Grants for Healthcare Organizations

Centers for Disease Control - NCCDPHP

In collaboration with U.S. Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention (CDC), state and federally funded programs, you will implement a project focused on reducing chronic disease rates within a defined geographic area within four communities: Lake County, Indiana; Sandusky and Toledo metro area, Ohio; Brownsville, Texas; Petersburg, Virginia. The goal is to demonstrate how individual and system-level interventions can encourage healthier habits and choices that can reduce the burden of chronic disease in children. When these lifestyle and community changes are maintained over time, reductions in the burden of chronic diseases among adults in the United States may eventually result.Findings from these demonstration programs will inform the potential replication of successful interventions in additional communities.Additionally, HHS will use findings from this demonstration project to inform future approaches to funding and measuring effects of novel efforts to improve health for individuals and communities.

U.S. National Science Foundation

The primary aim of the Mathematical Sciences Infrastructure Program is to foster the continuing health of the mathematical sciences research community as a whole. In addition,the program complements the <a title="DMS Workforce" href="https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503233" target="_blank">Workforce Program in the Mathematical Sciences</a> in its goal to increase the number of well-prepared U.S. based individuals who successfully pursue careers in the mathematical sciences and in other professions in which expertise in the mathematical sciences plays an increasingly important role. The DMS Infrastructure program invites projects that support core research in the mathematical sciences, including: 1) novel projects supporting research infrastructure across the mathematical sciences community; 2) training projects complementing the Workforce Program, and 3) conference, workshop, and travel support requests that include cross-disciplinary activities or have an impact at the national scale. Proposals under this solicitation submitted to DMS Infrastructure must show engagement in developing or enhancing the mathematical sciences research infrastructure in the U.S., including, but not limited to, broadening participation activities; professional development training; or involvement of students and early career researchers. Proposals must explain the regional or national scale impact of the activity that goes substantially beyond the submitting institution or the location of the event. Full proposals (with exception of conference proposals, which are subject to lead-time requirements) must be submitted close to one of the Full Proposal Target Dates. See below for more information about each category of Infrastructure projects. (1)Novel projects that serve to strengthen the research infrastructure: The DMS Infrastructure Program will consider novel projects that support and strengthen the research infrastructure across the mathematical sciences community. These projects most often cut across multiple sub-disciplines supported by DMS or involve interdisciplinary collaborations. The main goal of these projects should be to create a new research infrastructure or substantially enhance or transform an existing infrastructure with regional or national impact that goes substantially beyond the submitting institution or the location of the project. Full proposals must be submitted by the Full ProposalTarget Date. (2)Training projects: Training proposals submitted to DMS Infrastructure must not fit into one of the areas covered by solicitations in the <span style="text-decoration: underline;"><a title="DMS Workforce" href="https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503233" target="_blank">Workforce Program in the Mathematical Sciences</a></span>; they must be submitted by the Full ProposalTarget Date; and they must: <p style="padding-left: 30px;">A. Include a core research component for trainees in mathematical sciences; <p style="padding-left: 30px;">B. Demonstrate promise for an impact at the regional or national scale that goes substantially beyond the submitting institution or the location of the project; <p style="padding-left: 30px;">C. Satisfy at least one of the following criteria: <p style="padding-left: 60px;">i. Serve as models to be replicated,<br />ii. Promote partnerships with non-academic entities, minority-serving institutions, or community colleges, or<br />iii. Include a substantial broadening participation initiative. In addition, all proposals of this type must clearly identify: <ul> <li>Goals to be achieved;</li> <li>Specific new activities to be conducted, the way in which these address the goals, and the way in which the activities significantly differ from or enhance common practice;</li> <li>Measurable outcomes for the project;</li> <li>Plans and methods for assessment of progress toward the goals to be achieved, and for evaluation of the success of the activity;</li> <li>Recruitment, selection, and retention plans for participants, including members of underrepresented groups;</li> <li>Sustainability plans to continue the pursuit of the project's goals when funding terminates; and</li> <li>A budget commensurate with the proposed activity.</li> </ul> 3) Conferences, Symposia, Working Research Sessions, Travel Support Requests: Principal Investigators should carefully read the program solicitation<a title="DMS Conferences and Workshop Program Page" href="https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=11701" target="_blank">Conferences and Workshops in the Mathematical Sciences</a>to obtain important information regarding the substance of proposals for conferences, workshops, summer/winter schools, international travel support, and similar activities.Conference/workshop proposals that concern topics within a particular subdiscipline of mathematics or statistics should be submitted to the appropriate DMS disciplinary program(s). These submissions are subject to the lead-time requirements specified by the disciplinary program(s); see the program web pages listed on the<a title="Division of Mathematical Sciences" href="https://www.nsf.gov/div/index.jsp?div=DMS" target="_blank">DMS home page</a>. Conference/workshop proposals may be submitted to the DMS Infrastructure program only if the intended topical areas span a wide range of the mathematical sciences and are consequently not within the scope of DMS disciplinary programs. The required lead time for submission of such proposals is: <ul> <li>6 months in advance of the meeting date for proposals requesting no more than $50,000 to support a domestic meeting;</li> <li>9 months in advance of the meeting date for proposals requesting more than $50,000 to support a domestic meeting;</li> <li>12 months in advance of the meeting date for proposals requesting support for participation in a meeting taking place outside the United States.</li> </ul>

