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.
2,326
datasets available to search
ShareScore release 0.9.0
Dataset results
2,326 results for “clusters”
Figure S2 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 S2. – Spatial hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
Figure 2 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 2. – Spatial correlation matrix at a 522 km2 scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure 11 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 11. – Scophthalmus rhombus from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.
Figure S5 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 S5. – Spatial-temporal hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
FIGURE 1. Translucent 3D in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 1. Translucent 3D-models of Cambarellus diminutus sample C7tank in combination with 3D-models of calcite clusters, which precipitated inside the carcass during its decomposition in freshwater. 1.1 3D-model without calcite clusters on day 1. 1.2 3D-model on day 2 showing a small amount of calcite clusters inside the cephalothorax and the first tergite. 1.3 3D-model on day 4 showing a lot of calcite clusters inside the antennules, the left major propodus, the rostrum, the cephalothorax, the tergites, the uropods, and the telson. 1.4 3D-model on day 7, showing widespread calcite clusters at the inner side of the carapace of the carcass except the dorsal side of the cephalothorax and the tergites (see also Figure.4.1). 3D-models were reconstructed based on µ-CT data.
FIGURE 6. 3D in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 6. 3D-models and SEM-images of sample C3tank. 6.1 3D-model of the whole crayfish in dorso-lateral view. 6.2 3D-model of the chela of the first left pereiopod in combination with a SEM-image of the calcified muscle of the dactyl. 6.3 SEM-image of a calcified muscle from the inside of the dactyl of the chela of the first left pereiopod. 3Dmodels were reconstructed based on µ-CT data.
FIGURE 5 in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 5. SEM-images of several diverse calcite structures which precipitated inside the carcasses. 5.1 Bispherical structure with mineralized setae and a part of the cuticle layers. 5.2 and 5.3 Spherical structures. 5.4 Elliptical structure which is tapering at the left side. 5.5 Complex structure. 5.6 Bispherical structure with mineralized setae and a part of the cuticle layers.
FIGURE 8 in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 8. Hypothetical scenarios of calcium dissolution and precipitation of calcite clusters inside decomposing crayfish without (8.1-2) and with gastroliths in tank water (8.3-4). 8.1 Low pH-values around and inside the carcass caused by an enzymatic self-digestion (autolysis) and bacterial activity release dissolved calcium ions which migrate out of the carapace into the body cavity and into the environment (red arrows). 8.2 Increase of the pH-value inside the carcass caused by microbial activities during the putrefaction result in a precipitation of calcite clusters at the inner side of the carapace, consisting of previously dissolved calcium ions out of the cuticle layers. 8.3 Low pH-values around and inside the carcass caused by enzymatic self-digestion (autolysis) and bacterial activity resulted in an accumulation of dissolved calcium ions (red arrows). In addition, low pH conditions inside the stomach and decay of the "gastrolith-cavity-membrane" resulted in dissolving calcium ions from the gastroliths. 8.4 An increase of the pHvalue inside the carcass, along the inner side of the carapace, caused by microbial activities during the putrefaction resulted in a precipitation of calcite clusters by previously dissolved calcium ions out of the cuticle layers and gastroliths.
FIGURE 7 in Calcite precipitation forms crystal clusters and muscle mineralization during the decomposition of Cambarellus diminutus (Decapoda: Cambaridae) in freshwater
FIGURE 7. Representative Raman spectra of a mineralized muscle of Cambarellus diminutus (sample C3tank) and observed crystal clusters compared to Raman reference spectra of crystalline calcite and apatite, taken from the RRUFF Raman data base (*R040170, #R060070, Laetsch and Downs, 2006). Raman spectra of the mineralized muscle as well as of the crystal cluster exhibit all main Raman bands typically observed in well crystallized calcite, including the lattice modes, which are absent in amorphous calcium carbonate (Wang et al., 2011).
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West) in Bumps on the back: An unusual morphology in phylogenetically distinct Peridinium aff. cinctum (= Peridinium tuberosum; Peridiniales, Dinophyceae)
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West)
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae) in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae)
Data Sets "Modulation of electrical activity of proteinoid microspheres with chondroitin sulfate clusters"
<p>Data Sets "Modulation of electrical activity of proteinoid microspheres with chondroitin sulfate clusters"</p>
Fig. 2. Hierarchical cluster analysis with 2 in Proliferation of the invasive termite Coptotermes gestroi (Isoptera: Rhinotermitidae) on Grand Cayman and overall termite diversity on the Cayman Islands
Fig. 2. Hierarchical cluster analysis with 2 (a), 3 (b), 4 (c), and 5 (d) clusters for Coptotermes gestroi over Grand Cayman Island.
