Group items tagged

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 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 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.

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 funds available for awards in each program element offered in ROSES-2017 range from less than one to several million dollars, which allows for selection from a few to as many as several dozen proposals, depending upon 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 proposed work and/or program requirements.

The Institute for Research on Innovation and Science (IRIS) has issued a call for proposals for its 2020 Researcher Awards. IRIS seeks to enable fundamental research on the results of public and private investments that support discovery, innovation, and education on U.S. university campuses and accomplishes that goal by: 1) collecting and curating data from universities around the country; 2) cleaning, integrating, and protecting that data in its capacity as an IRB-approved data repository; 3) partnering with the U.S. Census Bureau to link university data to restricted federal micro-data; and 4) making the resulting datasets as broadly available to research users as the law and responsible research practice allow. Through the awards program, grants of up to $20,000 over up to fifteen months will be awarded in support of research using IRIS UMETRICS data to address questions about the social and economic returns to investments in research. Proposals submitted for IRIS Researcher Awards must emphasize the use of IRIS data in projects that address open issues in the study of science and technology and in science policy.

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 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 goal of this activity is to foster novel, transformative, multidisciplinary approaches that address the use of large data sets to create actionable knowledge for improving STEM teaching and learning environments (formal and informal) in the medium term, and to revolutionize learning in the longer term. These approaches will involve the work of learning scientists, STEM disciplinary experts, computer scientists, statisticians, database experts and educational researchers who design and study learning environments. Among the potential benefits of integrating approaches from these disciplines are improving student learning and engagement, optimizing personalized instruction, and supporting rapid decision making to help educators respond more effectively to the learning needs of individuals and groups of learners in multiple settings. These approaches may be risky but should have the potential to rapidly advance the field. The scope of this activity does not include infrastructure development focused on data base design and development for education domains. The new approaches envisioned in this solicitation will require the generation and use of data that range from micro-level data on individual learners, to data from online learning sources (such as massively open online courses), to meso-level data from the classroom that provide information to students and teachers about how learning is progressing, to macro-level data such as school, district, state, and national data, including data from federal science and policy agencies.

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.

The Division of Environmental Biology (DEB) Core Tracksupports research and training on evolutionary and ecological processes acting at the level of populations, species, communities, and ecosystems. DEB encourages research that elucidates fundamental principles that identify and explain the unity and diversity of life and its interactions with the environment over space and time. Research may incorporate field, laboratory, or collection-based approaches; observational or manipulative studies; synthesis activities; phylogenetic discovery projects; or theoretical approaches involving analytical, statistical, or computational modeling. Proposals should be submitted to the core clusters (Ecosystem Sciences, Evolutionary Processes, Population and Community Ecology, and Systematics and Biodiversity Sciences). DEB also encourages interdisciplinary proposals that cross conceptual boundaries and integrate over levels of biological organization or across multiple spatial and temporal scales.Research addressing ecology and ecosystem science in the marine biome should be directed to the Biological Oceanography Program in the Division of Ocean Sciences; research addressing evolution and systematics in the marine biome should be directed to the Evolutionary Processes or Systematics and Biodiversity Science programs in DEB. All DEB programs also encourage proposals that leverage NSF-supported data networks, databases, centers, and other forms of scientific infrastructure, including but not limited to the National Ecological Observatory Network (NEON), Environmental Data Initiative (EDI), and Integrated Digitized Biocollections (iDigBio). The Rules of Life Track supports integrative proposals that span population, species, community and ecosystem scales normally funded by DEB, to organismal, cellular and molecular scales typically funded by other divisions in the Biological Sciences.

n 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 tothe Division of Information and Intelligent Systems (IIS) within the Directorate for Computer and Information Science and Engineering (CISE),once received, the proposals will be managed by a cross-disciplinary team of NSF Program Directors. NSF'sHarnessing the Data Revolution (HDR)Big Idea 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.

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 (see https://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 Division of Information and Intelligent Systems (IIS) within the Directorate for Computer and Information Science and Engineering (CISE), once received, the proposals will be managed by a cross-disciplinary team of NSF Program Directors.

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 International Research Network Connections (IRNC) program supports high-performance network connectivity required by international science and engineering research and education collaborations involving the NSF research community. NSF expects to make 1-2 awards to link U.S. research networks with peer networks in Europe and Africa and leverage existing international network connectivity. High-performance network connections funded by this program are intended to support science and engineering research and education applications, and preference will be given to solutions that provide the best economy of scale and demonstrate the ability to support the largest communities of interest with the broadest services. Funded projects will assist the U.S. research and education community by enabling state-of-the-art international network services and access to increased collaboration and data services. Through extended international network connections, additional research and production network services will be enabled, complementing those currently offered or planned by domestic research networks.

