April 16, 2021
PA-20-272 - Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Administrative Supplement Clinical Trial Optional)
NOT-OD-21-119 - Notice of Change to Additional Information for NOT-OD-21-094
Office of The Director, National Institutes of Health (OD)
National Eye Institute (NEI)
National Human Genome Research Institute (NHGRI)
National Institute on Aging (NIA)
National Institute of Allergy and Infectious Diseases (NIAID)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
National Institute on Deafness and Other Communication Disorders (NIDCD)
National Institute of Dental and Craniofacial Research (NIDCR)
National Institute on Drug Abuse (NIDA)
National Institute of Environmental Health Sciences (NIEHS)
National Institute of General Medical Sciences (NIGMS)
National Institute of Mental Health (NIMH)
National Institute of Neurological Disorders and Stroke (NINDS)
National Institute on Minority Health and Health Disparities (NIMHD)
National Library of Medicine (NLM)
Fogarty International Center (FIC)
Office of Strategic Coordination (Common Fund)
All applications to this funding opportunity announcement should fall within the mission of the Institutes/Centers. The following NIH Offices may co-fund applications assigned to those Institutes/Centers.
Office of Research on Women's Health (ORWH)
This Notice announces the availability of supplements to active grants which are intended to support collaborations that bring together expertise in biomedicine, data management, and artificial intelligence and machine learning (AI/ML) to make NIH-supported data useful and usable for AI/ML analytics. This initiative is aligned with the NIH Strategic Plan for Data Science, which describes actions aimed at modernizing the biomedical research data ecosystem and making data FAIR (Findable, Accessible, Interoperable, and Reusable) with high impact for open science. For the purposes of this Notice, AI/ML is inclusive of machine learning (ML), deep learning (DL), and neural networks (NN).
Artificial intelligence and machine learning (AI/ML) are a collection of data-driven technologies with the potential to significantly advance biomedical research. NIH makes a wealth of biomedical data available and reusable to research communities however, not all of these data are able to be used efficiently and effectively by AI/ML applications. The goal of this Notice is to make the data generated through NIH-funded research AI/ML-ready and shared through repositories, knowledgebases or other data sharing resources.
For the purposes of this Notice, AI/ML is inclusive of machine learning (ML), deep learning (DL), and neural networks (NN). Making data AI/ML-ready is not simply formulaic. It requires engagement with and feedback from AI/ML applications. Furthermore, feedback from AI/ML applications can improve the understanding of the data to improve future re-use.
Some aspects of AI/ML-readiness are better understood than others. For example, AI/ML tools, such as PyTorch and TensorFlow, which are used to build and deploy AI/ML applications, both require specific data formats. Important biomedical AI/ML applications often require data from different sources to be combined, so making data FAIR through the use of data and metadata standards (ontologies, taxonomies, terminologies) is a foundational component of AI/ML-readiness.
Other aspects of data, such as the representation of information, presence of noise, specificity or uncertainty of labels, and the amount of data, can influence the computational feasibility of AI/ML learning and the accuracy of the resulting models in ways that are currently difficult to predict without testing.
In addition to AI/ML-readiness of the data and metadata themselves, there are increasing expectations around data documentation to include information about data provenance and bias that would help researchers make more informed and ethical decisions from the data. For example, imbalanced datasets can result in AI/ML algorithms that lead to biased clinical decisions and, potentially, a misalignment with NIH goals to improve minority health and reduce health disparities for marginalized populations.
AI/ML-readiness should be guided by a concern for human and clinical impact and therefore requires attention to ethical, legal, and social implications of AI/ML including but not limited to: (1) biases in datasets, algorithms, and applications; (2) concerns related to privacy and confidentiality; (3) impacts on disadvantaged or marginalized groups and health disparities; and (4) unintended, adverse social consequences of research and development.
It is the NIH vision to establish a modernized and integrated biomedical data ecosystem that adopts the latest data science technologies, including AI and ML, and best practice guidelines arising from community consensus, such as the FAIR principles and open-source development. This effort is described in the NIH Data Science Strategic Plan and led by the NIH Office of Data Science Strategy (ODSS).
