April 12, 2021
PA-20-272 - Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional)
Office of The Director, National Institutes of Health (OD)
National Eye Institute (NEI)
National Heart, Lung, and Blood Institute (NHLBI)
National Human Genome Research Institute (NHGRI)
National Institute on Aging (NIA)
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
National Institute of Allergy and Infectious Diseases (NIAID)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
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 of Nursing Research (NINR)
National Cancer Institute (NCI)
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)
The NIH Office of Data Science Strategy (ODSS) announces the availability of funds for Administrative Supplements to certain institutional research training, career development, or research education awards (see eligibility below). The funds will support the development and implementation of curricular or training activities at the interface of information science, artificial intelligence and machine learning (AI/ML), and biomedical sciences to develop the competencies and skills needed to make biomedical data FAIR (Findable, Accessible, Interoperable, and Reusable) and AI/ML-ready. For the purposes of this Notice, AI/ML is inclusive of machine learning (ML), deep learning (DL), and neural networks (NN).
This Notice will support creative, educational activities to develop the competencies and skills needed to make biomedical data FAIR and AI/ML-ready with a primary focus on:
All curriculum and training offerings developed must be aligned with NOT-OD-20-031, "Notice of NIH's Interest in Diversity".
Applicants are strongly encouraged to discuss potential requests with their Institute/Center (IC) Program Official before submitting the supplement request.
Artificial intelligence and machine learning (AI/ML) are a collection of data-driven technologies with the potential to significantly advance biomedical research. Much of this potential is unrealized, however, because biomedical data are not collected and prepared in ways that would allow them to be used efficiently and effectively by AI/ML applications. The task of making data FAIR and AI/ML-ready is not only algorithmic. It requires multi-disciplinary expertise, experimentation and, often, iterative feedback from AI/ML applications and experts. Particularly for biomedical data, 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.
Some aspects of AI/ML-readiness are relatively better understood than others. For example, popular AI/ML tools, such as PyTorch and TensorFlow, that are used to build and deploy AI/ML applications, each 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. In addition, 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. Furthermore, there are increasing expectations around data documentation to include information about data provenance and bias to help researchers make more informed and ethical decisions about AI/ML-applications.
Research Education Objectives
NIH seeks to train new scientists who are experts in FAIR and AI/ML-ready data, and are able to advance the field of data science for AI/ML in biomedicine. With this NOSI, NIH seeks to provide training opportunities for people from a variety of backgrounds and career stages to specialize in the competencies and skills needed to make data FAIR and AI/ML-ready, and in the skills needed to collaborate effectively with researchers across the fields of information sciences, biomedical research, and AI/ML.
It is recognized that different disciplinary areas and data types may need personnel with different sets of expertise and training experiences, including basic, clinical, population, behavioral and social sciences. Programs are not expected to cover all of these disciplines, but the educational experiences should prepare those who complete the program to work with a range of data types.
NIH expects that the outputs of activities funded under this NOSI will be made available to the broadest possible audience. Training modules and curriculum plans should be exportable and shared.
Applications that are not explicitly relevant to making data FAIR and AI/ML ready will be deemed as non-responsive to this initiative.out of scope.
NIH expects successful activities and training materials broadly available at no cost to the user. Dissemination plans that rely on fee-based access to activities and training materials will be deemed as non-responsive to this initiative and out of scope.
See IC specific eligibilities below. The parent award must be active when the supplemental application is submitted (e.g., within the originally reviewed and approved project period) and have a project end date of May 31, 2022 or later. Awards in a No-Cost Extension (NCE) are not eligible.
An applicant institution (normally identified by having a unique entity identifier such as a DUNS number or NIH Institutional Profile File - IPF) may submit only one application in response to this administrative supplement opportunity. For institutions with two or more eligible grants, it is expected that the Program Directors/Principal Investigators will cooperate to develop curricula and/or activities that are broadly applicable to individuals at their institution and that a single supplement request will be submitted through one of the eligible awards.
Applicants for this supplement must have an NCI-funded Ruth L. Kirschstein NRSA Institutional Research Training Grant (T32), Paul Calabresi Career Development Award for Clinical Oncology (K12), or one of the Cancer Research Education Grants Program (R25).
Applicants for this supplement must have an NEI-funded Ruth L. Kirschstein NRSA Institutional Research Training Grant (T32) or Institutional Career Development Award (K12).
Applicants for this supplement must have an NHGRI-funded training grant (T32) or research education program (R25) awarded through the following funding announcements and previous issuances:
Applicants for this supplement must have an NHLBI-funded Ruth L. Kirschstein NRSA Institutional Research Training Grants (T32, T35), Institutional Career Development Award (K12), Research in Residency (R38) or one of the following Research Education Programs (R25) funding announcements and previous issuances:
Applicants for this supplement must have NIA-funded institutional training (Ruth L. Kirschstein NRSA T32, T35) programs, research education programs (R25), or research education components linked to Center awards (RL5). Specific Funding Announcements (FOAs) listed below. Eligibility also applies to previous issuances of the FOAs:
Applicants must have an NIAAA-funded training (T32, T35) or research education program (R25) award. NIAAA encourages projects that use the NIAAA Data Archive or funded initiatives with data repositories, including Collaborative Studies on Genetics of Alcoholism (COGA) study, Adolescent Brain Cognitive Development (ABCD) Study, or the Monkey Alcohol Tissue Research Resource (MATRR).
Applicants for this supplement must have an NIAID-funded Ruth L. Kirschstein NRSA Institutional Research Training Grant (T32) or a NIAID Research Education Grant (R25).
Applicants for this supplement must have an NIAMS-funded Ruth L. Kirschstein NRSA Institutional Research Training Grant (T32).
Applicants for this supplement must have a NIDA-funded training grant (T32) or research education program (R25) awarded. NIDA encourages projects that use data focused on addiction and/or neuroscience, including, but not limited to, the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD Study collects multidimensional longitudinal data from nearly 12,000 children beginning at ages 9-10 and continuing for 10 years, and releases this data annually through the NIMH Data Archive.
Applicants for this supplement must have an NIEHS-funded Ruth L. Kirschstein NRSA Institutional Research Training Grant (T32).
Applicants for this supplement must have an NIGMS-funded training (T32, T34), Institutional Career Development (K12), or certain research education program (R25, see below) award. Only R25 awards in the following programs (and associated funding opportunity announcements) are eligible for this supplement:
Applicants for this supplement must have an NIMH-funded Ruth L. Kirschstein NRSA Institutional Research Training Grant (T32) or Short Courses for Mental Health Related Research (R25)award through PAR-20-096 or previous issuances.
Applicants for this supplement must have an NINR-funded Ruth L. Kirschstein NRSA Institutional Research Training Grant (T32).
Application and Submission Information
Applications for this initiative must be within scope of the parent award and must be submitted using the following opportunity or its subsequent reissued equivalent.
Application Review Information
NIH staff will consider whether the budget is fully justified and will evaluate applications using the following criteria:
Applicants are strongly encouraged to notify the program contact at the Institute supporting the parent award that a request has been submitted in response to this NOSI in order to facilitate efficient processing of the request.
Laura Biven, Ph.D.
NIH Office of Data Science Strategy