2,085 research outputs found
Ask Any Vegetable
No full textThis is a book about making animal forms out of common vegetables. As the eBay title for it proclaims: Very WEIRD! As the author writes in the foreword, Look long at an ordinary gourd of any sort and it will suggest many things to you (vi). This book is in this collection because of Fox and Crane on 24-25, Hare and Tortoise on 60-61, and Fox and Crow on 68-69. For the former scene, normal gourds were used to create the crane and the vase. For the fox an immature gourd was used; some clay was added, into which ears and eyes were stuck. A bit of cotton was pasted over the body to resemble fur, and the bushy tail was bult up of strands of corn silk. The fox's ears are feather-shaft ends (25). Did Aesop ever think that he would be getting into scenes made up of vegetables? The second scene is set in a forest whose trees are carrots. The rabbit is formed from a peanut, and the tortoise from a horse chestnut. The third scene represents some confusion or syncretism between FG and FC. The crow, which might be difficult to create, is cleverly left out of the scene. Prizes in the book go to the camel and leader on 36 (also on the front cover of the dust jacket), the resting sea lions on 53, and the sleeping student on 114. I would say that R.E. Eshmeyer was as crazy as I am, and that probably fits. He was also a man of the cloth.This is a hardbound book (hard cover)This book has a dust jacket (book cover)R.E. Eshmeye
Letter from R.E. Tracy, Supervisor, Sacramento-San Joaquin Area, to George H. Nakamura, May 15, 1944
No full textCorrespondence from R.E. Tracy to George Hideo Nakamura regarding a Government Bill of Lading.The Japanese American Archival Collection documents the people, places, and daily life of Japanese Americans, primarily those who lived in the once thriving community of pre-war Florin in the Sacramento region, as well as the conditions in American incarceration camps during World War II. The approximately 7,000 original items include personal and official letters, photographs, diaries, arts and crafts, newsletters, textiles, camps artifacts, yearbooks and other publications
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
New Jersey's Rising Seas and Changing Coastal Storms: Report of the 2019 Science and Technical Advisory Panel
No full textThe first New Jersey Science and Technical Advisory Panel (STAP) on Sea-Level Rise and Coastal Storms was convened by Rutgers University on behalf of the NJ Climate Change Alliance in 2015, culminating in a 2016 report that identified planning options for practitioners to enhance the resilience of New Jersey’s people, places, and assets to sea-level rise, coastal storms, and the resulting flood risk (Kopp et al., 2016). An innovative approach used to inform the 2016 report was the complementary convening of a panel of practitioners to offer insights on the application of the STAP science to state and local planning and decision-making. Following the same process, the same team at Rutgers University was engaged by the State of New Jersey Department of Environmental Protection to update the 2016 report based on the most current scientific information. Similar to the inaugural work, the 2019 STAP was charged with identifying and evaluating the most current science on sea-level rise projections and changing coastal storms, considering the implications for the practices and policies of local and regional stakeholders, and providing practical options for stakeholders to incorporate science into risk-based decision processes.
The 2019 STAP process recommended the following key updates to the 2016 STAP report:
Making available historical sea-level rise (SLR) information for New Jersey to provide a frame of reference for future projections;
Updating information on ice sheet dynamics;
Expanding consideration of tidal flooding; and
Expanding consideration of storm tide-related flooding.
This report integrates the 2019 key STAP updates and should be considered the most recent reference in this series.Please cite this report as: Kopp, R.E., C. Andrews, A. Broccoli, A. Garner, D. Kreeger, R. Leichenko, N. Lin, C. Little, J.A. Miller, J.K. Miller, K.G. Miller, R. Moss, P. Orton, A. Parris, D. Robinson, W. Sweet, J. Walker, C.P. Weaver, K. White, M. Campo, M. Kaplan, J. Herb, and L. Auermuller. New Jersey’s Rising Seas and Changing Coastal Storms: Report of the 2019 Science and Technical Advisory Panel. Rutgers, The State University of New Jersey. Prepared for the New Jersey Department of Environmental Protection. Trenton, New Jersey.
