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138 results for “emulator”
Data for "emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves"
<p>This dataset contains codes, data, tables, andd figures (high resolution) related to the following publication: Xiong, W., K. Tanaka, P. Ciais, D. J. A. Johansson, M. Lehtveer (2022) emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves. Submitted to arXiv on 23 December 2022.</p>
Measurement of interturn short-circuits emulation on dual three-phase PMS motor
<p>The published data contains measurement records made on a special permanent magnet synchronous motor (PMSM). The motor has a stator with winding taps. The winding taps can be used to emulate various severities of interturn short-circuits.</p> <p>A detailed machine description and modeling can be found in the paper "Interturn short circuit modelling in dual three-phase PMSM" (https://dx.doi.org/10.1109/IECON49645.2022.9968364). </p> <p>The file name specifies the measurement conditions according to</p> <p>spd10-5000rpm_flt<strong>N</strong>z<strong>P</strong>_<strong>XX</strong>NM.mat</p> <p><strong>N</strong> - Number of shorted turns (0-healthy operation)</p> <p><strong>P</strong> - Fault phase ( U or V)</p> <p><strong>XX </strong>- Load torque generated by a dynamometer</p> <p> </p> <p>For example, the file spd10-5000rpm_flt1zu_25NM.mat contains data where one coil turn in phase U was shorted, and the machine was producing 25 Nm torque.</p> <p>Measurement was performed by the microcontroller and synchronised with the control algorithm. The sample rate is 10 kHz for this reason. Each data file contains measured currents in stator coordinates as well as currents and voltages (control value) in rotor (dq) coordinates. These variables are measured for each three-phase sub-system. Measured motor position, speed and required motor speed are also included. </p>
Nicholls et al 2022 Emulator Changes
<p>Repository reproducing plots and processing used in Nicholls et al 2022 (https://doi.org/10.1029/2022GL099788).<br> <br> For questions and comments, please contact Zebedee Nicholls (zebedee.nicholls@climate-energy-college.org). For full details, please see https://gitlab.com/magicc/nicholls-et-al-2022-emulator-changes.</p>
WESN-emulated motor execution EEG data
<p>This dataset contains EEG measured during a motor execution task and processed as to emulate EEG originating from a wireless EEG sensor network composed of mini-EEG devices, as presented in [1]. It is a processed version of the original High Gamma dataset of [2].</p> <p>In mini-EEG devices, we cannot measure the potential between a given electrode and a distant reference (e.g. the mastoid or Cz electrode) , as we would in traditional EEG caps. Instead, we can only record the local potential between two nearby electrodes belonging to the same sensor device. To emulate this setting using a standard cap-EEG recording, we we can considers= each pair of electrodes within a certain maximum distance as a candidate electrode pair or node. By subtracting one channel from the other, we remove the common far-distance reference and obtain a signal that emulates the local potential of the node.</p> <p>We applied this method to the High Gamma dataset as follows. First, the 44 channels covering the motor cortex were selected. These channels are indicated in the <em>channel_labels.json</em> file. Then, the rereferencing between channels with a distance threshold of 3 cm was applied, yielding a set of 286 candidate electrode pairs or nodes. The <em>nodes.json</em> file indicates the specific pair of channels composing each of these nodes. These have an average inter-electrode distance of 1.98 cm and a standard deviation of 0.59 cm. Finally, we applied the preprocessing described in [2], i.e., resampling at 250 Hz, highpass filtering above 4 Hz, standardizing the per-node mean and variance to 0 and 1 respectively, and extracting a window of 4.5 seconds for each trial.</p> <p>[1] Strypsteen, Thomas, and Alexander Bertrand. "A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks." <em>arXiv preprint arXiv:2308.08379</em> (2023).</p> <p>[2] Schirrmeister, Robin Tibor, et al. "Deep learning with convolutional neural networks for EEG decoding and visualization." <em>Human brain mapping</em> 38.11 (2017): 5391-5420.</p>
Example inference dataset for the Ai2 Climate Emulator (ACE)
<h1>Dataset for Ai2 Climate Emulator</h1> <p> </p> <div>This dataset contains a minimal example set of files and configuration to use for inference with the Ai2 Climate Emulator (ACE). Please see https://github.com/ai2cm/ace to install the necessary software. The included checkpoint is the same ace checkpoint as referenced in (https://zenodo.org/records/10791087). See README.md for a description of the included files.<br><br>v1.1: The initial condition zarr stores were erroneously missing all values in the first upload. These files have been fixed in this update. </div> <div> </div>
