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218 results for “Physical Modelling”
A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
<p>Data used to generate the figures in "A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model" 2021. The zip files contains the hybrid forecasts and regridded ERA5 data used to verify the forecasts as well as the SPEEDY-LLR and ML-only runs. </p>
Generative models for hadron shower simulation in fundamental physics
<p>This is a subset of data used in our NeurIPS 2021 submission paper.</p>
Physics‐Based Narrowband Optical Parameters for Snow Albedo Simulation in Climate Models
<p>This is a supplementary file for a submitted paper"Physics-based effective broadband optical parameters for snow albedo simulation in climate models".</p> <p>The authors derived a set of snow optical properties that effective in broadband snow radiative transfer simulation. These parameters are physically-based. </p>
Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data
<p>Numerical codes and results for the article: Modeling water flow and solute transport in unsaturated soils using physics-informed neural networks trained with geoelectrical data</p>
Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>There are 2 zip folders in this repository.</p> <p>"a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 3 main folders:</p> <ul> <li>base_setup_files</li> <li>a3d_jgr_alpha1</li> <li> a3d_jgr_alpha3</li> </ul> <p>The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.</p> <p>To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p> <p>_________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 2 main folders: </p> <ul> <li>SNOWPACK_JGR_ALPHA1</li> <li>SNOWPACK_JGR_ALPHA3</li> </ul> <p>In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p>
Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)"
<p>This dataset contains the numerical model input files that were used to produce the model simulation presented in "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)".</p> <p>When using these files to run a model, most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time is provided for the first year of the simulation. This dataset does not include the ERA5 atmospheric data, which can be downloaded directly from https://doi.org/10.24381/cds.adbb2d47.</p> <p>Portions of the initial and boundary condition data were generated using E.U. Copernicus Marine Service Information: \url{https://doi.org/10.48670/moi-00021}, \url{https://doi.org/10.48670/moi-00148}. Refer to the manuscript for references to other data sources.</p>
Optimization scripts used for "Bayesian optimization of laser-plasma accelerators assisted by reduced physical models"
<p>This dataset contains the optimization scripts needed to reproduce the results from the article "Bayesian optimization of laser-plasma accelerators assisted by reduced physical models" by A. Ferran Pousa, S. Jalas, M. Kirchen, A. Martinez de la Ossa, M. Thévenet, J. Larson, S. Hudson, A. Huebl, J.-L. Vay, and R. Lehe.</p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication.</p> <p>Please cite the paper as follows:</p> <p>Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118</p> <p> </p>
Analysis datasets for NEMO_validation workflow Byrne et al 2023 GMD. "Using the COAsT Python package to develop a standardised validation workflow for ocean physics models"
<p>Analysis datasets in support of Byrne et al. (2023) "Using the COAsT Python package to develop a standardised validation workflow for ocean physics models", <em>Geoscientific Model Development</em>.</p> <p> </p> <p>The datasets are from a comparative analysis of two versions of the European shelf sea AMM15 (Atlantic Margin Model at 1.5km horizontal resolution) configuration. These are NEMO ocean model configurations with different code base versions. The configurations are CO7, which is based on NEMOv3.6, and CO9p0 (also referred to as P0.0), which is based on NEMOv4.0.4.</p>
Data of: Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing
<p>This dataset contains the data and models used in the research journal publication: "Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing " which was funded from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 101001567).</p> <p> </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>
Establishment of Sleep Quality, Physical and Mental Health and Occupational Burnout Management Model for Shift Nursing Staff and Evaluation of Its Effectiveness
ClinicalTrials.gov study NCT04423328. IPD Sharing: NO. Countries: 1. Publications: 1.
The Impact of the "Nutrition Enrichment and Healthy Living Model" ( NEHLM) on Diet Quality, Physical Activity and Dental Health Among Children From Socioeconomically Disadvantaged Families in Beer She
ClinicalTrials.gov study NCT01071551. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The relationship between physical education teachers' competence support and middle school students' participation in sports: a chain mediation model of perceived competence and exercise persistence
Open the record for dataset details and reuse information.
The effects of exploratory behavior on physical activity in a common animal model of human disease, zebrafish (Danio rerio)
Open the record for dataset details and reuse information.
Beyond the landscape: resistance modelling infers physical and behavioural gene flow barriers to a mobile carnivore across a metropolitan area
Open the record for dataset details and reuse information.
