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328 results for “Simulation results”
Data and morphometric results from a physical experiment simulating washover deposition
<p>This portfolio includes two sets of data, used and explained in Lazarus, Williams & Goldstein (preprint: <a href="https://doi.org/10.31223/X5JH1X">https://doi.org/10.31223/X5JH1X</a>):</p> <ol> <li>washover morphometry measured from a physical experiment to simulate washover deposition, conducted at the Total Environment Simulator (TES) at the University of Hull ('TES_washover_morphometry.csv');</li> <li>the terrestrial laser-scan data in which those morphometric characteristics of washover were measured ('Run01.zip' – 'Run22.zip'), with a table of experimental conditions for each trial (run).</li> </ol> <p> </p> <p><strong>Washover morphometry dataset</strong></p> <p><em><strong>TES_washover_morphometry.csv</strong></em> – Washover morphometry from the 22 experimental trials in the TES at Hull. Dataset includes: deposit ID, throat width (m), deposit width (m), intrusion length (m), deposit area (m<sup>2</sup>), deposit volume (m<sup>3</sup>), deposit perimeter (m), built fraction, forcing mode ("inundation" or "wave-driven"), and a dataset label ("TES").</p> <p> </p> <p><strong>Table of experimental conditions</strong></p> <p><em><strong>TES_parameters_trial_log_full_v2.pdf</strong></em> – Table of experimental conditions for each trial (run).</p> <p> </p> <p><strong>Data folders</strong></p> <p>These folders contain terrestrial laser-scan data from washover experiments at the Total Environment Simulator (TES) at the University of Hull. These files were used to identify and measure the individual washover deposits in each experimental trial.</p> <p>Each folder (Run01–Run22) contains data (in 'e57' file format) for a specific trial, and includes the following:</p> <p><em><strong>RunX_DOD.e57</strong></em> – This is a point cloud of the difference between the Pre_Processed and Post_Processed files. This is produced by subtracting the 'post' values from the 'pre' values.</p> <p><em><strong>RunX_Post_Processed.e57</strong></em> – This is a point cloud of the topography at the end of the experimental run, created from the raw data file by trimming out extraneous points.</p> <p><em><strong>RunX_Post_Raw.e57</strong></em> – This is a point cloud of the topography at the end of the experimental run, created in FARO Scene to merge two individual point clouds (covering the entire interior of the TES) into a non-proprietary file format.</p> <p><em><strong>RunX_Pre_Processed.e57</strong></em> – This is a point cloud of the topography prior to the experimental run, created from the raw data file by trimming out extraneous points.</p> <p><em><strong>RunX_Pre_Raw.e57</strong></em> – This is a point cloud of the topography prior to the experimental run, created in FARO Scene to merge two individual point clouds (covering the entire interior of the TES) into a non-proprietary file format.</p>
Variability due to climate and chemistry in observations of oxygenated Earth-analogue exoplanets: Simulations and results
<p>The Great Oxidation Event was a period during which Earth's atmospheric oxygen (O<sub>2</sub>) concentrations increased from ~10<sup>−5</sup> times its present atmospheric level (PAL) to near modern levels, marking the start of the Proterozoic geological eon 2.4 billion years ago. Using WACCM6, an Earth System Model, we simulate the atmosphere of Earth-analogue exoplanets with O<sub>2</sub> mixing ratios between 0.1% and 150% PAL. Using these simulations, we calculate the reflection/emission spectra over multiple orbits using the Planetary Spectrum Generator. We highlight how observer angle, albedo, chemistry, and clouds affect the simulated observations. We show that inter-annual climate variations, as well as short-term variations due to clouds, can be observed in our simulated atmospheres with a telescope concept such as LUVOIR or HabEx. Annual variability and seasonal variability can change the planet's reflected flux (including the reflected flux of key spectral features such as O<sub>2</sub> and H<sub>2</sub>O) by up to factors of 5 and 20, respectively, for the same planetary phase. This variability is best observed with a high-throughput coronagraph. For example, HabEx (4 m) with a starshade performs up to a factor of two times better than a LUVOIR B (6 m) style telescope. The variability and signal-to-noise ratio of some spectral features depends non-linearly on atmospheric O<sub>2</sub> concentration. This is caused by temperature and chemical column depth variations, as well as generally increased liquid and ice cloud content for atmospheres with O<sub>2</sub> concentrations of <1% PAL.</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Datasets containings simulation results
