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2,326 results for “clusters”
The Unified Cluster Catalogue
<p><a href="https://ucc.ar">The Unified Cluster Catalogue (UCC)</a></p> <p>See README.txt </p>
Mp4-Version of the supplementary Movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"
<p>Videos in 'mp4'-format of the 8 supplementary movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"</p>
Network of hotspot interactions cluster tau amyloid folds
<p>Datafiles associated with <strong>Network of hotspot interactions cluster tau amyloid folds.</strong></p> <p> </p> <p>The alascan files contain the averaged and raw outputs of the <em>in silico </em>alanine scan conducted on tau fibrils. The combo aggregation file contains ThT fluorescence data from the VQIVYK/VEVKSE alanine mutants and coaggregation experiments. The incorporation_vs_insilico file contains the results of the alanine scan top and bottom hits compared to the results of the incorporation experiment of alanine mutants on that position.</p> <p> </p> <p>Version 2: Files added providing data for how edge and center chains contribute to the total energetics, as well as a per-layer energy contribution to total deltaREU in the alanine scan of the PHF fibril.</p>
Dataset for Fast Li-ion Storage and Dynamics in TiO2 Nanoparticle Clusters Probed by Smart Scanning Electrochemical Cell Microscopy
<p>This dataset provides the raw data to the manuscript </p> <p><strong>"Fast Li-ion Storage and Dynamics in TiO<sub>2</sub> Nanoparticle Clusters Probed by Smart Scanning Electrochemical Cell Microscopy"</strong> published in Angewandte Chemie International Edition.</p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Powder XRD data for the commercial anatase TiO<sub>2</sub>.</li> <li>Cyclic voltammetry data from SECCM measurements.</li> <li>SEM images of the TiO<sub>2</sub> nanoparticles after SECCM tip landing and electrochemical measurement.</li> </ul>
Reproduction Package for the paper "The early evolution of young massive clusters. The kinematic history of NGC6611 / M16"
<p>This is a comprehensive reproduction package for the paper "The early evolution of young massive clusters. The kinematic history of NGC6611 / M16" by <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220708452S/abstract">Stoop et al. (2022)</a>.</p> <p>This reproduction package aims for open science, with the internal API designation of 'Gold'.</p> <p>This package aims to provide the raw data, and the software and scripts to produce the intermediate and end products. Software and scripts are also available to produce the figures and tables in the paper.</p> <p> </p>
AntiRef: reference clusters of human antibody sequences
<p><strong>Motivation:</strong> Biases in the human antibody repertoire result in publicly available antibody sequence datasets containing many duplicate or highly similar sequences. These redundant sequences are a barrier to rapid similarity searches and reduce the efficiency with which these datasets can be used to train statistical or machine learning models of human antibodies. Identity-based clustering provides a solution, however, the extremely large size of available antibody repertoire datasets make such clustering operations computationally intensive and potentially out of reach for many scientists and researchers who would benefit from such data.</p> <p><strong>Results:</strong> AntiRef (Antibody Reference Clusters), which is modeled after UniRef, provides clustered datasets of filtered human antibody sequences. Starting from a dataset of ~335M unique, full-length, productive human antibody sequences from the Observed Antibody Space repository, several AntiRef cluster sets were generated. Due to the modular nature of recombined antibody genes, the clustering thresholds used by UniRef (100, 90 and 50 percent identity) to cluster general protein sequences are suboptimal for antibody clustering. AntiRef provides reference antibody sequence datasets clustered at a range of relevant identity thresholds: 100, 99, 98, 96, 94, 92 and 90 percent. AntiRef90, which uses the lowest clustering threshold of any AntiRef dataset, is roughly one-third the size of the filtered input dataset and less than half the size of the non-redundant AntiRef100.