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855 results for “model system”
FIGURE 13 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 13. Female terminalia of Austroleptis papaveroi (Diptera, Austroleptidae): (a) photograph using a microscope; (b) drawing used as input for our 3D reconstruction (both from Fachin et al. 2018); (c)–(d) our closed contour selection traced from the adapted illustration; and (e)–(f) the resulting 3D model.
Dataset from Denmark for Modeling a Highly Renewable Power System
<p>This is a completed database of Danish electrical transmission system for modeling an electrical system with high penetration of renewable energy.</p>
A Model Instability Issue in the NCEP Global Forecast System Version 16 and Potential Solutions
<p>This dataset includes the GFSv16 model source code and the scripts and data to plot the figures for the manuscript</p> <p>" A Model Instability Issue in the NCEP Global Forecast System Version 16 and Potential Solutions" submitted to GMD</p>
Benchmark Systems for Paper: Optimization-Based Model Order Reduction of Port-Hamiltonian Descriptor Systems
<p>Benchmark Systems for Paper: Optimization-Based Model Order Reduction of Port-Hamiltonian Descriptor Systems</p> <p>Contains:</p> <p>Index 2 Oseen Models and Index1/2 RCL circuits in port-Hamiltonian form.</p>
Research data related to the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)"
<p><strong>Research Data related to the publication "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023) published in <em>Water Resources Research</em> </strong></p> <p>Dear reader,</p> <p>research data are provided for the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023). The authors hope that the research data allows for a better understanding of the paleo-modeling workflow. Feedback on the model files or questions regarding the modeling approach etc. can be addressed to the authors of the article, see contact details below. The research data comprises the following files:</p> <ul> <li>files related to the parameter estimation procedure using PEST (Doherty, 2021a,b) (see subfolder "<em>parameter_estimation</em>")</li> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for each model variant. Note that model variants consist of several time slice models, indicated by the corresponding file names, e.g., '<em>Model_BC_slice_01.py'</em> etc. (see '<em>scripts.zip</em>' in the subfolders 'Model BC', 'Model CP', 'Model NE-ND-NP', 'Model NE-NP', 'Model NG', 'Model NP', 'Model R1', 'Model R2', 'Model R3', 'Model R4', 'Model R5', 'Model R6', 'Model SS')</li> <li>simulation output files, including concentration and head data for each model stress period (3-D), mean/max. concentration and head data for each model stress period (2-D), as well as depth [mbgs] of different salinity interfaces (2-D), i.e., marking the transitions from fresher to more saline groundwater using thresholds of 0.45 ('<em>depth_interface_mbgs</em>'), 1, 5, 10 and 20 g TDS L<sup>-1</sup>, respectively (see subfolders '<em>output/npy_arrays'</em> within each model variant subfolder). Moreover, sea levels, time slice names and stress period numbers are provided in the '<em>output/npy_arrays'</em> subfolders as well as final concentrations and heads (3-D) for each time slice model of each model variant (e.g., '<em>Model_BC_slice_01_final_concentrations.npz</em>' and '<em>Model_BC_slice_01_final_heads.npz</em>'; see '<em>output.zip'</em> in the model variant subfolders)</li> <li>iMOD-Python (Visser and Bootsma, 2019) input files, such as digital elevation models, geologic models etc. (see subfolder '<em>imod_input'</em>). However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consult the corresponding meta-data files or get in touch with one of the authors for further information</li> <li>bash scripts for the execution of iMOD-Python .py- and iMOD-WQ .run-files in a linux environment (see subfolder '<em>bash_scripts'</em>)</li> <li>figure files as well as the corresponding .py and .m scripts and shape-files, where applicable (see subfolder '<em>figures'</em>); note that consent for re-distribution for some figure input files doesn't exist, compare corresponding meta-data files</li> <li>videos presenting the concentration evolution of the different model variants (vertically averaged concentrations & cross-sectonal view, see subfolder '<em>videos'</em>)</li> </ul> <p>Meta-data files are usually provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input data and .run-files were executed on the University Oldenburg High-Performance Cluster 'Carl', running simulations in parallel with 32 computational cores.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>Literature:</p> <p>Doherty, J. E., (2021a). PEST Model-Independent Parameter Estimation User Manual Part I: PEST, SENSAN and Global Optimisers. Watermark Numerical Computing. p.394.</p> <p>Doherty, J. E. (2021b). PEST Model-Independent Parameter Estimation User Manual Part II: PEST Utility Support Software. Watermark Numerical Computing. p.274.</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p><strong>If you have further questions, please, contact one of the following authors</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
dateset for "Design and Evaluation of an Efficient High-Precision Ocean Surface Wave Model with a Multiscale Grid System (MSG_Wav1.0)"
<p>Here is the dataset for the paper named "Design and Evaluation of an Efficient High-Precision Ocean Surface Wave Model with a Multiscale Grid System (MSG_Wav1.0)".</p>
Datasets associated with the publication: "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".
