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4,563 results for “correlations”
Students' Beliefs, Basic Psychological Needs, Classroom Participation, Foreign Language Anxiety, and Academic Adjustment: A Correlation Study in Tashkent, Uzbekistan
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Investigating the Eye Movement Characteristics of Basketball Players Executing 3-Point Shot at Varied Intensities and their Correlation with Shot Accuracy
<h2>不同强度三分球运动员眼球运动特征及其与投篮命中率的相关性研究</h2>
Digital image correlation displacements and strains around a growing fatigue crack in an AA2024-T3 aluminium alloy
<p>This repository contains the data used in the research article:</p> <p>Strohmann, Melching, Paysan, Dietrich, Requena, Breitbarth. Next generation fatigue crack growth experiments of aerospace materials. <em>Scientific Reports</em>, 2024, <a href="https://doi.org/10.1038/s41598-024-63915-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41598-024-63915-x</a>.</p> <p> </p> <p><strong>General Description</strong><br>The dataset contains digital image correlation (DIC) data of a growing fatigue crack in an AA2024-T3 alloy. For the experiment, two independent DIC measurement devices were used – a global full-field DIC and a local microscopic DIC. Thus, the dataset consists of two main directories. One for the 3D DIC data ("global_3d_dic") and a second one for the 2D microscopic DIC data ("local_2d_dic"). Both of these are described and connected by rich metadata.<br>The 3D DIC data directory contains two subdirectories (a "Nodemaps" directory and a "Connections" directory). Both of them contain 797 '.txt'-files. The "Nodemaps" give the DIC results (i.e. coordinates, displacement and strains) for every timestep throughout the experiment. These are usually maximum, minimum, and mean load of a certain load cycle. However, for few crack lengths, we obtained DIC data for a higher number (ca. 100) of images within one load cycle. The nodemaps' format and structure is optimized for data processing in the open-source Python package <a title="CrackPy" href="https://doi.org/10.5281/zenodo.10990494" target="_blank" rel="noopener">CrackPy</a>. The last integer number of each filename can be interpreted as a 'timestep' throughout the experiment. The "Connection" files represent the connections of the DIC facet center coordinates. These are necessary to export the "Nodemap" data to any mesh like dataset, e.g. for VTK. <br>The 2D microscopic DIC directory contains 3 subdirectories ("80", "90", "95") for predefined positions with respect to the specimen coordinate system. For each location, a number of DIC data are stored, again within two subdirectories "Nodemaps" and "Connections" as '.txt'-files. For the 2D DIC data, the last integer of each name cannot be correlated to a timestep. Instead, we provide a descriptive file "local_2d_microscopic_coordinates_by_nodemaps.csv" linking each and every "Nodemap"-file to its respective coordinates and timestep (i.e. the load cycles).</p> <p>To describe the data, we distinguish between<br>1. Higher-level metadata - these data contain information about the experiment and material. The data do not change between timesteps and are given within this description.<br>2. Timestep metadata - these data contain information about one timestep of the experiment and are stored in the header of each "Nodemap"-file.</p> <p> </p> <p><strong>Higher-level Metadata</strong><br>The experiment is described in detail in the reference publication by <a title="Strohmann et al. (2024)" href="https://www.researchsquare.com/article/rs-3128435/v1" target="_blank" rel="noopener">Strohmann et al. (2024)</a> and a summary is given below. Moreover, we provide a dictionary in javascript object notation explaining terms which are used in the higher-level metadata. We use such a dictionary since no standardized ontology is currently available. This dictionary is stored in the main directory as "higher_level_metadata_dictionary.json".</p> <p><em>Material </em><br>A commercially available AA2024-T3 aluminum alloy was tested in L-T orientation, i.e. rolling direction, L, parallel to the load axis. The specimen had a width W = 160 mm cut from a rolled sheet of 2 mm.</p> <p><em>Digital image correlation</em><br>For 3D DIC, we used a GOM Aramis 12M system with a facet size of 20 x 20 pixels and a 16 pixels facet distance. One facet, therefore, covers ~0.614 x 0.614 mm². For the 2D microscopic DIC we captured images using a Zeiss STEMI 206C light optical microscope (LOM), equipped with a Basler a2A5320-23µmPro global shutter CMOS camera. One image has a size of 10.2 x 5.7 mm², 5328 x 3040 Pixels and a facet size of 40x40 pixels (distance of facet center points 30 pixels). The LOM was mounted to a robotic arm, a KUKA lbr Iiwa Cobot.