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248 results for “Multiscale”
Dataset for Uncovering multiscale structure in the variability of larval zebrafish navigation
<p>Datasets needed for recreating the figures from the paper "Uncovering multiscale structure in the variability of larval zebrafish navigation"</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten ASCII
<p>Multiscale elevation models centered on Churfirsten, Switzerland</p> <p>Resolutions: 0.5, 2, 5, 10, 15, 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 3,000 × 2,500 height samples each</p> <p>File format: Esri ASCII grid</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Data and Source Codes used in "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"
<p>Data and Source Codes used in the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>Advection Test (ADV): East-West A_TST (100km, Cube), C_TST (25km, Cube), E_TST (5km, Cube),</p> <p> North-South K_TST (100km, Cube), M_TST (25km, Cube), O_TST (5km, Cube) </p> <p>Barotropic Test (BAR): A_TST5 (100km, Cube), Y_TST4 (100km, RLL), C_TST3 (5km, Cube), C_TST1 (5km, RLL)</p> <p>Baroclinic Test (BCL): J_TST30 (100km, Cube), J_TST20 (100km, RLL)</p>
Simulated tracer-gas distribution in a multiscale model of the human lung during multiple-breath nitrogene washout
<p><em><strong>Content</strong></em></p> <p><strong>baseline:</strong></p> <ul> <li>inletFlow (ASCII format data for flow rate at the mouth in m^3/s, sampling frequency 1kHz)</li> <li>primary_results (ASCII format data table with four colums: time in seconds, N2 concentration (normalized), <em>empty </em>-1, pleural pressure in Pascal)</li> </ul> <p><strong>compliance modification (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> <li>duct: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) witin the airway network. Sampling frequency 50Hz (separat vtk-file for each timestep). <em>Inspect for instance with the VisIt (Lawrence Livermore National Laboratory) free visualization software.</em></li> <li>lobule: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) within the trumpet lobules.</li> </ul> <p><strong>compliance modification (regional):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> <li>duct: (same format as described above)</li> <li>lobule: (same format as described above)</li> </ul> <p><strong>size modification (regional):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul> <p><strong>resistance modification (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul> <p><strong>healthy controls (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul>
Data set associated with the paper "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"
<p>New data set associated with the revision of the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: <br> I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>The title of the paper has been changed to "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"</p> <p>New simulated data set of the advection test is in the folder ADVEC_NEW; New simulated data set of the barotropic instability test is in the folder BARO_NEW; New simulated data set of the baroclinic instability test is in the folder BCL_NEW</p>
Dataset for the article "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry"
<p>This dataset contains additional material related to the article: "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry". The preprint is available at <a href="https://doi.org/10.26434/chemrxiv.7635986">https://doi.org/10.26434/chemrxiv.7635986</a>. Final article is available at <a href="https://doi.org/10.1021/acs.jctc.9b00093">https://doi.org/10.1021/acs.jctc.9b00093</a>.</p>
A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
<p>Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).</p>
Retrieving 2D laterally varying structures from multi-station surface wave dispersion curves using multiscale window analysis
<p>Here are the waveform data used in the Geophysical Journal International paper entitled "Retrieving 2D laterally varying structures from multi-station surface wave dispersion curves using multiscale window analysis". The dataset is used for the reader who wants to reproduce the result in the paper.</p>
Gramtools enables multiscale variation analysis with genome graphs
<p>Singularity container and data files supporting the article.</p> <p>Instructions to use these data are at: https://github.com/iqbal-lab-org/paper_gramtools_nesting </p>
Multiscale processes controlling niobium mobility during supergene weathering
