   7. Grew up in one of the following areas: a) a U.S. rural area, as designated by the Health Resources and Services Administration (HRSA) Rural Health Grants Eligibility Analyzer (https://data.hrsa.gov/tools/rural-health), or b) a Centers for Medicare and Medicaid Services-designated Low-Income and Health Professional Shortage Areas (qualifying zip codes are included in the file). Only one of the two possibilities in #7 can be used as a criterion for the disadvantaged background definition.
4. Literature shows that women from the above backgrounds (categories A and B) face particular challenges at the graduate level and beyond in scientific fields. (See, e.g., From the NIH: A Systems Approach to Increasing the Diversity of Biomedical Research Workforce https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008902/).

    

   Women have been shown to be underrepresented in doctorate-granting research institutions at senior faculty levels in most biomedical-relevant disciplines, and may also be underrepresented at other faculty levels in some scientific disciplines (See data from the National Science Foundation National Center for Science and Engineering Statistics: Women, Minorities, and Persons with Disabilities in Science and Engineering, special report available at https://www.nsf.gov/statistics/2017/nsf17310/, especially Table 9-23, describing science, engineering, and health doctorate holders employed in universities and 4-year colleges, by broad occupation, sex, years since doctorate, and faculty rank).

Applicants must be:

• Independent investigators, researchers or faculty (i.e., must meet their institutions’ and organization’s eligibility to apply for independent awards) or hold faculty/staff appointments.
• At least one Co-PI must be an early-career (assistant professor or equivalent) or mid-career (associate professor or equivalent) investigator.
• At least one Co-PI must work in AI/ML or computational methods
• At least one Co-PI must work in health disparities research or work in a divergent/disparate discipline or one of the major focus areas of the AIM-AHEAD program/North Stars
• This award is not intended for individuals in research training or fellowship positions.

Number of Applications

Only one application per institution (normally identified by having a unique DUNS number or NIH IPF number) is allowed as the lead institution for each focus area. An institution can apply as a lead or participant institution for multiple focus areas. Each application will select one focus area for its institutional capacity building.

Phase I Application Review Considerations

The following describe review considerations for Phase I applications.  Criteria for phase II will be released at the time of phase II application solicitation.  Each phase I application will be reviewed based on one Focus Area selected: 

1. Research Data Repository to Host AIM-AHEAD Generated Datasets:
   • To what extent the proposed planning phase of the research data repository encompass the needs of the types of data it will host (e.g., EHR, synthetic data, social determinants, etc.) to further health disparities research?
   • To what extent does the proposed planning phase of the research data repository implementation demonstrate capabilities and expertise in cybersecurity and attaining, maintaining, and providing services in security standards (such as FISMA, HIPAA, FedRAMP, etc.)?
   • To what extent the proposed application describe how the data will be maintained and governed to preserve privacy and autonomy, especially for populations with more data sensitivity needs?
   • To what extent does the proposed application demonstrate the potential to develop data infrastructure to improve access to data generated by AIM-AHEAD sponsored projects, including those by early-career and/or under-represented researchers?
   • To what extent does the application discuss how the team will work collaboratively with other DICB awardees across the focus areas?
2. Resource Center for Data Curation, Linkages, and Harmonization Datasets for the Consortium Stakeholders:
   • To what extent does the proposed application define the priorities and needs for linking and curating data sets from multiple sources and types of data?
   • To what extent does the application demonstrate an understanding of the linkages and harmonization and does this align with their design of the data and computing infrastructure and associated data governance models and training offerings?
   • To what extent does the application showcase the skills and knowledge of the team in data acquisition, data access models, cybersecurity, data sharing and management, data curation, linking, and harmonization for the datasets?
   • To what extent does the proposed application plan have potential to curate and offer data reflecting diverse and under-represented populations for AIM-AHEAD sponsored research?
   • To what extent does the application discuss how the team will work collaboratively with other DICB awardees across the focus areas?
3. Resource Center for Data Access and Data Governance:
   • To what extent does the application demonstrate needs assessment and pilot testing plans to test and develop different data and computing infrastructure, tools, and governance models as well as to determine options and preferences for data sharing, data access, oversight, and regulatory policies?
   • To what extent does the application include options for distributed/federated data sharing and supporting the consortium stakeholders that meet the social and technical needs of the consortium members who range from novice to expert in AI/ML and technical skills?
   • To what extent will the proposed plan expand the reach of AIM-AHEAD infrastructure for investigators and communities historically underrepresented in health-related sciences on a national basis?
   • To what extent does the application propose a plan to enhance the capacity of consortium institutions and organizations to prepare and link data for AI/ML and engage in AI/ML research through the development of tools and/or local infrastructure investments?
   • To what extent does the proposal have the expertise and expand representation to establish trusted data access and governance policies?
   • To what extent does the application discuss the support skills needed for ethical considerations in data management and data sharing, particularly for tribal sovereignty treaties, laws, regulations, and preferences regarding data, as well as social determinants of health data and data from vulnerable populations?
   • To what extent does the application discuss how the team will work collaboratively with other DICB awardees across the focus areas?
4. Resource Center to Provide Concierge Service to Support Open-source AI/ML Tools:
   • To what extent does the application address how the team will develop and organize a resource center to provide real-time support and concierge service for AI/ML tools, cloud computing, open-source machine learning/deep learning tools, data analysis and preprocessing and other support needed?
   • To what extent does the application address how the team will research, acquire, and develop new skills in open-source and cloud-based AI/ML and relevant software and technologies?
   • To what extent does the application propose plans for consideration of the needs and constraints regarding different data and computing infrastructure, tools, and governance models, including data policy and organizational models, in the proposed real-time support for AI/ML tools, cloud computing, open-source machine learning/deep learning tools, data analysis and preprocessing, and other support needed?
   • To what extent does the application include a detailed plan to establish a concierge service to support researchers and their use of the data sets in the consortium stakeholders?
   • To what extent does the application provide how the team will support workforce development in training in AI tools and infrastructure resources for AIM-AHEAD Consortium members with varying levels of AI/ML technical skills?
   • To what extent does the application discuss how the team will work collaboratively with other DICB awardees across the focus areas?

