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328 results for “Simulation results”
Dateset of results: yearly operation for AMTP and MAND - steady state simulation (D3.3 D5.1)
<p>Resuts of the steady state simulations carried out (in the frame of the ASTEP project) for the daily and yearly operation of the ASTEP concept. The dataset include data related to two reference cases and different options of design such as layout, locations, and demand variations. The results are related to the Deliverables D3.3 Design of the daily and yearly operation for AMTP and MAND and D5.1 Report on the integrated ASTEP model development and Conceptual Designs</p>
Results of RHD simulation of ns-prepulse with 3DLINE code for different target materials
<p>calc_NNNN_XX.vtk files are results of RHD simulations of fs-prepulse carried with code 3DLINE in rz-geometry. Units: cm, g/ccm, keV.</p> <p>Parameters of prepulse: <br> 940 ps of 1e10 W/cm2 at axis; <br> 60 ps exponentially rising up to 1e16 W/cm2 at axis; <br> wavelength 815 nm;<br> spatial profile - see intensity.txt.</p> <p>For further details contact IliaTsygvintsev@gmail.com.</p>
The simulation results of source distribution and influencing factors.
<p>This dataset contains simulation results of audible noise from UHV DC transmission lines based on digital twin. We provide six CSV files.</p> <p>Three source distributions are proposed and the ground audible noise under different distributions is calculated, which is compared with the BPA formula. The simulation data is stored in four files: (1)source_distribution_a.csv (2)source_distribution_b.csv (3) source_distribution_c.csv (4)source_distribution_formula.csv </p> <p>Two influencing factors including temperature and relative humidity in audible noise generation and propagation are analysed. The data is stored in two files: (1)temperature.csv (2)relative humidity.csv</p>
Models and simulation results of "A relatively dry mantle transition zone revealed by geomagnetic diurnal variations"
<p>Data and models used in Zhang et al. 2022: A relatively dry mantle transition zone revealed by geomagnetic diurnal variations. See ReadMe for more detailed description of file contents. Files includes:</p> <p>1. 3D Conductivity models (ascii) and corresponding perturbed magnetic field on the surface (Matalab Mat format):</p> <p>Global*Model, Secondary*Hs_1cpd.mat</p> <p>2. inhomogeneous surface conductance layer for thin-sheet modeling (Matlab Mat format):</p> <p>SurfaceCond1degx1deg.mat</p> <p>3. 1D inversion profiles to generate Figure 1 (Matalab Mat format)</p> <p>depth.mat, MultiMode*.mat</p> <p>4. PCA used for the data analysis (Matalab Mat format). </p> <p>Data_1-4cpd_Mode_1-5.mat</p> <p>5. Source estimates results used for the preferred conductivity model (Matalab Mat format).</p> <p>SphereCoeff_PerferedSource.mat</p> <p> </p>
Simulation Results of the Paper entitled "The Design of a Zone-Picking System with Cooperation Area between Neighboring Zones and Its Cooperation Methods" (submitted to International Journal of Production Research)
<p>Simulation Results of the Paper entitled “The Design of a Zone-Picking System with Cooperation Area between Neighboring Zones and Its Cooperation Methods” (submitted to International Journal of Production Research)</p>
Numerical simulation results of debris flow under different condition in Chutou gully
<p>We release four datasets that reflect the numerical simulation results of debris flow with a recurrence period of 100 years under different check dam conditions, including with and without check dams and the check dam breakage. The simulation parameters and topography were taken from the Chutou gully, Miansi Town, Wenchuan County, Sichuan Province, Southwestern China. The first dataset indicates the final flow depth distribution under the check dam breakage. The second dataset indicates the final flow depth distribution with check dams. The third dataset indicates the final flow depth distribution without check dams. The fourth dataset indicates the flow velocity evolution at different locations, columns A, E, I and M refer to different times, the rest refer to the flow velocity at different locations at corresponding times. The datasets can be used to analyse the effect of the check dam on debris flows or deposit evolution. The first three datasets can be viewed or edited through the ArcGIS software; the last dataset be viewed or edited through the Excel software.</p>
Model simulation results for "Enhanced seasonal amplitude of atmospheric CO2 by the changing Southern Ocean carbon sink"
<p>This dataset contains the seasonal variations of monthly mean atmospheric CO<sub>2</sub> concentration derived from GEOS-Chem model simulations during 2000-2016. Monthly terrestrial CO2 fluxes derived from CLM4.5-CN, used as an input dataset for the GEOS-Chem simulations, are also included.</p> <p>There are six sets of GEOS-Chem simulation results; "ctrl", "BIOfix", "OCNfix", and "FFfix" are the main experiments to evaluate the effects of changes in terrestrial CO<sub>2</sub> fluxes, air-sea CO<sub>2</sub> fluxes, and fossil fuel CO<sub>2</sub> emissions on the seasonal amplitude of atmospheric CO<sub>2</sub> over the globe; "ALLfix" and "OCNfix_SO" are additional experiments for identifying the effects of changes in the other factors (i.e., atmospheric transport and biomass burning) and regional changes in air-sea fluxes in the Southern Ocean. </p> <p>Detailed explanations for each simulation are described in the main text.</p> <p>*We recommend contacting us first if you want to utilize the dataset for study (yjm921@gmail.com).</p>
Simulation Results of the Distributed Schedule Optimization with Energy Storages using EO-COHDA
