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1,024 results for “Edge”
Dataset for the "a parameterization for cloud organization and propagation by evaporation-driven cold pools edges"
<p>When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub-cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid-scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry-to-wet 10-days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas.</p><p> </p>
QLKNN7D-edge training set
<p><strong>QLKNN7D-edge training set</strong></p> <p>This dataset contains a large-scale run of ~15 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The dataset is in a parameter regime typical of the L-mode near edge (pedestal forming region). QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/">https://gitlab.com/qualikiz-group/QuaLiKiz/</a>. This dataset was generated with QuaLiKiz 2.8.4, which includes numerical improvements increasing the robustness of strongly driven (high gradient) calculations typical of the L-mode near-edge. See <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/2.8.4">https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/2.8.4</a> for the in-repository tag.</p> <p>The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See <a href="https://doi.org/10.1063/1.5134126">https://doi.org/10.1063/1.5134126</a> for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on <a href="https://arxiv.org/abs/1911.05617">arXiv</a> and the older dataset on <a href="https://doi.org/10.5281/zenodo.3497066">Zenodo</a>. For an application example, see <a href="http://https://doi.org/10.1088/1741-4326/ac0d12">Van Mulders et al 2021</a>, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. An additional, larger, QuaLiKiz dataset is found at <a href="https://zenodo.org/record/8017522">https://zenodo.org/record/8017522</a>. Neither the QLKNN10D or QLKNN11D datasets include L-mode near-edge parameters. For any learned surrogates developed for QLKNN7D-edge, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended.</p> <p>Related repositories:</p> <ul> <li><a href="https://qualikiz.com">General QuaLiKiz documentation </a></li> <li><a href="https://qualikiz.com/QuaLiKiz/Input-and-output-variables">QuaLiKiz/QLKNN input/output variables naming scheme </a></li> <li><a href="https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop">Training, plotting, filtering, and auxiliary tools </a></li> <li><a href="https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools">QuaLiKiz related tools </a></li> <li><a href="https://gitlab.com/qualikiz-group/QLKNN-fortran">FORTRAN QLKNN implementation with wrapper for Python and MATLAB </a></li> <li><a href="https://gitlab.com/qualikiz-group/qlknn-hyper">Weights and biases of 'hyperrectangle style' QLKNN </a></li> </ul> <p> </p>
Dataset for Disturbance: a double-edged sword for restoration in a changing climate: Western Oregon and Washington upland prairies 2019-2020
Data associated with the paper submitted to Restoration Ecology in November 2021: Disturbance: a double-edged sword for restoration in a changing climate Alejandro Brambila1, Paul B. Reed1, Scott D. Bridgham1, Bitty A. Roy1, Bart R. Johnson2, Laurel Pfeifer-Meister1 and Lauren M. Hallett1 1. Institute of Ecology and Evolution, University of Oregon 2. Department of Landscape Architecture, University of Oregon In this project, we used this data to test how fire disturbance, designed to enhance restoration seeding success, combines with climate and initial vegetation conditions to shift perennial versus annual grass dominance and overall community diversity in Pacific Northwest grasslands. We seeded both native and introduced perennial grasses and native forbs in paired, replicated burned-unburned plots in three sites along a latitudinal climate gradient from southern Oregon to Washington. Past restoration and climate manipulations at each site had increased the variation of starting conditions between plots. This data is to be used with the script, full_disturbance_script.R, which can be accessed at https://github.com/HallettLab/hops. Includes the tables: plotkey.csv spkey.csv mixkey.csv vegplot.csv vegplot2020.csv
Extreme Drought in Grasslands Experiment (EDGE): High frequency measurements from the northern Chihuahuan Desert site, Sevilleta National Wildlife Refuge, NM, USA (2013-2023)
The Extreme Drought in Grasslands Experiment (EDGE) is distributed across six representative grassland ecosystems of the central United States. EDGE serves as an important research platform for understanding the resistance and resilience of these grassland ecosystems to extreme prolonged drought as well as to changes in precipitation seasonality. This data package contains high-frequency environmental sensor measurements from the northern Chihuahuan Desert site, dominated by black grama (Bouteloua eriopoda), located in the Sevilleta National Wildlife Refuge in central New Mexico.
