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260 results for “PCA”
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>
R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics
<p>This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.</p>
Dataset for Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients
<p>This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.</p> <p>Data are available in 2 different formats: </p> <p>- 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum) </p> <p>- 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".</p> <p>The code for R is available as a single file "Rcode.R".</p> <p> </p>
CMB heat flux PCA results
<p>Results of the CMB heat flux PCA from the Coltice et al. (2019) mantle convection model in the numpy (.npy) format. The PCA is computed on the snapshots of the simulations between 300 Myr and 1131 Myr in the simulation time.</p> <p>-avg_pattern.npy: Spherical harmonic decomposition of the CMB heat flux average pattern</p> <p>-patterns.npy: Spherical harmonic decomposition of the PCA components patterns</p> <p>-sing_val.npy: Singular value of the PCA components</p> <p>-weights.npy: Time dependent weights of the PCA components</p>
Fig. 14. PCA plot for standardized morphometric data for H in A new rupicolous species of gecko of the genus Hemidactylus Oken, 1817 from the Satpura Hills, Central India
Fig. 14. PCA plot for standardized morphometric data for H. chipkali sp. nov. (black), H. cf. murrayi (red) and H. treutleri (blue). Circles = male and squares = female.
A principal components (PCs) dataset of the leaf and canopy levels used for SIF retrieval in SFM-PCA approach
<p><span>A principal components (PCs) dataset (640–850 nm) generated using a principal component analysis approach to reconstruct the shape of the reflectance spectrum for the leaf and canopy levels. For the leaf level, a novel SIF-free leaf spectra dataset (n = 849, species = 95) collected at three sites in Beijing, China during June and August 2023, was used. For the canopy level, a total of 19,380 SIF-free spectra generated using a SCOPE model based on the measured leaf reflectance and transmittance was employed.</span></p>
PCA SUB ELITE YOUTH Handball DATA_SET
<p>The presented data base refers to results of study about PCA in Handball locomotion analysis of youth sub-elite handball players.</p>
Figure 6. PCA scatter plot for the 2 canonical variates generated from the 7 morphometric characters from 5 in Morphological and biometrical comparisons of the baculum in the genus Nannospalax Palmer, 1903 (Rodentia: Spalacidae) from Turkey with consideration of its taxonomic importance
Figure 6. PCA scatter plot for the 2 canonical variates generated from the 7 morphometric characters from 5 species.
Figure. PCA analysis based on GH-MspI, GH-AluI, PRL, and DGAT1 loci in Turkish native cattle breeds (Turkish Grey - TG, East Anatolian Red - EAR, Anatolian Black - AB, and South Anatolian Red - SAR). in Growth hormone (GH), prolactin (PRL), and diacylglycerol acyltransferase (DGAT1) gene polymorphisms in Turkish native cattle breeds
Figure. PCA analysis based on GH-MspI, GH-AluI, PRL, and DGAT1 loci in Turkish native cattle breeds (Turkish Grey - TG, East Anatolian Red - EAR, Anatolian Black - AB, and South Anatolian Red - SAR).
Figure 6. Normed PCA factorial graph F1 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 6. Normed PCA factorial graph F1 × F2 of two revealed geographical groups of Beringiana beringiana samples (blue circles indicate samples from Primorsky Krai, Kunashir, Sakhalin, and Iturup islands; red circles indicate samples from Kamchatka Peninsula). Ellipses show 95 % confidence interval.
Fig. 5. Relationships between the PCA 1 and CCA 1 in Integrity of fluvial fish communities is subject to environmental gradients in mountain streams, Sierra de Aroa, north Caribbean coast, Venezuela
Fig. 5. Relationships between the PCA 1 and CCA 1 axes with the species richness and rivers stations. The richness (A) was moderately explained, but the multivariate (B) and canonical (C) analyses showed functional relationships with the geographical location of the rivers.
Fig. 5. Correlation between PCA axis 1 in Fish assemblages of tropical floodplain lagoons: exploring the role of connectivity in a dry year
Fig. 5. Correlation between PCA axis 1 and species richness (a), density (b), and biomass (c) in connected [February (), May (), November ()] and disconnected lagoons, May (), August (), November ()]. Arrows indicate the direction of the limnological variables influence.
