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567 results for “Simulated datasets”
The P-V-T dataset of liquid Fe-C alloys from FP-MD simulations
<p>The <em>P</em>-<em>V</em>-<em>T</em> dataset of liquid Fe-C alloys with different carbon contents (<em>X</em><sub>C</sub> = 0, 2.1, 4.7, 7.6, and 11.2 wt%) under the <em>P</em>-<em>T</em> conditions of the outer core (~136-330 GPa, 4000-6000 K) are obtained via first-principles molecular dynamics simulations.</p>
Simulation Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies
<p>This is a supplementary simulation dataset to reproduce Fig. 3C,D of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <p>The zip folder contains the following 3 files in h5 format: front attack (out_Npred1_pred_angle0.0.h5), side attack (<span>out_Npred1_pred_angle1.5707963267948966.h5), </span><span>back attack (out_Npred1_pred_angle3.141592653589793.h5).</span></p> <p>Each file has the following "keys": KeysViewHDF5 ['circ_seg', 'end', 'endD', 'end_PosVel', 'fount', 'part', 'partD', 'pavas', 'pred', 'predD', 'start', 'start_fountain', 'start_pred', 'swarm', 'swarm_pred0', 'swarm_predD'].</p> <p>The key necessary to reproduce Fig. 3C,D of the aforementioned paper is "fount" (<HDF5 dataset "fount": shape (40, 1200, 100, 8), type "<f8">). Namely, "fount" data array consists of 40 simulation runs, 1200 time points, 100 agents, and 8 metrics. The metric with index "0" depicts the value of the Euclidean distance from the agent <em>i</em> to the simulated predator. The metric with index "1" depicts the value of the position angle (theta 1 in rad) of the agent <em>i</em> relative to the simulated predator. The metric with index "2" depicts the value of the flee angle (theta 2 in rad) of the agent <em>i</em> relative to the simulated predator.</p> <p>To estimate the start and the end of the fountain evasion, one can use the following script in Python:</p> <p>import numpy as np</p> <p>m = h5py.File(filename, "r")<br><br>for key in m.keys():<br> print(key)</p> <p>fount_runs = m[key]["fount"]</p> <p>for j in range(40):<br> fnt_start[j] = np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][0] <br> fnt_end[j] = np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][-1]</p>
ALPHA-g Preprocessed Simulation Dataset for Machine Learning
<p>Dataset generated for "Vertex Reconstruction with Deep Learning for ALPHA-g Radial Time Projection Chamber" 2024 preprint and "AI Meets Antimatter: Unveiling Antihydrogen Annihilations" paper for Machine Learning and the Physical Sciences workshop at NeurIPS'24 by Ferreira et al.</p> <p>The goal of this project was to create a PointNet-like deep learning model that could learn the relationship between simulated ALPHA-g detector data and the position of an antimatter annihilation on the walls of the ALPHA-g vacuum chamber. If this approach to antimatter annihilation event reconstruction works with real-world data then it can then be used as part of a larger analysis pipeline to better constrain the effect of gravity on antimatter.</p> <p>See the above papers for a much more detailed description of the experiment and data used but generally, the four important terms to know for interacting with this dataset are:</p> <table> <tbody> <tr> <td><strong>Term</strong></td> <td><strong>Meaning</strong></td> </tr> <tr> <td>Event</td> <td>An antimatter annihilation. Each event contains a number of spacepoints and one vertex.</td> </tr> <tr> <td>Spacepoints</td> <td>The many 3D points (x,y,z) that represent the ALPHA-g detector data, if more than 800 this dataset cuts them off at 800, prioritizing the ones with higher amplitudes, and if less than 800 it pads them with zeros such that all events have 800 entries.</td> </tr> <tr> <td>Vertex</td> <td>3D position of the antimatter annihilation. For this initial study, we just use the <em>z</em> coordinate since this is what is most important to measuring the effect of gravity. </td> </tr> <tr> <td>Helix Fit</td> <td>The state-of-the-art method currently used for antimatter annihilation event reconstruction within the ALPHA-g detector. The method doesn't use machine learning and instead involves fitting 3D helix functions to the spacepoints.</td> </tr> </tbody> </table> <p> </p> <p>Data is split into three separate HDF5 datasets: training (2,160,020 events), validation (261,654 events), and testing (288,919 events). Each dataset contains the following columns with each row representing one event:</p> <table> <tbody> <tr> <td><strong>Column Index</strong></td> <td><strong>Value [mm]</strong></td> </tr> <tr> <td>0</td> <td><em>x</em> coordinates of spacepoints</td> </tr> <tr> <td>1</td> <td><em>y</em> coordinates of spacepoints</td> </tr> <tr> <td>2</td> <td><em>z</em> coordinates of spacepoints</td> </tr> <tr> <td>3</td> <td> <p><em>z</em> coordinate of simulated vertex</p> </td> </tr> <tr> <td>4</td> <td> <p><em>z</em> coordinate of Helix Fit vertex prediction</p> </td> </tr> </tbody> </table> <p> </p> <p>The code used to generate the raw dataset is available at <code><a href="bitbucket.org/expalpha/alphasoft">bitbucket.org/expalpha/alphasoft</a></code> and the code used to process it into this preprocessed dataset as well as the downstream training and evaluation are available at <code><a href="gitlab.triumf.ca/alpha-ai/rTPC-AI">gitlab.triumf.ca/alpha-ai/rTPC-AI</a></code>.</p>
Processed extreme composite cyclone datasets from X-SHiELD simulations.
