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248 results for “Multiscale”
Experimental data in support of manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Data from: Do introduced apex predators suppress introduced mesopredators? A multiscale spatiotemporal study of dingoes and feral cats in Australia suggests not
<p>1. The role of apex predators in structuring ecosystems through the suppression of mesopredator activity and abundance is receiving increasing attention, largely due to the potential benefits for biodiversity conservation. In Australia, invasive mesopredators such as feral cats (Felis catus) have been identified as major contributors to Australia's mass mammal extinctions since European arrival. The introduced dingo (Canis familiaris) has been proposed as a novel way to suppress the impacts of feral cats, however scientific evidence of the dingo's suppressive role is equivocal. 2. We used camera traps to investigate whether a large introduced predator (dingo) suppresses the activity of an established introduced mesopredator (feral cat) across a national park site conserving endangered species, and an agricultural site supporting cattle grazing enterprises. 3. Feral cats and dingoes exhibited marked overlap in both temporal and spatial activity, indicating coexistence. Some temporal separation was evident at the agricultural site, however this reflected higher diurnal activity by dingoes, not a responsive shift in cat activity. Cat activity times were unrelated to dingo presence and did not differ between areas occupied by dingoes and dingo-free areas. There was no evidence of dingoes excluding cats from patches at either site, nor was there evidence of within-night fine-scale spatiotemporal avoidance of dingoes by cats. 4. Species co-occurrence models revealed dingoes had no negative effect on the probability of cat presence. The probability of detecting a cat on the national park was significantly higher in areas with dingoes than in dingo-free areas, while on agricultural land, cat detectability did not differ between areas with and without dingoes. Cats remained active, abundant and widespread across both sites, with evidence of cats hunting and breeding successfully in areas occupied by dingoes. 5. Synthesis and applications. Our findings indicate that feral cats can coexist with dingoes, without apparent suppression of cat activity, abundance, or fitness. Proposals to reintroduce or restore dingoes and other large predators to suppress invasive mesopredators and conserve biodiversity should be carefully evaluated on a site-by-site basis, as their ability to suppress cats and protect species of conservation significance will likely be context dependent.</p>
Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates
<p>The data of paper "Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates"<br>1. The BAs file include lammps input file of EMD, BAs model file and NNP model. One can use follw command to perform simulation.<br> lmp -in in.thermal<br> Besides, the ShengBTE results from 3-phonon and 4-phono are included in ShengBTE file.<br>2. The GaN file include lammps input file of EMD, GaN model file and NNP model. One can use follw command to perform simulation.<br> lmp -in in.thermal<br> Besides, the ShengBTE results from 4-phonon are included in ShengBTE file.<br>3. The BAs-GaN file include lammps input file of NEMD, heterostructure model file and NNP model. One can use follw command to perform simulation.<br> lmp -in in.thermal<br>4. The active learning framework that includes the initial file, running file, machine configuration file and other input files.It is worht to note that the machine configuration file may not normally work, duo to different cluster environment. </p> <p>5. The FEM file contains python scripts for building finite element models.</p>
Data from: Multiscale chromatin dynamics and high entropy in plant iPSC ancestors
<p>Plant protoplasts constitute the starting material to induce pluripotent cell masses <em>in vitro</em> competent for tissue regeneration. Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterized by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what are the factors influencing chromatin transitions during culturing is largely unknown. High-throughput imaging and a custom, supervised image analysis protocol extracting over 100 chromatin features unravelled a rapid, multiscale dynamics of chromatin patterns which trajectory strongly depends on nutrients availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating an intrinsic entropy as hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming <em>in vitro</em>.</p>
