Autism Data Science Initiative Funding Opportunities

Research Opportunity Announcement (ROA): OTA-25-006 Autism Data Science initiative

Navigation #Navigation

• Section 1: Overview Information
• Section 2: Objectives of this Opportunity
  • Background
  • Sources for Autism-Related Data
  • Scope and Program Components
• Section 3: Potential Award Information
• Section 4: Eligibility
  • Required
  • Desired
  • Organizations
• Section 5: Application Information and Submission
  • Application Overview
  • Letters of Intent
  • Application Format and Requirements
  • Cover Page
  • Abstract
  • Specific Aims
  • Biosketches of Senior/Key Personnel and Other Significant Contributors
  • Other Support Documents
  • Research Strategy
  • Community Engagement Plan
  • Resource Sharing Plan
  • Data Management and Sharing (DMS) Plan
  • Additional Items
  • Budget
  • Award Project Duration
  • Reporting Requirements
  • Submission Information
• Section 6: Objective Review Information
  • Reviewer Process
  • Reviewer Selection
  • Conflicts of Interest (COI)
  • Review Criteria
• ROA References

Section 1: Overview Information #section1

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Section 2: Objectives of this Opportunity #section2

The purpose of this Research Opportunity Announcement (ROA) is to invite applications from eligible organizations to support the Autism Data Science Initiative (ADSI). ADSI will bring together diverse data resources and community members with lived experience to explore novel contributors and/or to characterize the collective contributions of numerous factors to the causation of autism spectrum disorder (ASD), hereafter referred to as autism, and their potential role in increasing the prevalence of autism. ADSI will also seek to identify how existing treatments/interventions are used and better understand their outcomes to inform the design of future clinical studies. This initiative will achieve these goals through four strategic aims: 1) to create new integrated data resources, by applying innovative approaches across existing data from research studies or other valid sources,¹ with rigorous privacy protections, for use by the autism research community; 2) to identify and address gaps in available data through targeted data generation; 3) to support the analysis of these integrated data resources that link data on genetic and nongenetic factors (e.g., diagnostic, clinical, behavioral, neurophysiological, pharmaceutical and environmental exposures, complications of pregnancy and peri-natal events) to explore contributors to the causes of autism and/or to identify patterns associated with treatment/intervention outcomes and the use of services for autism; and 4) to provide a venue for replication of these analyses by independent teams to validate findings and increase transparency in the conduct of science.

¹#footnote1 A valid data source refers to any information that is methodologically sound, ethically obtained, and suitable for drawing meaningful and reproducible conclusions pertaining to the questions at hand.

Background #background

About 1 in 31 children in the United States has been identified with autism, which affects approximately 3-4 males for every female (CDC, 2025).  Core features of autism are difficulties with social communication, social interaction and restricted, repetitive patterns of behavior or interests. Autism can be reliably diagnosed by experienced healthcare specialists as early as age 2. It is an extremely heterogeneous condition with a variable clinical presentation and differing service and support needs. Family studies in autism have identified high heritability. Genetic studies of autism in tens of thousands of persons have revealed a complex genetic architecture, including associations with single-gene disorders or aneuploidies, rare de novo copy number variants, single nucleotide variants, and heritable common polygenic risks.  Autism risk also increases with parental age. Nongenetic factors (e.g., environmental chemicals, medications, maternal health) also contribute to the development and expression of autism but in general these are less well characterized and likely many remain to be discovered. The prevalence of autism among 8-year-olds has increased most years since the Centers for Disease Control and Prevention (CDC) began tracking the condition in 2000, and occurs across all racial, ethnic, and socioeconomic groups. This increase in autism prevalence is likely due to a combination of factors. Studies sponsored by the National Institutes of Health (NIH) and other federal and non-governmental organizations have identified multiple factors that may be driving this increase including, but not limited to, changes in diagnostic factors, (e.g., diagnostic ascertainment, diagnostic criteria and/or practices), greater parent and practitioner awareness, and increased development and availability of services and supports that motivate families to pursue a diagnosis. The contribution of other factors that may play a role in the rising prevalence are not well studied or understood and include changes in the patterns and composition of prenatal/early life environmental chemical exposures (e.g., air pollution), parental health factors (e.g., obstetric complications, cardiometabolic diagnoses, maternal stress, and infection and immune alterations), and birth factors (e.g., preterm or very preterm birth). One or more of these factors may be influenced by genetics or interact with genetic susceptibility, reflecting gene-environment interactions (GxE factors). One mechanism by which these nongenetic factors may alter risk is by increasing or decreasing gene expression through epigenetic mechanisms. The totality of nongenetic factors over the life course is represented by the concept of the exposome.  

Innovative new approaches that reflect an exposomics framework and that apply new analytic and statistical methods to data derived from human epidemiology and clinical settings are needed to advance the study of GxE interactions in autism. Observations of altered prenatal and early-life brain development and dysregulation of neuronal and immune function in people on the autism spectrum and in some non-human model systems can provide clues to identify causative factors. In addition, the male bias and sex-related differences in presentation and underlying biology may also be informative. Large datasets from NIH supported research with clinical data, such as neurophysiological (e.g., electroencephalography, evoked responses, magnetoencephalography, functional magnetic resonance imaging) or other biomarkers, can be leveraged for these purposes. The development of new approach methodologies (NAMs), including complex human-based in vitro models (e.g., brain organoids), has shown promise in addressing some of the knowledge gaps in autism etiology by enabling the experimental study of biologic response to potential neurodevelopmental toxicants or pharmacological treatments and their interaction with genetic susceptibility.  

