Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,563
datasets available to search
ShareScore release 0.9.0
Dataset results
4,563 results for “correlations”
Data bundle for "Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope"
<p>Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk)</p> <p>This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative <br> electron diffraction and X-ray spectroscopy, in the scanning electron microscope' <br> https://doi.org/10.1016/j.ultramic.2020.112944</p> <p>The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format.</p> <p>Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and <br> .mat results files are contained within. These are organised by Figure number in the paper.</p> <p>The provided results are divided into two major sections:<br> (1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information.<br> RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum. <br> (2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps. <br> </p>
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
NDVI Raster maps of Scotland for 2013-2016 used to analyse correlations between greenness, mortality and mental health.
<p>These files were used in the analysis for "Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study" Hyam. Submitted to RIO 2020.</p> <p><strong>Extract on construction of data</strong></p> <p>NDVI data was downloaded from the United States Geological Survey (USGS) Land Satellites Data System (LSDS) Science Research and Development (LSRD) (United States Geological Survey 2018). Which produces Level 2 and Level 3 data products from the Level 1 data of instruments aboard Landsat Satellites. For this study Surface Reflectance data generated by the Landsat Surface Reflectance Code (LaSRC) from the Operational Land Imager (OLI) instrument aboard the Landsat 8 satellite was used (United States Geological Survey 2018). The Surface Reflectance NDVI (sr_ndvi) product and Level-2 Pixel Quality Assessment band (pixel_qa) were downloaded for Landsat scenes 204/21, 205/21, 206/21, 204/20, 205/20, 206/20 WRS-2 (NASA 2018) for the calendar years 2013 to 2016. These scenes cover most of Scotland and include all the major urban areas. A full list of the 333 products is given in supplemental material. Suppl. material 2</p> <p>All of Scotland is over 54° North and so for many satellite images the sun is at too low an angle to give reliable surface reflectance data especially in the winter months. Scotland also has an oceanic climate so the ground is often obscured by cloud or mist. To build a detailed, contiguous NDVI map of the whole country therefore requires combining images taken on many satellite passes especially if points are to be sampled multiple times to overcome measurement errors. The images downloaded from USGS were therefore combined. A cloud free version of each NDVI image was created by setting the pixels that corresponded to cloud, snow or water in the Quality Assurance Assessment band to NA. These cloud free images were then combined into a single, mosaic stack of images to cover all of the study area and then averaged down to a single layer as a tiff image. This was done for two seasonal periods, Winter (October, November, December of 2013, 2014, 2015 and 2016 combined with January, February, March of 2014, 2015, 2016) and summer (April, May, June, July, August, September of 2014, 2015, and 2016). The resulting two images covering most of Scotland for winters and summers between 2013 and 2016 and formed the basis of subsequent analysis.</p> <p>These two files are included here along with a list of the Landsat products used to produce them.</p>
Graphs and Attributes used for the attribute-structure correlation pattern mining