U.S. National Science Foundation

The primary aim of the Mathematical Sciences Infrastructure Program is to foster the continuing health of the mathematical sciences research community as a whole. In addition,the program complements the Workforce Program in the Mathematical Sciences in its goal to increase the number of well-prepared U.S. based individuals who successfully pursue careers in the mathematical sciences and in other professions in which expertise in the mathematical sciences plays an increasingly important role. The DMS Infrastructure program invites projects that support core research in the mathematical sciences, including: 1) novel projects supporting research infrastructure across the mathematical sciences community; 2) training projects complementing the Workforce Program, and 3) conference, workshop, and travel support requests that include cross-disciplinary activities or have an impact at the national scale. Proposals under this solicitation submitted to DMS Infrastructure must show engagement in developing or enhancing the mathematical sciences research infrastructure in the U.S., including, but not limited to, broadening participation activities; professional development training; or involvement of students and early career researchers. Proposals must explain the regional or national scale impact of the activity that goes substantially beyond the submitting institution or the location of the event. Full proposals (with exception of conference proposals, which are subject to lead-time requirements) must be submitted close to one of the Full Proposal Target Dates. See below for more information about each category of Infrastructure projects. (1)Novel projects that serve to strengthen the research infrastructure: The DMS Infrastructure Program will consider novel projects that support and strengthen the research infrastructure across the mathematical sciences community. These projects most often cut across multiple sub-disciplines supported by DMS or involve interdisciplinary collaborations. The main goal of these projects should be to create a new research infrastructure or substantially enhance or transform an existing infrastructure with regional or national impact that goes substantially beyond the submitting institution or the location of the project. Full proposals must be submitted by the Full ProposalTarget Date. (2)Training projects: Training proposals submitted to DMS Infrastructure must not fit into one of the areas covered by solicitations in the Workforce Program in the Mathematical Sciences; they must be submitted by the Full ProposalTarget Date; and they must: A. Include a core research component for trainees in mathematical sciences; B. Demonstrate promise for an impact at the regional or national scale that goes substantially beyond the submitting institution or the location of the project; C. Satisfy at least one of the following criteria: i. Serve as models to be replicated,ii. Promote partnerships with non-academic entities, minority-serving institutions, or community colleges, oriii. Include a substantial broadening participation initiative. In addition, all proposals of this type must clearly identify: Goals to be achieved; Specific new activities to be conducted, the way in which these address the goals, and the way in which the activities significantly differ from or enhance common practice; Measurable outcomes for the project; Plans and methods for assessment of progress toward the goals to be achieved, and for evaluation of the success of the activity; Recruitment, selection, and retention plans for participants, including members of underrepresented groups; Sustainability plans to continue the pursuit of the project's goals when funding terminates; and A budget commensurate with the proposed activity. 3) Conferences, Symposia, Working Research Sessions, Travel Support Requests: Principal Investigators should carefully read the program solicitationConferences and Workshops in the Mathematical Sciencesto obtain important information regarding the substance of proposals for conferences, workshops, summer/winter schools, international travel support, and similar activities.Conference/workshop proposals that concern topics within a particular subdiscipline of mathematics or statistics should be submitted to the appropriate DMS disciplinary program(s). These submissions are subject to the lead-time requirements specified by the disciplinary program(s); see the program web pages listed on theDMS home page. Conference/workshop proposals may be submitted to the DMS Infrastructure program only if the intended topical areas span a wide range of the mathematical sciences and are consequently not within the scope of DMS disciplinary programs. The required lead time for submission of such proposals is: 6 months in advance of the meeting date for proposals requesting no more than $50,000 to support a domestic meeting; 9 months in advance of the meeting date for proposals requesting more than $50,000 to support a domestic meeting; 12 months in advance of the meeting date for proposals requesting support for participation in a meeting taking place outside the United States.

