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567 results for “Simulated datasets”
ConFiRMa dataset_02: simulation of tests on CRM strengthened masonry elements with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of tests on masonry elements strengthened through Composite Reinforced Mortar with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Masonry elements strengthened through Textile-Reinforced Mortar:application of the detailed level modelling with a free open-source Finite-Element code".</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p>
Simulated X-ray Photon Fluctuation Spectroscopy Dataset
<p>Dataset for simulated X-ray Photon Fluctuation Spectroscopy (XPFS) detector images and photon maps. XPFS is a X-ray speckle imaging technique used at SLAC National Accelerator Laboratory to study ultrafast materials dynamics. </p>
SIGMOID-TR: A Simulated Ground Motion Dataset for Turkey
<p><strong>SIGMOID-TR: A Simulated Ground Motion Dataset for Turkey</strong></p> <p>A simulated ground motion dataset for ten earthquake scenarios in Turkey (seven real, three hypothetical earthquakes).</p> <p>Each scenario consists of 180 combinations for input parameters, and ground motions are simulated at 104 sites for each set of combinations, resulting in 18720 simulated ground motions for each earthquake and 187200 motions in total.</p>
Dataset: "Balancing consumer and business value of recommender systems: A simulation-based analysis"
<p>The data files in this directory contain to the results of the simulations reported in the paper: "Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis" published in Electronic Commerce Research and Applications. The paper is available here: <a href="https://doi.org/10.1016/j.elerap.2022.101195">https://doi.org/10.1016/j.elerap.2022.101195</a></p> <p> </p>
Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models - Dataset
<p>Main data used in the following paper: Revaz, T.; Porté-Agel, F. Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3745. https://doi.org/10.3390/en14133745</p>
Simulator dataset and associated matlab routine
<p>This archive integrates 4 data files in csv format generated in ONERA's simulator.</p> <p>These files have been used for an ERF paper, also available on zenodo (10.5281/zenodo.6673941) :</p> <p>VERSATILE OFFLINE SIMULATION TOOL FOR SYSTEMS DESIGN</p>
HOWS-CL-25: Household Objects Within Simulation Dataset for Continual Learning
<p>HOWS-CL-25 (Household Objects Within Simulation dataset for Continual Learning) is a synthetic dataset especially designed for object classification on mobile robots operating in a changing environment (like a household), where it is important to learn new, never seen objects on the fly.<br> This dataset can also be used for other learning use-cases, like instance segmentation or depth estimation.<br> Or where household objects or continual learning are of interest.</p> <p>Our dataset contains 150,795 unique synthetic images using 25 different household categories with 925 3D models in total. For each of those categories, we generated about 6000 RGB images. In addition, we also provide a corresponding depth, segmentation, and normal image.</p> <p>The dataset was created with BlenderProc [Denninger et al. (2019)], a procedural pipeline to generate images for deep learning.<br> This tool created a virtual room with randomly textured floors, walls, and a light source with randomly chosen light intensity and color. After that, a 3D model is placed in the resulting room. This object gets customized by randomly assigning materials, including different textures, to achieve a diverse dataset. Moreover, each object might be deformed with a random<br> displacement texture.<br> We use 774 3D models from the ShapeNet dataset [A. X. Chang et al. (2015)] and the other models from various internet sites. Please note that we had to manually fix and filter most of the models with Blender before using them in the pipeline!</p> <p>For continual learning (CL), we provide two different loading schemes:<br> - Five sequences with five categories each<br> - Twelve sequences with three categories in the first and two in the other sequences.</p> <p>In addition to the RGB, depth, segmentation, and normal images, we also provide the calculated features of the RGB images (by ResNet50) as used in our RECALL paper.<br> In those two loading schemes, ten percent of the images are used for validation, where we ensure that an object instance is either in the training or the validation set, not in both. This avoids learning to recognize certain instances by heart.</p> <p>We recommend using those loading schemes to compare your approach with others.</p> <p>Here we provide three files for download:<br> - HOWS_CL_25.zip [124GB]: This is the original dataset with the RGB, depth, segmentation, and normal images, as well as the loading schemes. It is divided into three archive parts. To open the dataset, please ensure to download all three parts.<br> - HOWS_CL_25_hdf5_features.zip [2.5GB]: This only contains the calculated features from the RGB input by a ResNet50 in a .hdf5 file. Download this if you want to use the dataset for learning and/or want to compare your approach to our RECALL approach (where we used the same features).<br> - README.md: Some additional explanation.</p> <p>For further information and code examples, please have a look at our website: https://github.com/DLR-RM/RECALL.</p>
Dataset for "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations"
<p>This is a dataset for the article "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations", available at https://arxiv.org/abs/2403.10891 (Full citation data will be added upon final publication of the article.)</p> <p>This package provides figure data and representative configuration files associated with the systems studied in the article above. Additionally, for the hard sphere system, this package includes direct coexistence data for all reported system sizes and crystal orientations.</p> <p> </p> <p> </p>
Test dataset of VLT/SPHERE/IRDIS H2 observation Injected with simulated disks.