<p>## SCPM: An implementation of an algorithm for structural correlation pattern mining.</p> <p>The structural correlation measures how a set of attributes induces dense subgraphs in an attributed graph. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Structural correlation pattern mining is useful to analyze how different attribute sets are correlated to dense subgraphs in several real-life attributed graphs.</p> <p>**Relevant Publications**</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Structural correlation pattern mining for large graphs. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (MLG '10).</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Mining Attribute-structure Correlated Patterns in Large Attributed Graphs. In Proceedings of the VLDB Endowment (PVLDB '12).</p> <p>* Arlei Silva. Structural correlation pattern mining for large graphs. M.Sc Thesis, Computer Science Department, Universidade Federal de Minas Gerais, 2011.</p> <p>* Arlei Silva, Wagner Meira Jr. Structural correlation pattern mining for large graphs. Thesis and Dissertation Contest of the Brazilian Computer Society (CTD'12).</p> <p><br> ## HOW TO</p> <p>cd to trunk and run make<br> see README in trunk</p> <p><br> ## Datasets:</p> <p>### Description:</p> <p>#### ATTRIBUTE FILE:</p> <p>Format: Lists the attributes of each vertex from the graph.</p> <p> <VERTEX_ID>,<ATTRIBUTE_ID>,<ATTRIBUTE_ID>...,<ATTRIBUTE_ID></p> <p> Example: <br> 1,A,C <br> 2,A <br> 3,A,C,D <br> 4,A,D <br> 5,A,E <br> 6,A,B,C <br> 7,A,B,E <br> 8,A,B <br> 9,A,B <br> 10,A,B,D <br> 11,A,B</p> <p>#### GRAPH FILE:</p> <p>Format: Lists the neighbors of each vertex from the graph (adjacency list). Although the graph is undirected, each edge must be included in both directions.</p> <p> <VERTEX_ID>,<NEIGHBOR_ID>,<NEIGHBOR_ID>...,<NEIGHBOR_ID> <br> <br> Example: <br> 1,4 <br> 2,3 <br> 3,2,4,5,6,7 <br> 4,1,3,5,6 <br> 5,3,4,6 <br> 6,3,4,5,7,8,9,10 <br> 7,3,6,8,11 <br> 8,6,7,9,10,11 <br> 9,6,8,10,11 <br> 10,6,8,9,11 <br> 11,7,8,9,10</p> <p>### REAL DATASETS</p> <p>Lastfm:</p> <p>attributes: attrLastFm.csv.tar.bz2</p> <p>network: graphLastFm.csv.tar.gz</p> <p>DBLP:</p> <p>attributes: newAttrDBLP.csv.tar.bz2</p> <p>network: newGraphDBLP.csv.tar.bz2</p> <p>CITESEER:</p> <p>attributes: attrCiteseer.csv.tar.bz2</p> <p>network: graphCiteseer.csv.tar.bz2</p>
Data to "Point-wise correlations between 10-2 Humphrey visual field and OCT data in open angle glaucoma"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Cirafici, P., Maiello, G., Ancona, C., Masala, A., Traverso, C.E., & Iester M. (in press) Point-wise correlations between Humphrey visual field and OCT data in open angle glaucoma. Eye</p>
Second Ocular adnexal malignancies - Histology for Histology Correlation
<p>This table presents the histology of the first primary and histology of the orbital malignancy.</p> <p>Please note that the population in this table is different from the population in the paper. The first primary in this table includes all sites including the eye and orbit.</p>
Dataset of Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits
<p>Full datasets for the research entitled 'Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits'.</p>
Ionospheric Vertical Correlation Lengths Derived From IRI-2016 Model Errors
<p>Ionospheric vertical correlation lengths based on IRI-2016 model and Incoherent Scatter Radar (ISR) data.</p> <p><strong>Important! The analysis was performed in log space. </strong></p> <p>ISR used for this analysis:</p> <p>Jicamarca, Arecibo, Millstone Hill, Poker Flat ISR, and ResoluteBay North ISR.</p> <p>This metadata can be used for the construction of the covariance matrix for ionospheric data assimilation.</p> <p>Inside of the .nc file:</p> <p>lat=array of geomagnetic latitudes (degrees)<br> alt=arrays of altitudes (km)<br> vert_corr1=array of size (nalt, nlat), contains vertical correlation length above the reference point for different latitudes<br> vert_corr2=array of size (nalt, nlat), contains vertical correlation length below the reference point for different latitudes<br> </p>
Cr[CH(SiMe3)2]3/SiO2 catalysts for ethene polymerization: the correlation at amolecular level between the chromium loading and the microstructure of the produced polymer
