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This FOA solicits applications for new Behavioral and Social Sciences Research (BSSR) predoctoral training programs that focus on innovative computational and/or data science analytic approaches and their incorporation into training for the future BSSR health research workforce. The vision of the Advanced Data Analytics for BSSR training program is to support the development of a cohort of specialized predoctoral candidates who will possess advanced competencies in data science analytics to apply to an increasingly complex landscape of behavioral and social health-related big data.

The reproducibility and comparability of research on dietary supplements is enhanced by rigorous analytical characterization of key experimental materials and the publication of validated analytical methods that accurately and precisely characterized and quantify constituents in dietary supplement ingredients and products. This FOA builds on existing NIH awards to support the performance and publication of formal single-laboratory validation studies of quantitative analytical methods. The methods proposed for validation must be used to identify and quantify dietary supplement-relevant chemical constituents (i.e., active or marker chemical compounds, adulterants, contaminants) or their metabolites in experimental reagents, raw materials, and/or clinical specimens (e.g., urine or plasma samples). Methods must have been developed or utilized in fulfillment of the active parent grant's specific aims. Candidate constituents for quantitative method validation studies include (but are not limited to): phytochemicals, nutrients, and potentially deleterious substances such as pesticides and mycotoxins. Multi-laboratory validation studies will not be supported through this FOA.

The Science of Science & Innovation Policy (SciSIP) program supports research designed to advance the scientific basis of science and innovation policy. The program funds research to develop models, analytical tools, data and metrics that can be applied in the science policy decision making process and concern the use and allocation of scarce scientific resources. For example, research proposals may develop behavioral and analytical conceptualizations, frameworks or models that have applications across the broad array of science and innovation policy challenges. Proposals also may develop methodologies to analyze science, technology and innovation data, and to usefully convey that information to a variety of audiences. Proposals that create and improve science, engineering and innovation data, including the design of new metrics and indicators, particularly proposals that demonstrate the viability of collecting and analyzing data on knowledge generation and innovation in organizations, are encouraged.

This FOA encourages Research Conference Grant (R13) applications from institutions and organizations that propose to develop interdisciplinary research teams. Teams must include investigators from the social and/or behavioral sciences, and may include the life and/or physical sciences.  The goal is to broaden the scope of investigation into scientific problems, yield fresh and possibly unexpected insights, and increase the sophistication of theoretical, methodological, and analytical approaches by integrating the analytical strengths of two or more disparate scientific disciplines while addressing gaps in terminology, approach, and methodology.  This program will allow investigators from multiple disciplines to hold meetings in order to provide the foundation for developing interdisciplinary research projects.

As part of NSF's Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21) activity, the Directorate for Social, Behavioral and Economic Sciences (SBE) seeks to develop user-friendly large-scale next-generation data resources and relevant analytic techniques to advance fundamental research in SBE areas of study. Successful proposals will, within the financial resources provided by the award, construct such databases and/or relevant analytic techniques and produce a finished product that will enable new types of data-intensive research. The databases or techniques should have significant impacts, either across multiple fields or within broad disciplinary areas, by enabling new types of data-intensive research in the SBE sciences.

The National Center for Science and Engineering Statistics (NCSES) of the National Science Foundation (NSF) is one of the thirteen principal federal statistical agencies within the United States. It is responsible for the collection, acquisition, analysis, reporting and dissemination of objective, statistical data related to the science and engineering enterprise in the United States and other nations that is relevant and useful to practitioners, researchers, policymakers and the public. NCSES uses this information to prepare a number of statistical data reports as well as analytical reports including the National Science Board's biennial report, Science and Engineering (S&E) Indicators, and Women, Minorities and Persons with Disabilities in Science and Engineering. The Center would like to enhance its efforts to support analytic and methodological research in support of its surveys, and to engage in the education and training of researchers in the use of large-scale nationally representative datasets. NCSES welcomes efforts by the research community to use NCSES data for research on the science and technology enterprise, to develop improved survey methodologies for NCSES surveys, to create and improve indicators of S&T activities and resources, and strengthen methodologies to analyze and disseminate S&T statistical data. To that end, NCSES invites proposals for individual or multi-investigator research projects, doctoral dissertation improvement awards, workshops, experimental research, survey research and data collection and dissemination projects under its program for Research on the Science and Technology Enterprise: Statistics and Surveys.

