Key Dates

Related Announcements

January 26, 2023 – Ruth L. Kirschstein National Research Service Award (NRSA) Institutional Research Training Grant (Parent T32) – See NOFO PA-23-048.

Issued by

National Institute of Allergy and Infectious Diseases (NIAID)

Purpose

This Notice of Special Interest (NOSI) solicits competitive revision applications from existing T32 recipients to support additional training slots within the NIAID Data Science Training Program (NDSTP) for pre-doctoral data science training. Applications must propose a data science training program that will include research and mentoring opportunities, as well as coursework for pre-doctoral biomedical trainees. Through this NOSI, applicants may increase the number of training slots beyond the T32 maximum allowed; the training slots proposed via this NOSI must support trainees pursuing a data science curriculum, described below. It is anticipated that an application will propose 2-3 additional data science training slots and 3-4 awards are expected.

• Research and mentorship opportunities will include faculty mentorship for trainees in data science topics and peer-to-peer collaborative research activities (e.g., postdoctoral to graduate student, senior graduate student to junior graduate student, etc.) during which trainees from biomedical and computational programs will collaborate on research projects with supervision from researchers with expertise in both infectious and immune-mediated diseases (IID) and computational methods. For the purposes of this NOSI, IID may include the subject areas of infectious, immunologic, and allergic diseases.
• Coursework will include structured classes in computational methods, data management and sharing, and social and ethical considerations of data science technologies. Applications are expected to propose collaborations between departments of biomedical and computational sciences to develop courses that provide instruction in these areas of data science and will include direct application to IID subjects. An optional seminar series may be developed in addition to coursework.

Proposed programs must comply with the requirements of the parent T32 program and NIAID training grant policies.

Background

Data science, defined in the NIH Strategic Plan for Data Science, is a rapidly evolving field with methods that are relevant for IID research, including artificial intelligence (AI), machine-learning (ML), systems modeling, natural language processing (NLP), image analysis and structure-based design of diagnostics, therapeutics, and vaccines. Data science approaches and technologies enhance data curation, management, and sharing. Biomedical trainees with training in these areas will be prepared to advance FAIR (Findable, Accessible, Interoperable, and Reusable) compliant data, enhance open science, and improve research rigor and reproducibility. Data science training for predoctoral biomedical trainees will accelerate research by the next generation of IID researchers and enable researchers to comply with the 2023 NIH Data Management and Sharing Policy.

Despite the importance of data science across IID research, many biomedical students undertake self-directed data science training. There is a clear need for interdisciplinary, institutional training programs (e.g., between departments of computational and biomedical science) that apply data science to IID research for pre-doctoral students. The NDSTP program will develop data science training that will bridge divisions between computational and IID biomedical research through well-rounded training programs. This program will provide the following opportunities for trainees: data science coursework (and optional seminars), interdisciplinary mentorships, research collaborations, and technical research skills.

Data Science Program Objectives

This NOSI will support training slots for T32 grants for pre-doctoral trainees in the application of data science methods across IID research. Through the proposed NDSTP program, trainees will develop the capability to apply data science methods and technologies across IID domains. The program will incorporate (1) interdisciplinary faculty mentorships of trainees and peer-to-peer trainee collaborations to conduct research that applies data science to IID research, and (2) data science coursework and an optional seminar series that are developed and instructed by faculty from departments of (or faculty with expertise in) computational and biomedical sciences. Trainees will only be eligible to enroll in the NDSTP in their second or third year of pre-doctoral training. Applications should develop outcomes (e.g., credit sharing, a certificate program, etc.) that ensure trainees receive recognition for completion of the NDSTP.

Data Science Research and Mentorship

Programs are required to include faculty mentorship of trainees and research opportunities for trainees from faculty beyond the role of the course instructor. This may involve peer-to-peer trainee collaborations, and the conduct of research in data science applied to IID biomedical research. Research must occur under faculty supervision. It is encouraged that participating faculty members serve on a trainee’s dissertation committee. The program will provide trainees with a working knowledge of emerging data science methods applied to IID research to develop independent research at the interface of data science and IID biomedical science. Examples of data science research and trainee mentorship activities include, but are not limited to:

• Interdisciplinary research opportunities across data science topics that support dissertation research and/or data science training which ultimately lead to publications and/or conference presentations;
• Establishing formal mentorships for IID biomedical students from faculty in computational and/or data science departments beyond coursework instruction (e.g., serving on a dissertation committee, lab rotations and/or collaborative research projects)
• Developing peer-to-peer networks through new collaborative research projects and/or contributing to ongoing research projects with trainees in biomedical and computational disciplines with faculty supervision.