<p>This dataset contains the result of the evaluation of an approach to integrate energy storages in distributed flexibility negotiations. The data was created using the implemented approach on https://gitlab.com/digitalized-energy-systems/models/eo-cohda. To work with this results we highly recommend to use eo-cohda as well, because it provides a lot convenient utility functions for this.</p> <p>The dataset has been divided in two parts:</p> <ol> <li>the result of the negotiation, <ul> <li>format: hdf, readable using hdf-viewers/python</li> </ul> </li> <li>the generated schedules of the energy storages used for the negotiation. <ul> <li>format: binary, pickled real power schedules, readable using eo-cohda's utility methods.</li> </ul> </li> </ol> <p> </p>
Simulation results from Bishop et al. 2022
<p>Variant call format (VCF) file output by the Geonomics simulation used in Bishop et al. 2022. For more information on this dataset see the wingen R package repository on GitHub (<a href="https://github.com/AnushaPB/wingen">https://github.com/AnushaPB/wingen</a>)</p>
Simulation data results from SEAMANCORE model
<p>This dataset contains simulation data from the SEAMANCORE model for a region in the Indo-Pacific.</p> <p>Four different management alternatives were examined with 1100 model runs each to account for parameter uncertainty.</p> <p>This dataset was created by running the SEAMANCORE model (https://doi.org/10.5281/zenodo.7155783) with the different input parameters (input_parameters.zip). </p> <p>The results (SEAMANCORE.zip) are daily time series of proportion of benthos groups, biomass of fish functional groups, and fished biomass in the investigated area over 2280 days (~6 years) for the four different management alternatives and 1100 parameter specifications each.</p>
Supplementary Materials include results of simulation experiments to investigate the impact of phylogenetic regression with model violations.
<p>Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has sown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution—the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt lineage-specific evolutionary shifts, which current regression models are largely ill-equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations than the standard least-squares estimator, while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against unreplicated evolution and a better understanding of evolutionary model performance in challenging–yet biologically important–settings.</p>
Simulation results with the EULAG research model for the publication: "Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost"
<p>Supplementary material for the publication</p> <ul> <li>Mark Schlutow, Tobias Stacke, Tom Doerffel, et al. Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost. ESS Open Archive . January 24, 2024. <a href="https://doi.org/10.22541/essoar.170612558.81370785/v1">https://doi.org/10.22541/essoar.170612558.81370785/v1</a></li> </ul> <p>The material contains all simulation results and raw outputs that are necessary to reproduce the figures and statistics of the publication. </p>
2024 4Seasons SCREAMv1 Paper Simulation Results
<p>Globus Collection of simulation results for the 2024 SCREAMv1 four-seasons campaign. Used in the Donahue, AS et al. paper entitles "To exascale and beyond – The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), a performance portable global atmosphere model for cloud-resolving scales"</p> <p>Available on NERSC HPSS, contact corresponding author for access.</p>
Simulation results for "Integration of Plant and Microbial Oil Processing at Oilcane Biorefineries for More Sustainable Biofuel Production" publication
<p>Simulation results for "Integration of Plant and Microbial Oil Processing at Oilcane Biorefineries for More Sustainable Biofuel Production" publication.</p>
Mixed Precision Optimization Method acceleration time and numerical simulation results
<p>The stored data are the speedup ratios of the MPFlow method and other acceleration algorithms, as well as the elastic wave response data obtained by numerical simulation using the MPFlow method.</p>
Simulation results for "Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes"
<p>This dataset contains simulations results presented in the paper "Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes".</p> <p>The files are in `csv` file format, with data easily processable using the python library `sinter`.</p>
Measurement Results of Single-Bit Sigma-Delta Modulator and Simulation Data for DFT Based RSSI Calculation
<p>Measurement of single-bit output data stream of an low-IF receiver with sigma-delta ADC. Inphase and Quadraturephase signals can be found in raw_rx_*.csv files. Simulation results of DFT based RSSI calculation are found in sim_RSSI_*.csv. Measurement results of the RSSI dynamic range are located in meas_RSSI_dynamicRange.csv.</p>
Simulation results for PhD thesis
<p>This is the dataset used to generate the results for PhD thesis titled: "Source Location Privacy in Wireless Sensor Networks Under Practical Scenarios: Routing Protocols, Parameterisations and Trade-Offs".</p>
AMDADOS simulation results and scaling for IEEE Oceans paper
<p>Presents inputs datasets for the AMDADOS paper presented at IEEE Oceans 2018 (<a href="https://doi.org/10.5281/zenodo.1346168">10.5281/zenodo.1346168</a>) to enable replication of experiments</p>
Simulation results for the earthquake cycle including huge SSEs.
<p>The damped data for the simulation results shown in the paper titled "Nucleation for characteristic earthquakes in simulated cycles involving huge slow slip events on the deeper estension"(now in under review), authored by Ohtani, Makiko, N. Kame, and M. Nakatani.</p> <p> </p> <p> </p>
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.