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281 results for “community model”
COS-1m: the first community-scale open space maps of 31 major cities in China created with the layered occlusion perception model
<p>Correspondence to:<br>Shihong Du<br>Peking University<br>Beijing 100871, China<br>E-mail: shdu@pku.edu.cn</p> <p>COS-1m (Community Open Spaces) offers detailed, high-resolution maps of community-level open spaces across 31 major cities in China. Developed using the latest very high-resolution satellite imagery and a novel Layered Occlusion Perception Model (LOPM), this dataset ensures precise representation with a spatial resolution of 1 meter. In total, the COS-1m dataset covers an area of 46727.46 square kilometers.</p> <p>The COS-1m dataset addresses the critical challenge of layered occlusions in remote sensing imagery by accurately modeling and reconstructing the intricate layered structures of COS. This approach allows for the detailed delineation of occluded ground elements, overcoming the limitations of traditional single-layer mapping methods. The dataset reveals that, on average, 60.51 km² of COS area per city is occluded, constituting 10.18% of the total COS area, which had previously been overlooked.</p> <p>Key features of the COS-1m dataset include:</p> <ul> <li><strong>Four Types of Public Open Space Elements</strong>: Green spaces (1), blue spaces (2), squares (4), and walkways (6).</li> <li><strong>Three Types of Coupled Open Spaces</strong>: waterfront woodlands (3), shaded squares (5), and shaded walkways (7).</li> </ul> <p>The dual-layer mapping technique used in COS-1m provides a comprehensive overview of urban open spaces, capturing the vertical overlaps of various elements such as trees, water bodies, squares, and walkways. This dataset is a significant advancement in COS monitoring technology, filling a critical gap in community-scale Sustainable Development Goal (SDG) assessments. It provides urban planners and policymakers with valuable insights to promote more effective and sustainable urban development.</p> <p>COS-1m achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across the 31 cities, demonstrating its robustness and reliability as a tool for urban space analysis and planning.</p> <p>If you use this dataset, please cite: "Lei, Y., Zhang, X., Xiong, S., Tan, G., & Du, S. (2025). Developing Layered Occlusion Perception Model: Mapping community open spaces in 31 China cities. Remote Sensing of Environment, 316, 114498."</p> <p> </p>
Input data for the Community Water Model (CWatM) - a regional dataset covering Israel and the Ayalon Basin
<p>This data was used for the paper: 'Wastewater matters: Incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08)'. When using this data pleas cite the paper, alongside this Zenodo repository.</p> <p>Fridman, D., Smilovic, M., Burek, P., Tramberend, S., and Kahil, T. 2024. Wastewater matters: Incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08). <em>Geoscientific Model Development Discussions</em>, 1-26. [Preprint]</p> <p><strong><span>Background</span></strong></p> <ul> <li>This dataset was developed as part of the IIASA WINTER project, aiming to run high resolution hydrological<br>simulations in the river basins in Israel (Water Futures and Solutions for Israel (WFaS-Israel) | IIASA).</li> <li>Conducting a high-resolution (30 arcseconds) simulation can rely on a mix of upscaled coarse global, high-resolution global, and local datasets. Specifically, Hanasaki et al. (2022) stress the importance of local water management and use data for hyper-resolution hydrologic simulations.</li> <li>The dataset covers the terrestrial area of Israel and the Palestinian Authority. It also includes the cross-border and upstream river basin in the neighboring countries Egypt, Jordan, Syria, and Lebanon. The selected river basins of Ayalon and Sorek are in the central coastal area of Israel and vary by topography, land cover, and water management. The Ayalon stream drains the western downslopes of the Judea and Samaria mountains and outlets into the Yarkon stream (to the North), which later reaches the Mediterranean Sea. The Sorek stream drains the hills around South-West Jerusalem and flows Westwards until reaching the Mediterranean Sea.</li> </ul> <p><strong><span>Further Information</span></strong></p> <ul> <li>For details about the contents of this dataset please refer to the readme.txt</li> <li>For details about the data soruces and processing please refer to the dataset_description.pdf</li> </ul> <p>In case of additional questions please do not hesitate to write to us fridman@iiasa.ac.at</p>
Dataset for Exploring Western North Pacific Tropical Cyclone Activity in the High-Resolution Community Atmosphere Model
<p>This dataset contains results from high-resolution, tropical cyclone-permitting experiments using Community Atmosphere Model version 5.</p>
Influence of number of individuals and observations per individual on a model of community structure