This work was made possible with financial assistance from the Coastal Zone Management Act of 1972, as amended, as administered by the Office of Coastal Management, National Oceanic and Atmospheric Administration (NOAA) Program through the New Jersey Department of Environmental Protection, Coastal Management Program, Bureau of Climate Resilience Planning. The LocalizeSL sea-level rise projection framework used in this report was developed with grants to REK from the National Science Foundation (Grant ICER-1663807) and the National Aeronautics and Space Administration (Grant 80NSSC17K0698), as well as from the Rhodium Group (for whom REK has previously worked as a consultant) as part of the Climate Impact Lab collaboration."November 2019
The Indian biennale effect: the Kochi/Murziris Biennale 2012
No full textThe Kochi-Muziris Biennale, the most recent global art biennale, was launched in Kochi in the state of Kerala, India, in 2012. This essay considers the “biennale effect,” locating it within India's recent history of radical political modernization and in the context of the state's attempts to establish itself in terms of internationalism and contemporaneity via the arts. Pivotal to this discussion of the biennale effect is the recognition of a growing critical discourse about the biennale format by scholars, critics, and curators. The impact of the Indian biennale on the formerly Communist city of Kochi is also explored, including photographic documentation by the author, in the context of the contradictions and paradoxes raised by India's hosting of this global art event
Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Dataset README
No full textThis document is the README for the complete dataset.Quantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.README file last updated by DJ Rasmussen (), dmr2-at-princeton-dot-edu, Wed Jul 13 14:00:55 PDT 2016PLEASE click on README on the left bar to view the README file for the complete Climate Projections Dataset.This data set is intended to accompany these studies:(1) T. Houser, R.E. Kopp, S.M. Hsiang, M. Delgado, A.S. Jina, K. Larsen,M. Mastrandrea, S. Mohan, R. Muir-Wood, D.J. Rasmussen, J. Rising,and P. Wilson. (2015). American Climate Prospectus: Economic Risksin the United States. Columbia University Press. ISBN: 978-0231174565(2) D. J. Rasmussen, M. Meinshausen, and R. E. Kopp. (2016). Probability-weighted ensembles of U.S. county-level climate projections for climaterisk analysis. Journal of Applied Meteorology and Climatology. DOI: 10.1175/JAMC-D-15-0302.
Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Monte Carlo Pattern/Residual (MCPR) data set RCP45
No full textQuantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.
## Citation
This data set is intended to accompany these studies:
(1) T. Houser, R.E. Kopp, S.M. Hsiang, M. Delgado, A.S. Jina, K. Larsen, M. Mastrandrea, S. Mohan, R. Muir-Wood, D.J. Rasmussen, J. Rising, and P. Wilson. (2015). American Climate Prospectus: Economic Risks
in the United States. Columbia University Press. ISBN: 978-0231174565
(2) D. J. Rasmussen, M. Meinshausen, and R. E. Kopp. (2016). Probability-weighted ensembles of U.S. county-level climate projections for climaterisk analysis. Journal of Applied Meteorology and Climatology. DOI: 10.1175/JAMC-D-15-0302.1
Please cite these works when using any results generated with these projections.
Copyright (C) 2016 by ROBERT E. KOPP AND RHODIUM GROUP LLC
This dataset is made available under a non-Commercial Creative Commons License.
https://creativecommons.org/licenses/by-nc/3.0/us/
You are free to:
1. Share ‒ copy and redistribute the material in any medium or format
2. Remix, transform, and build upon the material
You must:
1. Attribution ‒ You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
2. You may not use the material for commercial purposes.
No warranties are given
Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Monte Carlo Pattern/Residual (MCPR) data set RCP85
No full textQuantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.