</p> <p><em>Fatigue crack growth</em><br>We used a standard uniaxial servo-hydraulic testing rig. We applied a cyclic load ranging from Fmin = 4.5 kN to Fmax = 15 kN, i.e. R=Fmin/Fmax = 0.3. Throughout the experiment, we measured the crack length using direct current potential drop (DCPD).</p> <p><em>Image acquisition during fatigue crack growth</em><br>We acquired reference images for the DIC calculations before the experiment. For the global DIC, this is simply an image of the unloaded specimen. For the local microscopic DIC, the reference images are acquired in a checker board pattern with an overlap of 70 %. The depth of focus was calibrated for each image individually following (see <a title="Paysan et al. (2023)" href="https://doi.org/10.1007/s11340-023-00964-9" target="_blank" rel="noopener">Paysan et al. (2023)</a>). Images were acquired every 0.5 mm of crack extension at minimum, maximum and 0.5(Fmax- Fmin).</p> <p> </p> <p><strong>Timestep Metadata</strong><br>The timestep-wise metadata is stored in the individual DIC output files, "Nodemaps". We explain the terms used in a second dictionary, "timestep_level_metadata_dictionary.json". For all DIC data, we stored all data coming from the machine controller, i.e. number of cycles, force, displacement of the cylinder and also potential and crack length calculated from the potential as well as current values for back face strain gauges at both back faces of the MT specimen. In addition, for the local microscopic DIC data, we also store the current location of the center point of the image with respect to the global coordinate system provided by the current position of the robot carrying the LOM.</p>
Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics (Data)
<p>Combined data for the manuscript `Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics` for all manuscript and supplemental information figures.</p> <p>Every folder contains the raw data and Jupyter notebook (python) for graph creation.</p> <p>Images are not enclosed but are shown in the manuscript.</p> <p> </p>
Datasets and simulation programs for the thesis 'Terahertz-Induced Dynamics in Strongly Correlated and Magnetic Materials'
<p>This dataset contains all the data and programs used to create the results in my thesis 'Terahertz-Induced Dynamics in Strongly Correlated and Magnetic Materials'.</p> <p>It is separated into individual chapters. Each chapter contains the data inside the figures, the raw data, if applicable, a program file, and a readme.txt offering additional information.</p>
The conformational plasticity of structurally unrelated lipid transport proteins correlates with their mode of action
<p>Supporting DATA files for "Conformational plasticity of structurally unrelated lipid transport proteins correlates with their mode of action".</p> <ol> <li><strong>zenodo_simulation_starting_files_and_result_pdbs.zip</strong> contains input files for atomistic and coarse-grained MD simulations, membrane binding interface results from coarse-grained simulations, and apo-like and holo-like conformations of lipid transfer domains arising from clustering of atomistic simulations</li> <li><strong>zenodo_data_and_codes.zip </strong>contains the raw data and/or scripts used for the analysis of the figures of the paper as well as a README file explaining the contents. The figure caption in the manuscript indicates the folder name that contains data relevant to that particular figure. <p>The trajectories have not been uploaded here, in case you are interested, please ask the corresponding author (stefano.vanni@unifr.ch).</p> </li> </ol> <p> </p>
Data from: Phenotypic correlates between clock genes and phenology among populations of Diederik cuckoo, Chrysococcyx caprius
<p>Dataset of Clock genes for Diederik Cuckoos collected as part of a migration genetics study.</p>
Data Related to "Charge-Transfer Contacts for the Measurement of Correlated States in Monolayer WSe2"
<p>Data related to "Charge-Transfer Contacts for the Measurement of Correlated States in Monolayer WSe2"</p>
Genetic diversity of Avena ventricosa populations along an ecogeographical transect in Cyprus is correlated to environmental variables
<p>genetic data</p>
Correlation analysis for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary data for reproducibility.</p>
FIGURE 4. Correlation plots. a in Taxonomic assessment of Australian Eocyzicus species (Crustacea: Branchiopoda: Spinicaudata)
FIGURE 4. Correlation plots. a, correlation between the total length of the carapace and the number of antennary segments. b, correlation between the number of "crowded" growth lines and the number of telsonic spines. c, correlation between total length of the carapace and the number of all thorax segments for E. richteri and E. breviantennus. d, correlation between total length of the carapace and the number of antennary segments for E. richteri sp. nov. and E. breviantennus sp. nov. Each point corresponds to one individual.