<p>ECMWF HRES and ERA5 datasets for the paper "The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models".</p> <p>Content: </p> <p>========================================================================================</p> <p>Storm Friederike:</p> <p>ECMWF HRES forecast, 18.01.2018 00:00 UTC - 23:00 UTC, hourly, GRIB-format.</p> <p>ECMWF ERA5 reanalysis, 16.01.2018 12:00 UTC - 19.01.2018 00:00 UTC, twelve-hourly data, GRIB-format.</p> <p>========================================================================================</p> <p>Storm Vladiana:</p> <p>ECMWF HRES analysis, 23.09.2016 00:00 UTC - 23.09.2016 00:00 UTC, six-hourly data, rotated North Pole (latitude: 51˚, longitude: 160˚), NetCDF-format.</p> <p>========================================================================================</p> <p>Storm Egon:</p> <p>ECMWF ERA5 reanalysis, 12.01.2017 00:00 UTC - 13.01.2017 06:00 UTC, six-hourly data, GRIB-format.</p>
Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling: Supplementary Model Files
<p>Supplementary code and model files for the manuscript entitled "Elucidating the Magma Plumbing System of Ol Doinyo Lengai (Natron Rift, Tanzania) Using Satellite Geodesy and Numerical Modeling". OlDoinyoLengai_code_and_models.zip contains all necessary Matlab code, functions, input and output files for the GNSS, InSAR, and joint inversions presented in our manuscript necessary to reproduce the results. dMODELS is an open source code developed by the United States Geological Survey. The originally published program is available here: https://pubs.usgs.gov/tm/13/b1/ and the revised software archived here will also be available through the USGS website code.usgs.gov/vsc/publications/OlDoinyoLengai or by contacting Maurizio Battaglia. With this manuscript we are providing an update to dMODELS that includes improved graphics and joint inversion capabilities for both InSAR and GNSS data. </p>
Earth system model simulation results (1980-2014) for snow analysis
<p>This dataset contains monthly output in 1984-2014 from simulations using E3SM. To extract them, you should first collect the files together and run <code>zip -F sd_simulation_output_sliced.zip --out sd_simulation_output.zip, then unzip sd_simulation_output.zip</code>.</p>
SESMG Model Definitions: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the files is one SESMG model definition used for the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". Further information can be found in this publication. The file names indicate to which sensitivity analysis of the study the individual model definition belongs to. Used acronyms: "ng" = natural gas.</p>
SESMG Model Results: "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis"
<p>Each of the folders contains SESMG results for a sensitivity analysis of the study "Potential-Risk and No-Regret Options for Urban Energy System Design - A Sensitivity Analysis". More information can be found in this publication. Each folder contains two subfolders. The "cost-minimum" subfolder contains the results for financially optimized systems, and the "emission-minimum" subfolder contains the results for GHG emission-optimized systems. Within these subfolders, the results for different gradations of the respective sensitivity parameters are stored in separate sub-subfolders. The 01_reference_total_ghg_emissions folder has a slightly different structure. Since the results are not separated into financially and emissions-optimized scenarios, the results of different gradations are stored directly in the main folder of this sensitivity analysis.</p>
Supporting data to reproduce figures and anaylisis presented in Bruciaferri et al. 2023 - submitted to Journal of Advances in Modeling Earth Systems (JAMES)
