Opportunity Information: Apply for RFA HG 14 020
Apply for RFA HG 14 020
- The National Institutes of Health in the education health sector is offering a public funding opportunity titled "Development of Software and Analysis Methods for Biomedical Big Data in Targeted Areas of High Need (U01)" 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 93.173 Research Related to Deafness and Communication Disorders 93.233 National Center on Sleep Disorders Research 93.242 Mental Health Research Grants 93.279 Drug Abuse and Addiction Research Programs 93.286 Discovery and Applied Research for Technological Innovations to Improve Human Health 93.310 Trans NIH Research Support 93.393 Cancer Cause and Prevention Research 93.394 Cancer Detection and Diagnosis Research 93.395 Cancer Treatment Research 93.396 Cancer Biology Research 93.399 Cancer Control 93.846 Arthritis, Musculoskeletal and Skin Diseases 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 93.879 Medical Library Assistance.
- This funding opportunity was created on Mar 26, 2014 and posted on Feb 18, 2014.
- Applicants must submit their applications by Jun 19, 2014. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- The funding agency has allocated a total of $6,500,000.00 to eligible and selected applicants.
- Each selected applicant is eligible to receive up to $300,000.00 in funding.
- Eligible applicants include: Public and State controlled institutions of higher education County governments City or township governments Native American tribal organizations (other than Federally recognized tribal governments) State governments Private institutions of higher education Special district governments Small businesses Others (see text field entitled Additional Information on Eligibility for clarification) For profit organizations other than small businesses Independent school districts Native American tribal governments (Federally recognized) Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education Public housing authorities/Indian housing authorities.
- Other Eligible Applicants include the following Alaska Native and Native Hawaiian Serving Institutions Asian American Native American Pacific Islander Serving Institutions (AANAPISISs) Eligible Agencies of the Federal Government Faith based or Community based Organizations Hispanic serving Institutions Historically Black Colleges and Universities (HBCUs) Indian/Native American Tribal Governments (Other than Federally Recognized) Regional Organizations Tribally Controlled Colleges and Universities (TCCUs) U.S. Territory or Possession Non domestic (non U.S.) Entities (Foreign Institutions) are not eligible to apply. Non domestic (non U.S.) components of U.S. Organizations are eligible to apply. Foreign components, as defined in the NIH Grants Policy Statement, are allowed.
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Opportunity Summary:
The NIH funding opportunity RFA-HG-14-020, titled "Development of Software and Analysis Methods for Biomedical Big Data in Targeted Areas of High Need (U01)," is a cooperative agreement program under the Big Data to Knowledge (BD2K) initiative. BD2K was created in response to the rapid growth of large-scale biomedical datasets and the practical difficulties researchers face when trying to store, manage, integrate, and analyze them. The core aim of this announcement is to fund the development of new computational analysis methods and usable software tools that help the broader biomedical research community turn big, complex datasets into reliable scientific knowledge.
This FOA is specifically targeted at four high-need areas that commonly limit progress in data-intensive biology and medicine. First, it seeks approaches for data compression and reduction, meaning methods that shrink or simplify large datasets while preserving the information researchers need for downstream analyses. Second, it prioritizes data visualization tools that help scientists explore patterns, outliers, uncertainty, and multi-dimensional structure in complex biomedical data, ideally in ways that support interpretation and decision-making rather than just producing static graphics. Third, it emphasizes data provenance, which covers tracking where data came from, how it was generated, and what processing steps were applied so results can be trusted, audited, reproduced, and reused. Fourth, it calls for improved data wrangling capabilities, meaning practical tools and methods to clean, transform, standardize, annotate, and merge datasets from different sources, formats, and standards, which is often one of the most time-consuming and error-prone parts of biomedical analysis.
Because the award mechanism is a U01 cooperative agreement, funded projects are expected to be collaborative in nature and to involve substantial interaction with NIH program staff compared with a typical investigator-initiated grant. In general terms, that structure is used when NIH anticipates an active partnership to help align deliverables with community needs, encourage interoperability, and ensure the resulting tools and methods are broadly useful. The focus is not simply on generating new academic insights, but on producing software and analytical methods that can be adopted by other researchers and that address real bottlenecks in biomedical big data workflows.
The opportunity was posted on February 18, 2014, with an application closing date of June 19, 2014, and an archive date of July 20, 2014. The estimated total funding level listed is $6.5 million, with an award ceiling of $300,000. The FOA sits within NIH's broader health and biomedical research mission and is associated with many CFDA program areas, spanning genomics, mental health, addiction, cancer, neurological disorders, infectious diseases, child health, and others, reflecting that big data challenges and tool needs cut across almost every modern biomedical field.
Eligibility is broad and includes public and private institutions of higher education, state and local governments, tribal organizations (including federally recognized tribal governments), nonprofits with and without 501(c)(3) status, small businesses and other for-profit organizations, independent school districts, public housing authorities/Indian housing authorities, and a range of mission-driven institutional types such as HBCUs, Hispanic-serving institutions, tribally controlled colleges and universities, and other designated serving institutions. Foreign institutions (non-U.S. entities) are not eligible to apply directly, but non-U.S. components of U.S. organizations may participate, and foreign components are allowed as defined by the NIH Grants Policy Statement. No cost sharing or matching requirement is listed, which typically lowers barriers for applicants who might otherwise need to supply non-federal funds.
Overall, this grant opportunity is best understood as part of NIH's effort to build the practical computational infrastructure for modern biomedical science: tools that make massive datasets easier to store and transmit, easier to interpret visually, easier to trust and reproduce through provenance tracking, and easier to prepare for analysis through robust wrangling. The intended outcome is a stronger, more capable research ecosystem where biomedical big data can be used more routinely, correctly, and efficiently to produce meaningful advances in health and disease research.
