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567 results for “Simulated datasets”
Dataset for Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor
<p><strong>SDC-Scissor tool for Cost-effective Simulation-based Test Selection in Self-driving Cars Software</strong></p> <p>This dataset provides test cases for self-driving cars with the BeamNG simulator. Check out the repository and demo video to get started.</p> <p><strong>GitHub:</strong> <a href="https://github.com/ChristianBirchler/sdc-scissor">github.com/ChristianBirchler/sdc-scissor</a></p> <p>This project extends the tool competition platform from the <a href="https://github.com/se2p/tool-competition-av">Cyber-Phisical Systems Testing Competition</a> which was part of the <a href="https://sbst21.github.io/">SBST Workshop in 2021</a>.</p> <p><strong>Usage</strong></p> <p><strong>Demo</strong></p> <p> <a href="https://youtu.be/Cn8p648KnfQ">YouTube Link</a></p> <p><strong>Installation</strong></p> <p>The tool can either be run with <a href="https://docs.docker.com/get-docker/">Docker</a> or locally using <a href="https://python-poetry.org/docs/">Poetry</a>.</p> <p>When running the simulations a working installation of <a href="https://beamng.gmbh/research/">BeamNG.research</a> is required. Additionally, this simulation cannot be run in a Docker container but must run locally.</p> <p>To install the application use one of the following approaches:</p> <ul> <li>Docker: <code>docker build --tag sdc-scissor .</code></li> <li>Poetry: <code>poetry install</code></li> </ul> <p><strong>Using the Tool</strong></p> <p>The tool can be used with the following two commands:</p> <ul> <li>Docker: <code>docker run --volume "$(pwd)/results:/out" --rm sdc-scissor [COMMAND] [OPTIONS]</code> (this will write all files written to <code>/out</code> to the local folder <code>results</code>)</li> <li>Poetry: <code>poetry run python sdc-scissor.py [COMMAND] [OPTIONS]</code></li> </ul> <p>There are multiple commands to use. For simplifying the documentation only the command and their options are described.</p> <ul> <li>Generation of tests: <ul> <li><code>generate-tests --out-path /path/to/store/tests</code></li> </ul> </li> <li>Automated labeling of Tests: <ul> <li><code>label-tests --road-scenarios /path/to/tests --result-folder /path/to/store/labeled/tests</code></li> <li><em>Note:</em> This only works locally with BeamNG.research installed</li> </ul> </li> <li>Model evaluation: <ul> <li><code>evaluate-models --dataset /path/to/train/set --save</code></li> </ul> </li> <li>Split train and test data: <ul> <li><code>split-train-test-data --scenarios /path/to/scenarios --train-dir /path/for/train/data --test-dir /path/for/test/data --train-ratio 0.8</code></li> </ul> </li> <li>Test outcome prediction: <ul> <li><code>predict-tests --scenarios /path/to/scenarios --classifier /path/to/model.joblib</code></li> </ul> </li> <li>Evaluation based on random strategy: <ul> <li><code>evaluate --scenarios /path/to/test/scenarios --classifier /path/to/model.joblib</code></li> </ul> </li> </ul> <p>The possible parameters are always documented with <code>--help</code>.</p> <p><strong>Linting</strong></p> <p>The tool is verified the linters <a href="https://flake8.pycqa.org/en/latest/">flake8</a> and <a href="https://pylint.org/">pylint</a>. These are automatically enabled in <a href="https://code.visualstudio.com/">Visual Studio Code</a> and can be run manually with the following commands:</p> <pre>poetry run flake8 . poetry run pylint **/*.py</pre> <p><strong>License</strong></p> <p>The software we developed is distributed under GNU GPL license. See the <a href="https://github.com/ChristianBirchler/sdc-scissor/blob/main/LICENSE.md">LICENSE.md</a> file.</p> <p><strong>Contacts</strong></p> <p>Christian Birchler - Zurich University of Applied Science (ZHAW), Switzerland - <a href="mailto:birc@zhaw.ch">birc@zhaw.ch</a></p> <p>Nicolas Ganz - Zurich University of Applied Science (ZHAW), Switzerland - <a href="mailto:gann@zhaw.ch">gann@zhaw.ch</a></p> <p>Sajad Khatiri - Zurich University of Applied Science (ZHAW), Switzerland - <a href="mailto:mazr@zhaw.ch">mazr@zhaw.ch</a></p> <p>Dr. Alessio Gambi - Passau University, Germany - <a href="mailto:alessio.gambi@uni-passau.de">alessio.gambi@uni-passau.de</a></p> <p>Dr. Sebastiano Panichella - Zurich University of Applied Science (ZHAW), Switzerland - <a href="mailto:panc@zhaw.ch">panc@zhaw.ch</a></p> <p><strong>References</strong></p> <ul> <li>Christian Birchler, Nicolas Ganz, Sajad Khatiri, Alessio Gambi, and Sebastiano Panichella. 