The International Research Network Connections (IRNC) program supports high-performance network connectivity required by international science and engineering research and education collaborations involving the NSF research community. NSF expects to make 1-2 awards to link U.S. research networks with peer networks in Europe and Africa and leverage existing international network connectivity. High-performance network connections funded by this program are intended to support science and engineering research and education applications, and preference will be given to solutions that provide the best economy of scale and demonstrate the ability to support the largest communities of interest with the broadest services. Funded projects will assist the U.S. research and education community by enabling state-of-the-art international network services and access to increased collaboration and data services. Through extended international network connections, additional research and production network services will be enabled, complementing those currently offered or planned by domestic research networks.

Awards will be made as grants, cooperative agreements, and inter- or intra-agency transfers depending on the nature of the proposing organization and/or project requirements. The period of performance for an award is up to 5 years. Note that it is NASA policy that all investigations involving non-U.S. organizations will be conducted on the basis of no exchange of funds. Prospective proposers are requested to submit any questions in writing to NASAMIRO@nasaprs.com no later than 10 business days before the proposal due date so that NASA will be able to respond. Every effort will be made to answer any questions received after that date, but there is no guarantee that an answer will be provided. Only Minority Serving Universities that are legally recognized by the Department of Education are eligible to apply for this NASA Research Announcement (NRA). No later than the due date for proposals, proposers to this NRA are required to have: 1) a Data Universal Numbering System (DUNS) number, 2) a valid registration with the System for Award Management (SAM) [formerly known as the Central Contractor Registry (CCR)], 3) a valid Commercial And Government Entity (CAGE) Code, 4) a valid registration with NASA Solicitation and Proposal Integrated Review and Evaluation System (NSPIRES) (this also applies to any entities proposed for subawards or subcontracts.) Consult Appendix H Section H.2.1 regarding teaming requirements and partnership guidelines. The mission of the National Aeronautics and Space Administration (NASA) Office of Education is to advance high quality Science, Technology, Engineering and Mathematics (STEM) education across the United States. To maximize these efforts, NASA has consolidated its education activities into the Minority University Research and Education Project (MUREP). MUREP supports training and development of students and faculty in STEM at targeted colleges and universities, and provides opportunities for research and education that attract and prepa

The goal of this NOAA Environmental Literacy Grants (ELG) Federal Funding Opportunity (FFO) is to build the capacity of informal educators (including interpreters and docents) and/or formal educators (pre- or in-service) to use NOAA data and data access tools to help K-12 students and/or the public understand and respond to global change. Successful projects will enhance educators' ability to use the wealth of scientific data, data visualizations, data access technologies, information products, and other assets available through NOAA (plus additional sources, if desired) to engage K-12 students and/or other members of the public in a minimum of two U.S. states or territories. Partnerships with NOAA entities and/or involvement of NOAA scientists to facilitate the use of such assets by educators are strongly encouraged. As an ultimate outcome, successful projects should aim to increase educators' effectiveness in promoting stewardship and increasing informed decision making by a diverse pool of K-12 students and/or other members of the public. The impact of the proposed project on the target educators must be measurable during the award period.

The National Science Foundation (NSF) seeks new proposals and supplemental funding requests to existing awards that support flexible personalized learning to prepare the science, technology, engineering and mathematics (STEM) workforce of the future. NSF envisions projects that collectively apply to all learners, from young children to those already in the workforce. In particular, we would like to support research that complements an anticipated future funding opportunity made possible by a gift from the Boeing Corporation, which was announced on September 24, 2018 (https://www.nsf.gov/news/news_summ.jsp?cntn_id=296700). The Boeing gift established a partnership between NSF and Boeing to accelerate training in crucial skill areas for the future U.S. workforce. It will be used to support design, development, implementation, and analysis of online courses in model-based engineering, model-based systems engineering, mechatronics, robotics, data science and sensor analytics, program management, and artificial intelligence. These courses will use personalized learning systems to maximize their effectiveness for diverse learners.