<p>These are the datasets containings the simulation results presented in "Approximate Bayes factors: flexible and likelihood-free model comparison with ABrox".</p>
Correcting for population stratification reduces false positive and false negative results in joint analyses of host and pathogen genomes [G2G-Simulator: Simulated dataset]
<p>Data associated with the paper 'Correcting for population stratification reduces false positive and false negative results in joint analyses of host and pathogen genomes'.</p> <p>It contains the raw simulated data from the 'G2G-Simulator' program. </p> <p>Those data need to be loaded in a R environment .</p> <p>You can reproduce plots present in the paper by parsing the R object using the script 'parse_paper_data.R' present in the G2G-Simulator GitHub repository (https://github.com/onaret/G2G-Simulator/paper/parse_paper_dataset.R). </p>
Data for: Field data and simulation results of the Seoul Halloween crowd-crush
<div> <div>This data contains field data and simulation results related to the Seoul Halloween crowd-crush that occurred in Itaewon, Seoul, in 2022. The dataset includes original mobile LTE data from the event, conversations between pedestrians and police, and simulation codes and results. The simulations were conducted using MATLAB and Fortran to analyze crowd dynamics and potential safety measures.</div> </div>
Results of MD simulations of collision cascades in hcp Zr at 600 K.
<p>This is a database consisting of pre- and post-processing scripts, input files and results from molecular dynamics (MD) simulations of collision cascades in LAMMPS. The database is associated with the paper: <a href="https://arxiv.org/abs/2405.03332">Molecular dynamics simulations of neutron induced collision cascades in Zr</a>. The material studied was hcp Zr at the temperature of 600 K. A detailed description of the record can be found in the <code>pdf</code> file, as well as in Emacs <code>org</code> and Jupyter notebooks (<code>ipynb</code>). Files also include Python code describing how to access and process the data.</p>
The shielding properties of textile FSS - simulations and results
<p>The data collection includes data from measurements and simulations of the S21 parameter, which determines the shielding properties of textile and foil based Frequency Selective Surface - FSS. The results of the work related to the attached data were published in Rac-Rumijowska, O., Pokryszka, P., Rybicki, T., Suchorska-Woźniak, P., Woźniak, M., Kaczkowska, K., & Karbownik, I. (2024). Influence of Flexible and Textile Substrates on Frequency-Selective Surfaces (FSS). Sensors, 24(5), 1704.</p>
Source Code and Simulation Results: Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna
<h3>Summary</h3> <p>This publication supplements the article "Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna" with tabulated data and Matlab code that allows the reproduction of the results. Within the article, the chiral behavior of single and double plasmonic nano antennas made from silver is numerically investigated with a focus on the coupling of a linear polarized dipole as an excitation source to the helix.</p> <h3>Simulation Setup - FEM Simulations</h3> <p>The script "run_wavlengthscan.m" allows to reproduce all simulations of the article. It can be chosen between the single and double helices, by specifying the keys parameter "keys.doppelhelix" where 0 gives a single and 1 a double helix. The number of turns can be specified by choosing "keys.case". The dipol is located within a 20nm thick hBN substrate layer, on glass (BK7). Results of the Purcell enhancement can be plotted using the scripts "display_results_single_helix.m" and "display_results_doublehelix.m" in the folder "results". The far-field plots can be reproduced using the scripts "display_farfiel_polarization_single_helix.m" and "display_farfiel_polarization_double_helix.m" of the folder "FunctionsAndScripts".