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1,774 results for “Acceleration”
Dry trajectories of SARS-CoV-2 RBD from accelerated molecular dynamics simulation
<p>These are supplementary files to the preprint/paper "SARS-CoV-2 spike protein unlikely to bind to integrins via the Arg-Gly-Asp (RGD) motif of the Receptor Binding Domain: evidence from structural analysis and microscale accelerated molecular dynamics" (http://dx.doi.org/10.1101/2021.05.24.445335).</p> <p>The attached code in Jupyter notebook can be run after installing the virtual environment using the `environment.yml `</p> <p>The file `data.zip` needs to be extracted to the same path where the notebook is run from</p>
A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories
<p>Containes input data for MD simulations of 3 HSP90- small compound complexes from the paper</p> <p>A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb - structure after NTP equilibration </li> <li>ref-equal-NTP.rst7 - coordinates after NTP equilibration</li> <li>ref-equal-NTP.crd - coordinates after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul> <p> </p>
Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery (Polygons)
<p>Automatically generated dataset of glacier outlines from the journal article: "Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery"</p> <p>Research paper: https://www.sciencedirect.com/science/article/pii/S0034425721005824</p> <p>More information can be found here: https://github.com/bevingtona/glacier_change_western_canada</p>
SEDflow: Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation
<p><a href="http://changhoonhahn.github.io/SEDflow">SEDflow</a> is an accelerated Bayesian SED modeling method that uses the <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220201809H/abstract">Hahn et al. (2022a)</a> PROVABGS SED model and Amortized Neural Posterior Estimation (ANPE) to derive posterior probability distributions of galaxy properties from optical photometry. SEDflow is<span class="math-tex">\(10^5\times\)</span> faster than conventional Markov Chain Monte Carlo sampling methods and takes ~1 second per galaxy to obtain posteriors. This repository includes all of the data used to train, validate, and test SEDflow.</p> <p>This repository also includes a value-added catalog with detailed physical properties of 33,884 galaxies in the NASA-Sloan Atlas (http://www.nsatlas.org/). The properties are inferred from optical photometry in the <em>u, g, r, i, z</em> bands using SEDflow. For more details on this catalog and SEDflow see the <a href="http://changhoonhahn.github.io/SEDflow">documentation</a> and Hahn & Melchior (2022). </p> <p>For each galaxy, the catalog provides posteriors of: </p> <ul> <li>log_mstar: log10 of stellar mass</li> <li>log_sfr_1gyr: log10 of average star formation rate over 1Gyr</li> <li>log_z_mw: log10 of mass-weighted metallicity</li> <li>beta1, beta2, beta3, beta4: coefficients of the non-negative matrix factorization (NMF) star formation history basis functions</li> <li>fburst: fraction of stellar mass formed by a starburst event</li> <li>tburst: time of the starburst event</li> <li>log_gamma1, log_gamma2: log10 of coefficients of the NMF metallicity history basis functions</li> <li>tau_bc: birth cloud optical depth</li> <li>tau_ism: diffuse dust optical depth</li> <li>n_dust: Calzetti (2001) dust index</li> </ul>
Acceleration Research on Novel Photovoltaic Materials
<p>Data and Simulation definiton file for SCAPS1D simulation that are the basis for figures 3-5 of publication DOI:10.1039/d2fd00085g, published in Faraday Discussions (2022)</p> <p>Device structure for the drift-diffusion simulation is: </p> <p>metal back-contact/p-type absorber(1 micron)/n-type buffer layer (30nm)/i-ZnO(80nm)/n-type ZnO(100nm)</p> <p>No interface recombination and no back contact recombination is assumed.</p>
Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering
<p>A dataset for the publication: T. Puttonen, M. Salmi, J. Partanen, Mechanical Properties and Fracture Characterization of Additive Manufacturing Polyamide 12 After Accelerated Weathering, 2021.</p> <p>The paper studies the mechanical properties and fracture mechanics of Additive Manufacturing (AM) polyamide 12 (PA12) in two build orientations exposed to a 1500-hour accelerated weathering cycle (ISO-4982-3) followed by tensile testing (ISO-527). Fracture surfaces of X and Z build orientation AM PA12 and X build orientation AM glass-filled PA12 were studied with scanning electron microscopy. The tested AM materials were PA12, glass-filled PA12, and carbon-reinforced PA12. The reference materials cut from sheet included glass-filled and molybdenum disulfide-filled PA66, PMMA, ABS, PC, and cast PA12.</p> <p>The dataset contains:</p> <p>- Full tensile test results in PDF format, and individual CSV files</p> <p>- A python script for tensile CSV data plotting</p> <p>- Overall pictures of all samples after tensile tests</p> <p>- 3D models and drawings for tensile samples, manufacturing files for a custom QUV holder assembly</p> <p>- SEM images of fracture surfaces for AM polyamide 12 (SLS), X and Z build orientation, and glass-filled polyamide 12 (SLS) in the X build orientation</p> <p> </p> <p>Version history:</p> <p>1.0.1: A partially corrupted version of the tensile test results PDF file replaced (Tensile_test_results.pdf)</p>
Data from "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad"
<p>This upload includes data shown in the figures of the Paper "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad".</p>
Along flow acceleration of the Greenland ice sheet
<p>The acceleration of Greenland ice flow derived from ITS_LIVE annual velocity data spanning 1985-2018.</p> <p>There are two variations of using either weighted and unweighted least squares in the estimation. The data files follow the format:</p> <ul> <li>weighted--ax.tif | acceleration in the x direction:</li> <li>weighted--ay.tif | acceleration in the y-direction.</li> <li>weighted--a.tif | acceleration in the dominant flow direction</li> <li>weighted--asigma.tif | standard error estimate of the "weighted--a" data.</li> <li>weighted--N.tif | Number of years with data for each grid point.</li> </ul> <p>All data are using a polar stereographic projection (EPSG:3413). </p> <p> </p> <p>This dataset was created as part of the study: Grinsted et al. 2022, Accelerating ice flow at the onset of the Northeast Greenland ice stream. Processing choices is detailed there. </p>
The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.
<p>This dataset is related to paper journal paper with DOI: <a href="http://dx.doi.org/10.1016/j.nima.2022.167083">10.1016/j.nima.2022.167083</a>.</p> <p>The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens. </p> <p>Data files were prepared by agnieszka.gierej@vub.be</p>
Dataset Global Warming Forecast using Acceleration Factors
<p>The dataset includes results of Global Warming forecast using four methods.</p> <p>The methods include a parabolic trendline of the last 61 years of global warming and cumulated CO2 emissions.</p> <p>Two other methods apply the velocity and the acceleration of global warming and cumulative CO2 emissions.</p> <p>The relation between the global surface temperature change and the change in the cumulative CO2 emissions was determined in previous publications as 0.000745°C/GtCO2.</p> <p>The average result from all four methods for the business as usual CO2 mitigation scenario is 4.4°C (4.1°C -5.0°C).</p> <p>According to this forecast, the global temperature change will reach 1.5°C in 2031 (9 years from now) and 2.0°C in 2047 (25 years from now).</p>
Data for "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning"
<p>Datasets and material for replicating plots and results from the paper "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning" <a href="https://scipost.org/SciPostPhys.15.1.018">SciPost Phys. 15, 018 (2023)</a>.</p> <p>You will find three data files and a ReadMe.txt:</p> <ul> <li><strong>couplings.tar.gz </strong>contains the random couplings of the system's Hamiltonian <span class="math-tex">\(H = \sum_{\langle ij \rangle}{J_{ij} \sigma_i \sigma_j}\)</span>;</li> <li><strong>datasets.tar.gz </strong>contains all the datasets generated by the <a href="https://www.dwavesys.com/">D-Wave</a> quantum computer. They are already split into train and validation and divided for the type of model and annealing time;</li> <li><strong>data_for_fig.tar.gz </strong>contains files for reproducing the plots of the article, almost all of them are saved in double format, .csv and .npy or .npz.</li> </ul> <p>We encourage you to download the GitHub code linked below to open all the listed data.</p> <p>All the data are zip, so to unzip them using</p> <pre><code class="language-bash">tar -xvf datasets.tar.gz</code></pre> <p>The code for training the Neural Networks and reproducing all the results is open access at <a href="https://doi.org/10.5281/zenodo.7118502">zenodo.7118502</a>.</p>
A study of comparative (2019-2023) trends and current acceleration in Particulate Matter (PM2.5) concentration in India