uploaded files: Correlative microscopy approach for biology using x-ray holography, x-ray scanning diffraction and STED microscopy
<p>The data uploaded here corresponds to a manuscript on x-ray /STED correlative imaging by the same authors published under the same title in Nature Communications in 2018.</p> <p>The provided data are subdivided into three parts:<br> 1. The 01_STED_fig2a.mat file contains the main results shown in Fig.2a (main article) as variables:<br> - STED_micrograph: the STED micrograph with each pixel representing single photon counts<br> - STED_dwell_time: the dwell time at each pixel position</p> <p>2. The 02_HOLO_fig2b.mat file contains the main results shown in Fig.2b (main article) as variables:<br> - I: the emptyimage devided, but not yet filtered hologram<br> - geo: a structure including the geometrical magnification M, the fresnel-number F, the waveguide-sample-distance z01, the sample-detector-distance z12, the effective propagation distance z_eff and the effective pixelsize dxeff<br> - lambda: the wavelength used for all x-ray experiments<br> - phi_raar: the reconstructed phasemap. Note, that for depicting the phase shifts, the matlab command angle(phi_raar) has to be used</p> <p>3. The 03_SCANNING_fig2c.mat file contains the main results shown in Fig.2c (main article) and Fig.4 (inset) as variables:<br> - darkfield: the x-ray dark field map of the scan area<br> - sSAXS_dwell_time: the dwell time for each scan point<br> - mask: the dark field mask applied on the diffraction patterns<br> - single_diff_image: a single diffraction pattern</p>
Supporting data for : "Differential responses to chemical cues correlate with task specialization in ant foragers"
<p>Data for : "Differential responses to chemical cues correlate with task specialization in ant foragers". </p>
Data and analysis for "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models"
<p>This is an archive of the raw data and analysis source code for the paper "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models". The archive contains:</p> <ul> <li><strong>devel.Rmd : </strong>Source code for the worksheet that contains the early development and figures for the paper.</li> <li><strong>devel.html</strong> : HTML rendering of devel.Rmd</li> <li><strong>lg-ensemble-stats.Rmd </strong>: Source code for the worksheet that contains the statistical analysis described in the paper.</li> <li><strong>lg-ensemble-stats.html</strong> : HTML rendering of lg-ensemble-stats.Rmd</li> <li><strong>cc-analysis.Rmd </strong>: Analysis of the compromise conjecture raised by some readers of the paper</li> <li><strong>cc-analysis.nb.html</strong> : HTML rendering of cc-analysis.Rmd</li> <li><strong>data.tar.bz2 </strong>: Input data for the analyses above.</li> </ul> <p>The source code in this archive is written in R and requires the R runtime environment. It also uses the fldgen package, version 1.0.0, which is available at <a href="https://github.com/JGCRI/fldgen">https://github.com/JGCRI/fldgen</a></p> <p> </p>
Localized admittance and correlation
<p>Localized admittance and correlation data from Broquet & Wieczorek (2019)</p>
Neural correlates of top-down modulation of haptic shape versus roughness perception
<p>Exploring an object’s shape by touch also renders information about its surface roughness. It has been suggested that shape and roughness are processed distinctly in the brain, a result based on comparing brain activation when exploring objects that differed in one of these features. To investigate the neural mechanisms of top-down control on haptic perception of shape and roughness, we presented the same multidimensional objects but varied the relevance of each feature. Specifically, participants explored two objects that varied in shape (oblongness of cuboids) and surface roughness. They either had to compare the shape or the roughness in an alternative-forced-choice-task. Moreover, we examined whether the activation strength of the identified brain regions as measured by functional magnetic resonance imaging (fMRI) can predict the behavioral performance in the haptic discrimination task. We observed a widespread network of activation for shape and roughness perception comprising bilateral pre- and postcentral gyrus, cerebellum, and insula. Task-relevance of the object’s shape increased activation in the right supramarginal gyrus (SMG/BA40) and the right precentral gyrus (PreCG/BA44) suggesting that activation in these areas does not merely reflect stimulus-driven processes, such as exploring shape, but also entails top-down controlled processes driven by task-relevance. Moreover, the strength of the SMG/PreCG activation predicted individual performance in the shape but not in the roughness discrimination task. No activation was found for the reversed contrast (roughness > shape). We conclude that macrogeometric properties, such as shape, can be modulated by top-down mechanisms whereas roughness, a microgeometric feature, seems to be processed automatically.</p>
text-fig. 58. Diagrams showing how character distribution in a cladogram indicates gaps in the fossil record, a, arrangement of three taxa in a cladogram; synapomorphies a-f support the monophyly of the clade (B-C). B, if gradual acquisition of characters is assumed, several taxa must be missing in the phylogeny between taxon A and clade (B-C). The number of taxa missing is positively (though not necessarily linearly) correlated with the number of synapomorphies that diagnose any given node. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 58. Diagrams showing how character distribution in a cladogram indicates gaps in the fossil record, a, arrangement of three taxa in a cladogram; synapomorphies a-f support the monophyly of the clade (B-C). B, if gradual acquisition of characters is assumed, several taxa must be missing in the phylogeny between taxon A and clade (B-C). The number of taxa missing is positively (though not necessarily linearly) correlated with the number of synapomorphies that diagnose any given node.