</p> <p>The template for the mesh is contained in the folder "generate_grid_file", where the parameters of the helix (for example: radius, tube radius, and pitch height) can be modified.</p> <p>Within the folder "project3D" all required .jcm files are stored. Copy the "grid.jcm" file with the geometry of interest to this folder to perform simulations.</p> <p>All required keys parameters for the JCM template files (.jcmt, jcmpt) are set within the functions "set_numerical_parameter.m" and "set_physical_parameters.m", contained in the folder "FunctionsAndScripts". Therein, the function "set_sources.m" specifies the parameters for the dipole excitation, such as the position, and the strength (equivalent to the polarization).</p> <h3>Semi-Analytical Model</h3> <p>The Jupyter notebook "Semi_Analytical_Plasmonic_Helix.ipynb" contains the commented Python script for the semi-analytical design tool used to obtain far-field radiation patterns of the single helix. This semi-analytical design tool is based on an analytical model developed in [4]. The script can be divided into three parts. First, the single helix is defined, and a linear wavelength scaling law [5] is used to determine the illuminating wavelengths at which Fabry-Pérot resonances occur. Second, the overlap integral between the mode current on the helix and the incident electric field is evaluated for a given direction of incident light. Thirdly, the direction of incidence is varied to obtain the far-field radiation patterns. The script allows for the radiation patterns to be exported as a .csv file. Alternatively, the radiation patterns can be plotted directly using the provided single_plot functions.</p> <h3>Material</h3> <p>The material data has been taken from the <a href="https://refractiveindex.info/" target="_blank" rel="noopener">refractiveindex.info</a> database. For silver the data is taken from tabulated data from Johnson and Christy [1] . The dispersion relation for hBN comes from [2] and tabulated data for glass (BK7) from [3]. The MATLAB script "material_properties_plot.m" plots the material fits above the wavelengths of interest. The required tabulated data is given in the folder "material_data".</p> <p>With 'material_properties_plot.m' the fits to the material data can be reproduced and plotted.</p> <h3>Usage</h3> <p>The .zip folder Helix_FEM contains all data and scripts to reproduce the plots from the 3D FEM simulations.</p> <p>The Jupyter Notebook Semi_Analytical_Plasmonic_Helix reprouces the results from the semi-analytical model.</p> <h3>Requirements</h3> <ul> <li>JCMsuite (at least 5.4.0)</li> <li>MATLAB (tested with version R2023b)</li> <li>Python (tested with Version 3.10.9)</li> <li>Jupyter Notebook (tested with 6.5.2) </li> </ul> <p>To run the simulations with JCMsuite you must replace corresponding placeholders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <h3>References</h3> <p>[1] P. B. Johnson and R.-W. Christy, “Optical constants of the noble metals,” Phys. Rev. B 6, 4370 (1972).</p> <p>[2] S.-Y. Lee, T.-Y. Jeong, S. Jung, and K.-J. Yee, “Refractive index dispersion of hexagonal boron nitride in the visible and near-infrared,” Phys. Status Solidi B 256, 1800417 (2019).</p> <p>[3] “SCHOTT Zemax catalogue 2017-01-20b,” (2017).</p> <div>[4] K. Höflich et al., "Resonant behavior of a single plasmonic helix." Optica 6, 1098(2019).</div> <div> </div> <div>[5]L. Novotny, "Effective wavelength scaling for optical antennas", Phys. Rev. Lett. 98,266802 (2007).</div>
Mechano-circadian simulation results
<p>Simulation results associated with the manuscript, "Computational modeling establishes mechanotransduction as a potent modulator of the mammalian circadian clock". See README file for descriptions of individual results files. All are stored as .m files for use in MATLAB. README reproduced below for easy access:</p> <ul> <li>bayesianAnalysis.mat: This should be loaded after initializing UQLab and before running any of the scripts. It contains all the results from MCMC for Bayesian parameter estimation in a single variable, `myBayesianAnalysis`.</li> <li>pSol_MAP.mat: This can be loaded for running simulations using only the maximum a posteriori parameters, contains a single variable `pSol`.