## Citation
This data set is intended to accompany these studies:
(1) T. Houser, R.E. Kopp, S.M. Hsiang, M. Delgado, A.S. Jina, K. Larsen,
M. Mastrandrea, S. Mohan, R. Muir-Wood, D.J. Rasmussen, J. Rising,
and P. Wilson. (2015). American Climate Prospectus: Economic Risks
in the United States. Columbia University Press. ISBN: 978-0231174565
(2) D. J. Rasmussen, M. Meinshausen, and R. E. Kopp. (2016). Probability-
weighted ensembles of U.S. county-level climate projections for climate
risk analysis. Journal of Applied Meteorology and Climatology. DOI: 10.1175/JAMC-D-15-0302.1
Please cite these works when using any results generated with these projections.
Copyright (C) 2016 by ROBERT E. KOPP AND RHODIUM GROUP LLC
This dataset is made available under a non-Commercial Creative Commons License.
https://creativecommons.org/licenses/by-nc/3.0/us/
You are free to:
1. Share ‒ copy and redistribute the material in any medium or format
2. Remix, transform, and build upon the material
You must:
1. Attribution ‒ You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
2. You may not use the material for commercial purposes.
No warranties are given
Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Monte Carlo Pattern/Residual (MCPR) data set RCP60
No full textQuantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.
## Citation
This data set is intended to accompany these studies:
(1) T. Houser, R.E. Kopp, S.M. Hsiang, M. Delgado, A.S. Jina, K. Larsen,
M. Mastrandrea, S. Mohan, R. Muir-Wood, D.J. Rasmussen, J. Rising,
and P. Wilson. (2015). American Climate Prospectus: Economic Risks
in the United States. Columbia University Press. ISBN: 978-0231174565
(2) D. J. Rasmussen, M. Meinshausen, and R. E. Kopp. (2016). Probability-
weighted ensembles of U.S. county-level climate projections for climate
risk analysis. Journal of Applied Meteorology and Climatology. DOI: 10.1175/JAMC-D-15-0302.1
Please cite these works when using any results generated with these projections.
Copyright © 2016 by ROBERT E. KOPP AND RHODIUM GROUP LLC
This dataset is made available under a non-Commercial Creative Commons License.
https://creativecommons.org/licenses/by-nc/3.0/us/
You are free to:
1. Share ‒ copy and redistribute the material in any medium or format
2. Remix, transform, and build upon the material
You must:
1. Attribution ‒ You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
2. You may not use the material for commercial purposes.
No warranties are given
Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Source Code
No full textSource code for the study as represented by the following abstract:Quantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.README file last updated by DJ Rasmussen (), dmr2-at-princeton-dot-edu, Wed Jul 13 14:00:55 PDT 2016 PLEASE click on README on the left bar to view the README file for the Climate Projection Code.This data set is intended to accompany these studies: (1) T. Houser, R.E. Kopp, S.M. Hsiang, M. Delgado, A.S. Jina, K. Larsen, M. Mastrandrea, S. Mohan, R. Muir-Wood, D.J. Rasmussen, J. Rising, and P. Wilson. (2015). American Climate Prospectus: Economic Risks in the United States. Columbia University Press. ISBN: 978-0231174565 (2) D. J. Rasmussen, M. Meinshausen, and R. E. Kopp. (2016). Probability- weighted ensembles of U.S. county-level climate projections for climate risk analysis. Journal of Applied Meteorology and Climatology. DOI: 10.1175/JAMC-D-15-0302.1Please cite these works when using any results generated with these projections. ---- Copyright (C) 2016 by ROBERT E. KOPP AND RHODIUM GROUP LLC This dataset is made available under a non-Commercial Creative Commons License. https://creativecommons.org/licenses/by-nc/3.0/us/ You are free to: 1. Share — copy and redistribute the material in any medium or format 2. Remix, transform, and build upon the material You must: 1. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. 2. You may not use the material for commercial purposes. No warranties are given
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