Despite improvements in autism screening and diagnosis, people on the autism spectrum have poor long-term outcomes resulting in higher health care utilization. People on the autism spectrum are dying much younger than expected, including by suicide, and are at increased risk of health problems (e.g., cardiovascular and metabolic conditions, epilepsy, sleep problems, pain management). Approximately three out of every four people on the autism spectrum have a co-occurring mental health condition, including anxiety, depression, ADHD, or substance use disorder, among others. Individuals on the autism spectrum will require different supports and interventions across the life span - from the point of first concern to later adulthood. Without treatments and supports, these problems negatively impact quality of life, educational and employment outcomes, and community participation. Significant advancements have been made in the development of novel interventions for autism. However, most of the intervention research has targeted early childhood, and research on autism in adolescence and adulthood is scarce. As the field of autism intervention research has included older individuals, outcomes have expanded to include mental health, educational and vocational functioning, self-determination, and quality of life. The rapid growth of this population has stretched the capacity of multiple service systems to deliver education, health and mental health services, and home and community-based care, all of which influence healthy outcomes and wellbeing. Pharmacologic treatment studies have also been conducted, although none have shown efficacy in affecting the core features of autism. There is limited understanding of how the complexity of genetic and nongenetic etiologies, together with variation in phenotype, is predictive of treatment response. There are few data available to determine the effectiveness of interventions that are accessible in standard community settings using existing workforces.  This ‘research to practice’ gap is a major impediment to improving the lives of individuals on the autism spectrum.  

Given the significant increase in prevalence of autism over the past 25 years, enhanced, cutting-edge research is urgently needed to identify and understand the full complement of factors contributing to this rise. Additionally, the Interagency Autism Coordinating Committee and the Autism Collaboration, Accountability, Research, Education, and Support Act of 2024 have highlighted the increased need for evidence-based treatments and interventions that have a more immediate impact on improving the lives of those living with autism. Identifying effective and scalable interventions across the lifespan addressing the spectrum of abilities, identifying predictors of treatment response, and improving our understanding of mechanisms of co-occurring conditions that could serve as treatment/service targets in future clinical trials are critical to advancing autism research. These areas could be catalyzed with progressive data science methodologies and rich, extant data resources. 

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Sources for Autism-Related Data #sourcesardata

NIH and many other federal agencies have contributed to the development of rich data resources related to autism over at least the last three decades. Researchers responding to this ROA should aim to leverage existing data, such as those available through NIH-funded repositories. A non-exhaustive list of data sources can be found on this website (ADSI Data Resources page). Additionally, applicants may propose to pool large cohort data from existing federally funded datasets and/or use alternative valid data sources, which could include both public and private data. The proposed use of valid foreign datasets may also be permissible with appropriate data use agreements provided there are no foreign subawards. In addition, applicants may propose research that will enhance existing dataset/s by generating a limited amount of new data. Examples include but are not limited to the use of extant biospecimens to characterize metabolic or epigenetic markers, the mechanistic impacts of environmental chemical exposures in brain organoids or other human-centric models, generation of new environmental exposure estimates using geographic information system (GIS)-based modeling, or adding additional common outcome, implementation, or service measures across multiple on-going community-based clinical trials. This ROA will not support data generation in non-human animal models. However, there are many existing data resources that are derived from animal experimentation (e.g. toxicity data, functional data of autism risk genes) where comparable human data are unavailable. When combined with human data (clinical, epidemiological, observational, etc.), these non-human datasets can enrich the data aggregation, hypothesis testing, and exploratory analyses that will be supported through this initiative. All use of data within this initiative shall be compliant with privacy and confidentiality requirements, applicable federal and state laws and regulations, HHS and NIH policy, determinations of any involved Institutional Review Board, data use limitations from informed consent documentation, associated data use agreements, and data repository policies. 

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Scope and Program Components #scopecomponents

This proposed Autism Data Science Initiative (ADSI) aims to leverage existing data sources through innovative integration and analyses to advance understanding of contributors to autism diagnosis and increases in prevalence of the condition, and to enhance understanding of effective and scalable interventions and services. NIH currently supports a large number of studies of factors that contribute to autism. This initiative is not intended to enable incremental extensions of existing research or studies that can be funded by other NIH mechanisms but instead will support transformative and high impact research addressing a knowledge gap that deserves special emphasis and that requires collaborative and interdisciplinary data science approaches. ADSI projects will use data science technologies to investigate one or more of the following priority areas: 1) what newly emerging and/or understudied factors may contribute to autism; 2) how multiple disparate factors (e.g., exposomics) interact to contribute collectively to risk and resilience; 3) how those factors, in combination with diagnostic and other factors, may contribute to increased prevalence over time; and 4) how the use of multiple data sources can characterize utilization patterns of autism treatment, intervention, and services to pinpoint potential treatment/service targets by identifying effective and scalable interventions across the lifespan, predictors of treatment response, or mechanisms of co-occurring conditions that could inform the design or approach in future trials. Investigators could propose research questions that address more than one of these priority areas. As a multifactorial condition with complex etiology, a full understanding of the factors contributing to autism must draw on the interests, expertise, and data resources of several NIH Institutes and Centers (ICs) to enable integration of research in toxicology and environmental health, neuroscience, genetics, maternal and child health, and translational science. To ensure rigor in research and transparency in reporting methods and outcomes, this initiative will support independent replication and validation activities to verify the reliability and reproducibility of these new analyses.

The overarching goal of this initiative is to further our understanding of autism and develop new knowledge that could be used to improve health outcomes for people on the autism spectrum. This can be facilitated by ensuring strong community engagement in the research being conducted (i.e., project planning, methodology, data analytic plans, review of results, dissemination strategies, etc.). Community-engaged research can take myriad forms but essentially involves the bidirectional relationship between community partners and the research team in guiding the research project and providing necessary transparency. Community-engaged research approaches have the potential to make research more relevant to the partners who will use the research, including those who are at high risk for adverse health outcomes. The autism community is broad and includes people on the autism spectrum, their family members, clinicians and service providers, and advocacy organizations. Incorporating perspectives of community and practice partners and setting characteristics during intervention development and testing helps to ensure resulting treatments are scalable and can be disseminated into practice, given typically available resources, service and financing structures, service use patterns, and utility for end-users. Applications to this funding opportunity must provide a plan for community engaged research. No specific approach is required but applicants must describe how their plan includes relevant invested parties as collaborators at a level of involvement that is feasible for the community partner and is appropriate for the project to enhance the impact of the research.  

The ADSI will achieve these objectives through four interrelated tasks shown in Table 1.