<p>This metadata file gathers diffraction, spectroscopic and geochemical raw data supporting the manuscript entitled “<strong>Multiscale processes controlling niobium mobility during supergene weathering</strong>“ submitted to <em>Geochimica et Cosmochima Acta</em>.</p> <p><strong>- X-ray diffraction:</strong> "spinner_PIMI_xx.xrdml". Duricrust: PIMI01, 02. Bauxitic horizon: PIMI03. Lateritic horizon: PIMI04, 05. Clayey mottled zone: PIMI06,07.</p> <p><strong>- X-ray absorption data:</strong></p> <p><strong>1) References: </strong>"XAS_L3/K-EDGE_<em>references</em>". Raw X-ray absorption spectra of Nb mineralogial references at the Nb L<sub>3</sub>- and K-edges.</p> <p><strong>2) Bulk sample of laterite: </strong>"XAS_L3/K-EDGE_<em>pimi_XX</em>". Raw X-ray absorption spectra of lateritic bulk samples from Pitinga.</p> <p><strong>3) Microanalyses: </strong>"MICRO_XAS_L3_<em>minerals</em>". Micro-XAS spectra collected on thin sections from Pitinga.</p> <p>- <strong>Bulk chemistry </strong>"geochemistry_pitinga". Bulk geochemical analyses of Pitinga lateritic regolith.</p>
Multiscale assessment of oviposition habitat associations and implications for management in the spotted lanternfly (Lycorma delicatula), an emerging invasive pest
<p>1. Control of incipient invaders—established invasive species in the early stages of spreading—can be inhibited by incomplete knowledge of the species' habitat use. By identifying consistent habitat associations for incipient invaders early, control efforts can be more effective. Yet, because habitat associations are the result of multiscale processes, approaches are needed for integrating data collected across scales to identify them.</p> <p>2. We employed a hierarchical, multiscale approach to identify oviposition habitat associations in the spotted lanternfly (<em>Lycorma</em> <em>delicatula</em>), an incipient invasive species of high concern in the United States. We targeted four oviposition habitat spatial scales most likely to be used by lanternflies and the spatial scales of explanatory habitat variables most easily used by managers to locate egg masses to control.</p> <p>3. Spotted lanternflies exhibited oviposition habitat associations at the landscape, site, and tree scales. Overall, lanternflies oviposited more frequently at sites and on trees with low canopy cover in the surrounding landscape indicating higher use of human-impacted habitat. Additionally, they oviposited more frequently on trees from the <em>Acer</em> genus and in the crowns of larger trees beyond the reach of managers without special equipment. The duration a site had been invaded had opposing effects on oviposition at the site and tree scales.</p> <p>4. Despite high variation in the number of eggs per egg mass, no habitat variables explained this variation, suggesting more work is needed to understand spotted lanternfly reproductive output.</p> <p>5. Synthesis and applications. Our results indicate a multiscale approach is needed for spotted lanternfly control with unique strategies for locating egg masses at sites and on trees that vary in invasion duration. Specifically, at younger sites at the invasion edge, managers should expect patchy colonization of sites, yet when a site is colonized, many trees will have egg masses. Comparatively, older sites at the invasion core are more likely to have egg masses present, yet often at a lower density, which may make them difficult to find on individual trees. Based on our results, we assert that multiscale investigations of habitat associations would likely inform the control of other incipient invasive species as well. </p>
HiCube: Interactive visualization of multiscale and multimodal Hi-C and 3D genome data
<p>Test dataset for HiCube.</p> <p>HiCube is a lightweight web application for interactive visualization and exploration of diverse types of genomics data at multiscale resolutions. Especially, HiCube displays synchronized views of Hi-C contact maps and three-dimensional (3D) genome structures with user-friendly annotation and configuration tools, thereby facilitating the study of 3D genome organization and function.</p> <p>HiCube is implemented in Javascript and can be installed via NPM. The source code is freely available at GitHub (https://github.com/wmalab/HiCube).</p>
An atomistically informed multiscale approach to the intrusion and extrusion in hydrophobic nanopores
<p>Data from Molecular Dynamics simulations.</p> <p>Files ending in .dat are the "measurement" files and are the files required to compute the free energy and diffusivity from the simulation data. The normal sintax is eqX.dat where X is the imposed filling of the pore. The names of the folders represent the pressure at which the filling was taken (0 MPa, -20 MPa, 60 MPa)</p> <p>We also attach one trajectory file, 0.xyz and one LAMMPS output file, 0.log, which should be enough to reproduce the simulation script, in conjuction with 0.data, the initial condition.</p>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Data from: "Multiscale variability in nutrients and secondary metabolites in a bat-dispersed neotropical fruit"
