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Dataset results
14 results for “kernel density”
End-condition for solution small angle X-ray scattering measurements by kernel density estimation
<p>The set of python scripts and some datasets for estimating the minimum X-ray exposure time for X-ray solution scattering experiments using statistical and mathematical approaches.</p> <p>We apply a statistical inequality to estimate the kernel density estimation (KDE) method’s error to determine the minimum X-ray exposure time.</p> <p>Please refer to the following article, </p> <p>End-condition for solution small angle X-ray scattering measurements by kernel density estimation<br> Science and Technology of Advanced Materials: Methods, Volume 2 Issue 1, pages 426-434 (2022)<br> DOI: 10.1080/27660400.2022.2140021<br> <a href="https://doi.org/10.1080/27660400.2022.2140021">https://doi.org/10.1080/27660400.2022.2140021</a></p>
Figure reproduction for "Accelerating small angle scattering experiments on anisotropic samples using kernel density estimation"
<p>These datasets and a Jupyter notebook reproduce figures in <a href="https://www.nature.com/articles/s41598-018-37345-5">a publication by Saito et al in Scientific Reports</a>. The notebook also serves as a demo for kernel density estimation (smoothing) of 2D data using Python. Details are described in the notebook. If you have no idea about ipynb format, please see HTML version with your web browser instead. It contains exactly the same codes and results as ipynb version.</p>
Datasets for the article "The temperature and density of a solar flare kernel measured from extreme ultraviolet lines of O IV"
<p>This entry contains the following files:</p><p>20120309_030933_kernel_fe8_shift.save<br>20120309_030933_kernel_fe8_shift_fits.txt<br>20110814_055342_qs_offlimb_si10.save<br>20110814_055342_qs_offlimb_si10_fits.txt</p><p>The .save files are IDL save files that can be restored into IDL using the restore command.</p><p>The 20120309 save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the flare kernel for the EIS short wavelength (SW) channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the long-wavelength (LW) channel.<br>map185 - An IDL map structure containing the Fe VIII 185.21 image that was used to select the flare kernel.<br>mask185 - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20120309_030933_kernel_fe8<i>s</i>hift_fits.txt. This file can be read with read_line_fits.pro in Solarsoft.</p><p>The 20110814 dataset is used to obtain an off-limb coronal spectrum for calibration purposes. The save file contains:</p><p>swspec - An IDL structure containing a 1D spectrum of the off-limb region for the EIS SW channel. The format is that returned by eis-mask-spectrum.pro.<br>lwspec - As above, but for the LW channel.<br>map - An IDL map structure containing the Si X 272 image that was used to select off-limb region.<br>mask - An IDL structure containing the pixel mask that is used as input to eis-mask-spectrum.pro.</p><p>The Gaussian fits to the spectra (as performed with the routine spec-gauss-eis.pro) are stored in 20110814_055342_qs_offlimb_si10_fits.txt. This file can be read with read_line_fits.pro in Solarsoft. </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p><p> </p>
DTM files from wildlife–vehicle collisions using kernel density estimation (KDE)
<p>21 CSV files that contain the Digital Terrain Model (DTM) from wildlife–vehicle collisions (WVC) hotspots using kernel density estimation (KDE) in Spain between 2016 and 2021. Data source of each WVC record is the Spanish General Directorate of Traffic (DGT).</p> <p>The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).</p> <p>This dataset is the output of the KDE analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
<p>The mapping population consists of 166 recombinant inbred lines (RILs) derived from a cross between HD3086 and HI1500.</p> <p><strong>Phenotypic data</strong><br>The RILs population along with parents were evaluated under four conditions namely timely sown irrigation (TSIR) taken as control, timely sown restricted irrigation (TSRI), late sown irrigation (LSIR), and late sown restricted irrigation (LSRI) conditions at Delhi, and under restricted irrigation condition at Indore. From each plot, 20 random spikes were harvested and spikes from each plot were threshed separately. While cleaning, care was taken to prevent metal and dust contamination. The grain iron concentration (GFeC) and grain zinc concentration (GZnC) were measured using Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000 M/s Oxford Inc, USA). The thousand kernel weight (TKW) was recorded by counting 1000 grains manually and weighted with an electronic balance.</p> <p><strong>Genotypic data</strong><br>DNA was extracted from 21 days old seedlings using CTAB method (Murray and Thompson, 1980). Genomic DNA quality was determined using 0.8% agarose gel electrophoresis with λ DNA as the standard and quantified using nanodrop. The 35K SNP Axiom breeders' array was used for genotyping of parents and the RILs population.</p>
Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
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Figure 3. a, linear discriminant function illustrating shape variation between iguanids. Kernel density ellipses for each species illustrate 90 in Morphological and performance modifications in the world's only marine lizard, the Galápagos marine iguana, Amblyrhynchus cristatus
Figure 3. a, linear discriminant function illustrating shape variation between iguanids. Kernel density ellipses for each species illustrate 90% and 70% of the data distribution. b, graph of morphometric trait loadings from LD analysis.
Data from: A spatial kernel density method to estimate diet composition of fish
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Kernel Density Estimation of metal axes from Denmark, England, Wales and the Netherlands
<p>As part of a Master's thesis, Kernel Density Estimations (KDEs) using likelihood cross-validation bandwidth were calculated for metal axes found by private metal detectorists. The data derives from England and Wales (Portable Antiquities Scheme), Denmark (Digitale Metaldetektorfund) and the Netherlands (Portable Antiquities Scheme). Calculations were performed with the 'density' function in R. </p>
Clustering and kernel density estimation for assessment of measurable residual disease by flow cytometry
<p>Flow cytometry raw data and supplementary table S1.</p>
A Unified View of Vibrational Spectroscopy Simulation through Kernel Density Estimations
<p>Please see ref:</p><p>Botella, R.; Kistanov, A., A. <i>J. Phys. Chem. Lett.</i> <strong>2023</strong>, 14, 3691-3697</p>
Data and trained models for "Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules"
<p>Data and trained models for "Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules", https://github.com/CIA-CCTB/FRCnet</p>
Mixture Density Mercer Kernels
We present a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density estimate. We show how to convert the ensemble estimates into a Mercer Kernel, describe the properties of this new kernel function, and give examples of the performance of this kernel on unsupervised clustering of synthetic data and also in the domain of unsupervised multispectral image understanding.
Mixture Density Mercer Kernels: A Method to Learn Kernels
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density estimate. We show how to convert the ensemble estimates into a Mercer Kernel, describe the properties of this new kernel function, and give examples of the performance of this kernel on unsupervised clustering of synthetic data and also in the domain of unsupervised multispectral image understanding.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.