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Dataset results
567 results for “Simulated datasets”
Simulation Dataset: The antimicrobial fibupeptide lugdunin forms water-filled channel structures in lipid membranes
<p>The Zenodo repository contains data to the molecular dynamics simulations in the following manuscript: </p> <p><strong>The antimicrobial fibupeptide lugdunin forms water-filled channel structures in lipid membranes </strong></p> <p>by</p> <p>Dominik Ruppelt, Marius F. W. Trollmann, Taulant Dema, Sebastian N. Wirtz, Hendrik <br>Flegel, Sophia Mönnikes, Stephanie Grond, Rainer A. Böckmann*, Claudia Steinem*</p> <p>0) Lugdunin-parameterization</p> <p>1) Lugdunin-stack-stability</p> <p> - trans_configuration.gro: Artifical lugdunin stack in trans configuration (see SFig. 23 a).</p> <p> - gauche_configuration.gro: Artifical lugdunin stack in gauche configuration (see SFig. 23 b)</p> <p> - DMPC</p> <p> - DOPC</p> <p> - POPC</p> <p> - POPC_CHOL</p> <p>2) Partitioning-of-Lugdunin-in-Membranes</p> <p> - POPC</p> <p> - POPC_CHOL</p> <p> - GRAMPOS</p> <p>3) PMF-calculations</p> <p> - GRAMPOS_WT</p> <p> - POPC_CHOL_MUT</p> <p> - POPC_CHOL_WT</p> <p> - POPC_MUT</p> <p> - POPC_WT</p>
Dataset for Atomistic simulations on liquid Mg-Sr alloys assisted with deep learning potential
<p>This Dataset is for the paper "Atomistic simulations on liquid Mg-Sr alloys assisted with deep learning potential," including an example script, interatomic potentials, and training data.</p>
Fly to Target and Turn Dataset -- Gaussian Splatting Holodeck and PyBullet Simulator
<p>Gaussian Splatting and PyBullet Simulation Datasets in 3Hz, 9Hz, and mixed (1-10Hz) frequency framerates. The task is to fly to the red or blue target, then turn left if the target is red and turn right if the target is blue. Velocity labels are also provided for each timestep for velocity in the xyz directions and the yaw rate. </p> <p> </p>
Dataset for Sakata et al. (2024) "Effects of an Intrinsic Magnetic Field on Ion Escape from Ancient Mars Based on MAESTRO Multifluid MHD Simulations"
<div> <p>This dataset contains the simulation results in Sakata et al. (2024), Effects of an Intrinsic Magnetic Field on Ion Escape from Ancient Mars Based on MAESTRO Multifluid MHD Simulations, Journal of Geophysical Research: Space Physics.</p> </div>
Simulation Results in the Paper "Propagation of Slow Slip Events on Rough Faults: Clustering, Back Propagation, and Re-rupturing" [Dataset]
<p>Data file "simulations.mat" contains the 5 simulations of slow slip events on flat or rough faults. </p> <table> <tbody> <tr> <td>structure array</td> <td>description</td> <td>reference</td> </tr> <tr> <td>s0</td> <td> 2.5 km long flat fault</td> <td>Fig. 2b</td> </tr> <tr> <td>s1</td> <td>2.5 km long rough fault</td> <td>Fig. 2c</td> </tr> <tr> <td>s2</td> <td>10 km long rough fault</td> <td>Fig. 5</td> </tr> <tr> <td>s3</td> <td>10 km long fractal fault</td> <td>Fig. 7</td> </tr> </tbody> </table> <p>structure array consists of:</p> <p>t: time (s)</p> <p>x: distance (m)</p> <p>v: slip rate (m/s)</p> <p>slip: accumulated slip (m)</p> <p>tau: shear stress (Pa)</p> <p>sigma: normal stress (Pa)</p> <p>notes: description</p> <p> </p> <p>Data file "catalog.mat" contains 3 simulated slow slip events' catalogs on flat and rough faults, c0, c1, and c2, in Fig. 4a, 4b, and 4c, respectively.</p> <p>It consists of:</p> <p>l: rupture length (m)</p> <p>time: time (s)</p> <p>notes: description</p>
The simulated datasets used for multispeaker anonymization