Additionally, all Phase I applications will also be reviewed based on the following criteria:

• To what extent the application clearly articulate goals and expected outcomes in one of the four focus areas of DICB?
• To what extent is the proposed approach reasonable to achieve the goals of the project? What is the likelihood of a successful outcome? Are the measures of success clearly defined?
• To what extent the team have expertise to accomplish the proposed work? Does the team include researchers with AI/ML or computational expertise and health disparities expertise?
• To what extent the application align with the goals of the AIM-AHEAD program? To what extent is the project likely to advance these overall goals?
• To what extent does the proposed partner have the potential to link, curate, and harmonize data reflecting diverse and under-represented populations for AIM-AHEAD-sponsored research?
• To what extent does the proposed partner have the potential to develop and contribute infrastructure to improve access to data for under-represented researchers?

Budget 

The planning award proposals may request funding support commensurate with the project scope of up to $500,000 over 12 months in total costs (direct and indirect). We anticipate funding a total of 8 awards (minimum of two awards per focus area).

The award may be used for salary and fringe benefits of the principal investigator(s), collaborating investigator(s), other key-personnel, and for project-related expenses, such as salaries of technical personnel essential to the conduct of the project, supplies, equipment, cloud infrastructure, software, travel to the AIM-AHEAD Annual Meeting, data management, and publication costs, etc. Funds will not support general staff or administrative support. 

Available Support from AIM-AHEAD Functional Cores:

Data and Research Core

The Data and Research Core will work with the DICB awardees:

• To help prioritize data curation and linking opportunities that will be of the highest value to AIM-AHEAD Consortium members
• To provide guidance and lessons learned on data use agreements necessary to participate as data contributors to AIM-AHEAD-sponsored programs
• To provide feedback on proposed data governance structures and processes

Infrastructure Core

The Infrastructure Core will provide the DICB awardees with:

• AI/ML Assessment Tool and Maturity Model to pre-evaluate applicants or awardees and tailor the program based on their current capabilities and resources
• Existing models for working with HBCUs and MSIs to build infrastructure capacity:
• The External Infrastructure Core Working Group consists of 25+ HBCUs and MSIs to provide input requirements, incorporation of ethics, product testing and evaluation, and thought leadership
• Cloud-based framework for centralized infrastructure design and build-out by and for HBCUs and MSIs
• Model for customized local infrastructure design and build-out
• Centralized and local EHR Data Resources
• Open-source data and AI/ML tools:
• Team of solution architects, data scientists, and software engineers to provide training and mentorship
• Administrative and operational support models

