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NSF's Harnessing the Data Revolution (HDR) Big Idea is a national-scale activity to enable new modes of data-driven discovery that will allow fundamental questions to be asked and answered at the frontiers of science and engineering. Through this NSF-wide activity, HDR will generate new knowledge and understanding, and accelerate discovery and innovation. The HDR vision is realized through an interrelated set of efforts in: Foundations of data science; Algorithms and systems for data science; Data-intensive science and engineering; Data cyberinfrastructure; and Education and workforce development. Each of these efforts is designed to amplify the intrinsically multidisciplinary nature of the emerging field of data science. The HDR Big Idea will establish theoretical, technical, and ethical frameworks that will be applied to tackle data-intensive problems in science and engineering, contributing to data-driven decision-making that impacts society. This solicitation describes one or more Ideas Lab(s) on Data-Intensive Research in Science and Engineering (DIRSE) as part of the HDR Institutes activity. These Ideas Labs represent one path of a conceptualization phase aimed at developing Institutes as part of the NSF investment in the HDR Big Idea.

This Funding Opportunity Announcement (FOA) solicits applications for a single Coordinating Center to support novel data acquisition strategies, data harmonization, analysis and dissemination activities on emerging and current drug abuse trends across the United States. The Coordinating Center will (1) Maintain a Scientific Advisory Group; (2) Maintain and refine an Early Warning Network composed of local experts on drug abuse data from the selected communities, as well as NIDA-supported community-based researchers, to assist in the ongoing monitoring and interpretation of data; (3) Maintain key community-level indicators for monitoring drug abuse trends and early identification of new synthetic drugs and emerging issues including establishing harmonization of indicators and of presentation and analysis of indicators across the selected communities; (4) Continue to identify and maintain novel sources of data including treatment admissions data, national drug use among adults and youth, law enforcement seizures, and drug poisoning death; (5) Conduct cross-site data analyses from the harmonized Coordinating Center data; (6) Continue to disseminate and identify novel ways to execute dissemination and publication plans of results and findings from the Coordinating Center data, including development and maintenance of a website for disseminating data and findings; (7) Conduct webinars on topics of interest to stakeholders; (8) Conduct on the ground epidemiologic investigations on topics of immediate crisis or need, providing functional feedback to impacted communities towards optimizing current and future response; (9) Provide operational, administrative and logistical support for the Coordinating Center data harmonization and dissemination initiative.

The Bureau of Justice Statistics (BJS) seeks an applicant to conduct the collection, analysis, and dissemination activities for the Annual Surveys of Probation and Parole (ASPP) for the collection years 2020 through 2024. The current funding is for the first 3 years of the award; the final 2 years will be funded upon successful completion of 2020-2022 data. The ASPP are two separate data collections, independently referred to as the Annual Probation Survey and Annual Parole Survey. Since 1980, the ASPP have collected aggregate data on the number of persons supervised on probation or parole (i.e., post-custody community supervision), together referred to as the community supervision population. The ASPP obtain aggregated data from administrative records maintained by state probation and/or parole agencies; local agencies (municipal, county, or court); and the federal system. The ASPP are core BJS data collections and are the only national data collections that describe the size, change, movements, outcomes, and characteristics of the community supervision populations at the national, federal, and state levels. Together with data from the National Prisoner Statistics (NPS) Program, which collects counts of persons incarcerated in federal and state prisons, and data from the Annual Survey of Jails, which collects counts of persons held in local jails, ASPP data are used to estimate the total number of persons supervised by the adult correctional systems in the United States. Collectively, these data collections are also critical for tracking the level and change in the correctional populations over time and enhancing the understanding of the flow of offenders through and eventually out of the criminal justice system.

What we will consider funding: Innovative ideas for improving measurement of coverage and equity data for use by program staff and managers. Specifically, their ability to collect and deliver the right data at the right time to the appropriate audiences would benefit from: Incorporating advances in technology to support decision makers in planning and executing program strategies Integration of immunization data systems, as well as the ability to address data use demands from multiple stakeholders. Enabling a culture that supports data quality and use e.g. provides feedback on data at multiple levels. Alignment of incentives to promote reporting of accurate data above coverage estimates. Innovations in process efficiency toward improved service delivery. These may stem from lean healthcare, or other approaches, but should have the end goal of improving the experience of healthcare workers, caregivers, or both.

