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5,805 results for “Data model”
Data for paper "Parametric Timed Model Checking for Guaranteeing Timed Opacity"
<p>Our zip contains all necessary scripts, models, binaries and instructions to reproduce the experiments of our paper<br> <em>Parametric Timed Model Checking for Guaranteeing Timed Opacity</em> published in the proceedings of ATVA 2019.</p> <p>It allows interested readers to reproduce exactly the content of <strong>Table 1</strong> and <strong>Table 2</strong> of our paper.<br> In addition, all result files are generated, containing the synthesized constraints, and the execution times of IMITATOR.</p>
Data for "Evaluation of Low Impact Development and Nature-Based Solutions for stormwater management: a fully distributed modelling approach"
<p>The data set corresponds the data used in the paper : “Evaluation of Low Impact Development and Nature-Based Solutions for stormwater management: a fully distributed modelling approach”, published in 2019 in the Journal “Hydrology and Earth System Sciences” (http://www.hydrol-earth-syst-sci.net/).</p> <p>More precisely it corresponds to the simulation input data that used in the Multi-Hydro model and the simulated discharge of the five scenarios investigated in the paper.</p>
CESM1.2 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"
<p>CESM1.2-CAM5 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"</p> <p><strong>Citation: </strong>Zhu, J., & Poulsen, C. J. (2019). Quantifying the cloud particle-size feedback in an Earth system model. <em>Geophysical Research Letters</em>, <em>46</em>, 10910–10917. <a href="https://doi.org/10.1029/2019GL083829">https://doi.org/10.1029/2019GL083829</a></p> <p>Data include:</p> <p>(1) cloud liquid particle size for liquid (AREL) and ice (AREI), grid box averaged cloud liquid (CLDLIQ) and ice (CLDICE), fractional occurrence of liquid (FREQL) and ice (FREQI), and surface temperature (TS) in the preindustrial and 2xCO2 experiments; and<br> (2) the cloud feedback (lam_CLDTOT) and cloud particle-size feedback (lam_CLDEFR3L) from our PRP-based method.</p>
Data for the manuscript: TOWARDS AN INTEGRATED SOIL-PLANT-ATMOSPHERE MODELING ENVIRONMENT: IMPLEMENTATION OF A DYNAMIC PLANT UPTAKE MODULE FOR THE HYDRUS MODEL
<p>Data used in the manuscript for the theoretical and experimental validation, and for the Global Sensitivity Analysis. For a thorough description, please refer to the manuscript.</p>
Model and sample data for MNIST classification
<p>Model and sample data for MNIST classification. The data is used in conjunction with <a href="https://github.com/freitaglab/LightToInformation">https://github.com/freitaglab/LightToInformation</a>.</p>
ETIN-MIP (Extra-Tropical Interaction Model Intercomparison Project) data
<p>This is long term mean atmospheric data of ETIN-MIP (Extra-Tropical Interaction Model Intercomparison Project)</p> <p>Detail descriptions about the experiments and preliminary results would be published in Kang et al., 2019 (BAMS, in preparation)</p> <p> </p> <p>Data description</p> <p>CTL : 31-150 year mean<br> NEXT : 101-150 year mean<br> SEXT : 101-150 year mean<br> STRO : 101-150 year mean<br> For MIROC model, STRO experiment does not exist<br> For NORESM model, NEXT experiment is not available</p> <p> </p> <p>Full data is accessible through FTP sever with personal contact<br> Please send e-mail if you want further data or have any question about the data<br> E-mail adress : hanjunkim0617@gmail.com</p>
FESOM model data for Atlantic Water inflow to the Arctic Ocean
<p>The FESOM model data used in a paper (Intensification of the Arctic Ocean warming by sea ice decline: The Nordic Seas as a switchyard) submitted to GRL for review.</p>
Mathematical modeling and data & Survey
<p>This research examines brand loyalty and team identification among fans of National Football League (NFL) teams and how loyalty to football, NFL teams and athletes is impacted by the changing dynamics of the fanbase. Brand loyalty in sports is complex and fan attachment to their teams often provoke intense emotional responses. The research employed a Likert-type survey questionnaire pre- and post-test and an emotional response pictorial assessment using Self-Assessment Manakin (SAM) to measure emotional dimensions. Descriptive and parametric statistics were used to measure the relationships between NFL team loyalty (team commitment and team identification), emotional response (pleasure-arousal-dominance) and team and player social activist behaviors. The research also assesses the relationships between demographic and background and team and player activist behaviors to determine changes in fan dynamics. The findings show moderate positive relationships between team loyalty, team and player activist behaviors, younger age and middle income. This document is for survey and mathematical modeling process analysis and data collection</p>
GIS data for the maps in publication Spatial perspectives enhance modeling of nanomaterial risks