Table 1: ADSI Task Areas and Task Area Restrictions 

Applicants may propose activities in the four tasks with the following limitations and conditions:  

• Applicants may not propose activities under Task I, Dataset Aggregation or Task II, Data Generation, alone; Tasks I and II must be combined with Task III.
• Applicants may propose activities under Task III, Data Analysis, alone. Task I, Dataset Aggregation and Task II, Data Generation are not required.
• If an applicant proposes activities to perform any analyses under Task III, Data Analysis, that leverages the formation of a novel dataset/s developed under Task I, Dataset Aggregation, data analysis activities under Task III will be restricted until the awardee can demonstrate they have successfully completed all tasks and requirements for Task I, Dataset Aggregation
• If an applicant proposes activities in support of Task IV, Model Validation or Method Replication, they will be ineligible to receive support for activities in Tasks I – III within this ROA.  

Task I – Dataset Aggregation  

Task I will result in the formation of a novel dataset/s that could address (1) a variety of known or novel contributors to the risk for autism, or (2) patterns of treatment, intervention, and services and associated outcomes. The novel datasets that will be developed under this initiative will involve collaborative research to integrate multiple data sources. Many existing large-scale datasets have been created and maintained by multiple national research institutes, agencies, and organizations from the private and non-profit sectors. Projects should propose the use of existing datasets whenever possible. Applicants can propose the aggregation of only human datasets (clinical, epidemiological, observational etc.) or both human and non-human (animal models, NAMs, etc.) datasets, but the exclusive use of non-human datasets is not allowed. Acquisition of source data and development and preparation for analysis of secondary dataset/s will be an iterative process, ensuring that all governance considerations of source data are met, that the Recipients are authorized to access the proposed data sets and/or biospecimens, and that data sharing is maximized per the NIH Data Management and Sharing (DMS) Policy. Prior to aggregating the dataset/s, the team must assess the feasibility of their data strategy – the plan for acquisition, integration, and analysis of existing data sources, as well as sharing of novel datasets for Task IV in the ROA and broad sharing per the NIH DMS Policy. Because much of the data will be controlled access, use of controlled access data must adhere to the updated security standards in the NIH Security Best Practices for Users of Controlled-Access Data.    

Applicants are encouraged to reference relevant components of the NICHD Record Linkage Implementation Checklist in developing this data strategy.  This Checklist is a resource for guiding decisions that must be made prior to designing and implementing a strategy for linking data from multiple sources and sharing and using the linked datasets for research. It emphasizes the need for collaboration amongst researchers, data repositories, and other stakeholders to design and implement such a strategy. It also includes a step for evaluating rules that apply to each dataset contributed to the linked datasets, which may require engagement with data governance experts.  Originally designed for teams aiming to implement individual-level record linkage across multiple data sources, this Checklist guides any effort aiming to reuse and/or integrate existing data from multiple sources.  

In Task I, applicants should plan to demonstrate the feasibility of acquisition, integration, use, and sharing of the existing datasets proposed for developing the novel dataset/s.   

Specifically:  

1. Identify which existing data sources will be used to develop the novel dataset/s.
2. Describe where and how the data will be analyzed.
3. Obtain approval or authorization to access, use, and link, if relevant, the identified datasets from a data repository or other data source.
4. Identify policies and rules that apply to each identified dataset, to understand what can and cannot be done when analyzing and/or integrating the datasets for research use and sharing, and how the novel dataset/s will inherit rules and controls from the original datasets.  This may require reviewing a variety of data governance documents (consent forms, IRB determinations, data submission/use agreements, repository policies, federal or state laws) and analyzing whether they conflict (thus possibly prohibiting the linkage) or how they intersect (thus impacting how the linked data can be shared and used). Proposals should describe any constraints on broad data sharing (e.g., informed consent, privacy concerns) in the Data Management and Sharing Plan.  
5. Propose controls to mitigate re-identifiability risk when sharing the data for validation purposes under Task IV and with the research community for secondary analysis.  This information should be described in detail in the proposal's Data Management and Sharing Plan.
6. If novel dataset aggregation will involve privacy-preserving record linkage, describe in the proposal how each of the technical factors (Checklist items 6, 7, and 8) will be addressed   

Recipients shall demonstrate they have satisfied these conditions, through delivery of a strategy (addressing the relevant elements of the Checklist) and associated determination of feasibility, for NIH review and approval prior to beginning activities associated with Task III, Data Analysis.  

Task II – Data Generation  

While this ROA is primarily focused on reuse and analysis of existing data, in limited cases where applicants can identify an unmet need for additional data to augment existing data, applicants may propose new data generation activities. This could include analysis of existing biospecimens in clinical or human epidemiology studies, use of geospatial models for exposure assignment/estimation, and qualitative data collection or surveys in a previously identified population. Applicants can also propose to use human-centric new approach methodologies (NAMs), including but not limited to microphysiologic systems, such as brain organoids. If included, these new approaches must be complementary to the proposed activities under Task III and, if also proposed, any activities under Task I.  Studies proposing to use non-human animal models are not allowed. The applicant must describe why the new data are needed to advance autism research, provide a justification for the approach proposed to generate the new data, and confirm that the data are not available from any other public data sources. The applicant must also demonstrate the feasibility of collecting the proposed data in 18 months or less and, if proposing new data collection from research participants, IRB approval for the research protocol and data sharing plan. If an awardee proposes activities under Task I, Dataset Aggregation and Task II, those activities may occur in parallel provided that the applicant has demonstrated the data needed are not available through any other public data source. Awardees will be required to maximize sharing of newly collected dataset/s to support reproducibility and new research, in alignment with the NIH Data Management and Sharing Policy. As stated in that policy, NIH strongly encourages the use of established repositories to the extent possible for preserving and sharing scientific data. A list of potential repositories for autism-relevant data sets is provided on the following webpage (ADSI Data Resources page).  

Task III – Data Analysis  

Data Analysis will entail rich analysis and rigorous evaluation utilizing advanced data science methodologies, such as artificial intelligence (AI), machine learning (ML), and/or other advanced statistical methods. Activities proposed must focus on the analysis of identified human dataset/s or a combination of human and non-human dataset/s; analysis restricted to non-human dataset/s is not permitted. Applicants should,

• Explicitly state the hypotheses to be tested or if the analyses proposed are hypothesis-generating.
• Pre-register planned variables for analysis or methods proposed for automated or machine learning based models for variable identification.  

