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A Bold New Approach for Space Exploration and Discovery
Office of Science and Technology Policy National Aeronautics and Space Administration FACT SHEET A Bold New Approach for Space Exploration and Discovery • Adds $6 billion to NASA’s budget over five years and draws upon American ingenuity to enable us to embark on an ambitious 21st century program of human space exploration and observation of the Earth and the Universe. • Ends NASA’s Constellation program, which was planning to use an approach similar to the Apollo program to return astronauts to the Moon 50 years after that program’s triumphs. An independent panel found that Constellation was years behind schedule and would require large budget increases to land even a handful of astronauts back on the Moon before 2030. Instead, we are launching a bold new effort that invests in American ingenuity to develop more capable and innovative technologies for future space exploration. • In consultation with our partners, extends operation of the International Space Station likely to 2020 or beyond and enhances its utilization, bringing nations together in a common pursuit of discovery in space. • Initiates several new programs to transform the state of the art in space technologies, including flagship exploration technology development and demonstration programs, investments in early-stage advanced concepts, potential “game-changing” technologies, and new propulsion technologies – all intended to increase the reach and reduce the costs of future human space exploration as well as other NASA, government, and commercial space activities. • Enhances the Nation’s global climate change research and monitoring system, accelerating decadal survey missions and re-flying a satellite that will identify global carbon sources and sinks. -
The U.S. Department of Energy's Ten-Year-Plans for the Office Of
U.S. DEPARTMENT OF ENERGY The U.S. Department of Energy’s Ten-Year-Plans for the Office of Science National Laboratories FY 2019 FY 2019 Annual Laboratory Plans for the Office of Science National Laboratories i Table of Contents Introduction ................................................................................................................................................................1 Ames Laboratory ........................................................................................................................................................3 Lab-at-a-Glance ......................................................................................................................................................3 Mission and Overview ............................................................................................................................................3 Core Capabilities .....................................................................................................................................................4 Science Strategy for the Future ..............................................................................................................................8 Infrastructure .........................................................................................................................................................8 Argonne National Laboratory ................................................................................................................................. -
Automating Data Science: Prospects and Challenges
Final m/s version of paper accepted (April 2021) for publication in Communications of the ACM. Please cite the journal version when it is published; this will contain the final version of the figures. Automating Data Science: Prospects and Challenges Tijl De Bie Luc De Raedt IDLab – Dept. of Electronics and Information Systems Dept. of Computer Science AASS Ghent University KU Leuven Örebro University Belgium Belgium Sweden José Hernández-Orallo Holger H. Hoos vrAIn LIACS Universitat Politècnica de València Universiteit Leiden Spain The Netherlands Padhraic Smyth Christopher K. I. Williams Department of Computer Science School of Informatics Alan Turing Institute University of California, Irvine University of Edinburgh London USA United Kingdom United Kingdom May 13, 2021 Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights arXiv:2105.05699v1 [cs.DB] 12 May 2021 • Automation in data science aims to facilitate and transform the work of data scientists, not to replace them. • Important parts of data science are already being automated, especially in the modeling stages, where techniques such as automated machine learning (Au- toML) are gaining traction. • Other aspects are harder to automate, not only because of technological chal- lenges, but because open-ended and context-dependent tasks require human in- teraction. 1 Introduction Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems. Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization [10]. -
Agency Information
