Opportunity Information: Apply for RFA HG 14 004
Apply for RFA HG 14 004
- The National Institutes of Health in the education environment food and nutrition health income security and social services sector is offering a public funding opportunity titled "Predoctoral Training in Biomedical Big Data Science (T32)" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.113 Environmental Health 93.121 Oral Diseases and Disorders Research 93.173 Research Related to Deafness and Communication Disorders 93.213 Research and Training in Complementary and Integrative Health 93.242 Mental Health Research Grants 93.273 Alcohol Research Programs 93.279 Drug Abuse and Addiction Research Programs 93.286 Discovery and Applied Research for Technological Innovations to Improve Human Health 93.307 Minority Health and Health Disparities Research 93.310 Trans NIH Research Support 93.361 Nursing Research 93.398 Cancer Research Manpower 93.846 Arthritis, Musculoskeletal and Skin Diseases Research 93.847 Diabetes, Digestive, and Kidney Diseases Extramural Research 93.853 Extramural Research Programs in the Neurosciences and Neurological Disorders 93.855 Allergy and Infectious Diseases Research 93.856 Microbiology and Infectious Diseases Research 93.865 Child Health and Human Development Extramural Research 93.866 Aging Research 93.867 Vision Research 93.879 Medical Library Assistance.
- This funding opportunity was created on Apr 22, 2014 and posted on Apr 22, 2014.
- Applicants must submit their applications by Jul 27, 2015. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- The funding agency has allocated a total of $6,000,000.00 to eligible and selected applicants.
- The number of recipients for this funding is limited to 3 candidate(s).
- Eligible applicants include: Native American tribal governments (Federally recognized) Public and State controlled institutions of higher education Private institutions of higher education Native American tribal organizations (other than Federally recognized tribal governments) Others (see text field entitled Additional Information on Eligibility for clarification).
- Other Eligible Applicants include the following Alaska Native and Native Hawaiian Serving Institutions Asian American Native American Pacific Islander Serving Institutions (AANAPISISs) Hispanic serving Institutions Historically Black Colleges and Universities (HBCUs) Indian/Native American Tribal Governments (Other than Federally Recognized) Tribally Controlled Colleges and Universities (TCCUs) U.S. Territory or Possession The sponsoring institution must assure support for the proposed program. Appropriate institutional commitment to the program includes the provision of adequate staff, facilities, and educational resources that can contribute to the planned program. The applicant institution(s) must have a strong and high quality research program in biomedical Big Data Science that includes diverse and complex data types, such as genomic, other omic, imaging, phenotypic, exposure, etc., both in the biomedical sciences. The applicant institution(s) must have the requisite faculty and facilities to conduct the proposed institutional program. In many cases, it is anticipated that the proposed program will complement, but be distinct from, other ongoing research training programs occurring at the applicant institution. It is expected that a substantial number of program faculty will have active biomedical Big Data Science research projects involving a variety of data types in the biomedical sciences in which participating trainees may gain relevant experiences consistent with their research interests and goals.
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Opportunity Summary:
The Predoctoral Training in Biomedical Big Data Science (T32) opportunity, Funding Opportunity Number RFA-HG-14-004, is a National Institutes of Health (NIH) institutional training grant designed to build and support graduate-level training programs focused on biomedical Big Data Science. The central goal is to develop the next generation of scientists who can create and apply computational, statistical, and quantitative methods to handle the rapidly expanding scale, complexity, and diversity of data generated in modern biomedical research. In practical terms, NIH is seeking training programs that will produce researchers capable not only of analyzing large biomedical datasets, but also of inventing new tools, technologies, and methodological approaches that help the broader biomedical research community extract meaningful, actionable insights from these data.
This FOA emphasizes structured, program-level training rather than individual fellowships. The intent is for institutions to propose comprehensive predoctoral training environments that combine rigorous coursework, mentored research, and professional development in areas directly relevant to Big Data challenges in biomedicine. The program focus is explicitly biomedical, and the training is meant to prepare participants for long-term careers developing methods and technologies that increase the value of complex biomedical datasets. The announcement highlights that biomedical Big Data can involve many kinds of information, including (as examples) genomic and other "omics" data, imaging data, phenotypic or clinical data, and exposure-related data, among others. A strong application would therefore reflect an environment where trainees can engage with multiple data types and real scientific problems, rather than training narrowly around a single dataset or single disciplinary lens.
