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155 results for “Cluster analysis”
Globular Cluster Abundances from High-Resolution, Integrated-Light Spectroscopy. II. Expanding the Metallicity Range for Old Clusters and Updated Analysis Techniques
<p>Data from:</p> <p> Globular Cluster Abundances from High-Resolution, Integrated-Light Spectroscopy.<br> II. Expanding the Metallicity Range for Old Clusters and Updated Analysis Techniques (Astrophysical Journal)</p> <p> J. E. Colucci, R. A. Bernstein, A. McWilliam, Observatories of the Carnegie Institution for Science</p> <p>This repository contains reduced globular cluster integrated light echelle spectra in IRAF readable format. <br> NOTE: Spectra are *not* flux calibrated or doppler corrected. Sky/Background emission and absorption lines <br> are present. See reference paper for data reduction details.</p> <p>For each globular cluster:<br> <br> 1. *Approximately* normalized spectra are found in files ending with "ils_normalized.fits." The echelle<br> blaze function normalization was performed with an order by order fit to spectra of a reference G-type star.</p> <p> 2. Unnormalized spectra are found in files ending with "ils.fits." These spectra are not flux calibrated so do not<br> use the count values in each order for science purposes. </p> <p><br> Spectra for the globular clusters NGC 104, NGC 362, NGC 2808, NGC 6093, NGC 6397, NGC 6752 were <br> taken with the DuPont telescope. A reference star spectrum associated with the DuPont data is included : hr914_std.fits</p> <p>Spectra for the globular clusters NGC 6388, NGC 6440, NGC 6441, NGC 6528, NGC 6553 were taken with the <br> MIKE spectrograph on Magellan Clay. A reference star spectrum associated with this data is included: ltt9239_std.fits</p> <p>Spectra for the globular cluster Fornax 3 was taken with the MIKE spectrograph on Magellan Clay on a different run. <br> A reference star spectrum associated with this data is included: hd033771_std.fits</p> <p>This research was supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-1302710.</p>
Strategic Environmental Messaging: Identifying the Most Impactful Communication Characteristics Through Cluster Analysis
<p>Datasets for study 1 & 2 for the manuscript Strategic Environmental Messaging: Identifying the Most Impactful Communication Characteristics Through Cluster Analysis</p>
Dataset on Spatial Analysis and Clustering of Deforestation in the Amazon Biome: Spatio-Temporal Patterns and Priority Areas
<p>The dataset was developed with the aim of facilitating the development of a methodology to identify and evaluate deforestation patterns and trends in the Amazon. This innovative method combines deforestation alerts from the Real-Time Deforestation Detection System (DETER) with detailed information on various land categories, including environmental protection areas, settlements, rural properties, undesignated public forests, indigenous lands, and conservation units. The integration of this robust data allowed for the precise identification of areas at risk of deforestation, significantly strengthening monitoring and control activities aimed at combating deforestation in the Amazon region.</p> <p> </p> <p><strong>Spatial resolution</strong></p> <p>The data are available with a spatial resolution of 25 x 25 km (625 km²) and cover the Amazon biome.</p> <p> </p> <p><strong>Temporal resolution </strong></p> <p>Period of observed data: 2017 and 2021</p> <p> </p> <p><strong>Coordinate reference system</strong> </p> <p>Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)</p> <p> </p> <p><strong>Data format</strong></p> <p>Data is provided as Shapefile.</p> <p> </p> <p><strong>Dataset usage</strong> </p> <p>It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.</p> <p> </p> <p><strong>Publication & further information</strong></p> <p>For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).</p>
Reproducible analysis from SiRCle (Signature Regulatory Clustering)
<p>This contains the code and the data including the updates made during revisions for the manuscript: <strong><a href="https://www.biorxiv.org/content/10.1101/2022.07.02.498058v1.abstract">SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer.</a> </strong></p> <p> </p> <p><strong>The data have been generated from CPTAC and TCGA.</strong> This includes no new data in this study.</p>
Integrated Analysis of Seismic Sources and Structures: Understanding Earthquake Clustering during Hydraulic Fracturing
<p>The uploaded files include the 3D velocity model, 2D seismic reflection profiles, and horizontal slice utilized in this study.</p>
Model-based analysis of tuberculosis genotype clusters in the United States reveals high degree of heterogeneity in transmission, and state-level differences across California, Florida, New York, and Texas.