Extreme Drought in Grasslands Experiment (EDGE): High frequency measurements from the southern Great Plains site, Sevilleta National Wildlife Refuge, NM, USA (2013-2023)
The Extreme Drought in Grasslands Experiment (EDGE) is distributed across six representative grassland ecosystems of the central United States. EDGE serves as an important research platform for understanding the resistance and resilience of these grassland ecosystems to extreme prolonged drought as well as to changes in precipitation seasonality. This data package contains high-frequency environmental sensor measurements from the southern Great Plains site, dominated by blue grama (Bouteloua gracilis), located in the Sevilleta National Wildlife Refuge in central New Mexico.
Seed dispersal data for Warneke et al "Habitat fragmentation alters the distance of abiotic seed dispersal through edge effects and direction of dispersal"
This csv file contains seed dispersal data for five species (Carphephorus bellidifolius, Aristida beyrichiana, Liatris squarrulosa, Sorghastrum secundum, and Anthenantia villosa). Data were collected at the Savannah River Site, near Aiken, South Carolina, United States. Data were collected between November 17, 2009, to January 22, 2010 and were collected using the methods outlined in this document.
A dataset based on two graph coverage criteria: prime-path and edge coverage
<p>This repository contains 462 instances from 6 projects. The dataset structure contains 43 columns, in which 18 columns are the source code metrics of the application methods under test, 18 columns are the source code metrics of test methods, and seven columns are the test case metrics. </p>
Execution Traces of an MNIST Workflow on a Serverless Edge Testbed
<p>For the evaluation of a Serverless Edge Computing platform, we built an Edge Cloud testbed consisting of several machines:</p> <ul> <li>A Cloud VM</li> <li>An Nvidia Jetson TX</li> <li>Four Raspberry Pi 3b+</li> <li>Two Intel NUCs with i5 processors</li> </ul> <p>We were interested in profiling these devices with a Machine Learning workflow deployed as a serverless application. To that end, we implemented three functions: Preprocessing, Training, and Serving as OpenFaaS functions. The workflow trains an MNIST model.</p> <p> </p>
Data for an unusually dense population of Sphodros rufipes (Latreille 1829) (Mygalomorphae, Atypidae) at the edge of its range on Tuckernuck Island, Massachusetts
<p>Data submitted in fulfillment of a 2008 Nantucket Biodiversity Initiative grant.</p> <p>Paper Abstract: We counted and measured <em>Sphodros rufipes</em> (Latreille 1829) pursewebs in two survey plots on Tuckernuck Island, Massachusetts. Tuckernuck is 50 Km south of Cape Cod, Massachusetts, and is entirely owned by private landowners or conservation organizations; biological research activities are supported and encouraged by residents on a limited basis. Our objectives were to quantify web density and determine the main components of the <em>S. rufipes</em> diet. We counted 479 webs in the two plots and report web densities between 0.058 and 0.18 webs/m<sup>2</sup>; denser than previously reported populations. Contrary to most previously published literature on <em>S. rufipes,</em> we noted the predominance of the grass-like sedge, <em>Carex pensylvanica,</em> rather than trees, as a web support. However, we also offer the first report of <em>S. rufipes </em>using a conifer (<em>Pinus rigida</em>) as a web support. Coleopterans and isopods made up 79 percent of the prey parts collected from 56 pursewebs. We suggest that the Tuckernuck population offers an opportunity to collect important long-term demographic data.</p> <p>Datasets:<br> sphodrosWebLocations.csv - data from two specific areas<br> sphodrosSpiderMeasurements.csv - measurements of live spiders borrowed from their webs<br> sphodrosRandomWebLocations.csv - data for webs found by happenstance<br> sphodrosDiet.csv - diet data from body parts collected from Sphodros webs<br> sphodrosDataDictionary.csv</p>
Figure 1 in The hermit crab Sympagurus dimorphus (Anomura: Parapaguridae) at the edge of its range in the south-western Atlantic Ocean: population and morphometry features
Figure 1. Sympagurus dimorphus. Densities (number of individuals/100 m2) in the sampling areas from 2002 through 2006. Left column shows presence/absence data including all sampling sites, and right column shows densities on a relative scale.