</li> <li>SobolAnalysis.mat: results of Sobol sensitivity analysis stored in single variable `SobolAnalysis` for use with UQLab.</li> <li>circStored.mat: Contains variable `circStoreCell` to generate phase diagrams in CircadianAnalysis file.</li> <li>BranchesStored.mat: Contains variable `BranchesStored` with the coordinates of Hopf bifurcation curves in the MRTF-YAP/TAZ phase plane. Generated by ddeBifCircadian.m, for use with CircadianAnalysis.m.</li> <li>nMechCircStored.mat and nMechBranches.mat: Alterative versions of the above, corresponding to tests of different coupling Hill coefficients [1,1.5,2,3,4.5]</li> <li>StiffnessTests.mat: results from population level simulations for Figure 4, with substrate stiffnesses [0.1, 0.3, 1, 3, 10, 30, 100, 300, 1e7]. Data stored in single variable `popSeq`, generated by MechanoCircadian_main.m</li> <li>WTTests.mat: tests from Abenza et al for Figure 5, population level data in single variable `popSeq`, see MechanoCircadian_main.m for more details</li> <li>StiffnessTestsYAPMutant.mat: same as stiffnessTests above, but for YAP mutant 5SA-YAP (see settings in MechanoCircadian_main.m)</li> <li>StiffnessTestsLaminMutant.mat: same as stiffnessTests above, but for YAP mutant 5SA-YAP (see settings in MechanoCircadian_main.m)</li> <li>AltMutantTests.mat: tests for Figure S5, all at substrate stiffness = 30 kPa. 6 populations, corresponding to 0.5x YAP mutant, 2x YAP mutant, 4x YAP mutant, 1.5x NPC opening (Lamin mutant), 3x NPC opening (Lamin mutant), 5x NPC opening (Lamin mutant) - all stored in `popSeq` as above</li> </ul>
Evaluation datasets and results of the paper "A Framework for Measuring the Quality of Business Process Simulation Models"
<p>Datasets and files used in the evaluation of the publication entitled "A Framework for Measuring the Quality of Business Process Simulation Models", where:</p> <ul> <li><strong><em>BPS-models/</em></strong>: folder containing the BPS models used in the evaluation (the BPS models discovered by ServiceMiner are not included due to privacy reasons). <ul> <li>The BPS models discovered by SIMOD are composed of <em>i)</em> a BPMN file with the process model structure, and <em>ii)</em> a JSON file with the parameters of the simulation. These files correspond to the format of Prosimos simulation engine (<a href="https://prosimos.cloud.ut.ee/">https://prosimos.cloud.ut.ee/</a>).</li> <li>The BPS models of the Loan Application and Procure to Pay processes are composed of a BPMN file with both the process model structure and parameters, corresponding to the format of the BIMP simulator used in APROMORE (<a href="https://apromore.com/">https://apromore.com/</a>).</li> </ul> </li> <li><em><strong>measures/</strong></em>: folder containing the distance values of each measure reported in the paper.</li> <li><em><strong>original-event-logs/</strong></em>: folder containing the (train and test) event logs used in the evaluation.</li> <li><em><strong>simulated-logs/</strong></em>: folder containing the simulated logs evaluated in the paper (synthetic, SIMOD, and ServiceMiner).</li> <li><em><strong>ComputeLogDistance.py</strong></em>: script to compute the distance measures proposed in the paper.</li> </ul> <p> </p> <p>To evaluate the distance measures of a set of simulated event logs in the folder <em>simulated_logs/</em> against the test log <em>test_event_log.csv.gz</em>, run:<br><em> python ComputeLogDistance.py -cfld test_event_log.csv.gz simulated_logs/</em></p> <p>*The flag <em>-cfld</em> is optional, due to the high computational complexity of the CFLD measure.</p> <p><strong>WARNING</strong>: set the column names of each log accordingly (where <em>log_1_ids</em> are the IDs of the test log, and <em>log_2_ids</em> the IDs of the simulated logs). Examples:</p> <pre><code># Column IDs for the (train/test) real-life logs, and the SIMOD simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='start_time', end_time='end_time', resource='resource' ) # Column IDs for the Loan Application and Procure to Pay simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='Start_Time', end_time='End_Time', resource='resource' ) # Column IDs for the ServiceMiner simulated logs. EventLogIDs( case='case_id', activity='Activity', start_time='start_time', end_time='end_time', resource='Resource' )</code></pre> <p> </p>
Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.