<p> </p> <p>The simulated datasets of the paper: </p> <p>"A Benchmark for Multi-speaker Anonymization," (submitted to IEEE TIFS)<br><a href="https://arxiv.org/abs/2407.05608">https://arxiv.org/abs/2407.05608</a></p> <p>The authors are Xiaoxiao Miao, Ruijie Tao, Chang Zeng, Xin Wang</p> <p>This data is meant to be used with our open-source implementation, which can be found here: <a href="https://github.com/xiaoxiaomiao323/MSA">https://github.com/xiaoxiaomiao323/MSA</a></p> <p>This study is partially supported by JST, PRESTO Grant Number JPMJPR23P9, Japan, SIT-ICT Academic Discretionary Fund, and Ministry of Education, Singapore, under its Academic Research Tier 1 (R-R13-A405-0005) and its SIT's Ignition grant (STEM) (R-IE3-A405-0005).</p> <p>More information about the directory structure and how to use the data can be found in the READMEs on GitHub.</p> <p> </p> <p> </p>
[Dataset] Intensity and Dimensionality-Dependent Dynamics of Laser-Proton Acceleration in 1D, 2D, and 3D Particle-in-Cell Simulations
<p>The included Jupyter Notebooks (.ipynb) were used to generate figures and make calculations for the work. The resulting image files are saved in Figure_Outputs.tar.gz. The other archives (1D.tar.gz, 2DS.tar.gz, 2DP.tar.gz, 3D.tar.gz, laser_focus.tar.gz, and EField.tar.gz) are compressed directories containing EPOCH PIC simulation outputs for the paper (relevant directories should be extracted to replicate analysis with provided files). </p> <p>Since the last version, a new figure (Figure 6, an ion phase space) has been added. Notebooks for later figures have been renumbered and the figure outputs have been updated. </p>
Accuracy Study and Improvement of Network Simulation in the SimGrid Framework (datasets)
<p>Data sets and code for https://hal.inria.fr/inria-00361031 and the Rescience replication.</p> <p>The content.org file is the content of the Rescience article from which all the commands for the replication can be found.</p>
The simulated datasets used in VirStrain's paper.
<p>These are simulated datasets used in VirStrain's paper. More information can be found in the README.txt in the "simulated_data_VirStrain.zip".</p>
An evaluation dataset for the skills of CMIP5 and CMIP6 models in simulating climate of China
<p>General circulation model (GCM) simulations archived by the Coupled Model Intercomparison Project (CMIP) are crucial tools for climate science. However, with various GCM results simulated by different countries and institutions, researchers have difficulty in choosing appropriate models for their unique study area. To this end, this dataset provieds Tayler skill scores of 28 GCMs in simulating temperature and precipitation of 631 reference sites across China under daily, monthly and seasonally scales. These scores are calculated based on the observations of meteorological stations and historical simulations of GCMs during 1970-2005. </p> <p>The dataset is very important for researchers to select locally appropriate GCMs. For example, researchers concerned with climate change of Beijing could firstly download the GCMs with sound performance at station 54511 (i.e., NorESM2-LM, INM-CM5-0 and MPI-ESM1-2-LR for temperature and NorESM1-M, IPSL-CM5A-LR and INM-CM4 for precipitation), and then conduct the further works of downscaling.</p>
Simulated dataset of dryland vegetation model
<p>This file contains simulated output from a drylands vegetation model used to produce figs 3, S3, S4 and S6 in the paper "Remotely-sensed slowing down in spatially patterned dryland ecosystems" authored by Michiel P. Veldhuis, Ricardo Martinez-Garcia, Vincent Deblauwe, Robert M. Pringle, Corina E. Tarnita & Vasilis Dakos </p>
Molecular Dynamics simulation dataset for : Hypoxia increases the methylated histones to prevent histone clipping and redistribution of heterochromatin during Raf-induced senescence.