Edge mode engineering for optimal ultracoherent SiN membrane designs
<p>Raw dataset for all the figures of the article entitled</p> <p>'Edge mode engineering for optimal ultracoherent SiN membrane designs',</p> <p>and corresponding scripts for data analysis.</p>
Data for "Sub-7-femtosecond conical-intersection dynamics probed at the carbon K-edge"
<p>Data sets underlying Figs. 1-4, and S1-S6, S8-S20 of the paper entitled "Sub-7-femtosecond conical-intersection dynamics probed at the carbon K-edge".</p>
Input data for 'forest_carbon_edge_effects'
<p>1. af.tif: Land-cover from MODIS for the continent of Africa clipped to the tropical regions to match the biomass dataset; 16 classes defined by the UMD classification. From Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168–182.<br /> 2. af_biov2ct1.tif: Above-ground biomass for the tropical regions of Africa; biomass measured as tons/ha. From Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182–185.<br /> 3. am.tif: Land-cover from MODIS for the Americas; 16 classes defined by the UMD classification. From Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168–182.<br /> 4: am_biov2ct1.tif: Above-ground biomass for the tropical regions of the Americas; biomass measured as tons/ha. From Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182–185.5: anthrome_0.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(0): No data. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 5: anthrome_11.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(11):Urban. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 6: anthrome_12.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(12):Mixed settlements. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 7: anthrome_21.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(21):Rice villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 8: anthrome_22.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(22):Irrigated villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 9: anthrome_23.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(23):Rainfed villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 10: anthrome_24.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(24):Pastoral villages. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 11: anthrome_31.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(31):Residential irrigated croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 12: anthrome_32.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(32):Residential rainfed croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 13: anthrome_33.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(33):Populated croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 14: anthrome_34.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(34):Remote croplands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 15: anthrome_41.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(41):Residential rangelands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 16: anthrome_42.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(42):Populated rangelands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 17: anthrome_43.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(43):Remote rangelands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 18: anthrome_51.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(51):Residential woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 19: anthrome_52.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(52):Populated woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 20: anthrome_53.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(53):Remote woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 21: anthrome_54.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(54):Inhabited treeless and barren lands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 22: anthrome_61.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(61):Wild woodlands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 23: anthrome_62.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. Value(62):Wild treeless and barren lands. From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 24: as.tif: Land-cover from MODIS for the continent of Asia; 16 classes defined by the UMD classification. From Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 114:168–182.<br /> 25: as_biov2ct1.tif: Above-ground biomass for the tropical regions of Asia; biomass measured as tons/ha. From Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, P. S. A. Beck, R. Dubayah, M. A. Friedl, S. Samanta, and R. A. Houghton. 