All of comparative biology depends on knowledge of the evolutionary relationships (phylogeny) of living and extinct organisms. In addition, understanding biodiversity and how it changes over time is only possible when Earth's diversity is organized into a phylogenetic framework. The goals of the Genealogy of Life (GoLife) program are to resolve the phylogenetic history of life and to integrate this genealogical architecture with underlying organismal data. The ultimate vision of this program is an open access, universal Genealogy of Life that will provide the comparative framework necessary for testing questions in systematics, evolutionary biology, ecology, and other fields. A further strategic integration of this genealogy of life with data layers from genomic, phenotypic, spatial, ecological and temporal data will produce a grand synthesis of biodiversity and evolutionary sciences. The resulting knowledge infrastructure will enable synthetic research on biological dynamics throughout the history of life on Earth, within current ecosystems, and for predictive modeling of the future evolution of life.Projects submitted to this program should emphasize increased efficiency in contributing to a complete Genealogy of Life and integration of various types of organismal data with phylogenies.This program also seeks to broadly train next generation, integrative phylogenetic biologists, creating the human resource infrastructure and workforce needed to tackle emerging research questions in comparative biology. Projects should train students for diverse careers by exposing them to the multidisciplinary areas of research within the proposal.

A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications,healthcare informatics and business analytics. A salient feature of these emerging domains is the large and continuously streaming data sets that these applications generate, which must be processed efficiently enough to support real-time learning and decision making based on these data. This challenge requires novel hardware techniques and machine-learning architectures.This solicitation seeks to lay the foundation for next-generation co-design of RTML algorithms and hardware, with the principal focus on developing novel hardware architectures and learning algorithms in which all stages of training (including incremental training, hyperparameter estimation, and deployment) can be performed in real time. The National Science Foundation (NSF) and the Defense Advanced Research Projects Agency (DARPA) are teaming up through this Real-Time Machine Learning (RTML) program to explore high-performance, energy-efficient hardware and machine-learning architectures that can learn from a continuous stream of new data in real time, through opportunities for post-award collaboration between researchers supported by DARPA and NSF.

DMREF is the primary program by which NSF participates in the Materials Genome Initiative (MGI) for Global Competitiveness. MGI recognizes the importance of materials science and engineering to the well-being and advancement of society and aims to "deploy advanced materials at least twice as fast as possible today, at a fraction of the cost." MGI integrates materials discovery, development, property optimization, and systems design with a shared computational framework. This framework facilitates collaboration and coordination of research activities, analytical tools, experimental results, and critical evaluation in pursuit of the MGI goals. Consistent with theMGI Strategic Plan, DMREFhighlights four sets of goals: · Leading a culture shift in materials science and engineering research to encourage and facilitate an integrated team approach; · Integrating experimentation, computation, and theory and equipping the materials scienceand engineering communities with advanced tools and techniques; · Making digital data accessible, findable,and useful to the community; and · Creating a world-class materials science and engineering workforce that is trained for careers in academia or industry. Accordingly, DMREF will support activities that significantly accelerate materials discovery and/or development by building the fundamental knowledge base needed to design and make materials and/or devices with specific and desired functions or properties.

CubeSat constellations and swarms have been identified as a new paradigm for space-based measurements to address high-priority science questions in multiple disciplines. However, the full potential of CubeSat constellations and swarms for scientific studies has not yet been realized because of: i) the limitations of some of the existing key CubeSat technology, ii) knowledge gaps in the design and optimization of CubeSat technology for swarms and constellations, and iii) the increasing cost of more sophisticated CubeSat technology. The technology challenges include high bandwidth communications in CubeSat-to-CubeSat and CubeSat-to-ground scenarios, circuits and sensors miniaturization, on-board signal processing, and power generation. The vision of a satellite mission consisting of 10-100 CubeSats will require focused investment and development in a myriad of CubeSat-related technologies to build a cost-effective constellation or swarm of CubeSats. This will require transformative approaches for designing and building CubeSat subsystems and sensors, and innovative production approaches that will reduce the cost of implementing large-scale constellation missions.Spectrum allocations for data transmission and possible electromagnetic interference between or within constellations of CubeSats are issues that also will need to be considered. This solicitation describes an Ideas Lab focused onCubeSat Innovations to push the envelope of space-based research capabilities by simultaneously developing enabling technologies in several domains, including propulsion systems, sensor design, electronic circuits, antennas, satellite-to-ground and satellite-to-satellite communications and wireless networking, and power management. The vision of this Ideas Lab is to support research and engineering technology development efforts that will lead to new science missions in geospace and atmospheric sciences using self-organizing CubeSat constellations/swarms.