<p>Processed extreme cyclones composites datasets during 2020-2022 from X-SHiELD simulations (see Cheng et al. (2022) and Harris et al. (2023) for details on the X-SHiELD model and experiment setups), including total column water vapour (TCWV in the paper, but here shortened as iwp), total precipitation (pr), and 10-m wind speeds for both the current climate and a warming scenario with a uniform 4-K increase in sea surface temperature.</p>
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
<p><span>The datasets store both motion and observation information of a single fluorescent sub-diffraction limit-sized particle moving in a three-dimensional confined environment. The confined motion is following a nonlinear model driven by non-Gaussian noise, the observation is formed by engineered Double-helix (DH) point spread function (PSF) and captured by scientific complementary metal-oxide semiconductor (sCMOS) camera. Based on our prior computationally efficient application of Sequential Monte Carlo - Expectation Maximization (SMC-EM), we extended it to handle the DH-PSF for encoding the three-dimensional position of the particle in two-dimensional image plane of the camera. We focus on studying the datasets at low signal and low signal-to-background ratio (SBR). Based on the datasets across different SBR and confinement lengths, a quantitative comparison is conducted to show that in the low signal regime, the SMC-EM approach outperforms the other methods while at higher signal-to-background levels, SMC-EM and the MLE-based methods perform equally well and both are significantly better than fitting to the MSD. In addition, our results indicate that at smaller confinement lengths where the nonlinearities dominate the motion model, the SMC-EM approach is superior to the alternative approaches. </span></p>
Simulated Bending Moment Dataset
<p>This dataset includes two sets. One set has 600 data records, corresponding to the training set referred to in Section 11.4.1 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book, whereas the other set has 10,000 data records. Each set has three columns of data (other than the serial number). The first column is the wind speed, simulated using a Rayleigh distribution, and the second and third columns are, respectively, the simulated flapwise and edgewise bending moments, in the unit of kN-m. The flapwise and edgewise bending moments are simulated from TurbSim and FAST. The simulations were conducted by Dr. Eunshin Byon at the University of Michigan.</p>
Datasets for "LAPS v1.0.0: Lagrangian Advection of Particles at Sea, a Matlabprogram to simulate the displacement of particles in the ocean."