<p>Data for reproducing figures and the analysis of</p> <p>Diego Bruciaferri, Catherine Guiavarc’h, Helene T. Hewitt, James Harle, Mattia Almansi and Pierre Mathiot. Localised general vertical coordinates for quasi-Eulerian ocean models: the Nordic overflows test-case, submitted to JAMES.</p> <p>Data includes (© Crown copyright Met Office):</p> <p>1) models_geometry: bathymetry, horizontal grid and domain files needed to run the models and analyse their results.</p> <p>2) hpge: output data from HPG error idealised test.</p> <p>3) ideal_ovf: output data from the idealised overflow experiment</p> <p>4) realistic: output data from the realistic simulations</p>
Dataset: Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture
<p>Dataset for Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture manuscript submitted to Plant Phenomics. The dataset contains raw and processed root architecture images, RhizoVision trait outputs, and the associated R scripts for statistical analysis and model parameterization.</p>
Deliverable 3.3: Report on multi-cells stack system and shunt current distribution modelling
<p>Data for reproducing the results presented in Deliverable 3.3 "Report on multi-cells stack system and shunt current distribution modelling" belonging to the CompBat EU project, DOI:<a href="https://doi.org/10.3030/875565">10.3030/875565</a></p>
Supporting data for manuscript: "Global-scale evaluation of coastal ocean alkalinity enhancement in a fully-coupled Earth system model"
<p>Supporting data for manuscript: "Global-scale evaluation of coastal<br> ocean alkalinity enhancement in a fully-coupled Earth system model"</p> <p>Authors: Julien Palmieri and Andrew Yool</p> <p>Institute: National Oceanography Centre, European Way, Southampton<br> SO14 3ZH, UK</p> <p>This repository consists of four main sets of files:</p> <p>1. Matlab scripts used for analysis, figure plotting and table<br> preparation</p> <p> Filenames of the format: Figure_??.m</p> <p>2. Raw netCDF output files from UKESM1 for six model experiments</p> <p> Filenames of the format: medusa_c*.nc</p> <p>3. BGCVal processed timeseries shelve files</p> <p> Filenames of the format: u-c*.shelve.txt</p> <p>4. CMM2 processed timeseries netCDF files</p> <p> Filenames of the format: c*_global.nc</p> <p>File sets 2-4 are read and processed by script files in file set 1<br> </p>
Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum
<p>Simulation data (TS, FSNT, and FLNT) and apap cloud feedback analysis for CESM2 LGM simulation</p> <p><strong>Simulation boundary condition files in 1-degree resolution: boundary_condition_files.zip</strong></p> <p><strong>Please cite: </strong></p> <p>Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., & DiNezio, P. (2021). Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum. <em>Geophysical Research Letters</em>, <em>n/a</em>(n/a), e2020GL091220. https://doi.org/10.1029/2020GL091220</p>
Magmatic fingerprints of subduction initiation and mature subduction: Numerical modelling and observations from the Izu-Bonin-Mariana system (Supplementary Material)
<p>Video of the reference model as well as three end-member models described in the manuscript. Additionally, the source code that was used to run the models and the initial model setup for each model presented can be found. The numbering of the models is equivalent to the numbering used in the paper (Ritter et al., 2024 in Front. Earth Sci.)</p>
Data and model code: Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050
<p>Dataset for <em>Assessing the Implications of Hydrogen Blending on the European Energy System towards 2050</em></p>
Models and data for: State estimation of a physical system without governing equations
<p>Contains the accompanying data and models for the paper State estimation of a physical system with unknown governing equations. Accompanying code can be found here: https://github.com/coursekevin/svise</p> <p> </p>
Feasibility Study Using Zone-MPC Controller (Zone-Model Predictive Control) and Health Monitoring System (HMS)
ClinicalTrials.gov study NCT01472406. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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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.