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4,563 results for “correlations”
Data set of correlations between stocks world wide
<p>This data set contains intraday (1 hour format) correlations for one month (December 2021) from more than 2000 Stocks, Indices, Forex and Futures of major Stock exchanges world wide. It is an example of the outcome from data processing inside Infore project. The data set contains more than 2 million files.</p>
Data of FigS7, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS7, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS7.PNG). The Corresponding raw data and subsequent data analysis obtained contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt) and one file as csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv).</p>
Correlating NanoSIMS images with ultra-high resolution EM images in Look@NanoSIMS
<p>Supplement of the study by Spataro et al. (2022) describes how to perform correlative image analysis in Look@NanoSIMS. This repository contains files used as an example in that analysis.</p> <p>* Rat7SNR_21.tif = ultra-high resolution EM image (4096 x 3072 pixels)<br> * Spataro-Sept-2018_3.im.zip = zipped raw data produced by NanoSIMS 50L (256 x 256 pixels)<br> * Spataro-Sept-2018_3.zip = zipped folder containing data generated by Look@NanoSIMS</p> <p>To start the analysis, download all files to a folder on your computer (preferably in the same folder) and unzip the file Spataro-Sept-2018_3.zip. The latter step will create a folder Spataro-Sept-2018_3 containing files generated by Look@NanoSIMS when analysing data in Spataro-Sept-2018_3.im.zip and Rat7SNR_21.tif. The files include information about the alignment of individual planes (xyalign.mat), coordinates of the pairs of reference points in the EM and NanoSIMS images (points_10x.mat), regions of interest defined for the resampled (cells_10x.mat) and original NanoSIMS data (cells_1x.mat), and Look@NanoSIMS preferences saved for the resampled (prefs_10x.mat) and original (prefs_1x.mat and prefs.mat) NanoSIMS data. You can use these files to reproduce the analysis described in the Supplement of Spataro et al. (2022).</p> <p>Reference:</p> <p>S. Spataro, B. Maco, S. Escrig, L. Jensen, L. Polerecky, G. Knott, A. Meibom, and B. L. Schneider (2022). Alpha-synuclein-induced changes to neuronal metabolism revealed by stable isotope labeling and ultra-high-resolution imaging.</p>
High-resolution basin-wide correlations with dynamic time-warping: code and data for a case study from the Usseln Limestone (Late Devonian, Rhenish Massif, Germany)
<p>This dataset accompanies the manuscript of Wichern et al. (GRL, 2024), entitled "Decoding Deep-Time Rhythms: Probing the limit of Stratigraphic Correlation in the Usseln Limestone's (Late Devonian) Time-Specific Facies". It contains both datasets and code. </p> <p>The dataset concerns samples collected from the Usseln Limestone, a rock unit that underlies the Late Devonian Kellwasser Crisis deposits in the Rhenish Massif, western Germany. The data consists of high-resolution thin-section composite photos, as well as micro-XRF scanning data (both maps and depth records) for three localities. The code contains the workflow to structure and plot the micro-XRF maps and convert them to depth records, as well as the workflow to analyse these depth records using dynamic time warping. Further analytical details can be found in the supporting material of the associated manuscript.</p>
Data for the article "Mechanically induced correlated errors on superconducting qubits with relaxation times exceeding 0.4 milliseconds"
<p>Here you will find all the raw data and data processing scripts for the plots presented in the article "Mechanically induced correlated errors on superconducting qubits with relaxation times exceeding 0.4 milliseconds."</p>
Numerical data for "Nonlocal correlations transmitted between quantum dots via short topological superconductor"
<p>Raw numerical data used to produce figures 2-8 in the article <em>Nonlocal correlations transmitted between quantum dots </em><em>via short topological superconductor</em> (arXive preprint <a href="http://arxiv.org/abs/2405.06630">http://arxiv.org/abs/2405.06630</a>), and other data obtained within the same project. See file headers for details concerning the content.</p>
Correlative microscopy of mice cerebellar Purkinje cells from 20x confocal tissue imaging to super-resolution 93x 3D STED of dendritic spines
<p>This Dataset concerns the paper entitled "<em>From tissues to segmentation: a modular framework for multi-scale neuron isolation</em>" by Cauzzo et al. <strong>Nature Comm (2024).</strong></p> <p>S.Cauzzo<sup>$</sup>, E. Bruno, D. Boulet, P. Nazac, M. Basile, A. L. Callara, F. Tozzi, A. Ahluwalia, C. Magliaro, L. Danglot<sup>$</sup><sup>*</sup>, N. Vanello<sup>$</sup><sup>*</sup> *shared senior authorship: Lydia.danglot@inserm.fr ; nicola.vanello@unipi.it</p> <p><sup>$</sup> corresponding authors : cauzzo.simone@gmail.com ; Lydia.danglot@inserm.fr ; nicola.vanello@unipi.it</p> <p> </p>