Measurement Dataset of Thermal Fault Emulation of a 46Ah High-Power Kokam Nano Pouch Cell via Uniform and Local Heating
<h1>Preface</h1> <p>This dataset contains experimental data that supplement the article <em>Thermal fault detection by changes in electrical behaviour in lithium-ion cells </em>(<a href="https://doi.org/10.1016/j.jpowsour.2021.229572" target="_blank" rel="noopener">10.1016/j.jpowsour.2021.229572</a>) in the Journal of Power Sources. This dataset extends the already published cell characteristics (see <a href="https://doi.org/10.17632/g443f7cn7p.2" target="_blank" rel="noopener">10.17632/g443f7cn7p.2</a>) by all measured quantities associated with the conducted study. Therefore, the dataset includes sensor readings that have not been described in the before mentioned documents due to space limitations. <em><br></em></p> <p>The published data belongs to the master thesis <em>Development of a model-based method for the early detection of safety-critical heating of lithium-ion cells (transl.), Klink</em> <em>(2020), TU Clausthal</em> that is connected to a study thankfully funded by the European Automobile Manufacturers' Association (ACEA).</p> <h1>Structure</h1> <p>The repository is subdivided in four directories (.zip) based on the content. Within these directories, the individual datasets can be found. While every dataset contains three different file types, the corresponding files can be identified based on the identical filenames. The following file types are provided:</p> <table> <tbody> <tr> <td><strong>File type</strong></td> <td><strong>Content</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>*.png</td> <td>Simple graph of the provided data.</td> <td>Missing values are interpolated.</td> </tr> <tr> <td>*.csv</td> <td>Tabular data of the dataset.</td> <td>Columns are separated by ";", the decimal point is ".".</td> </tr> <tr> <td>*.pickle</td> <td>Pickled object of a <a href="https://pandas.pydata.org/docs/index.html" target="_blank" rel="noopener">pandas</a> dataframe (Python) of the data. Preserve index and data types.</td> <td>Pickled with pandas version 2.2.2 using the pickle protocol 5</td> </tr> </tbody> </table> <p>The index and column names of the tabular time series have the following name scheme: X_Y_Z </p> <table> <tbody> <tr> <td><strong>Placeholder</strong></td> <td><strong>Description</strong></td> <td><strong>Example</strong></td> </tr> <tr> <td>X</td> <td>Quantity symbol</td> <td>U for voltage, I for current</td> </tr> <tr> <td>Y</td> <td>[optional] Additional index</td> <td><em>meas </em>for measured quantities</td> </tr> <tr> <td>Z</td> <td>Unit</td> <td>s for seconds, V for volt</td> </tr> </tbody> </table> <h1>Content</h1> <p>The dataset contains the data of both experiments for validation and for investigation of the fault characteristics of the conducted thermal abuse test. While the electrical quantities have been recorded using a battery test stand from Keysight/Scienlab (SL60/200/12BT4C) the temperature readings have been measured by type K thermocouples and recorded with data logger from PCE instruments. For all tests, the temperature sample rate has been set to 1 Hz. Please refer to the attached schematics in <em>SensorPositions.zip</em> for the placement of the individual thermocouples. In addition, T_5 represents the surrounding and T_2 is on the backside of T_1. The sensor positions T_7 and T_8 are added only for the uniform heating where T_7 is located between heating element and cell and T_8 central at the heating plate. Within the referenced article, only T_1 has been used. </p> <p>For details on the experimental setup, please refer to the method section of the linked article. </p> <h2>1. Validation</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>The data contains the electrical load of the cell with an extended WLTC driving cycle that has been scaled to approx. 400 A as well as the corresponding temperature at T_1. The test was conducted within a climatic chamber at 20°C. This data can be used to either parameterize a model of the cell or to validate a model based on other parameter such as the linked parameter set.