<p>The data, run results and analysis scripts used to produce all of the results presented in the paper "LAPS v1.0.0: Lagrangian Advection of Particles at Sea, a Matlab program to simulate the displacement of particles in the ocean."</p>
Simulated in-the-wild Dataset (SWAD)
<p>Simulated in-the-wild dataset, including 44 images, containing a total of 117 Artcodes.</p>
``HiPen'': a new dataset for validating (S)QM/MM free energy simulations
<p>Calculating free energy differences between levels of theory (i.e., <span class="math-tex">\(\Delta A^{low \to high}\)</span>) is integral to performing indirect (S)QM/MM free energy simulations. However, connecting levels of theory via free energy simulations has proved difficult due to (1) bond/angle degrees of freedom, (2) dihedral degrees of freedom, and (3) solvent arrangement differences between levels of theory, largely due to partial charge differences between levels of theory. In order to improve calculation of (S)QM/MM free energy simulations, the free energy simulation community should begin to compare methods based on convergence success relative to overall computational time and resource requirements. We have begun to compile such a dataset by calculating <span class="math-tex">\(\Delta A^{MM \to SCC-DFTB}\)</span> in gas phase for 22 drug-like molecules, as seen in our recent publication, Kearns, et al. <strong>2018</strong>, <em>Molecules</em>, Submitted, and we hope that future practitioners will do the same. With this work we hope to provide a standard for comparison for future FES methodologies; additionally, in the near future we hope to continue to add to this dataset including results in more complicated environments such as in solution and in enzyme. All data can be found in our publication and in the accompanying Supporting Information; raw data (such as simulation trajectories and raw data files) can be made available upon request. The purpose of this dataset publication is to make available all starting coordinates, topologies, parameter sets, and input files necessary to replicating the results published in our work.</p>
Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America : Dataset
<p>This dataset is linked to the paper “Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-16-1661-2023). It contains the installer of the model, its user guide, as well as all the input files (inventory, thinning, meteorology and soil horizons files for each stand used in the evaluation and calibration steps), the R scripts and associated data used to analyse the model outputs.</p>
Simulated Dataset
<p>Simulated dataset to test ZONODO dataset publication.</p>
Dataset supporting "From mouth to gut: microfluidic in vitro simulation of human gastro-intestinal digestion and intestinal permeability"
<p>Manuscript abstract:</p> <p>Reproducible <em>in vitro</em> studies of bioaccessibility, intestinal absorption and bioavailability are key to the successful development of novel foods and drugs aiming for oral administration. There is currently a lack of methods that offer the finesse required to study these parameters for valuable molecules in small volumes - as is the case of nanomaterials under research. Here, we describe a modular microfluidic-based platform for total simulation of the human gastro-intestinal tract. Digestion-chips and cell-based gut-chips were fabricated from PDMS by soft lithography. On-chip digestion was validated using a fluorescently-labelled casein derivative, which followed typical Michaelis-Menten kinetics and showed temporal resolution and good agreement with well-established bench-top protocols. Irreversible inhibition of serine proteases using Pefabloc® SC and a 1:6 dilution was sufficient to mitigate the cytotoxicity of simulated digestion fluids. Caco-2/HT29-MTX co-cultures were grown on-chip under continuous flow for 7 days to obtain a differentiated cell monolayer forming a 3D villi-like epithelium with clear tight junction formation, and with an apparent permeability (P<sub>app</sub>) of Lucifer Yellow closely approximating values reported <em>ex vivo</em> (3.7x10<sup>-6</sup> ± 1.4x10<sup>-6</sup> vs 4.0x10<sup>-6</sup> ± 2.2x10<sup>-6</sup>). Digesta from the Digestion-chips were flowed through the Gut-Chip demonstrating the capacity to study sample digestion and intestinal permeability in a single microfluidic platform holding great promise for its use in pharmacokinetics studies.</p>
Performance and limits of a shallow-water model for landslide-generated tsunamis: from laboratory experiments to simulations of flank collapses at Montagne Pelée (Martinique) - DATASETS