2022. Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor. In 2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE.</li> </ul> <p><strong>If you use this tool in your research, please cite the following papers:</strong></p> <pre><code>@INPROCEEDINGS{Birchler2022, author={Birchler, Christian and Ganz, Nicolas and Khatiri, Sajad and Gambi, Alessio, and Panichella, Sebastiano}, booktitle={2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), title={Cost-effective Simulationbased Test Selection in Self-driving Cars Software with SDC-Scissor}, year={2022}, }</code></pre>
Template dataset for cell simulation
<p>Dataset with templates of actual cells observed through fluorescent microscopy. The zip archive contains:</p> <ul> <li>a library of 3D cell and nucleus shapes, in addition of 2D protrusion areas.</li> <li>precomputed distance maps.</li> <li>an index in a CSV file.</li> </ul> <p> </p> <p>Original dataset comes from https://doi.org/10.5281/zenodo.1413489</p> <p>Template dataset initially used by https://github.com/fish-quant/sim-fish/tree/main</p>
Dataset: High-accuracy simulations of highly spinning binary neutron star systems
<p><strong>Dataset for Gravitational Waveforms for the work of Dudi et al., "High-accuracy simulations of highly spinning binary neutron star systems"; arXiv: 2108.10429 </strong></p> <p> </p> <p><strong>The naming of the files: </strong>EOS_SpinSetup_NumberOfPoints_Mode.dat<br> EOS: SLy<br> SpinSetup: dd - down down; ud - up, down; uu037 -- up, up with dimensionless spin of 0.37; uu057 -- up, up with dimensionless spin of 0.57<br> NumberOfPoints: Points inside the finest level: 96, 144, 192, 240<br> Mode: Only 2,2-mode available <br> </p>
BLE Ray-Tracing Simulation Dataset
<p>BLE ray-tracing propagation data generated via Altair Feko WinProp software in a simulation environment of dimensions 14m x 7m. Four horizontal facing APs are placed in the corners of the room at 2.5m height and at 45 degrees azimuth rotation pointing towards the center of the room. The elevation angle of all APs is 45 degrees pointing downwards. Three transmitting frequencies are simulated i.e., 2402, 2426 and 2480 MHz, corresponding to the three advertising BLE channels (numbered 37, 38 and 39, respectively) and two antenna polarizations (omni-directional), i.e., horizontal and vertical. The tag is positioned at a fixed height of 1.5 m (z-dimension) and 2450 signal samples are collected per room configuration, evenly distributed across the room. Data was collected for 9 different room configurations with varying furniture, furniture material and anchor points positions and orientation as shown below. <br> </p> <table> <tbody> <tr> <td> <table> <thead> <tr> <th>Room Setup</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>testbench_01</td> <td>No line-of-sight blocking furniture</td> </tr> <tr> <td>testbench_01_furniture_low</td> <td>One line-of-sight blocking furniture</td> </tr> <tr> <td>testbench_01_furniture_mid</td> <td>Three line-of-sight blocking furniture</td> </tr> <tr> <td>testbench_01_furniture_high</td> <td>Six line-of-sight blocking furniture</td> </tr> <tr> <td>testbench_01_furniture_low_concrete</td> <td>Same as low but with concrete furniture</td> </tr> <tr> <td>testbench_01_furniture_mid_concrete</td> <td>Same as mid but with concrete furniture</td> </tr> <tr> <td>testbench_01_furniture_high_concrete</td> <td>Same as high but with concrete furniture</td> </tr> <tr> <td>testbench_01_rotated_anchors</td> <td>Same as testbench_01 but the anchors have been rotated clockwise by 5 degrees</td> </tr> <tr> <td>testbench_01_translated_anchors</td> <td>Same as testbench_01 but the anchors have been translated by 10cm</td> </tr> </tbody> </table> </td> </tr> </tbody> </table> <p><br> For each room setup, data is split into 12 json files - 6 for the tag signal data collected from