<p>Test data sets used in Juillard et. al. (2023) and , Juillard et. al. (2024), used to compare three different algorithms for processing data sets using ADI alone and to compare the three different strategies: RDI, ADI, and ARDI.</p> <p>This test pipeline consists of a total of 60 test data sets composed of five different disk morphologies, injected at three different contrast levels ($10^{-3}$, $10^{-4}$, and $10^{-5}$), into four different observing ADI sequences of stars without any known circumstellar signal, reflecting different observing conditions. </p> <div><strong><u>CONTENT </u></strong></div> <div> </div> <div>You will find in this folder the battery of test dataset in the compressed folder "test_cubes_sphere.zip". It contains 60 folders, one for each test dataset, with two files in each: "angle.fits" and "cube.fits"</div> <div> </div> <div>Additionaly there folders containg the empty cubes, injected disks, star flux, mask that reprensent the location of the apperture where the flux of the disk was integrated to compute contrast. The Jupyter notebook "asses_quality_of_disk_estimate.ipynb" shows how to compare a disk estimate. We also provided an example disk estimate (file 'X_3_2_0.fits') to try out the notebook.</div> <div> </div> <div>Finally, a folder named "Ref_lib_sphere" containes the different set of references frames library used in the publication Juillard et. al. (2024). In this folder, "ref_lib_X" corresponds to the optimal references for testing using empty cube X, "randref" corresponds to "shuffled" (same for every cube), and "randref_nooverlap_X" corresponds to "excluded." </div> <div>In the “Optimal” case, we used the most correlated frames, which is the same selection used in the first series, where we compare ADI, RDI, and ARDI. In the “Shuffled” case, we randomly selected a sample from the reference libraries dedicated to each of our four ADI test cubessequences into one common reference library. In this test, the random selection contains 25% of frames from the “Optimal” reference library. In the “Excluded” case, we selected for each ADI cube a sample only from references dedicated to the three other ADI cubes, creating a selection that excludes optimal references. The PCC computed for each of the three selections of references is presented in Fig. 5 of Juillard et. al. (2024).</div> <div> </div> <div><span><strong>ABOUT THE DATA</strong></span></div> <p>The data sets, obtained through the High-Contrast Data Center (HCDC), were acquired using the Infrared Dual-Band Imager and Spectrograph (IRDIS, Dohlen et al. 2008; Vigan et al. 2010) camera of the Spectro-Polarimetric High-contrast Exoplanet Research coronagraphic system on the Very Large Telescope (VLT/SPHERE, Beuzit et al. 2019). The test data sets all consist of the $H$2 channel from the dual-band $H$23 set. They were chosen to exhibit a diverse range of characteristics, including low Strehl ratio with a 26\degr\ rotation (ID #1), wind-driven halo (ID #2), an unstable speckle field (ID #3), and good Strehl ratio with an 80\degr\ field rotation (ID #4). The raw data processed with the data handling software (Pavlov et al. 2008) of the HCDC (Delorme et al. 2017), which performs dark, flat, and bad pixel correction on a coronagraphic sequence. <br>For future reference, we computed the mean and standard deviation of the Pearson correlation coefficients (PCC) between each unique pair of frames in the ADI cube. The mean PCC are as follows: Cube #1: $\mu = 0.99$; Cube #2: $\mu = 0.97$; Cube #3: $\mu = 0.93$; Cube #4: $\mu = 0.96$, with standard deviations below $0.001$ for all the cubes.