<p>These files include the datasets utilized to perform geospatial modeling in the publication: Spatial perspectives enhance modeling of nanomaterial risks in the Journal of Industrial Ecology. </p> <p>The following data sources were used in this modeling effort:</p> <p><strong>National Hydrography Dataset (NHD): United States Geological Survey (USGS)</strong></p> <p>Upstate NY Lakes, ponds, streams, rivers, springs, and wells</p> <p><strong>Critical Environmental Areas in New York State: New York State Department of Environmental Conservation </strong></p> <p>Areas designated as critical under 6 NYCRR Part 617: “ecological, geological, or hydrological sensitivity that may be adversely affected by any change” (NY DEC)</p> <p><strong>National Land Cover Dataset (NLCD): United States Geological Survey (USGS)</strong></p> <p>National Land Cover Database classification schemes based primarily on Landsat data (2011)</p> <p><strong>Elevation Data: United States Geological Survey (USGS)</strong></p> <p>Digital Elevation Models (10-meter) for New York, elevation values were derived from USGS contour lines mapped at a scale of 1:24,000. </p> <p><strong>Interstate Highway: Federal Highway Administration’s National Transportation Atlas Database</strong></p> <p>Rural and urban highways for New York</p> <p> </p> <p><strong>Other references</strong></p> <p>Bureau, U.S. Census., American community survey 5-year estimates. 2017.</p> <p>EPA, Toxics Resource Inventory. 2019</p> <p> </p> <p>.</p>
Data: Trait-based food web model reveals the underlying mechanisms of biodiversity-ecosystem functioning relationships
<p>Data and code related to 'Trait-based food web model reveals the underlying mechanisms of biodiversity-ecosystem functioning relationships' to reproduce figures and analyses.</p>
Rainfall-Runoff Modeling Using Crowdsourced Water Level Data
<p>Input data, geodata, model outputs, and Python scripts used for running and analyzing the hydrological model <a href="https://philippkraft.github.io/cmf/">CMF </a>(parameterized using <a href="https://github.com/thouska/spotpy">SPOTPY</a>) in the frame of the publication "Rainfall-Runoff Modeling Using Crowdsourced Water Level Data" by Weeser et al. (Water Resource Research).</p> <p>The folder WRR_CrowdMod_2019_08_23.zip contains:</p> <ul> <li>Folder <em>input_data</em>: Input data used for the model</li> <li>Folder <em>script_model</em>: The model including the SPOTPY set-up for calibration and validation <ul> <li>Subfolder <em>parameter_validation</em>: Parameter sets used during validation for all analyzed scenarios</li> <li>Subfolder <em>parameter_fluxes</em>: Parameter sets used for the analysis of the fluxes</li> </ul> </li> <li>Folder <em>calibration</em>: Model output during calibration with all 10<sup>6</sup> runs</li> <li>Folder <em>validation</em>: Model outputs generated during validation <ul> <li>Subfolder <em>accepted_simulation_results</em>: modeled discharge by using all accepted parameter sets for each scenario</li> </ul> </li> <li>Folder <em>Fluxes</em>: The fluxes released by the different model components</li> <li>4 Jupyter notebooks: <ul> <li>1_calibration: Script for analyzing the model output generated during calibration. Generates the parameter sets used for validation</li> <li>2_validation: Analyze the results during validation</li> <li>3_fig5_calibration_validation: Script used to generate figure 5</li> <li>4_fig6_fluxes: Script used to generate figure 6 showing the fluxes within the model during validation</li> </ul> </li> </ul> <p>The folder geodata contains two shapefiles representing the spatial data of the catchment.</p>
Hcropland30: A hybrid 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model
<p><strong>Hcropland30</strong><strong>:</strong><strong>A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model</strong></p> <p><strong>***Please note this dataset is undergoing peer review***</strong></p> <p><strong>Version</strong>: <strong>1.0</strong></p> <p><strong>Authors</strong>: Qiong Hu <sup>a, 1</sup>, Zhiwen Cai<sup> b, 1</sup>, Liangzhi You<sup> c, d</sup>, Steffen Fritz<sup> e</sup>, Xinyu Zhang<sup> c</sup>, He Yin<sup> f</sup>, Haodong Wei<sup>c</sup>, Jingya Yang<sup> g</sup>, Zexuan Li<sup> a</sup>, Qiangyi Yu<sup> g</sup>, Hao Wu<sup> a</sup>, Baodong Xu<sup> b *</sup>, Wenbin Wu<sup> g, *</sup></p> <p><em><sup>a</sup></em><em> Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China</em></p> <p><em><sup>b</sup></em><em> College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>c</sup></em><em> Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>d </sup></em><em>International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA</em></p> <p><em><sup>e </sup></em><em>Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria</em></p> <p><em><sup>f </sup></em><em>Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA</em></p> <p><a name="_Hlk166672711"></a><em><sup>g </sup></em><em>State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China</em></p> <p><strong> </strong></p> <p><strong>Introduction</strong></p> <p>We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.