Opportunity Information: Apply for RFA HG 14 006
Apply for RFA HG 14 006
- The National Institutes of Health in the education health sector is offering a public funding opportunity titled "Revisions to Add Biomedical Big Data Training to Active NLM Institutional Training Grants in Biomedical Informatics (T15)" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.172 Human Genome Research 93.310 Trans NIH Research Support 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 28, 2016. (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) Others (see text field entitled Additional Information on Eligibility for clarification) Native American tribal organizations (other than Federally recognized tribal governments) Private institutions of higher education Public and State controlled institutions of higher education.
- 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) Only institutions with currently active T15 institutional training grants are eligible to apply for revisions (competitive supplements) for the expressed purpose of training the next generation of scientists who will develop computational and quantitative approaches and tools needed by the biomedical research community to work with biomedical Big Data in the biomedical sciences. 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 basic and clinical 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 basic and clinical sciences in which participating trainees may gain relevant experiences consistent with their research interests and goals.
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Opportunity Summary:
Revisions to Add Biomedical Big Data Training to Active NLM Institutional Training Grants in Biomedical Informatics (T15) (Funding Opportunity Number RFA-HG-14-006) is a National Institutes of Health discretionary grant opportunity in the education and health activity area. It was posted on April 22, 2014, and remained open through July 28, 2016 (archived August 28, 2016). The overall goal is not to start new training programs from scratch, but to fund competitive revisions (often described as competitive supplements) to institutions that already hold active NLM T15 institutional training grants in biomedical informatics. Through these revisions, NIH sought to add a dedicated Biomedical Big Data Science training track to existing T15 programs, expanding what those established training environments could offer in response to the rapid growth of complex biomedical datasets.
The central purpose of the FOA is workforce development: preparing the next generation of scientists to build computational and quantitative methods, along with practical tools, that biomedical researchers can use to extract value from large-scale, diverse, and increasingly complex biomedical data. In plain terms, the initiative targets the skills needed to handle modern biomedical data problems, where the challenge is not just storing or moving data, but making it analyzable, trustworthy, and scientifically meaningful. The FOA emphasizes careers focused on developing new technologies and methods, meaning trainees are expected to learn how to create and improve approaches for data integration, analysis, and interpretation rather than only applying existing software in routine ways.
Eligibility is intentionally narrow on the program side: only institutions with currently active NLM T15 institutional training grants could apply for these revisions. Even though the general applicant categories listed include a wide range of institution types (public and private higher education institutions, federally recognized tribal governments, tribal organizations, and several designated serving institutions such as HBCUs, Hispanic-serving institutions, AANAPISIs, TCCUs, and Alaska Native/Native Hawaiian-serving institutions), the gatekeeping requirement is the presence of an active T15 award. The sponsoring institution also had to demonstrate and formally assure strong institutional support, including adequate staff, facilities, and educational resources to run the expanded training track effectively.
A major expectation of the FOA is that the applicant institution already has a strong, high-quality research base in biomedical Big Data Science. NIH highlights the need for work spanning diverse and complex data types, explicitly calling out areas like genomic and other "omic" data, imaging, phenotypic data, and exposure-related data, and it stresses relevance to both basic and clinical science contexts. This matters because the training environment is expected to be research-intensive and hands-on: trainees should be able to participate directly in active projects where large, heterogeneous datasets are being generated, curated, integrated, analyzed, and used to answer biomedical questions. The FOA also anticipates that a substantial number of program faculty will themselves have active Big Data Science research projects involving multiple data types, so trainees can find mentorship and project experiences aligned with different interests and career goals.
Structurally, these revisions were expected to complement existing training programs at the institution while still being distinct enough to justify a specific Big Data Science track. That is, NIH was looking for deliberate curriculum and training design tailored to Big Data challenges, not a relabeling of standard biomedical informatics training. The implied end-state is a pipeline of trainees who can operate at the intersection of computing, statistics, and biomedical science, and who are prepared to develop the next wave of methods and platforms needed by the broader research community.
In terms of scale and competition, NIH anticipated making about three awards, with an estimated total funding level of $6,000,000 across the opportunity. The FOA lists CFDA associations including Human Genome Research (93.172), Trans-NIH Research Support (93.310), and Medical Library Assistance (93.879), reflecting the cross-cutting nature of Big Data training and its relevance across biomedical domains. There was no cost sharing or matching requirement, reducing barriers for eligible institutions while keeping the emphasis on demonstrated institutional commitment and research capacity.