</p> <p><br>The injected disks represent a range of scenarios for both debris and protoplanetary disks. As detailed in Juillard et.al 2023, this selection consists of two 75\degr\ inclined disks with varying sharpness levels (A and B), a 45\degr\ inclined disk with two concentric rings (C), a nearly face-on disk with azimuthal flux variation (D), and a hydrodynamical simulation of a disk with embedded spiral structures and a companion (E). <br>The contrast of the injected disks is determined by measuring the integrated flux within a full width at half-maximum (FWHM)-sized aperture, centered at the peak intensity of the disk, and then dividing this value by the integrated flux within an FWHM-sized aperture of the stellar point spread function. However, we made an exception for the synthetic disk E, where we measured the flux at the companion location.</p> <p>The reference frames were selected from a set of archival IRDIS observations taken with the same filter, coronagraph, and exposure time as the test data sets. These reference targets were observed between 2014 December 11 and 2021 June 1, and the raw data were calibrated through the same process as the test data sets. For each test data set, the PCC was calculated between the frames of the data set and the reference targets, excluding any observations of the data set star taken at different epochs. The PCC was calculated within a circular annulus between 0\farcs31 and 0\farcs67, which captures both the dominant speckle region and position of the waffle pattern, used for precise star centering of a coronagraphic sequence (Zurlo et al. 2014), if it was included in the observation. For each frame in the data set, the 300 best correlated reference frames were identified, and those that appeared in this selection for more than 30\% of the data set frames were selected for the final reference library. </p>
nextGEMS cycle3 datasets: statistical summaries for streamed data from climate simulations
<p>This Zenodo holds the datasets used in the paper "Statistical summaries for streamed data from climate simulations" by Katherine Grayson. All the data comes from the nextGEMS cycle 3 and has been regridded for plotting purposes with resolution given in the title of each data set. The wind speed data set has been made by taking the square root of the squared and summed v10 and u10 components respectively. All data has been retrieved and regridded through the AQUA reader on the Levante supercomputer, developed as part of the Destination Earth initative. The source code to create all the figures using this data can be found in https://github.com/kat-grayson/one_pass_algorithms_paper/tree/main </p>
Dataset for Paper Titled "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations"
<div>The attached two files were used for analysis in the paper "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations." The NetCDF file included planetary boundary layer heights derived from lidar and radiosondes. The CSV file detailed the model configurations for selected case days in simulation sets ID1-5.</div> <div> <div> <p> </p> </div> </div>
bin3C - simulated community and associated sequencing datasets
<p>We simulated a human gut microbiome comprising 63 genomes from the GTDB annotated with an isolation source of faeces. No two genomes are more than 96% similar in terms of ANI.</p> <p>A Generalized Pareto distribution was used to model an abundance profile, which was assigned in random order to the references. There is a 50:1 difference between the most and least abundant member.</p> <p>Illumina shotgun and Hi-C reads were simulated using MetaART and sim3C (https://github.com/cerebis/sim3C).</p> <p>A sweep was performed over depth of coverage, by serially subsampling initial high depth readsets. Shotgun depth was parameterised by the most abundant at 250x, while Hi-C was parameterised by the number of pairs (200 million pairs).</p> <p>Shotgun was subsampled once, at half depth (125x), while Hi-C was subsampled 4 times (12.5, 25, 50, 100, 200 million pairs).</p> <p>The random seed used throughout was 12345.</p> <p>These simulated readsets were then analyzed using bin3C to retrieve metagenome-assembled genomes (MAGs). The resulting genome bins were validated using CheckM to estimate completeness and contamination.</p> <p> </p> <p> </p> <p> </p>
Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling: Datasets.
