Notice Number: NOT-HG-14-022
Update: The following update relating to this announcement has been issued:
Release Date: February 7, 2014
Estimated Publication Date of Announcement: Spring 2014
First Estimated Application Due Date: Summer 2014
Earliest Estimated Award Date: Spring 2015
Earliest Estimated Start Date: Spring 2015
The NIH is announcing its intent to publish a Funding Opportunity Announcement (FOA) to allow revisions (competitive supplements) to add a Big Data Science track to ongoing T32 institutional training grants for the 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. The aim of this initiative is to train a cadre of scientists who have the knowledge and skill sets in scientific disciplines relevant to Big Data Science in the biomedical sciences.
In this context, the term "Big Data" is used in the broadest sense to include biological, biomedical, behavioral, social, environmental, and clinical studies that relate to understanding health and disease. This predoctoral training initiative is different from most currently funded NIH training programs in that it will: (1) require that trainees to become proficient at the intersection of three scientific areas – computer science/informatics, statistics/mathematics, and biomedical science; (2) expect active participation of training faculty from all of these three scientific disciplines who will work collaboratively across disciplines as co-mentors of trainees in Big Data Science; and (3) develop the skills required to participate in a team approach to solving data-intensive biomedical problems, while also nurturing the skills necessary to be an independent investigator in Big Data science.
This Notice is being provided to allow potential applicants sufficient time to develop meaningful collaborations and responsive projects.
The FOA is expected to be published in the spring of 2014 with an expected application due date in the summer of 2014.
This FOA will utilize the T32 activity code. Details of the planned FOA are provided below.
The BD2K training program initiative is being developed in response to information from three sources. First, a report from the Data and Informatics Working Group (DIWG) of the Advisory Committee to the NIH Director recommended that NIH “Build Capacity by Training the Work Force in the Relevant Quantitative Sciences such as Bioinformatics, Biomathematics, Biostatistics, and Clinical Informatics” (Report). Second, a Request for Information (NOT-HG-13-003; Report) was issued by the NIH to solicit input from the extramural community on what types of knowledge and skills are needed by individuals to effectively manage and utilize Big Data. Third, a workshop (Report) was held by NIH that addressed the knowledge, skills, and resources needed to organize, process, manage, analyze, and visualize large, complex data sets.
Description of the BD2K Training Program
Institutional training grants are a key component of a broader BD2K effort to train leaders who have the skills and depth of knowledge to develop new quantitative approaches and tools to maximize the value of the growing amount and complexity of biomedical Big Data. It is becoming increasingly clear that there are many new opportunities presented by the availability of multidimensional data including, but not limited to, imaging, phenotypic, molecular (including –omics), clinical, behavioral, environmental, and multiple small data sets. At the same time, there are enormous challenges to computing across and integrating these data and to extracting useful biological knowledge from them. The purpose of the BD2K training program is to produce graduates who will have the required multidisciplinary skill sets to become leaders in the effort to develop new quantitative approaches and tools needed by the biomedical research community to harness the opportunities Big Data provides.
It is essential that a revision to an ongoing training program significantly expand the scope of a currently funded training program. It is expected that the revision will support a separate training track that encompasses the requisite Big Data elements, whether it is built de novo or drawn from existing elements. Because Big Data science is interdisciplinary in nature, it is expected that trainees will acquire during the training experience competency in all three relevant areas –computer science/informatics, and statistics/mathematics, and biomedical science – and expertise in aspects of data science that are essential to biomedical science. The training should include those aspects of computer science/informatics and statistics/mathematics that are directly relevant to the biomedical sciences.
Primary Organizational Focus of the Training Grant Program
Since the focus of the training is in the area of developing new approaches and tools for manipulating, analyzing, and interpreting Big Data, the PDs/PIs for this type of training program would collectively encompass expertise from all three major scientific areas, including demonstrated research leadership in computer science/informatics, statistics/mathematics, and biomedical science. Participating training faculty should include investigators who develop new technologies and practical tools, who generate and utilize Big Data, and who have a variety of biomedical expertise, from clinical to basic sciences, and with multiple disease specialties. Applications for training programs that focus exclusively on one or two of the BD2K relevant scientific areas or on just a few diseases will not be considered responsive. The primary PD(s)/PI(s) must ensure that the appropriate faculty work collaboratively and in a sustained manner across scientific disciplines and organizational lines to jointly mentor trainees.
NIH strongly encourages institutions that currently have multiple NIH training grants to consider using revisions to draw on and take advantage of existing training activities through collaborative approaches to expand beyond what their current training programs offer to create a unique, effective Big Data training opportunity. Review criteria will include an evaluation of the strength of the leadership to develop collaborations across departments and the usage of innovative approaches to build on existing programs.
Additional Information Relevant to Applying for a Revision
Since it is expected that trainees will be appointed for a minimum of two years, the training grant to which the revision will be made should have a minimum of three years remaining at the time of award.
NIH is planning to publish FOAs for new T32s (reference NOT-HG-14-011) and revisions to T15 institutional training grants (reference NOT-HG-14-023). Many institutions have T32 and T15 institutional training grants supported by the NIH. Given the limited amount of funds for these combined initiatives, institutions are encouraged to consider combining the expertise in Big Data within an institution and submitting only one application, whether for: (a) a new T32 institutional training grant or (b) a revision to a T32 or T15 institutional training grant.
Applications are not being solicited at this time.