<p>Datasets of the 6 presented experiments with 4mm beads presented in the paper "Performance and limits of a shallow-water model for landslide-generated tsunamis: from laboratory experiments to simulations of flank collapses at Montagne Pelée (Martinique)".</p> <p>Each datasets (csv file) corresponds to the hand picked profile of either the water free surface or the granular material at 0.1 second of interval. </p> <p>The third dataset for each experiment corespond of the gauges records.</p>
Dataset for "Harmonic Structure of Lower Hybrid Waves Driven by Energetic Ions at 4000 km Altitude: PIC simulation"
<p>To produce the figures shown in the paper, you can launch Gnuplot and execute the following command:</p> <p>gnuplot> load "<em>(filename).</em>gp" (e.g. load "fig1.gp")</p> <p>For Figs. 1 and 4, you can input the filename in the scripts to produce the figures.</p> <p>For Figs. 2 and 3, you can set a flag variable, <em>flag</em>, as <em>flag=1</em> to produce the figure in case A or as <em>flag=2</em> to produce the figure in case B.</p> <p>The label in the plotted figures is the same as in the paper.</p> <p>We prepare CSV files so you can produce the figures with other software.</p>
Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level - MD simulations Dataset
<p>Models of SARS-CoV-2 virus spike protein bound to 1-3 of ACE2 receptors embedded in lipid nanodisks. Systems include all files in GROMACS format needed to reproduce simulations performed in the "Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level" article.</p> <p> </p> <table> <caption>Details</caption> <thead> <tr> <th scope="col">system</th> <th scope="col">box size (x-y-z) [nm]</th> <th scope="col">Number of atoms</th> </tr> </thead> <tbody> <tr> <td>Spike +<br> 1x ACE2, full length</td> <td>33.44834 28.96711 57.95573</td> <td>5,665,217</td> </tr> <tr> <td>Spike +<br> 1x ACE2, truncated</td> <td>28.05757 24.29856 46.74417</td> <td>3,203,907</td> </tr> <tr> <td>Spike +<br> 2x ACE2, truncated</td> <td>28.30864 21.23141 48.40873</td> <td>2,936,398</td> </tr> <tr> <td>Spike +<br> 3x ACE2, truncated</td> <td>28.32733 21.24544 48.30436</td> <td>2,936,588</td> </tr> </tbody> </table>
Simulation datasets for: Intense surface winds from gravity wave breaking in simulations of a destructive macroburst
<p>Shortly after 0600 UTC (midnight local time) 9 June 2020, a convective line produced severe winds across parts of northeast Colorado that caused extensive damage, especially in the town of Akron. High-resolution observations showed gusts exceeding 50 m s<sup>−1</sup>, accompanied by extremely large pressure fluctuations, including a 5-hPa pressure surge in 19 s immediately following the strongest winds and a 15-hPa pressure drop in the following 3 min. Numerical simulations of this event (using the WRF Model) and with horizontally homogeneous initial conditions (using Cloud Model 1) reveal that the severe winds in this event were associated with gravity wave dynamics. In a very stable postfrontal environment, elevated convection initiated and led to a long-lived gravity wave. Strong low-level vertical wind shear supported the amplification and eventual breaking of this wave, resulting in at least two sequential strong downbursts. This wave-breaking mechanism is different from the usual downburst mechanism associated with negative buoyancy resulting from latent cooling. The model output reproduces key features of the high-resolution observations, including similar convective structures, large temperature and pressure fluctuations, and intense near-surface wind speeds. The findings of this study reveal a series of previously unexplored mesoscale and storm-scale processes that can result in destructive winds.</p> <p><strong>Significance Statement </strong></p> <p>Downbursts of intense wind can produce significant damage, as was the case on 9 June 2020 in Akron, Colorado. Past research on downbursts has shown that they occur when raindrops, graupel, and hail in thunderstorms evaporate and melt, cooling the air and causing it to sink rapidly. In this research, we used numerical models of the atmosphere, along with high-resolution observations, to show that the Akron downburst was different. Unlike typical lines of thunderstorms, those responsible for the Akron macroburst produced a wave in the atmosphere, which broke, resulting in rapidly sinking air and severe surface winds.</p>
Dataset of Simulation of Dawn-to-Dusk Electric Field in the Jovian Inner Magnetosphere via Region 2-like Field-Aligned Current
<p>This dataset contains the input parameters and the simulated results used for figures in the paper "Simulation of Dawn-to-Dusk Electric Field in the Jovian Inner Magnetosphere via Region 2-like Field-Aligned Current" by Y. Nakamura, K. Terada, C. Tao, N. Terada, Y. Kasaba, F. Leblanc, H. Kita, A. Nakamizo, A. Yoshikawa, S. Ohtani, F. Tsuchiya, M. Kagitani, T. Sakanoi, G. Murakami, K. Yoshioka, T. Kimura, A. Yamazaki, and I. Yoshikawa.</p> <p>Detailed informations about the data files can be found in "README.txt".</p>
