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2,326 results for “clusters”
FIGURE 3 in Pinanga gruezoi (Arecaceae), a new slender clustering palm from the Philippines with notes on an amended description of P. samarana
FIGURE 3. The distribution map of Pinanga gruezoi Adorador & Fernando and closely related slender clustering species based on herbarium records. The gray-shaded area approximates the c. 150 meters below sea level bathymetry line which shows several Pleistocene Aggregated Island Complexes (PAIC) (Heaney 1986).
Online material to publication "Exploring the dynamical evolution of Cepheid multiplicity in star clusters and its implications for B-star multiplicity at birth"
<p>The description of the movie can be found in the file movie_description.pdf.</p> <p>The full paper can be found here: https://ui.adsabs.harvard.edu/abs/2024arXiv240907530D/abstract</p>
Database of clustered vOTUs recovered from a viromics prescribed burn study of forest soil
<p>Database of dereplicated viral operational taxonomic units (vOTUs) recovered from a viromics (viral-size fraction metagenomics) prescribed burn study of forest soil</p>
Supplemental Online Material for "Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution": Interactive Figures
<p>This repository contains the interactive figures accompanying the manuscript "<em>Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution</em>". These interactive visualizations provide a dynamic exploration of some of the data and results discussed in the paper. The following interactive figures are included:</p> <ul> <li><code>Figure-1.html</code>: Interactive version of Figure 1 from the paper, showing a time series heatmap of the 14-day simple moving average of reported COVID-19 cases for each of Europe's 298 NUTS 2 regions, from 02/10/2020 - 07/12/2020.</li> <li><code>Figure-2.html</code>: Interactive version of Figure 2, which visualizes two heatmap dendrograms showcasing the pairwise distance matrices and hierarchical clustering results for (a) the Euclidean distance and (b) the Dynamic Time Warping distance.</li> <li><code>cluster_maps.html</code>: Interactive cluster maps, allowing for detailed exploration of the clusters discussed in the paper. Users can select between different distance metrics (Euclidean or Dynamic Time Warping) and adjust the number of clusters to visualize, providing greater flexibility in exploring the clustering patterns. This figure is not included as a static figure in the paper but is provided merely as supplemental online material.</li> </ul> <p>These interactive figures are best viewed using a modern web browser (e.g., Chrome, Firefox ...). For all visualizations, upon hovering over the figure, a label appears displaying the NUTS 2 region and the country it is in, providing additional geographic context.</p>
Source data for Figures and Tables in "Photoinduced hydrogen dissociation in thymine predicted by coupled cluster theory"
<p>Source data for figures and tables in "Unexpected hydrogen dissociation in thymine predicted by coupled cluster theory".<br><br>This work has received funding from the Norwegian Research Council through FRINATEK project 275506, the European Research Council (ERC)<br>under the European Union’s Horizon 2020 Research and Innovation Program<br>(Grant No.~101020016), the AMOS program within the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division. <br>We acknowledge computing resources through UNINETT Sigma2--the National Infrastructure for High Performance Computing and Data Storage in Norway, project NN2962k.</p>
Sub-functionalization and epigenetic regulation of a biosynthetic gene cluster in Solanaceae
<p>Datasets for the code analysis in <em>Priego-Cubero, Knoch et al., 2024 </em>(https://doi.org/10.1101/2024.10.02.615186)</p>
Data for "Imaging magnetic spiral phases, skyrmion clusters, and skyrmion displacements at the surface of bulk Cu2OSeO3"
<div> <p>Data from all the figures in the main text of "Imaging magnetic spiral phases, skyrmion clusters, and skyrmion displacements at the surface of bulk Cu2OSeO3". All files contain the data displayed in the paper.</p> <p> </p> </div>
LJ_clustering_kernels
Open the record for dataset details and reuse information.