The NIH Research Education Program (R25) supports research education activities in the mission areas of the NIH. The over-arching goal of this OBSSR and participating institutes and centers R25 program is to support educational activities that develop cross-cutting methodologies and analytics that are needed to more rapidly advance behavioral and social sciences research (BSSR) and are not already well addressed by existing educational programs widely available to the research community. Methodological domains of focus include but are not limited to innovative data collection methodologies and analytic techniques, analysis and linking of big data, or needed but underused designs to advance research across the translational spectrum. Priority will be given to courses that address an important and cross-cutting educational need, that fill a gap in the field not already well addressed by other opportunities and that include a plan for increased reach and sustainability of the training both during and beyond the funding period. Over the period of support, it is expected that the course will be refined, improved, and sufficiently well-documented and resourced for dissemination of the program when the period of support ends. To accomplish the stated over-arching goal, this FOA will support creative educational activities with a primary focus on Courses for Skills Development

As part of NSF's Harnessing the Data Revolution (HDR), the Directorate for Social, Behavioral and Economic Sciences (SBE) seeks to develop user-friendly large-scale next-generation data resources and relevant analytic techniques to advance fundamental research in SBE areas of study. Successful proposals will, within the financial resources provided by the award, construct such databases and/or relevant analytic techniques and produce a finished product that will enable new types of data-intensive research. The databases or techniques should have significant impacts, either across multiple fields or within broad disciplinary areas, by enabling new types of data-intensive research in the SBE sciences.

The Directorate for Social, Behavioral and Economic Sciences (SBE) seeks to develop user-friendly large-scale next-generation data resources and relevant analytic techniques to advance fundamental research in SBE areas of study. Successful proposals will, within the financial resources provided by the award, construct such databases and/or relevant analytic techniques and produce a finished product that will enable new types of data-intensive research. The databases or techniques should have significant impacts, either across multiple fields or within broad disciplinary areas, by enabling new types of data-intensive research in the SBE sciences. Human Networks and Data Science (HNDS) is a two-track program. It supports research and infrastructure that uses data science to advance understanding of a full range of human networks. HNDS research will identify ways in which dynamic, distributed, and heterogeneous data can provide novel answers to fundamental questions about individual and group behavior. HNDS is especially interested in proposals that leverage data-rich insights about human networks to support improved health, prosperity, and security.

As part of NSF's Harnessing the Data Revolution (HDR), the Directorate for Social, Behavioral and Economic Sciences (SBE) seeks to develop user-friendly large-scale next-generation data resources and relevant analytic techniques to advance fundamental research in SBE areas of study. Successful proposals will, within the financial resources provided by the award, construct such databases and/or relevant analytic techniques and produce a finished product that will enable new types of data-intensive research. The databases or techniques should have significant impacts, either across multiple fields or within broad disciplinary areas, by enabling new types of data-intensive research in the SBE sciences.

Hazards SEES seeks research projects that will productively cross the boundaries of the atmospheric and geospace, earth, and ocean sciences; computer and information science; cyberinfrastructure; engineering; mathematics and statistics; and social, economic, and behavioral sciences. Successful proposals will integrate across these multiple disciplines to promote research that advances new paradigms that contribute to creating a society resilient to hazards. Hazards SEES intends to transform hazards and disaster research by fostering the development of interdisciplinary research that allows for appropriately targeted data collection, integration, and management; modeling (including predictive models for real-time decision making); visualization and simulation; data analytics and data-driven discovery; real-time sensing; cross-cutting knowledge development; and synthesis of applicable models and theory. 

As part of NSF's Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21) activity, the Directorate for Social, Behavioral, and Economic Sciences (SBE), the Directorate for Education and Human Resources (EHR), and the Office of Cyberinfrastructure seek to enable research communities to develop visions, teams, and capabilities dedicated to creating new, large-scale, next-generation data resources and relevant analytic techniques to advance fundamental research for the SBE and EHR areas of research.  Successful proposals will outline activities that will have significant impacts across multiple fields by enabling new types of data-intensive research.  Investigators should think broadly and create a vision that extends intellectually across multiple disciplines and that includes--but is not limited to--the SBE or EHR areas of research.

This Funding Opportunity Announcement (FOA) aims to stimulate the broader research community to utilize a resource funded through the American Recovery and Reinvestment Act of 2009 (Recovery Act) to generate and evaluate hypotheses about the complex interrelationships and multi-directional influences among genetics, brain maturation, neurocognitive function, and psychiatric symptoms during development. This FOA is a strategic effort to disseminate this data resource, stimulate the broader research community to use the resource, and accelerate research on neurodevelopment and trajectories of risk for mental illness. Secondary goals of this initiative are to foster collaborations among researchers from diverse fields of expertise, enhance diversity of research questions and analytic approaches, advance methods for integration across data modalities and levels of analyses (i.e., imaging, genomics, behavior), and encourage inclusion of early stage investigators among these collaborations.

