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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.

Social science research on many topics has often been hampered by the limitations of survey data. However, the digital age has rapidly increased access to large and comprehensive data sources such as public and private administrative databases, and unique new sources of information from online transactions, social-media interactions, and internet searches. New computational tools also allow for the extraction, coding, and analysis of large volumes of text. Advances in analytical methods for exploiting and analyzing data have accompanied the rise of these data. The emergence of these new data also raises questions about access, privacy and confidentiality. The Russell Sage Foundation's initiative on Computational Social Science (CSS) supports innovative social science research that brings new data and methods to bear on questions of interest in its core programs in Behavioral Economics, Future of Work, Race, Ethnicity and Immigration, and Social Inequality. Limited consideration will be given to questions that pertain to core methodologies, such as causal inference and innovations in data collection.

The purpose of this FOA is to solicit an application for the next 5-year cycle of the Midlife in the United States (MIDUS) Study, a National Longitudinal Study of Health and Well-being. The goals of this next phase are to complete the third wave of longitudinal data collection and enhance content in the area of daily stress; complete the second wave of data collection of clinical biomarkers and affective neuroscience assessments; continue innovative sub-studies such as how psychosocial influences affect gene expression and novel methods to track and reinstate non-responders; connect these content areas through innovative analyses to data on health, functioning, personality, cognitive status, affective functioning, economic well-being, social relationships, and well-being; and maintain and enhance data distribution and user support. A central goal of the MIDUS study is to support data dissemination, user support of public use files, and encourage data use broadly by the scientific community.

The purpose of this funding is to expand funded states’ ability to advance and evaluate state-level prevention for opioid overuse, misuse, abuse, and overdose in states with a high burden of drug overdose deaths. The original FOA (CDC-RFA-CE16-1606) advanced a two-tiered strategy to allow for the fact that the needs of states vary considerably. Some states need support to build their ability to collect, analyze, and apply data and develop a strategy to combat the epidemic. These states were funded for the Planning and Data (base) component alone. Other states need resources and support to implement a comprehensive prevention program. These states were funded for both the Planning and Data component and the Prevention in Action (enhanced) component. States funded under the Planning and Data (base) component are expected to improve their data collection and analysis around the epidemic and work with their communities to develop a strategy for building a larger, more comprehensive program in the future

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 purpose of this NOFO is to enhance the capacity of surveillance programs to implement a population-based, multiple source surveillance program for autism spectrum disorder (ASD). The project will fund sites to participate in the Autism and Developmental Disabilities Monitoring (ADDM) Network and will enhance surveillance activities at both prior and newly participating sites through two funding components. Component A is required for all applicants, while applying for Component B funding is optional. Component A funds surveillance of autism spectrum disorder (ASD) among 4-year-old and 8-year-old children. Component A activities include: adhere to standardized ADDM Network methodology; renew or establish agreements for access to BOTH health and education data sources (electronic and paper records); staff training and continued education; report clean, de-identified data with vital records/census linkages to CDC; submit at least two manuscripts; engage partners and stakeholders in activities aimed at increasing use of ADDM data; and create and implement a strategic plan for performance monitoring and evaluation. Component B funds conducting follow-up at age 16 years of children abstracted for ASD at age 8 years in 2010 and 2012, including collecting data on characteristics, functioning, and educational service delivery (including transition planning). These data will inform public health strategies to improve identification and services for children with ASD.

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 General Social Survey (GSS) is a nationally representativeinterview survey of the United States adult population that collects data on a wide range of topics: behavioral items such as group membership and participation; personal psychological evaluations including measures of well-being, misanthropy, and life satisfaction; attitudinal questions on such public issues as crime and punishment, race relations, gender roles, and spending priorities; and demographic characteristics of respondents and their parents.The GSS has provided data on contemporary American society since 1972, serving as a barometer of social change and trends in attitudes, behaviors, and attributes of the United States adult population.In 1984, the GSS stimulated cross-national research by collaborating with Australia, Britain, and Germany to develop data collection programs modeled on the GSS.This program of comparative cross-national research, called the International Social Survey Program (ISSP), now includes 43 nations and enables researchers and analysts to place findings and trends from the United States within a comparative context. Since its inception, the GSS has completed 32 in-person, cross-sectional surveys of the adult household population of the United States with response rates that exceed 60 percent. The survey is currently fielded biennially.Data from the GSS are made available to scholars, students and the public for research, analysis and educational activities within 12 months of data collection.

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 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.

Over the last decade, SBE has actively encouraged and supported communities to develop visions, teams, and capabilities dedicated to the creation of new, large-scale, next-generation data resources and relevant analytic techniques to advance fundamental research in the SBE sciences. This has been done both through the NSF 14-517 Building Community and Capacity for Data-Intensive Research in the Social, Behavioral, and Economic Sciences and in Education and Human Resources (BCC-SBE/EHR) competition and other mechanisms, such as NSF 17-594 Research Coordination Networks (RCNs). In 2015 SBE began the Resource Implementations for Data Intensive Research in the Social, Behavioral and Economic Sciences (RIDIR) competition. The current solicitation seeks to build upon those earlier efforts and extends the data infrastructure focus of the former RIDIR competition for the coming year.

