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2,326 results for “clusters”
Clustering dark energy imprints on cosmological observables of the gravitational field
<p>This file contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clustering dark energy imprints on cosmological observables of the gravitational field" (<a href="https://arxiv.org/abs/2007.04968">https://arxiv.org/abs/2007.04968</a>).</p> <p><br> This paper has also been published in MNRAS which can be accessed at this link: <a href="https://doi.org/10.1093/mnras/staa3589">https://doi.org/10.1093/mnras/staa3589</a>.</p> <p>Directories</p> <ul> <li><strong>codes</strong>: This directory includes the codes to generate and post-process the simulation data.</li> <li><strong>data</strong>: This directory contains the data generated as part of the project.</li> <li><strong>jupyter_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper.</li> <li><strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>jupyter_notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "<strong>data</strong>" directory to access the simulation data.</li> </ol> <p><br> If you have any feedback or request feel free to email farbod.hassani@gmail.com</p>
DATASET - Automated grain sizing from UAV imagery of a gravel-bed river: benchmarking of three object-based methods and analysis of particle-size clustering
<p>Dataset used to compute the grain size distributions from in-field line sampling and digitally on orthoimages with automated methodologies and by manual labelling. It also contains the data used to produce spatial statistics.</p>
Microscopy-based assessment of H3K27-acetylation at RNA polymerase II clusters during early stages of zebrafish embryo development
<p>This repository contains the data and MatLab analysis scripts of the analysis of histone 3 lysine 27 acetylation (H3K27ac) at RNA polymerase II clusters over the course of zygotic genome activation and gastrulation of zebrafish embryos. Samples were prepared and images were recorded by Marcel Sobucki in Lennart Hilbert's laboratory, images were analyzed by Lennart Hilbert.</p> <p>To analyse date data, the raw image data are first extracted into MatLab-native files using the <a href="https://zenodo.org/api/files/2f2192f4-a05c-4969-ba0a-4f235b837709/MultiPosition_extraction_nd2.m">MultiPosition_extraction_nd2.m</a> script. The actual analysis is then carried out using the <a href="https://zenodo.org/api/files/2f2192f4-a05c-4969-ba0a-4f235b837709/ClusterAnalysis.m">ClusterAnalysis.m</a> script. Example microscopy images were produced using the <a href="https://zenodo.org/api/files/2f2192f4-a05c-4969-ba0a-4f235b837709/ExampleImages.m">ExampleImages.m</a> script. The extracted data can be reviewed using the <a href="https://zenodo.org/api/files/2f2192f4-a05c-4969-ba0a-4f235b837709/ReviewExtractedStacks.m">ReviewExtractedStacks.m</a> script.</p> <p>Additional data needed to be stored in an extra repository due to size limitations: <a href="https://doi.org/10.5281/zenodo.8028508">https://doi.org/10.5281/zenodo.8028508</a></p>
Microscopy-based assessment of RNA polymerase II clusters in zebrafish embryos treated with JQ1 and flavopiridol (additional data)
<p>This repository contains the data and MatLab analysis scripts of the analysis of histone 3 lysine 27 acetylation (H3K27ac) at RNA polymerase II clusters in zebrafish embryos treated with the inhibitors JQ1 or flavopiridol. Samples were prepared and images were recorded by Süheyla Eroglu-Kayikci and Elisa Kämmer in Lennart Hilbert's laboratory, images were analyzed by Lennart Hilbert.</p> <p>This repository contains additional data to the following main repository: <a href="https://doi.org/10.5281/zenodo.8019764">https://doi.org/10.5281/zenodo.8019764</a></p>
Microscopy-based assessment of RNA polymerase II clusters in zebrafish embryos treated with JQ1 and flavopiridol