Data and Code for the paper 'Indication of long-range correlations governing city size'
<div> <h1>Summary</h1> <p>This repository contains the preprocessed data necessary for constructing the city network as described in the related paper, as well as the code to do the Shortest-path Fluctuation Analysis (SFA) on those networks.</p> <div> <h2>Under the <code>data</code> folder</h2> <ul> <li> <p>In the <code>Node_list</code> folder, each <em><code>CSV</code></em> file has the name convention like <code>AL_1000_node_list.csv</code>, for example, this means the table contains the list of <strong>nodes</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 3 columns with column names, and without row names.</p> <ul> <li> <p>Each row in the table is a record of one node, the <strong><em>node ID</em></strong> is the <code>row_id</code> of the record in the table, the first record has a row_id of <strong>0</strong>.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <em><code>X</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <em><code>Y</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>third</strong> column in the table is the <strong>Size</strong> of the node, i.e., the number of pixels of this city.</p> </li> </ul> </li> <li> <p>In the <code>Edge_list</code> folder, each <em><code>TXT</code></em> file has the name convention like <code>AL_1000_edges.txt</code>, for example, this means the table contains the list of <strong>edges</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 2 columns without column name, without row name.</p> <ul> <li> <p>Each row in the table is a record of one edge that is composed of a pair of nodes, the <strong><em>node ID</em></strong> is the <code>row_id</code> of that node in the <strong>node_list</strong> table.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <strong><em>ID</em></strong> of the node on one side of an edge.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <strong><em>ID</em></strong> of the node on the other side of an edge.</p> </li> </ul> </li> </ul> </div> <div> <h2>Under the <code>code</code> folder</h2> <ul> <li> <p>The <code>SFA_args_LSPT_out.cpp</code> file contains the <strong>C++</strong> code to do the SFA calculation, detailed information can be found in the documentation of the code file.</p> </li> <li> <p>The <code>SFA_config.ini</code> file contains some configuration parameters for running the compiled program, details can also be found in the file. This file has to be put in the same folder as the compiled executable file.</p> </li> </ul> </div> </div>
Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach
<p>This is the dataset used in the analysis "Identification of Southeast Asian <em>Anopheles </em>mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach". It consists in 3584 raw mass spectra (mzXML file format) of the head of 359 <em>Anopheles </em>mosquito specimens collected in Karen (Kayin state) in Myanmar between 2020 and 2022 and associated metadata (Rdata file format) including sample information (taxonomy.Rdata) and spectra information (metadata.Rdata).</p>
Mud and organic content are strongly correlated with microplastic contamination in a meandering riverbed - Data sets
<p>This is the dataset relating to publication "Microplastics distribution in a meandering riverbed reveasl mud content as a universal normalizer for microplastic contamination in aquatic environments" by Van Daele, M., Van Bastelaere, B., de Clercq, J., Meyer, I., Vercauteren, M. and Asselman, J.</p> <p>It contains the microplastic and sedimentological data that support the findings of that publication, with a seperate file for data obtained from riberbed sediments and the water column. It further contains a file with all source data for the graphs in the figures of the main manuscript and the Supplementary Information<span><span>.</span></span></p>
Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)
<p>This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory.</p> <p>Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information.</p> <p>You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset.</p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication. </p> <p>30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values.</p> <p>The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set.</p> <p>The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s.</p> <p>The files are plain text files with the data points separated by "space" along rows and "new line" along columns.</p>