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, Computer Simulation and Analysis</li> <li>Files are in <strong>txt</strong>, <strong>spc</strong>, <strong>par</strong>, <strong>opj</strong>, and <strong>m</strong> format</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements in <strong>spc</strong> and <strong>par</strong> formats</li> <li>EPR spectroscopic simulation and analyses in<strong> m</strong> format</li> <li>UV-vis and IR spectroscopic measurements and analyses; and polymer analyses in <strong>opj</strong> format</li> </ul> </li> <li>Are the data <strong>generated</strong> (e.g. by a machine) or <strong>collected</strong> (e.g. by means of a survey)? <ul> <li>EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker.</li> <li>FT-IR spectroscopic measurements were generated by Vertex70 produced by Bruker equipped with an MCT detector.</li> <li>UV-Vis-NIR spectroscopic measurements were generated by Cary5000 spectrophotometer produced by Varian equipped with a reflectance sphere.</li> </ul> </li> <li><strong>If t</strong> <ul> <li>Files in <strong>PARACAT_WP4_20201229_01_EPR</strong> folder includes EPR spectroscopic measurements and computer simulations/analyses, original data are in spc/par formats; files in m format were used to process the data of the same name and saved the results in txt format.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>GC</strong> – Gas Chromatography, <strong>GPC</strong> – Gel Permeation Chromatography</li> <li>definitions of variables: <strong>Magnetic field, Wavenumber, Pressure</strong></li> <li>units of measurement: <strong>Gauss (G), milli Tesla (mT), cm<sup>-1</sup>, milli bar (mbar)</strong></li> <li>abbreviations: <strong>IR/EPR/UV </strong>are data relate to measurements of IR/EPR/UV-Vis spectroscopy.</li> <li>abbreviation: <strong>CO/C2H4</strong> are data relate to methods with CO/ethene dosage.</li> <li>abbreviation: <strong>polym</strong> is data relates to polymer analysis conducted by GC and GPC</li> </ul> </li> </ul>
Dataset: Neural correlates of error prediction in a complex motor task
<p>There are two files for each subject:</p> <p>1. errorsegments_sub##.mat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below).</p> <p>2. hitsegments_sub##.mat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 5 cm) in the task (segment and electrode information can be found below).</p> <p>The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodes</p> <p>datapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.</p> <p>segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. </p> <p>electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]</p> <p> </p> <p> </p>
Data and code for "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations"
<p>This record contains data, scripts, and models associated with the publication "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations".</p> <h3>Databases</h3> <p>The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access.</p> <h3>Models</h3> <p>The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.</p> <h3>Primitive structures</h3> <p>Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.</p> <h3>Tools for analyzing tilt angles</h3> <p><strong><code>standardize-cell-orientation.py</code></strong><br>Converts each frame in `movie.xyz` to the standardized cell setting and writes the resulting trajectory to `movie-standardized.xyz`.<br>This is useful when working with triclinic cells.</p> <p><code><strong>analyze-tilt-angles-for-orthorhombic-cells.py</strong></code><br>Analyzes the distribution of tilt angles for each frame in a trajectory.<br>The analysis assumes that the B-B bonds are oriented along the Cartesian coordinate system either without further rotation or after a "simple" rotation of the entire cell.</p> <p><code><strong>angle-analysis.ovito</strong></code><br>Ovito script that approximately implements the procedure used in <code>analyze-tilt-angles-for-orthorhombic-cells.py</code>.</p>
Efficient PCA denoising of spatially correlated redundant MRI data
<p>MRI data used for the study: "Henriques, Ianus, Novello, Jovicich, Jespersen, Shemesh. Efficient PCA denoising of spatially correlated redundant MRI data. Imaging Neuroscience (In Press)."</p><p><strong>Preclinical scanner data</strong></p><p>All animal experiments for the collection of these datasets were preapproved by the institutional and national authorities and carried out according to European Directive 2010/63.