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3,655 results for “Structural data”
Data from: Functional connectivity and structural analysis of trial spinal cord stimulation responders in failed back surgery syndrome
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Data from: Extensive intraspecific gene order and gene structural variations in upland cotton cultivars
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GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033
GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product.
PHOENIX MARS ATMOSPHERIC STRUCTURE EXP REDUCED DATA V1.0
The purpose of this report is to describe the methodology used to produce Reduced Data Records (RDRs) for the Phoenix Atmospheric Structure Experiment (ASE) from its Experimental Data Records (EDRs). These RDRs include vertical profiles of atmospheric density, pressure, and temperature.
GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033
GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product.
Single-cell RNA-sequencing data of microfluidic spinal cord-like structures
GEO Series GSE300459. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Data of mammalian interaction structure manuscript by She et al.
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Data associated to the manuscript "On the structure of hybrid water-in-salt electrolytes"
<p>Contains molecular dynamics input files and experimental data used in the article: "On the structure of hybrid water-in-salt electrolytes"</p>
SubsurfaceBreaks v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces: Input and Processed Data
<p>This companion dataset relates to the manuscript "<strong>SubsurfaceBreaks</strong> <strong>v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces"</strong>, by Michał Michalak, Christian Gerhards and Peter Menzel.</p> <p>There are several groups of files:</p> <ul> <li>a file with parameters (params.txt) of the generated homoclinal interfaces (slopes) such as dip angle, dip direction, level of noise).</li> <li>files 0-999 are generated using the code from GitHub. (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_synthetic_subsurface_slopes) for generating synthetic slopes. Every slope is in a separate file (.txt files) and it is possible to upload the slope to ParaView for further inspection: Delaunay triangulation, normal vectors and dip vectors have their own .vtu files. The .txt files (0-999) can be uploaded for training using the Python script (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_subsurface_slopes_training_testing_evaluating_revision.ipynb).</li> <li>KSH_input.txt corresponds to real data from Kraków-Silesian Homocline. Every row corresponds to a point representing a geological horizon separating Middle Jurassic geological units: Kościeliska sandstones from ore-bearing clays. This data set can be used to calculate geometric attributes using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes).</li> <li>KSH_input_output_0 corresponds to an output file from processing the KSH_input.txt file using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes). This file should be uploaded to the Python script to identify fault-related features on a real subsurface slope.</li> </ul>
Data from: The mediating effect of perceived Coach's emotional support to Life satisfaction, Curiosity and Sports engagement: a Partial Least Square-Structural Equation Model
<p>This data set is from the study titled, "The mediating effect of perceived Coach’s emotional support to Life satisfaction, Curiosity and Sports engagement: a Partial Least Square-Structural Equation Model."</p>
Supporting data for 'Key structural features, thermodynamics and kinetics along the pathway between active and inactive forms of thrombin'
<p>This data set contains the raw data used for the preparation of the manuscript 'Key structural features, thermodynamics and kinetics along the pathway between active and inactive forms of thrombin'. It is separated into following parts:</p> <ul> <li>X-ray: Data used in the analysis of the PDB structures of thrombin, including a list of the PDB IDs, results of the PCA and values of the introduced features in the structures.</li> <li>cMD: Input parameters, topologies and starting coordinates of the five classical MD simulation that are based on different PDB structures, resulting trajectories, projection on X-ray PCA, RMSD values and values of the introduced features during the simulations.</li> <li>TMD: Input parameters, topologies and starting coordinates of the Targeted MD simulations, resulting trajectories, projection on X-ray PCA, RMSD values and values of the introduced features during the simulations.</li> <li>Seeded-Sims: Input parameters, topologies and starting coordinates of classical MD simulations that are started from cluster representatives of the TMD simulations and resulting trajectories.</li> <li>MSM: Values of the introduced features of the trajectories started from the TMD seeds, results of the TICA and of the denity-based clustering, representative structures of the states E, I1, I2 and E* and distribution of the introduced features in these states.</li> </ul> <p>A more detailed description of the included files can be found in the <em>README</em> files in each folder. Additional information about software versions can also be found there.</p>
Data from "Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics"
