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218 results for “Physical model”
Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites: modeling data
<p>These data are related to:</p> <p>Moreau, J., Kohout, T., Wünnemann K., Halodova, P., Haloda, J., 2019.<br> Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites.<br> Icarus, 332, 50-65. <a href="https://doi.org/10.1016/j.icarus.2019.06.004">https://doi.org/10.1016/j.icarus.2019.06.004</a></p> <p>Any use of these files, scripts (partial or complete) in research papers, please reference the paper above + Moreau et al. (2017, 2018) (references compiled in the above-mentioned paper).</p> <p>To use these files, you will need:<br> - authorized access to the iSALE shock physics code (iSALE-Dellen version) re-compiled with our modifications, with reference<br> to the manual in your work<br> - access to the pySALEPlot tool for iSALE users made by T. Davison acknowledged in your work<br> - running the iSALE models to generate the different jdata.dat files (average size of a jdata.file is 7 Go)<br> - python<br> - Ubuntu or macOS</p> <p> </p> <p>(more info in README.txt file)</p>
WASHTREET. Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.
<p><strong>WASHTREET</strong><strong> - </strong><strong>Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.</strong></p> <p>This dataset contains raw data and surface elevations results from the application of the Structure from Motion (SfM) photogrammetric technique in a 36 m<sup>2</sup> full-scale urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). This work is part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The accurately measurement of the surface elevations is needed for a proper representation of surface flow, which is key in the detachment and transport of solids in the model surface. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>)</p> <p>A detailed description of experimental procedure and data collected can be consulted in ‘<em>1_ExperimentalProcedure.pdf’</em>. Raw images taken as input for the SfM software are included in ‘<em>2_RawImages.zip’</em>. Then, the point cloud resulted is provided in ‘<em>3_SFM_RawPointCloud.ply</em>’. This point cloud was processed and the final elevation map with a resolution of 5 mm is included in ‘<em>4_SfM_ElevationMap(m).xyz</em>’.</p> <p>Further details of the physical model and hydraulic and sediment transport experiments can be consulted in the dataset <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET - Hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques are provided in the dataset <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13.<a href="http://doi.org/10.1038/s41597-020-0384-z"> https://doi.org/10.1038/s41597-020-0384-z</a></li> </ul>
Data-driven physics-based modeling of pedestrian dynamics
<p>Python package to create physics-based pedestrian models from crowd measurements</p> <p>Github: <a href="https://github.com/c-pouw/physics-based-pedestrian-modeling">https://github.com/c-pouw/physics-based-pedestrian-modeling</a></p>
Repository for: "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model"
<p>Set of numerical experiments supporting the paper entitled "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model" by Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard and Marco Toffolon. Submitted to GMD. </p> <p>The folder contains: </p> <p>simulations: DYNO-PODS + Delft3D experiments on Lake Morat. See https://github.com/louisXW/DYNO-pods for more insights on DYNO-PODS and instructions for installation.</p> <p>scripts: extraction and plotting scripts</p> <p>source_code: modified Delft3D src as available at: https://github.com/eawag-surface-waters-research/Delft3D/tree/d3d4/research/surface_heat_transfer</p>
Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model
<p>Database corresponding to the research work submitted to Water Resources Research :<br> "Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model"<br> Fanny Picourlat (fanny.picourlat@lsce.ipsl.fr), Emmanuel Mouche, Claude Mugler</p> <p> </p> <p>"Geomorphic_Analysis" directory ------------------------------------------------------------------------------</p> <p>Little Washita geomophic analysis results. Analysis conducted on the 100 m resolution DEM (from USGS datadase, accessible at https://www.usgs.gov/core-science-systems/national-geospatial-program/small-scale-data) using a flow paths modeling algorithm developped by Maquin (2016).</p> <p> - Hillslopes_Length.csv : List of hillslopes lengths [m]<br> <br> - Hillslopes_MeanSlopes.csv : List of hillslopes mean slopes [%]<br> <br> <br> "3DREF_model_19981999" directory ------------------------------------------------------------------------------<br> Three-dimensional reference model files : exemple of the 1998-1999 water year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.</p> <p> - parameters_summary.pdf : parameters summary table.<br> <br> - "Outputs" directory ---------------------------------------------<br> Output files that form paper's figures.<br> <br> - LWo.hydrograph.Hydrographe_USGS1_07327550.dat : Streamflow at USGS1<br> - LWo.water_balance.dat : Water balance<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used for plotting depth to water table.<br> - LWo.olf.dat : Surface domain variables for all nodes for all output times. File used for plotting evapotranspiration at the closest node from the Ameriflux station.<br> <br> <br> "Hillslope_Model" directory -----------------------------------------------------------------------------------<br> Equivalent hillslope model files for the 20-year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.<br> <br> - "Outputs" directory ----------------------------------------------<br> <br> - LWo.water_balance.dat : Water balance. File used for plotting hillslope discharge and evapotranspiration.<br> - LWo.observation_well_flow.Well_1.dat : Outputs at nodes located at x=100m. File used for plotting depth to water table.<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used to extract the "post-processed" files described below.</p> <p> - "Post_processed" directory -------------------------------<br> Data extracted from the output file LWo.pm.dat.