Correlator data for determination of the I=1 pion-pion scattering amplitude and timelike pion form factor from Nf=2+1 lattice QCD
<p>Bootstrap samples of all correlation functions involved in the analysis of pion-pion scattering data and the timelike pion form factor described in "The I =1 pion-pion scattering amplitude and timelike pion form factor from N f = 2 + 1 lattice QCD". Additionally, an analysis file is provided for each ensemble which stored the analysis choices made in that work. These data are intended for use with the Jupyter notebook located in https://github.com/ebatz/jupan, which provides an interface. This notebook performs the entire analysis chain discussed in the above paper. </p>
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
Supporting transcriptomic data and code for: "Rapid and dose-dependent Natural Killer (NK) cell modulation and cytokine correlations after human rVSV-ZEBOV Ebolavirus vaccination"
<p>The counts_NK.csv file contains gene expression data (counts) for genes in the Ion Ampliseq human Gene expression kit panel. Data were obtained from whole blood RNA. Subjects were vaccinated with a high dose of the rVSV-ZEBOV vaccine against Ebola virus disease in the Geneva clinical trial.</p> <p>The Descriptive_Table_NK_2.csv contains descriptive data of the subjects for differential expression analysis.</p> <p>The NK_analysis_code.R file contains the code used for analysis.</p>
Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images
<p>Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images </p>
Data-driven criteria for quantum correlations
<p>Dataset of 3-qubit density matrices used for training deep-learning models for quantum correlations identicfication introduced in two papers:</p> <ul> <li> <div>M Krawczyk, J Pawłowski, MM Maśka, K Roszak Physical Review A 109, 022405 (2024).</div> <div> </div> </li> <li> <div>J Pawłowski, M Krawczyk, Physical Review Applied 22, 014068 (2024).</div> <div> </div> </li> </ul>
Data For Scalco et al. Clinicopathological correlates of quantitative Amyloid-B Pathology in the Temporal Cortex: Machine learning analysis of 131 cases from an ADRC
<p>Dataset containing 131 de-identified whole slide images (WSIs) with a respective data dictionary. </p> <p><strong>Paper</strong>: Scalco, R., Oliveira, L.C., Lai, Z. et al. Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases. acta neuropathol commun 12, 134 (2024). https://doi.org/10.1186/s40478-024-01827-7</p> <p><strong>Details</strong>: A total of 131 .svs. WSIs, de-identified using svs-deidentifier v 0.9.1-beta (https://github.com/pearcetm/svs-deidentifier/releases). Dataset is uploaded in batches due to Zenodo data upload limitations.</p> <p><strong>Slide curation/preparation</strong>: All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 between 20x and 40x magnification.</p> <p><strong>Code:</strong> Please refer to <a href="https://github.com/ucdrubinet/BrainSec">https://github.com/ucdrubinet/BrainSec</a> and <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p>
Dataset from: Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions
<p>Measures of functionality, proprioception, self reported status and joint acoustic emissions (AE) were recorded for a sample of general population. Specifically, threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded using two sensors in different frequency ranges and three modes of AE event detection were investigated during cycling with 30 and 60 rpm cadences.</p>
Metrics for two-sample tests: results on Mixture of Gaussians and Correlated Gaussians models
<p>The repository includes version 1.0 (v1.0) of the code and results corresponding to the GitHub repository <a href="https://github.com/TwoSampleTests/GenerativeModelsMetrics">GenerativeModelsMetrics</a>.</p> <p>Publishing information and arXiv identifier will be added after publication of the main manuscript related to the data.</p>
Experimental data related to the publication: "Phase Composition of AlTiNbMoV, AlTiNbTaZr and AlTiNbMoCr Refractory Complex Concentrated Alloys: A Correlation of Predictions and Experiment
<h2>EDS data</h2> <ul> <li>SPC and SPD files: EDAX Genesis raw data for EDS maps of as-sintered condition (Figures 1, 5 and 10).</li> <li><em>EDS_homogenized.h5</em> file: EDAX APEX raw data for maps (Figures 2, 6 and 11) and linescans (Figures 3, 7 and 12) of the homogenized condition</li> </ul> <h2>XRD data</h2> <p>ASCII tab-separated files for XRD diffractograms (Figures 4, 9 and 14). Contains measured intensity and calculated intensities for the displayed phases.</p> <h2>CALPHAD data</h2> <p>ASCII tab-separated files to recreate Figure 15 (resulting phase fractions only).</p> <p> </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.