Data Science Coursework

Data science coursework must align with the goals of the NIH Strategic Plan for Data Science and include coursework in the topic areas of (1) computational methods; (2) data management and sharing; and (3) social and ethical considerations of data science technologies. Faculty from schools of computational and biomedical sciences must collaborate to develop data science coursework with applications in biomedical research. The courses must be developed jointly between faculty with biomedical and computational expertise and must have direct application of data science to IID. Alternatively, programs could modify existing courses to incorporate relevant topics in data science. Potential coursework may include, but is not limited to:

• Computational training in AI, ML, knowledge graphs, automated reasoning, natural-language processing, image analysis, and systems modeling;
• Methods and technologies for data curation, management, and data re-use, including FAIR;
• Training in effective data sharing methods, approaches to improve the rigor and reproducibility of data to advance FAIR, machine readable, and AI readiness;
• The ethical, social, and cultural impact of computational approaches in IID research.

Transformative Revisions

Applications to this NOSI must demonstrate transformative revisions to their existing T32 program beyond data science activities outlined in the aims of the current award. To be considered “transformative revisions”, existing programs must add data science mentoring and research opportunities, and coursework that aligns with the NIH Strategic Plan for Data Science, including computational methods, data management and sharing, and social and ethical considerations of data science technologies. These activities must be developed collaboratively between departments of computational and biomedical sciences. Existing T32 programs that focus on traditional IID approaches with computationally intense components including, epidemiology, statistics, or bioinformatics must propose transformative revisions to incorporate data science mentorship, research and coursework to the existing program.

Potential Data Science Topic Areas

Existing IID T32 Programs may choose to develop new opportunities for pre-doctoral trainees in a range of data science methods. Examples of topics that fit the scope of this NOSI may incorporate, but are not limited to, the following areas:

• Approaches such as graph neural networks, random forest classifiers, explainable, sequence-to-structure models, molecular docking scoring, regression analyses, sequence-to-function models, NLP and generative models for application to drug-target interactions models for anti-infective drug activity, and therapeutic design.
• The incorporation of new computational methods, such as AI/ML, knowledge graphs, automated reasoning, NLP, image analysis, and systems modeling;
• Data science approaches to mine, enhance, evaluate, and (re)use data from electronic health records (EHR), clinical trials, epidemiology studies, molecular data, registry data, etc.;
• The development of re-usable computational tools, research software, and/or AI/ML algorithms for secondary analysis;
• Methods for secure federated learning, data visiting and tabularization, or approaches to improve the speed and accuracy of data analysis and re-analysis;
• Advanced statistical and mathematical modeling methods, graph and network theory approaches for predictive, interventional, and prognostic applications;
• Data management and sharing technologies, such as generalizable tools and workflows. Tools and workflows could include approaches for data and/or metadata acquisition and annotation, data processing, provenance, standards, ontologies, as well as data exchange formats for domain-specific data.

Applications proposing any of the following will NOT be supported under this NOSI: 

• Programs that do not include institutional level collaborations between departments of computational and biomedical science.
• Existing T32 programs that include data science and/or traditional IID approaches with computationally intense components including, epidemiology, statistics, or bioinformatics that do not propose transformative revisions to incorporate data science mentorship, research, and coursework to the existing program.
• Programs that do not include faculty mentorship and research opportunities from faculty beyond the role of course instructor.
• Programs that do not include the development of data science coursework with direct application to IID subject areas.
• Programs that do not support pre-doctoral students in their 2^nd and 3^rd year of training.
• Programs that do not support the NIAID mission.

Pre application Webinar

A pre-application webinar will be held. Please see the FAQ web page for further details and updates. 

Application and Submission Information

Applications for this initiative must be submitted using the following opportunity or its subsequent reissued equivalent.

• PA-23-048 – Ruth L. Kirschstein National Research Service Award (NRSA) Institutional Research Training Grant (Parent T32).

All instructions in the SF424 (R&R) Application Guide and PA-23-048 must be followed, with the following additions:

• Application Due Date(s) – September 25, 2024 (Non-AIDS), January 7, 2025 (AIDS), by 5:00 PM local time of applicant organization.
• For funding consideration, applicants must include “NOT-AI-24-012” (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.
• Requests may be for the entire award project period of the parent award.
• Applicants must identify the number of trainee slots (e.g., 2-3 additional data science training slots) they wish to add. 
• Applicants are strongly encouraged to contact the Program Officer assigned to the parent award to determine appropriateness prior to submission.
• The proposed project period must be within the project period of the parent award. New trainees must be appointed initially for a minimum of 9 months.
• The peer-reviewed NIH-funded parent grant must have at least 24 months of active grant support remaining after the submission deadline for the submitted application. An award in a no-cost extension is not eligible.

Applications nonresponsive to terms of this NOSI will not be considered for the NOSI initiative.

Inquiries

Please direct all inquiries to the contacts in Section VII of the listed notice of funding opportunity with the following additions/substitutions:

Scientific/Research Contact

Meghan Hartwick, Ph.D.
Office of Data Science and Emerging Technologies (ODSET)
National Institute for Allergy and Infectious Diseases (NIAID)
Telephone: 301-761-6549
Email: datascience-foa@niaid.nih.gov