As applicable to the dataset and proposed hypothesis testing/generation, researchers should:

• Analyze the identified or selected dataset/s to test for relationships among variables of interest or combinations of variables and autism diagnosis and/or increased prevalence of autism over time.  
• Assess, test, validate, and/or strengthen the understanding of autism core features (e.g., social-communication deficits, or restrictive and repetitive behaviors) and associated phenotypes (e.g., intellectual disability, speech and language abilities and differences, co-occurring medical or mental health conditions) within this dataset/s for improved accuracy and better understanding of the condition.
• Analyze identified or selected dataset/s to enable comparison of outcomes of different interventions and services, conditioned on characteristics of the individual receiving treatment, the intervention/service setting, and/or provider characteristics.  
• Analyze identified or selected dataset/s to test hypotheses linking a specific risk for autism to services utilization data, treatments/interventions, and outcomes.

All applicants are required to maximize sharing of newly developed dataset/s to support reproducibility and new research, in alignment with NIH Data Management and Sharing Policy. As stated in that policy, NIH strongly encourages the use of established repositories to the extent possible for preserving and sharing scientific data. Some potential repositories for autism-relevant data sets are listed on the following website (ADSI Data Resources page). Any data sharing limitations for the proposed dataset/s and resources should be clearly described in the application’s Data Management and Sharing Plan. 

Task IV – Model Validation or Method Replication

In support of the larger goals of this program, to better understand the causes of and interventions for autism, the program will also explicitly support a parallel effort to replicate the results of the research studies funded through Tasks I – III of this ROA to increase confidence in results. Applicants seeking to provide independent model validation or method replication studies are not eligible to propose activities under Tasks I – III of this ROA. Model Validation or Method Replication studies will involve either the independent validation of models and tools, or the unbiased duplication of study conditions and methods to reproduce results from research funded by this ROA. Replication studies will not be directed toward the reproduction of results from research outside of the initiative. This ROA will not support the use of in non-human animal models for model validation or method replication.  

Model validation efforts will establish that the model is accurately capturing the mechanism it is intended to measure, and that its performance can be relied on to provide a particular interpretation in the specific context. Method replication studies will attempt to duplicate as closely as possible the methods employed in the program’s primary research studies to test the reliability of those results and reproducibility of the findings. These efforts will use new data (i.e., data not used in the original study) when feasible to test the same scientific hypotheses as a previous study. Application of methods and tools to similar data from different sources will help to determine the robustness of the results beyond the original context.  

Research activities in this program rely on several data sources, some of which may be unique. Data privacy and security are both important concerns. It is expected that scientists conducting replication studies will need to work with those doing the primary research in the program to determine from the outset: (a) what data (and metadata) will be necessary and available to support replication studies, and (b) what methods and technologies will be necessary to replicate the research. For example, it may be advisable to set aside a portion of the available data to be used in a replication experiment, isolating it from the initial research study.  

Validation and/or replication studies may include data generation directed at replication of preclinical laboratory studies, data identification, curation, harmonization, linkage, and analysis. Validation and Replication teams will:

• Coordinate with investigators funded under this ROA to validate their models and tools and/or replicate their methods to arrive at the same findings as the original studies.
• Provide public documentation of how models and methods were replicated, as well as any adaptation needed to conduct similar research using new data or in new contexts.
• Assess, test, and validate approaches and findings.  
• Document any challenges and barriers to implementing these models/methods.  

Preparatory work, including coordination with program researchers, is expected to occur from the outset. While the replication team(s) selected will be expected to work with the other funded researchers to transfer and duplicate methods and tools, the bulk of the duplication effort will, of necessity, occur on a later timeline than the original research, and will need to be completed quickly, generally within one calendar year of the conclusion of the primary research study. Replication researchers must be available for consultation with the primary researchers throughout the period of award to ensure that the replicated study has high fidelity to the original study.   

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Section 3: Potential Award Information #awardinfo

The Autism Data Science Initiative (ADSI) will be coordinated and managed through the NIH Common Fund, whose Other Transactions will be governed by 42 U.S. Code § 282 (n)(1)(b). NIH funds to conduct this initiative will be awarded only after successful completion of both stages of application and review. It is anticipated this ROA will result in 10-25 awards, with a budget not generally exceeding $5,000,000 total costs per award, contingent upon the availability of funds and a sufficient number of meritorious applications. If proposed Task IV activities include replication of a large number of Task III results, a larger budget request may be appropriate. Proposed activities must be completed within 24 months for applications including only Tasks I and III. Applications that include a combination of tasks which include Task II and applications for Task IV must be completed within 36 months.  

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Section 4: Eligibility #eligibility

This ROA will establish the ADSI. Applications from senior/key personnel and groups with the following characteristics are encouraged. 

Required #required

• For all tasks: A team that includes expertise or demonstrated experience in autism research (e.g., working with and analyzing autism-related data), or data science pertaining to autism or other developmental disabilities.
• For all tasks: A team that includes expertise and demonstrated experience in community-engaged research.
• Task I: A strong track record of successfully building and/or integrating datasets from disparate data sources/repositories with adherence to data source governance based on expert policy analysis and through implementation of sustainable data integration strategies.
• Task II: Demonstrated expertise in the development and use of proposed methodologies or human-centric models for new data generation (e.g., genetic, exposomic, metabolomic, geo-coding, modeling, surveys, qualitative methods, NAMs).
• Task III: Demonstrated success exploring and analyzing novel, multi-modal integrated datasets using advanced data science methodologies, such as but not limited to AI, ML, advanced statistical methods and others and adherence to best practices for model transparency and re-use; and expertise in appropriately using data from large repositories containing phenotypic, genomic, environmental, or longitudinal/temporal information; electronic health information data; and/or data from other administrative or real-world data sources for scientific research.
• Task IV – Demonstrated expertise in validation, data science, computational reproducibility, and the science of replication; experience using best practices for replication and reproducibility in their prior work.  

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Desired #desired

• A team with the ability to initiate research activities quickly and complete them within a rapid time frame. This may include having IRB approval or exemptions in place and existing data use agreements that would cover the proposed work.
• Expertise or demonstrated experience in compliance with policy, governance, and/or regulations related to access, use, linkage, and sharing of sensitive data.  
• Existing access to relevant datasets and the ability to conduct comprehensive analysis.
• A team with a demonstrable history of working together, to rapidly and rigorously achieve the goals of a project.
• Successful track record of working productively with community and/or federal partners to complete complicated, time-sensitive tasks, demonstrating mutual respect for all engaged.
• Expertise or demonstrated experience in biostatistics, epidemiology, study design, and/or research synthesis and meta-analysis.  
• Expertise or demonstrated experience in open science methodologies, to include broad sharing through public repositories of well-annotated datasets and computational models.