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current for WLTC emulation</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_meas_C</td> <td>Cell surface temperature</td> </tr> </tbody> </table> <h2>2. ThermalCalibration</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>For each heating setup (uniform, local) this directory contains one data set. Within this experiment, the cell was pulsed with short high current (150 A) pulses to achieve a constant thermal heating power without changing the SOC. Based on the temperature response, a thermal model can be parameterized for both heating setups. Please note, that the electrical sample rate was higher and no interpolation was conducted. </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>3. UniformThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, the cell went into thermal runaway during a charging procedure. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. Temperature readings of 9999°C (Upper range) due to sensor failure have been replaced by NaN. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>4. LocalThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td> <p>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, a charging process and observation, no thermal runaway occurred. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. It seems that the heat transfer into the cell could have been optimized, as shown by the relatively low cell temperature despite the hot heating element. Nevertheless, this experiment can be used to investigate online detection of small cell changes due to local heating - even without thermal runaway. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</p> </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table>
Dataset from VR Streaming Server (Emulated) and Radio Access Network for Streaming Traffic
<p>The dataset contains an experiment in a site where UEs attach to a gNodeB that provides access to a streaming server that is stressed with high demanding transcoding workloads to emulate VR/AR processes. The UEs are realized through the Remote UE mode enabled by Amarisoft Simbox emulator, and the gNodeB is realized through the Amarisoft Callbox, which also provides the user plane function. The emulated VR streaming server is deployed as a Nginx pod in a Kubernetes cluster.</p> <p>We rely on MonB5G sampling functions that feed monitoring data (CPU and RAN parameters) to the monitoring system. A streaming video server has been deployed with the help of a NGINX server. It provides video-on-demand and video streaming, which can be accessed by any user (or UE) for real-time reproduction. This VR video streaming emulation aids to assess the performance of the network and therefore the benefits that each solution has brought. The video “Big Buck Bunny” with h.264 encoding and a resolution of 1920x1080p has been used for the experiments.The description of dataset features are:<br> 1-) Index Number,<br> 2-) Time: Time of the experiment,<br> 3-) N: number of VR streaming clients,<br> 4-) C: Average CPU of VR streaming server [mc]<br> 5-) O: Outbound traffic at the server average outbound traffic (O) flowing from the data interface of the video server. <br> 6-) R: Instantaneous downlink bit rate [Mbps],</p> <p>The original video file information:</p> <table> <tbody> <tr> <td> <p>Video codec </p> </td> <td> <p>Advanced Video Codec (AVC) </p> </td> </tr> <tr> <td> <p>Width </p> </td> <td> <p>1920 pixels </p> </td> </tr> <tr> <td> <p>Height </p> </td> <td> <p>1080 pixels </p> </td> </tr> <tr> <td> <p>Display aspect radio </p> </td> <td> <p>16:9 </p> </td> </tr> <tr> <td> <p>Duration </p> </td> <td> <p>10 min 34 s </p> </td> </tr> <tr> <td> <p>Max Bitrate </p> </td> <td> <p>16.7 Mb/s </p> </td> </tr> <tr> <td> <p>Frame rate </p> </td> <td> <p>30 FPS </p> </td> </tr> </tbody> </table>
Data package for paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events
<p>This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events".</p>
Emulator-based decomposition for structural sensitivity of core-level spectra
<p>We explore the sensitivity of several core-level spectroscopic methods to the underlying atomistic structure by using the water molecule as our test system. We first define a metric that measures the magnitude of spectral change as a function of the structure, which allows for identifying structural regions with high spectral sensitivity. We then apply machine-learning-emulator-based decomposition of the structural parameter space for maximal explained spectral variance, first on overall spectral profile and then on chosen integrated regions of interest therein. The presented method recovers more spectral variance than partial least squares fitting and the observed behavior is well in line with the aforementioned metric for spectral sensitivity. The analysis method is able to independently identify spectroscopically dominant degrees of freedom, and to quantify their effect and significance.</p>
Aerosol Microphysics Emulation Dataset
<p>This dataset contains input/output data of one time step of the M7 aerosol microphysics model. It is created to enable the use of machine learning to emulate the model part. It contains input and output pairs for training, validation and testing from separate days of the year. The code can be found <a href="https://github.com/paulaharder/aerosol-microphysics-emulation">here</a>. </p>