<p>Files are Initial structures of Cathepsin L(CTSL) with histone H3 peptides (naive and K23me3) and their Molecular Dynamics simulation trajectories. AutoDock Vina and Charmm were used in H3 peptide docking. </p> <p>Wild type CTSL was modeled with apo-Cathepsin L C25A mutant(PDB ID : 3K24) using Charmm-GUI. Trimethylated histone H3 peptide was generated with PyMol. H3 peptide Molecular Dynamics simulations were performed with GPU-accelerated OpenMM.</p>
Simulated datasets of twelve satellites capable for SIF monitoring.
<p>The simulated datasets of twelve satellites capable for SIF monitoring, which were used in the paper entitled<em><strong> Investigating the potential accuracy of spaceborne solar-induced chlorophyll fluorescence retrieval for twelve capable satellites based on simulation data</strong></em><strong>.</strong> </p> <p>The datasets contain the test and training datasets, the corresponding wavelengths, as well as the SIF intensities at 685 (SIF_685_true) and 740 nm (SIF_740_true) simulated by SCOPE.</p>
[Dataset] Determining the origin of tidal oscillations in the ionospheric transition region with EISCAT radar and global simulation data
<p>Preprocessed data</p>
SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations
<p><strong>SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations</strong></p> <p>Availability of domain-specific datasets is a challenge for object detection. In particular, conducting onsite experiments is risky and time-consuming for the maritime domain. It is not always possible to capture challenging situations and incorporate variations in the environment. Therefore, we present a simulation-based dataset for maritime environments consisting of 9471 high-resolution (1920x1080) images resembling the real world with precise annotations.</p> <p>The dataset was acquired using a simulation tool, AILiveSim [17]. AILiveSim is a 3D simulation platform for targeted scalable development and integration of autonomous systems through digital twins. The tool provides a realistic 3D model of the route of the watercraft extended from the city of Turku to Ruisalo in South-West Finland.</p> <p>Our dataset incorporates diversity of objects, weather, illumination, visible proportion and scale in images that resemble the real world. </p> <p>Please cite the following publication when using the dataset:</p> <p>M. Raza, H. Prokopova, S. Huseynzade, S. Azimi and S. Lafond, "SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations," <em>OCEANS 2022, Hampton Roads</em>, Hampton Roads, VA, USA, 2022, pp. 1-5, doi: 10.1109/OCEANS47191.2022.9977182.</p> <p>The publication is available at: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977182">https://doi.org/10.1109/OCEANS47191.2022.9977182</a></p> <p>A preprint version of the publication is available at <a href="https://arxiv.org/abs/2211.05237">https://arxiv.org/abs/2211.05237</a></p> <p> </p> <p> </p>
Model output dataset used in "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China"
<p>This dataset accompanies the paper by Zhang et al. "Sensitivity of Heavy Convective Precipitation Simulations to Changes in Land-atmosphere Exchange Processes over China".</p> <p>Three heavy precipitation events were modeled using the WRF v3.9 model:</p> <p>(1) The_21_July_Beijing_Rainstorm_Simulation<br> (2) The_30_July_Ningxia_rainstorm_Simulation<br> (3) The_19_June_Jiangxi_rainstorm_Simulation</p> <p>Furthermore, three cases were designed for each heavy precipitation event: (1) control experiment (DEFAULT), using the default M-O option (<em>C<sub>zil</sub></em> ~ 0); (2) constant <em>C<sub>zil</sub></em> (CZIL0.01, CZIL0.05, CZIL0.1, CZIL0.3, CZIL0.5 and CZIL0.8), with <em>C<sub>zil</sub></em> values of 0.01, 0.05, 0.1, 0.3, 0.5, and 0.8; (3) a dynamic canopy-height dependent <em>C<sub>zil</sub></em> (NEWCZIL).</p> <p>Plain Language Summary for this paper:<br> Over recent decades, the frequent occurrence of heavy precipitation events has caused devastating ecological and socioeconomic impacts, such as agriculture losses, infrastructure damage, and casualties. High-resolution atmospheric modeling at a convection-permitting grid spacing (≤4 km) provides valuable applications for predicting heavy precipitation. Precipitation can be strongly affected by the energy and moisture exchanges between land surface and atmosphere. However, the representation of land-atmosphere interactions in atmospheric models and the responses of precipitation to land-atmosphere exchange efficiency remain great uncertainties. This study performed 3-km high-resolution atmospheric modeling with a dynamic vegetation-type-dependent land-atmosphere exchange scheme for three typical heavy precipitation events that occurred over areas with different dominant land-cover types. The results showed that land-atmosphere exchange efficiency mainly affected the precipitation intensity as well as the onset and peak time of precipitation. The dynamic exchange scheme modifies the efficiency of land-atmosphere exchanges to match local land cover conditions and could reproduce well the field observations, especially the intensity and location of the heaviest rainfall which usually serve as the most concerned variable in a major rainstorm event. Our findings show that the dynamical scheme could help achieve more accurate precipitation simulations.</p>
Dataset wih MD simulations of fish aquaporin - wt and different mutants.