The Brain and Propranolol Pharmacokinetics in the Elderly-Figure 1.(a)Results of the Monte-Carlo simulations to describe pharmacokinetics of young patients with validation from the Taegtmeyer 2014 publication(Taegtmeyer et al., 2014)
<p>Propranolol has been found to be therapeutically effective, to obtain a clinical response by<br> beta-adrenoceptor blockade, at plasma levels of greater than 20 ng/mL(Coltart et al., 1971;<br> Frishman, 1988; Johnsson and Regàrdh, 1976). Thus, to display the data, we used highlighted<br> plasma concentration where the pharmacokinetic curve falls below 20ng/mL threshold for<br> therapeutic efficacy in the patient’s plasma.</p>
Figure 2.(a)Results of the Monte-Carlo simulations describing a population of elderly patients after a single oral dose of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>In effort to identify the recommended Propranolol dosage for elderly patients, we identified<br> the patient package inserts from the Food and Drug Administration (FDA) Inderal label, who<br> manufacture propranolol. Based from FDA Wyeth Propranolol label, for dosing in the geriatric<br> population,the label states that there were not sufficient numbers of clinical study participants who<br> were 65-years and older to properly determine the difference in response young and elderly<br> patients.</p>
Figure 4. Simulation (Monte-Carlo, n=200) results elderly patients taking a 10mg oral dose resulting in similar Cmax, maximum plasma concentration, to the young patients taking a 40mg oral dose. The dotted lines illustrate the 10th and 90th percentiles of plasma levels of the elderly population with a 10mg oral administration of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Thus, the package insert (see 1) recommends clinicians start at the lower end of the dosing<br> range, without further details.<br> Similarly, Pfizer manufactures Inderal® LA (Propranolol HCI), which is the long-acting<br> form of propranolol and their package insert (see 2) states, “There is no information available for<br> elderly patients.” Though the kinetics for the long-acting formdiffers from the standard form,<br> manufactured by Wyeth, we would suspect a 10mg dose for the elderly would achieve a similar<br> maximum plasma concentration (Cmax) to that of the younger patient cohort.This 10mg, which is<br> 25% of the original 40mg, dosing schedule is based on our simulations at 10mg in the geriatric<br> population.</p>
Time-averaged simulation results and in vivo measurements to show the impact of red blood cells on the flow field in the cortical microvasculature
<p>The dataset contains 5 files. 4 of them are time-averaged results of blood flow simulations with discrete red blood cell (RBC) tracking in realistic microvascular networks. The 5th file contains median values of RBC velocity measurements at capillary bifurcations in the somatosensory cortex of the mouse.</p> <p>Further notes on the simulation results:<br> - The realistic microvascular networks are from the mouse parietal cortex and have first been published in Blinder et al., 2013, Nature Neuroscience (<a href="https://doi.org/10.1038/nn.3426">https://doi.org/10.1038/nn.3426</a>).<br> - The numerical model to simulate blood flow in realistic microvascular networks has been described in Schmid et al., 2017, PLOS Computational Biology (<a href="https://doi.org/10.1371/journal.pcbi.1005392">https://doi.org/10.1371/journal.pcbi.1005392</a>).<br> - MVN1 and MVN2 stands for microvascular network 1 and 2, respectively.<br> - wRBCs and wpPs stands for 'with red blood cells' and 'with passive particles'. These terms describe two different numerical models: The wRBC-model accounts for all RBC related flow phenomena. The wpP neglects the phase-separation and the Fahraeus-Lindqvist effect, i.e. RBCs and flow are decoupled. Further details are available from Schmid et al. (2019, <a href="https://doi.org/10.1371/journal.pcbi.1007231">https://doi.org/10.1371/journal.pcbi.1007231</a>)</p> <p><strong>File format: </strong>pickle (Python)</p> <p><strong>Files 1 - 4 </strong>(Time-averaged simulation results):<br> Filenames: MVN1_wpPs.tar.bz2, MVN2_wpPs.tar.bz2, MVN1_wRBCs.tar.bz2, MVN2_wRBCs.tar.bz2</p> <p>Each compressed folder contains two files:<br> <br> edgesDict.pkl: dictionary with edge/vessel related data: </p> <ul> <li>flow: Flow rate in vessel [um^3/ms]</li> <li>length: Vessel length [um] (Tortuosity is considered)</li> <li>htt: Tube hematocrit in vessel [-]</li> <li>diameter: Effective vessel diameter [um]</li> <li>connectivity: Vertex indices, e.g. start and end vertex of the corresponding vessel</li> </ul> <p>verticesDict.pkl: dictionary with vertex/bifurcation related data:</p> <ul> <li>index: Index of the current vertex </li> <li>coords: Coordinates to describe the position of the vertex [um]</li> <li>pressure: Pressure at the vertex [mmHg]</li> </ul> <p> </p> <p><strong>File 5</strong> (in vivo RBC velocity measurements):<br> Filename: measurementDict.pkl</p> <p>keys:</p> <ul> <li>divergent_d1: divergent bifurcation, RBC velocity measurement in daughter vessel 1</li> <li>divergent_d2: divergent bifurcation, RBC velocity measurement in daughter vessel 2</li> <li>convergent_m1: convergent bifurcation, RBC velocity measurement in mother vessel 1</li> <li>convergent_m2: convergent bifurcation, RBC velocity measurement in mother vessel 2</li> </ul> <p><br> Data structure: list of list,<br> e.g. daughter vessel 1:<br> [[bif.1 - measure.1, bif.1 - measure.2, bif.1 - measure.3], [bif.2 - measure.1, bif.2 - measure.2, bif.2 - measure.3],...]