the 4 anchor points and 6 for the properties of each anchor point. The 6 different files correspond to the different channel-polarization combinations as indicated by their names. </p> <p>Each entry of the anchor json files contains the following information:</p> <ul> <li>anchor: anchor's index</li> <li>x_anchor, y_anchor, z_anchor: anchor point's coordinates</li> <li>az_anchor: anchor's horizontal rotation</li> <li>el_anchor: anchor's vertical rotation</li> <li>reference_power: received signal strength (RSS) reference value in dB</li> </ul> <p>Each entry of the tag json files contains the following information:</p> <ul> <li>anchor: anchor's index</li> <li>point: point's index</li> <li>x_tag, y_tag, z_tag: tag point's coordinates</li> <li>los: point is in line of sight of anchor {0=false,1=true}</li> <li>relative power: RSS value in dB</li> <li>pdda_input_real: in-phase components of the anchor's antennas' measurements</li> <li>pdda_input_image: quadrature-phase components of the anchor's antennas' measurements</li> <li>pdda_phi: pdda prediction for azimuth angle</li> <li>pdda_theta: pdda prediction for elevation angle</li> <li>pdda_out_az: pdda's spatial power spectrum for azimuth angle</li> <li>pdda_out_el: pdda's spatial power spectrum for elevation angle</li> <li>true_phi: actual azimuth angle</li> <li>true_theta: actual azimuth angle</li> </ul>
Vasculature Segmentation Validation Dataset - Part I - Simulated Tubes and Filter Responses
<p>Dataset to allow exploration of data enhancement, segmentation, and validation for: https://www.biorxiv.org/content/10.1101/2020.07.21.213843v1 and associated future publications</p> <p><strong>Dataset description:</strong></p> <ul> <li><em><strong>SimulatedTubes</strong></em>: Computationally produced tubes (3 different sizes) that are hollow, filled, or filled with Gaussian distribution. To study filter responses, artificial noise and blurring were added.</li> <li><em><strong>DataExFilterResponse</strong></em>: Example data of the head vasculature of 4dpf of zebrafish, where the response to different filters is tested (i.e. GF - general filtering and TF - tubular filtering / Sato enhancement; for TF also different scale sizes are examined; see also: https://www.mdpi.com/2313-433X/5/1/14).</li> </ul> <p><em><strong>Data acquisition:</strong></em><br> Experiments were performed according to the rules and guidelines of institutional and UK Home Office regulations under the Home Office Project Licence 70/8588 held by TC. Maintenance of adult zebrafish Tg(kdrl:HRAS-mCherry)s916 (Chi et al., 2008) was performed as described in standard husbandry protocols (Westerfield, 1993). Embryos, obtained from controlled mating, were kept in E3 medium buffer with methylene blue and staged as previously described (Kimmel et al., 1995).</p> <p>Embryos were embedded in 2% LMP-agarose with 0.01% Tricaine in E3 (MS-222, Sigma). Data were acquired using a Zeiss Z.1 light sheet microscope, Plan-Apochromat 20x/1.0 Corr nd=1.38 objective, dual-side illumination with online fusion, activated Pivot Scan, image acquisition chamber incubation at 28°C, with a scientific complementary metal-oxide semiconductor (sCMOS) detection unit. Data properties can be summarised as: 16bit image depth, voxel dimensions in x, y and z of 1920 x 1920 x 400-600, respectively, giving a voxel size of 0.33 x 0.33 x 0.5 µm). </p> <p><em><strong>Contact</strong></em>: kugler.elisabeth[at]gmail.com</p> <p><em><strong>Useful code links</strong></em>: </p> <ul> <li>https://github.com/ElisabethKugler/ZFVascularQuantification</li> <li>https://github.com/ElisabethKugler/Matlab3D-ImageAnalysis</li> </ul>
Datasets for Simulation-based Anomaly Detection for Multileptons at the LHC
<p>The simulated background and signal data used for a signal model agnostic machine learning search. This search examined the decay of the Higgs boson to leptons working off of LHC data from the Atlas experiment. Details are provided in the paper entitled "Simulation-based Anomaly Detection for Multileptons at the LHC". </p>
Dataset: Code Comparison for Fast Flavor Instability Simulation