The goals of the Resilient Interdependent Infrastructure Processes and Systems (RIPS) solicitation are (1) to foster an interdisciplinary research community that discovers new knowledge for the design and operation of infrastructures as processes and services  (2) to enhance the understanding and design of interdependent critical infrastructure systems (ICIs) and processes that provide essential goods and services despite disruptions and failures from any cause, natural, technological, or malicious, and (3) to create the knowledge for innovation in ICIs to advance society with new goods and services. The objectives of this solicitation are: Create theoretical frameworks and multidisciplinary computational models of interdependent infrastructure systems, processes and services, capable of analytical prediction of complex behaviors, in response to system and policy changes. Synthesize new approaches to increase resilience, interoperations, performance, and readiness in ICIs. Understand organizational, social, psychological, legal, political and economic obstacles to improving ICI's, and identifying strategies for overcoming those obstacles. The RIPS solicitation seeks proposals with transformative ideas that will ensure ICIs services are effective, efficient, dependable, adaptable, resilient, safe, and secure.  Successful proposals are expected to study multiple infrastructures focusing on them as interdependent systems that deliver services, enabling a new interdisciplinary paradigm in infrastructure research.  Proposals that do not broadly integrate across the cyber-physical, engineering and social, behavioral and economic (SBE) sciences may be returned without review. 

The Science of Science & Innovation Policy (SciSIP) program supports research designed to advance the scientific basis of science and innovation policy. Research funded by the program thus develops, improves and expands models, analytical tools, data and metrics that can be applied in the science policy decision making process.

The Methodology, Measurement, and Statistics (MMS) Program is an interdisciplinary program in the Directorate for Social, Behavioral, and Economic Sciences that supports the development of innovative analytical and statistical methods and models for those sciences. MMS seeks proposals that are methodologically innovative, grounded in theory, and have potential utility for multiple fields within the social, behavioral, and economicsciences. As part of its larger portfolio, the MMS Program partners with a consortium of federal statistical agencies to support research proposals that further the production and use of official statistics. The MMS Program provides support through a number of different funding mechanisms. The following mechanisms are addressed in this solicitation: Regular Research Awards Awards for conferences and community-development activities Doctoral Dissertation Research Improvement (DDRI) Grants Research Experience for Undergraduates (REU) Supplements

The Methodology, Measurement, and Statistics (MMS) Program is an interdisciplinary program in the Directorate for Social, Behavioral, and Economic Sciences that supports the development of innovative analytical and statistical methods and models for those sciences. MMS seeks proposals that are methodologically innovative, grounded in theory, and have potential utility for multiple fields within the social, behavioral, and economic sciences. As part of its larger portfolio, the MMS Program partners with a consortium of federal statistical agencies to support research proposals that further the production and use of official statistics.

The Methodology, Measurement, and Statistics (MMS) Program is an interdisciplinary program in the Directorate for Social, Behavioral, and Economic Sciences that supports the development of innovative analytical and statistical methods and models for those sciences. MMS seeks proposals that are methodologically innovative, grounded in theory, and have potential utility for multiple fields within the social, behavioral, and economicsciences. As part of its larger portfolio, the MMS Program partners with a consortium of federal statistical agencies to support research proposals that further the production and use of official statistics. The MMS Program provides support through a number of different funding mechanisms. The following mechanisms are addressed in this solicitation: Regular Research Awards Awards for conferences and community-development activities Doctoral Dissertation Research Improvement (DDRI) Grants Research Experience for Undergraduates (REU) Supplements

This Funding Opportunity Announcement (FOA) invites applications that seek to apply one or more innovative methodologies in communication research across the cancer control continuum, from prevention, early detection, diagnosis, treatment, and survivorship, to end of life. Applications to this FOA should utilize one or more of the following analytic approaches, methods, and data sources, including but not limited to social media data mining, Natural Language Processing (NLP) techniques, online social network analysis, crowdsourcing research tools (e.g., mTurk), online search data, Ecological Momentary Assessment, neuroscience and biobehavioral approaches to communication, and geographic information systems. Studies should assess outcomes related to cancer prevention and control (e.g., knowledge, attitudes, beliefs, perceived risk, decision making in screening and treatment, information inequalities, social support, shared decision making, persuasion, caregiving, behavioral intentions, preventive behaviors, and policy support, among others). This FOA utilizes the Exploratory/Developmental Grant (R21) mechanism, which supports investigation of novel scientific ideas or new model systems, tools, or technologies that have the potential for significant impact on biomedical or biobehavioral research.

The Methodology, Measurement, and Statistics (MMS) Program is an interdisciplinary program in the Directorate for Social, Behavioral, and Economic Sciences that supports the development of innovative, analytical, and statistical methods and models for those sciences. MMS seeks proposals that are methodologically innovative, grounded in theory, and have potential utility for multiple fields within the social and behavioral sciences. As part of its larger portfolio, the MMS Program partners with a consortium of federal statistical agencies to support research proposals that further the development of new and innovative approaches to surveys and to the analysis of survey data.