Dataset for the article "Transforming ceria into 2-dimensional clusters enhances catalytic activity"
<p>CONTCAR files of the structures discussed in the article "Transforming ceria into 2-dimensional clusters enhances catalytic activity"</p>
Online Tables for the thesis "Clustering approaches for patient stratification in psychiatry" by Jonas Hagenberg
<p>This repository contains the online tables that accompany my thesis "Clustering approaches for patient stratification in psychiatry".</p> <p>In the following, I list the description of all tables:</p> <p> </p> <div> <div> <p>1: Information about somatic diseases and medication separated by status (participants labeled as cases and used in the clustering as well as controls without a DSM-IV diagnosis). N data denotes the number of individuals who had the information available. The medication information was not available for the OPTIMA cohort. N denotes the individuals who were affected by the disease or took the medication.</p> <p>2: Missingness and coefficient of variation information of the immune marker data for 237 participants used in the clustering and 36 controls without a DSM-IV diagnosis. The coefficient of variation was calculated from three internal controls that were run in duplicates.</p> <p>3: Variable importance of all variables the initial clustering. The variable importance is calculated as the F-value from an ANOVA model with the clusters as independent variables. The variable importance cannot be interpreted as a p-value as the variables were already used in the clustering, thereby inflating the p-values. Full version of supplementary table 3.</p> <p>4: Variable importance of all gene sets in the initial clustering. Full version of supplementary table 8.</p> <p>5: Variable importance of all variables the secondary analysis corrected for age, sex and BMI. Full version of supplementary table 9.</p> <p>6: Variable importance of all variables the exploratory analysis including cell type proportions. Full version of supplementary table 10.</p> <p>7: Variable importance of all gene sets in the exploratory analysis including cell type proportions. Full version of supplementary table 11.</p> <p>8: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP, IL-6 and BMI. Full version of supplementary table 12.</p> <p>9: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP, IL-6 and BMI. Full version of supplementary table 13.</p> <p>10: Enriched hallmark gene sets with regard to CRP calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>11: Enriched hallmark gene sets with regard to IL-6 calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>12: Enriched hallmark gene sets with regard to BMI calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>13: Enriched GO biological pathway gene sets with regard to CRP calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type. Full version of supplementary table 15.</p> <p>14: Enriched GO biological pathway gene sets with regard to IL-6 calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>15: Enriched GO biological pathway gene sets with regard to BMI calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type.</p> <p>16: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and IL-6.</p> <p>17: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and IL-6.</p> <p>18: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and BMI.</p> <p>19: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and BMI.</p> <p>20: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained IL-6 and BMI.</p> <p>21: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained IL-6 and BMI.</p> <p>22: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained only CRP.</p> <p>23: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained only IL-6.</p> <p>24: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained only BMI.</p> <p>25: Variable importance of initial multi-omics clustering of the DEGs identified in the single cell data set.</p> </div> </div>
Dataset for: Electron attachment to CCl3COOH molecule and clusters
<p>Raw dataset for the publication with excel assignmnets file.</p>
Data from: Phylogeography of var gene repertoires reveals fine-scale geospatial clustering of Plasmodium falciparum populations in a highly endemic area
Plasmodium falciparum malaria is a major global health problem that is being targeted for progressive elimination. Knowledge of local disease transmission patterns in endemic countries is critical to these elimination efforts. To investigate fine-scale patterns of malaria transmission, we have compared repertoires of rapidly evolving var genes in a highly endemic area. A total of 3680 high quality DBLα sequences were obtained from 68 P. falciparum isolates from ten villages spread over two distinct catchment areas on the north coast of Papua New Guinea (PNG). Modeling of the extent of var gene diversity in the two parasite populations predicts more than twice as many var gene alleles circulating within each catchment (Mugil=906; Wosera=1094) than previously recognized in PNG (Amele=369). In addition, there were limited levels of var gene sharing between populations, consistent with local parasite population structure. Phylogeographic analyses demonstrate that while neutrally evolving microsatellite markers identified population structure only at the catchment level, var gene repertoires reveal further fine-scale geospatial clustering of parasite isolates. The clustering of parasite isolates by village in Mugil, but not in Wosera was consistent with the physical and cultural isolation of the human populations in the two catchments. The study highlights the micro-heterogeneity of P. falciparum transmission in highly endemic areas and demonstrates the potential of var genes as markers of local patterns of parasite population structure.