The Data Science Corps is one of the components of the HDR ecosystem enabling education and workforce development by focusing on building capacity for harnessing the data revolution at the local, state, and national levels to help unleash the power of data in the service of science and society. The Data Science Corps will provide practical experiences, teach new skills, and offer learning opportunities in different settings. This solicitation prompts the community to envision creative educational pathways that will transform data science education and expand the data science talent pool by enabling the participation of undergraduate and Master's degree students with diverse backgrounds, experiences, skills, and technical maturity in the Data Science Corps. These activities are envisioned to be inherently collaborative, with a lead organization and one or more collaborating organizations.

This NOFO will sustain and continue progress made through the previous CDC SI Cooperative Agreement. The purpose of this NOFO is to promote evidence-based decision making for an AIDS-Free generation by monitoring data quality at the site-level and collecting, analyzing and disseminating data at all levels. NOFO objectives are to: (1) improve capacity of M&E systems to oversee data quality and data use for decision making (including capacity building of M&E staff); (2) enhance district-led and nationally supported evidence-based programming (triangulating data from multiple data sources); (3) improve the understanding of HIV burden, incidence, loss to follow-up, linkages, and referral services across interventions (95-95-95) and facilities; (4) support regular updates to DHIS2 and electronic registers (including use of unique identifiers) to reflect changes to MOH and/or PEPFAR indicators; and (5) improve and/or develop interoperability of DHIS2 with other data systems in country.

USTRANSCOM/AMC is seeking to understand, apply, experiment, and further develop innovative methods of using probabilistic modeling and fused-data analysis of aviation enterprise data sources to reveal previously unknown patterns, trends and indicators. From these findings, AMC seeks to build processes and initiate actions to enhance air operations enterprise safety, improve training effectiveness and increase operational efficiency. The challenge is to understand the feasibility and potential to glean currently unanticipated and or hidden information from structured and unstructured data sets. Data sets may include, but are not limited to: - Military Flight Operations Quality Assurance (MFOQA) - Flight Simulator Operations Quality Assurance (SOQA) - Line Operations Safety Audit (LOSA) - Aircrew Safety Action Program (ASAP) - Safety investigation findings - Training data, records, and reports - Standardization (audit) reports - Maintenance records and reports - Aircrew scheduling data

Our Data-Driven Discovery Initiative seeks to advance the people and practices of data-intensive science, to take advantage of the increasing volume, velocity, and variety of scientific data to make new discoveries. Within this initiative, we're supporting data-driven discovery investigators - individuals who exemplify multidisciplinary, data-driven science, coalescing natural sciences with methods from statistics and computer science. These innovators are striking out in new directions and are willing to take risks with the potential of huge payoffs in some aspect of data-intensive science. Successful applicants must make a strong case for developments in the natural sciences (biology, physics, astronomy, etc.) or science enabling methodologies (statistics, machine learning, scalable algorithms, etc.), and applicants that credibly combine the two are especially encouraged. Note that the Science Program does not fund disease targeted research. It is anticipated that the DDD initiative will make about 15 awards at ~$1,500,000 each, at $200K-$300K/year for five years.

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. For example, research proposals may develop behavioral and analytical conceptualizations, frameworks or models that have applications across a broad array of SciSIP challenges, including the relationship between broader participation and innovation or creativity. Proposals may also develop methodologies to analyze science and technology data, and to convey the information to a variety of audiences. Researchers are also encouraged to create or improve science and engineering data, metrics and indicators reflecting current discovery, particularly proposals that demonstrate the viability of collecting and analyzing data on knowledge generation and innovation in organizations. Among the many research topics supported are:examinations of the ways in which the contexts, structures and processes of science and engineering research are affected by policy decision, the evaluation of the tangible and intangible returns from investments in science and from investments in research and development, the study of structures and processes that facilitate the development of usable knowledge, theories of creative processes and their transformation into social and economic outcomes, the collection, analysis and visualization of new data describing the scientific and engineering enterprise. The SciSIP program invites the participation of researchers from all of the social, behavioral and economic sciences as well as those working in domain-specific applications such as chemistry, biology, physics, or nanotechnology. The program welcomes proposals for individual or multi-investigator research projects, doctoral dissertation improvement awards, conferences, wo