Рис. 1. Регион иссΛеΑований: A — его поΛожение на карте Восточной Азии; B — общий виΑ Буреинско-Хинганской низменности; C — карта-схема ΑебеΑинского стационара Хинганского заповеΑника. УсΛовные обозначения: I — Хинганский заповеΑник (вкΛючает Αва кΛастера); II — заказник «Ганукан». 1 — Антоновское воΑохраниΛище; 2 — оз. ΔоΛгое; 3 — оз. Гусиное; 4 — оз. Третье ΑебеΑиное Fig. 1. Study region: A — study region on the map of the East Asia; B — Burea-Khingan (Arkhara) lowland; C — Lebedinsky Station. Notes: I — two clusters of Khingan Nature Reserve; II — Ganukan Sanctuary. 1 — Antonovskoye Reservoir; 2 — Dolgoye Lake; 3 — Gusinoye Lake; 4 — Lebedinoye Lake in The results of long-term observation of waterfowl spring migration in Khingan Nature Reserve, Eastern Russia
Рис. 1. Регион иссΛеΑований: A — его поΛожение на карте Восточной Азии; B — общий виΑ Буреинско-Хинганской низменности; C — карта-схема ΑебеΑинского стационара Хинганского заповеΑника. УсΛовные обозначения: I — Хинганский заповеΑник (вкΛючает Αва кΛастера); II — заказник «Ганукан». 1 — Антоновское воΑохраниΛище; 2 — оз. ΔоΛгое; 3 — оз. Гусиное; 4 — оз. Третье ΑебеΑиное Fig. 1. Study region: A — study region on the map of the East Asia; B — Burea-Khingan (Arkhara) lowland; C — Lebedinsky Station. Notes: I — two clusters of Khingan Nature Reserve; II — Ganukan Sanctuary. 1 — Antonovskoye Reservoir; 2 — Dolgoye Lake; 3 — Gusinoye Lake; 4 — Lebedinoye Lake
Turbulence pattern files used for star cluster formation in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** these files are automatically downloaded by Phantom on running the code **</p> <p>The files here are sample cubes containing turbulent driving patterns for the velocity field (vx, vy and vz) used to initiate star cluster formation simulations in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code</p> <p>These can be used to set up initial conditions for a set of simulations similar to those shown in <a href="http://adsabs.harvard.edu/abs/2003MNRAS.339..577B">Bate, Bonnell & Bromm (2003)</a>. The files here are not the original driving patterns used in the BBB03 simulations, but have the same structure, and give a default driving pattern that can be used without having to re-generate the files. A similar set of files was used for the simulations published in <a href="https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..105L">Liptai et al. (2017)</a>.</p> <p>The files were generated with a piece of code written by Volker Bromm, which was originally part of Matthew Bate's sphNG simulation code.</p> <p>For details of how to read these files, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/setup/velfield_fromcubes.f90">src/setup/velfield_fromcubes.f90</a>)</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 4.Performance based on no. of tumor pixel & execution time
<p>In this paper we segmented the brain tumors in axial view of MR images with the help of<br> unsupervised clustering method i.e. K-means clustering. The unsupervised clustering methods gave<br> the better results than traditional method.<br> The performance analysis and comparison is done f on the basis of no. of tumor pixels in<br> segmented brain tumor and the execution time for the same. Regarding the no. of tumor pixels, Kmeans<br> clustering gave a better result than the other methods. The clustering algorithms were tested<br> with a data base of 20 MRI brain images. K-means clustering achieved almost 90%result</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 3:(a) Input MR Image (b) Enhanced Image (c) Segmented Tumor (d) Located brain tumor
<p>Figure 3 shows three different original brain MR images, contrast enhancement of the<br> images, segmented images using K-means algorithm and finally located tumor. Fig 1.4 shows the<br> performance of the unsupervised clustering methods with the no. of tumor pixels and execution<br> time to locate the brain tumor.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 2. Stages of software implementation
<p>The algorithm has two stages, first is pre-processing of given MRI image and after that<br> segmentation and then perform morphological operations.</p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 1. Diagnosis Rate in different Countrie
<p>In MRI images, the amount of data is too much for manual segmentation. The procedure is<br> tedious, time, labor consuming, subjective and requires expertise. This gave way to methods that are<br> computer-aided with user interaction at varying levels. These methods are automatic and objective<br> and the results are highly reproducible. We designed software tool for locating brain tumor, based<br> on unsupervised clustering methods and analyzed its performance</p>
Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>This paper mainly focuses on automated detection of White Matter Lesions of brain using<br> fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the<br> representation of an image into a meaningful image and makes it easier to analyze. As a first step,<br> MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient<br> image enhancement techniques. The pre-processed image is subjected to clustering. The clustering<br> algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC)<br> and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to<br> initialization and are easily trapped in local optima. In order to obtain an optimized result, the<br> clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic<br> global optimization tool which is used in many optimization problems. Figure 2 represents overall<br> process of automatic detection of WMLs of brain. Since MS lesions present different characteristics<br> from lesions in elderly individuals there are many clustering models to determine the accuracy but<br> those methods are not directly applicable to predict the accurate lesions because of the decreased<br> contrast between White Matter and Grey Matter in elderly people. The proposed clustering models<br> are derived by extending the objective functions of FCM and Possibilistic clustering with a<br> Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and<br> is shown to be more robust to noise and other artifacts than competing approaches.</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.