Quadrennial Technology Review 2015 Chapter 1: Energy Challenges Supplemental Information Additional Information on Energy Challenges Agency Information Representative DOE Science and Energy Program Workshops U.S. DEPARTMENT OF ENERGY Quadrennial Technology Review 2015 Agency Information Chapter 1: Supplemental Information Agency Information Many Federal Departments and Agencies conduct research, development, demonstration, and deployment (RDD&D) activities, policy and regulatory activities, and other actions related to clean energy technologies, as do many State Agencies. The following websites provide a starting point for examining some of the excellent work done by these Federal Agencies—with a primary focus on RDD&D activities, as well as those providing data and information helpful for informing those activities. State and local activities are not covered here. This is not a complete list across agencies or within agencies, only a starting point. For all of these agencies, searching their websites for the energy-related topics of interest is the best way to get up-to-date information. For regulatory and policy issues, the work done by the Quadrennial Energy Review provides a useful overview and framework.1 AGENCY/DIVISION and URL: Executive Office of the President (EOP) https://www.whitehouse.gov/administration/eop Council on Environmental Quality (CEQ) https://www.whitehouse.gov/administration/eop/ceq National Security Council (NSC) https://www.whitehouse.gov/administration/eop/nsc Office of Management and Budget (OMB) https://www.whitehouse.gov/omb Office of Science and Technology Policy (OSTP) https://www.whitehouse.gov/administration/eop/ostp U.S. Department of Agriculture (USDA) http://www.usda.gov/wps/portal/usda/usdahome http://www.usda.gov/wps/portal/usda/usdahome?navid=ENERGY http://www.usda.gov/oce/energy/ http://www.ars.usda.gov/research/programs/programs.htm?NP_CODE=307 http://www.usda.gov/oce/reports/index.htm#renewable http://www.rd.usda.gov/about-rd/agencies/rural-utilities-service U.S. -
Direct Federal Financial Interventions and Subsidies in Energy in Fiscal Year 2016
Direct Federal Financial Interventions and Subsidies in Energy in Fiscal Year 2016 April 2018 Independent Statistics & Analysis U.S. Department of Energy www.eia.gov Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the U.S. Department of Energy or other federal agencies. U.S. Energy Information Administration | Financial Interventions and Subsidies i April 2018 Contacts This report, Direct Federal Financial Interventions and Subsidies in Energy in Fiscal Year 2016, was prepared under the general guidance of Ian Mead, Assistant Administrator for Energy Analysis; Jim Turnure at 202/586-1762 (email, [email protected]), Director, Office of Energy Consumption and Efficiency Analysis; and Shirley Neff, Senior Advisor, EIA. Technical information concerning the content of the report also may be obtained from Mark Schipper at 202/586-1136 (email, [email protected]) and technical information on the subsidies and support to the electric power industry may be obtained from Chris Namovicz at 202/586-7120 (email, [email protected]). Contributing authors, by fuel or technology subsidy and support issue areas, are as follows • Richard Bowers and Fred Mayes–renewables (electricity) subsidies and support -
Agricultural Research: Background and Issues
Agricultural Research: Background and Issues Updated October 2, 2020 Congressional Research Service https://crsreports.congress.gov R40819 SUMMARY R40819 Agricultural Research: Background and Issues October 2, 2020 The U.S. Department of Agriculture (USDA) Research, Education, and Economics (REE) mission area funds billions of dollars annually for biological, physical, and social Genevieve K. Croft science research that is related to agriculture, food, and natural resources. Four agencies Analyst in Agricultural carry out REE responsibilities: the Agricultural Research Service (ARS), the National Policy Institute of Food and Agriculture (NIFA), the National Agricultural Statistics Service (NASS), and the Economic Research Service (ERS). The Under Secretary for REE, who oversees the REE agencies, holds the title of USDA Chief Scientist and is responsible for coordinating research, education, and extension activities across the entire department. The Office of the Chief Scientist (OCS)—a staff office within the Office of the Under Secretary for REE—supports this coordination role. Discretionary funding for the REE mission area totaled approximately $3.4 billion in FY2020, and mandatory funding from the 2018 farm bill adds another $177 million per year on average. USDA administers federal funding to states and local partners through its extramural research agency: NIFA. NIFA administers this extramural funding through capacity grants (allocated to the states based on formulas in statute) and competitive grants (awarded based on a peer-review process). USDA also conducts its own research at its intramural research agencies: ARS, NASS, and ERS. Debates over the direction of public agricultural research and the nature of how it is funded continue. Ongoing issues include whether federal funding is sufficient to support agricultural research, education, and extension activities; the different roles of extramural versus intramural research; and the implications of allocating extramural funds via capacity grants versus competitive grants. -