Proposals nonresponsive to the terms of this ROA will not be considered. Projects considered unresponsive to this announcement would include:  

• Novel intervention studies
• Clinical trials
• Secondary analyses of datasets that are not representative of or do not include autistic populations  
• Studies proposing animal model generation
• Development of novel datasets that cannot be accessed by other researchers for Task IV, Model Validation or Method Replication
• Development of commercial products
• Analyses that mostly focus on other developmental disabilities or conditions 

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Organizations #organizations

Non-domestic (non-U.S.) entities (foreign applicants) are not eligible to apply. Non-domestic (non-U.S.) components of U.S. organizations are not eligible to apply. Foreign components are not allowed. 

The following domestic entities are eligible to apply under this ROA:

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
• Historically Black Colleges and Universities
• Tribally Controlled Colleges and Universities
• Alaska Native and Native Hawaiian Serving Institutions
• Asian American Native American Pacific Islander Serving Institutions

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)
• Faith-Based or Community-Based Organizations
• Regional Organizations

For-Profit Organizations

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

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)
• Eligible Agencies of the Federal Government, including NIH Intramural Research Program (NIH IRP)
• U.S. Territory or Possession Other
• Independent School Districts
• Native American Tribal Organizations (other than federally recognized tribal governments) 

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Section 5: Application Information and Submission #section5

Application Overview #appoverview

Letters of Intent #LOI

Interested applicants may submit a Letter of Intent (LOI) of no more than 1 page with the names, email addresses, and organizational affiliations for the Contact PI and the Recipient Business Official/Signing Official and key personnel. LOIs will be reviewed by NIH staff only to assess eligibility and to identify conflicts of interest for potential reviewers and NIH will not be providing any feedback. LOIs must be submitted by email as a PDF attachment to [email protected] by June 6, 2025. LOIs submitted by other means may not be considered. LOIs are optional, not required.

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Application Format and Requirements #appformat

Applications should clearly and fully demonstrate the applicant/s capabilities, knowledge, and experience in the past five years. Applications shall also include a separate budget.

Plans must be submitted by the due date, in text-recognizable PDF (Adobe) format, use Arial 10-point font with 1” margins, and be single-spaced. The application may not exceed 13 pages (excluding biosketches, budget, letters of support, and bibliography). 

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Cover Page (no more than 1 page) #coverpage

• Number and title of this ROA
• Project title
• Principal Investigator(s) first and last name, title, institution, mailing address, email address, and phone number. If multiple Principal Investigators are named, then the contact Principal Investigator is clearly identified.
• Table listing other involved personnel as first and last name, institution, and roles (multiple Principal Investigators, co-Investigators, collaborators, contractors, authors of letters of support, etc.)
• Name and address of the submitting organization and department, if any, with the organizational Unique Entity Identifier number and employment identification number provided
• Authorized organizational representative first and last name, title, institution, mailing address, email address, and phone number
• Proposed project period dates
• Confirmation that the work involves data from human subjects
• Proposed budget per year (direct and total) 

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Abstract (no more than 1 page)  #abstract

The project abstract is a succinct and accurate description of the proposed work and should be able to stand on its own (separate from the application). It should be informative to other persons working in the same or related fields and understandable to a scientifically literate reader. If the application is funded, the project abstract will be entered into an NIH database and made available on the NIH Research Portfolio Online Reporting Tool Expenditures and Results (RePORTER) and will become public information. 

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Specific Aims (no more than 1 page) #specificaims

State concisely the goals of the proposed research and summarize the expected outcome(s) for each Task Area being proposed, including the impact that the results of the research will have on the research field. List succinctly the specific objectives of the proposed work for each Task Area being proposed. 

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Biosketches of Senior/Key Personnel and Other Significant Contributors (no more than 3 pages per individual) #biosketches

At a minimum, the information in the biosketch should include the name and position title, education/training (including institution, degree, date (or expected date), and field; list of positions and employment in chronological order (including dates); and a personal statement that briefly describes the individual’s role in the project and why they are well-suited for this role. The format, downloadable as a Microsoft Word file from https://grants.nih.gov/grants-process/write-application/forms-directory/biosketch, used for an NIH grant application is acceptable. 

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Other Support Documents (no page limitation) #othersuppdocts

Provide Other Support for all key personnel using the NIH grant application format as found here: https://grants.nih.gov/grants-process/write-application/forms-directory/other-support 

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Research Strategy (no more than 6 pages) #researchstrategy

The Research Strategy is organized into eight sections to facilitate review. Sections 1, and 6-8 are required of all applicants. Sections 2-5 correspond to the four task areas supported under this ROA. Applicants will complete some, but not all, of Sections 2-5, based on the activities proposed in their application. Additional information is provided in each section below. 

Section 1: The potential impact of the work to be done if it were successfully implemented (required)

At a minimum for each Task being included in the proposal:

• Describe the scientific question(s) (i.e., user stories that articulate the desired outcomes for a given analysis or analyses) and resultant impact of the proposed work.
• Describe each of the considered/identified data source/s and data type/s that are being proposed to be utilized in the development of novel dataset/s and their related applicability to achieving the aims described in the ROA. Include origin of the data, metadata information, collection details, data use/reuse limitations, a summary of the process for gaining authorization to access the data, and any other relevant items for each data source. Data sources can include, but are not limited to:
  • Valid public or private sources including other federal/state/local data sources and/or foundation supported data sources
  • Investigator datasets
  • Data hosted by data repositories
  • Electronic health records
  • Administrative datasets
  • Industry-supported research datasets 

Section 2: Dataset Aggregation (optional, see requirements below)

If the proposal includes activities under Task I, Dataset Aggregation, plans for access and integrating data sources for dataset aggregation or generation of new data, describe the governance and technical decisions that will enable the proposed use of the selected data sources and, if applicable, the use of novel integrated dataset/s for proposed analysis and sharing, based on the NICHD ODSS Record Linkage Implementation Checklist (PDF 238 KB). Refer to the guidance in Section 2, Task I description for additional details. If no activities are proposed under Task I, Dataset Aggregation, this section should not be included. 