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2:182–185.<br /> 26-30: ecoregions_projected.(.dbf/.prj/.qpj/.shp/.shx): Terrestrial Ecoregions of the World is a biogeographic regionalization of the Earth’s terrestrial biodiversity. Units are ecoregions, defined as relatively large units of land or water containing a distinct assemblage of natural communities sharing a large majority of species, dynamics, and environmental conditions. From Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.<br /> 31: fi_average.tif: Average fire density 1997-2011. Based on the modified algorithm 1 product of World Fire atlas (WFA, ESA-ESRIN) dataset. UNEP/GRID-Europe compiled the monthly data and processed the global fire density. Unit is expected average number of event per 0.1 decimal degree pixel per year multiplied by 100 (e.g. 64 value means 0.64 events per year) and slightly smoothed. From UNEP, DEWA, GRID -Europe, Collection: Global Estimated Risk Index for Multiple Hazards. Web. 30 Sep 2014,http://preview.grid.unep.ch/index.php?preview=data&events=fires.<br /> 32: gl_anthrome.tif: Anthromes (Anthropogenic Biomes, or "human biomes") represent the global ecological patterns created by sustained direct human interactions with ecosystems. All values(see items 5-24). From Ellis, E. C., K. Klein Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography xx:xxx-xxx. DOI: 10.1111/j.1466-8238.2010.00540.x<br /> 33: glbctd1t0503m.tif: Gridded Livestock of the World: Cattle. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014).<br /> 34: glbgtd1t0503m.tif: Gridded Livestock of the World: Goats. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014).<br /> 35: glbpgd1t0503m.tif: Gridded Livestock of the World: Pigs. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014).<br /> 36: glbshd1t0503m.tif: Gridded Livestock of the World: Sheep. Number per square kilometer. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones. From Robinson, T. P. et al. Mapping the Global Distribution of Livestock. PLoS One 9, e96084 (2014).<br /> 37: glds00ag.tif: Gridded Population Density of the World, Version 3: (GPWv3): Population Density Grid. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. From CIESIN, IFPRI, Bank, T. W. & CIAT, Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid. (2011). Web. 26 Sep 2014. http://dx.doi.org/10.7927/H4R20Z93<br /> 38: glds00g.tif: Gridded Population Density of the World, Version 3: (GPWv3): Population Density Grid. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative units, is used to assign population values to grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. From CIESIN, IFPRI, Bank, T. W. & CIAT, Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Population Density Grid. (2011). Web. 26 Sep 2014. http://dx.doi.org/10.7927/H4R20Z93<br /> 39: global_elevation.tiff: GTOPO30 is a global digital elevation model (DEM) with a horizontal grid spacing of 30-arc seconds (0.008333333333333 degrees or approximately 1 kilometer), resulting in a DEM having dimensions of 21,600 rows and 43,200 columns. The horizontal coordinate system is decimal degrees of latitude and longitude referenced to World Geodetic System 84 (WGS84). The vertical units represent elevation in meters above mean sea level. The elevation values range from -407 to 8,752 meters. In the DEM, ocean areas have been masked as no data and have been assigned a value of -9999. Lowland coastal areas have an elevation of at least 1 meter (so in the event that a user reassigns the ocean value from -9999 to 0 the land boundary portrayal will be maintained). Small islands in the ocean less than approximately 1 square kilometer are not represented. GTOPO30 was derived from several raster and vector sources of topographic information. These sources include: Digital Terrain Elevation Data, Digital Chart of the World, USGS 1-degree Digital Elevation Models, Army Map Service 1:1,000,000-scale Maps, International 1:1,000,000-scale Map of the World, Peru 1:1,000,000-scale Map, New Zealand DEM, and Antarctic digital Database. GTOPO30 was developed to meet the needs of the geospatial data user community for regional and continental scale topographic data. The data are suitable for many regional and continental applications, such as climate modeling, continental-scale land cover mapping, extraction ofdrainage features for hydrologic modeling and geometric and atmospheric correction of medium and coarse resolution satellite image