<p>MD trajectories and final minimized structures after 100 ns MD simulation for wt, pT38, pY107, L117A, Y107A and Y107E variants of fish aquaporin.</p> <p>All simulations performed using the YASARA software (www.yasara.org). </p>
Simulated Long-Film image dataset
<p>This dataset contains simulated 10,368 Long-Film images and vertebrae centroid labels from projection of public CT images. The CT images are from SpineWeb [1] and The Cancer Imaging Archive [2]. Details of usage and generation process can be found in [3].</p> <p>[1] B. Glocker, J. Feulner, A. Criminisi, D.R. Haynor, E. Konukoglu, Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans., Med. Image Comput. Comput. Assist. Interv. 15 (2012) 590–8. https://doi.org/10.1007/978-3-642-33454-2_73.<br> [2] K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, F. Prior, The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository., J. Digit. Imaging. 26 (2013) 1045–57. https://doi.org/10.1007/s10278-013-9622-7.<br> [3] Huang Y, Jones CK, Zhang X, Johnston A, Waktola S, Aygun N, Witham TF, Bydon A, Theodore N, Helm PA, Siewerdsen JH, Uneri A. "Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images." Computer Methods and Programs in Biomedicine, 2022 (under review)</p> <p> </p>
Dataset: Polystyrene and SARS-CoV-2 S protein MD simulations
<p>Coordinates (input files and equilibrated structures) corresponding to the MD simulations reported in Sahihi and Faraudo, J. Chem. Inf. Model. 2022, 62, 16, 3814–3824 <a href="https://doi.org/10.1021/acs.jcim.2c00562">https://doi.org/10.1021/acs.jcim.2c00562</a> . Please cite this publication and the dataset in any use of the data.</p> <p>The coordinate files in pdb format include fully glycosylated structures of the S1 subunit of SARS-CoV-2 spike protein (up and down confirmations) and a polystyrene slab.</p>
Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"
<p>Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"</p> <p>Yuhang Zhang1, Aizhong Ye1*, Phu Nguyen2, Bita Analui2, Soroosh Sorooshian2, Kuolin Hsu2</p> <p>1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.</p> <p>2 Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA.</p> <p>## Dataset </p> <p>Streamflow simulations from one observed precipitation (CMA) and three satellite precipitation products (PDIR, IMERG-F, and GSMaP) for 522 sub-basins.</p> <p>- Q-CMA (streamflow reference)<br> - Q-PDIR (uncorrected)<br> - Q-IMERGF (uncorrected)<br> - Q-GSMAP (uncorrected)</p> <p>### Data structure</p> <p>- Head section (row1-row5)<br> - SubNO: 522 <br> - BeginT: 2003-01-01 00:00 <br> - EndT: 2019-12-31 00:00 <br> - Interval: 1440s (daily)<br> - Revise: 10 (scaling factor to keep int datatype)<br> - Point1 Point2 ... (Subbasin No.)<br> - Data section<br> - 6209 rows, 522 cols</p> <p>## Results</p> <p>Two post-processing model results for test period (2015-1-1 to 2018-12-31).</p> <p>### Data structure</p> <p>- 1462 rows, every row denotes each day from 2015-1-1 to 2018-12-31</p> <p>- 100 columns, every column denotes each quantile from 0.005 to 0.995, total 100 quantiles.</p> <p>### qrf-output</p> <p>- pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs)</p> <p>### lstm-output</p> <p>- pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs)</p> <p> </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.