<p>This repository contains the data and MatLab analysis scripts of the analysis of histone 3 lysine 27 acetylation (H3K27ac) at RNA polymerase II clusters in zebrafish embryos treated with the inhibitors JQ1 or flavopiridol. Samples were prepared and images were recorded by Süheyla Eroglu-Kayikci and Elisa Kämmer in Lennart Hilbert's laboratory, images were analyzed by Lennart Hilbert.</p> <p>To analyse date data, the raw image data are first extracted into MatLab-native files using the <a href="https://zenodo.org/api/files/2f2192f4-a05c-4969-ba0a-4f235b837709/MultiPosition_extraction_nd2.m">MultiPosition_extraction_nd2.m</a> script. The actual analysis is then carried out using the <a href="https://zenodo.org/api/files/2f2192f4-a05c-4969-ba0a-4f235b837709/ClusterAnalysis.m">ClusterAnalysis.m</a> script. Example microscopy images were produced using the <a href="https://zenodo.org/api/files/2f2192f4-a05c-4969-ba0a-4f235b837709/ExampleImages.m">ExampleImages.m</a> script. The extracted data can be reviewed using the <a href="https://zenodo.org/api/files/2f2192f4-a05c-4969-ba0a-4f235b837709/ReviewExtractedStacks.m">ReviewExtractedStacks.m</a> script.</p> <p>Additional data, stored in separate repository due to size limits: <a href="https://doi.org/10.5281/zenodo.8023349">https://doi.org/10.5281/zenodo.8023349</a></p>
Mongodb Database dump for TOSEM submission "Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement"
<p>The Mongodb Database dump for TOSEM submission "Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement"</p>
Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering (data)
<p>MIBI-TOF data for lymph node dataset reported in Liu et al., Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering</p> <p>1. mibi_single_channel_tifs.zip: Single-channel MIBI-TOF images</p> <p>Folders are labeled according to the field-of-view (FOV) number. Each folder contains single-channel TIFFs for each marker in the panel. Images are 1024x1024 pixels, 500 um. See paper for details.</p> <p>2. segmentation.zip: Segmentation output of MIBI-TOF images</p> <p>Cell segmentation was performed using Mesmer (Greenwald NF, Nature Biotechnology 2021). Output of Mesmer that delineates the single cells in each of the images is included.</p> <p>3. source_data.zip: Source data files for figures</p> <ul> <li>pixel_ccs_allpreprocessing.csv: Cluster consistency score (CCS) for all pixels using all preprocessing steps, related to Fig. 2d-f, Supp. Fig. 4,5,9,10</li> <li>pixel_ccs_nopixelnorm.csv: CCS for all pixels where pixel normalization was left out, related to Fig. 2f, Supp. Fig. 6</li> <li>pixel_ccs_nochannelnorm.csv: CCS for all pixels where channel normalization was left out, related to Fig. 2f, Supp. Fig. 8</li> <li>pixel_ccs_passes1.csv: CCS for all pixels where 1 pass was used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_passes100.csv: CCS for all pixels where 100 passes were used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_sigma0.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma1.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma3.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma0_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma1_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma2_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 2 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma3_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_nodes15.csv: CCS for all pixels where 15 nodes were used for SOM training, related to Supp. Fig. 9e</li> <li>pixel_ccs_threshold80.csv: CCS for all pixels where a threshold of 80% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_ccs_threshold98.csv: CCS for all pixels where a threshold of 98% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_info_comparison_table.csv: Number of pixels that were assigned to a cluster outside of cell segmentation masks, related to Fig. 3d</li> <li>single_cell_pixel_composition_table.csv: Pixel composition information for each single cell, related to Fig. 5, Supp. Fig 16</li> <li>single_cell_integrated_expression_table.csv: Integrated expression per cell, output by Mesmer, related to Fig. 5, Supp. Fig. 16</li> <li>cell_silhouette_scores.csv: Silhouette scores for comparing integrated expression and pixel composition, related to Fig. 