Doe-Fy2021-Laboratory-Table 1.Pdf
DOE/CF-0168 Department of Energy FY 2021 Congressional Budget Request Laboratory Tables Preliminary February 2020 Office of Chief Financial Officer DOE/CF-0168 Department of Energy FY 2021 Congressional Budget Request Laboratory Tables Preliminary The numbers depicted in this document represent the gross level of DOE budget authority for the years displayed. The figures include discretionary and supplemental funding. They do not consider revenues/receipts, use of prior year balances, deferrals, rescissions, or other adjustments appropriated as offsets to the DOE appropriations by the Congress. February 2020 Office of Chief Financial Officer Printed with soy ink on recycled paper Table of Contents Laboratory Table by Congressional Control Laboratory Table Summary......................................................................................................................................1 Laboratory Table by Congressional Control4 Ames Laboratory..................................................................................................................................................4 Ames Site Office...................................................................................................................................................5 Argonne National Laboratory...............................................................................................................................6 Argonne Site Office..............................................................................................................................................9 -
Undergraduate Internships
Internship Programs https://internships.fnal.gov/ Undergraduate Internships Community College Internships (CCI) Summer technical training experience C for community college students. CCI is sponsored by the Department of Energy, Office of Science. Helen Edwards Summer Internship A ten-week summer internship for students majoring in physics and engineering at universities in Europe. Lee Teng Internship Accelerator science and engineering internship designed to attract undergraduate students to the exciting and challenging world of particle accelerator physics and technology. Summer Internships in Science and Technology (SIST) Undergraduate sophomores and juniors majoring in physics, engineering (mechanical, electrical and computer), materials science, mathematics and computer science conduct research with Fermilab scientists and engineers. Science Undergraduate Laboratory Internship (SULI) Sponsored by the Department of Energy Office of Science, undergraduate physics or engineering majors work with scientists or engineers on projects at the frontier of particle physics research. VetTech Internship Program Military veterans provide routine technical support for an assigned experiment or support group. The program assumes apprentices level expertise, general military experience and/or knowledge-based on academic study. Managed by Fermi Research Alliance, LLC for the U.S. Department of Energy Office of Science www.fnal.gov Graduate Internships Computational Science Graduate Fellowship (CSGF) CSGF provides outstanding benefits and opportunities -
ARPA-E): Background, Status, and Selected Issues for Congress
Order Code RL34497 Advanced Research Projects Agency - Energy (ARPA-E): Background, Status, and Selected Issues for Congress Updated August 12, 2008 Deborah D. Stine Specialist in Science and Technology Policy Resources, Science, and Industry Division Advanced Research Projects Agency - Energy (ARPA-E): Background, Status, and Selected Issues for Congress Summary In August 2007, Congress authorized the establishment of the Advanced Research Projects Agency - Energy (ARPA-E) within the Department of Energy (DOE) as part of the America COMPETES Act (P.L. 110-69). Modeled on the Defense Advanced Research Projects Agency (DARPA), ARPA-E would support transformational energy technology research projects with the goal of enhancing the nation’s economic and energy security. Proponents of ARPA-E contend that additional science and technology would help respond to the nation’s need for clean, affordable, and reliable energy. Opponents question whether ARPA-E is necessary to develop new technologies, when existing energy technologies are not fully utilized due to insufficient policies to encourage their implementation. ARPA-E proponents counter that ARPA-E is needed to catalyze the energy marketplace by accelerating research that will bridge the gap between basic research and industrial product development. The Bush Administration questions whether the DARPA model can be used for the energy sector and is concerned that it might redirect funds away from current DOE research activities, particularly the DOE Office of Science. Instead, the President’s FY2009 budget requests funding for six new technology transfer collaborations. ARPA-E proponents doubt that DOE can achieve ARPA-E’s goals with its existing structure and personnel, as opposed to the ARPA-E’s innovative R&D management design. -
