Wednesday, January 14, 2015

NIH Big Data to Knowledge (BD2K) Enhancing Diversity in Biomedical Data Science (R25)


On January 13, 2015, a new funding opportunity announcement was released entitled NIH Big Data to Knowledge (BD2K) Enhancing Diversity in Biomedical Data Science (R25). The over-arching goal of this BD2K R25 program is to support educational activities that enhance the diversity of the biomedical, behavioral, and clinical research workforce. To accomplish the stated over-arching goal, this FOA will support creative educational activities with a primary focus on research experiences for students and faculty, and for curriculum development. 

The primary purpose of the NIH BD2K Enhancing Diversity in Biomedical Data Science program is to provide resources for eligible institutions to implement innovative approaches to research education for diverse students in Big Data science, including those from underrepresented backgrounds in biomedical research. Higher education institutions listed in the FOA are eligible to apply. Some institutions provide unique opportunities for access to students from diverse backgrounds underrepresented in biomedical and behavioral research. Accordingly, the NIH Big Data to Knowledge (BD2K) program strongly encourages applications from the following institutions: Historically Black Colleges and Universities (HBCUs), Tribally Controlled Colleges and Universities (TCCUs), Hispanic-Serving Institutions (HSIs), Alaska Native and Native Hawaiian-Serving Institutions, and institutions serving individuals living with disabilities. Applicants must collaborate with at least one NIH BD2K Center [NIH BD2K Centers] across the nation to develop the BD2K R25 program at the applicant institution. Refer to RFA-MD-15-005 for details. 

"Collaborative activities with the NIH BD2K Centers may include, but are not limited to: short-term research experiences for students and faculty at the NIH BD2K Centers, and hands-on projects; developing and/or disseminating curriculum materials that will be used at the applicant institution, and/or in a joint-instructional capacity with BD2K faculty. - See more at:"

List of BD2K centers:

UNIVERSITY OF PITTSBURGH (Super computing center?)

UW Madison (past Spelman REU program)


Report of a workshop, very informative

Who to Train: The BD2K workforce will need both quantitative (statistical and computational)
expertise and biomedical domain expertise, taken together as “data science” expertise.
Examples of biomedical fields that already incorporate varying amounts and mixtures of
quantitative expertise are bioinformatics, computational biology, biomedical informatics,
biostatistics, and quantitative biology. Both basic and clinical researchers at all career levels
need to receive training.
 When to Train: Training is needed at all career stages: exposure courses for
undergraduates, cross-training for graduate students and postdoctoral fellows, training as
needed for researchers at all levels to facilitate their work, refresher courses or certificates in
specific competencies for mid-level researchers, and relevant continuing medical education
courses for clinical professionals.
 What to Train: Both long- and short-term training is needed, and efforts should be guided by
the competency level required for the technical knowledge and skills to be gained. The
technical knowledge and skills needed include: (1) computational and informatics skills; (2)

mathematics and statistics expertise; and (3) domain science knowledge.

How to Train: Several ways to cross-train biomedical and quantitative scientists were
suggested, including through (1) new or expansion of existing long-term research training
programs (which can incorporate activities such as boot camps, joint and team coursework,
delayed laboratory rotations, dual or team mentoring, clinical and industrial externships, and
team challenges); (2) short-term courses and hands-on immersive experiments (which can
span short courses, certificate programs, immersive workshops, summer institutes, clinical
immersion and shadowing, and continuing medical education opportunities); (3) curricula for
biomedical Big Data; (4) technology-enabled learning systems and environments (e.g., webbased
courses and Massive Open Online Courses (MOOCs) to offer training to a much
larger audience; and (5) a training laboratory that has tools and resources for self-directed

learning and exploration. 

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