Dataset for solar energy intake correlated with weather forecast data
<p>This is the first release.</p>
Dataset for: Regional Correlations in the layered deposits of Arabia Terra, Mars
<p><strong>Dataset for: Regional Correlations in the layered deposits of Arabia Terra, Mars</strong></p> <p><strong>Overview:</strong></p> <p>This repository contains the map-projected HiRISE Digital Elevation Models (DEMs) and the map-projected HiRISE image for each DEM and for each site in the study. Also contained in the repository is a GeoPackage file (beds_2019_08_28_09_29.gpkg) that contains the dip corrected bed thickness measurements, longitude and latitude positions, and error information for each bed measured in the study. GeoPackage files supersede shapefiles as a standard geospatial data format and can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS. For more information about GeoPackage files, please use <a href="https://www.geopackage.org/">https://www.geopackage.org/</a> as a resource. A more detailed description of columns in the beds_2019_08_28_09_29.gpkg file is described below in a dedicated section. Table S1 from the supplementary is also included as an excel spreadsheet file (table_s1.xlsx).</p> <p><strong>HiRISE DEMs and Images:</strong></p> <p>Each HiRISE DEM, and corresponding map-projected image used in the study are included in this repository as GeoTiff files (ending with .tif). The file names correspond to the combination of the HiRISE Image IDs listed in Table 1 that were used to produce the DEM for the site, with the image with the smallest emission angle (most-nadir) listed first. Files ending with “_align_1-DEM-adj.tif” are the DEM files containing the 1 meter per pixel elevation values, and files ending with “_align_1-DRG.tif” are the corresponding map-projected HiRISE (left) image. Table 1 Image Pairs correspond to filenames in this repository in the following way: In Table 1, Sera Crater corresponds to HiRISE Image Pair: PSP_001902_1890/PSP_002047_1890, which corresponds to files: “PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif” for the DEM file and “PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif” for the map-projected image file. Each site is listed below with the DEM and map-projected image filenames that correspond to the site as listed in Table 1. The DEM and Image files can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.</p> <p>· Sera</p> <p>o DEM: PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif</p> <p>o Image: PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif</p> <p>· Banes</p> <p>o DEM: ESP_013611_1910_ESP_014033_1910_align_1-DEM-adj.tif</p> <p>o Image: ESP_013611_1910_ESP_014033_1910_align_1-DRG.tif</p> <p>· Wulai 1</p> <p>o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif</p> <p>o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif</p> <p>· Wulai 2</p> <p>o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif</p> <p>o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif</p> <p>· Jiji</p> <p>o DEM: ESP_016657_1890_ESP_017013_1890_align_1-DEM-adj.tif</p> <p>o Image: ESP_016657_1890_ESP_017013_1890_align_1-DRG.tif</p> <p>· Alofi</p> <p>o DEM: ESP_051825_1900_ESP_051970_1900_align_1-DEM-adj.tif</p> <p>o Image: ESP_051825_1900_ESP_051970_1900_align_1-DRG.tif</p> <p>· Yelapa</p> <p>o DEM: ESP_015958_1835_ESP_016235_1835_align_1-DEM-adj.tif</p> <p>o Image: ESP_015958_1835_ESP_016235_1835_align_1-DRG.tif</p> <p>· Danielson 1</p> <p>o DEM: PSP_002733_1880_PSP_002878_1880_align_1-DEM-adj.tif</p> <p>o Image: PSP_002733_1880_PSP_002878_1880_align_1-DRG.tif</p> <p>· Danielson 2</p> <p>o DEM: PSP_008205_1880_PSP_008930_1880_align_1-DEM-adj.tif</p> <p>o Image: PSP_008205_1880_PSP_008930_1880_align_1-DRG.tif</p> <p>· Firsoff</p> <p>o DEM: ESP_047184_1820_ESP_039404_1820_align_1-DEM-adj.tif</p> <p>o Image: ESP_047184_1820_ESP_039404_1820_align_1-DRG.tif</p> <p>· Kaporo</p> <p>o DEM: PSP_002363_1800_PSP_002508_1800_align_1-DEM-adj.tif</p> <p>o Image: PSP_002363_1800_PSP_002508_1800_align_1-DRG.tif</p> <p> </p> <p><strong>Description of beds_2019_08_28_09_29.gpkg:</strong></p> <p>The GeoPackage file “beds_2019_08_28_09_29.gpkg” contains the dip corrected bed thickness measurements among other columns described below. The file can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.