</p> <p> - TAB_xzy_sat.csv : Saturation for all nodes for all output times. Used for defining the seepage face extension Xs(t).<br> - x(t).csv : x coordinate (for all output times) of the intersection point between roots ending limit and water table. Used for defining the water table slope tan(i(t)).</p> <p><br> "Params" directory --------------------------------------------------------------------------------------------<br> Parameters files for both 3DREF model (1998-1999 simulation) and hillslope model (20-year simulation).<br> <br> - "Topography" directory --------------------------------------------<br> - maillage_hgs_corr3_riv.2dm : Horizontal 3D mesh file<br> - fichier_altitude_grok_corr3_riv_b.txt : 3D surface nodes elevation [m]<br> <br> - "Props" directory -------------------------------------------------<br> - LW.mprops : Subsurface material parameters<br> - LW.oprops : Surface domain parameters<br> - LW_LAI.etprops : Vegetation parameters<br> <br> - "Zones_vg" directory ----------------------------------------------<br> - zone_1_ele.txt : Bare soil 3D elements IDs<br> - zone_2_ele.txt : Deciduous forest 3D elements IDs<br> - zone_3_ele.txt : Evergreen forest 3D elements IDs<br> - zone_4_ele.txt : Shrubs 3D elements IDs<br> - zone_5_ele.txt : Grassland 3D elements IDs<br> - zone_6_ele.txt : Pasture 3D elements IDs<br> - zone_7_ele.txt : Crops 3D elements IDs<br> <br> - "Forcings" directory ----------------------------------------------<br> - NARR_day_9899.csv : Daily rainfall [m/s] on 1998-1999<br> - NARR_day_etp_9899.csv : Daily Potential Evapotranspiration [m/s] on 1998-1999<br> - NARR_day_9313.csv : Daily rainfall [m/s] on 1993-2013<br> - NARR_day_etp_9313.csv : Daily Potential Evapotranspiration [m/s] on 1993-2013<br> <br> - "Output_times" directory ------------------------------------------<br> - output_times_365d.csv : Output times [s] for 365 days<br> - output_times_20y.csv : Output times [s] for 20 years<br> <br> - "Rivers" directory ------------------------------------------------<br> - no_rivers_ele.txt : No river 3D elements IDs<br> - rivers_ele.txt : River 3D elements IDs<br> <br> - "LAI" directory ---------------------------------------------------<br> LAI files (1st column : time [s], 2nd column : LAI [-])<br> - LAI_2_hydro.csv : Deciduous forest LAI over one water year<br> - LAI_4_5_6_hydro.csv : Shrubs, grassland and pasture LAI over one water year<br> - LAI_winterwheat_OAlaoui.csv : Crops LAI over one water year<br> - LAI_eq_20y.csv : Equivalent LAI (for hillslope model) over 20 years<br> <br> "Analytical_Model" directory -----------------------------------------------------------------------------------</p> <p> - Analytical_model.py : Analytical model code for the 20-year simulation of the equivalent hillslope water balance.</p> <p> </p>
Data set of paper Model-Driven System-Performance Engineering for Cyber-Physical Systems
<p>This data set contains the raw and processed data of the paper <em>Model-Driven System-Performance Engineering for Cyber-Physical Systems</em>, published in the proceedings of ESWEEK’21.</p>
A 1D coupled physical-biogeochemical model for the North Atlantic for studying vertical carbon flux parameterizations
<p>This repository provides the model output and code for analysis in the following article:</p> <p>Wang, B., & Fennel, K. (2023). An assessment of vertical carbon flux parameterizations using backscatter data from BGC Argo. <em>Geophysical Research Letters</em>, 50, e2022GL101220. <a href="https://doi.org/10.1029/2022GL101220">https://doi.org/10.1029/2022GL101220</a></p> <p> </p>
A habitat connectivity reality check for fish physical habitat model results and decision making for river restoration
<ol> <li>Fish physical habitat models are a tool for guiding restoration efforts in lotic ecosystems but often they overestimate restoration outcomes because currently they do not incorporate habitat connectivity. This persistent issue can, in extreme cases, result in little or no improvement to fish populations after the restoration, wasting valuable conservation resources.</li> <li>We present a case study where practitioners applied a fish habitat model for multiple life history stages of gravel spawning fishes to a 52 kilometer stretch of the Iller River but did so at a microscale implementation (every 200 meters). This approach provided an opportunity to assess the connectivity of gravel spawning fishes to find suitable habitats for all life history stages and seasonal movements.</li> <li>We used the assessed habitat estimates (availability of distinct habitat types within the 200 m reaches) to calculate the minimum distance a fish would need to go as it hypothetically “grew up” from egg to full spawning adult. We call this technique a reality check as it results in a decisive understanding of which areas were ultimately necessary to fulfill the life cycle of gravel spawning fishes, which standard assessments do not show.</li> <li>Our results show that complete connectivity still require long movement distances for vulnerable life stages to find suitable habitat. This contradicts standard practice, as restoration schemes and decision making often assume that connectivity inherently leads to more fish production without added habitat restoration.</li> <li>We recommend practitioners should perform this habitat connectivity approach when assessments implement fish habitat suitability models at similar scales. As a result, decision makers can evaluate proposed restoration sites and measures more realistically.</li> </ol>
The influence of twin tunnel excavation on single and group pile loading by physical modeling
<p>This study uses small-scale physical models to assess the interaction between piles and twin tunnels, considering varying tunnel distances and surface loads. The excavation process of the tunnels is simulated by gradually releasing air pressure from rubber tubes while hydraulic jacks apply loads to aluminum piles. Surface linear variable differential transformers (LVDTs) and strain gauges are utilized for measuring pile responses. Data related to bending moment and axial forces extracted from tests are also provided.</p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
Open the record for dataset details and reuse information.