<p>Social network analysis is increasingly applied to understand animal groups. However, it is rarely feasible to observe every interaction among all individuals in natural populations. Studies have assessed how missing information affects estimates of individual network positions, but less attention has been paid to metrics that characterize overall network structure such as modularity, clustering coefficient, and density. In cases such as groups displaying fission-fusion dynamics, where subgroups break apart and rejoin in changing conformations, missing information may affect estimates of global network structure differently than in groups with distinctly separated communities due to the influence single individuals can have on the connectivity of the network. Using a bat maternity group showing fission-fusion dynamics, we quantify the effect of missing data on global network measures including community detection. In our system, estimating the number of communities was less reliable than detecting community structure. Further, reliably assorting individual bats into communities required fewer individuals and fewer observations per individual than to estimate the number of communities. Specifically, our metrics of global network structure (i.e., graph density, clustering coefficient, R<sub>com</sub>) approached the 'real' values with increasing numbers of observations per individual and, as the number of individuals included increased, the variance in these estimates decreased. Similar to previous studies, we recommend that more observations per individual should be prioritized over including more individuals when resources are limited. We recommend caution when making conclusions about animal social networks when a substantial number of individuals or observations are missing, and when possible, suggest subsampling large datasets to observe how estimates are influenced by sampling intensity. Our study serves as an example of the reliability, or lack thereof, of global network measures with missing information, but further work is needed to determine how estimates will vary with different data collection methods, network structures, and sampling periods.</p>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>7_experiments.zip contains modified model code and output data of each experiment in this study.</li> <li>off-line test.zip contains off-line test code and output data.</li> <li>plot_scripts.zip are the NCL scripts used for figures in the paper.</li> </ul>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>Figs&Table are the NCL scripts used for figures and table in the paper.</li> <li>Model_Results contains output data of each experiment in this study.</li> <li>Mods_Scripts contains modified model code.</li> <li>Offline_Code contains off-line test code.</li> </ul>
Tool and python programs for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model"
<p>Here is the content:</p> <p> * file dir_list which contains information about each file's content</p> <p> * the tool is used to alter a data file by keeping a specific number of significant digits for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model'</p> <p> * pythons program and its associated data to create each figure and table in the paper (data for Table 07 is not included due to size is larger than 50GB)</p>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The code, scripts, and data used in the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>All Figures&Table and their corresponding NCL scripts are under the directory of Figs&Table. </li> <li>The modified model code, corresponding original model code, and model run scripts are under the directory of Mods_Scripts.</li> <li>The postprocessing NCL scripts, which select useful variables from simulation results, are under the directory of PostProcessing.</li> <li>The zonal mean data from model results used for making figures and corresponding data processing scripts are under the directory of Model_Results.</li> <li>The FORTRAN code used for offline tests is under the directory of Offline_Code.</li> <li>The code, data, and NCL scripts used for the figures and table in the Appendix are under the directory of Appendix.</li> </ul>
Fig. 4 in The ant fauna of Castelporziano Presidential Reserve (Rome, Italy) as a model for the analysis of ant community structure in relation to environmental variation in Mediterranean ecosystems
Fig. 4 Multidimentional scaling performed on abundance ranks of ant species during May and July 2006. Symbols represent the 28 transects analysed (see text for details) and they are sorted according to trapping period
Fig. 3 in The ant fauna of Castelporziano Presidential Reserve (Rome, Italy) as a model for the analysis of ant community structure in relation to environmental variation in Mediterranean ecosystems
Fig. 3 Overall ant abundance (mean) from pitfall trapping along the 7 sampled sites during May and July
Fig. 2 in The ant fauna of Castelporziano Presidential Reserve (Rome, Italy) as a model for the analysis of ant community structure in relation to environmental variation in Mediterranean ecosystems
Fig. 2 Ant species richness (mean) from pitfall trapping along the 7 sampled sites during May and July
Fig. 1 in The ant fauna of Castelporziano Presidential Reserve (Rome, Italy) as a model for the analysis of ant community structure in relation to environmental variation in Mediterranean ecosystems
Fig. 1 Species accumulation curves of low vegetation sites (A) and of high vegetation sites (B). The values on y-axis correspond to Sobs (Mao Tau) values as described in Colwell (2005). Each line represents a different site type