</p> <p> </p> <p>(<strong>Column_Name:</strong> Description)</p> <p><strong>sitewkn:</strong> Site name corresponding to the bed (i.e. Danielson 1)</p> <p><strong>section:</strong> Section ID of the bed (sections contain multiple beds)</p> <p><strong>meansl:</strong> The mean slope (dip) in degrees for the section</p> <p><strong>meanaz:</strong> The mean azimuth (dip-direction) in degrees for the section</p> <p><strong>ang_error:</strong> Angular error for a section derived from individual azimuths in the section</p> <p><strong>B_1:</strong> Plane coefficient 1 for the section</p> <p><strong>B_2:</strong> Plane coefficient 2 for the section</p> <p><strong>lon:</strong> Longitude of the centroid of the Bed</p> <p><strong>lat:</strong> Latitude of the centroid of the Bed</p> <p><strong>thickness:</strong> Thickness of the bed BEFORE dip correction</p> <p><strong>dipcor_thick:</strong> Dip-corrected bed thickness</p> <p><strong>lon1:</strong> Longitude of the centroid of the lower layer for the bed (each bed has a lower and upper layer)</p> <p><strong>lon2:</strong> Longitude of the centroid of the upper layer for the bed</p> <p><strong>lat1:</strong> Latitude of the centroid of the lower layer for the bed</p> <p><strong>lat2:</strong> Latitude of the centroid of the upper layer for the bed</p> <p><strong>meanc1:</strong> Mean stratigraphic position of the lower layer for the bed</p> <p><strong>meanc2:</strong> Mean stratigraphic position of the upper layer for the bed</p> <p><strong>uuid1:</strong> Universally unique identifier of the lower layer for the bed</p> <p><strong>uuid2:</strong> Universally unique identifier of the upper layer for the bed</p> <p><strong>stdc1:</strong> Standard deviation of the stratigraphic position of the lower layer for the bed</p> <p><strong>stdc2:</strong> Standard deviation of the stratigraphic position of the upper layer for the bed</p> <p><strong>sl1:</strong> Individual Slope (dip) of the lower layer for the bed</p> <p><strong>sl2:</strong> Individual Slope (dip) of the upper layer for the bed<strong> </strong></p> <p><strong>az1:</strong> Individual Azimuth (dip-direction) of the lower layer for the bed</p> <p><strong>az2:</strong> Individual Azimuth (dip-direction) of the upper layer for the bed</p> <p><strong>meanz:</strong> Mean elevation of the bed</p> <p><strong>meanz1:</strong> Mean elevation of the lower layer for the bed</p> <p><strong>meanz2:</strong> Mean elevation of the upper layer for the bed</p> <p><strong>rperr1:</strong> Regression error for the plane fit of the lower layer for the bed</p> <p><strong>rperr2:</strong> Regression error for the plane fit of the upper layer for the bed</p> <p><strong>rpstdr1:</strong> Standard deviation of the residuals for the plane fit of the lower layer for the bed</p> <p><strong>rpstdr2:</strong> Standard deviation of the residuals for the plane fit of the upper layer for the bed</p>
Correlation between the introduction of gastrointestinal PCR panels and the antibiotic prescription in outpatients with gastroentiritis
<p>Excel table summarising the data of 2 cohorts having symptoms of gastroenteritis:</p> <p>- cohort from 2016 investigated by traditional methods</p> <p>- cohort from 2017 investigated by gastrointestinal PCR panels</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.