On equilibrating non-periodic molecular dynamics samples for coupled particle-continuum simulations of amorphous polymers: dataset
<p><strong>Abstract:</strong><br> (from [1])<br> In the context of fracture simulations of polymers, the molecular mechanisms in the vicinity of the<br> crack tip are of particular interest. Nevertheless, to keep the computational cost to a minimum, a<br> coarser resolution must be used in the remaining regions of the numerical sample. For the specific<br> case of amorphous polymers, the Capriccio method bridges the gap between the length and time scales<br> involved at the different levels of resolution by concurrently coupling molecular dynamics (MD) with<br> the finite element method (FEM). Within the scope of the Capriccio approach, the coupling to the<br> molecular MD region introduces non-periodic, so-called stochastic boundary conditions (SBC). In<br> similarity to typical simulations under periodic boundary conditions (PBC), the SBC MD simulations<br> must reach an equilibrium state before mechanical loads are exerted on the coupled systems. In this<br> contribution, we hence extensively study the equilibration properties of non-periodic MD samples<br> using the Capriccio method. We demonstrate that the relaxation behavior of an MD-FE coupled<br> MD domain utilizing non-periodic boundary conditions is rather insensitive to the specific coupling<br> parameters of the method chosen to implement the boundary conditions. The behavior of an exemplary<br> system equilibrated with the parameter set considered as optimal is further studied under uniaxial<br> tension and we observe some peculiarities in view of creep and relaxation phenomena. This raises<br> important questions to be addressed in the further development of the Capriccio method.<br> <br> <strong>Contact:</strong><br> Felix Weber<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen<br> Germany<br> <br> <strong>Context:</strong><br> This dataset contains the results presented in [1] and related data.</p> <p><strong>Content:</strong><br> Throughout this data set, Lammps [2] real units are used. The following folders contain the results obtained under periodic boundary conditions:<br> - biax_PBC: biaxial loading <br> - equil_PBC: equilibration<br> - ut_PBC: uniaxial tension<br> Each simulation directory contains:<br> - input.prm: input parameters of the specific simulation (read by the input file)<br> - job.out: simulation log file<br> - meta.info: meta data of the specific simulation run<br> - Lammps input file (*.in) of the specific simulation<br> - Lammps data file (*.data, molecular style) of the investigated sample<br> - LAMMPS_out: resulting Lammps data and restart (*.rst) files and simulation results (Lammps thermo_out) in <br> tabulated form, an overview of the columns is given in the respective folders</p> <p>The following folders contain the results obtained under stochastic boundary conditions using the Capriccio method [3]:<br> - best: equilibration with the parameter set considered to be most suitable for the MD-FE coupled equilibration<br> - biax: biaxial loading<br> - bridging: equilibration with different adaptivity levels of the bridging domain<br> - descr_obs: equilibration with a Lagrangian frame for the description of the observation region<br> - dpd: equilibration with different thicknesses of the dissipative particle dynamics (DPD) region<br> - friccoeff: equilibration with different friction coefficients applied in the dissipative particle dynamics region<br> - fur: equilibration with different numbers of fur beads <br> - gausspoints: equilibration with different numbers of quadrature points per direction <br> - min: equilibration applying an initial, static energy minimization<br> - nodes: equilibration with a higher number of finite element nodes<br> - shifted: equilibration with particle systems obtained at different positions and points in time within the periodic master system<br> - smdcub: equilibration with different remaining stiffness ratios for the cubic modified weighting factor<br> - smdlin: equilibration with different remaining stiffness ratios for the linear modified weighting factor<br> - timestepsize: equilibration with different molecular dynamics time step sizes<br> - ut: uniaxial tension<br> - ut_friccoeff: uniaxial tension with different friction coefficients applied in the dissipative particle dynamics region<br> - weighting: equilibration with different energy weighting functions<br> - youngsmod: equilibration with different Young's moduli<br> Each simulation directory contains:<br> - input_files: Abaqus [4] input file (*.inp) and Lammps data file (*.data, molecular style) of the investigated sample<br> - MD_data: Results evaluated in the molecular dynamics region. MD_data contains the following subfolders: <br> anchorforces (dumped force components on the anchor points (AP) in kcal/mol, files anchorforce_[load step]_[MD-FE iteration].AF), <br> data (resulting Lammps data files *.