The applicant organization must demonstrate that it already has a strong, high-quality research base in biomedical Big Data Science and can offer the faculty expertise and infrastructure needed to support trainees effectively. NIH expects that a substantial portion of the program faculty will be actively leading research projects in biomedical Big Data Science, ideally spanning diverse and complex data modalities. This matters because the T32 mechanism is built around mentored research experiences, so reviewers typically look for evidence that trainees will have access to well-funded, productive mentors and projects where Big Data methods are central to the scientific questions being pursued. The FOA also notes that the proposed training program may complement other existing research training programs at the institution, but it should be distinct in its goals and design, meaning it should have its own identity, curriculum structure, mentorship plan, and outcomes centered on biomedical Big Data Science.
Eligibility is broad and includes public and state-controlled institutions of higher education, private institutions of higher education, and several categories of minority-serving and tribal institutions. Specifically mentioned eligible applicants include federally recognized Native American tribal governments, tribal organizations (including those not federally recognized, as clarified), and institutions such as Historically Black Colleges and Universities (HBCUs), Hispanic-serving Institutions, Alaska Native and Native Hawaiian-serving Institutions, Asian American and Native American Pacific Islander-serving Institutions (AANAPISIs), Tribally Controlled Colleges and Universities (TCCUs), and U.S. territories or possessions. Regardless of institution type, NIH requires clear institutional commitment to the proposed program. That commitment typically means the institution must be able to assure support through adequate staffing, appropriate facilities, and educational resources that directly strengthen the training experience, rather than relying solely on grant funds to create basic capacity.
From an administrative and funding perspective, this is a discretionary grant under the NIH training mechanism, with an expected number of awards listed as three and an estimated total funding level of $6,000,000. The FOA was posted on April 22, 2014, with a closing date of July 27, 2015, and an archive date of August 27, 2015, indicating this specific announcement is historical rather than currently active. The program is tied to multiple CFDA numbers spanning a wide range of biomedical and health-related domains (for example, environmental health, mental health, drug abuse, cancer research manpower, diabetes and kidney diseases, neurology, infectious diseases, child health, aging, vision research, and others). The breadth of CFDA associations reflects how Big Data methods are relevant across many NIH institutes and mission areas, and it signals that trainees and faculty projects may cut across disease areas and traditional departmental boundaries as long as the core emphasis remains on biomedical Big Data Science.
There is no cost sharing or matching requirement stated for this opportunity. The sponsoring institution is responsible for assuring the viability of the program and providing the environment needed for high-quality training. For applicants and stakeholders, the key takeaway is that NIH is investing in institutional training ecosystems that can consistently produce PhD-level researchers fluent in computational and quantitative thinking, grounded in biomedical problems, and capable of building the next wave of data-driven methods and tools that biomedical science increasingly depends on. For reference and official details, the FOA points to the NIH Grants Guide link at http://grants.nih.gov/grants/guide/rfa-files/RFA-HG-14-004.html, and provides NIH Office of Extramural Research (OER) webmaster contacts for access or technical issues.
Frequently Asked Questions (FAQs)
1) What is the Predoctoral Training in Biomedical Big Data Science (T32) opportunity?
It is an NIH institutional training grant (T32) focused on supporting graduate-level (predoctoral) training programs in biomedical Big Data Science. The aim is to develop scientists who can create and apply computational, statistical, and quantitative approaches to biomedical data that are rapidly increasing in scale, complexity, and diversity.
2) What is the Funding Opportunity Number (FOA) for this program?
The Funding Opportunity Number is RFA-HG-14-004.
3) What is the main goal of this FOA?
The central goal is to build training programs that produce the next generation of researchers who can both analyze large biomedical datasets and invent new tools, technologies, and methods that help the biomedical research community extract meaningful and actionable insights from complex data.
4) Is this funding for individual students or for institutions?
This FOA emphasizes structured, program-level training rather than individual fellowships. Institutions propose comprehensive predoctoral training environments, and the award supports that institutional training program.
5) What kinds of training activities does NIH expect in a strong T32 program under this FOA?
NIH is seeking programs that integrate rigorous coursework, mentored research experiences, and professional development, all focused on biomedical Big Data challenges. The program is meant to prepare trainees for long-term careers developing methods and technologies that increase the value of complex biomedical datasets.
6) What does NIH mean by "biomedical Big Data" in this FOA?