<br> bif.: bifurcation, measure.: measurement.<br> The order of bifurcations is the same for 'divergent_d1' and 'divergent_d2' (and for 'convergent_m1' and 'convergent_m2'). </p> <p> </p>
Simulated Neutral Landscape Models and Scaling Results for Testing of Multi-Dimensional Grid-Point Scaling Algorithm
<p>The data package contains (1) simulations of neutral landscape models of categorical data for benchmark testing of scaling algorithms, and (2) scaling results for testing consistency and sensitivity of the newly developed Multi-Dimensional Grid-Point (MDGP) scaling algorithm.</p> <p>Neutral landscapes were generated using the "nlmpy" python module. The MDGP scaling algorithm and the test framework were implemented in R (https://github.com/gannd/landscapeScaling). </p>
bin3C - CheckM result for bin3C solution of the simulated community at full depth
<p>Supplementary data table S2 from the manuscript</p> <p><strong>bin3C :</strong><strong> Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)</strong></p> <p>CheckM validation result for the simulated community at full shotgun and Hi-C depth of coverage.</p>
VERTEX 2.0 Rat Neocortex Simulation Results
<p>The results of simulations of electric field stimulation in rat neocortex.</p> <p>Three stimulation paradigms have been used, a single pulse, a paired pulse, and theta burst stimulation.</p> <p>The VERTEX simulator code that generated these results is available at https://github.com/haeste/Vertex_2.</p>
Simulation results from HadGEM-UKCA perturbed parameter ensembles for Yoshioka et al. 2019 JAMES
<p>This dataset was created from perturbed parameter ensembles (PPEs) using HadGEM-UKCA atmospheric composition climate model and used in Yoshioka et al. (Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and their Radiative Forcing) submitted to The Journal of Advances in Modeling Earth Systems (JAMES). It contains the following data;</p> <p>N50_sfc_data.tar.gz contains simulated number concentrations of particles larger than 50 nm from one-at-a-time screening experiments used in Figure 1 of the paper. teaca-teacl are job IDs of different experiments where values of parameters RAIN_FRAC and CLOUD_ICE_THRESH were varied;<br> teaca: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.7<br> teacb: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.5<br> teacc: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.3<br> teacd: RAIN_FRAC=0.6, CLOUD_ICE_THRESH=0.1<br> teace: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.7<br> teacf: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.5<br> teacg: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.3<br> teach: RAIN_FRAC=0.4, CLOUD_ICE_THRESH=0.1<br> teaci: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.7<br> teacj: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.5<br> teack: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.3<br> teacl: RAIN_FRAC=0.2, CLOUD_ICE_THRESH=0.1</p> <p>CCN0p2_Stations_for_figure6.xlsx contains cloud condensation nuclei concentration at 0.2% supersaturation calculated for the AER ensemble of simulations at station locations used to create Figure 6 of the paper.</p> <p>Mean_Variance_CCN0p2_L9_AER_2008ANN.nc and Mean_Variance_CCN0p2_L9_AER-ATM_2006ANN.nc are emulator means and variances of annual average cloud condensation nuclei concentration at 0.2% supersaturation at model level 9 (~650m) in AER and AER-ATM ensembles and used in Figures 7 and 8.</p> <p>Mean_Variance_AOD550_AER_2008ANN.nc and Mean_Variance_AOD550_AER-ATM_2006ANN.nc are emulator means and variances of annual average aerosol optical depth at 550 nm in AER and AER-ATM ensembles and used in Figures 7 and 8.</p> <p>Mean_Variance_RFnet_AER_2008ANN.nc and Mean_Variance_ERF_AER-ATM_2006ANN.nc are emulator means and variances of annual average aerosol radiative forcing in AER ensemble and aerosol effective radiative forcing in AER_ATM ensemble and used in Figures 7, 8 and 9.</p> <p>Mean_Variance_SeaSalt_Load_AER_2008ANN.nc and Mean_Variance_SeaSalt_Load_AER-ATM_2006ANN.nc are emulator means and variances of annual average column mass loading of sea salt aerosol in AER and AER-ATM ensembles and used in Figure 9.</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.