Toward a Physics based Model of Hypervelocity dust Impacts,
<p>Data used in the preparations of certain figures of the manuscript</p>
Experimental Data for: Research Perspective on Supporting Software Engineering via Physical 3D Models
<p>Experimental data for the experiment presented in the technical report 1507: "Research Perspective on Supporting Software Engineering via Physical 3D Models"</p>
Quantifying the Impact of Vertical Resolution on the Representation of Marine Boundary Layer Physics for Global-Scale Models
<p>Data supporting the findings of DOI: 10.1175/MWR-D-23-0078.1, a 2023 Monthly Weather Review publication with the same title as this dataset.</p>
Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics (Model Data)
<p>Model Data to reproduce plots from article "Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics". README contains information on where to access model and visualization tools.</p>
Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.
<p>Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.</p>
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
<p>Here are the datasets for our publication entitled "<a href="https://www.nature.com/articles/s41467-024-48779-z">Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</a>" published in Nature Communications. </p> <p>The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature. A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.</p> <p>Other details can be found in "Data Introduction.pdf" file.</p> <p>The <strong>Python Code</strong> for reading and preprocessing this dataset is available at: <a href="https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library">https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library</a></p> <p>Summary of articles using the this dataset: <a href="https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary">https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary</a></p> <p> </p> <p>If you find this data helpful, please consider citing our paper:</p> <p>Wang, F., Zhai, Z., Zhao, Z. <em>et al.</em> Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. <em>Nat Commun</em> <strong>15</strong>, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z</p>
Lightning Prediction in the Tehran Region Using the WRF Model with Multiple Physical Parameterizations and an Ensemble Approach
<p><span>The Grid Analysis and Display System (</span>GrADS)<span> </span><span>and</span><span> </span><span>Python</span><span> </span><span>scripts and the output data from simulations that we used in this study.</span></p>
Climate model (CM2.6) and regional model (ACM) processed output used to investigate the physical drivers and biogeochemical effects of the weakening of the northwest North Atlantic Shelfbreak Jet (Garcia-Suarez & Fennel., 2024; JAMES)
<p>Key processed output from the climate model GFDL CM2.6 and the regional Atlantic Canada model (ACM) used to investigate the physical drivers and the biogeochemical effects of the weakening of the shelfbreak jet in the northwest North Atlantic Ocean. The dataset includes all model variables required to reproduce the key results in <em>Garcia-Suarez & Fennel (2024, JAMES)</em>. See <em>GarciaSuarezandFennel_JAMES_CM26_ACM_data_README_v2.txt</em> for more details.</p>
Substorm Dataset and code (Substorm Identification With The WINDMI Magnetosphere - Ionosphere Nonlinear Physics Model)
<p>The archive contains MATLAB Live Script (<code>.mlx</code>) and Simulink model (<code>.slx</code>) files.</p> <ul> <li> <p><strong><code>.mlx</code> files:</strong> MATLAB Live Scripts are interactive files that combine code, output, and formatted text in a single document. They are used for data analysis, visualization, and sharing workflows in an easy-to-read format. These files can be opened in MATLAB's Live Editor for an interactive coding experience.</p> </li> <li> <p><strong><code>.slx</code> files:</strong> Simulink model files are used in MATLAB's Simulink environment for graphical modeling and simulation of dynamic systems. They are particularly useful for designing and analyzing systems with time-dependent behaviors, such as control systems or physical processes.</p> </li> </ul>
ScienceDex guides
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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.