<p>Original data and R code accompanying our paper in <em>Ecology & Evolution</em> by Gelambi M. & Whitehead, S. R. (2023). </p> <p>Ripe fleshy fruits contain not only nutrients but also a diverse array of many secondary metabolites. Nutrients serve as a reward for mutualists, whereas defensive metabolites protect the fruit against pests and predators. The composition of these chemical traits is highly variable, both across different plants and even within repeating structures on the same individual plant. This intraspecific and intraindividual variation has important fitness consequences for both plants and animals, yet patterns of variation and covariation in nutrients and secondary metabolites are not well understood, especially at smaller scales. Here, we investigate the multiscale variation and covariation between nutrients and defensive metabolites in <em>Piper sancti-felicis </em>ripe fruits. Means and variances of sugars, proteins, phenolics, and alkenylphenols vary greatly among plants, and at least 50% of the trait variation occurs at the intraindividual level. Also, we found that proteins, but not sugars, were correlated with phenolics and alkenylphenols at multiple scales, suggesting trait variation in protein content may be more constrained than sugars. Our findings emphasize the importance of examining patterns across scales and provide the groundwork to better understand how complex patterns of variation and covariation in nutrients and defensive metabolites shape ecological interactions surrounding fruits.</p>
The data used for the paper "Adapting a multiscale approach to assess the compositional diversity of landscapes" (Landscape Ecology)
<p>The data consists of a single SimpleFeatures (sf) object with 267,813 rows, i.e. hexagons covering Hungary, and 89 variables. The following variables are contained by the data (Table 1.).</p> <table> <caption>Table 1. Main attributes of the variables. For explanation of the vegetation type codes implied in the table by "XX", see the Supplementary Material S2 of Konrád et al. 2023.</caption> <thead> <tr> <th scope="col">Variable</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>unique id of hexagons</td> </tr> <tr> <td>Bakony</td> <td>logical mask for subsetting the landscape Bakony</td> </tr> <tr> <td>Baranya</td> <td>logical mask for subsetting the landscape Baranya Hills</td> </tr> <tr> <td>ExternalSomogy</td> <td>logical mask for subsetting the landscape External Somogy</td> </tr> <tr> <td>HunGreatPlain</td> <td>logical mask for subsetting the landscape Hungarian Great Plain</td> </tr> <tr> <td>KorosMarosInterfluve</td> <td>logical mask for subsetting the landscape Körös-Maros Interfluve</td> </tr> <tr> <td>Mezofold</td> <td>logical mask for subsetting the landscape Mezőföld & Velence Hills</td> </tr> <tr> <td>NorthernHunMountains</td> <td>logical mask for subsetting the landscape Northern Hungarian Mountains</td> </tr> <tr> <td>Orseg</td> <td>logical mask for subsetting the landscape Őrség</td> </tr> <tr> <td>SouthernBukk</td> <td>logical mask for subsetting the landscape Southern Bükk</td> </tr> <tr> <td>binarized_potential_XX</td> <td>binarized potential vegetation data of the XX vegetation type</td> </tr> <tr> <td>binarized_actual_XX</td> <td> <p>binarized potential vegetation data of the XX vegetation type</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>References</strong><br> Konrád KD, Bede-Fazekas Á, Bartha S, Somodi I (2023) Adapting a multiscale approach to assess the compositional diversity of landscapes. <em>Landscape Ecology.</em></p> <p> </p>
Dataset_Multiscale Fast Fourier Transform homogenization of additively manufactured fiber reinforced composites from component-wise description of morphology
<p>Original micro-CT imaging data and output of processing via the OpenFiberSeg software of additively manufactured carbon fiber reinforced composites. </p> <p>This dataset accompanies the publication in Composite Science and Technology available at https://doi.org/10.1016/j.compscitech.2023.110261</p> <p> </p>
MM and QMMM trayectories from "Conformational Changes and ATP Hydrolysis in Zika Helicase. The Molecular Basis of a Biomolecular Motor Unveiled by Multiscale Simulations"
<p>Here are provided two trayectories from the results of "Conformational Changes and ATP Hydrolysis in Zika Helicase. The Molecular Basis of a Biomolecular Motor Unveiled by Multiscale Simulations".</p> <p><em>ZikaNS3h-MMtrajectory.nc</em> contains a Molecular Mechanics MD simulation of the system with ATP bound to the active site.</p> <p><em>TS1-mech1-QMMMtrajectory.nc</em> contains a QMMM MD simulation of the system with TS1 of the base-assisted mechanism bound to the active site.</p> <p><em>ZikaNS3h.prmtop</em> is the parameter file.</p> <p> </p>
Multiscale simulation data for demonstrating the analogy between grain boundaries and Brownian ratchets
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Data from: Towards a better ecological understanding of metacommunity stability: A multiscale framework to disentangle population variability and synchrony effects
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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.