SPT results - Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury
<p>Results of the study "Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury"</p>
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
<p><strong>Supplementary Data</strong> </p> <ol> <li><strong>Supplementary Data 1</strong> contains the input (non-watertight) surface meshes of the block (shown in Figure 2a) reconstructed within the context of the EPFL-KAUST collaboration, and the corresponding output (watertight) meshes generated by Ultraliser.</li> <li><strong>Supplementary Data 2 </strong>contains a set of 20 non-watertight meshes that were randomly selected from the block shown in <strong>Supplementary Figure S54</strong> and another set of the their watertight counterparts.</li> <li><strong>Supplementary Data 3</strong> contains a set of 25 neuronal morphologies with different morphological types and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 4</strong> contains a set of 25 synthetic astroglial morphologies 15 and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 5</strong> contains the vascular morphology (shown in <strong>Supplementary Fig. S83</strong>) and a corresponding multi-partitioned watertight mesh.</li> <li><strong>Supplementary Data 6</strong> contains the datasets used for the comparative analysis shown in <strong>Supplementary Section 13</strong>.<br> <br> Neuronal, astrocytic and vascular morphologies are stored in SWC, H5 and VMV file formats respectively. The file structures of the SWC and VMV formats are publicly available online. The H5 files of the complete astrocyte cells can be made available from corresponding authors upon request. All the surface meshes are stored in Wavefront OBJ files. Additional STL meshes are generated to be used for TetGen to create corresponding tetrahedral meshes. All the input and generated data files are publicly available on Zenodo (10.5281/zenodo.7105941).</li> </ol> <p><strong>Data Sources</strong> </p> <ol> <li>Cellular and subcellular NGV meshes segmented from the volume shown in Figure 2 are provided by the collaborating co-authors affiliated with KAUST.</li> <li>Neuronal meshes shown in Figure 3, Supplementary Figures S55 - S75 and Supplementary Figures S85 are publicly available from the MICrONS program.</li> <li>Neuronal morphologies shown in Figure 4, Supplementary Figures S80 - S81 and Supplementary Figure S86 are publicly available from NeuroMorpho.Org.</li> <li>Astrocytic morphologies (Figure 5 and Supplementary Figure S82) are provided by Eleftherios Zisis.</li> <li>Vascular morphologies (rat’s cerebral microvasculature) shown in Figure 6 and Supplementary Figures S83 - S84 are courtesy of Bruno Weber, University of Zürich (UZH).</li> <li>The vascular morphology of the arterial arborizations shown in Supplementary Figure S88 is available from the Brain Vasculature (BraVa) database (cng.gmu.edu/brava).</li> </ol>
Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α-Fig. 1df
<p>smTIRF-FRET Data for Fig 1, for "Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α"</p>
Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Study at the Fête des Lumières in Lyon
<p>We present one of the first comprehensive field datasets capturing dense pedestrian dynamics across multiple scales, ranging from macroscopic crowd flows over distances of several hundred meters to microscopic individual trajectories, including approximately 7,000 recorded trajectories.</p> <p>The dataset also includes a sample of GPS traces, statistics on contact and push interactions, as well as a catalog of non-standard crowd phenomena observed in video recordings. Data were collected during the 2022 Festival of Lights in Lyon, France, within the framework of the French-German <a href="We%20present%20one%20of%20the%20first%20comprehensive%20field%20datasets%20capturing%20dense%20pedestrian%20dynamics%20across%20multiple%20scales,%20ranging%20from%20macroscopic%20crowd%20flows%20over%20distances%20of%20several%20hundred%20meters%20to%20microscopic%20individual%20trajectories,%20including%20approximately%207,000%20recorded%20trajectories.