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The Long Term Research in Environmental Biology (LTREB) Program supports the generation of extended time series of data to address important questions in evolutionary biology, ecology, and ecosystem science. Research areas include, but are not limited to, the effects of natural selection or other evolutionary processes on populations, communities, or ecosystems; the effects of interspecific interactions that vary over time and space; population or community dynamics for organisms that have extended life spans and long turnover times; feedbacks between ecological and evolutionary processes; pools of materials such as nutrients in soils that turn over at intermediate to longer time scales; and external forcing functions such as climatic cycles that operate over long return intervals. The Program intends to support decadal projects. Funding for an initial, 5-year period requires submission of a preliminary proposal and, if invited, submission of a full proposal that includes a 15-page project description. Proposals for the second five years of support (renewal proposals) are limited to an eight-page project description and do not require a preliminary proposal. Continuation of an LTREB project beyond an initial ten year award will require submission of a new preliminary proposal that presents a new decadal research plan.?? Successful LTREB proposals address three essential components: A Decadal Research Plan that clearly articulates important questions that cannot be addressed with data that have already been collected, but could be answered if ten additional years of data were collected. This plan is not a research timeline or management plan. It is a concise justification for ten additional years of support in order to advance understanding of key concepts, questions, or theories in environmental biology.Core Data: LTREB proposals require that the author has studied a particular phenomenon or process for at least six years up to the present or for long enough to gene

Over the past few years, Project Open Data (https://project-open-data.cio.gov/) has sought to identify and share best practices, examples, and software code to assist federal agencies with opening up access to data. Moreover, there have been efforts to scale up "open data" across various application sectors, including health, energy, climate, education and learning, finance, public safety, and global development, unlocking valuable data and improving decision making by making data resources more open and accessible to innovators and the public. NSF has established a national network of Big Data Regional Innovation Hubs and Spokes (BD Hubs and Spokes), comprising members from academia, industry, and government, with the goal of igniting new public-private partnerships across the Nation in big data research and development as well as training and education. Facilitating access to data is one of the objectives of the BD Hubs and Spokes. Collectively, these initiatives constitute an important first step in supporting the growing and interdisciplinary data science research community, which requires access to real-world datasets, e.g., as training data that can further data science, including machine learning capabilities, and enhance knowledge and decision making in various application sectors.

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.

NSF's Directorate for Computer and Information Science and Engineering (CISE) initiated the National Network of Big Data Regional Innovation Hubs (BD Hubs) program in FY 2015 (NSF 15-562). Four Big Data Hubs (BD Hubs)—Midwest, Northeast, South, and West—were established, one in each of the four Census Regions of the United States[1]. The BD Hubs provide the ability to engage local or regional stakeholders, e.g., city, county, and state governments, local industry and non-profits, and regional academic institutions, in big data research, and permit a focus on regional issues. These collaborative activities and partnerships play a critical role in building and sustaining a successful national big data innovation ecosystem. This solicitation continues the operation of a national network of BD Hubs. It builds on demonstrated strengths of the program, which has grown to include a set of BD Spokes affiliated with the BD Hubs, and is responsive to the recent developments in data science. For instance, the recently released report on Data Science for Undergraduates: Opportunities and Options from the National Academies of Sciences, Engineering, and Medicine exemplifies the urgency of multi-faceted education and training in data science. The BD Hubs will continue to nucleate regional collaborations and multi-sector projects, while fostering innovation in data science. The NSF BD Hubs program is aligned with NSF’s Harnessing the Data Revolution (HDR) Big Idea, one of NSF’s 10 Big Ideas for Future Investment. HDR is a visionary, national-scale activity to enable new modes of data-driven discovery, allowing fundamentally new questions to be asked and answered in science and engineering frontiers, generating new knowledge and understanding, and accelerating discovery and innovation.

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.

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.

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.