<p>Data and codes for the publication</p>
Code and data for spatial and temporal magnitude clustering analysis
<p>Code used for performing spatial and temporal seismic magnitude clustering analysis. Includes documentation (README.txt) with steps on how to implement the code. The public datasets used for this study can be accessed at the following locations: </p> <ul> <li><strong>Southern California Catalog: </strong> <ul> <li>SCEDC (2013): Southern California Earthquake Center.<br> Caltech.Dataset. doi:<a href="https://dx.doi.org/10.7909/C3WD3xH1">10.7909/C3WD3xH1</a></li> </ul> </li> <li><strong>Northern California Catalog:</strong> <ul> <li>NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC.</li> </ul> </li> <li><strong>Mixed-mode Laboratory Catalog:</strong> <ul> <li>Lin, Qing, et al. "Opening and mixed mode fracture processes in a quasi-brittle material via digital imaging." <em>Engineering Fracture Mechanics</em> 131 (2014): 176-193.</li> </ul> </li> <li><strong>ETAS Code:</strong> <ul> <li>Leila Mizrahi, Shyam Nandan, Stefan Wiemer 2021;<br> Embracing Data Incompleteness for Better Earthquake Forecasting. (Section 3.1)<br> <em>Journal of Geophysical Research: Solid Earth</em>; doi: <a href="https://doi.org/10.1029/2021JB022379">https://doi.org/10.1029/2021JB022379</a></li> </ul> </li> </ul>
Original NGS dataset from publication "Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center".
<p>Original hepatitis C virus hypervariable region 1 NGS sequences in fastq format from patients analyzed in the study "Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center". </p> <p> </p>
Performance Analysis of Single Board Computer Clusters
<p>This dataset contains the outputs from HPL runs used to measure performance of 16 nodes clusters built using Raspberry Pi 3 Model B, Raspberry Pi 3 Model B+, and Odroid C2. These clusters were constructed using the Pi Stack PCB which is available from https://doi.org/10.5258/SOTON/D0379.</p>
Analysis of constrained simulations of the Coma cluster and of its surrounding cosmic web
<p>The advent of wide-area spectroscopic galaxy surveys has allowed us to start investigating the properties of the filaments of the cosmic web. How filaments connect to clusters and how these connections impact cluster evolution is a hot topic in astrophysics, of interest for ongoing experiments and future facilities (from both the gas phase perspective, e.g. eROSITA, and the galaxy distribution, e.g. Euclid). The average connectivity (number of connected filaments) of a few observed and simulated clusters has been measured and it has been found that it scales with cluster mass. We applied a cosmic web detection algorithm (DisPerSE) to the Sloan Digital Sky Survey (SDSS) to detect the filaments from the galaxy distribution. We then detected three secure filaments connecting to the Coma cluster. This discovery lead to the developing of a further investigation based on constrained numerical simulations, which allow us to reproduce in detail a portion of the nearby Universe, recreating observed clusters including Coma. We analysed these simulations, with the aim of studying the evolution of the filaments around Coma throughout cosmic history and determining the impact of matter accretion channeled through these structures on the evolution of the Coma cluster. In this talk I will review our previous results and introduce the findings we obtained with the study of our constrained numerical simulations.</p>
Characterisation of night-time outdoor lighting in small urban centres using cluster analysis of remotely sensed light emissions (Dataset)
<p>Data used for the paper "Characterisation of night-time outdoor lighting in small urban centres using cluster analysis of remotely sensed light emissions". </p>
Dataset - Characterization of groups of informal carers. A cluster analysis.
<pre>This is the dataset for the "Characterization of groups of informal caregivers. A cluster analysis using the example of Saxony."</pre>
Initial application of the noise-sorted scanning clustering algorithm to the analysis of composition-dependent organic aerosol thermal desorption measurements
Open the record for dataset details and reuse information.