U.S. National Science Foundation

Science of Learning and Augmented Intelligence (SL) supports potentially transformative research that develops basic theoretical insights and fundamental knowledge about principles, processes and mechanisms of learning, and about augmented intelligence &mdash; how human cognitive function can be augmented through interactions with others or with technology, or through variations in context. The program supportsresearch addressing learning in individuals and in groups, across a wide range of domains at one or more levels of analysis, including molecular and cellular mechanisms; brain systems; cognitive, affective and behavioral processes; and social and cultural influences. The program also supports research on augmented intelligence that clearly articulates principled ways in which human approaches to learning and related processes, such as in design, complex decision-making and problem-solving, can be improved through interactions with others or through the use of artificial intelligence in technology. These could include ways of using knowledge about human functioning to improve the design of collaborative technologies that have the capacity to learn to adapt to humans. For both aspects of the program, there is special interest in collaborative and collective models of learning and intelligence that are supported by the unprecedented speed and scale of technological connectivity.This includes emphasis on how people and technology working together in new ways and at scale can achieve more than either can attain alone. The program also seeks explanations for how the emergent intelligence of groups, organizations and networks intersects with processes of learning, behavior and cognition in individuals. Projects that are convergent or interdisciplinary may be especially valuable in advancing basic understanding of these areas, but research within a single discipline or methodology is also appropriate.Connections between proposed research and specific technological, educational and workforce applications will be considered as valuable broader impacts but are not necessarily central to the intellectual merit of proposed research. The program supports a variety of approaches, including experiments, field studies, surveys, computational modeling, and artificial intelligence or machine learning methods. Examples of general research questions within scope of Science of Learning and Augmented Intelligence (SL)include: <ul type="disc"> <li>What are the underlying mechanisms that support transfer of learning from one context to another or from one domain to another?How is learning generalized from a small set of specific experiences?What is the basis for robust learning that is resilient against potential interference from new experiences?How is learning consolidated and reconsolidated from transient experience to stable memory?</li> <li>How do human interactions with technologies, imbued with artificial intelligence, provide improved human task performance?What models best describe the interplay of the individual and collaborative processes that lead to co-creation of knowledge and collective intelligence? In what ways do the capacities and constraints of human cognition inform improved methods of human-artificial intelligence collaboration?</li> <li>How can we integrate research findings and insights across levels of analysis, relating understanding of cellular and molecular mechanisms of learning in the neurons, to circuit and systems-level computations of learning in the brain, to cognitive, affective, social and behavioral processes of learning? What is the relationship between assembly of new networks (development) and learning new knowledge in a maturing or mature brain? What concepts, tools (including Big Data, machine learning, and other computational models) or questions will provide the most productive linkages across levels of analysis?</li> <li>How can insights from biological learners contribute and derive new theoretical perspectives to artificial intelligence, neuromorphic engineering, materials science and nanotechnology? How can the ability of biological systems to learn from relatively few examples improve efficiency of artificial systems?How do learning systems (biological and artificial) address complex issues of causal reasoning?How can knowledge about the ways in which humans learn help in the design of human-machine interfaces?</li> </ul>

U.S. National Science Foundation

Science of Learning and Augmented Intelligence (SL) supports potentially transformative research that develops basic theoretical insights and fundamental knowledge about principles, processes and mechanisms of learning, and about augmented intelligence how human cognitive function can be augmented through interactions with others or with technology, or through variations in context. The program supportsresearch addressing learning in individuals and in groups, across a wide range of domains at one or more levels of analysis, including molecular and cellular mechanisms; brain systems; cognitive, affective and behavioral processes; and social and cultural influences. The program also supports research on augmented intelligence that clearly articulates principled ways in which human approaches to learning and related processes, such as in design, complex decision-making and problem-solving, can be improved through interactions with others or through the use of artificial intelligence in technology. These could include ways of using knowledge about human functioning to improve the design of collaborative technologies that have the capacity to learn to adapt to humans. For both aspects of the program, there is special interest in collaborative and collective models of learning and intelligence that are supported by the unprecedented speed and scale of technological connectivity.This includes emphasis on how people and technology working together in new ways and at scale can achieve more than either can attain alone. The program also seeks explanations for how the emergent intelligence of groups, organizations and networks intersects with processes of learning, behavior and cognition in individuals. Projects that are convergent or interdisciplinary may be especially valuable in advancing basic understanding of these areas, but research within a single discipline or methodology is also appropriate.Connections between proposed research and specific technological, educational and workforce applications will be considered as valuable broader impacts but are not necessarily central to the intellectual merit of proposed research. The program supports a variety of approaches, including experiments, field studies, surveys, computational modeling, and artificial intelligence or machine learning methods. Examples of general research questions within scope of Science of Learning and Augmented Intelligence (SL)include: What are the underlying mechanisms that support transfer of learning from one context to another or from one domain to another?How is learning generalized from a small set of specific experiences?What is the basis for robust learning that is resilient against potential interference from new experiences?How is learning consolidated and reconsolidated from transient experience to stable memory? How do human interactions with technologies, imbued with artificial intelligence, provide improved human task performance?What models best describe the interplay of the individual and collaborative processes that lead to co-creation of knowledge and collective intelligence? In what ways do the capacities and constraints of human cognition inform improved methods of human-artificial intelligence collaboration? How can we integrate research findings and insights across levels of analysis, relating understanding of cellular and molecular mechanisms of learning in the neurons, to circuit and systems-level computations of learning in the brain, to cognitive, affective, social and behavioral processes of learning? What is the relationship between assembly of new networks (development) and learning new knowledge in a maturing or mature brain? What concepts, tools (including Big Data, machine learning, and other computational models) or questions will provide the most productive linkages across levels of analysis? How can insights from biological learners contribute and derive new theoretical perspectives to artificial intelligence, neuromorphic engineering, materials science and nanotechnology? How can the ability of biological systems to learn from relatively few examples improve efficiency of artificial systems?How do learning systems (biological and artificial) address complex issues of causal reasoning?How can knowledge about the ways in which humans learn help in the design of human-machine interfaces?