Data From: Emulation of Cardiac Mechanics using Graph Neural Networks
<p>Contains simulation results of the forward displacement from beginning to end-diastole for approximately 3000 synthetically generated left ventricle geometries.</p> <p>The simulation results are split into training, validation and test data.</p> <p>The data is described in detail in a forthcoming publication in <em>Computer Methods in Applied Mechanics and Engineering</em> - further information will be provided upon publication. A GitHub repository will also be made available, with code for processing the simulation data and training a Graph Neural Network emulator.</p>
Exoplanet atmosphere evolution: emulation with neural networks: supplementary data
<p>Supplementary data for 'Exoplanet atmosphere evolution: emulation with neural networks'. Includes MCMC chain for Bayesian Hierarchical Model (BHM) including samples of core mass for all planets, as well 5 hyper parameters (see paper for details). Additionally, a machine readable version of Table 1 is made available.</p>
STEMMUS SCOPE emulator train test example data of 2014
<div> <div>The "csv" file contains land-atmosphere variables and latent heat flux (LEtot) simulated by STEMMUS-SCOPE (soil-plant model), version 1.5.0, see GitHub repository <a href="https://github.com/EcoExtreML/STEMMUS_SCOPE" target="_blank" rel="noopener">STEMMUS-SCOPE</a>. The data spreads over 19 Fluxnet sites and for the year 2014 with hourly intervals. For more information see <a href="https://research-software-directory.org/projects/ecoextreml" target="_blank" rel="noopener">EcoExtreML project</a>.</div> <br> <div>This data was used as training data pairs to develop an emulator using a random forests regression algorithm, the "onnx" file. The target variable is "latent heat flux (LEtot)" and features are land-atmosphere variables. For more information about the emulator, see GitHub repository <a href="https://github.com/EcoExtreML/Emulator" target="_blank" rel="noopener">STEMMUS-SCOPE Emulator</a>.</div> <div> </div> <div>The model and data are used to create a tutorial on applying an explainability method, for example, Kernel SHAP using the package <a href="https://dianna.readthedocs.io/en/latest/" target="_blank" rel="noopener">DIANNA</a>. For more information see <a href="https://www.esciencecenter.nl/projects/deep-insight-and-neural-networks-analysis-dianna/" target="_blank" rel="noopener">Deep Insight and Neural Network Analysis (DIANNA) project</a>.</div> </div>
Data for: Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use
<p>This dataset contains the code and the data files needed to create the figures shown in the paper titled "Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use".</p>
TARDIS DALEK emulator dataset 1
<p>Dataset of ~80000 spectra and parameters created on the MSU and NYU supercomputers with TARDIS (github.com/tardis-sn/tardis) git hash c23cfb2895f043c25aa961e8a92df1baa87edd46 </p> <p>All the files needed to create this should be in this dataset. Loading the parameters requires numpy and pandas</p> <p>import numpy as np</p> <p>import pandas as pd</p> <p>spectra = np.load('grid_log_uniform_v1_part2_interp_spectra.npy')</p> <p>parameters = pd.read_hdf('grid_log_uniform_v1_part2.h5')</p> <p> </p> <p> </p>
Proxies' response times measured by clients in an emulated community network
<p>13 virtual nodes were deployed in Planetlab testbed (https://www.planet-lab.org) to emulate a small community network with 8 clients and 5 proxies. Each client probed all proxies every 10 seconds during two days. The same file (http://ovh.net/files/1Mb.dat) was requested in all probes. A probe was considered successful if the file was completely downloaded by the client. In this case, the response time was registered by the client, considering the time elapsed from the moment the client sent the request until the last byte of the response was received.</p> <p>This dataset contains the proxies' response times that were measured by clients in sucessful probes.</p>
Training data set for: Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries
<h2>Overview</h2> <p>This is a synthetic volcano deformation dataset accompanying the publication of <em><strong>Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries</strong></em>,<em><strong> </strong></em>on the journal <em>Volcanica</em>. Synthetic, quasi-static deformation is computed for magma chambers of various geometries, parameterized as spheroids or superpositions of spherical harmonics. Surface deformation is computed using the boundary element method (BEM) of Nikkhoo & Walter (2015). Please reference our paper for details of computational methods.</p> <p>The dataset contains 50,000 realizations of magma chamber geometries/orientations/centroid depths and associated deformation fields. Surface deformation fields are sampled at discrete locations, with a uniform random distribution within [Lh x Lh], and a distribution that concentrates near the chamber (at radial distances, r = 10^(-3 <em> random number) * </em>Lh/2). Note this dataset contains only a small fraction of the total dataset. In total, 824,393 realizations of magma chambers were used to train our emulators. For accessing the complete training data set, please contact the authors. </p> <p>Each .mat file contains the deformation field associated with a single chamber geometry. Use visData.m to visualize chamber geometry and associated surface displacement. Each file contains two MATLAB structures, "input" and "output". </p> <h2>Naming of each zip file</h2> <p>The numbers after the underscore, N:M, indicate that this file contains N of the M total chamber realizations for this particular setup. </p> <p><a href="../api/records/13800065/draft/files/sph_20AspRatios_1e4:151211.zip.zip/content" target="_blank" rel="noopener noreferrer">sph_20AspRatios_1e4:151211.zip</a>: deformation corresponding to spheroidal magma chambers parameterized by aspect ratios. </p> <p><a href="../api/records/13800065/draft/files/sh_complex_1e4:152283.zip/content" target="_blank" rel="noopener noreferrer">sh_complex_1e4:152283.zip</a>: deformation corresponding to chamber geometry produced by superposition of spherical harmonic modes. </p> <p><a href="../api/records/13800065/draft/files/sh_mode_approx_1e4:138380.zip/content" target="_blank" rel="noopener noreferrer">sh_mode_approx_1e4:138380.zip</a>: deformation corresponding to chamber geometries corresponding to individual spherical harmonic modes, combined with a spherical mode (the spherical mode prevents chamber surfaces from having zero radii locally)</p> <p><a href="../api/records/13800065/draft/files/sh_spheroid_approx1e4:202272.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_approx1e4:202272.zip</a>: deformation corresponding to chambers approximating spheroids, but parameterized by spherical harmonics.</p> <p><a href="../api/records/13800065/draft/files/sh_spheroid_perturb_1e4:180247.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_perturb_1e4:180247.zip</a>: same as above, but with additional random perturbations parameterized in spherical harmonics.</p> <h2>Variables in each file</h2> <p><strong>Input</strong> contains the following fields:</p> <p><strong>dp2mu</strong>: pressure change to shear modulus ratio.</p> <p><strong>dx</strong>, <strong>dy</strong>, <strong>dz</strong>: the coordinates of chamber centroid [meters]</p> <p><strong>mu: </strong>dimensionless crustal shear modulus (always set to 1)</p> <p><strong>nu</strong>: crustal Poisson's ratio (always set to 0.25)</p> <p><strong>Ns</strong>: number of points on the surface where displacements are computed</p> <p><strong>Lh</strong>, <strong>Lv</strong>: horizontal and vertical dimensions of the model domain [meters]. Lh is determined such that at the edge of the model domain, the displacement magnitude is below 10 percent of the maximum. Lv = Lh/2 + abs(dz)</p> <p>for the spheroids -----------------------------------------------------------------------------------------------------------</p> <p>the input files contain</p> <p><strong>asp</strong>: aspect ratio of chamber (length of the semi-major axis divided by that of the semi-minor axis)</p> <p><strong>ra</strong>, <strong>rb</strong>: semi-major, -minor, axis length [meters]</p> <p><strong>thetax</strong>, <strong>thetay</strong>, <strong>thetaz</strong>: counterclockwise rotation angles with regard to x, y, z axis [degrees]. thetax = [0, 90] degrees, thetay = 0 degrees, thetaz = 360 degrees.</p> <p>for the general geometries--------------------------------------------------------------------------------------------------</p> <p>the input files contain</p> <p><strong>ls</strong>, <strong>ms</strong>, <strong>fs</strong>: degree, order, coefficients of spherical harmonic modes. Spherical harmonics are sampled up to degree 5. fs is a complex vector of coefficients such that the resulting shape is real. </p> <p><strong>normF</strong>: normalization factor applied to the shape parameterized by ls, ms, fs, such that the shape as a maximum radius of unity.</p> <p><strong>rmax</strong>: scale factor to scale the spherical harmonics parameterized shape to real dimensions [meters].