This ROSES-2014 NRA (NNH14ZDA001N) solicits basic and applied research in support of NASA's Science Mission Directorate (SMD). This NRA covers all aspects of basic and applied supporting research and technology in space and Earth sciences, including, but not limited to: theory, modeling, and analysis of SMD science data; aircraft, scientific balloon, sounding rocket, International Space Station, CubeSat, and suborbital reusable launch vehicle investigations; development of experiment techniques suitable for future SMD space missions; development of concepts for future SMD space missions; development of advanced technologies relevant to SMD missions; development of techniques for and the laboratory analysis of both extraterrestrial samples returned by spacecraft, as well as terrestrial samples that support or otherwise help verify observations from SMD Earth system science missions; determination of atomic and composition parameters needed to analyze space data, as well as returned samples from the Earth or space; Earth surface observations and field campaigns that support SMD science missions; development of integrated Earth system models; development of systems for applying Earth science research data to societal needs; and development of applied information systems applicable to SMD objectives and data. Awards range from under $100K per year for focused, limited efforts (e.g., data analysis) to more than $1M per year for extensive activities (e.g., development of science experiment hardware). The funds available for awards in each program element offered in this ROSES-2014 NRA range from less than one to several million dollars, which allows selection from a few to as many as several dozen proposals depending on the program objectives and the submission of proposals of merit. Awards will be made as grants, cooperative agreements, contracts, and inter- or intraagency transfers, depending on the nature of the proposing organization and/or program requirements. The

The Office of Advanced Scientific Computing Research (ASCR) in the Office of Science (SC), U.S. Department of Energy (DOE), hereby invites applications for basic research that significantly advances management, analysis and visualization of data in disciplines supported by DOE in the context of emerging architectures for extreme scale computing platforms. The purpose of this announcement is to invite applications for basic computer science research on five major themes: 1. Usability and user interface design; 2. In situ methods for data management, analysis and visualization; 3. Design of in situ workflows to support data management, processing, analysis and visualization; 4. New approaches to scalable interactive visual analytic environments; and/or 5. Proxy applications or workflows and/or simulations for data management, analysis and visualization software to support co-design of extreme scale systems. The supported research will lay the foundation for building the software infrastructure to support scientific data management, analysis and visualization in the context of extreme scale computing.

The BIGDATA program seeks novel approaches in computer science, statistics, computational science, and mathematics, along with innovative applications in domain science, including social and behavioral sciences, geosciences, education, biology, the physical sciences, and engineering that lead towards the further development of the interdisciplinary field of data science. The solicitation invites two types of proposals: "Foundations" (F): those developing or studying fundamental theories, techniques, methodologies, technologies of broad applicability to Big Data problems; and "Innovative Applications" (IA): those developing techniques, methodologies and technologies of key importance to a Big Data problem directly impacting at least one specific application. Therefore, 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. While Innovative Applications (IA) proposals may address critical big data challenges within a specific domain, a high level of innovation is expected in all proposals and proposals should, in general, strive to provide solutions with potential for a broader impact on data science and its applications. IA proposals may focus on novel theoretical analysis and/or on experimental evaluation of techniques and methodologies within a specific domain. Proposals in all areas of sciences and engineering covered by participating directorates at NSF are welcome.

The NIDCR-funded FaceBase data repository and knowledgebase (www.facebase.org) was established to advance craniofacial research by creating comprehensive datasets on craniofacial development and dysmorphologies and disseminating them to the wider craniofacial research community. Since FaceBase's inception, the data management and integration hub has been charged with the critical functions of collecting datasets and analytic tools developed by dataset-generating spoke projects, curating and integrating those resources, and disseminating them. This Request for Applications (RFA) is a limited competition for renewal applications for the Data Management and Integration Hub. As in the past, the FaceBase 3 hub will continue to be responsible for integration and presentation of existing FaceBase datasets, development or adoption of new approaches for data search and visualization, and outreach to the wider craniofacial research community. In addition, the hub will assume an important new mission that of working with investigators within the craniofacial research community to make their datasets compatible with FaceBase's data models, curating and integrating these new datasets with those already held by FaceBase.

The purpose of this funding opportunity announcement (FOA) is to increase the impact of NICHD-funded research within the scientific mission of the NICHD Population Dynamics Branch (PDB) by providing research infrastructure to: promote data sharing; support the development of procedures and technologies for data sharing; disseminate best practices in data sharing; provide a resource that catalogs NICHD-funded data available for secondary analysis; and promote the secondary analysis of data collected through NICHD grants to research teams outside the original grantees.

The Data Incubator is a Cornell-funded data science training organization. We run a free advanced 8-week fellowship (think data science bootcamp) for PhDs looking to enter industry. A variety of innovative companies partner with The Data Incubator for their hiring and training needs, including LinkedIn, Genentech, Capital One, Pfizer, and many others. The program is free for admitted Fellows. Fellows have the option to participate in the program either in person in New York, San Francisco Bay Area, Seattle, Boston, Washington DC, or online.

