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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.

The purpose of this Kirschstein-NRSA predoctoral fellowship (F31) award is to enhance the diversity of the health-related research workforce by supporting the research training of predoctoral students from population groups that have been shown to be underrepresented in the biomedical, behavioral, or clinical research workforce. Such individuals include those from underrepresented racial and ethnic groups, those with disabilities, and those from disadvantaged backgrounds. Through this award program, promising predoctoral students will obtain individualized, mentored research training from outstanding faculty sponsors while conducting well-defined research projects in scientific health-related fields relevant to the missions of the participating NIH Institutes and Centers. The proposed mentored research training is expected to clearly enhance the individuals potential to develop into a productive, independent research scientist.

The purpose of the Kirschstein-NRSA postdoctoral fellowship is to enhance the research training of promising postdoctoral candidates who have the potential to become productive, independent investigators in scientific health-related research fields relevant to the missions of the participating NIH Institutes and Centers.

The purpose of the Kirschstein-NRSA predoctoral fellowship (F31) award is to enable promising predoctoral students to obtain individualized, mentored research training from outstanding faculty sponsors while conducting dissertation research in scientific health-related fields relevant to the missions of the participating NIH Institutes and Centers. The proposed mentored research training must reflect the applicants dissertation research project and is expected to clearly enhance the individuals potential to develop into a productive, independent research scientist.

The purpose of the Kirschstein-NRSA, dual-doctoral degree, predoctoral fellowship (F30) is to enhance the integrated research and clinical training of promising predoctoral students, who are matriculated in a combined MD/PhD or other dual-doctoral degree training program (e.g. DO/PhD, DDS/PhD, AuD/PhD, DVM/PhD), and who intend careers as physician-scientists or other clinician-scientists. Applicants must propose an integrated research and clinical training plan and a dissertation research project in scientific health-related fields relevant to the missions of the participating NIH Institutes and Centers. The fellowship experience is expected to clearly enhance the individuals potential to develop into productive, independent physician-scientists or other clinician-scientists.

In response to the spectacular opportunities and immense challenges presented by the dawning era of "Big Data" in biomedical research, NIH has developed the Big Data to Knowledge (BD2K) initiative with the mission of enabling the biomedical research community to use the various types of Big Data for research. The purpose of this Funding Opportunity Announcement (FOA) is to solicit development of analysis methods and software in the four topic areas of data compression/reduction, data visualization, data provenance, and data wrangling as part of the overall BD2K initiative.