<p>This is the data provided in the article "Code Comparison for Fast Flavor Instability Simulation". The python scripts produce all of the plots present in the publication from the *.txt data files. The filenames of the *.txt files indicate the author of the dataset and the figure that the data/script produces. The meaning of each column of data is described in README.md. README.md also contains the git repository locations and commit hashes for the open-source codes used to produce this data.</p> <p>Figure 1: Psur-t</p> <p>Figure 2: P-u-t5000</p> <p>Figure 3: Sfft-k-t0 and Sfft-k-t5000</p> <p>Figure 4: deltaELN-t</p> <p>Figure 5: deltaP-t</p> <p> </p>
Dataset for I. Latella, A. Campa, L. Casetti, P. Di Cintio, J. M. Rubi, and S. Ruffo, Monte Carlo simulations in the unconstrained ensemble, Phys. Rev. E 103, L061303 (2021)
<p>This dataset contains data associated to plots published in the paper I. Latella, A. Campa, L. Casetti, P. Di Cintio, J. M. Rubi, and S. Ruffo, Monte Carlo simulations in the unconstrained ensemble, Phys. Rev. E 103, L061303 (2021), https://doi.org/10.1103/PhysRevE.103.L061303</p>
Dataset of micromagnetic simulations on magnetic unstable zone (#2022JB024876)
<p>The files attached below are all the raw data that support the figures in paper #2022JB024876.</p>
Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets
<p>Using spectral reflectance and random forest method for modeling soil surface changes induced by simulated rainfall - datasets</p> <p>The impact of simulated rainfall on the soil surface roughness of different soil types with various initial surface states and the differences between their spectral characteristics were studied under laboratory conditions. The soil samples were collected from a horizon of fields near Poznań, western Poland. The physical and physicochemical properties of each soil sample were determined. Then, the part of the soil materials, consisting of natural aggregates, were used to form three soil surface roughness. </p> <p>An explanation of the table column names in the “soils properties.csv” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“sand” - Sand content in the soil sample in %.</p> </li> <li> <p>“silt” – Silt content in the soil sample in %.</p> </li> <li> <p>“clay” – Clay content in the soil sample in %.</p> </li> <li> <p>pHH2O” - The pH of the soil sample determined in water. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“pHKCl” – The pH of the soil sample determined in KCl. The soil pH was determined by the potentiometry method.</p> </li> <li> <p>“SOC” – Organic matter content in soil was determined by oxidation titration using K2Cr2O7 with H2SO4 on the block mineralization.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “rainfall doses.csv” file:</p> <p> </p> <ul> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p>“rainfall dose” - One-time amount of rainfall dose expressed in millimeters.</p> </li> <li> <p>“accumulated rainfall” – Summation of rainfall after each successive dose expressed in millimeters.</p> </li> </ul> <p> </p> <p>An explanation of the table column names in the “soil measurements” file:</p> <p> </p> <ul> <li> <p>“textural classification” - Name of the granulometric group. Soil texture was determined by the hydrometer method according to standard PN-R-04032.</p> </li> <li> <p>“rainfall simulation” - Rainfall simulation number.</p> </li> <li> <p> “reflectance” - The amount of radiation reflected from the soil surface under the influence of successive rainfalls and expressed in nanometres. </p> </li> <li> <p>“roughness state” - The size of the roughness: R1 is the lowest soil roughness state, R2 represents medium soil roughness, and R3 represents the greatest roughness.</p> </li> <li> <p>“T3D” - Tortuosity index is a surface roughness index. It was calculated from DEM (Digital Elevation Model). It expresses the ratio between the true surface of DEM and its flat horizontal area.</p> </li> <li> <p>“HSD” - Height Standard Deviation is the second surface roughness index. It was calculated from DEM and expressed in millimeters. </p> </li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><br> </p> <p> </p>
Datasets for "High-resolution, basin-scale simulations reveal the impact of intermediate zonal jets on the Atlantic oxygen minimum zones".
