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2,326 results for “clusters”
Figure 6 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 6. – Absolute values of spatial-temporal hierarchical clustering at a 522 km2 scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values in percentage (red). The light grey numbers represent the edge number of the tree.
Figure 1. – Eastern English Channel spatial grid using a in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 1. – Eastern English Channel spatial grid using a triangular mesh at a 522 km2 (A), 782 km2 (B) and 1043 km2 (C) average scale with the geographic coordinates in WGS84 of all the English Channel groundfish hauls survey from 1995 to 2014 (blue). The red points are the vertices used to define the mesh.
Figure 10 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 10. – Alosa sp. from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.
Figure 4 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 4. – Spatial hierarchical clustering at a 522 km2 scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed in percentage (red). The light grey numbers represent the edge number of the tree.
Figure 9 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 9. – Eastern English Channel absolute spatiotemporal community from low (blue) to high (red) median densities of numbers/ km2 in log scale for communities, AST522c1 (A), AST522c2 (B).
Figure 8 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 8. – Eastern English Channel spatiotemporal community from low (blue) to high (red) median densities of numbers/ km2 in log scale for communities ST522c1 (A), ST522c2 (B).
Figure S6 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S6. – Eastern English Channel spatiotemporal community from low (blue) to high (red) median densities of numbers/ km2 in log scale for communities ST782c1 (A), ST782c2 (B), ST1043c1 (C), ST1043c2 (D).
Figure S1 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S1. – Spatial correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
FIGURE 4. 4.1 in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 4. 4.1 Anterior view from the cephalothorax into the abdominal region of a translucent 3D model of Cambarellus diminutus sample C7tank in combination with 3D models of calcite clusters on day 7). 4.2 SEM image of the carapace surface with a plumose seta and some kind of bacteria. 4.3 SEM image of the surface of a calcite cluster with parts of the cuticle layers and bacteria. Abbreviations: A, abdominal segment; B, bacteria; C, cuticle; CC, calcite cluster; In, intestine; P, plumose seta. 3D-models were reconstructed based on µ-CT data.
FIGURE 3 in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 3. Comparison of the maximum volume of precipitated calcite of samples C1tank to C10tank and C1dist. to C10dist. in dependence of the body size. Specimens without gastroliths (which were in the intermoult phase) are marked with a circle. Specimens with gastroliths are marked by using a filled triangle (early premoult phase), filled squares (late premoult phase) and filled circles (postmoult phase). Also shown are regression lines for individuals without gastroliths (R2 = 0.39) and with gastroliths (R2 = 0.08).
Figure 1. Unweighted Pair Group Method with Arithmetic Mean-dendrogram showing clustering pattern for 26 in Intra and inter-monkey transmission of bacteria in wild black capuchins monkeys (Sapajus nigritus): a preliminary study
Figure 1. Unweighted Pair Group Method with Arithmetic Mean-dendrogram showing clustering pattern for 26 genotypes of enterococci isolated from paired oral (O) and rectal (R) swabs of black capuchin monkeys (Sapajus nigritus-SN). UPGMA using Sorensen-Dice coefficients of similarity (> 75%).
Dataset for Evaluating geopolitical gas supply chain security in the EU: A literature-based index and a clustering analysis
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Data used to produce the results for the article "Clustering to characterize extreme marine conditions for the benthic region of Northeastern Pacific continental margin"
<div>This repository contains a zip file with all of the modelled environmental data needed to reproduce the results in Holdsworth et al 2024 (currently in prep, but the link will be included eventually). Regional ocean model simulations of the Canadian Northeastern Pacific ocean were conducted using the <strong>N</strong>ucleus for <strong>E</strong>uropean <strong>M</strong>odelling of the <strong>O</strong>cean v 3.6 (NEMO). The hindcast extended from 1996 to the end of 2019. <br> </div> <div>The variables included are: <div>T - potential temperature </div> <div>S - salinity</div> <div>O2 - dissolved oxygen</div> <div>DIC - dissolved inorganic carbon, </div> <div>ALK- total alkalinity</div> <div>OmegaA - the aragonite saturation state</div> <div>AOU - apparent oxygen utilization</div> </div> <div> </div> <div>processed/daily/clim: Contains the climatological values for relevant variables in each cluster.</div> <div> </div> <div>AOU, T, and OmegaA were used for kmeans clustering in Holdsworth et al. 2024. </div> <div> </div> <div>processed/daily/by_year/by_cluster/n_clusters_6/: Contains the interannual data for each cluster. </div>
Data for cluster generation for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary data for reproducibility. </p>
Binary Approach to Ternary Cluster Expansions: NO–O–Vacancy System on Pt(111)
<p>Cluster expansions (CEs) provide an exact framework for representing the configurational energy of interacting adsorbates at a surface. Coupled with Monte Carlo methods, they can be used to predict both equilibrium and dynamic processes at surfaces. In this work, we propose a three-binary-to-single-ternary (TBST) fitting procedure, in which a ternary CE is approximated as a linear combination of the three binary CEs (O–vac, NO–vac, and NO–O) obtained by fitting to the three binary legs. We first construct a full ternary CE by fitting to a database of density functional theory (DFT) computed energies of configurations across a full range of adsorbate configurations and then construct a second ternary using the TBST approach. We compare two approaches for the NO–O–vacancy system on the (111) surface of Pt, a system of relevance to the catalytic oxidation of NO. We find that the TBST model matches the ternary CE to within 0.018 eV/site across a wide range of configurations. Further, surface coverages and NO oxidation rates extracted from Monte Carlo simulations show that the two models are qualitatively consistent over the range of conditions of practical interest.</p>
Explaining the luminosity spread in young clusters: proto and pre-main sequence stellar evolution in a molecular cloud environment
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2018MNRAS.474.1176J">Jensen & Haugbølle (2018)</a>. MESA version 8845.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stx2844">10.1093/mnras/stx2844</a></p>
Simulated function placements in predefined mixed cloud-edge clusters
<p>Experiments in a simulation environment to evaluate the placement quality of the Skippy Scheduler, an optimized container scheduler for serverless edge computing in Kubernetes.</p>
Data clustering sample 0107
<p>Telegram's contest of data clustering. First sample of data.</p>
Dataset for molecular dynamics simulations of coalescence of Pd and AuPd clusters
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SpatialCVGAE: Spatial Domain Identification via Consensus Clustering Integrated Variational Graph Autoencoder
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ScienceDex guides
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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.