</p><p>A mouse brain (C57BL/6J) was extracted via transcardial perfusion with 4% Paraformaldehyde (PFA), immersed in 4% PFA solution for 24 h, washed in Phosphate-Buffered Saline (PBS) solution for at least 24 h, and then placed on a 10 mm NMR tube filled with Flourinert (Sigma Aldrich, Lisbon, PT), which was sealed using paraffin film. </p><p>The MRI experiments were performed on a 16.4 T Bruker Aeon Ascend scanner (Bruker, Karlsruhe, Germany), interfaced with an Avance IIIHD console, and equipped with a gradient system capable of producing up to 3000 mT/m in all directions. A constant temperature of 37oC was maintained throughout the experiments using the probe's variable temperature capability. </p><p>Two distinct diffusion-weighted datasets were then acquired using Bruker's standard "Diffusion Tensor Imaging EPI":</p><ul><li><i>Dataset1 </i>(<strong>MB_exp1.nii</strong> and its brain mask<strong> MB_exp1_mask.nii</strong>): For this dataset, we modulated the amount of spatial correlations by acquiring EPI datasets with parameters optimized to mitigate noise spatial correlations, particularly avoiding k-space undersampling acquisition during EPI's gradient ramps and without using partial Fourier, which minimize regridding.</li><li><i>Dataset2 </i>(<strong>MB_exp2.nii</strong> and its brain mask<strong> MB_exp2_mask.nii</strong>): The second dataset was acquired with identical resolution, number of acquisitions, etc., but with large factors inducing spatial correlations, including k-space sampling during gradient ramps (default Bruker's acquisition and reconstruction procedures for acquisition speed) and with a significant phase partial Fourier factor of 6/8 (note for partial Fourier acquisitions, EPI data is reconstructed with zero-padding, according to the default reconstruction procedures by Bruker's pre-clinical reconstruction software Paravision 6.0.1).</li></ul><p>All datasets are acquired for the following diffusion-weighted parameters: 30 gradient directions for b-values 1, 2 and 3 ms/μm2 (Δ = 15 ms, δ = 1.5 ms), and 20 consecutive b-value=0 acquisitions - b-values and diffusion gradient directions are saved in files: <strong>MB.bval</strong> / <strong>MB.bvec</strong>.</p><p>Other acquisition parameters: TR/TE = 3000/50 ms, 9 coronal slices, Field of View = 12×12 mm2, matrix size 80×80, in-plane voxel resolution of 150×150 μm2, slice thickness = 0.7 mm, number of averages = 2, number of segments = 1, double sampling acquisition.</p><ul><li><i>Gold standard acquisitions for dataset 2 </i>(<strong>MB_exp2_20averages.nii</strong>): For a gold standard reference, the second dataset was also repeated for 20 averages. Note, since this dataset is aligned to <strong>MB_exp2.nii</strong> you can use <strong>MB_exp2_mask.nii </strong>for its brain mask.</li></ul><p>For all datasets, Spatial drifts in the image domain were first corrected using a sub-pixel registration technique (Guizar-Sicairos et al., 2008).</p><p> </p><p><strong>Clinical scanner data</strong></p><p>Experiments were approved by the Ethical Committee of the University of Trento and the participant signed an informed consent. </p><p>MRI data was a acquired for a healthy control (male, 54 years) using a 3T MAGNETOM PRISMA scanner (Siemens Healthcare, Erlangen, Germany) equipped with a 64-channel head-neck RF receive coil. </p><p>Diffusion MRI data was acquired using a monopolar single diffusion encoding EPI PGSE (Feinberg et al., 2010; Moeller et al., 2010; Xu et al., 2013) along 30 diffusion gradient directions for five non-zero b-values = 1, 2, 3, 4.5 and 6 ms/μm2 (Δ = 39.1 ms, δ = 26.3 ms) and 17 interspersed b-value=0 acquisitions. b-values and diffusion gradient directions are saved in files: <strong>HB.bval</strong> / <strong>HB.bvec</strong>. Note, only the masked version of these dataset (<strong>HB_masked.nii</strong> and its brain mask <strong>HB_mask.nii</strong>) is provided to guarantee that data privacy standards are met. For noise maps covering all FOV, the noise maps computed as the std of the 5 first repeating unmasked b = 0 acquisitions are provided in file <strong>stdS0i.nii.</strong></p><p>Other acquisition parameters were the following: TR/TE = 4000/80 ms, 63 axial slices, Field of View = 220×220 mm2, matrix size 110×110, isotropic resolution of 2 mm, 6/8 phase partial Fourier, parallel imaging with GRAPPA 2, simultaneous multi-slice factor 3. All diffusion MRI data was reconstructed using zero-padding, which is the default procedure for data acquired with partial Fourier above 70%. </p>
Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023).