<p>This repository contains the 7-year averaged files from runs TATL10 and TATL3 used in the manuscript "High-resolution, basin-scale simulations reveal the impact of intermediate zonal jets on the Atlantic oxygen minimum zones".</p> <p>Additional files include the CARS oxygen climatology and the CMEMS model output used in the study.</p> <p> </p>
Dataset 2 for the reproduction of figures used in "High-resolution, basin-scale simulations reveal the impact of intermediate zonal jets on the Atlantic oxygen minimum zones""
<p>This is the second dataset with data necessary for the reproduction of the figures used in the manuscript.</p> <p> </p>
Biases of STRUCTURE software when exploring introduction routes: Datasets, STRUCTURE outputs, simulation and analysis pipeline
<p>This archive is associated with the article “Biases of STRUCTURE software when exploring introduction routes of invasive species”. Authors: Eric Lombaert, Thomas Guillemaud & Emeline Deleury.</p> <p>The file contains the 22,500 simulated datasets, the corresponding 900,000 STRUCTURE outputs and the summary statistics files. It also contains SIM_STRUCT which is a home-made pipeline developed for the purpose of carrying out analyzes as described in the manuscript. It can be used to simulate and summarize datasets, and to perform STRUCTURE analyses in batch on those simulated datasets. It is currently based on several softwares such as DIYABC, ARLSUMSTAT and STRUCTURE, as well as on some home-made PERL scripts. A tutorial is included.<br> See the Readme file for details.</p>
Simulations dataset and pre-trained models of "Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory" Ph.D. project
<p>Ph.D. project datasets and models release, <br><em>Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory.</em></p>
Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing - Simulated datasets
<p>This repository contains the reads, assemblies, and references required to replicate the <strong>simulated</strong> results presented in the paper: https://doi.org/10.1101/2023.01.31.526521</p>
Dataset belonging to SNF project: Use of physiologically based pharmacokinetic modelling to simulate dosing requirements of long-acting intramuscular antiretroviral drugs in special populations and to manage drug-drug interactions
Open the record for dataset details and reuse information.
Simulated datasets for dihiggs to (bb)(γγ) with higgs-sector SMEFT effects
<p>Analysis dataset used for the report "Deriving Constraints on di-Higgs SMEFT couplings with Neural Simulation-based Inference" (<a href="https://arxiv.org/abs/2405.15847">arXiv link</a>). We provide dihiggs signal events <em>gg -> hh -> (bb)(γγ)</em> and QCD background events <em>pp -> bbγγ </em>at both a 14 TeV and 100 TeV collider setup. All events are generated with MadGraph 3.5.1, Pythia 8.306, and Delphes 3.5.0.</p> <p>For the hard process (<em>pp -> hh</em>) for the signal events, we use the <a href="https://feynrules.irmp.ucl.ac.be/wiki/SMEFTatNLO" target="_blank" rel="noopener">SMEFT@NLO</a> MadGraph model to incorporate the effects of three dimension-6 SMEFT operators (c_p, c_{dp}, c_{tp}). We select 10 values ("morphing basis points") for the vector (c_p, c_{dp}, c_{tp}) (see Table IV of the accompanying report). At each morphing basis point, we generate a number of signal events; for each event, we also calculate the weights to the other 9 basis points. All of this data is stored in the .h5 files. We use MadSpin to carry out the higgs decays.</p> <p>These datasets can be used as input into the <a href="https://github.com/madminer-tool/madminer/blob/b7f7916dde5b315cecaafd113e88a37b9464c420/madminer/sampling/sampleaugmenter.py#L16" target="_blank" rel="noopener">MadMiner SampleAugmenter</a> function, which uses the weights associated with the signal events to generate detector-level events at arbitrary (c_p, c_{dp}, c_{tp}) points.</p> <p>All scripts used to generate and process this data are available at <a href="https://github.com/rmastand/nsbi_for_dihiggs" target="_blank" rel="noopener">this repository</a>. Please refer to the accompanying paper for more details on the analysis. </p> <p> </p> <p> </p> <p> </p>
Ground-state dataset "Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states"