</p> <p><strong>Datasets:</strong> AntiRef comprises a series of datasets, each representing one of several clustering thresholds. AntiRef datasets were generated by a nested clustering procedure similar to UniRef which, proceeding in order of decreasing stringency, clusters the representative sequences from the preceding round of clustering. AntiRef datasets can be found at the following links:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.7474657">AntiRef100</a>: representative sequences resulting from clustering all filtered AntiRef input sequences at 100% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7475961">AntiRef99</a>: representative sequences resulting from clustering AntiRef100 at 99% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7476040">AntiRef98</a>: representative sequences resulting from clustering AntiRef99 at 98% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487182">AntiRef96</a>: representative sequences resulting from clustering AntiRef98 at 96% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487199">AntiRef94</a>: representative sequences resulting from clustering AntiRef96 at 94% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487264">AntiRef92</a>: representative sequences resulting from clustering AntiRef94 at 92% identity.</li> <li><a href="https://doi.org/10.5281/zenodo.7487298">AntiRef90</a>: representative sequences resulting from clustering AntiRef92 at 90% identity.</li> </ul> <p><strong>Files:</strong> The following files are included in the primary AntiRef data repository:</p> <ul> <li><em>antiref_cluster-manifest.csv.gz:</em> A compressed CSV file containing the cluster assignments for every sequence in the AntiRef input dataset. For each AntiRef round, cluster names correspond to the sequence ID of the representative sequence (as determined by MMSeqs2). The nested clustering process conserves cluster names between iterations, meaning the clustering lineage of any sequence can easily be traced across all AntiRef datasets.</li> <li><em>download_heavy.txt</em>: A plain text file (generated by the <a href="http://opig.stats.ox.ac.uk/webapps/oas/">Observed Antibody Space</a>) containing the commands necessary to download all antibody heavy chain sequences used to create AntiRef.</li> <li><em>download_light.txt:</em> A plain text file (generated by the <a href="http://opig.stats.ox.ac.uk/webapps/oas/">Observed Antibody Space</a>) containing the commands necessary to download all antibody light chain sequences used to create AntiRef.</li> </ul> <p><strong>Code:</strong> All code used to generate AntiRef (data download, filtering, and clustering) is available under the MIT license on <a href="http://www.github.com/briney/antiref">GitHub</a>.</p>
Text-fig. 45. Scanning electron microscope (SEM) images of "Staminate structure"; Catefica locality, Portugal. a–c) Staminate structure in oblique apical (a), oblique basal (b) and lateral (c) views showing distinct stalk and head with a cluster of about 20 stamens; note bracts at the base of the structure (asterisks) and probable secretory openings in the anther tissues (arrows). Specimen, Catefica 358-S135451 (a–c). Scale bars = 600 Μm (a–c). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 45. Scanning electron microscope (SEM) images of "Staminate structure"; Catefica locality, Portugal. a–c) Staminate structure in oblique apical (a), oblique basal (b) and lateral (c) views showing distinct stalk and head with a cluster of about 20 stamens; note bracts at the base of the structure (asterisks) and probable secretory openings in the anther tissues (arrows). Specimen, Catefica 358-S135451 (a–c). Scale bars = 600 Μm (a–c).