For reference and full details, NIH provided an official announcement page (http://grants.nih.gov/grants/guide/rfa-files/RFA-HG-14-006.html) and directed applicants having technical access issues to the NIH Office of Extramural Research webmaster at FBOWebmaster@OD.NIH.GOV.
Frequently Asked Questions (FAQs)
What is the name of this funding opportunity?
The opportunity is titled "Revisions to Add Biomedical Big Data Training to Active NLM Institutional Training Grants in Biomedical Informatics (T15)" and it is associated with Funding Opportunity Number (FON) RFA-HG-14-006.
Which agency is offering this grant opportunity?
This is a National Institutes of Health (NIH) discretionary grant opportunity. The revisions are specifically tied to active National Library of Medicine (NLM) T15 institutional training grants in biomedical informatics.
What is the main goal of the FOA?
The main goal is workforce development: expanding existing, active NLM T15 training programs by adding a dedicated Biomedical Big Data Science training track. The intent is to prepare scientists to develop new computational and quantitative methods and practical tools for working with large-scale, complex biomedical data.
Is this FOA meant to create brand-new training programs?
No. The FOA is not intended to start new training programs from scratch. It is intended to fund competitive revisions (often described as competitive supplements) to institutions that already hold active NLM T15 institutional training grants.
Who is eligible to apply?
Eligibility is intentionally narrow: only institutions with currently active NLM T15 institutional training grants in biomedical informatics can apply for these revisions.
The FOA lists many types of institutions. Does that mean any of them can apply?
Not by itself. While the general applicant categories include many institution types (including public and private higher education institutions, federally recognized tribal governments, tribal organizations, and designated serving institutions such as HBCUs, Hispanic-serving institutions, AANAPISIs, TCCUs, and Alaska Native/Native Hawaiian-serving institutions), the key gatekeeping requirement remains having an active NLM T15 award.
What does NIH expect from the institution in terms of support and resources?
The sponsoring institution must demonstrate and formally assure strong institutional support, including adequate staff, facilities, and educational resources to operate the expanded Big Data Science training track effectively.
What kind of research environment is expected for the Big Data training track?
The FOA expects the institution to already have a strong, high-quality, research-intensive base in biomedical Big Data Science. The training environment should be hands-on, with trainees participating in active projects where large, heterogeneous datasets are generated, curated, integrated, analyzed, and used to answer biomedical questions.
What types of biomedical data does the FOA emphasize?
The FOA explicitly calls out diverse and complex data types including genomic and other "omic" data, imaging data, phenotypic data, and exposure-related data. It also stresses relevance in both basic science and clinical science contexts.
What kinds of careers or skill sets is this training intended to support?
The FOA emphasizes careers focused on developing new technologies and methods for Big Data. Trainees are expected to learn how to create and improve approaches for data integration, analysis, and interpretation, rather than only applying existing software in routine ways.
How should the proposed Big Data Science track relate to the existing T15 program?
The revisions were expected to complement existing training programs at the institution while still being distinct enough to justify a specific Biomedical Big Data Science track. NIH was looking for deliberate curriculum and training design tailored to Big Data challenges, not a relabeling of standard biomedical informatics training.
What kind of faculty involvement does NIH anticipate?
The FOA anticipates that a substantial number of program faculty will have active Big Data Science research projects involving multiple data types, so trainees can access mentorship and project experiences across different interests and career goals.
How many awards did NIH expect to make and what was the estimated total funding level?
NIH anticipated making about three awards. The FOA estimated a total funding level of $6,000,000 across the opportunity.
Is cost sharing or matching required?
No. The FOA states there was no cost sharing or matching requirement.
What activity area does this opportunity fall under?
The opportunity is in the education and health activity area.
When was the FOA posted, and when did it close?
It was posted on April 22, 2014, and remained open through July 28, 2016. It was archived on August 28, 2016.
Where can applicants find the official announcement and full details?
NIH provided the official announcement page at http://grants.nih.gov/grants/guide/rfa-files/RFA-HG-14-006.html.
Who should be contacted for technical access issues related to the application or announcement?
For technical access issues, NIH directed applicants to contact the NIH Office of Extramural Research webmaster at FBOWebmaster@OD.NIH.GOV.
What CFDA program associations were listed for this opportunity?
The FOA lists CFDA associations including Human Genome Research (93.172), Trans-NIH Research Support (93.310), and Medical Library Assistance (93.879).
Why does the FOA emphasize Big Data now?
Based on the FOA description, the revisions were motivated by the rapid growth of complex biomedical datasets and the need for training that goes beyond data storage or movement, focusing instead on making data analyzable, trustworthy, and scientifically meaningful through new methods and tools.
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