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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.

This Funding Opportunity Announcement (FOA) solicits applications to develop web-accessible data archives to capture, store, and curate data related to BRAIN Initiative activities.  The data archives will work with the research community to incorporate tools that allow users to analyze and visualize the data, but the creation of such tools is not part of this FOA.  The data archives will use appropriate standards to describe the data, but the creation of such standards is not part of this FOA. A goal of this program is to advance research by creating a community resource data archive with appropriate standards and summary information that is broadly available and accessible to the research community for furthering research.

This Funding Opportunity Announcement (FOA) solicits applications to develop web-accessible data archives to capture, store, and curate data related to BRAIN Initiative activities.  The data archives will work with the research community to incorporate tools that allow users to analyze and visualize the data, but the creation of such tools is not part of this FOA.  The data archives will use appropriate standards to describe the data, but the creation of such standards is not part of this FOA. A goal of this program is to advance research by creating a community resource data archive with appropriate standards and summary information that is broadly available and accessible to the research community for furthering research.

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.

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.

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.

NSF is interested in supporting activities by the NSF Cyberinfrastructure community in the analysis of existing benchmarks, and in the development of new benchmarks, that measure real-world performance and effectiveness of large-scale computing systems for science and engineering discovery. Research, development, and use of performance benchmarks in high-performance computing (HPC) has been active for over 20 years, as evidenced by the development of LINPACK and the emergence of the TOP500 list in the early 1990s, followed by the development of the HPC Challenge Benchmark and the current HPCG effort (http://tiny.cc/hpcg). There have been efforts to provide benchmarks that include real applications, such as the SPEC High Performance Computing Benchmarks (http://spec.org/benchmarks.html#hpg), the Blue Waters SPP suite (http://www.ncsa.illinois.edu/assets/pdf/news/BW1year_apps.pdf), and the NERSC SSP (https://www.nersc.gov/users/computational-systems/nersc-8-system-cori/nersc-8-procurement/trinity-nersc-8-rfp/nersc-8-trinity-benchmarks/ssp/). Recent efforts have sought to broaden the set of relevant benchmarks to more effectively cover performance under different application environments such as data-intensive analysis (e.g., Graph500). Energy efficiency has also emerged in recent years as a relevant and increasingly important area of measurement and profiling for HPC systems (e.g., Green500). In addition to HPC, the Big Data community has gained interest in benchmarking; reference approaches to measuring and characterizing system performance for large-scale data analysis hardware and software systems remains an area of research, development, and community discussion (e.g., on the Big Data Top 100). Industry and academe have convened an ongoing series of workshops and meetings on the topic of Big Data benchmarking (http://clds.ucsd.edu/bdbc/workshops). Given the emergence of inference-based computing, the growing role of data analysis, changes in scientific workflow du

SoftwareInfrastructure for Sustained Innovation (SI2) is a long-term investment focused on realizing a portion of the Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21, http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504730) vision and catalyzing new thinking, paradigms and practices in science and engineering. CIF21 envisions a linked cyberinfrastructure architecture that integrates large-scale computing, high-speed networks, massive data archives, instruments and major facilities, observatories, experiments, and embedded sensors and actuators, across the nation and the world, and that enables research at unprecedented scales, complexity, resolution, and accuracy by integrating computation, data, and experiments in novel ways. Software is a primary modality through which CIF21 innovation and discovery will be realized. It permeates all aspects and layers of cyberinfrastructure (from application codes and frameworks, programming systems, libraries and system software, to middleware, operating systems, networking and the low-level drivers). The CIF21 software infrastructure must address the complexity of this cyberinfrastructure, accommodating: disruptive hardware trends; ever-increasing data volumes; data integrity, privacy, and confidentiality; security; complex application structures and behaviors; and emerging concerns such as fault-tolerance and energy efficiency. The programs must focus on building robust, reliable and sustainable software that will support and advance sustained scientific innovation and discovery.
 The Division of Advanced Cyberinfrastructure in the Computer & Information Science & Engineering Directorate (CISE/ACI) is partnering with Directorates and Offices across the NSF to support SI2, a long-term comprehensive program focused on realizing a sustained software infrastructure that is an integral part of CIF21.

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.

In response to the spectacular opportunities and immense challenges presented by the dawning era of "Big Data" in biomedical research, NIH has developed the Big Data to Knowledge (BD2K) initiative with the mission of enabling the biomedical research community to use the various types of Big Data for research. The purpose of this Funding Opportunity Announcement (FOA) is to solicit development of analysis methods and software in the four topic areas of data compression/reduction, data visualization, data provenance, and data wrangling as part of the overall BD2K initiative.

This Funding Opportunity Announcement (FOA) solicits applications to develop web-accessible data archives to capture, store, and curate data related to BRAIN Initiative activities. The data archives will work with the research community to incorporate tools that allow users to analyze and visualize the data, but the creation of such tools is not part of this FOA. The data archives will use appropriate standards to describe the data, but the creation of such standards is not part of this FOA. A goal of this program is to advance research by creating a community resource data archive with appropriate standards and summary information that is broadly available and accessible to the research community for furthering research.

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.

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.

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.

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.

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.

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

Today, complex data-its form, manipulation, and interpretation-are as important to humanities study as more traditional research materials. Datasets, for example, may represent digitized historical records, high-quality image data, or even multimedia collections, all of which are increasing in number due to the availability and affordability of mass data storage devices and international initiatives to create digital content. Moreover, extensive networking capabilities, sophisticated analytical tools, and new collaboration platforms are simultaneously providing and improving interactive access to and analysis of these data as well as a multitude of other resources. The Institutes for Advanced Topics in the Digital Humanities program seeks to enable humanities scholars in the United States to incorporate advances like these into their scholarship and teaching.

Today, complex data-its form, manipulation, and interpretation-are as important to humanities study as more traditional research materials. Datasets, for example, may represent digitized historical records, high-quality image data, or even multimedia collections, all of which are increasing in number due to the availability and affordability of mass data storage devices and international initiatives to create digital content. Moreover, extensive networking capabilities, sophisticated analytical tools, and new collaboration platforms are simultaneously providing and improving interactive access to and analysis of these data as well as a multitude of other resources. The Institutes for Advanced Topics in the Digital Humanities program seeks to enable humanities scholars in the United States to incorporate advances like these into their scholarship and teaching.

The Synergistic Population and Data Science Award mechanism supports multi-institutional partnerships that will leverage existing prostate cancer patient datasets or cohorts to answer questions that will make a significant impact in addressing the FY19 PCRP Overarching Challenges. Thus, applications must be responsive to at least one of the FY19 PCRP Overarching Challenges. The award is intended to support both the development of infrastructure to facilitate access to multiple datasets/cohorts by a team of investigators, as well as conducting hypothesis-based studies utilizing the multi-institutional datasets. To qualify for this mechanism, a minimum of three institutions and three well-annotated patient datasets/cohorts is required. Dataset Coordination: Central to the pursuit of the hypothesis proposed in the application is the development of the infrastructure to facilitate multi-institutional access to the identified datasets/cohorts. The application must describe the key elements that will need to be in place in order to facilitate the multi-institution collaboration, including: * Administrative and management plans for decision-making and allocation of resources * Description of the dataset logistics, including storage, how data will be shared among PIs/institutions, and plans for data harmonization * Description of key members and roles of the multi-institutional research team * Communication plans among the research team members to discuss research progress and results * Regulatory plan, including coordination of local Institutional Review Board (IRB) approvals * Data security and integrity measures * Intellectual property plan * Plan for disseminating information and distributing data to the prostate cancer research community

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.