<p><span> For PM<sub>2.5</sub><span> </span>monitoring model, the data was procured from the Central Pollution Control Board’s functional and selected air monitoring stations. The data is available online at the <span> Central Pollution Control Board but in form of daily trends with numerous air quality monitoring stations in an area; monthly and Annual average level especially PM2.5 trends processed from the original data. </span></span></p>
Accelerated Mechanophore Activation and Drug Release in Network Core-Structured Star Polymers Using High-Intensity Focused Ultrasound
<div>Data of the associated manuscript and supporting information sorted after Figures, Schemes, and Tables.</div>
Experimental data, analysis scripts and simulations for "Emittance preservation in a plasma-wakefield accelerator"
<p>This dataset presents the experimental data, the analysis scripts and the accompanying simulations for the article <em>"Emittance preservation in a plasma-wakefield accelerator"</em> by C. A. Lindstrøm <em>et al</em>. [<a href="https://doi.org/10.1038/s41467-024-50320-1">Nat. Commun. 15, 6097 (2024)</a>].</p> <p>The data was collected at the FLASHForward facility at DESY (Hamburg, Germany). Simulations were performed using <a href="https://doi.org/10.5281/zenodo.5639467" target="_blank" rel="noopener">HiPACE++ v23.11</a>.</p> <p><strong>Folder structure:</strong></p> <ul> <li>Folders containing experimental data: <ul> <li>Folder <code>1A_DATA__OBJECT_PLANE_SCANS</code> contains all data from object-plane scans (emittance measurements).</li> <li>Folder <code>1B_DATA__SPECTRUM_MEASUREMENT</code> contains all data from energy-spectrum measurements.</li> <li>Folder <code>1C_DATA__TWO_BPM_TOMOGRAPHY</code> contains all data from two-BPM tomography measurements.</li> <li>Folder <code>1D_DATA__BEAM_RECONSTRUCTION</code> contains all data from beam-reconstruction measurements (including longitudinal-phase-space measurements).</li> <li>Folder <code>1E_DATA__PLASMA_DENSITY</code> contains all data from plasma-density measurements (spectral-line broadening).</li> </ul> </li> <li>Folder <code>2_ANALYSIS</code> contains all the data-analysis scripts, required for plotting experimental figures.</li> <li>Folder <code>3_SIMULATION</code> contains all simulation scripts, required for generating 6D beam phase spaces and plotting simulation figures.</li> <li>Folder <code>4_FIGURES</code> contains all figure-plotting scripts (17 figures total).</li> </ul> <p><br><strong>Dataset structure:</strong></p> <ul> <li>Each dataset is identified by a 5-digit number (e.g., <code>14275</code>)</li> <li>Metadata and beam-synchronous scalar values are contained in a <code>.mat</code> dataset file (e.g., <code>14275.mat</code>).</li> <li>The dataset file has the following fields: <ul> <li><code>.metadata</code> containing all the generic metadata</li> <li><code>.state</code> containing all the <em>non-beam-synchronous</em> data (once per dataset; magnet settings etc.)</li> <li><code>.scalars</code> containing all the <em>beam-synchronous scalar</em> data (once per shot; BPM readings etc.)</li> <li><code>.vectors</code> containing all the <em>beam-synchronous vector</em> data (once per shot; scope traces etc.)</li> <li><code>.images</code> containing all the <em>beam-synchronous image</em> data, with relative URLs (once per shot; spectrometer images etc.)</li> </ul> </li> <li>The corresponding images (linked from the <code>.mat</code> file) are contained in the <code>images</code> folder, sorted by scan step.</li> </ul> <p><br><strong>Instructions for plotting all figures*:</strong></p> <ol> <li>Change directory to <code>4_FIGURES/</code></li> <li>In MATLAB, run <code>plot_all_figures();</code></li> <li>The 4 main figures and 13 supplementary figures will be plotted</li> </ol> <p><strong>Instructions for generating the 6D phase space for simulations*:</strong></p> <ol> <li>Change directory to<code> 3_SIMULATION/input_beam_generation/</code></li> <li>In MATLAB, run <code>generate_beam_and_plasma();</code></li> <li>The full analysis will up to several minutes (the files are stored in the <code>_files</code> folder)</li> </ol> <p><strong>Instructions for performing HiPACE++ simulations*:</strong></p> <ol> <li>Change directory to e.g. <code>3_SIMULATION/simulations/experimental_cell_50mm/</code></li> <li>The HiPACE++ input file is called <code>input_file</code></li> <li>This file refers to the plasma profile (<code>plasma_short.csv</code>) and beam files (<code>beam.h5</code> and <code>driver.h5</code>) found in <code>3_SIMULATION/run_notebooks/inputs/</code></li> </ol> <p><strong>Instructions for re-performing all the analysis*:</strong></p> <ol> <li>Change directory to <code>2_ANALYSIS/</code></li> <li>In MATLAB, run <code>run_all_analyses();</code></li> <li>The full analysis will up to several hours (the files are stored in various <code>_files</code> folders)</li> </ol> <p><em>* The scripts use UNIX system calls and are only compatible with Linux and Mac, but not Windows.</em></p>