Text-fig. 20. Synchrotron radiation X-ray tomographic microscopy (SRXTM, a) and scanning electron microscope (SEM, b–h) images of fruits of Appomattoxia sp. (a–d) and anther and pollen of Goczania rugosa (e–h); Catefica locality, Portugal. a) Surface rendering of fruit in lateral view showing densely spaced hairs, some with delicate coiled tips; b) Fruit in lateral view showing short, densely spaced hairs and apical stigmatic region; c, d) Detail of fruit surface and hairs from fruit in (b); e) Fragmentary anther showing four pollen sacs; f) Proximal view of pollen grains from an abraded anther showing microechinate surface of pollen wall and clusters of small, spiny orbicules; g, h) Proximal (g) and distal (h) views of pollen grains from an isolated pollen sac, showing short colpus (h), tectate pollen wall and microechinate surface ornamentation. Specimens, Catefica 49-S174913 (a), Catefica 49-S107794 (b–d), Catefica 50-S170391 (e), Catefica 49-S170138 (f), Catefica 49-S170143 (g, h). Scale bars = 300 Μm (a, b, e), 100 Μm (c), 50 Μm (d), 6 Μm (f–h). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 20. Synchrotron radiation X-ray tomographic microscopy (SRXTM, a) and scanning electron microscope (SEM, b–h) images of fruits of Appomattoxia sp. (a–d) and anther and pollen of Goczania rugosa (e–h); Catefica locality, Portugal. a) Surface rendering of fruit in lateral view showing densely spaced hairs, some with delicate coiled tips; b) Fruit in lateral view showing short, densely spaced hairs and apical stigmatic region; c, d) Detail of fruit surface and hairs from fruit in (b); e) Fragmentary anther showing four pollen sacs; f) Proximal view of pollen grains from an abraded anther showing microechinate surface of pollen wall and clusters of small, spiny orbicules; g, h) Proximal (g) and distal (h) views of pollen grains from an isolated pollen sac, showing short colpus (h), tectate pollen wall and microechinate surface ornamentation. Specimens, Catefica 49-S174913 (a), Catefica 49-S107794 (b–d), Catefica 50-S170391 (e), Catefica 49-S170138 (f), Catefica 49-S170143 (g, h). Scale bars = 300 Μm (a, b, e), 100 Μm (c), 50 Μm (d), 6 Μm (f–h).
Text-fig. 4. Taphonomic features of the studied localities of ammonoids. a: Sandstone slab with fragmentary remains of productid and spiriferid brachiopods, orthocerids, coiled nautiloids and ammonoids (stratigraphic level No. 3). b: Shell debris cluster and fragment of crushed ammonoid conch (stratigraphic level No. 1). c: Epibionts on the surface of an ammonoid conch (stratigraphic level No. 5). d: Cluster of bivalves, gastropods and cephalopods remains in a siderite nodule (stratigraphic level No. 5). e: Fragment of an ammonoid conch (stratigraphic level No. 3). f: Fragment of an ammonoid conch (?) with terminal aperture and brachiopod valve (stratigraphic level No. 3). g: Specimen of?Anthracoceratites sp. with conch injuries (shown by arrows) (stratigraphic level No. 8). h, i: Bioerosion trace fossils Cyclopuncta girtyi ELIAS, 1958 on the fragments of cephalopod conchs (stratigraphic level No. 5). j: Limonitized conchs of the ammonoid (stratigraphic level No. 7). k: Fragment of an ammonoid conch (stratigraphic level No. 5). Scale bars 10 mm. in Late Bashkirian Ammonoids From The Mospyne Formation Of The Donets Basin, Ukraine
Text-fig. 4. Taphonomic features of the studied localities of ammonoids. a: Sandstone slab with fragmentary remains of productid and spiriferid brachiopods, orthocerids, coiled nautiloids and ammonoids (stratigraphic level No. 3). b: Shell debris cluster and fragment of crushed ammonoid conch (stratigraphic level No. 1). c: Epibionts on the surface of an ammonoid conch (stratigraphic level No. 5). d: Cluster of bivalves, gastropods and cephalopods remains in a siderite nodule (stratigraphic level No. 5). e: Fragment of an ammonoid conch (stratigraphic level No. 3). f: Fragment of an ammonoid conch (?) with terminal aperture and brachiopod valve (stratigraphic level No. 3). g: Specimen of?Anthracoceratites sp. with conch injuries (shown by arrows) (stratigraphic level No. 8). h, i: Bioerosion trace fossils Cyclopuncta girtyi ELIAS, 1958 on the fragments of cephalopod conchs (stratigraphic level No. 5). j: Limonitized conchs of the ammonoid (stratigraphic level No. 7). k: Fragment of an ammonoid conch (stratigraphic level No. 5). Scale bars 10 mm.