[load step].[MD-FE iteration].data), Density (dumped mass density in the observation region in kg/m^3), <br> Energy (dumped total (kinetic + potential), angle, bond, and pair energies in kcal/(mol*Angstrom)), Strain (integral strains in the<br> observation region in x-, y-, and z-direction calculated by means of the Matlab [5] script calc_OBSstrain.m),<br> Stress (stresses in the observation region in MPa), and Temperature (temperature in the observation region in K)<br> - job.out: simulation log file<br> - meta.info: meta data of the specific Lammps simulation<br> Information on the subfolders is given in the respective folders.</p> <p><strong>References:</strong><br> [1] F. Weber, M. Ries, C. Bauer, C. R. Wick, S. Pfaller, "On equilibrating non-periodic molecular dynamics samples for coupled<br> particle-continuum simulations of amorphous polymers", Forces in Mechanics, 2023, 10, 100159.<br> [2] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, <br> S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "LAMMPS - a flexible simulation tool <br> for particle-based materials modeling at the atomic, meso, and continuum scales", Computer Physics Communications, 2022, 271, 108171.<br> [3] S. Pfaller, M. Rahimi, G. Possart, P. Steinmann, F. Müller-Plathe, M. C. Böhm, "An Arlequin-based method to couple molecular dynamics <br> and finite element simulations of amorphous polymers and nanocomposites", Computer Methods in Applied Mechanics and Engineering, <br> 2013, 260, 109-129. <br> [4] Dassault Systèmes, "Abaqus documentation", URL: https://abaqus-docs.mit.edu/2017/English/SIMACAEEXCRefMap/simaexc-c-docproc.htm.<br> [5] The MathWorks, Inc, "MATLAB. The Language of Technical Computing", URL: https://de.mathworks.com/help/matlab/.</p> <p><strong>Funding:</strong><br> This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -<br> 377472739/GRK 2423/1-2019. The authors are very grateful for this support. Sebastian Pfaller is furthermore<br> funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 396414850 (Individual<br> Research Grant ’Identifikation von Interphaseneigenschaften in Nanokompositen’).</p>
X-ray scattering datasets and simulations associated with the publication "Bio-SAXS of Single-Stranded DNA-Binding Proteins: Radiation Protection by the Compatible Solute Ectoine"
<p>This dataset contains the processed and analysed small-angle X-ray scattering data associated with all samples from the publications "Bio-SAXS of Single-Stranded DNA-Binding Proteins: Radiation Protection by the Compatible Solute Ectoine" (<a href="https://doi.org/10.1039/D2CP05053F">https://doi.org/10.1039/D2CP05053F</a>)</p> <p>Files associated with McSAS3 analyses are included, alongside the relevant SAXS data, with datasets labelled in accordance to the protein (G5P), its concentration (1, 2 or 4 mg/mL), and if Ectoine is present (Ect) or absent (Pure). PEPSIsaxs simulations of the GVP monomer (PDB structure: 1GV5 ) and dimer are also included.[1]</p> <p>TOPAS-bioSAXS-dosimetry extension for TOPAS-nBio based particle scattering simulations can be obtained from <a href="https://github.com/MarcBHahn/TOPAS-bioSAXS-dosimetry">https://github.com/MarcBHahn/TOPAS-bioSAXS-dosimetry</a> which is further described in <a href="https://doi.org/10.26272/opus4-55751">https://doi.org/10.26272/opus4-55751</a>.</p> <p>This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant number 442240902 (HA 8528/2-1 and SE 2999/2-1). We acknowledge Diamond Light Source for time on Beamline B21 under Proposal SM29806. This work has been supported by iNEXT-Discovery, grant number 871037, funded by the Horizon 2020 program of the European Commission.</p> <p>[1] S. Su, Y.-G. Gao, H. Zhang, T. C. Terwilliger and A. H.-J. Wang, Protein Science, 1997, 6, 771–780.</p> <p> </p> <p><br> </p>
Dataset related to the publication "Simulation of pressure-induced cavity deformation – the 18SIB04 Quantumpascal EMPIR project"
<p>The data set consists of; The published paper, all figures that present measurement or simulation data in .png and .fig format and the underlying data plotted in the figures in text format. The published plots were generated from the fig files. The text files were generated by reading the plotted data from the fig files. The files are named Fig_XX were XX corresponds to the figure number in the publication. The format of the text file is as follows. Before every data set there is a header consisting of; The number of the subplot where the data is plotted (Plot: XX), the number of the data set in the sub plot (DataSet: XX), and the color of the line or marker in the plot (Color: XX). The description of what each color represents can be found in the publication.</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.