At a minimum:

• Describe how the research team and other parties involved in the proposed linkage contributed to the development of the linkage strategy that addresses all pertinent elements of the checklist.
• For human data sources, describe how autism case ascertainment was/will be done in the proposed dataset/s (e.g., autism diagnostic determination, ICD code, ADOS, ADI-R, verbal and non-verbal intellectual functioning, adaptive functioning, parent report) and how they were/will be validated.
• Describe an analysis of data governance for each data source to be linked, to determine whether the data can be linked, and if so, how the respective rules and controls impact the strategy for linking, using, and sharing the linked dataset for this project. Describe how the team will address these governance considerations in their work.
• Describe the proposed technical plans for linking each data source/s to create the novel dataset/s and the scope of the datasets anticipated to be part of the linkage. Include the expected method and/or software that would be used to link the dataset/s and the platform environment(s) where the linkages will be performed, and the novel dataset/s will be created.
• Describe how the team will maximize sharing of the linked dataset, in alignment with the NIH Data Management and Sharing Policy, taking into consideration NIH Guidance on Protecting Privacy when sharing scientific data. Propose potential processes and controls for mitigating re-identifiability of the data that may be introduced by any/all linkages.
• Describe the process and potential security requirements needed to protect personally identifiable information elements, if relevant. For NIH policy regarding the use of controlled-access data, please refer to NIH Security Best Practices for Users of Controlled-Access Data.
• Describe contingency plans for alternate data sources/types, software and platforms if alternate sources and models are necessary. 

Section 3: Data Generation (optional, see requirements below).

If the proposal includes activities under Task II, Data Generation, this section should describe how the applicant will generate new data to enhance any existing dataset/s that could address (1) a variety of known or novel contributors to the risk for autism, or (2) patterns of treatment/intervention and services and associated outcomes. Any proposal including the Data Generation task will require a data management and sharing plan (see below). If no activities are proposed under Task II, Data Generation, this section should not be included.

At a minimum:  

• Describe the rationale and need for the generation of new data and why existing data are insufficient or cannot be linked to answer specific research questions to be addressed in the proposal. New data generated must include a Data Management and Sharing Plan with the application.
• Describe the methods for generation of new data including any data standards, validation and reliability of selected methods and any human-centric models that are proposed. For data generated from biospecimens this should include methods for original specimen collection, preservation and storage or for the access to and appropriate use and analysis of banked biospecimens.  
• If proposing the use of NAMs or human-centric models, describe the relevance of the proposed model and selected endpoints to autism.  
• Describe the technical plans for linking newly generated data to existing data source/s, where feasible.
• Describe strategy and contingency plans for generation of new data within a rapid timeframe. 

Section 4: Data Analysis (optional, see requirements below).

If the proposal includes activities under Task III, Data Analysis, this section should describe how the proposed analysis utilizes advanced data science methodologies, such as artificial intelligence (AI), machine learning (ML), and/or other advanced statistical methods to achieve the overarching aims described in this ROA, as well as the development, annotation, and sharing of newly-developed models for Task IV and for sharing with the broad researcher community. If no activities are proposed under Task III, Data Analysis, this section should not be included.

At a minimum:  

• Describe the types of data science methodology (AI/ML, advanced statistical methods, or other) that are anticipated to be used for exploring the dataset/s  
• Demonstrate how each data science methodology proposed will ensure fidelity of the analysis and achieve the overarching aims described in the ROA.
• Provide evidence to demonstrate successful use of these methodologies and sharing of models and/or software in similar endeavors.
• Describe the skillset, capacity, and capability to conduct these data science endeavors.
• Describe the secure platform environment(s) where the novel dataset/s will be hosted during analysis and the environment’s capacity to support the expected scale and complexity of the analysis (e.g., cloud-based systems for large-scale compute).
• Describe the plan for sharing newly developed models while adhering to NIH guidance on using controlled-access in generative AI and other relevant policy considerations and guidance for AI in research: Artificial Intelligence - Office of Science Policy.  

Section 5: Model Validation or Method Replication (optional; see requirements below)

If the proposal includes activities under Task IV, Model Validation or Method Replication, this section should describe applicant’s technical capabilities and subject matter expertise that will enable teams to carry out the replication study. If no activities are proposed under Task IV, Model Validation or Method Replication, this section should not be included.

At a minimum:

• Describe the replication and validation team’s technical capabilities, capacity, skillsets, and subject matter expertise. Examples of relevant scientific expertise may include, but are not limited to study design, statistical analysis, evaluation, autism screening and diagnosis, genetics, neurobiology, epidemiology, and social and environmental determinants of health. Include the types of data science methodology (AI/ML, advanced statistical methods, or other) that the team has expertise in.  
• Provide evidence of previous experience conducting replication and validation research.
• Describe the organizational structure and operational plan to conduct replication and validation studies.  
• Describe plans to engage with researchers from original studies throughout the preparatory phase and replication phase.
• Provide timelines and milestones to demonstrate feasibility of completing a replication study within 36 months, accounting for preliminary preparatory work, replication activities, and validation activities.  
• Describe the platform environment(s) where the linked data will be hosted during analysis and the environment’s capacity to support the expected scale and complexity of the analysis (e.g., cloud-based systems for large-scale compute).    
• Describe the process and potential security requirements needed to protect personally identifiable information elements, if relevant.   

Section 6: Experience with sharing and coordination; nimbleness to course correct (required).

• Applicants should include past performance that demonstrates the team’s experience sharing data, resources, methods, algorithms, newly created tools or software, and other resources with the research community. For projects that propose AI/ML applications, describe how model transparency will be achieved.
• Describe the team’s experience demonstrating nimbleness to correct course, as needed, using alternate strategies to address unforeseen challenges and changes in scope.
• Applicants proposing activities under Task I – III should describe how they will plan to collaborate with the teams under Task IV, Model Validation or Method Replication
• Applicants proposing activities under Task IV should describe how they will plan to collaborate and coordinate with the research teams funded under Tasks I-III to understand the scope, methods, and data sources used in their research to prepare for the model validation and method replication activities  

Section 7: Past performance and expertise of the team members and complementarity with other groups (required).