<p>Datasets related to the publication [1].<br> Including:</p> <ul> <li>KRAS G12X mutations derived from COSMIC v.79 [http://cancer.sanger.ac.uk/cosmic/] (KRAS_G12X_mut_COSMICv79..xlsx)</li> <li>RMSFs (300-2000ns) of GDP-systems (300_2000rmsf_GDP_systems_RAW_AVG_SE.xlsx)</li> <li>RMSFs (300-2000ns) of GTP-systems (300_2000RMSF_GTP_systems_RAW_AVG_SE.xlsx)</li> <li>PyInteraph analysis data for salt-bridges and hydrophobic clusters (.dat files for each system in the PyInteraph_data.zip-file)</li> <li>Backbone trajectories for each system (residues 4-164; frames for every 1ns). Last number (e.g. _1) refers to the replica of the simulated system.</li> <li>backbone_4-164.gro/.pdb/.tpr -files (resid 4-164) </li> </ul> <p><br> [1] Pantsar T et al. Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling. <em>PLoS Comput Biol Submitted</em> (2018)</p>
Simulated Arabidopsis thaliana sequencing datasets for chloroplast assembler benchmarking
<p><strong>Changes</strong></p> <ul> <li>Fixed non-circular sampling from chloroplast and mitochondrion in version 1.1.0</li> <li>Fixed off-by-one error in reverse read in version 1.0.0</li> </ul> <p><strong>Purpose and Documentation</strong></p> <p>See: <a href="https://github.com/chloroExtractorTeam/benchmark">github.com/chloroExtractorTeam/benchmark</a></p> <p><strong>Original data</strong><br> The original <em>Arabidopsis thaliana </em>sequences were downloaded from TAIR: </p> <p>The Arabidopsis Information Resource (<a>TAIR</a>) on www.arabidopsis.org, Mar 22, 2019 available under the <a href="http://www.arabidopsis.org/doc/about/tair_terms_of_use/417">TAIR Terms of Use</a> </p> <p><em>Tanya Z. Berardini, Leonore Reiser, Donghui Li, Yarik Mezheritsky, Robert Muller, Emily Strait and Eva Huala. "The Arabidopsis Information Resource: Making and mining the "gold standard" annotated reference plant genome." genesis 2015 <a href="https://doi.org/10.1002/dvg.22877">doi:10.1002/dvg.22877</a></em></p> <p><strong>Programs used to generate this data</strong><br> - <a href="https://github.com/shenwei356/seqkit">seqkit</a> (v0.10.1): Shen W, Le S, Li Y, Hu F (2016) "SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation." PLOS ONE 11(10): e0163962. <a href="https://doi.org/10.1371/journal.pone.0163962">doi:10.1371/journal.pone.0163962</a></p> <p> </p>
Simulated dataset (Almeida et al., 2018 - GigaScience) treated with various clustering programs to evaluate ReClustOR efficiency and constitency
<p>ReClustOR is a novel clustering method that overcomes some of the problems associated with classical ‘<em>heuristic’</em>clustering methods and consequently increases the stability and quality of the reconstructed OTUs. Moreover, the OTUs database defined with ReClustOR can be used as reference(s) with gradual enrichment of it, with new studies and samples. In this way, huge datasets like<em> </em>the Earth Microbiome Project can be easily used as references for smaller projects, thereby increasing the quality of comparisons between studies and datasets</p> <p>Here, we propose a new approach called ReClustOR (for RE-CLUSTering method using an Open-Reference approach) to improve OTU consistency (see https://doi.org/10.5281/zenodo.2597402). This new strategy combines two of the previously-described clustering methods. Firstly, a classical clustering<em> </em>method (<em>e.g. </em>SWARM, or VSEARCH) is used to define OTU centroids and create a reference database. Secondly, a closed- or open-reference method (depending on the user’s choice) is computed for all reads which are not considered as OTU centroids. Contrary to the classical clustering methods, each read is compared to all centroids using a distance-based greedy clustering technique (Edgar, 2010; He et al., 2015), and then assigned to the nearest one, thereby fixing the erroneous assignments of reads to OTUs.