Model configuration and input files for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the configuration and input files to reproduce the regional CESM/MOM6 simulation in: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". <br><br>The initial, open boundary conditions and monthly means for the sponge layers were generated from the GLORYS12V1 reanalysis: <a href="https://doi.org/10.3389/feart.2021.698876">https://doi.org/10.3389/feart.2021.698876</a></p> <p>The tidal amplitudes and phases were generated from the TPXO model: <a href="https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2">https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2 </a></p> <p>The monthly Chlorophyll-a climatology was generated from the SeaWifs mission dataset: <a href="10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018">10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018</a></p> <p>The river runoff to ocean was generated from the GloFAS dataset: <a href="https://doi.org/10.24381/cds.a4fdd6b9">https://doi.org/10.24381/cds.a4fdd6b9 </a></p> <p>The topography was generated from the Shuttle Radar Topography Mission: <a href="https://doi.org/10.1029/2019EA000658">https://doi.org/10.1029/2019EA000658</a> and smoothed with a Cressman weighted interpolation scheme.</p> <p>This collection does not include the JRA55-do atmospheric forcing dataset: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ocemod.2018.07.002" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ocemod.2018.07.002</a> </p> <p>Steps to setup a regional configuration of CESM/MOM6 can be found here: <a href="https://github.com/NCAR/regional_cesm_mom6.git">https://github.com/NCAR/regional_cesm_mom6.git</a> </p> <p> </p>
Model output for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the model output fields used in : "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". <br><br>daily_surface_fields.tar: contains daily output of sea surface salinity, surface u and v velocity components, sea-surface height and mixed layer depths. Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename.</p> <p>monthly_3D_tracers.tar: contains monthly mean output of temperature and salinity for the full 3D field (lat,lon,depth). Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename.</p> <p> </p> <p> </p>
SDGs Action Model in Proverty Alleviation in Village Communities
<p>This material has presented on 2nd International Conference on Advance Research in Social and Economic Science in October 25, 2023.</p>
On the increase of climate sensitivity and cloud feedback with warming in the Community Atmosphere Models
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2020). On the Increase of Climate Sensitivity and Cloud Feedback With Warming in the Community Atmosphere Models. <em>Geophysical Research Letters</em>, <em>47</em>(18), e2020GL089143. <a href="https://doi.org/10.1029/2020GL089143">https://doi.org/10.1029/2020GL089143</a></p> <p> </p> <p><strong>Upadated on July 3, 2024</strong>: add more timeseries of precipitation and energy fluxes for analysis in Bonan, Schneider, & Zhu (2024).</p> <p>David Bonan, Tapio Schneider, Jiang Zhu. Precipitation over a wide range of climates simulated with comprehensive GCMs. <em>ESS Open Archive .</em> April 25, 2024.<br><span>DOI: <a href="https://doi.org/10.22541/essoar.171405864.45942692/v1" target="_blank" rel="noopener noreferrer">10.22541/essoar.171405864.45942692/v1</a></span> </p>
iCESM1.2 restart file from "The Connected Isotopic Water Cycle in the Community Earth System Model Version 1"
<p>iCESM1.2 restart file at year 1850</p> <p><strong>Citation: </strong>Brady, E. C., Stevenson, S., Bailey, D., Liu, Z., Noone, D., Nusbaumer, J., … Zhu, J. (2019). The Connected Isotopic Water Cycle in the Community Earth System Model Version 1. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>, 2547–2566. https://doi.org/10.1029/2019MS001663</p> <p><strong>iCESM1.2 GitHub:</strong> https://github.com/NCAR/iCESM1.2</p> <p>A CLM land surface data set is included: surfdata_1.9x2.5_simyr1850_c140303.nc.</p>
R-scripts for the calculation of HW and Bioclimatic models in: "Small vertebrate and mollusc community response to the Holocene environment and climate changes in the Kraków-Częstochowa Upland (Poland)"
<p>HW_Holocene: Tables and R script used for calculation of HW percentage values.</p> <p>PalBER_Bioclimaticmodel_modified: Tables and R script used for the calculation of the climate values through Bioclimatic model. Modified after Royer et al., 2020.</p>
Integration of prosumer peer-to-peer trading decisions into energy community modelling
<p>Peer-to-peer (P2P) exchange of renewable energy is an attractive option to empower citizens to actively participate in the energy transition. Whereas previous research has assessed P2P communities primarily from a techno-economic perspective, little is yet known about prosumer preferences for solar power trading. Importantly, the impacts of community members’ trading decisions on key performance indicators such as individual electricity bills, community autarky, and grid stress remain unknown. Here, we assess P2P trading strategies of German homeowners based on an online experimental study. We simulate how various decision-making strategies impact the performance of P2P communities. The findings suggest that community autarky is slightly higher when prosumers are enabled to trade energy compared to when they merely aim to maximize their self-consumption. Our analysis moreover shows that P2P energy trading based on human decision-making may lead to financial benefits for prosumers and traditional consumers, and reduced stress for the grid.</p>
Model dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation"
<p>This is the global 1-deg CLM5-SNICAR model simulation dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation".</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.