<p>Datasets and script of the manuscript “Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics” authored by R. Muñoz*, M. Enríquez, F. Bongers, R.D. López-Mendoza, C. Miguel-Talonia & J.A. Meave*, published in Frontiers in Forests and Global Change (2023).</p> <p>* Correspondence: R. Muñoz (rod.munozaviles@gmail.com) & J.A. Meave (jorge.meave@ciencias.unam.mx)</p> <p>The original publication can be found in https://doi.org/10.3389/ffgc.2023.1082207</p> <p> </p> <p><strong>TERMS OF USE FOR THE CURRENT DATASETS AND SCRIPTS</strong></p> <p>All data and scripts associated with the current publication are intended ONLY for the reproduction and validation of the analyses conducted in the manuscript cited above. Use of this data for other purposes (for example, other publications or meta-analyses) is strictly forbidden without prior consent from the corresponding authors (R. Muñoz and/or J.A. Meave, contact details above).</p> <p> </p> <p><strong>FOLDER STRUCTURE</strong></p> <p>The ZIP folder is structured in the following manner:</p> <p>– Munoz et al 2023 Frontiers.zip</p> <p> – READ ME.txt</p> <p> – Script Munoz et al 2023 Frontiers.R</p> <p> – Data source</p> <p> – Dataset Munoz et al 2023 Frontiers stand data.csv</p> <p> – Dataset Munoz et al 2023 Frontiers species matrix.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ONI.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ENSO events.csv</p> <p> </p> <p><strong>DESCRIPTION OF SCRIPT</strong></p> <p>The script provided in the root of the ZIP folder (Script Munoz et al 2023 Frontiers.R) allows to reproduce the analyses, figures and tables supporting the original publication in Frontiers. When executed in full, the script generates a new folder named “Figures” where all figures are stored in their raw, unedited version. The figures for publication were later edited in Adobe Illustrator to enhance their visual appearance.</p> <p> </p> <p><strong>DESCRIPTION OF DATASETS</strong></p> <p>Four datasets are provided in this ZIP file (“Data source” folder):</p> <p>1. Dataset Munoz et al 2023 Frontiers stand data.csv (<em>Stand data</em>)</p> <p>2. Dataset Munoz et al 2023 Frontiers species matrix.csv (<em>Species matrix</em>)</p> <p>3. Dataset Munoz et al 2023 Frontiers ONI.csv (<em>ONI</em>)</p> <p>4. Dataset Munoz et al 2023 Frontiers ENSO events.csv (<em>ENSO events</em>)</p> <p> </p> <p><em>STAND DATA </em>contains information about the seven forest attributes included in the study, per substrate and year. It contains the following variables:</p> <ol> <li>Year: Year of measurement</li> <li>Plot: Plot code</li> <li>Set: Can only be “MatCan” (Mature Canopy)</li> <li>Subset: Either “Lim” (limestone) or “Phy" (phyllite)</li> <li>Dynamics: Whether there is a previous measurement allowing the estimation of dynamic rates (e.g., net change; FALSE/TRUE) </li> <li>Basal: Basal area expressed in m2/ha</li> <li>DeltaBasal: Annual net change in basal area</li> <li>R.basal: Annual change in basal area due to recruitment</li> <li>G.basal: Annual change in basal area due to growth</li> <li>M.basal: Annual change in basal area due to mortality</li> <li>AGB: Aboveground biomass expressed in Mg/ha, estimated from the allometric equation of Chave et al. 2014 (including DBH, height and WD)</li> <li>DeltaAGB: Annual net change in AGB</li> <li>R.agb: Annual change in AGB due to recruitment</li> <li>G.agb: Annual change in AGB due to growth</li> <li>M.agb: Annual change in AGB due to mortality</li> <li>Dens: Tree density expressed in individuals/ha</li> <li>DeltaDens: Annual net change in tree density</li> <li>R.dens: Annual change in tree density due to recruitment</li> <li>G.dens: Annual change in tree density due to “growth”. Here, “growth” is a term introduced to account for small differences in tree densities between years due to changes in the extrapolation factor of a tree. Due to the nested sampling design of the vegetation survey, sometimes trees change their extrapolation factor as they grow larger. Thus, is a tree changes extrapolation factor, those differences (that are neither recruitment or mortality) are added up here.</li> <li>M.dens: Annual change in tree density due to mortality</li> <li>Species: Species richness expressed in spp/plot. Redundant with “q0” column.</li> <li>DeltaSpecies: Annual net change in species richness</li> <li>R.species: Annual change in species richness due to recruitment</li> <li>M.species: Annual change in species richness due to mortality</li> <li>Height: Average plot canopy height expressed in m</li> <li>q0: Hill number of order 0 expressed in species effective number (species richness)</li> <li>q1: Hill number of order 1 expressed in species effective number (typical species)</li> <li>q2: Hill number of order 2 expressed in species effective number (dominant species)</li> </ol> <p> </p> <p><em>SPECIES MATRIX</em> contains an abundance matrix per species, plot and year. It contains the following variables:</p> <ol> <li>PlotYear: This column actually does not have a name to it in the file, but is the first column in the dataset, It contains the three-character identifier for the plot and the four numbers of the year of measurement. For instance, “BER2008” would represent the observations made for the plot BER in 2008.</li> <li>treat: This indicates whether the plot is located on limestone (1) or phyllite (2) substrate</li> <li>sp001-sp127: indicates the abundance (in number of individuals per plot) of a given species. Species numbers were assigned randomly, thus they do not match the order of the table provided in Supplementary Material 3 of the publication in Frontiers.</li> </ol> <p> </p> <p><em>ONI</em> contains the Oceanic El Niño Index values per month and year. It is a “year by month” contingency matrix, where years are presented in the rows name, and months are presented in the columns name. ONI values are given in Celsius degrees, and they represent the 3-month rolling average of the temperature anomaly in the Nino3.4 region. The data source and details of this dataset can be found at the NOAA webpage (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php).</p> <p> </p> <p><em>ENSO EVENTS</em> contains the occurrence of events of El Niño (warm and dry episodes) and La Niña (cold and wet episodes). It contains the following variables:</p> <ol> <li>Year: Year</li> <li>Month: Month</li> <li>ONI: Oceanic El Niño Index (see ONI dataset description above)</li> <li>Year.cont: Time as a continuous variable (instead of having years and months separately, for plotting)</li> <li>Nino: El Niño (warm and dry) episode occurrence (“1” indicates occurrence)</li> <li>Nina: La Niña (cold and wet) episode occurrence (“1” indicates occurrence)</li> </ol>
RBP Footprint Grand Challenge: An evaluation of novel computational approaches to RNA-binding protein target prediction from structural data
GEO Series GSE227455. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
PHOENIX MARS ATMOSPHERIC STRUCTURE EXPERIMENT DATA V1.0
Unknown
Expression data from normal human spheroid-forming keratinocytes in monolayer mass culture and from corresponding cornified-like spheroid ring structures.