</p> <p> </p> <p><strong>Dataset</strong></p> <p><strong><em><u>1. Hcropland30</u></em></strong><strong>:</strong> A hybrid 30-m global cropland map in 2020</p> <p>****<strong>Data format</strong>: GeoTiff</p> <p>****<strong>Spatial resolution</strong>: 30 m</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Values</strong>: 1 denotes cropland and 0 denotes non-cropland</p> <p>The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data.</p> <p><strong><em><u>2. </u></em></strong><strong><em><u>1°×1° </u></em></strong><strong><em><u>Grids</u></em></strong><strong>:</strong> This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>Id:</strong> The grid’s ID number.</p> <p><strong>area:</strong> The area of the grid.</p> <p><strong>mode:</strong> Indicates the representative sample grid.</p> <p><strong>climate:</strong> The climate type the grid belongs to.</p> <p><strong>dem: </strong>Average DEM value of the grid.</p> <p><strong>ndvi_s1 to ndvi_s4:</strong> Average NDVI values for four seasons within the grid.</p> <p><strong>esa, esri, fcs30, fromglc, glad, globeland30:</strong> Proportion of cropland pixels of different publicly available cropland products.</p> <p><strong>inconsistent:</strong> Proportion of inconsistent pixels within the grid according to different public cropland products.</p> <p><strong>hcropland30:</strong> Proportion of cropland pixels of our Hcropland30 dataset.</p> <p><strong><em><u>3. Samples</u></em></strong>: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>type:</strong> 1 denotes cropland sample and 0 denotes non-cropland sample.</p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite the following paper:</p> <p>Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted.</p> <p><strong>License</strong></p> <p>The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).</p> <p><strong>Disclaimer</strong></p> <p>This dataset is provided as-is, without any warranty, express or implied. The dataset author is not</p> <p>responsible for any errors or omissions in the data, or for any consequences arising from the use</p> <p>of the data.</p> <p><strong>Contact</strong></p> <p>If you have any questions or feedback regarding the dataset, please contact the dataset author</p> <p>Qiong Hu (huqiong@ccnu.edu.cn)</p>
Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c in Pliocene origins, Pleistocene refugia, and postglacial range expansions in southern devil scorpions (Vaejovidae: Vaejovis carolinianus)
Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c. Localities used to test and train the model are indicated by
Data and machine-learning model for fcc-FeHx at the CMB conditions
<p>Data including melting temperatures and sound velocities. The machine-learning model was trained by DeePMD-kit. The initial configuration for two-phase coexistence simulations is provided.</p>
FESOM2.1 model data used in the paper 'Atmospheric blocking slows ocean-driven melting of Greenland's largest glacier tongue'
<p>This data set includes the data necessary to reproduce the findings of McPherson et al., in revision, and recreate the figures in the manuscript.</p> <p>The output of model simulations with the global ocean sea ice model FESOM2.1 is provided. </p> <p>In particular, the data set includes:</p> <ol> <li>long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (<a target="_blank" rel="noopener noreferrer">fesom.mean.1970_2021.t.s.u.v.mat</a>)</li> <li>air-sea heat flux anomaly and anomalous wind field at 10m, taking mean winter (DJF) conditions between 2014 - 2016 from the mean of the years 2017 - 2020 (fesom.anom.fh.wind.mat)</li> <li>Atlantic Water temperature and velocity anomalies, taking mean winter (DJF) conditions between 2014 - 2016 from the mean of the years 2017 - 2020 (fesom.anom.t.u.v.mat)</li> </ol> <p>Each file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas) needed for plotting the data.</p>
Data for Model Counting in the Wild (KR-24 paper)
<h2>Contents</h2> <ol> <li><strong>Model Counting Benchmarks:</strong> Found in the zip file <code>model_counting_benchmarks.zip</code>.</li> <li><strong>Projected Counting Benchmarks</strong>: Found in the zip file <code>projected_counting_benchmarks.zip</code>.</li> <li><strong>Logfiles of Running counters</strong>: Stored in the <code>logfiles.zip</code>.</li> <li><strong>Binaries for Counters: </strong>Provided in the archive: <code>bins.zip</code>.</li> </ol> <h2>Benchmarks</h2> <p>Each benchmark file is systematically prefixed to indicate its source or category as referenced in the paper. For example, benchmarks from the Network Reliability Benchmark Set are prefixed with <code>netrel.</code></p> <h2>Logfiles</h2> <p>Logfiles are organized into folders corresponding to each counter used in the experiments.</p> <h2>Binaries</h2> <p>Contains binaries for each counter used in the experiments, along with a <code>commands.txt</code> file that provides instructions for running them.</p>
Masked Conditional Diffusion Model with GNN for Spatial Transcriptomics Data Imputation
Open the record for dataset details and reuse information.
zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation
<p>Data and code used to generate the analyses and figures in paper "zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation" are provided here.</p>
Data and code from: Improving accuracy and reproducibility of cartilage T2 mapping in the OAI dataset through extended phase graph modeling
<p>The repository contains the OAI data used to run the experiments reported in the study "Improving accuracy and reproducibility of cartilage T2 mapping in the OAI dataset through extended phase graph modeling", along with their segmentation and processed T2 maps with the different fitting algorithms.</p> <p>The <em><strong>code_repository</strong></em> folder contains a snapshot of the <a href="https://github.com/barma7/EPGfit_for_cartilage_T2_mapping">GitHub repository</a> at the time of the submission of the manuscript. Please visit the GitHub repository for the latest version. </p>
Replication Package: Anomaly detection via runtime monitoring data for structural equation modeling
<p>This replication package contains the following information:</p> <ul> <li><strong>Data extraction from literature & interviews: </strong><em>Generation Structural & Measurement Model via literature and interviews.xlsx</em> - here you can find the mapping of the extracted phrases to inductively summarise information regarding the structural and measurement models.</li> <li><strong>Dataset</strong> of runtime monitoring data extracted from TrainTicket via EvoMaster: <br> <ul> <li><em>TrainTicket faults classification.xlxs:</em> Describes the datasets and their faults, in which microservice the fault is injected for better explainability of the obtained results</li> <li><em>IndicatorDescriptionbasedonAnomalyDetectionToolsInterviews.xlsx:</em> description and mapping of selected indicators to the defined parameters from <a href="https://arxiv.org/abs/2408.07816" target="_blank" rel="noopener">previous work </a></li> <li>Unfortunately, the size of the datasets generated via EvoMaster and their injected faults are too big to upload here, thus, they will be available here: <a href="https://uibkacat-my.sharepoint.com/:f:/g/personal/monika_steidl_uibk_ac_at/EjLMt8SYWwtJtp2YuSaqavcBKJoCQ3b5H_l_OY0ifbVRCA?e=fatKyD" target="_blank" rel="noopener">Datasets with injected anomalies</a><br> <ul> <li>the error description can be found <a href="https://github.com/FudanSELab/train-ticket/wiki/Fault-Description" target="_blank" rel="noopener">here</a></li> <li>the datasets are named ts-error-<em>indicatorOfError</em>-reset.zip because the databases are getting reset so that no anomalies are introduced with wrong database entries</li> </ul> </li> </ul> </li> <li><strong>Code</strong> for handling and transforming data to extract indicators describing the whole system's and microservices' behavior from the collected runtime monitoring data collected from TrainTicket:<br> <ul> <li><a href="https://github.com/moniSt13/ConTest-Parsing" target="_blank" rel="noopener">link to the Github repository</a></li> </ul> </li> <li><strong>reports</strong> regarding the established PLS-SEM model using previously handled and transformed runtime monitoring data. Please be aware that opening the reports can leas to out of memory due to their size: <ul> <li><em>Assessment of Measurement Model: MeasurementModel_TrainTicket_erorcleaned.zip & MeasurementModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em> </li> <li><em>Assessment of Structural Model: StructuralModel_TrainTicket_errorcleaned.zip & StructuralModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em></li> </ul> </li> </ul> <p><br><br>---------------------------------------</p> <p><em>Future work </em>not elaborated in the associated paper due to space restrictions:</p> <ul> <li><strong>reports regarding F5 error</strong>: PLS-SEM model results without interpretation and further mediating effects between microservices included: F5_error.zip</li> </ul> <p> </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.