Figure reproduction for "Accelerating small angle scattering experiments on anisotropic samples using kernel density estimation"
<p>These datasets and a Jupyter notebook reproduce figures in <a href="https://www.nature.com/articles/s41598-018-37345-5">a publication by Saito et al in Scientific Reports</a>. The notebook also serves as a demo for kernel density estimation (smoothing) of 2D data using Python. Details are described in the notebook. If you have no idea about ipynb format, please see HTML version with your web browser instead. It contains exactly the same codes and results as ipynb version.</p>
Software, Dataset, and Techreport: Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration
<p>This upload contains a techreport titled "Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration" together with the software (with documentation) and dataset generating the results. The software is also available on GitHub at https://github.com/croci/mpfem-paper-experiments-2024/ . The GitHub version may be updated in the future. This upload corresponds to commit number 8506dd368b84655201c8c72b1307239b9b4e43fd . See README.md file for installation instructions. The manuscript is also available on the arXiv: https://arxiv.org/abs/2410.12614.</p>
Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations
<p>Datasets used in 'Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations', published in AI4Mat-NeurIPS-2024.</p> <ul> <li>pcfs_g2_2d_n50000_20240623_nstage200_maxdelay66_.h5 was used for inputs and predictions in Fig. 1 and Fig. 2</li> <li>pcfs_g2_2d_n50000_20240820_nstage100_maxdelay120.h5 was used for inputs and predictions in Fig. 3</li> </ul> <p> </p>
First estimation of global trends in nocturnal power emissions reveals acceleration of light pollution
<p>The power emitted by different countries at night is based on DMSP and VIIRS data. Inclued also, some extra data from Spain, Portugal, Italy, UK and Greece.</p>
Reference dataset of multi-objective and multi-fidelity optimization in laser-plasma acceleration
<p>This repository contains a dataset used for the article "<em>Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration</em>" (<a href="https://arxiv.org/abs/2210.03484">arXiv:2210.03484</a>). The dataset consists of 2443 FBPIC particle-in-cell simulations of a laser wakefield accelerator that were selected using a Bayesian optimizer. The goal of the optimization was to perform multi-objective multi-fidelity optimization of electron beam parameters. The dataset contains simulations of different resolutions, accordingly with differing fidelities. The typical runtime at lowest (highest) resolution is approximately 1 (90) minutes.</p> <p>In the dataset we have <em>train_x </em>and <em>train_obj </em>numpy arrays with dimensions <em>(n,5)</em> and<em> (n,3)</em>, respectively. Here <em>n</em> is the number of FBPIC simulations. The five columns in <em>train_x </em>are [plasma density, upramp length, laser focus, downramp length, fidelity]. The fidelity parameter controls the resolution and hence the runtime of the simulation. The three columns in the <em>train_obj </em>are the [total charge, distance of median to target energy, bandwidth of electron beams]. For the distance, the target energy is fixed to 300 MeV and for the bandwidth is defined by the median absolute deviation around the median. The two columns have negative values since the optimizer assumes a maximization of all objectives while the distance and bandwidth in this study were being minimized.</p> <p>The different folders contain data of different kind of single and multi-objectives that were used to produce figures 2, 3, 5 in the associated paper. For more details please see the referred article. The folder "combined" contains the data of all simulations together and is most suitable for (5D x 3D) surrogate model generation.</p>
Stability of the Modulator in a Plasma-Modulated Plasma Accelerator
<p>Input decks for the particle-in-cell code WarpX used in a new study to simulate the modulator stage of a recently proposed laser-plasma accelerator scheme [Phys. Rev. Lett. <strong>127</strong>, 184801 (2021)], dubbed the Plasma-Modulated Plasma Accelerator (P-MoPA). </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.