This opportunity is intended to support collaborations that bring together expertise in biomedicine, data management, and AI/ML to improve the AI/ML-readiness of data generated from NIH-funded research and shared through repositories, knowledgebases or other data sharing resources.
Applications submitted in response to this NOSI are strongly encouraged to include the following information:
These supplements may be used to support a variety of activities including, but not limited to, the following:
These efforts are expected to be informed by best practices in data management and engagement with the AI/ML community.
Significant skills in data management and AI/ML are expected to be needed to identify and address gaps in AI-readiness. Thus, supplements are primarily intended to provide support for data management and AI/ML collaborators, engagement events such as hackathons, and computing and storage costs required to improve the AI-readiness of data.
The scope of each proposed project is defined by and limited to the aims of the funded project for which the supplement is being sought.
Applicants partnering with industry to test novel methods or infrastructures may be considered.
The integration of causal models and causal inference in AI/ML is within scope.
A broad range of projects involving the management of data repositories, or other shared data resources are eligible regardless of the scientific area of emphasis. Both open and controlled access data, including clinical data, are within scope.
Awardees should be willing to participate in virtual meetings organized by NIH.
Applications that are not appropriate and out of scope for this NOSI include:
Application and Submission Information
To be eligible, the parent award must be able to receive funds in FY2021 (Oct. 1, 2020 - Sept. 30, 2021) and not be in the final year or in a no-cost extension period at the time of the award. The parent award should end on or after Sept. 30, 2022.
One-time supplement budget requests cannot exceed $200,000 direct costs. The number of awards will be contingent on availability of funds and receipt of meritorious applications. It is currently anticipated that 50 awards will be made.
Eligible Activity Codes:
Additional funds may be awarded as supplements to parent awards using any Activity Code that is listed in PA-20-272 with the following exceptions.
Small business activity codes (such as R41, R42, R43, R44, U44, and Fast Track) are excluded as well as G20, PS1, P60, R13, U13, U42, and UG1 awards.
Note that not all participating NIH Institutes and Centers (ICs) support all the activity codes that may otherwise be allowed. Applicants are therefore strongly encouraged to consult the program officer of the parent grant to confirm eligibility.
Centers and multi-project grant mechanisms are eligible but must provide a strong justification for why existing funds cannot be reallocated toward the proposed project.
For awards that are already primarily funded to deliver reusable data to the community, applicants should provide strong justification for why additional funds are needed to support AI/ML-readiness given that these activities could have been supported through the parent award.
Applications for this initiative must be submitted using PA-20-272 - Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional) or its subsequent reissued equivalent.
Administrative Evaluation Process
Submitted applications must follow the guidelines of the IC that funds the parent grant. Administrative Supplements do not receive peer review. Each IC will conduct administrative reviews of applications submitted to their IC separately. The most meritorious applications will be evaluated by a trans-NIH panel of NIH staff and supported based upon availability of funds. The criteria described below will be considered in the administrative evaluation process:
It is strongly recommended that the applicants contact their respective program officers at the Institute supporting the parent award in advance to:
Investigators planning to submit an application in response to this NOSI are also strongly encouraged to contact and discuss their proposed research/aims with the scientific contact listed on this NOSI in advance of the application receipt date.
Following submission, applicants are strongly encouraged to notify the program contact at the IC supporting the parent award that a request has been submitted in response to this NOSI in order to facilitate efficient processing of the request.
The Office of Research on Women’s Health (ORWH), which is part of the Office of the Director of the National Institutes of Health (NIH), works in partnership with 27 NIH Institutes and Centers (ICs) to promote women’s health research within and beyond the NIH scientific community. Within the focus of this funding announcement, additional co-funding may be available from ORWH for projects that focus on assessing outcomes related to the health of women and health disparities. In particular, ORWH is interested in understanding the social and health implications of using data that are imbalanced with respect to the representation of underserved women in AI/ML applications.