5d</li> <li>cell_silhouette_scores_undefinedremoved.csv: Silhouette scores for comparing integrated expression and pixel composition where undefined cells were removed, related to Fig. 16g</li> <li>cell_silhouette_scores_preprocessed.csv: Silhouette scores for comparing integrated expression and pixel composition where pixels were preprocessed before integrating expression, related to Fig. 17d</li> <li>cell_silhouette_scores_ilastik_cellprofiler.csv: Silhouette scores for comparing integrated expression and pixel composition where segmentation masks were obtained using Ilastik/CellProfiler, related to Fig. 18b</li> <li>cell_ccs_pixel_composition.csv: CCS for all cells using pixel composition for clustering, related to Supp. Fig. 16e, 17c</li> <li>cell_ccs_integrated_expression.csv: CCS for all cells using integrated expression for clustering, related to Supp. Fig 16e-f</li> <li>cell_ccs_integrated_expression_preprocessed.csv: CCS for all cells using integrated expression for clustering where data was preprocessed before integrating, related to Supp. Fig 17c</li> <li>cytof_ccs.csv: CCS of the CyTOF dataset used as a benchmark, related to Supp. Fig. 4c,d</li> <li>scrnaseq_ccs.csv: CCS of the scRNA-seq dataset used as a benchmark, related to Supp. Fig. 4c,e</li> <li>pixel_phenotype_maps: TIFFs where pixel value corresponds to pixel cluster number as reported in the paper</li> <li>cell_phenotype_maps: TIFFs where pixel value corresponds to cell cluster number as reported in the paper</li> <li>runtime_analysis_pixel.csv: Runtime analysis of pixel clustering in Pixie, related to Supp. Fig. 22a</li> <li>runtime_analysis_cell.csv: Runtime analysis of cell clustering in Pixie, related to Supp. Fig. 22b</li> <li>runtime_clustering_algorithm.csv: Runtime analysis of different clustering algorithms, related to Supp. Fig. 22c</li> </ul>
Repository for 'Surface-induced phase separation of reconstituted nascent integrin clusters on lipid membranes'
<p>Surface-induced phase separation of reconstituted nascent integrin clusters on lipid membranes (PNAS)</p> <p>The structure of this repository is as follows:</p> <p>1. FRAP_curves.zip -- contains FRAP curves </p> <ul> <li>FRAP_curves_for_fig2F_figS5</li> <li>FRAP_curves_for_fig2G</li> <li>FRAP_curves_for_fig2H</li> <li>FRAP_curves_for_fig2I</li> <li>FRAP_curves_for_fig2J</li> <li>FRAP_curves_for_fig2K</li> <li>FRAP_curves_for_figS8&E</li> </ul> <p>2. Images.zip -- contains confocal images epifluorescence images</p> <ul> <li>Images_for_fig1G_fig1H</li> <li>Images_for_fig3D</li> <li>Images_for_figS1</li> <li>Images_for_figS2C</li> <li>Images_for_figS3C</li> <li>Images_for_figS6</li> </ul> <p>3. Source_data.xlsx -- contains the data for plotting the Figures</p>
Orientation-dependent propulsion of active Brownian spheres: from self-advection to programmable cluster shapes
<p>Supplemental data for the following manuscript: Stephan Bröker, Jens Bickmann, Michael te Vrugt, Michael E. Cates, Raphael Wittkowski, "Orientation-dependent propulsion of active Brownian spheres: from self-advection to programmable cluster shapes".</p>
Clusternets: A deep learning approach to probe clustering dark energy
<p>This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clusternets: A deep learning approach to probe clustering dark energy"</p> <p>Directories</p> <ul> <li><strong>Codes</strong>: This directory contains different codes used to generate and post-process the simulation data, including k-evolution, gevolution, Pylians, CLASS, and LATfield2.</li> <li><strong>Final figures and data</strong>: This directory includes the data for the power spectra, simulation settings and Jupyter notebooks to produce the figures.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>Final figures and data</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>Codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Use the simulation setting files in the "<strong>Final figures and data</strong>" directory to replicate the simulations.</li> </ol> <p>Note: Some of the simulations may require high computational resources and may take a significant amount of time to run.<br> <br> If you have any feedback or request feel free to email chgeniamirsbu@gmail.com and farbod.hassani@gmail.com</p>