Solid-state-like high harmonic generation from cluster molecules with rotational periodicities
<p><span>High harmonic generation (HHG) from solid-state crystals in strong laser fields has been understood by the band structure of the solids, which is based on the periodic boundary condition (PBC) due to translational invariance. For the systems with PBC due to rotational invariance, an analogous Bloch theorem can be applied. Considering a ring-type cluster of cyclo[18]carbon as an example, we develop a quasi-band model and predict the solid state-like HHG in this system. Under the irradiation of linearly polarized laser field, cyclo[18]carbon exhibits solid-state-like HHG originated from intra-band oscillations and inter-band transitions, which in turn is promising to optically detect the symmetry and geometry of controversial structures. Our results based on the Liouville-von-Neumann equations are well reproduced by the time-dependent density functional theory calculations and are foundational in providing a connection linking the HHG physics of gases and solids.</span></p>
Dataset for Unsupervised Clustering of CFG Files
<p>This dataset consists of control flow graphs (CFGs) of malware and benign binary files. Due to the security risk of sharing malware binary files online, these binary files are not shared. However, the hash values of the binaries are given for information.</p> <p>The dataset is associated with the software available at https://github.com/MSUSEL/unsupervised-graph (link may change). A manuscript outlining the problem and software solution, titled "Malware Detection Using Unsupervised Clustering of Binary File Control Flow Graphs," is under review for publication in IEEE Transactions on Information Forensics and Security. Upon publication, the related identifiers for both the code and the paper will be updated.</p>
Dataset: Core excitations and ionizations of uranyl in Cs2UO2Cl4 from relativistic embedded damped response time-dependent density functional theory and equation of motion coupled cluster calculations
<p>This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper titled "Core excitations and ionizations of uranyl in Cs2UO2Cl4 from relativistic embedded damped response time-dependent density functional theory and equation of motion coupled cluster calculations", by Wilken Aldair Misael and Andre Severo Pereira Gomes. It also contains the figures used in the manuscript.</p>
Companion data of Summarizing task-based applications behavior over many nodes through progression clustering
<p>This is the companion data for the paper *Summarizing task-based applications behavior over many nodes through progression clustering* by Lucas Leandro Nesi, Vinícius Garcia Pinto, Lucas Mello Schnorr, and Arnaud Legrand accept for publication in 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (<a href="https://www.pdp2023.org/">PDP 2023</a>). The remaining of the companion is at: https://gitlab.com/lnesi/companion-pdp-2023.</p>
Streptomyces Biosynthetic Gene Clusters
<p>A collection of genbank files 11975 biosynthetic gene clusters from generated using antiSMASH v6.0.1</p>
Dataset for CSP_via_Clustering
<p>This repository shares the dataset for Content Security Policy (CSP) analysis via clustering. The dataset includes a homepage CSP cluster dataset, a homepage request dataset, a homepage HTML dataset, and a subpage analysis dataset.</p>
Archived Data - Genetic breaks caused by ancient forest fragmentation: phylogeography of Staudtia kamerunensis (Myristicaceae) reveals distinct clusters in the Congo Basin
<p>List of the 400 genotyped <em>Staudtia kamerunensis</em><em> </em>accessions from Central Africa included in Vanden Abeele & Matvijev et al. 2023 - Tree Genetics & Genomes, and the corresponding alleles for each of the 14 microsatellite markers (0-0 indicates missing alleles)</p>
Cluster expansions in icet format for direct download
<p>This record contains cluster expansions (CEs) in <a href="https://icet.materialsmodeling.org/">icet</a> format from the following three publications</p> <ul> <li><em>High-Throughput Characterization of Transition Metal Dichalcogenide Alloys: Thermodynamic Stability and Electronic Band Alignment</em>, <a href="10.1021/acs.chemmater.2c01176">DOI:10.1021/acs.chemmater.2c01176</a></li> <li><em>Hydrogen-Driven Surface Segregation in Pd Alloys from Atomic-Scale Simulations</em>, <a href="https://doi.org/10.1021/acs.jpcc.1c00575">DOI: 10.1021/acs.jpcc.1c00575</a></li> <li><em>To Every Rule There is an Exception: A Rational Extension of Loewenstein's Rule</em> ,<a href="https://doi.org/10.1002/anie.202013256">DOI: 10.1002/anie.202013256</a></li> </ul> <p>They are compiled here to enable easy access via, e.g., <code>curl</code> or <code>wget</code>.</p>