GEO Series GSE94244. Homo sapiens. 6 samples. Type: Expression profiling by array.
Data in "From dome dune to barchan dune: airflow structure changes measured with particle image velocimetry in a wind tunnel"
<p>The dataset of side view and top view were stored as Tecplot file format. These data were used in Figures 5, 10, 12, 13 and 14. </p>
Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.
<p>Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.</p> <p> </p> <p>This is the abstract:</p> <p>Congenital bicuspid aortic valve (BAV) consists of two fused cusps and represents a major risk factor for calcific valvular stenosis. Herein, a fully coupled fluid-structure interaction (FSI) BAV model was developed from patient-specific magnetic resonance imaging (MRI) and compared against in vivo 4-dimensional flow MRI (4D Flow). FSI simulation compared well with 4D Flow, confirming direction and magnitude of the flow jet impinging onto the aortic wall as well as location and extension of secondary flows and vortices developing at systole: the systolic flow jet originating from an elliptical 1.6 cm<sup>2</sup> orifice reached a peak velocity of 252.2 cm/s, 0.6% lower than 4D Flow, progressively impinging on the ascending aorta convexity. The FSI model predicted a peak flow rate of 22.4 L/min, 6.7% higher than 4D Flow, and provided BAV leaflets mechanical and flow-induced shear stresses, not directly attainable from MRI. At systole, the ventricular side of the non-fused leaflet revealed the highest wall shear stress (WSS) average magnitude, up to 14.6 Pa along the free margin, with WSS progressively decreasing towards the belly. During diastole, the aortic side of the fused leaflet exhibited the highest diastolic maximum principal stress, up to 322 kPa within the attachment region. Systematic comparison with ground-truth non-invasive MRI can improve the computational model ability to reproduce native BAV hemodynamics and biomechanical response more realistically, and shed light on their role in BAV patients' risk for developing complications; this approach may further contribute to the validation of advanced FSI simulations designed to assess BAV biomechanics.</p> <p> </p>
Data and Script for Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
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Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers
<p>The 12 VS scenarios considered in this study employing six training-test data partitions<strong> </strong>(A-F). All training sets employ the same set of 371 actives (WO2015160641A2), but differ on the considered set of inactives and hence are uniquely identified by the latter (either TrueInactives, DeepCoys, RandomDecoys or ActivesOnly). Likewise, all test sets employ the same 297 actives (WO201503820A1), none of them also included in the training set, but different sets of inactives (TrueInactives or DeepCoys). </p> <p> </p> <table align="center"> <caption>Table 1. Six virtual screening scenarios corresponding to six pairs of training-test data for each type of SFs (classification or regression)</caption> <thead> <tr> <th scope="col">Partition ID</th> <th scope="col">Training set</th> <th scope="col">Test set</th> <th scope="col">Type</th> </tr> </thead> <tbody> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Regression</td> </tr> </tbody> </table> <p> </p>
Data for: Aprisco Field Station: The spatial structure of a new experimental site focused on agroecology
<p>Data for the data paper "<strong>Aprisco Field Station: The spatial structure of a new experimental site focused on agroecology</strong>". The data contains on the onehand tree locations of 50 1-hectare plots mapped at the Dehesa of the Aprisco Field Station next to the Monfragüe National Park, Torrejón el Rubio, Cáceres, Spain. This data set (TreeDataCorchuelas.xlsx) includes exact location of the trees and diameter at breast hight. On the other hand, the whole farm of 150 hectares including the 50 1-hectare plots was mapped with a drone to get a digital surface model (i.e. digital terrain model [ESP: modelo digital del terreno - MDT (MDT.tif)]) and a vegetation height model (digital elevation model [ESP: modelo digital de elevaciones - MDE (MDE.tif)]) that allows to identify all the trees, shrubs and other vegetation, as well as canopy height and canopy size calculations of all the trees. A 3d model of the digital elevation model is added as pdf (modelo3d.pdf). </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.