data. An example of a recent application derived from GTOPO30 is HYDRO1k, a geographic database (at a resolution of 1 km) developed to provide comprehensive and consistent global coverage of topographically derived data sets, including streams, drainage basins, and ancillary layers . HYDRO1k provides a suite of geo-referenced data sets, both raster and vector, which will be of value for all users who need to organize, evaluate, or process hydrologic information on a continental scale. The raster data sets are the hydrologically correct DEM, derived flow directions, flow accumulations, slope, aspect, and a compound topographic (wetness) index. The derived streamlines and basins are distributed as vector data sets. GTOPO30 was developed through a collaborative effort led by staff at the U.S. Geological Survey's EROS EDC. The following organizations participated by contributing funding or source data: the National Aeronautics and Space Administration (NASA), the United Nations Environment Programme/Global Resource Information Database (UNEP/GRID), the U.S. Agency for International Development (USAID), the Instituto Nacional de Estadistica Geografica e Informatica (INEGI) of Mexico, the Geographical Survey Institute (GSI) of Japan, Manaaki Whenua Landcare Research of New Zealand, and the Scientific Committee on Antarctic Research (SCAR). From Grenlee S., Gesch, D, available online [http://webmap.ornl.gov/wcsdown/dataset.jsp?ds_id=10003] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A..<br /> 40: global_precip.tiff: The Global Precipitation Climatology Centre (GPCC), which is operated by the Deutscher Wetterdienst (National Meteorological Service of Germany), is a component of the Global Precipitation Climatology Project (GPCP) with the main emphasis on the treatment of the global in-situ observations. The GPCC simultaneously contributes to the Global Climate Observing System (GCOS) and other international research and climate monitoring projects. This rain gauge-only data set was acquired from GPCC and resampled to 0.5 degree grid boxes for use in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II. The GPCC collects precipitation data which are locally observed at rain gauge stations and distributed as CLIMAT and SYNOP reports via the Global Telecommunication System of the World Weather Watch (GTS) of the World Meteorological Organization (WMO). The Centre acquires additional monthly precipitation data from meteorological and hydrological networks which are operated by national services. Meeson B., Los, S, Landis, D., Hall F., Collatz, G., Brown de Colstoun, E. available online [http://webmap.ornl.gov/wcsdown/wcsdown.jsp?dg_id=995_20] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A..<br /> 41: global_soil_types.tiff: A global data set of soil types is available at 1-degree latitude by 1-degree longitude resolution. There are 26 soil units based on Zobler’s assessment of FAO Soil Units (Zobler, 1986). The data set was compiled as part of an effort to improve modeling of the hydrologic cycle portion of global climate models. A more extensive version of these data, including 106 soil units as well as soil texture and slope, is available from NCAR, Scientific Computing Division, Data Support Section; the more extensive data set is entitled "Staub and Rosenweig's GISS Soil & Sfc Slope, 1-Deg" [http://www.dss.ucar.edu/datasets/ds770.0/]. A help file prepared by Matthews and Fung (1987) (soil1x1.help) is provided as a companion file. Image of 26 soil types available at 1-degree by 1-degree resolution. Additional documentation from Zobler’s assessment of FAO soil units is available from the NASA Center for Scientific Information. <br /> 42: global_water_capacity: Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. It is often assumed to be spatially invariant in large-scalecomputations of the soil-water balance. Empirical evidence, however, suggests that this assumption is incorrect. This data set provides an estimate of the global distribution of plant-extractable water capacity of soil. A representative soil profile, characterized by horizon (layer) particle size data and thickness, was created for each soil unit mapped by FAO (Food and Agriculture Organization of the United Nations)/Unesco. Soil organic matter was estimated empirically from climate data. Plant rooting depths and ground coverages were obtained from a vegetation characteristic data set. At each 0.5 x 0.5 degree grid cell where vegetation is present, unit available water capacity (cm water per cm soil) was