The FOA highlights that biomedical Big Data can include many kinds of information, such as genomic and other "omics" data, imaging data, phenotypic or clinical data, and exposure-related data, among other examples.
7) Does the FOA encourage training across multiple data types and research problems?
Yes. The description indicates that a strong application would reflect an environment where trainees can engage with multiple data types and real scientific problems, rather than being narrowly centered on a single dataset or a single disciplinary perspective.
8) What level of trainees is this program intended to support?
The FOA is specifically described as predoctoral training and is intended to support graduate-level training programs that produce PhD-level researchers in biomedical Big Data Science.
9) What kinds of careers are trainees expected to be prepared for?
The stated intent is to prepare participants for long-term careers developing methods and technologies that increase the value and usability of complex biomedical datasets, including the invention of new tools and methodological approaches.
10) What is required of the applicant institution in terms of research base and environment?
The applicant organization must demonstrate an existing strong, high-quality research base in biomedical Big Data Science and must be able to offer faculty expertise and infrastructure needed to support trainees effectively.
11) What does NIH expect regarding program faculty and mentorship?
NIH expects that a substantial portion of the program faculty will be actively leading research projects in biomedical Big Data Science. Because the T32 mechanism is built around mentored research, reviewers typically look for evidence that trainees will have access to productive mentors and well-supported projects where Big Data methods are central to the scientific questions.
12) Can the proposed program complement other training programs at the institution?
Yes. The FOA notes that the proposed program may complement other existing research training programs at the institution.
13) If a program complements existing training programs, does it still need to be distinct?
Yes. The FOA indicates the program should be distinct in its goals and design, with its own identity, curriculum structure, mentorship plan, and outcomes centered on biomedical Big Data Science.
14) What types of organizations are eligible to apply?
Eligibility is broad and includes public and state-controlled institutions of higher education, private institutions of higher education, and several categories of minority-serving and tribal institutions.
15) Are tribal governments or tribal organizations eligible applicants under this FOA?
Yes. The FOA specifically mentions federally recognized Native American tribal governments and tribal organizations, and clarifies that tribal organizations (including those not federally recognized) are eligible as described.
16) Which minority-serving institutions are explicitly mentioned as eligible?
The FOA explicitly mentions institutions such as Historically Black Colleges and Universities (HBCUs), Hispanic-serving Institutions, Alaska Native and Native Hawaiian-serving Institutions, Asian American and Native American Pacific Islander-serving Institutions (AANAPISIs), and Tribally Controlled Colleges and Universities (TCCUs).
17) Are U.S. territories or possessions included in eligibility?
Yes. The FOA includes U.S. territories or possessions among the eligible applicant categories.
18) What does NIH mean by "institutional commitment" for this training grant?
NIH requires clear institutional commitment to the proposed program. As described, this typically means the institution can assure support through adequate staffing, appropriate facilities, and educational resources that strengthen the training experience, rather than relying only on grant funds to create basic capacity.
19) What is the expected number of awards and total funding level for this FOA?
The FOA lists an expected number of awards of three and an estimated total funding level of $6,000,000.
20) What type of grant mechanism is used for this opportunity?
It is a discretionary grant under the NIH training mechanism (T32), focused on institutional training programs.
21) Is cost sharing or matching required?
No cost sharing or matching requirement is stated for this opportunity.
22) What are the key dates for this FOA?
The FOA was posted on April 22, 2014. The closing date is July 27, 2015. The archive date is August 27, 2015.
23) Is this FOA currently active?
Based on the listed closing and archive dates, this specific announcement is historical rather than currently active.
24) Why are multiple CFDA numbers associated with this FOA?
The FOA is tied to multiple CFDA numbers across many biomedical and health-related domains. This reflects that Big Data methods are relevant across many NIH institutes and mission areas, and it signals that trainee and faculty projects may cut across disease areas and traditional departmental boundaries, as long as the core emphasis remains biomedical Big Data Science.
25) Does the FOA restrict the program to a single disease area or institute mission?
The information provided indicates broad relevance across NIH mission areas (as reflected in the multiple CFDA associations), while maintaining a core emphasis on biomedical Big Data Science.
26) What is the official FOA link for reference?
The FOA points to the NIH Grants Guide at: http://grants.nih.gov/grants/guide/rfa-files/RFA-HG-14-004.html
27) Who is listed for help with access or technical issues related to the FOA?
The FOA provides NIH Office of Extramural Research (OER) webmaster contacts for access or technical issues.
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