<p>Dataset for publication "Efficient magnetic switching in a correlated spin glass", Nature Communications volume 14, Article number: 6127 (2023), DOI 10.1038/s41467-023-41718-4, include images, data used for generate that images, input files, converged potential files used for the calculations on SPR-KKR package 8.6. and raw data files.</p>
An efficient not-only-linear correlation coefficient based on clustering: Supplementary Files
<p>Supplementary Files for the manuscript "An efficient not-only-linear correlation coefficient based on machine learning" available at https://doi.org/10.1101/2022.06.15.496326</p> <ul> <li>Supplementary File 1: All pairwise gene correlations using Pearson, Spearman and CCC among the top 5,000 genes in GTEx’s whole blood with the largest variance. Columns indicates whether the gene pair was categorized in the top or bottom 30% of each coefficient, the correlation value, and the significance of the association. Significance is only present for the top 10 gene pairs of each intersection in the “Disagreements” group (Figure 3a, right) where CCC disagrees with Pearson, Spearman or both.</li> <li>Supplementary File 2: Percentiles of the coefficient values for the top 5,000 genes in GTEx’s whole blood.</li> <li>Supplementary File 3: Pearson, Spearman and CCC correlations values and their significance for two gene pairs (<em>UTY</em> - <em>KDM6A</em> and <em>DDX3Y</em> - <em>KDM6A</em>) across all tissues in GTEx.</li> </ul>
Data from: "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (Cervus canadensis) population"
<p>Raw and processed datasets used for analysis in "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (<em>Cervus canadensis</em>) population" by Hooven et al., published in <em>Mammalian Biology</em>. Datasets are as follows:</p> <p>Pregnancy.csv - Raw dataset detailing year and date of capture, individual identifier, and measured intrinsic variables, along with confirmed or predicted pregnancy/calf viability status.</p> <p>Pregnancy_final_mass.csv - Raw dataset after body mass estimation for individuals that were not weighed. </p> <p>all_confirmed_preg.csv - Subset of raw data for all individuals with confirmed pregnancy status (via lab PSPB assay).</p> <p>preg_ageclass.csv - Subset of all_confirmed_preg dataset including all individuals with general age classification (e.g., adult or subadult).</p> <p>preg_numeric.csv - Subset of all_confirmed_preg dataset including all individuals with numeric age value (from incisiform canine cementum annuli).</p> <p>all_fns.csv - Subset of dataset including all individuals with confirmed or predicted fetal/early neonatal survival ("offspring viability") status.</p> <p>fns_ageclass.csv - Subset of all_fns.csv including all individuals with general age classification.</p> <p>fns_numeric.csv - Subset of all_fns.csv including all individuals with numeric age values.</p> <p>parameter_est.csv - Parameter estimates from top-performing generalized linear mixed models for both pregnancy and offpsinrg viability, for plotting.</p>
Investigating the correlation of control parameters in reversed wood biomass supply chains (NCN) DEC-2020/39/I/HS4/03533
<p>Results of calculations of mutual correlations between parameters of reversed wood biomass supply chains, referring to the statistical yearbooks of the State Forests (in Poland). Identification of predictions of increasing values of individual parameters of reversed wood biomass supply chains.<br>The study was financed by National Science Centre in Poland under agreement DEC-2020/39/I/HS4/03533</p>
Data from: Evolutionary potential and constraints in an aposematic species: Genetic correlations between warning coloration and fitness components in wood tiger moths
<p>Phenotypic data and pedigrees of two laboratory populations of wood tiger moths (<em>Arctia plantaginis</em>) of Finnish (=FIN) and Estonian (=EST) ancestry.</p> <p><strong>Pedigree: </strong><br>ID: individual identifier<br>sire = Father<br>dam=mother</p> <p><strong>Pheno.data: </strong><br>ID: individual identifier<br>Sex: 1=male; 2=female<br>hatchingdate: date when larva hatched<br>pupadate: date of pupation<br>adultdate: date of exclusion<br>Pupa.Weight: weight of pupa [mg]<br>Female.Colour = hindwing colour of females. In this species hindwing colour in females varies continuously from yellow to red. It was quantified by visual matching of hinwdings against a colour scale ranging from 1 = yellow to 6 = red. <br>Signal.Size = larva signal size. Larvae show an orange patch of variable size on the back of their black body. The size is given as number of segments<br>Egg.N = egg number produced by the individual<br>Off.N = offspring number. Larvae were counted 2-3 weeks after egg laying</p> <p> </p>
Sociocultural correlates of exclusive breast feeding among postpartum mothers in Kwara state
<p>The data is about the determinants of exclusive breast feeding among postpartum mothers in Kwara state, Nigeria</p>
Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows
<div> <div><span># Data repository for the paper</span></div> <br> <div><span># </span><span>_Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows_</span></div> <br> <div><span>Corresponding author:</span></div> <div><span>Berend.van.Wachem@multiflow.org</span></div> <br> <div><span>This repository consists of the data and exemplary python scripts for the paper "Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows" by Christian Gorges, Victor Chéron, Anjali Chopra, Fabian Denner and Berend van Wachem. The data stored in this repository have the following data format:</span></div> <br> <div><span>-</span><span> .csv files consisting the raw data of the simulations used for the coefficient plots in the results' chapter of the paper</span></div> <div><span> </span></div> <div><span>-</span><span> .py files containing python scripts serving as examples on how to use and plot the raw data of the .csv files and the correlations</span></div> <br> <div><span>The main folders of this repository are named as the non-spherical particle shapes (Oblate, Prolate, Rod-like) and a folder with the data on which the correlations are based.</span></div> <br> <div><span>The folders named after the non-spherical particle shapes contain the raw simulation data. For instance, the Oblate folder contains the individual .csv files of all simulations of the oblate spheroid for all Reynolds numbers, Mach numbers, and angles of attack.</span></div> <br> <div><span>The folder Correlations/ consists of the temporally averaged drag, lift and torque coefficients, which are written in .csv files and stored in the folder ResultsCoefficients/, as well as Python scripts for plotting the correlations. </span></div> <br> <div><span>The naming style of the raw data files and the subfolders for each section is explained in the following:</span></div> <br> <div><span>The file names of the .csv files within the particle shape folders consist of the Reynolds number, followed by the Mach number and the angle of attack. For example "log_Re100M2_0_alpha_90.csv" consists of the data for a Reynolds number of 100, a Mach number of 2.0 and an angle of attack of 90 degrees. The content in the .csv files is given as: "%f,%f,%f,%f\n" which corresponds to "Physical time, drag coefficient, lift coefficient, torque coefficient". The first row in each file gives the headers of each column.</span></div> <br> <div><span>The .csv files in the folder Correlations/ResultsCoefficients/ are split per coefficient, shape, and particle Reynolds numbers, which can be identified by the name of the .csv file. For instance, the results obtained for the lift coefficient of</span></div> <div><span>the prolate spheroid particle for at a particle Reynolds numbers 100 for all orientation angles and Mach numbers are given in the file:</span></div> <div><span>"Prolate_100_CL.csv". In these files, the results are ordered per orientation angle (rows) and Mach</span></div> <div><span>number (column). </span></div> <br> <div><span>The python scripts have been tested with Python 3.11.5.</span></div> <br> <div><span>PlotCoefficients.py is an example python script to read the .csv files and plot the aerodynamic force coefficients as it is done in the results section of the paper.</span></div> <br> <div><span>The python scripts in the directory Correlations/ are split in three main functions in two files:</span></div> <div><span>-</span><span> Getter.py (read the .csv files storing the coefficients - separate functions</span></div> <div><span> for the drag, lift and torque coefficients)</span></div> <div><span>-</span><span> ManuscriptCorrelation.py with all the correlations derived in this work for an</span></div> <div><span> effective implementation in any solver, and a plotting function to have visual</span></div> <div><span> representation of the correlations.</span></div> <div><span>-</span><span> generalmain.py (calls Getter and Plotter)</span></div> <br> <div><span>The Getter is called from the generalmain.py file. (run python3 generalmain.py) so that all coefficients can be gathered in a 3D array.</span></div> <div><span>First dimension : Reynolds number</span></div> <div><span>Second dimension : Orientation angle</span></div> <div><span>Third dimension : Mach number</span></div> <div><span>The user just needs to give the absolute path to the folder ResultsCoefficients/.</span></div> <br> <div><span>This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 447633787.</span></div> </div>
Correlation Between Insulation Resistance and Temperature Measurement Error in Type K and Type N Mineral Insulated, Metal Sheathed Thermocouples
<p>Mineral insulated, metal sheathed (MI) Type K and Type N thermocouples are<br> widely used in industry for process monitoring and control. One factor that limits<br> their accuracy is the dramatic decrease in the insulation resistance at temperatures<br> above about 600 °C which results in temperature measurement errors due to electrical<br> shunting. In this work the insulation resistance of a cohort of representative MI<br> thermocouples was characterised at temperatures up to 1160 °C, with simultaneous<br> measurements of the error in indicated temperature by in situ comparison with a reference<br> Type R thermocouple. Intriguingly, there appears to be a systematic relationship<br> between the insulation resistance and the error in the indicated temperature. At<br> a given temperature, as the insulation resistance decreases, there is a corresponding<br> increasingly negative error in the temperature measurement. Although the measurements<br> have a relatively large uncertainty (up to about 1 °C in temperature error and<br> up to about 10 % in insulation resistance measurement), the trend is apparent at all<br> temperatures above 600 °C, which suggests that it is real. Furthermore, the correlation<br> disappears at temperatures below about 600 °C, which is consistent with the<br> well-established diminution of insulation resistance breakdown effects below that<br> temperature. This raises the intriguing possibility of using the as-new MI thermocouple<br> calibration as an indicator of insulation resistance breakdown: large deviations<br> of the electromotive force (emf) in the negative direction could indicate a correspondingly<br> low insulation resistance.</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.