A real dataset for evaluating hybrid clustering algorithm
<p>This is a real dataset for testing our hybrid clustering algorithm, and details can be found in our manuscript.</p>
Datasets S1 ~ S6 for evaluating hybrid clustering algorithm
<p>These datasets are used to evaluate our hybrid clustering algorithm. For details of our algorithm, please refer to https://github.com/junhaiqi/Hybrid_clustering.git.</p>
Ultrahigh Mass Activity Pt Entities Consisting of Pt Single atoms, Clusters, and Nanoparticles for Improved Hydrogen Evolution Reaction
<p>X-ray absorption spectroscopy data in an Athena project file. </p>
K-means clustering of the DEMIX data set
The DEMIX database (Guth, 2023) contains statistics from 6 test 1 arc second DEMs (ALOS, ASTER, CopDEM, FABDEM, NASADEM, and SRTM) compared to high resolution reference DEMs. The database contains 236 DEMIX tiles (Guth and others, 2023) and has 7 tile characteristics for each tile. A K-means clustering of the database, and an additional set of 4 land cover and landform classifications for the 236 DEMIX tiles produced variable numbers of clusters. This data set includes five databases including the clustering results, a figure showing the scatterplots showing the relations among the tile characteristics in the database, and map showing the location of the clusters. References: Guth, P. L., 2023. DEMIX GIS Database Version 2 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8062008 Guth, Peter L., Peter Strobl, Kevin Gross, & Serge Riazanoff. (2023). DEMIX 10k Tile Data Set (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7504791
Supplementary Material: A novel coupled-cluster singles and doubles implementation that combines the exploitation of point-group symmetry and Cholesky decomposition of the two-electron integrals
<p>This dataset contains all molecular geometries (in Angstrom) used to test the CD-CCSD implementation.</p>
Data from The Kinematics, Metallicities, and Orbits of Six Recently Discovered Galactic Star Clusters with Magellan/M2FS Spectroscopy
<p>Pace, Koposov, Walker et al 2023</p> <p>Catalog level data products from Magellan/M2FS and AAT/AAOmega spectroscopy (fits, npy formats) including membership information</p> <p>Summary table (Table 2) in machine readable formats (npy, fits, and csv formats)</p> <p>Updates (v2): The original M2FS sample had a bug in the application of the systematic errors. All errors have been corrected. The summary table has been updated accordingly. </p>
FIGURE 25. Zombia antillarum. A. Habit, with clustered stems. B. Leaf segments. C in A revision of Coccothrinax, Hemithrinax, Leucothrinax, Thrinax, and Zombia (Arecaceae)
FIGURE 25. Zombia antillarum. A. Habit, with clustered stems. B. Leaf segments. C. Stout, woody leaf sheath fibers forming rings of horizontal or reflexed spines at the sheath apex.
Effect of cyclic ageing on the early-stage clustering in Al-Zn-Mg(-Cu) alloys
<p>Raw data of all tensile tests has been put up along with the pos and range files for all the datasets that have been used in the paper. Further analysis can be done using IVAS software (CAMECA) for the results presented.</p>
STM time laps of Co clusters on graphene during CO2 exposure
<p>Co clusters deposited on graphene/Ni(100) are exposed to CO2 and monitored by STM.</p>
Unsupervised segmentation and clustering time series approach to Southern Africa rainfall regime changes.
<p>The datasets were sourced from Southern Africa countries namely Malawi, Mozambique, South Africa, and Zimbabwe where recorded historical rainfall data was available. In each country different meteorological stations were selected and were then categorized into coastal, sub-humid and semi-arid zones based on agro-ecological regions. The following information was collected for each meteorological service station; country, meteorological service station name, agroecological region, year , recorded rainfall, and stations geographical coordinates. </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.