At a minimum:

• Identify key personnel, project leads, and other personnel.
• Specify effort levels and specific roles for each person.
• If applicable, detail community partners and specific roles for each partner.
• Describe how key personnel and partners will accomplish the objective(s).
• Describe how the project will leverage the expertise of the federal and other relevant partners who are part of the ADSI team.
• Include applicable past performance for the team and any prior experience working together.
• Detail a leadership plan for plans that involve multiple Principal Investigators.

Section 8: Project Management Plan (required)

• Include a project management plan with defined timeline and milestones.
• Include risks and dependencies for 2- and 5-months post award at a minimum.
• Include any graphs, pictures, or data tables in the body of the text. 

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Community Engagement Plan (no more than 1 page) #commengagement

All applications should include a Community Engagement Plan that proposes or describes how community engagement strategies and community-engaged research will be employed during the project period. The plan should:

• Identify community partners that represent communities of interest; this can include but is not limited to people on the autism spectrum, their family members or caregivers, clinicians and service providers, and advocacy organizations. Specific individuals who will serve as community partners do not need to be identified at the time of application, but applicants must describe proposed number, type, identification, and prior representation in research.  
• Describe how the community partners will be collaboratively engaged in the research project (e.g., activities, frequency and duration of involvement). The community engagement plan should justify how the planned partners and level of involvement will enhance the research project.  
• Demonstrate the feasibility of engaging the community partners in the research at the planned level of involvement. Demonstration of feasibility could include (but is not limited to) letters of support and/or formal roles on the application (e.g., co-investigator, consultant).  

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Resource Sharing Plan (no more than 1 page) #resourcesharing

All applications should include a Resource Sharing Plan addressing how models, workflows, and/or pipelines created or used with support from this funding will be shared with the wider scientific community in a timely manner that would enable other researchers to replicate and build on for future research efforts. Plans should align to open-source practices and other NIH Best Practices for Sharing Research Software Frequently Asked Questions as much as possible. Data sharing plans should not be included in this section. Data sharing information must be described in the Data Management and Sharing (DMS) Plan. The Resource Sharing Plan will be considered during objective review and by program staff as award decisions are being made as appropriate and consistent with achieving the goals of the program. 

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Data Management and Sharing (DMS) Plan (no more than 2 pages) #dms

The Final NIH Policy for Data Management and Sharing (NOT-OD-21-013) expects researchers to maximize the sharing of scientific data and data to be accessible as soon as possible and no later than the time of an associated publication or the end of the award period, whichever comes first. NIH requires all applications submitted in response to this ROA to include a DMS Plan. The DMS Plan is expected to address the elements as described in Supplemental Information to the NIH Policy for Data Management and Sharing: Elements of an NIH Data Management and Sharing Plan (NOT-OD-21-014). For applications that aim to analyze existing data, DMS Plans should describe where and how other researchers can access that data to enable reproducibility and reuse. For this opportunity, the DMS Plan should reflect the goals of the program including respecting governance requirements for sharing the linked dataset/s, based on a detailed policy analysis. The DMS Plan will be reviewed and approved by NIH program staff prior to award. Awardees will be required to comply with their approved DMS Plan and any approved updates.

For human data, NIH expects awardees to share data through broad-sharing data repositories.

NICHD ODSS also provides additional DMS Policy Resources. 

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Additional Items #additionalitems

• A budget with justification using the SF-424 form (review the Budget section that follows this section for details)
• A letter of support from the applicant’s organization indicating institutional commitment for the project (e.g., relaying support for contributions) including, but not limited to, support for training activities or ADSI meetings, licenses, and other resources; and preparations to enter into negotiated other transactions agreements
• Letters of support from proposed collaborators or parties who hold governance over data sources (e.g., health care partners)
• A bibliography (not to exceed 1 page) 

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Budget (no page limitation) #budget

NIH may elect to negotiate any or all elements of the proposed budget. Proposals must provide a realistic budget and cost estimate for performing the work for each year. Provide the overall expected cost for the project including but not limited to each of the following categories:

• Personnel (to include the fulltime base salary for all key and other personnel)
• Equipment
• Travel (to include a breakout of all costs associated with each proposed trip)
• Subawards/subcontracts/consultants
• Other direct costs
• Total costs (with indirect costs included)
• Proposed cost share contributions (if applicable)

Applicants must provide a budget justification for all budget items. Subrecipient/subaward budgets must include a breakdown of costs and a budget justification. Applicants should provide one budget and one justification per institute or organization in the application. Applicants should also prepare a Gantt chart organized by Task Area that shows how the funding requested aligns to each of the proposed milestones. Budgets will be reassessed as the projects proceed and may be increased or decreased depending on progress, the needs of the program, and funds available. Total budget for the 24- to 36-month proposal generally should not exceed $5,000,000 total costs. If proposed Task IV activities include replication of a large number of Task III results, a larger budget request may be appropriate. 

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Award Project Duration #duration

The period of performance anticipated for this OTA will be a 24-month base period for applicants with activities proposed only under Tasks I and III. Applicants who propose a combination of activities that include Task II and applicants who propose Task IV may have up to a 36-month base period. Applications that do not include data collection activities under Task II or model validation and method replication activities under Task IV must not exceed 24 months.  

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Reporting Requirements #reporting

All awards made under this ROA will be subject to financial and programmatic reporting requirements. At a minimum, awardees will be required to submit an annual progress report and annual financial report but may be required to report more frequently (e.g., monthly or quarterly). Additional information about the reporting requirements can be found in the Terms and Conditions, available at ADSI Terms and Conditions of Award page. 

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Submission Information #submission

Complete applications must be submitted under OTA-25-006 via NIH eRA Commons ASSIST no later than the “Application Due Date” shown at the top of this notice, by 5 pm local time of the applicant organization. 

Late applications submitted to this ROA will not be accepted.

Full applications must be submitted via the NIH eRA ASSIST system. To submit an application via ASSIST, the applicant organization must have already registered for and been granted the following, which may take several weeks to complete:  

• System for Award Management (SAM): https://sam.gov/content/home – Applicants must complete and maintain an active registration, which requires renewal at least annually.
• Unique Entity Identifier (UEI): A UEI is issued as part of the SAM.gov registration process.
• eRA Commons: Once the unique organization identifier (UEI after April 2022) is established organizations can register with eRA Commons (https://www.era.nih.gov) in tandem with completing their full SAM and Grants.gov registrations.