</p> <p>To highlight the improvements provided by ReClustOR in describing microbial diversity in terms of ecological diversity metrics (<em>e.g. </em>richness, OTU composition, Shannon, 1/Simpson) and taxonomic composition, a simulated dataset was subjected to: (i) ESV definition, (ii) multiple conventional <em>de novo</em> methods (<em>i.e.</em> a homemade <em>de novo </em>clustering close to CRUNCHCLUST, VSEARCH and SWARM), and (iii) ReClustOR computation. This dataset is a simulated one (Almeida et al., 2018), containing a diverse set of <em>genera</em> commonly found in three ecosystems different ecosystems: human gut, ocean and soil. The clustering methods were compared for: (i) their ability to describe microbial richness, (ii) the congruence between OTU assignments and sequences taxonomy, (iii) the robustness of each defined OTU, and (iv) their ability to efficiently describe the microbial community based on OTU composition.</p> <p>Here, the simulated dataset (00_Raw_data) and all steps of analysis are available to resue them to test ReClustOR, and also to have a better understanding of files and data produced by this program. More details are available in the Tree_of_data.tree file.</p>
A Modified Doyle-Fuller-Newman Model Enables the Macroscale Physical Simulation of Dual-ion Batteries - Dataset and Software
<p>This dataset contains:</p> <p>- all the raw cycling data of the three-electrode cell used to gather the experimental data for the model validation (VMP data, exported with EC-LAB);<br>- the specific, processed data used in the model validation step (0.2C discharge, 5C discharge, EIS data);<br>- the COMSOL dual-ion battery model (version 6.0). IMPORTANT: activate the "Electric potential at the positive electrode current collector (only for EIS)" boundary condition when simulating impedance spectroscopy, and deactivate it when simulating charge/discharge curves; the charge-discharge profile can be modified by changing the duration of the test, the C-rate, and the conditions set in the "Events" section.</p> <p>Update: Fixed the model to work also in the 6.2 version of COMSOL (Substituted Dleff with Dleffxx in the modified weak expression of the cathode mass conservation equation). Download the new version!</p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Input files
<p>This dataset contains the parent input used to generate the simulation files of the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets, see "Related work" section. A report describing this dataset will be made available on BEL-Float project website by November 2024: https://www.owi-lab.be/bel-float.</p>
Structured Power Grid Simulation Dataset for Machine Learning: Failure and Survival Events in Grid2Op's L2RPN WCCI 2022 Environment
<p>This dataset was developed for and used in the paper titled <em>"Fault Detection for Agents in Power Grid Topology Optimization: A Comprehensive Analysis"</em> by Malte Lehna, Mohamed Hassouna, Dmitry Degtyar, Sven Tomforde, and Christoph Scholz, presented at the <em>Workshop on Machine Learning for Sustainable Power Systems (ML4SPS)</em>, part of <em>ECML PKDD 2024</em>. While the paper is pending formal publication, a preprint version is available on arXiv.</p> <p>The dataset contains structured training, validation, and test data comprising failure and survival events observed in transmission power grid simulations. These were generated using Grid2Op with the WCCI 2022 L2RPN environment. Each data instance is labeled with one of four classes, representing survival or impending failure in 1, 3, and 5 timesteps. This dataset was used to train, validate and test machine learning models that predict grid agent failures in topology optimization tasks. </p>
Dataset for simulation of a low-carbon urban energy system using the Backbone model
<p>The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.</p> <p>The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).</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.