The Qualitative Analysis of Repertory Grid Data: Interpretive Clustering (datasets only)
<p>Datasets accompanying the publication "The Qualitative Analysis of Repertory Grid Data: Interpretive Clustering" by </p> <p>Burr, King, and Heckmann.</p>
Data from: Cluster analysis successfully identifies clinically meaningful knee valgus moment patterns: frequency of early peaks reflects sex-specific ACL injury incidence
Background: Biomechanical studies of ACL injury risk factors frequently analyze only a fraction of the relevant data, and typically not in accordance with the injury mechanism. Extracting a peak value within a time series of relevance to ACL injuries is challenging due to differences in the relative timing and size of the peak value of interest. Aims/hypotheses: The aim was to cluster analyze the knee valgus moment time series curve shape in the early stance phase. We hypothesized that 1a) There would be few discrete curve shapes, 1b) there would be a shape reflecting an early peak of the knee valgus moment, 2a) youth athletes of both sexes would show similar frequencies of early peaks, 2b) adolescent girls would have greater early peak frequencies. Methods: N = 213 (39% boys) youth soccer and team handball athletes (phase 1) and N = 35 (45% boys) with 5 year follow-up data (phase 2) were recorded performing a change of direction task with 3D motion analysis and a force plate. The time series of the first 30% of stance phase were cluster analyzed based on Euclidean distances in two steps; shape-based main clusters with a transformed time series, and magnitude based sub-clusters with body weight normalized time series. Group differences (sex, phase) in curve shape frequencies, and shape-magnitude frequencies were tested with chi-squared tests. Results: Six discrete shape-clusters and 14 magnitude based sub-clusters were formed. Phase 1 boys had greater frequency of early peaks than phase 1 girls (38% vs 25% respectively, P < 0.001 for full test). Phase 2 girls had greater frequency of early peaks than phase 2 boys (42% vs 21% respectively, P < 0.001 for full test). Conclusions: Cluster analysis can reveal different patterns of curve shapes in biomechanical data, which likely reflect different movement strategies. The early peak shape is relatable to the ACL injury mechanism as the timing of its peak moment is consistent with the timing of injury. Greater frequency of early peaks demonstrated by Phase 2 girls is consistent with their higher risk of ACL injury in sports.
Behavioral Data Cluster Analysis
<p>The data set includes the main behavioral readouts (mean % prepulse inhibition, social preference index, % alternation in the Y-maze, and total distance moved in the open field) used for cluster analyses in the poly(I:C)-based mouse model of maternal immune activation. </p>
Post-processed datasets for scRNA-seq clustering analysis in PPML-Omics
Open the record for dataset details and reuse information.
Three-Dimensional Segmentation Assisted with Clustering Analysis for Surface and Volume Measurements of Equine Incisor in Multidetector Computed Tomography Data Sets
<p>The dataset contains computed tomography (CT) images of head horses with annotations of 12 segments corresponding to areas with teeth. Imaged animals: 49 horses. Measured animal features such as surface area, and volume are included. The study was supported by the National Science Centre, Poland as a part of the project<br>Miniatura 6 No 2022/06/X/ST6/00431. </p>
FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter. in Termite coprolites (Blattodea: Isoptera) from the Early Cretaceous of eastern Inner Mongolia, Northeast China
FIGURE. Scatter plots (N=200) and linear regression lines of the length and diameter of termite coprolites from the Lower Cretaceous Huolinhe Formation in eastern Inner Mongolia, China. The grey shading represents the 95% confidence interval of linear relationship. Note scatter plots depicting a k-means clustering analysis reveals three groups, indicated by circles of different colours; stars of different colour mean the clusters centroids which are the average length and diameter.
Supplementary material 1 from: Wang J-h, Zheng X-d (2017) Comparison of the genetic relationship between nine Cephalopod species based on cluster analysis of karyotype evolutionary distance. Comparative Cytogenetics 11(3): 477-494. https://doi.org/10.3897/compcytogen.v11i3.12752
Chromosome relative length, supplemental formulae : Explanation note: Chromosome relative length, supplemental formulae and all of the original images are made available under the online digital repository Figshare, and it is free to access, in adherence to the principle of open data, more details in https://figshare.com/s/8d21a0db9ffe1f17d279
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.