Applications must be prepared and submitted using NIH’s eRA ASSIST. Complete applications must be submitted by the Recipient Business Official (RBO). The organization must be registered in eRA Commons with one person designated as the contact Principal Investigator (PI) and one person designated as the RBO.

Applicants must submit the full application via the NIH eRA Commons ASSIST system no later than Friday, June 27, 2025, by 5 pm local time of applicant organization. Here are instructions for submitting via the NIH eRA ASSIST system including specific guidance for OTAs: https://www.era.nih.gov/help-tutorials/assist/era-training-assist.htm. Technical assistance is available from the eRA Service Desk: https://www.era.nih.gov/need-help. 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. 

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Section 6: Objective Review Information #section6

Reviewer Process #reviewerprocess

Applications will undergo an objective scientific review. This ensures the assessment of scientific or technical merit of applications by individuals with knowledge and expertise equivalent to that of the individuals whose applications of support they are reviewing. 

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Reviewer Selection #reviewerselection

This review will be conducted with both internal and external subject matter experts, with review panels representing expertise across autism-related fields such as autism and other developmental disability, genomics and other -omics, environmental exposures, epidemiology, health information, health systems and services research, clinical trials methodology and human subjects protection, implementation science, data science, new approach methodologies, and OT expertise. 

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Conflicts of Interest (COI) #coi

Reviewers shall disclose any COI that might preclude their participation in the review process, as per NIH guidelines. Each reviewer must certify, under penalty of perjury (U.S. Code Title 18, Chapter 47, Section 1001), that, to the best of their knowledge, they have disclosed all COI they may have with the applications or research and development contract proposals; and they fully understand the confidential nature of the review process. COI of review panel members are appropriately managed during the review process in accordance with standard NIH policies. 

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Review Criteria #reviewcriteria

The objective review will consider:

1. The significance of the proposed work to address one or both ADSI goals: 1) identifying factors that contribute to autism and the increasing prevalence over time and 2) identifying effective and scalable interventions and services, or predictors of treatment response. The potential impact of the proposed work to advance our understanding of autism generate new avenues to pursue in autism research.
2. For projects proposing Task I Dataset Aggregation - The innovation of project design and data science methodology proposed to generate a valuable dataset/s that can be explored for the multi-faceted aspects of autism risk and prevalence, and/or to identify effective and scalable interventions and services, or predictors of treatment response.
3. For projects proposing Task I Dataset Aggregation - The rigor and quality of the plans to generate the novel dataset/s using multiple data sources, ensuring pertinent governance and technical considerations are met.
4. For projects proposing Task II Data Generation – The rationale for generating new data, how it can enhance existing data, and how it will contribute to analyses in Task III. The strength of the rationale and feasibility of data generation methods and any human-centric models being proposed.  
5. For projects proposing Task II Data Generation and Task III Data Analysis- The quality and fidelity of the proposed data science analysis and interpretation to analyze and test the dataset/s and to test, validate, and/or strengthen understanding of the causative or risk factors driving the increase of autism prevalence over time, and/or to identify effective and scalable interventions and services, or predictors of treatment response.
6. For projects proposing Task III Data Analysis – The proposed analysis appropriately distinguishes between specific hypothesis testing and discovery/exploratory analyses. If specific hypotheses are to be tested, the relevance to the ADSI goals, and the rationale for pursuing these hypotheses is described clearly. For exploratory analyses, the feasibility and potential impact of the proposed exploration to inform future research is described clearly.
7. For proposing Task IV Model Validation or Method Replication - The PI and key personnel have a broad set of experience and expertise suited to performing validation and replication studies, including prior experience and expertise in study design, statistical analysis, and meta-analysis. The team has experience working collaboratively and demonstrates flexibility when encountering research challenges.  
8. The feasibility of the project management plan, and the interrelated phases proposed, to achieve dataset aggregation using disparate data sources and to conduct data science methodology within the dataset/s dictionaries, ontologies, and methodologies.
9. The viability of the milestones and timeline proposed, and the soundness of contingency plans with capacity to course correct.
10. The expertise of senior/key personnel and team members with the proposed work to enable successful execution of the ADSI.
11. The suitability and feasibility of the Community Engagement Plan to support ADSI goals, and whether the plan demonstrates appropriate meaningful engagement with community partners.
12. The suitability of the Resource Sharing Plan and the DMS Plan to support the ADSI goals and to comply with the NIH DMS Policy, and whether these plans demonstrate appropriate plans for sharing models and de-identified data with the wider scientific community while adhering to governance requirements of source datasets.
13. The appropriateness of the proposed budget for the scope of work and justification of any third-party subcontracts.

Reviewers will be asked to provide scores and comments on applications. Funding decisions will be based on the outcome of application review and reviewer discussion. It is anticipated that up to 25 awards will be made; however, this may vary depending on funds available and applications received. Agreements for all awards will be negotiated with eligible entities whose applications are determined to be the most advantageous and provide the best value to the ADSI. Following the review of proposals, NIH may assemble teams from all or parts of applications to establish an ADSI team. Individual components from distinct applications may be selectively funded to achieve the goals set forth herein. During the duration of the projects, awardees will work collaboratively to enhance the value of the overall initiative, for example NIH may request that awardees participate in activities to identify common data elements for inclusion across the proposed studies, and that could potentially be used more broadly by the autism research community. Additionally, if, over the duration of the project, some of the components either gain relevance or lose relevance to programmatic goals, the funding for such components may be increased, decreased, or discontinued.

NIH reserves the right to:

• Invite all, some, one, or none of the Principal Investigators submitting applications in response to this solicitation to present their application in a web-based videoconference or teleconference
• Share applications between and among any applicant(s) as necessary for configuring teams, economizing work, and prioritizing activities
• Select for negotiation all, some, one, or none of the proposals received in response to this solicitation
• Accept proposals in their entirety or to select only portions of plans for award

Appeals of the objective review will not be accepted in response to this ROA. 

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ROA References #roarefs

NICHD. (n.d.). Record Linkage Implementation Checklist. Available at: E2_Record-Linkage-Implementation-Checklist/README.md at main · NIH-NICHD-Ecosystem/E2_Record-Linkage-Implementation-Checklist · GitHub 

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