Artificial Intelligence Research in Northern Ireland and the Potential for a Regional Centre of Excellence
Artificial Intelligence Research in Northern Ireland and the Potential for a Regional Centre of Excellence Review Prepared by Ken Guy1 and Rob Procter2 with inputs from Franz Kiraly3 and Adrian Weller4 on behalf of The Alan Turing Institute5 April 2019 1 Ken Guy, Director, Wise Guys Ltd. 2 Professor Rob Procter, Turing Fellow, University of Warwick 3 Dr. Franz Kiraly, Turing Fellow, University College London 4 Dr. Adrian Weller, Programme Director and Turing Fellow, University of Cambridge 5 Although prepared on behalf of The Alan Turing Institute, responsibility for the views expressed in this document lie solely with the two main authors, Ken Guy and Rob Procter. Contents Foreword by Dr. Robert Grundy, Chair of Matrix ................................................................... i Foreword by Tom Gray, Chair of the Review Panel ............................................................. ii Executive Summary ................................................................................................................ vi 1. Introduction ....................................................................................................................... 11 1.1 Objectives ......................................................................................................................... 12 1.2 Expectations ..................................................................................................................... 13 1.3 Methodology ................................................................................................................... -
Big Data and Computing Building a Vision for ARS Information Management
Big Data and Computing Building a Vision for ARS Information Management Workshop Summary Feb. 5-6, 2013 USDA Agricultural Research Service TABLE OF CONTENTS TABLE OF CONTENTS ............................................................................................................................. 2 INTRODUCTION ....................................................................................................................................... 3 BIG DATA AND COMPUTING ................................................................................................................ 5 DEFINING RESEARCH NEEDS ............................................................................................................................................................... 5 STEPS TO ACHIEVE THE VISION: SPECIFIC RECOMMENDATIONS .................................................................................................... 6 CONCLUSIONS .......................................................................................................................................... 8 TABLE 1. SUMMARY OF RECOMMENDATIONS. ................................................................................................................................. 9 ACKNOWLEDGEMENTS...................................................................................................................................................................... 10 APPENDIX 1: EXAMPLES OF HOW IMPROVED BIG DATA CAPACITY WILL INCREASE ARS SCIENTIFIC CAPACITY ......................................................................................................................... -
De-Foa-0002449
DEPARTMENT OF ENERGY OFFICE OF SCIENCE BASIC ENERGY SCIENCES MATERIALS AND CHEMICAL SCIENCES RESEARCH FOR QUANTUM INFORMATION SCIENCE FUNDING OPPORTUNITY ANNOUNCEMENT (FOA) NUMBER: DE-FOA-0002449 FOA TYPE: INITIAL CFDA NUMBER: 81.049 FOA Issue Date: December 17, 2020 Submission Deadline for Pre-Applications: January, 27, 2021 at 5:00 PM Eastern A Pre-Application is required Pre-Application Response Date: March 1, 2021 Submission Deadline for Applications: April 14, 2021 at 11:59 PM Eastern Table of Contents SECTION I – FUNDING OPPORTUNITY DESCRIPTION ...................................................... 1 SECTION II – AWARD INFORMATION ................................................................................. 5 A. TYPE OF AWARD INSTRUMENT .................................................................................. 5 B. ESTIMATED FUNDING ................................................................................................... 5 C. MAXIMUM AND MINIMUM AWARD SIZE .................................................................. 5 D. EXPECTED NUMBER OF AWARDS .............................................................................. 6 E. ANTICIPATED AWARD SIZE ......................................................................................... 6 F. PERIOD OF PERFORMANCE .......................................................................................... 6 G. TYPE OF APPLICATION ................................................................................................. 6 SECTION III – ELIGIBILITY