estimated from the sand, clay, and organic content of each profile horizon, and integrated over horizon thickness. Summation of the integrated values over the lesser of profile depth and root depth produced an estimate of the plant-extractable water capacity of soil. The global average of the estimated plant-extractable water capacities of soil is 8.6 cm (Greenland, Antarctica and bare soil areas excluded). Estimates are less than 5, 10 and 15 cm - over approximately 30, 60, and 89 per cent of the area, respectively. Estimates reflect the combined effects of soil texture, soil organic content, and plant root depth or profile depth. The most influential and uncertain parameter is the depth over which the plant-extractable water capacity of soil is computed, which is usually limited by root depth. Soil texture exerts a lesser, but still substantial, influence. Organic content, except where concentrations are very high, has relatively little effect. The file is available in an ascii array format. The format is such that j=1 corresponds to the grid cell bounded by 90.0 and 89.5 degrees south latitude (centered on 89.75) and i=1 corresponds to the grid cell bounded by 0.0 and 0.5 degrees east longitude (centered on 0.25). No data are given for land ice grid cells, most of which occur in Antarctica and Greenland, or for other unvegetated areas. A value of -99.0 indicates either a water grid cell or a land ice grid cell. A value of -1.0 indicates that vegetation is absent (and the plant-extractable water capacity of soil is undefined). Units are cm. The data file may be read as follows: dimension whcdat(720,360) do j=1,360 read(iunit,'(36f5.1)') (whcdat(i,j),i=1,720) enddo Data Citation The data set should be cited as follows: Dunne, K. A., and Cort J. Willmott. 2000. Global Distribution of Plant-extractable Water Capacity of Soil (Dunne). Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.43-49: ilf2000_last_proj(.cpg/.dbf/.prj/.qpj/.shp/.shx/.tif): Intact Forest Landscape, 2000 (IFL2000). The world's IFL map is a spatial database (scale 1:1,000,000) that shows the extent of the intact forest landscapes (IFL) for year 2000. IFL is an unbroken expanse of natural ecosystems within the zone of current forest extent, showing no signs of significant human activity, and large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. From Potapov P., Yaroshenko A., Turubanova S., Dubinin M., Laestadius L., Thies C., Aksenov D., Egorov A., Yesipova Y., Glushkov I., Karpachevskiy M., Kostikova A., Manisha A., Tsybikova E., Zhuravleva I. 2008. Mapping the World's Intact Forest Landscapes by Remote Sensing. Ecology and Society, 13 (2) http://www.ecologyandsociety.org/vol13/iss2/art51/<br /> 50: lighted_area_luminosity.tif: NASA Earth Observation Satellite.</p>
Input dataset for carbon forest edge effects analysis
<p>Dataset that is used for the calculation of forest edge biomass effect from the following github project: [DOI forest_carbon_edge_effect] (http://dx.doi.org/10.5281/zenodo.15697)</p> <p>Contains global biomass, landcover data, anthrome, soil, elevation, water capacity, fire, luminosityr, cattle, goat, sheep, human population, and anthrome data.</p>
Distance from forest edge in the Pantropics
<p>distancefromforestedge_pantropics.zip - a zipped geotiff file in WGS84 coordinates whose pixel values indicate the distance in meters to the nearest forest edge as defined by: Baccini, A., Goetz, S.J., Walker, W.S., Laporte, N.T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P.S.A., Dubayah, R., Friedl, M.A., Samanta, S., Houghton, R.A., 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change 2, 182–185.</p> <p>regression_coefficients_as_shapefile - for calculating biomass storage within 100km grid cells across the pantropics. projected spatially as an ESRI Shapefile where the methods are defined as:</p> <p>method 1: Biomass= θ_1-θ_2⋅exp(-θ_3⋅Distance)</p> <p>method 2: Biomass= β_0+β_1⋅ln(Distance)</p> <p>method 3: Biomass = \eta_0+\eta_1 * Distance</p>
Grid Based Global Carbon Edge Regression Coefficents