Microsatellite genotypes, cluster membership and metadata of Central European wolves (Canis lupus)
<p class="Normalny1">Local extinction and recolonization events can shape genetic structure of subdivided animal populations. The gray wolf (<i>Canis lupus</i>) was extirpated from most of Europe, but recently recolonized big part of its historical range. An exceptionally dynamic expansion of wolf population is observed in the western part of the Great European Plain. Nonetheless, genetic consequences of this process have not yet been fully understood. We aimed to assess genetic diversity of this recently established wolf population in Western Poland (WPL), determine its origin and provide novel data regarding the population genetic structure of the grey wolf in Central Europe. We utilized both spatially explicit and non-explicit Bayesian clustering approaches, as well as a model-independent, multivariate method DAPC, to infer genetic structure in large dataset of wolf microsatellite genotypes. To put the patterns observed in studied population into a broader biogeographic context we also analyzed a mtDNA control region fragment widely used in previous studies.</p> <p>In comparison to a source population, we found slightly reduced allelic richness and heterozygosity in the newly recolonized areas west of the Vistula river. We discovered relatively strong west-east structuring in lowland wolves, probably reflecting founder-flush and allele surfing during range expansion, resulting in clear distinction of WPL, eastern lowland and Carpathian genetic groups. Interestingly, wolves from recently recolonized mountainous areas (Sudetes Mts, SW Poland) clustered together with lowland, but not Carpathian wolf populations. We also identified an area in Central Poland that seems to be a melting pot of western, lowland eastern and Carpathian wolves. We conclude that the process of dynamic recolonization of Central European lowlands lead to the formation of a new, genetically distinct wolf population. Together with the settlement and establishment of packs in mountains by lowland wolves and vice versa, it suggests that demographic dynamics and possibly anthropogenic barriers rather than ecological factors (e.g. natal habitat-biased dispersal patterns) shape the current wolf gene<span>tic structure in Central Europe.</span></p>
Open Datasets - available file for Design of Experimental (RSM) model, ANOVA table, SEM, VSM and Zeta potecial data for the nanocrystal clusters based on iron oxide.
<p>The open-access data from the statistical model for the design of experiment optimization and as well as the experimental data are available for the community. The data are based on an obtained results published in the article: https://www.mdpi.com/2079-4991/11/2/360</p>
FIGURE 2. Calamus flavinervis. A. Leaf sheath, with proximal pinnae oriented towards the sheath. B. Leaf, showing clustered pinnae. C in Four new species of Calamus (Arecaceae) from Vietnam
FIGURE 2. Calamus flavinervis. A. Leaf sheath, with proximal pinnae oriented towards the sheath. B. Leaf, showing clustered pinnae. C. Distalmost pinnae, showing cluster of pinnae with the distalmost pair oriented away from the sheath and the adjacent pair oriented at a 45º angle to the rachis. D. Partial pistillate inflorescence. (A–D from Henderson & Nguyen Quoc Dung 3869).
FIGURE 1. Miconia povedae Kriebel & Oviedo. A. Fertile branch and adaxial leaf surfaces. B. Abaxial leaf surfaces. C. Inflorescence showing clustered sessile flowers. D in A new species of Miconia from the remaining primary forest at Las Cruces Biological Station in Costa Rica
FIGURE 1. Miconia povedae Kriebel & Oviedo. A. Fertile branch and adaxial leaf surfaces. B. Abaxial leaf surfaces. C. Inflorescence showing clustered sessile flowers. D. Close up of the flower. E. Flower cluster terminating an inflorescence branch. F. Flower bud. G. View of the hypanthium from outside, note the acute calyx teeth on the rounded calyx lobes. H. Longitudinal cut of a flower with petals, stamens, and style removed. I. Ovary apex with minute glands. J. Petal. K. Lateral view of the stamen. L. Style. Photos: A–D by F. Oviedo (HLDG); E–L by R. Kriebel (NY). All photos of the type.
The role of collision speed, cloud density, and turbulence in the formation of young massive clusters via cloud-cloud collisions
<p>This repository contains the essential unprocessed data to recreate the results in this paper (10.1093/mnras/staa2857; <a href="https://ui.adsabs.harvard.edu/abs/2020MNRAS.499.1099L/abstract">Liow and Dobbs 2020</a>), which contains the following directories:</p> <p><strong>initial_conditions </strong></p> <ul> <li>This directory contains the initial condition dumpfiles. There are 24 main models in this paper. They are named XY_abc, where 'X' is either 'B', 'C', or 'D', representing low, standard, and high initial cloud density respectively; 'Y' is either '1' or '2', representing low and high turbulence; 'abc' is a three-digit number representing the collision Mach number.</li> <li>Additional models in appendices have prefixes attached to the filenames. The prefixes are either 'R' (resolution test models; Appendix A), 'T' (random turbulent seeds models; Appendix B), or 'M' (massive clouds models; Appendix D). The value after 'R' shows the number of SPH particles used, a.k.a. resolution.