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The purpose of this Funding Opportunity Announcement (FOA) is to solicit revisions (competitive supplements) to add a Big Data Science track to currently funded T32 institutional training grants 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 (see definition under Funding Opportunity Description). This proposed training initiative should prepare qualified individuals for careers in developing new technologies and methods that will allow biomedical researchers to maximize the value of the growing volume and complexity of biomedical data.

This funding opportunity announcement (FOA) is issued to enable submission of program project grant applications that propose to conduct innovative, interactive, high impact research.Applications should address significant scientific questions that are important for the mission of NINDS, via a synergistic collaboration between outstanding scientists who might not otherwise collaborate. The program project grant is designed to support research in which the funding of several interdependent highly meritorious projects as a group offers significant scientific advantages over support of these same projects as individual research grants.

This funding opportunity announcement (FOA) supports high impact efforts to make resources available to neuroscience researchers. Projects should engage in one or more of the following activities: propagation of cutting edge reagents or techniques, dissemination of resources to new user groups, or innovative approaches to increase the scale/efficiency of resource production and delivery. Applications focused on technology or software development are not responsive to this FOA, as the focus is on dissemination or provision of resources. Use of existing technologies to develop new reagents or genetic lines of clear value may be appropriate. Projects should address compelling needs of broad communities of neuroscience researchers or should offer unique services that otherwise would be unavailable. Projects must support the NINDS mission.

The NIH Research Education Program (R25) supports research education activities in the mission areas of the NIH. The goal of this NIH Blueprint R25 program is to support educational activities that enhance the diversity of the biomedical, behavioral and clinical research workforce. To this end, this funding opportunity announcement encourages the development of creative educational activities with a primary focus on Research Experiences, Courses for Skills Development, and Mentoring Activities.

This funding opportunity announcement (FOA) encourages Small Business Innovation Research (SBIR) grant applications from small business concerns (SBCs) that propose development of a broad base of innovative technologies in biomedical computing, informatics, and Big Data Science that will support rapid progress in areas of scientific opportunity in biomedical research. It is expected that this research and development is conducted in the context of important biomedical and behavioral research problems. As such, applications are intended to develop enabling technologies that could apply to the interests of most NIH Institutes and Centers and range from basic biomedicine to research in all relevant organ systems and diseases. Major themes of research include collaborative environments; data integration; analysis and modeling methodologies; and novel computer science and statistical approaches. New opportunities are also emerging as large and complex data sets are becoming increasingly available to the research community.

This funding opportunity announcement (FOA) encourages Small Business Technology Transfer (STTR) grant applications from small business concerns (SBCs) that propose development of a broad base of innovative technologies in biomedical computing, informatics, and Big Data Science that will support rapid progress in areas of scientific opportunity in biomedical research. It is expected that this research and development is conducted in the context of important biomedical and behavioral research problems. As such, applications are intended to develop enabling technologies that could apply to the interests of most NIH Institutes and Centers and range from basic biomedicine to research in all relevant organ systems and diseases. Major themes of research include collaborative environments; data integration; analysis and modeling methodologies; and novel computer science and statistical approaches. New opportunities are also emerging as large and complex data sets are becoming increasingly available to the research community.

A rich body of evidence suggests that cognitive processes are associated with particular patterns of neural activity. These data indicate that oscillatory rhythms, their co-modulation across frequency bands, spike-phase correlations, spike population dynamics, and other patterns might be useful drivers of therapeutic development for cognitive improvement in neuropsychiatric disorders. This initiative encourages applications to test whether modifying electrophysiological patterns during behavior can improve cognitive abilities. Applications should use experimental designs that incorporate active manipulations to address at least one, and ideally more, of the following topics: (1) in behaving animals, determine which parameters of neural coordination, when manipulated in isolation, improve particular aspects of cognition; (2) in animals or humans, determine how particular abnormalities at the cellular or molecular level, such as specific receptor dysfunction, affect the coordination of electrophysiological patterns during behavior; (3) determine whether in vivo, systems-level electrophysiological changes in behaving animals predict analogous electrophysiological and cognitive improvements in normal humans or clinical populations; and (4) use systems-level computational modeling to develop a principled understanding of the function and mechanisms by which oscillatory and other electrophysiological temporal dynamic patterns unfold across the brain (cortically and subcortically) to impact cognition.

The NIH is interested in promoting a broad base of research and development of technologies in biomedical computing, informatics, and Big Data Science that will support rapid progress in areas of scientific opportunity in biomedical research. It is expected that this research and development is conducted in the context of important biomedical and behavioral research problems. As such, applications are intended to develop enabling technologies that could apply to the interests of most NIH Institutes and Centers and range from basic biomedicine and including research to all relevant organ systems and diseases. Major themes of research include collaborative environments; data integration; analysis and modeling methodologies; and novel computer science and statistical approaches. New opportunities are also emerging as large and complex data sets are becoming increasingly available to the research community. This initiative aims to address biomedical research areas in biomedical computing, informatics, and Big Data science through the early stage development of new software, tools and related resources, as well as the fundamental research (e.g., methodologies and approaches) leading up to that development.

A rich body of evidence suggests that cognitive processes are associated with particular patterns of neural activity. These data indicate that oscillatory rhythms, their co-modulation across frequency bands, spike-phase correlations, spike population dynamics, and other patterns might be useful drivers of therapeutic development for cognitive improvement in neuropsychiatric disorders. This initiative encourages applications to test whether modifying electrophysiological patterns during behavior can improve cognitive abilities. Applications should use experimental designs that incorporate active manipulations to address at least one, and ideally more, of the following topics: (1) in behaving animals, determine which parameters of neural coordination, when manipulated in isolation, improve particular aspects of cognition; (2) in animals or humans, determine how particular abnormalities at the cellular or molecular level, such as specific receptor dysfunction, affect the coordination of electrophysiological patterns during behavior; (3) determine whether in vivo, systems-level electrophysiological changes in behaving animals predict analogous electrophysiological and cognitive improvements in normal humans or clinical populations; and (4) use systems-level computational modeling to develop a principled understanding of the function and mechanisms by which oscillatory and other electrophysiological temporal dynamic patterns unfold across the brain (cortically and subcortically) to impact cognition.

The goal of this program announcement is to support the extended development, maintenance, testing, evaluation, hardening and dissemination of existing biomedical software. The NIH is interested in promoting a broad base of research and development of technologies in biomedical computing, informatics, and Big Data Science that will support rapid progress in areas of scientific opportunity in biomedical research. It is expected that this research and development is conducted in the context of important biomedical and behavioral research problems and that domain researchers are consulted to make sure that the software is relevant to users. As such, applications are intended to develop enabling technologies that could apply to the interests of most NIH Institutes and Centers and range from basic biomedicine and including research to all relevant organ systems and diseases. Major themes of research include collaborative environments; data integration; analysis and modeling methodologies; and novel computer science and statistical approaches. New opportunities are also emerging as large and complex data sets are becoming increasingly available to the research community. The proposed work should apply best practices and proven methods for software design, construction, and implementation to extend the applicability of existing technologies in biomedical computing, informatics and big data science to a broader biomedical research community.