<p>Grid cell based regression coefficients for predicting global biomass in the pantropics.</p> <p>To better account for the variability within a continent, we constructed 100-km grid cells throughout the pantropics. In grid cells where the majority of pixels were from forest biomes, we consider three candidate regression models to represent the relationship between biomass density and distance to forest edge. In particular, we consider:</p> <ol> <li>Asymptotic: <span class="math-tex">\(\mathrm{Biomass} = \theta_1-\theta_2\cdot\exp(-\theta_3\cdot\mathrm{Distance})\)</span>,</li> <li>Logarithmic: <span class="math-tex">\(\mathrm{Biomass}=\beta_0+\beta_1\ln\cdot(\mathrm{Distance})\)</span> , or</li> <li>Linear: <span class="math-tex">\(\mathrm{Biomass}=\eta_0+\eta_1\cdot Distance\)</span></li> </ol> <p>Then, for each grid cell, the candidate with the highest R<sup>2</sup> is used to best represent the relationship between density and distance to forest edge. Models (2) and (3) were deemed as suitable (and more simplistic) alternatives in cells where higher distances were generally not observed and as a result the forest core was not firmly established. We also note that in the vast majority of grid cells, model (1) was optimal. For each cell the magnitude and distance of the edge effect were again estimated. In cells using models (2) or (3) the forest core (<span class="math-tex">\(\theta_1\)</span>) was estimated as the average biomass density at the largest observed distance in the cell.</p>
Grid Based Global Carbon Edge Regression Coefficients and Aggregations
<p>Grid cell based regression coefficients for predicting global biomass in the pantropics.</p> <p>To better account for the variability within a continent, we constructed 100-km grid cells throughout the pantropics. In grid cells where the majority of pixels were from forest biomes, we consider three candidate regression models to represent the relationship between biomass density and distance to forest edge. In particular, we consider:</p> <ol> <li>Asymptotic: <span class="math-tex">Biomass=θ1−θ2⋅exp(−θ3⋅Distance)</span>,</li> <li>Logarithmic: <span class="math-tex">Biomass=β0+β1ln⋅(Distance)</span> , or</li> <li>Linear: <span class="math-tex">Biomass=η0+η1⋅Distance</span></li> </ol> <p>Then, for each grid cell, the candidate with the highest R2 is used to best represent the relationship between density and distance to forest edge. Models (2) and (3) were deemed as suitable (and more simplistic) alternatives in cells where higher distances were generally not observed and as a result the forest core was not firmly established. We also note that in the vast majority of grid cells, model (1) was optimal. For each cell the magnitude and distance of the edge effect were again estimated. In cells using models (2) or (3) the forest core (<span class="math-tex">θ1</span>) was estimated as the average biomass density at the largest observed distance in the cell.</p> <p>This dataset also contains a shapefile parameter analysis of both proportion and total area of all forest landcover types (1-5).</p>
Skyrmions at the edge: Confinement effects in Fe/Ir(111)
<p>We have employed spin-polarized scanning tunneling microscopy and Monte-Carlo simulations to investigate the effect of lateral confinement onto the nanoskyrmion lattice in Fe/Ir(111). We find a strong coupling of one diagonal of the square magnetic unit cell to the close-packed edges of Fe nanostructures. In triangular islands this coupling in combination with the mismatching symmetries of the islands and of the square nanoskyrmion lattice leads to frustration and triple-domain states. In direct vicinity to ferromagnetic NiFe islands, the surrounding skyrmion lattice forms additional domains. In this case a side of the square magnetic unit cell prefers a parallel orientation to the ferromagnetic edge. These experimental findings can be reproduced and explained by Monte-Carlo simulations. Here, the single-domain state of a triangular island is lower in energy, but nevertheless multi-domain states occur due to the combined effect of entropy and an intrinsic domain wall pinning arising from the skyrmionic character of the spin texture.</p>
A JWST inventory of protoplanetary disk ices. The edge-on protoplanetary disk HH 48 NE, seen with the Ice Age ERS program
<p>JWST NIRSpec G395H spectrum for HH 48 NE edge-on disk, as analyzed in Sturm et al. (2023). DOI: 10.1051/0004-6361/202347512</p>
Рис. 4. МестонахожΑение Chrysopa viridinervis Jakowleff, 1869 на опушке ХваΛынского Λеса, Саратовская обΛасть. Fig. 4. The locality of Chrysopa viridinervis Jakowleff, 1869 at the edge of the Khvalynsk Forest, Saratov Region. in New data on Neuropterida from the southern part of the European Russia
Рис. 4. МестонахожΑение Chrysopa viridinervis Jakowleff, 1869 на опушке ХваΛынского Λеса, Саратовская обΛасть. Fig. 4. The locality of Chrysopa viridinervis Jakowleff, 1869 at the edge of the Khvalynsk Forest, Saratov Region.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.