</li> <li>The dumpfiles were produced using PHANTOM v1.3.0 (Price et al. 2018; https://phantomsph.bitbucket.io/) in binary format and can be visualised using SPLASH v2.8.0 (Price 2007; https://splash-viz.readthedocs.io/en/latest/intro.html) with code units 100,000 MSun and 10 pc. </li> </ul> <p><strong>ten_percents</strong> </p> <ul> <li>This directory contains the dumpfiles at t<sub>10%</sub>, the time when 10% of the gas mass is converted to sink mass. Most analysis in the paper was done at this snapshot. They can be visualised using SPLASH similar to the initial condition dumpfiles.</li> </ul> <p><strong>ev_files</strong></p> <ul> <li>This directory contains ASCII files that track the global properties (energies, total linear/angular momentum, etc) throughout the simulation. The suffixes '_0?' right before '.ev', not relevant to the analysis, show the number of times the simulations were continued. </li> </ul> <p><strong>all_turbulent_fields</strong></p> <ul> <li>This directory contains turbulent fields used in most models ('turb001' and 'turb002') and in the random turbulent seed models (otherwise), written in binary format. See 'README' for the random seeds used, and 'velfield.f' for the code to generate the turbulent fields.</li> </ul> <p>Not included in this repository are the intermediate dumpfiles and the sink particle data, but the data shown in this repository is sufficient to reproduce the time and snapshots in the paper. Post-processed data (e.g. global star formation rates, sink particle information at t<sub>10% </sub>and other specific snapshots) and results are not included here to preserve data authenticity.</p> <p>Readers are welcome to contact the authors for the post-processed data and more information.</p>
Cluster configurations of a generalized Deffuant model on hypergraph ensembles
<p>## Data</p> <p>For each measured combination of the confidence and system size, there is one gzipped<br> file. For different ensembles, we collected data in different ranges and quality.<br> The paramters are:</p> <p>* Number of samples `m` per parameter combination<br> * Range `r` of confidences epsilon<br> * Distances `d` between values of epsilon (basically the resolution of the data)<br> * Largest size `N_max`</p> <p>The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where<br> `{N}` signals the system size of the simulation and `{epsilon}` is the confidence<br> value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`).<br> The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two).</p> <p>We present the data for each ensemble in one folder (after unpacking the tar archive).<br> Note that some parameter values are missing, if they did not converge in reasonable time.</p> <p><br> * Barabasi Albert with a mean degree of `c=9` and hyperedge size of `k=3`: `ba_c9_k3`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Barabasi Albert with a mean degree of `c=10` and hyperedge size of `k=5`: `ba_c10_k5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=3`: `er_c10_k3`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=4`: `er_c10_k4`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=5`: `er_c10_k5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=10` hyperedge size `k=6`: `er_c10_k6`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 65536`<br> * Erdos-Renyi with a mean degree of `c=150` hyperedge size `k=6`: `er_c150_k6`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`<br> * Erdos-Renyi with a mean degree of `c_3=5` and `c_5=5`: `er_c3_5_c5_5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`<br> * Erdos-Renyi with a mean degree of `c_3=30/8` and `c_5=50/8`: `er_c3_375_c5_625`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`<br> * Lattice a mean degree of `c=12` hyperedge size `k=3`: `lat_c12_k3`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32761`<br> * Lattice a mean degree of `c=15` hyperedge size `k=5`: `lat_c15_k5`<br> * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 16384`</p> <p>## Data format</p> <p>Each final state is encoded as three lines:</p> <p>* The convergence time is a single integer with a line prefix '# sweeps: '<br> * The positions of all clusters in opinion space with a line prefix '# ' (unsorted)<br> * The number of agents in each of the clusters without a line prefix</p> <p><br> ## Python example for reading the format</p> <p>An example script, which visualizes the S vs eps graph for the largest size of the `er_c10_k3`<br> case, with a function to read this format is given in `example.py`.</p>
Analysis of RNA polymerase II clusters in fixed embryos injected with antigen-binding fragments
<p>Data and scripts for the analysis of RNA polymerase II clusters. The data set includes data obtained from fixed zebrafish embryos injected with antigen-binding fragments, CellProfiler pipelines for the initial analysis of images are provided, along with Python scripts to export data into CSV format and execute downstream analysis.</p>
Example micrographs of RNA polymerase II localization to the zebrafish microRNA miR-430 cluster
<p>Contains the raw images and MatLab scripts to show localization of recruited (Ser5P) and elongating (Ser2P) RNA polymerase II to the zebrafish microRNA miR-430 cluster. Embryos were fixed in the oblong stage of development, labeled by immunofluorescence, and images were recorded with an instant-SIM microscope.</p> <p>To produce example images, first run the extraction script, then the example images script. You need to add the bfmatlab importer function from OME for data import before running the extraction script. Otherwise, extracted data are also provided with this repository,</p>
ScienceDex guides
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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.