Weigh-in-motion (WIM) systems are a vital means for collecting traffic data-critical input for pavement and bridge designs-used for making transportation and freight planning decisions and in highway safety investigations. There are, however, many potential sources of error in WIM measurements which make it difficult for data collectors to evaluate data accuracy and consistency. For over a decade, the Federal Highway Administration (FHWA) Long-Term Pavement Performance (LTPP) program collected a massive amount of WIM data, along with information about the performance of WIM equipment. This includes the WIM validation and calibration data from 24 LTPP Specific Pavement Studies (SPS) test sites across North America. This and other data sets provide an opportunity to develop more advanced WIM tools to help state highway practitioners perform WIM site selection, sensor selection, maintenance, development of calibration procedures including frequency, and data quality acceptance. These tools could help improve WIM data accuracy and consistency by considering factors such as temperature and seasonal effects, vehicle speed, pavement condition, changes in truck population and configurations, data sampling frequencies, system age, and other factors.

The MPS DC is expected to be the central clearinghouse for TC data management, and will incorporate novel approaches and technologies for data management, data mining and meta-analyses, and data sharing across many organs and tissues, diseases, data types, and TC platforms. The MPS Data center is expected to provide different levels of public and tiered access to TC information for basic and clinical researchers, academic and practicing physicians, the pharmaceutical industry, NIH, FDA and other government agencies, patients, and the lay public. The MPS Data Center will work with IQ Consortium members to develop and make available a secure, customizable coordinated data management system for collection, storage, and analyses of diverse data types from multiple TC platforms being developed and used for drug screening, safety and efficacy testing.

To demonstrate and build on these emerging technologies, a wide range of institutions, both public and private, have initiated and invested in major pilot programs. These efforts are also supported by U.S. DOT through several federal initiatives such as the following: · CV Pilot Deployment Program, · The Smart City Challenge, · Advanced Transportation and Congestion Management Technologies Deployment Program of FHWA As these efforts continue to expand, the amount and quality of data surrounding the application of emerging technologies is also expanding. In response, an improved collaborative approach to data analytics has the potential to improve our ability to address transportation planning and policy questions critical to informed and effective decision-making at state and local public agencies. State and local transportation agencies are eager to learn from the experiences of early adopters of changing and emerging transportation technologies. Formulating a framework that establishes specific procedures for identifying, collecting, aggregating, analyzing, and disseminating data should significantly contribute to effective transportation decision-making. The objectives of this research are the following: 1. To develop a framework for identifying, collecting, aggregating, analyzing, and disseminating data from emerging public and private transportation technologies. This framework will address, at a minimum, data from CV/AV deployments as well as other data linked to smart city and related transportation initiatives. 2. To outline a process for using this framework to help decision-makers incorporate data from emerging technologies into transportation planning and policy.

In 2016, the National Science Foundation (NSF) unveiled a set of "Big Ideas," 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering (seehttps://www.nsf.gov/news/special_reports/big_ideas/index.jsp). The Big Ideas represent unique opportunities to position our Nation at the cutting edge of global science and engineering leadership by bringing together diverse disciplinary perspectives to support convergence research. As such, when responding to this solicitation, even though proposals must be submitted to the Directorate for Computer & Information Science & Engineering/Division of Computing and Communication Foundations (CISE/CCF), once received, the proposals will be managed by a cross-disciplinary team of NSF Program Directors. NSF'sHarnessing the Data Revolution (HDR) Big Ideais 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.

This Funding Opportunity Announcement (FOA) intends to support a Brain Cell Data Center (BCDC) that will work with other BICCN Centers and interested researchers to establish a web-accessible information system to capture, store, analyze, curate, and display all data and metadata on brain cell types, and their connectivity. The BCDC is expected to: (1) lead the effort to establish spatial and semantic standards for managing heterogeneous brain cell census data types and information; (2) lead the effort to collect and register multimodal brain cell census data to common brain coordinate systems; (3) generate searchable 2D and 3D digital brain atlases for cell census data; and (4) generate a unified and comprehensive brain cell knowledge base that integrates all existing brain cell census data and information across diverse repositories.  A central goal of this and the three companion FOAs is to build a brain cell census resource that can be widely used throughout the research community.