Social vulnerability to flooding in Ecuador : input variables, PCA vs Expert composite indices
<p><strong>Social vulnerability indices are used to better understand and predict the consequences of disasters, and support the development of improved disaster management policies. This research specifically supports the Ecuadorian Red Cross in generating a flood-specific social vulnerability index to inform flash flood early action protocol.</strong></p> <p>The dataset presents the results from the analysis of the social vulnerability to flooding in Ecuador, from individual input variables to the composite indices outputs. The results are available at the Parroquia level in Ecuador (admin level 3), for 1032 Parroquia excluding the Galapagos Islands.</p> <ul> <li>The dataset comprises, for each Parroquia, the estimation of <strong>15 variables characterizing the social vulnerability to flooding specific to Ecuador context</strong>. The variables are selected from literature review and consultation with Ecuadorian Red Cross disaster practitioners : <em>Disability, Poverty incidence, Gini Index, Agricultural labor share, Vectorborne disease incidence, Waterborne disease incidence, Social Security affiliation, Education level, Sanitation, Driking water access, Power access, Road travel time, Wall structure, Mobile access and Internet access.</em> All variables are normalized from 0 to 1, directed toward increasing vulnerability, and renamed accordingly.</li> <li>In addition, the <strong>Administrative level names, PCODE, calculated Area, population density,</strong> as well as related <strong>sub-regions</strong> are also referenced.</li> <li>Individual variables are integrated into <strong>composite vulnerability indices</strong>, using two different approaches: i) the Principal Component Analysis approach, using the first component <strong>PCA(n=1) </strong>and the first 5 components <strong>PCA(n=5)</strong> separately ; ii) the <strong>expert judgement weighting</strong> of the variables. The output composite indices, normalized from 0 to 1 are presented in 3 separated columns.</li> </ul> <p> </p>
PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTDOC Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTDOC instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTGJON Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTGJON instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
Raw Data and PCA Filtering of Apache Point Observatory NMSU 1m StellaCam Observations of LCROSS
<p>This archive contains the raw data and data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the StellaCam instrument on the Apache Point Observatory NMSU 1m telescope.</p> <p>Full details about the raw data are available in Chanover, N. J. et al. Results from the NMSU-NASA Marshall Space Flight Center LCROSS observational campaign. <em>J. Geophys. Res. (Planets)</em> <strong>116</strong>, E08003 (2011). <a href="https://doi.org/10.1029/2010JE003761">https://doi.org/10.1029/2010JE003761</a></p> <p>We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Apache Point Observatory 3.5m Agile Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Agile instrument on the Apache Point Observatory 3.5m telescope. We use principal component analysis (PCA) filtering both to improve the coregistration of the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of MMT Observatory 6.5m CCD47 Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the CCD47 instrument on the MMT Observatory 6.5m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA involvement subtypes
<p>Involvement of the PCA (Posterior Cerebral Artery) i.e stenosis or occlusion is presented along the different segments of the artery.</p> <p>This involvement is seen in moyamoya patients.</p>
PCA scores from contemporary Egyptian and Old Kingdom Egypt sample used for testing method by Musilová et al.(2016)
<p>Presented datasets consist of separate <strong>.csv</strong> files, that contain PCA scores and information about sex and population affinity. Each sample (Old Kingdom, contemporary Egypt, pooled sample etc.) has two files with PCA scores, one for shape and for form (shape + size together) as well as separate .csv file with information about sex/population. PCA scores were obtained after principal component analysis was applied to each dataset, that consisted of 3D models of skulls. PCA scores together with information about sex/population were used for training SVM for classification of sex and population. Details about samples bellow.</p> <p>The contemporary Egypt sample consists 96 CT scans, 49 males and 47 females, 3D models were obtained from DICOM data using Avizo. The Old Kingdom period sample consists of 54 3D models of skulls of individuals from Abusir and Giza, specifically 32 males and 22 females. 3D data from both samples are stored at the Laboratory of 3D Imaging and Analytical Methods, Department of Anthropology and Human Genetics, Faculty of Science, Charles university.</p> <p>Presented datasets were assembled and used as a part of study focused on testing the reliability of sex estimation method developed by Musilová et al.(2016) on non-European population. The datasets are made publicly available to enable reproducibility of the aforementioned work and to ensure data sharing and engagment for other studies focused on sex estimation using skull.</p>
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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.