%20%20The%20dataset%20also%20includes%20a%20sample%20of%20GPS%20traces,%20statistics%20on%20contact%20and%20push%20interactions,%20as%20well%20as%20a%20catalog%20of%20non-standard%20crowd%20phenomena%20observed%20in%20video%20recordings.%20Data%20were%20collected%20during%20the%202022%20Festival%20of%20Lights%20in%20Lyon,%20France,%20within%20the%20framework%20of%20the%20French-German%20MADRAS%20project,%20covering%20pedestrian%20densities%20up%20to%204%20individuals%20per%20square%20meter." target="_blank" rel="noopener">MADRAS project</a>, covering pedestrian densities up to 4 individuals per square meter.</p>
Research data supporting "Multiscale Molecular Modelling of ATP-Fueled Supramolecular Polymerisation and Depolymerisation"
<p>Raw research data supporting the publication Perego C. et al., <em>ChemSystemsChem</em> <strong>2021</strong>, DOI: <a href="https://doi.org/10.1002/syst.202000038">https://doi.org/10.1002/syst.202000038</a></p>
Multiscale Temporal Response of Salt Intrusion to Transient River and Ocean Forcing
<p>Model and validation data presented in Payo Payo et al, Multiscale Temporal Response of Salt Intrusion to Transient River and Ocean Forcing (JGR-Oceans, 2021, submitted).</p> <p>We used a 3-dimensional unstructured hydrodynamic model 3-dimensional unstructured hydrodynamic model (FVCOM Finite Volume Community Ocean Model)understand river and tide forcing to disentangle the multiscale temporal pattern of salt intrusion in estuaries subject to transient forcing due to river and tide. We implemented the Pearl River Delta as a case study for 2007/2008.</p> <p>The dataset consists of:</p> <p>·Measured and modelled salinity and elevation data at the validation stations.</p> <p>·River discharge data</p> <p>·SSH data</p> <p>·Salt intrusion length</p>
Multiscale Spatial Patterns in Giant Dike Swarms Identified through Objective Feature Extraction Datasets
<p>S1 - Linked dike clusters for the Columbia River Flood Basalt group including the four identified subswarms: Chief Joseph, Monument, Ice Harbor, and Steens as compiled in Morriss et al., 2020. This dataset uses the a UTM Zone 11N projection (EPSG:26911).</p> <p>S2 - Linked dike clusters for the Deccan Traps including the four identified subswarms: Saurashtra, Narmada-Tapi, Central and Coastal. Due to their overlap Central and Coastal Swarms have been combined in this dataset into the Central Swarm. This dataset uses the a WGS 84 projection (EPSG:3857). </p> <p>S3 - Dike segment data for Spanish Peaks and Dike Mountain located in the Rio Grande Rift of Colorado. This dataset was digitized using QGIS based on the map by Johnson (1961). This dataset uses the a UTM Zone13N projection (EPSG:32613). The file includes the start, end points, and midpoints of the dikes; segment length; calculated $\rho$ and $\theta$ for the Hough Transform; the origin used for the Hough Transform which is different for each subswarm (xc,yc); dike rock type if known; and a unique identification calculated based on the start and endpoints. This dataset has been preprocessed to remove curving dikes and is the data set used to produce later products (Data set S4). </p> <p>S4 - Linked dike clusters for the Spanish Peaks and Dike Mountain. This dataset was produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. This dataset uses the a UTM Zone 13N projection (EPSG:32613). </p> <p> </p> <p> </p> <p>These datasets were produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. The datasets are in the format of a CSV file but can be read into GIS programs using Well Known Text (WKT) linestring. TThe file includes the start and end points of the average line in the cluster and it's mid points, cluster length and width (Xstart, Xend, Xmid, Ymid, in meters and UTM coordinates, Dike Cluster Width or R\_Width, Dike Cluster Length or R\_Length all in meters); calculated average $\rho$ and $\theta$ for the Hough Transform $\rho$ units measured in meters, $\theta$ units measured in degrees, unless otherwise stated); the origin used for the Hough Transform which is different for each subswarm ($xc$,$yc$, meters in UTM coordinates); average slope and intercept (AvgSlope, AvgIntercept meters); range and standard deviation for $\rho$ and $\theta$ for all objects in the cluster ($\rho$ units measured in meters, $\theta$ units measured in degrees); cluster size (Size); sum of segment lengths in a cluster (SegmentLSum, meters); whether the cluster crosses between negative and positive values (ClusterCrossesZero, boolean); overlap as calculated in the main text where the length of overlap is normalized by the sum of segment lengths in a cluster; maximum number of overlapping segments (nOverlapingSegments); twist angle which is the difference in angle betweeen the average cluster line and the average line formed by cluster midpoints (EnEchelonAngleDiff, degrees); the p-value for the midpoint line fit of the segments where $p<0.05$ is considered to be a significant fit (EEPValue); the maximum, median, and minimum segment nearest neighbors distances in the cluster which is calculated using the cartesian midpoints of each segment and normalized by the Cluster Length (MaxSegNNDist, MedianSegNNDist, MinSegNNDist); characterization of each cluster as filtered or not, filtered clusters are of size greater than $3$ and have a MaxSegNNDist of less than $0.5$ (TrustFilter, boolean); the date edited (Date\_Changed), and the clustering parameters used for each cluster (Rho\_Threshold in meters, Theta\_Threshold in degrees) and a unique identification calculated based on the start and endpoints (ClusterHash). </p>
Tailoring magnetic hysteresis of Fe-Ni additive manufactured permalloy via multiphysics-multiscale simulations: Temperature-dependent parameters, thermodynamic database, results, and utilities
<p>This dataset contains temperature-dependent parameters and thermodynamic database, supplementary data and utilities of the publication "Tailoring magnetic hysteresis of additive manufactured Fe-Ni permalloy via multiphysics-multiscale simulations of process-property relationships" (<a href="http://doi.org/10.1038/s41524-023-01058-9">Yang et al., 2023</a>).</p> <p>We performed non-isothermal phase-field simulations of SLS process of the Fe<sub>21.5</sub>Ni<sub>78.5</sub> permalloy and subsequential mesoscopic thermo-elasto-plastic calculations and nanoscopic chemical order-disorder (<span>\(\gamma/\gamma'\)</span>) transition simulations as well as micromagnetic hysteresis calculations on nanostructures. Temperature-dependent parameters are employed. We then investigate the dependence of the fusion zone size, the residual stress and plastic strain, and the magnetic hysteresis of AM-produced Fe<sub>21.5</sub>Ni<sub>78.5 </sub>on beam power and scan speed.</p> <p>This dataset contains:</p> <ul> <li><em>feni_cac.tdb</em>: Thermodynamic database of the Fe-Ni binary system based on <a href="https://doi.org/10.1016/j.intermet.2010.02.026">Cacciamani et al., 2010</a></li> <li><em>average_values.csv</em>: Average quantities for creating the contours in Fig. 6a, 6b, 7a, 7b, 8a, and Supp. Fig. 10a, 10b.</li> <li><em>mesostructures.zip</em>: Containing resampled mesostructures from SLS single scan simulations (final timestep) with associated temperature, stress, and strain evolution. Nodal values are explained in Table 1. Naming pattern is <ul> <li>SLS-TEP__<power>-<scan_speed>__.e</li> </ul> </li> <li><em>parameters.zip</em>: Containing temperature-dependent parameters for performing SLS simulations and thermo-elasto-plastic calculations with fine (1K) temperature increments. The same temperature-dependent parameters with coarse temperature increments are already listed as Supp. Table 1, 2.</li> <li><em>sampled_point_data.zip</em>: Containing mechanical quantities on sampled points and corresponding results of nanoscopic <span>\(\gamma'\)</span> phase fraction (<span>\(\Psi_{\gamma'}\)</span>) and magnetic coercivity <span>\(H_\mathrm{c}\)</span>. Naming pattern is <ul> <li>mech__<power>-<scan_speed>__.csv</li> <li>Psi__<power>-<scan_speed>__.csv</li> <li>Hc__<power>-<scan_speed>__.csv</li> </ul> </li> <li><em>utilities.zip</em>: Containing Python utilities to perform calculations of free energy density and related thermodynamic quantities, extracting parameters from <em>feni_cac.tdb. </em><br><strong>Notice: </strong><a href="https://pycalphad.org/docs/latest/">pyCALPHAD</a> (ver 0.8.4) is requested for performing the Python utilities.