Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
218
datasets available to search
ShareScore release 0.9.0
Dataset results
218 results for “Physical Modelling”
Permeable pavement hydraulic performance and clogging experiments using a full-scale urban drainage physical model
<p>This dataset contains the results from 15 tests conducted used a 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 POREDRAIN project.</p> <p><br>The objective of the tests is to analyse the hydraulic performance of a porous asphalt layer of the PA-16 type and the impact of clogging on the hydrological behaviour and water quality of the effluent. The porous asphalt was used to retrofit an impervious concrete surface of a 36 m² full-scale street section physical model, which consist of a rainfall simulator placed over the street surface. The behaviour of the porous asphalt layer was assessed by adding surface sediment loads between simulated rainfall events. Stormwater flow discharges were collected from two gully pots and an outlet lateral channel. </p>
Supplemental information and Data for: Colloidal physics modeling reveals how per-ribosome productivity increases with growth rate in E. coli
<p>Faster growing cells must synthesize proteins more quickly. Increased ribosome abundance only partly accounts for increases in total protein synthesis rates. The productivity of individual ribosomes must increase too, almost doubling by an unknown mechanism. Prior models point to diffusive transport as a limiting factor but surface a paradox: faster growing cells are more crowded, yet crowding slows diffusion. We suspected physical crowding, transport, and stoichiometry, considered together, might reveal a more nuanced explanation. To investigate, we built a first-principles physics-based model of <em>E. coli</em> cytoplasm in which Brownian motion and diffusion arise directly from physical interactions between individual molecules of finite size, density, and physiological abundance. Using our microscopically-detailed model, we predict that physical transport of individual ternary complexes accounts for ~80% of translation elongation latency. We also find that volumetric crowding increases at faster growth even as cytoplasmic mass density remains relatively constant. Despite slowed diffusion, we predict that improved proximity between ternary complexes and ribosomes wins out, illustrating a simple physics-based mechanism for how individual elongating ribosomes become more productive. We speculate how crowding imposes a physical limit on growth rate and undergirds cellular behavior more broadly. Unfitted colloidal-scale modeling offers systems biology a complementary "physics engine" for exploring how cellular-scale behaviors arise from physical transport and reactions among individual molecules.</p>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 2
<p>This dataset includes model outputs averaged from day 2 to day 15 using the multiscale modeling framework (MMF, also referred to as ``superparameterization'') for</p> <ul> <li>Low-resolution MMF (LR): SP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_32_x_120z1200m.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 32, crm_ny = 1, crm_dx = 1200 m, crm_dt = 5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>High-resolution MMF (HR): UP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 64, crm_ny = 1, crm_dx = 200 m, crm_dt = 0.5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>Same as HR, but considers hyperviscosity with tau = 30s (HRh30): UPhyperlag30_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HR, but considers hyperviscosity with tau = 150s (HRh15): UPhyperlag15_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs15) with tau = 30s and sigmag = 1.5: HPhyper_sedi15_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs12) with tau = 30s and sigmag = 1.2: HPhyper_sedi12_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> </ul>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 1
<p>This dataset includes 6-month simulations using the ne30pg2 grid. Monthly averaged output files from six experiments are included.</p> <ul> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1 <ul> <li>The config options for this control simulation is <pre>CAM_CONFIG_OPTS = -mach summit -phys default -use_MMF -crm samxx -nlev 60 -crm_nz 50 -crm_dt 10 -crm_dx 2000 -crm_nx 64 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -rad rrtmgp -rrtmgpxx -MMF_microphysics_scheme sam1mom -chem none -nlev 125 -crm_nz 115 -crm_dt 2 -crm_dx 200 -crm_nx 256 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -use_MMF_VT -cppdefs ' -DMMF_ESMT -DMMF_USE_ESMT -DMMF_HYPERVISCOSITY -DMMF_SEDIMENTATION ' </pre> </li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED <ul> <li>The HV.SED control case is the same as the control (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1), but considered both hyperciscosity and sedimentation processes.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_1E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_5E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_5E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_5E-05.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_8E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_8E-05.</li> </ul> </li> </ul>
Preliminary data of drifting snow mass flux from the lower SPC at MOSAiC (2020-01-26 to 2020-02-04) for the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>Preliminary data of lower SPC massflux from MOSAiC, for the time period 2020-01-26 -- 2020-02-04.</p> <p>1-h averaged time series of mass flux (kg/m²/h) to compare with the ALPINE3D simulation results.</p> <p>Will soon be replaced with a DOI / Repositiry at the Arctic Data Centre from BAS.</p>
Supplementary Data: Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider
<p><strong>Supplementary Data</strong></p> <p><em>Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider</em></p> <p>This record contains the full dataset generated for the study "<em>Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider</em>". It contains the following files:</p> <ul> <li>data_cross_sections_full_exact.csv - a CSV file with the soft breaking parameters M1, M2, mu and tanb, the neutralino/chargino masses, the cross sections for neutralino-neutralino, chargino-neutralino and chargino-chargino production at the LHC operating at a CM energy of 13 TeV, and the branching ratios of the unstable charginos/neutralinos. </li> <li>benchmark_points.zip - this zip file contains the SLHA files for the four benchmark points as shown in the paper, together with the prospino output for the cross section. </li> </ul> <p> </p>
Acoustical-electrical models of tight rocks based on digital rock physics and double-porosity theory
<p>These data are the compressional-wave waveform and electrical and CT data obtained by Ba et al. through ultrasonic experimental and conductivity and CT scan measurements on tight-oil rocks.</p> <p>Details are given in the uploaded introduction document regarding the format of dataset.</p>
Model output 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 output files that were used in the analysis presented in "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)", submitted to Geoscientific Model Development.</p>
Two-fluid Physical Modeling of Superconducting Resonators in the ARTEMIS Framework
<p>Input files, data files and scripts to replicate results in "Two-fluid Physical Modeling of Superconducting Resonators in the ARTEMIS Framework" (Jambunathan et el.)</p> <p> </p> <p>Please direct any questions to the corresponding author, Revathi Jambunathan (rjambunathan [at] lbl.gov)</p> <p>Artemis simulations were run using the development branch of artemis <a href="https://github.com/ECP-WarpX/artemis">https://github.com/ECP-WarpX/artemis </a></p> <p>Some simulations in this paper were performed with different commit hashes of artemis and amrex, however, the input files should still work with the most recent development branch of artemis.</p> <p>In the attached tar file, the input files, data, and scripts used to analyse simulation results shown in Figures 1, 2, 3, 4, and 6 of the paper are provided. To run the simulations on perlmutter GPUs, the code is compiled with USE_GPU=TRUE and USE_LLG=FALSE</p> <p> </p> <p> </p> <p> </p>
Dataset for the SIAM MPI23 project "Model inversion for complex physical systems using low-dimensional surrogates"
<p>This dataset contains 20,000 synthetic simulations of a simplified two-dimensional confined aquifer model of the Hanford Site. The inputs are the Kosambi-Karhunen-Loève expansion (KKLE) coefficients of the input log-transmissivity field. The outputs are the corresponding stationary pressure responses observed at 323 observation wells. Also included are the arrays necessary to reconstruct the log-transmissivity inputs from the KKLE coefficients.</p>
Data used in the physical-biogeochemical model of Danjiangkou Reservoir
<p>The date set includes meteorological, hydrological, water quality, and organic carbon loading data obtained in 2009 for the Danjiangkou Reservoir in China. The data were used to set the boundary conditions of the physical-biogeochemical model, which was adopted to simulate reservoir methane dynamics in the Danjiangkou Reservoir.</p>
Data for A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers
<p>This repository contains dataset for the paper entitled "A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers". The magnitude of inverse energy cascade flux is revised in version 2.0 according to Xia et al. (2009) (https://doi.org/10.1063/1.3275861). </p>
Large Linguistic Model for Clinical Reaoning of Physical Therapy Students
ClinicalTrials.gov study NCT06809634. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
Open the record for dataset details and reuse information.
Supplemental information and Data for: Colloidal physics modeling reveals how per-ribosome productivity increases with growth rate in E. coli
Open the record for dataset details and reuse information.
A validated physical model of the thermoelectric drift of Pt-Rh thermocouples above 1200 °C
<p>Data associated with a validated physical model of the thermoelectric drift of Pt-Rh thermocouples above 1200 °C (Metrologia 57 (2020) 025009) https://doi.org/10.1088/1681-7575/ab71b3</p>
Dataset for a physics informed deep learning method with adaptively weighted loss for modeling soil water flows
<p>The data for the 11 scenarios generated by Hydrus-1D is located in data.zip</p> <p>The code for the physics-informed neural networks with adaptively weighted loss used to simulate water flow in loam soils is located at PINN_adaptively_weighted_loss_loam.zip</p>
Physical Unclonable In-Memory Computing for Simultaneous Protecting Private Data and Deep Learning Models
Open the record for dataset details and reuse information.
Data repository for Lin et al. (2022) "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling"
This dataset contains the necessary data and plotting tools supporting the paper titled "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling", by Lin et al., 2022. The data set contains thermospheric mass density simulated by MAGE, TIEGCM, DTM, and MSIS for the 1-6 February 2022 geomagnetic storm event.
QMC Raw Data for Disentangling the Physics of the Attractive Hubbard Model via the Accessible and Symmetry-Resolved Entanglement Entropies
<p><strong>Data Summary</strong></p> <p>Raw data of 'Disentangling the Physics of the Attractive Hubbard Model via the Accessible and Symmetry-Resolved Entanglement Entropies'.</p> <p>The default Julia RNG generates random seeds, with the seed number corresponding to the last four digits of each file name..</p> <p>For more details, please check README.md on the GitHub repository.</p> <p>The scripts for processing the raw data are located in the data folder within the same repository.</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.