Adverse childhood experiences, depressive symptoms, functional dependence, and physical activity: A moderated mediation model
<p>This release contains R scripts to analyze the links between adverse childhood experiences (ACEs), depression, functional dependence, and physical activity, using the SHARE panel data survey.</p>
Replication Package for A Systematic Literature Review of Model-driven Security Engineering for Cyber-physical Systems
<p>This package contains supplemental material for the paper "A Systematic Literature Review of Model-driven Security Engineering for Cyber-physical Systems".</p> <p>In particular, we provide:</p> <ul> <li>The survey protocol</li> <li>The used search strings</li> <li>The search results for each library</li> <li>The data extraction template</li> <li>The data extraction sheet for each selected approach</li> <li>The list of all publications and their exclusion stage</li> </ul>
WASHTREET - Hydraulic, wash-off and sediment transport experimental data obtained in an urban drainage physical model
<p><strong>WASHTREET</strong><strong> -</strong> <strong>Hydraulic, wash-off and sediment transport experimental data obtained in an urban drainage physical model.</strong></p> <p>This dataset contains the results from the tests carried out at a laboratory physical model in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain) as part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>. The objective of the project is to perform a series of high-resolution experiments where urban surface wash-off and sediment transport through gully pots and pipes were accurately measured in laboratory-controlled conditions in a separate drainage system.</p> <p>The experimental facility is a 36 m2 full-scale street section and consists of a rainfall simulator placed over a concrete street surface with two gully pots that drain runoff into an underground pipe system. Further details of the physical model are provided in ‘1_Physical_model_description.pdf’. Two zip files with rain intensity distributions (‘2_Rain_intensity_maps.zip’) and model topographies (‘3_Elevation_data.zip’) complete physical model information as accurately measured inputs for hydraulic, wash-off and sediment transport experiments. ‘4_Hydraulic_tests_description.pdf’ describes experimental procedure, equipment, measuring points, results and data set files of the hydraulic characterization of the experiments. Data regarding these hydraulic tests is included in ‘5_Hydraulic_tests.zip’. In these tests, flow in both gully pots and in the pipe system outlet, and a total of 6 surface and 6 pipe depths were measured by ultrasound distance sensors for the different simulated rains.</p> <p>‘6_Washoff_tests_description.pdf’ includes information of the experimental initial conditions, the different sediment granulometries used, measuring points, experimental procedure and result files regarding wash-off and sediment transport experiments. Data files of a total of 23 tests are included in ‘7_Wash-off_tests.zip’. In these experiments, an initial mass of sediment is distributed over the model surface, and the wash-off and sediment transport processes are measured during a steady and uniform rainfall by total suspended solids (TSS) and particle size distribution (PSD) samples at the entrance of gully pots and at the pipe system outlet. Online turbidity measurements at pipe system outlet, pipe depths and flow at pipe system outlet are also measured during the experiments. Results regarding mass balances, which are performed at the end of the experiment to assess the final distribution of sediments, are also included. At last, some relevant photos and videos taken during the experiments are provided in ‘8_Multimedia.zip’. </p> <p>Flow measurements have been used in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">10.1016/j.jhydrol.2019.05.003</a>), together with the related datasets <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a> and <a href="http://www.doi.org/10.5281/zenodo.3241337">WASHTREET - Structure from Motion data</a>, to calibrate a 2D shallow water model.</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> </p> <p>Derived publications:</p> <ul> <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="https://doi.org/10.1038/s41597-020-0384-z">https://doi.org/10.1038/s41597-020-0384-z</a></li> <li>Naves, J., Rieckermann, J., Cea, L., Puertas, J., & Anta, J. (2020). Global and local sensitivity analysis to improve the understanding of physically-based urban wash-off models from high-resolution laboratory experiments. <em>Science of The Total Environment</em>, <em>709</em>, 136152. <a href="https://doi.org/10.1016/j.scitotenv.2019.136152">https://doi.org/10.1016/j.scitotenv.2019.136152</a></li> <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). Development and Calibration of a New Dripper-Based Rainfall Simulator for Large-Scale Sediment Wash-Off Studies. <em>Water</em>, <em>12</em>(1), 152. <a href="https://doi.org/10.3390/w12010152">https://doi.org/10.3390/w12010152</a></li> </ul>
Ultrasonic Imaging of Spherical Physical Models
<p>This dataset contains multiple SEGY format seismic datasets from a series of ultrasonic physical modeling surveys involving spherical bodies in a lab setting.</p> <p>The README document in this repository provides an overview of the available datasets, in raw and processed versions. </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
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.