<h1>2D QUANTUM EDWARDS-ANDERSON GROUND-STATE DATASET:</h1> <p>The dataset contains coupling and energy data for 50 instances of a 2D quantum Edwards-Anderson model at Gamma (transverse field) = 1.8, featuring N=LxL=100 spins on a square lattice of side-length L=10 with periodic boundary conditions. The couplings are sampled from a Gaussian distribution with zero mean and unit variance.<br>The dataset consists of two text files containing coupling values and the corresponding ground-state energies.</p> <h2>Coupling Data (`coup_dataset.txt`)</h2> <p>The file `coup_dataset.txt` contains fifty sets of coupling data. Each set consists of three columns representing the indices `i`, `j`, and the coupling value `J_ij`, respectively.<br>The spin indices range from 1 to 100, ordered progressively by rows. Each set of coupling data is separated by two empty lines.</p> <h2>Energy Data (eng_dataset.txt)</h2> <p>The file `eng_dataset.txt` contains fifty rows of energy data corresponding to the coupling sets in `coup_dataset.txt`. Each row contains two columns representing the energy value and its associated statistical error-bar, rounded to the fifth decimal digit.</p>
Simulated Cucurbit Tobamovirus Testing Dataset for Illustrating Possible Data Format Only
<p>This dataset contains wholly simulated and not particularly realistic data. It is intended to demonstrate the format of a possible dataset of tobamovirus testing results for cucurbit imports into Australia.</p> <p>If permission is obtained to publish real data, the real data will contain de-identified data on testing for virus presence in cucurbit seed lots where importation into Australia was attempted, over a period between 2019 and 2021. Each row represents a seed lot. For each lot, a number of groups of 400 seeds were tested for tobamovirus contamination, returning a presence or absence result for each group.There is no identifier or name representing the lot, consignment, importer or source country. The ID variable on the file was randomly assigned.</p> <p>Other authors will be added to the real data page subject to their permission and preference.</p> <p>For more information on the data, see </p> <p>Dall DJ, Lovelock DA, Penrose LDJ, Constable FE. Prevalences of Tobamovirus Contamination in Seed Lots of Tomato and Capsicum. <em>Viruses</em>. 2023; 15(4):883. https://doi.org/10.3390/v15040883</p> <p> </p> <p><strong>Variable Definitions</strong></p> <table> <tbody> <tr> <td> <p>Variable Name</p> </td> <td> <p>Values</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>ID</p> </td> <td> <p>1, …, 800 (will be 793 in the real data)</p> </td> <td> <p>Randomised lot identifier number. The data set contains one row per lot.</p> </td> </tr> <tr> <td> <p>species</p> </td> <td> <p>tomato, capsicum</p> </td> <td> <p>species of cucurbit being imported</p> </td> </tr> <tr> <td> <p>year</p> </td> <td>2019, 2020, 2021</td> <td> <p>year in which importation was attempted</p> </td> </tr> <tr> <td> <p>month</p> </td> <td> <p>1, …, 12</p> </td> <td> <p>month in which importation was attempted</p> </td> </tr> <tr> <td> <p>NUM_TESTED</p> </td> <td> <p>1, ...., 50</p> </td> <td> <p>Number of groups tested. More groups were sampled from larger seed lots.</p> </td> </tr> <tr> <td>NUM_POS</td> <td>0, ..., 50</td> <td>Number of groups where virus contamination was detected.</td> </tr> <tr> <td> <p>vspecies</p> </td> <td> <p>PMMoV, PSTVd, PSTVd1, TASVd, TMV, ToBRFV, ToMMV, ToMV, blank</p> </td> <td> <p>Species of tobamovirus detected (equal to "blank", i.e., empty string, when no virus was detected.</p> </td> </tr> </tbody> </table> <p><strong>Author Field</strong></p> <p>If permission is obtained, this section will state that I am distributing this data with the permission of the data owner. (Other creators/contributers may be added to the "Creator" or "Contributer" section if they give permission for their names to appear.)</p>
Dataset of "Particle simulation study of obliquely propagating whistler waves in low-beta plasmas"
<p>This dataset contain all necessary output data to make the plots contained in the article. It also includes parameter files to run simulations.<br><br>Dataset list:</p> <p>parameter files/*.py: Input parameter files<br>rms_amplitude.npz: RMS maximum wave amplitude for each run<br>run_1-distributions/*.npz: Electron distribution function at different moments.<br>run_1-Bz-*.npz: 2D Wave field or wave number spectrum data of run 1<br>run_1-freq_spectrum.npz: spacially averaged wave fequency spectrum of Bz<br>run_1-spectrogram.npz: wave Bz spectrogram recorded at y=0<br>scalars_run*.npz: raw scalar output of each run, containing wave and particle enegy profiles<br>summary_data.npz: a summary data containing info of each run (init paras, maximum wave amplitude, init linear growth rate, etc)<br>temperature_profile_run*.npz: the time evolution of parallel and perpendicular temerature of each run<br><br></p> <p>* Not all raw datas are included in this repository due to the limit of file size. One can directly contact the author for more detailed datas.</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.