Reconstitution of phase-separated signaling clusters and actin polymerization on supported lipid bilayers
<p>Liquid–liquid phase separation driven by weak interactions between multivalent molecules contributes to the cellular organization by promoting the formation of biomolecular condensates. At membranes, phase separation can promote the assembly of transmembrane proteins with their cytoplasmic binding partners into micron-sized membrane-associated condensates. For example, phase separation promotes clustering of nephrin, a transmembrane adhesion molecule, resulting in increased Arp2/3 complex-dependent actin polymerization. In vitro reconstitution is a powerful approach to understanding phase separation in biological systems. With a bottom-up approach, we can determine the molecules necessary and sufficient for phase separation, map the phase diagram by quantifying de-mixing over a range of molecular concentrations, assess the material properties of the condensed phase using fluorescence recovery after photobleaching (FRAP), and even determine how phase separation impacts downstream biochemical activity. Here, we describe a detailed protocol to reconstitute nephrin clusters on supported lipid bilayers with purified recombinant protein. We also describe how to measure Arp2/3 complex-dependent actin polymerization on bilayers using fluorescence microscopy. These different protocols can be performed independently or combined as needed. These general techniques can be applied to reconstitute and study phase-separated signaling clusters of many different receptors or to generally understand how actin polymerization is regulated at membranes.</p>
PhageHostLearn - training data and cluster analysis
<p>These data comprise the processed phage RBP and <em>Klebsiella </em>K-loci sequence data to train our PhageHostLearn system, along with ESM-2 embeddings of the RBPs and loci, as well as results from the cluster analyses of K-loci proteins and RBPs at 50% identity with CD-HIT.</p>
Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning
<p><strong>Data Description</strong>:</p> <p>To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.</p> <p>Files in collection (32):</p> <p>Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts</p> <p>gNATSGO TIF files:</p> <p>├── available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]<br> ├── available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]<br> ├── caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]<br> ├── caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]<br> ├── cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]<br> ├── cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]<br> ├── clay_30arc_30cm_us.tif [30 cm depth soil clay content]<br> ├── clay_30arc_100cm_us.tif [100 cm depth soil clay content]<br> ├── depthWT_30arc_us.tif [depth to water table]<br> ├── kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]<br> ├── kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]<br> ├── ph_30arc_100cm_us.tif [100 cm depth soil pH]<br> ├── ph_30arc_100cm_us.tif [30 cm depth soil pH]<br> ├── pondingFre_30arc_us.tif [ponding frequency]<br> ├── sand_30arc_30cm_us.tif [30 cm depth soil sand content]<br> ├── sand_30arc_100cm_us.tif [100 cm depth soil sand content]<br> ├── silt_30arc_30cm_us.tif [30 cm depth soil silt content]<br> ├── silt_30arc_100cm_us.tif [100 cm depth soil silt content]<br> ├── water_content_30arc_30cm_us.tif [30 cm depth soil water content]<br> └── water_content_30arc_100cm_us.tif [100 cm depth soil water content]</p> <p>SOC TIF files:</p> <p>├──30cm SOC mean.tif [30 cm depth soil SOC]<br> ├──100cm SOC mean.tif [100 cm depth soil SOC]<br> ├──30cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]<br> └──100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation]</p> <p>site observations csv files:</p> <p>ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data</p> <p>ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data</p> <p><br> <strong>Data format</strong>:</p> <p>Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.</p> <p><strong>Geospatial projection</strong>: </p> <pre><code>GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> <p> </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.