The HDR Institutes activity seeks to create an integrated fabric of interrelated institutes that can accelerate discovery and innovation in multiple areas of data-intensive science and engineering. The HDR Institutes will achieve this by harnessing diverse data sources and developing and applying new methodologies, technologies, and infrastructure for data management and analysis. The HDR Institutes will support convergence between science and engineering research communities as well as expertise in data science foundations, systems, applications, and cyberinfrastructure. In addition, the HDR Institutes will enable breakthroughs in science and engineering through collaborative, co-designed programs to formulate innovative data-intensive approaches to address critical national challenges.

The BIGDATA program seeks novel approaches in computer science, statistics, computational science, and mathematics leading towards the further development of the interdisciplinary field of data science. The program also seeks innovative applications in domain science, including social and behavioral sciences, education, physical sciences, and engineering, where data science and the availability of big data are creating new opportunities for research and insights not previously possible. The solicitation invites two categories of proposals: Foundations (BIGDATA: 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 (BIGDATA: 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.

This Broad Agency Announcement (BAA) seeks to provide research and development for forming a revolutionary approach to information fusion and analysis by leveraging service-oriented architecture, open standards, and cutting-edge fusion and analytical algorithms to provide real-time (or near real-time) intelligence for decision makers. This BAA shall research and develop novel techniques to assist users with discovering the golden nuggets in the data - potential approaches include fusing diverse data sources, filtering noise, and leveraging pattern learning to derive patterns of life. Further, technical capabilities developed under this BAA will minimize user time spent gathering data and reporting data, while preserving and providing more time for analysis. This will be accomplished through several means to include a data framework that can easily and quickly connect to sundry data sources, a rich, intuitive personalized workspace and experience, a variety of user-defined visualization displays, machine learning to assist and automate mundane tasks, and a custom report generation tool.

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 Federal Highway Administration (FHWA) and the American Association of State Highway Transportation Officials (AASHTO) have adopted its use and translated the variables into civil engineering applications, specifically pavement design. FHWA's Long Term Pavement Performance (LTPP) Climate Tool provides access to the MERRA-2 database and generates site-specific climate data in compatible formats for AASHTO Pavement ME Design (Pavement ME). AASHTO will soon use MERRA-2 climatic data for AASHTOWare applications. MERRA-2 data sets include accurate hourly solar radiation values based on measured cloud-covered fractions which can significantly improve the accuracy of predicted pavement temperatures. Studies reveal challenges in matching the predictions from the Enhanced Integrated Climatic Model (EICM) with field observations and pavement performance. Variations in measurement of climatic attributes have also been reported to Operating Weather Stations (OWS). MERRA-2 data provides opportunities for enhancements to the climatic parameters and climatic module calculations for pavement design using Pavement ME. It provides improved climatology, higher frequency outputs including hourly data updates, and additional locations beyond the United States. Available data categories include: temperature, precipitation, surface pressure, cloud cover, humidity, wind speed, and solar radiation. The objectives of this project are to (1) evaluate the impact of using NASA's Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) through the FHWA LTPP Climate Tool to improve the climatic inputs and related models for Pavement ME; (2) enhance and simplify climate input parameters for Pavement ME that can be implemented by transportation agencies; and (3) develop climate-related models based on identified parameter enhancements

The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in making research data and artificial intelligence (AI) models findable, accessible, interoperable, and reusable (FAIR1) to facilitate the development of new AI applications in SC's congressionally authorized mission space, which includes the advancement of AI research and development. In particular, ASCR is interested in supporting FAIR benchmark data for AI; and FAIR frameworks for relating data and AI models. For this FOA, AI is inclusive of, for example, machine learning (ML), deep learning (DL), neural networks (NN), computer vision, and natural language processing (NLP). Data, in this context, are the digital artifacts used to generate AI models and/or employed in combination with AI models during inference. An AI model is an inference method that can be used to perform a "task," such as prediction, diagnosis, or classification. The model is developed using training data or other knowledge. An AI task is the inference activity performed by an artificially intelligent system.

The 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 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.