FAQs: NIH RFA-HG-14-020 (U01) - Development of Software and Analysis Methods for Biomedical Big Data in Targeted Areas of High Need (BD2K)
1) What is RFA-HG-14-020?
RFA-HG-14-020 is an NIH funding opportunity announcement (FOA) titled "Development of Software and Analysis Methods for Biomedical Big Data in Targeted Areas of High Need (U01)." It supports the development of computational analysis methods and usable software tools for biomedical big data as part of the Big Data to Knowledge (BD2K) initiative.
2) What is the purpose of this funding opportunity?
The purpose is to fund new analysis methods and software tools that help the broader biomedical research community turn very large and complex datasets into reliable scientific knowledge. The emphasis is on practical, adoptable tools and methods that address real bottlenecks in biomedical big data workflows.
3) What is BD2K and why was it created?
BD2K (Big Data to Knowledge) is an NIH initiative created in response to the rapid growth of large-scale biomedical datasets and the practical challenges researchers face in storing, managing, integrating, and analyzing those data.
4) What award mechanism does this FOA use?
This FOA uses the U01 cooperative agreement mechanism.
5) What does a U01 cooperative agreement imply for how projects are run?
A U01 cooperative agreement generally involves substantial interaction with NIH program staff compared with a typical investigator-initiated grant. Projects are expected to be collaborative, with an active NIH partnership to help align deliverables with community needs, encourage interoperability, and support broad usefulness of the resulting tools and methods.
6) What kinds of outputs is NIH trying to fund here?
The FOA is aimed at producing usable software tools and computational analysis methods that other researchers can adopt. The focus goes beyond generating academic insights and emphasizes tools and methods that address common, high-impact bottlenecks in biomedical big data work.
7) What are the four targeted high-need areas in this announcement?
The FOA targets four areas of high need:
- Data compression and reduction
- Data visualization
- Data provenance
- Data wrangling
8) What does "data compression and reduction" mean in this FOA?
It refers to methods that shrink or simplify large datasets while preserving the information needed for downstream analyses, helping make data easier to store, move, and work with without losing essential content.
9) What types of data visualization tools are emphasized?
The FOA prioritizes visualization tools that help researchers explore patterns, outliers, uncertainty, and multi-dimensional structure in complex biomedical data. The stated intent is to support interpretation and decision-making, not just produce static graphics.
10) What is meant by "data provenance" in this opportunity?
Data provenance covers tracking where data came from, how it was generated, and what processing steps were applied. The goal is to enable trust, auditing, reproducibility, and reuse of results by maintaining a clear record of data origins and transformations.
11) What does "data wrangling" include in the context of this FOA?
Data wrangling refers to practical tools and methods to clean, transform, standardize, annotate, and merge datasets from different sources, formats, and standards. The FOA highlights wrangling as one of the most time-consuming and error-prone parts of biomedical analysis.
12) Is this FOA meant for a specific disease area?
No. While the FOA is associated with many CFDA program areas (including genomics, mental health, addiction, cancer, neurological disorders, infectious diseases, child health, and others), it is fundamentally cross-cutting because biomedical big data challenges appear across nearly all modern biomedical fields.
13) How much total funding is estimated for this opportunity?
The estimated total funding level listed is $6.5 million.
14) What is the award ceiling for individual awards?
The award ceiling is listed as $300,000.
15) When was this funding opportunity posted?
The FOA was posted on February 18, 2014.
16) What was the application closing date?
The application closing date was June 19, 2014.
17) When was the FOA archived?
The archive date was July 20, 2014.
18) Who is eligible to apply?
Eligibility is broad and includes:
- Public and private institutions of higher education
- State and local governments
- Tribal organizations, including federally recognized tribal governments
- Nonprofits with and without 501(c)(3) status
- Small businesses and other for-profit organizations
- Independent school districts
- Public housing authorities / Indian housing authorities
- Mission-driven institutional types such as HBCUs, Hispanic-serving institutions, tribally controlled colleges and universities, and other designated serving institutions
19) Are foreign (non-U.S.) institutions eligible to apply directly?
No. Foreign institutions (non-U.S. entities) are not eligible to apply directly under this FOA.
20) Can foreign components participate in projects funded by this FOA?
Yes. Non-U.S. components of U.S. organizations may participate, and foreign components are allowed as defined by the NIH Grants Policy Statement.
21) Is cost sharing or matching required?
No cost sharing or matching requirement is listed for this opportunity.
22) What is the bigger picture goal behind funding tools like these?
This FOA is part of NIH's effort to build practical computational infrastructure for modern biomedical science. The intended outcome is a stronger research ecosystem where biomedical big data can be stored and transmitted more easily, interpreted more effectively, trusted and reproduced through provenance tracking, and prepared for analysis more efficiently through robust wrangling.
23) Why does the FOA emphasize interoperability and community needs?
Because the mechanism is a cooperative agreement and the goal is broad adoption, NIH anticipates active collaboration to align deliverables with community needs and to encourage interoperability so tools and methods can be used widely across biomedical research settings.
24) What kinds of research challenges is this FOA trying to reduce?
It targets common bottlenecks in data-intensive biomedical research, including difficulties with data size (compression/reduction), understanding complex results (visualization), ensuring trust and reproducibility (provenance), and the heavy workload of cleaning and integrating heterogeneous datasets (wrangling).
25) Is this opportunity focused more on theory or on deployable tools?
Based on the description, the emphasis is on usable software tools and practical analysis methods that other researchers can adopt, rather than on producing only conceptual or purely academic outcomes.
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