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 funding opportunity announcement (FOA) is to invite applications for the Data Coordinating Center (DCC) of the Early Phase Pain Investigation Clinical Network (EPPIC-Net). EPPIC-Net will serve as the cornerstone of the NIHs Helping to End Addiction Long-term (HEAL) Partnership. EPPIC-Net will provide a robust and readily accessible infrastructure for carrying out in depth phenotyping and biomarker studies in patients with specific pain conditions, and the rapid design and performance of high-quality Phase 2 clinical trials to test promising novel therapeutics for pain from partners in academia or industry. Studies will bring intense focus to patients with well-defined pain conditions and high unmet therapeutic needs. EPPIC-Net will consist of one Clinical Coordinating Center (CCC), one Data Coordinating Center (DCC) and approximately 10 specialized clinical centers (hubs). As the main data manager for pain research in the HEAL Initiative, the EPPIC-Net DCC will host and manage clinical, neuroimaging, biomarker, omics, and preclinical data from EPPIC-Net and other components of the HEAL Initiatives pain research program.

The National Science Foundation's (NSF's) Directorates for Computer & Information Science & Engineering (CISE) and Mathematical & Physical Sciences (MPS) recently launched the Transdisciplinary Research in Principles of Data Science (TRIPODS) Phase I program with the goal of promoting long-term, interdisciplinary research and training activities that engage theoretical computer scientists, statisticians, and mathematicians in developing the theoretical foundations of data science. Twelve TRIPODS Phase I Institutes were established in FY17 (see https://www.nsf.gov/news/news_summ.jsp?cntn_id=242888). The Partnerships between Science and Engineering Fields and the NSF TRIPODS Institutes (TRIPODS + X) solicitation seeks to expand the scope of the TRIPODS program beyond the foundations community by engaging researchers across other NSF disciplines and the TRIPODS research teams in collaborative activities. TRIPODS + X projects will foster relationships between researchers in science & engineering domains and foundational data scientists by leveraging existing NSF investments in the TRIPODS organizations. Working in concert with a TRIPODS organization, a TRIPODS + X project would focus on data-driven research challenges motivated by applications in one or more science and engineering domains or other activities aimed at building robust data science communities.

The CDS&E-MSS program accepts proposals that confront and embrace the host of mathematical and statistical challenges presented to the scientific and engineering communities by the ever-expanding role of computational modeling and simulation on the one hand, and the explosion in production of digital and observational data on the other. The goal of the program is to promote the creation and development of the next generation of mathematical and statistical theories and tools that will be essential for addressing such issues. To this end, the program will support fundamental research in mathematics and statistics whose primary emphasis will be on meeting the aforementioned computational and data-related challenges. This program is part of the wider Computational and Data-enabled Science and Engineering (CDS&E) enterprise in NSF that seeks to address this emerging discipline

The CDS&E-MSS program accepts proposals that confront and embrace the host of mathematical and statistical challenges presented to the scientific and engineering communities by the ever-expanding role of computational modeling and simulation on the one hand, and the explosion in production of digital and observational data on the other. The goal of the program is to promote the creation and development of the next generation of mathematical and statistical theories and tools that will be essential for addressing such issues. To this end, the program will support fundamental research in mathematics and statistics whose primary emphasis will be on meeting the aforementioned computational and data-related challenges. This program is part of the wider Computational and Data-enabled Science and Engineering (CDS&E) enterprise in NSF that seeks to address this emerging discipline

Effective solutions will bring together cyberinfrastructure expertise and domain researchers, to ensure that the resulting cyberinfrastructure address researchers' data needs. The activities should address the data challenges arising in a disciplinary or cross-disciplinary context. (Throughout this solicitation, 'community' refers to a group of researchers interested in solving one or more linked scientific questions, while 'domains' and 'disciplines' refer to areas of expertise or application.) The projects should stimulate data-driven scientific discoveries and innovations, and address broad community needs, nationally and internationally.

Effective solutions will bring together cyberinfrastructure expertise and domain researchers, to ensure that the resulting cyberinfrastructure address researchers' data needs. The activities should address the data challenges arising in a disciplinary or cross-disciplinary context. (Throughout this solicitation, 'community' refers to a group of researchers interested in solving one or more linked scientific questions, while 'domains' and 'disciplines' refer to areas of expertise or application.) The projects should stimulate data-driven scientific discoveries and innovations, and address broad community needs, nationally and internationally.