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Agriculture Decisions;
Up to 1988, the December issue contains a cumulative list of decisions reported for the year, by act, docket numbers arranged in consecutive order, and cumulative subject-index, by act.
SEC Docket
None
Enhancing the Postdoctoral Experience for Scientists and Engineers
The concept of postdoctoral training came to science and engineering about a century ago. Since the 1960s, the performance of research in the United States has increasingly relied on these recent PhDs who work on a full-time, but on a temporary basis, to gain additional research experience in preparation for a professional research career. Such experiences are increasingly seen as central to careers in research, but for many, the postdoctoral experience falls short of expectations. Some postdocs indicate that they have not received the recognition, standing or compensation that is commensurate with their experience and skills. Is this the case? If so, how can the postdoctoral experience be enhanced for the over 40,000 individuals who hold these positions at university, government, and industry laboratories? This new book offers its assessment of the postdoctoral experience and provides principles, action points, and recommendations for enhancing that experience.
Research and Development Data Needs
This report contains the proceedings of a one-day workshop organized by the National Research Council's Board on Science, Technology, and Economic Policy (STEP), in conjunction with a study by a panel of the NRC Committee on National Statistics (CNSTAT). This combined activity was commissioned by the Science Resources Statistics Division (SRS) of the National Science Foundation (NSF) to recommend improvements in the Foundation's portfolio of surveys of research and development spending by the federal government, state governments, private industry, the nation's universities and colleges, and other nonprofit institutions.
Enhancing Urban Sustainability with Data, Modeling, and Simulation
On January 30-31, 2019 the Board on Mathematical Sciences and Analytics, in collaboration with the Board on Energy and Environmental Systems and the Computer Science and Telecommunications Board, convened a workshop in Washington, D.C. to explore the frontiers of mathematics and data science needs for sustainable urban communities. The workshop strengthened the emerging interdisciplinary network of practitioners, business leaders, government officials, nonprofit stakeholders, academics, and policy makers using data, modeling, and simulation for urban and community sustainability, and addressed common challenges that the community faces. Presentations highlighted urban sustainability research efforts and programs under way, including research into air quality, water management, waste disposal, and social equity and discussed promising urban sustainability research questions that improved use of big data, modeling, and simulation can help address. This publication summarizes the presentation and discussion of the workshop.
Engineering, Social Justice, and Sustainable Community Development
Engineering, Social Justice, and Sustainable Community Development is the first in a series of biennial workshops on the theme of engineering ethics and engineering leadership. This workshop addresses conflicting positive goals for engineering projects in impoverished areas and areas in crisis. These conflicts arise domestically as well as in international arenas. The goals of project sponsors and participants, which are often implicit, include protecting human welfare, ensuring social justice, and striving for environmental sustainability alongside the more often explicit goal of economic development or progress. The workshop, summarized in this volume, discussed how to achieve the following: Improve research in engineering ethics. Improve engineering practice in situations of crisis and conflict. Improve engineering education in ethics and social issues. Involve professional societies in these efforts.
Roundtable on Data Science Postsecondary Education
Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.