Data Science Training Core

The Data Science Training Core will provide support to the DICB awardees by:

• Identifying training needs
• Recommending customized training modules
• Supporting HelpDesk related to data science training
• Synergizing with ongoing DSTC programs such as practicum, professional development, and outreaches

Letter of Intent (Optional)

Applicants are encouraged to submit a letter of intent. The information it contains allows the program committee to estimate the potential review workload and plan the review.

The letter of intent should include the following:

1. Title of the proposed project
2. Name, address, and email address of the Principal Investigator(s)
3. Names of other key personnel
4. Participating institutions and organizations

The letter of intent should be sent by May 30th at 5 pm EST to info@aim-ahead.net with the subject line "DICB Letter of Intent." For more information, contact the Program Chair using our HelpDesk link: https://helpdesk.aim-ahead.net/ticket/create/DICB.

Application Instructions 

Required Format:

Arial font and no smaller than 11 point; margins at least 0.5 inches (sides, top and bottom) and single-spaced lines. Submit as a single Word or pdf document to the application portal.

Required Elements of the Proposal

1. Cover Page (1 page limit): The cover Page should include:
   • Project title (The title should describe the project using concise and informative language)
   • DICB Focus Area 
   • PI/PD, Institution Name, Address, Contact info
   • Co-I/Key personnel, Institution Name
   • Requested Amount (12-month period)
2. Project Summary (1 page limit): Provide a succinct description of the proposed work, including the project’s long-term objectives and a description of the research design and methods for the entire AI/ML project.  
3. Project Description: The project description should contain the following components adhering to the page limits. 
   1. Specific Aims (1 page, please submit as one file): Provide a clear, concise summary of the aims of the work proposed and its relationship to your long-term goals. Applicants should provide the overall goals of the entire project, list separate Specific Aims to be accomplished, outline the proposed plans, and summarize the expected outcomes.
   2. Project Plan (5 pages, please submit as one file)
      1. Background and Significance:

         Applicants should include sufficient background to demonstrate how the proposed project will meet the needs of the selected DICB focus area and summarize their expertise relevant to accomplishing the project's specific aims. Applicants should concisely describe their institution's compelling needs and potential for capacity building in AI data and infrastructure and how it relates to capacity building AI and health disparities research at your institution. They should describe clearly which DICB focus area their application is focusing on. Applicants should describe any existing capacity in AI data and infrastructure at their institution and how the proposed proposal significantly enhances the capacity at their institution through mutually beneficial collaboration with AIM-AHEAD. Applicants must describe their project plan for carrying out the proposed activities, with specific milestones and mechanisms to assess success. The significance section will assess the potential impact on the AIM-AHEAD mission.

      2. Collaboration:

         Applicants are urged to form a multi-disciplinary and multi-institutional partnership (with research-intensive institutions) and describe their proposed collaboration activities with the partner(s) and the benefits of the collaborative activities. Applicants should describe the outcomes and benefits of capacity building that will be featured in a full proposal. Also, it is incumbent upon the applicant to make a clear link between the project and the mission of AIM-AHEAD.

      3. Previous Work and Relevant Expertise: Applicants must describe concisely previous work related to the proposed project that will help establish the investigator(s) experience and competence to pursue the proposed project.
      4. Phase I Timeline: Applicants should describe in detail the timelines of proposed activities, including institutional gap assessment, capacity development activities, collaboration activities with partner institutions, and AIM-AHEAD consortium partners for the DICB focus area selected.
4. References Cited (maximum 3 pages): List only references cited in the Project Description or supplementary documents of the proposal.
5. Detailed Budget and Budget Justification (Please submit as one file): The budget justification should entail a narrative explanation of each component of the cost required for the proposed work. The budget explanations should focus on how each budget item is required to achieve the project’s objectives and how the estimated costs in the budget were calculated.
6. Facilities, Equipment, and Other Resources (Please submit as one file): Facilities, Equipment, and Other Resources should describe the resources needed and those available for the proposed research project. Collaborators should also indicate the same. Applicants should convey how the scientific environment in which the project will be conducted contributes to the probability of success.
7. Senior Personnel Documents (Please submit all personnel documents as one file):
   Biographical Sketches: Biographical sketches are required for the PI, any co-PIs, and each participating Senior Personnel listed in the Project Description (including research scientists, staff, and /or students). All biographical sketches submitted in response to this solicitation are expected to follow the NIH format. 
8. Current and Pending Support (Please submit as one file): Disclose any previous funding from NIH or AIM-AHEAD, Federal, and other funding sources related to AI/ML and the CFP objectives.