</li> </ul> <p>Table 1. Nodal values in an exodus file Nodal value name Symbol Meaning Unit T <span>\(T\)</span> Normalized Temperature by <span>\(T_\mathrm{M}\)</span> - c <span>\(\rho\)</span> Substance order parameter - pb <span>\(\xi\)</span> Fusion zone indicator - eps (eps_11, eps_12, eps_13, eps_22, eps_23, eps_33) <span>\({\varepsilon}\)</span> Strain - epsp (epsp_11, epsp_12, epsp_13, epsp_22, epsp_23, epsp_33) <span>\({\varepsilon}_\mathrm{pl}\)</span> Plastic Strain - peeq <span>\(p_\mathrm{e}\)</span> Accumulated plastic strain - sigma (sigma_11, sigma_12, sigma_13, sigma_22, sigma_23, sigma_33) <span>\({\sigma}\)</span> Stress MPa vonmises <span>\(\sigma_\mathrm{e}\)</span> von Mises stress MPa u (u_X, u_Y, u_Z) <span>\(\mathbf{u}\)</span> Displacement μm</p> <p> </p> <p><strong>Notice</strong>: The raw transient outputs are not cured in this dataset due to the vast file size. Please contact the authors to acquire related files/utilities.</p>
Data for Transient 2D IR spectroscopy and multiscale simulations reveal vibrational couplings in the Cyanobacteriochrome Slr1393-g3
<p>Data used in the Manuscript Transient 2D IR spectroscopy and multiscale simulations reveal vibrational couplings in the Cyanobacteriochrome Slr1393-g3</p>
Data & Figures on the Multiscale Spatiotemporal Characterization of Embodied Environmental Efficiency of Building Structures in Geneva from 1850 to 2018
<p>Process data, figures, and figure data resulting from the study on Multiscale Spatiotemporal Characterization of Embodied Environmental Efficiency of Building Structures in Geneva from 1850 to 2018.</p>
Do introduced apex predators suppress introduced mesopredators? A multiscale spatiotemporal study of dingoes and feral cats in Australia suggests not
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UNO experimental dataset for: Multiscale bedform reorganization at the onset of substantial suspended sediment transport
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Data from: Multiscale chromatin dynamics and high entropy in plant iPSC ancestors
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Data for: Multiscale bedform reorganization at the onset of substantial suspended sediment transport
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Data of Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network
<pre>Data from title = "Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network", journal = "Computer Methods in Applied Mechanics and Engineering", pages = "112693", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2019.112693", author = "Wu, Ling and Zulueta, Kepa and Major, Zoltan and Arriaga, Aitor and Noels, Ludovic" </pre>
Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
<p>The dynamics and organization of the actin cytoskeleton are crucial to many cellular events such as motility, polarization, cell shaping, and cell division. The intracellular and extracellular signaling associated with this cytoskeletal network is communicated through cell membranes. Hence the organization of membrane macromolecules and actin filament assembly are highly interdependent. Although the actin-membrane linkage is known to happen through many routes, the major class of interactions is through the direct interaction of actin-binding proteins with the lipid class containing poly-phosphatidylinositols (PPIs). Among the PPIs, phosphatidylinositol bisphosphate (PI(4,5)P<sub>2</sub>) acts as a significant factor controlling actin polymerization in the proximity of the membrane by binding to actin-associated proteins. The molecular interactions between these actin-binding proteins and the membrane lipids remain elusive. Here, using molecular modeling, analytical theory, and experimental methods, we investigate the binding of three different actin-binding proteins, mDia2, NWASP, and gelsolin, to membranes containing PI(4,5)P<sub>2</sub> lipids. We perform molecular dynamics simulations on the protein-bilayer system and analyze the membrane binding in the form of hydrogen bonds and salt bridges at various PI(4,5)P<sub>2</sub> and cholesterol concentrations. Our experimental study with PI(4,5)P<sub>2</sub>-containing large unilamellar vesicles mimics the computational experiments. Using the multivalencies of the proteins obtained in molecular simulations and the cooperative binding mechanisms of the proteins, we also propose a multivalent binding model that predicts the actin filament distributions at various PI(4,5)P<sub>2 </sub>and protein concentrations.</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.