</p> <p>=============================================================================================</p> <p>Output contains the following fields,</p> <p><strong>X</strong>, <strong>Y</strong>, <strong>Z</strong>: coordinates of points where displacement vectors are computed [meters]</p> <p><strong>Ux</strong>, <strong>Uy</strong>, <strong>Uz</strong>: displacements in x, y, z directions [meters]</p> <p><strong>P</strong>, <strong>T</strong>: coordinates [meters] of vertices for the triangular mesh used in BEM calculation, and the connectivity matrix </p> <p><strong>C</strong>: coordinates [meters] of the center of each triangular element</p> <p><strong>that</strong>, <strong>dhat</strong>, <strong>nhat</strong>: unit vectors for orthogonal coordinate systems local to each triangular element. that ("t-hat") extends from vertex one to vertex two, nhat is outward normal, and dhat = cross (nhat, that).</p> <p>Reference:</p> <p>1. Nikkhoo, M., & Walter, T. R. (2015). Triangular dislocation: an analytical, artefact-free solution. <em>Geophysical Journal International</em>, <em>201</em>(2), 1119-1141.</p>
Output from Linear Inverse Models (LIMs) emulating the observed spatiotemporal statistics of Australian precipitation and global sea surface temperatures
<p><strong>Data repository for <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em></strong></p> <p>This repository contains LIM data underpinning the paper <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em> [doi: 10.1016/j.wace.2024.100734 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.wace.2024.100734" target="_blank" rel="noopener">available online in <em>Weather and Climate Extremes</em> 17 October 2024</a>]. All other datasets used in the paper are freely available online (see Data Availability statement in the paper for details). </p> <p>The repository contains 12 netcdf files, which together comprise the Linear Inverse Model (LIM) outputs described in the paper. <strong>In all cases, please see the paper for important details on the data and how they were produced.</strong> </p> <p><em>Global LIMs</em></p> <ul> <li>`LIM5000_COBE-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the Australian Gridded Climate Dataset v2 (AGCD) and global SST data from 'Centennial in situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables version 2' (COBE)</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and global SST data from US National Oceanic and Atmospheric Administration 'Extended Reconstruction SST version 5’ (ERSST)</li> </ul> </li> <li>`LIM5000_COBE-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from ERSST</li> </ul> </li> </ul> <p><em>Tropical Pacific Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from ERSST</li> </ul> </li> </ul> <p><em>Indian Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from ERSST</li> </ul> </li> </ul> <p><strong>How to cite this</strong> <strong>repository</strong></p> <p>If using this data, please cite the original publication, available from <a href="https://www.sciencedirect.com/science/article/pii/S2212094724000951" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S2212094724000951.</a> </p>
Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output
<p>We produce a dataset that uses pattern scaling, a common method of emulating climate models. Our dataset is built on the Pangeo CMIP6 archive, which has the advantage that we don't need to actually download the climate model output. Here we demonstrate the utility of our dataset, called Pangeo-Enabled ESM Pattern Scaling (PEEPS). The dataset, which is encapsulated in a Jupyter notebook (and replicated in a Python file), is flexible and can be extended to multiple scenarios and multiple variables, as long as they are in the Pangeo-accessible archive.</p>
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets
<p>This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology.</p> <p>Every folder contains two csv files:</p> <p>- parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis</p> <p>- outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample</p> <p>In ventricular_cell_model, there are four folders:</p> <p>- ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features</p> <p>- contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0).</p> <p>- contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1).</p> <p>- contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic.</p> <p>The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient.</p> <p>The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model.</p> <p>The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation.</p> <p>The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model.</p> <p>Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function.</p> <p>The details about the model can be found in the original publication.</p>
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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.