   Collaborators and Other Affiliations Information. 

9. Other Supplementary Documents (Please submit all supplementary documents as one file):
   1. Institutional Letters of Support: Two letters of support are required: These letters must be signed by the leadership of the institution (e.g., University president, provost, or college/school dean as applicable) to show support for the partnership and commitment to additional resources necessary to ensure that these partnerships will have the maximum sustainability. These letters should also identify the specific number of positions wholly dedicated to AI data and infrastructure under the partnership. In addition, if American Indians are involved, a Letter of Commitment from the Tribal Nation Leader is required.

      This letter should include the following:

      1. Signed letters of support from institutional leadership, for example, deans, provost, and vice president of research. The letter should demonstrate institutional assessment of current capacity, and institutional need for capacity building in AI data and infrastructure capacity as well as commitment and support for the proposed activities.
      2. Signed letters of support from institutional leadership in IT, security, research infrastructure/data science, or similar department leadership to show institutional capacity and support for this project.
   2. Letter(s) of collaboration between MSI and Resource Intensive Partner Institutions
   3. A detailed description of Cloud Computing, Data Infrastructure, and Cybersecurity Resources (if applicable).
   4. Project Personnel and Partner Organizations (required).

Progress and Post-Award Reporting 

Awardees are to enhance workforce capabilities and capacity at minority-serving institutions necessary for building and increasing data and infrastructure for capacity at minority-serving institutions in Artificial Intelligence and Machine Learning (AI/ML) models to develop institutional capabilities of this emerging technology in addressing health disparities research and inequities and develop a trained workforce in these technologies. Therefore, it is expected of the applicants to provide evidence of the following:

• Awardees will also participate in monthly awardee meetings (via Zoom).
• Awardees will submit monthly reports and budget reports. 
• Awardees will participate in meetings with the AIM-AHEAD Coordinating Center to participate in overall AIM-AHEAD data and infrastructure strategies.
• Awardees will participate in AIM-AHEAD annual meetings.
• Awardees will act as AIM-AHEAD community ambassadors to help onboard colleagues.
• Awardees must be willing to attend and present the results of their work at future AIM-AHEAD events and volunteer to review future AIM-AHEAD programs.
• Awardees agree to have AIM-AHEAD promote the project online through websites, social media, and other communication channels.
• All awardees will be expected to be involved in AIM-AHEAD activities during the year, including attending two annual program-wide meetings.
• Awardees must provide a summary of project status about milestones listed in the proposal, challenges faced and plans to overcome those challenges, usage of funds, and next steps. 
• By the project end date, awardees must provide a final report of progress, findings, usage of funds, and a list of publications, grant applications, articles, and conference talks emerging from the project.

Request Pre-Application Consultation

We strongly recommend applicants to request a pre-application consultation with a member of the DICB Committee in order to receive feedback on the following:

1. Whether the proposal is responsive to the CFP and how to modify the proposal to be more responsive
2. Identifying resources and support available through the AIM-AHEAD Cores and/or other sources

Submit your request for consultation before May 19, 2023. To submit your request for a consultation, you’ll need to provide the following information: PI Name, PI’s Organization, Email, Proposal Title, DICB Focus Area, and Questions for discussion.

Request Pre-Application Consultation Link: https://helpdesk.aim-ahead.net/ticket/create/DICB

Questions:

Please refer to the Frequently Asked Questions document before creating a help-desk ticket.

AIM-AHEAD Data and Infrastructure Capacity Building FAQs

If your question is not answered in the above FAQ document, please create a help-desk ticket using the link below.

https://helpdesk.aim-ahead.net/ticket/create/DICB