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 NIH Research Education Program (R25) supports research education activities in the mission areas of the NIH.  The over-arching goal of this BD2K  R25 program is to support educational activities that complement and/or enhance the training of a workforce to meet the nation's biomedical, behavioral and clinical research needs.    To accomplish the stated over-arching goal, this FOA will support creative educational activities with a primary focus on     Curriculum or Methods Development  in Big Data Science to augment current institutional curricula for the training of predoctoral level biomedical scientists and provide concentrated instruction in the tools, approaches and quantitative analysis concepts in data science. To facilitate the integration of data science into biomedical curricula nationally, this FOA seeks to support a cohort of institutions that will work collaboratively and collectively to produce curricular materials that are findable, accessible, interoperable, and reusable (FAIR).

The solicitation invites two categories of proposals: -Foundations (F): those developing or studying fundamental theories, techniques, methodologies, and technologies of broad applicability to big data problems, motivated by specific data challenges and requirements; and -Innovative Applications (IA): those engaged in translational activities that employ new big data techniques, methodologies, and technologies to address and solve problems in specific application domains. Projects in this category must be collaborative, involving researchers from domain disciplines and one or more methodological disciplines, e.g., computer science, statistics, mathematics, simulation and modeling, etc.

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 Adolescent Brain Cognitive Development (ABCD) Study is collecting data on health and mental health, cognitive function, substance use, cultural and environmental factors, and brain structure and function from youth starting when they are 9-10 years-old repeatedly for 10 years and makes that data available to the scientific community through the NIMH Data Archive. The purpose of this Funding Opportunity Announcement (FOA) is to encourage applications proposing the analysis of this public use dataset to increase knowledge of adolescent health and development. More information about the ABCD Study may be found on the ABCD Study web page (www.abcdstudy.org).

This funding opportunity announcement (FOA) seeks to leverage data from existing basic, clinical, and intervention research on suicide risk and behaviors as well as social media and healthcare records data, by encouraging the integration of existing data sets for novel secondary analyses aimed at identifying potential biological, experiential, and other predictors and moderators of suicide risk. The use of dimensional variables and inclusion of multiple levels of analyses is particularly encouraged. A secondary goal of this FOA is to support innovative projects that will generate foundational work for research studies on suicide-related behaviors that inform a Research Domain Criteria (RDoC) approach in this area.

This Funding Opportunity Announcement (FOA) encourages the submission of investigator-initiated research grant applications to support the secondary analysis of existing data sets with the goal of enhancing our understanding of patterns of alcohol consumption and the epidemiology of alcohol-related problems. Research grants for the Secondary Analysis of Existing Alcohol Epidemiology Data Sets are intended to provide support for studies that utilize currently available data sets to increase our understanding of the incidence, prevalence and etiology of alcohol related problems and disorders in the population, as well as the risk and protective factors associated with them. Research that employs analytic techniques which demonstrate or promote methodological advances in alcohol-related epidemiologic research is also of interest. Also Listed under (R03).

The purpose of this FOA is to provide support for applications that focus on data replication and the scalability of novel, targeted addiction treatments. Consistent with an experimental therapeutics approach, studies that would fall under the R33 (Phase II) would include studies with theory-derived targets (based on putative mechanisms of ATOD) and clearly defined hypotheses about how the proposed treatment directed at changing the target relates to clinical outcomes. In addition, studies that include preliminary efficacy with respect to target engagement and validation (i.e., data to establish the relation between target and symptom reduction and functional outcomes) are essential to the Phase II arm (R33). Studies of novel treatments include, but are not limited to behavioral, pharmacological, biologics-based, cognitive, device-based, interpersonal, physiological, or combined approaches. This FOA provides support for replication studies of addiction treatment across 2 or more settings. Specifically, this phase will focus on clinical trials that apply the target in a treatment setting (testing, refinement, and/or adaptation) to evaluate the efficacy and replicability of larger trials. Ultimately, the goal of this FOA is to replicate findings of mechanisms and processes underlying treatments or replication of strategies into novel intervention approaches that can be efficiently tested and replicated for their promise to address substance use disorder outcomes. Furthermore, these studies build on data that have been demonstrated with sufficient signal of target engagement from an analogous R21-like study to justify the proposed R33 trials.

The Substance Abuse and Mental Health Services Administration (SAMHSA) is accepting applications for fiscal year (FY) 2019 Mental and Substance Use Disorder (SUD) Practitioner Data grant. The purpose of this program is to provide comprehensive data and analysis on individuals who comprise the prevention and treatment fields to address mental and substance use disorders. The goal of the program is to provide valid data on the existing practitioners and usable information to SAMHSA on which to make policy and planning decisions.