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2,326 results for “clusters”
Data from "Linked Coupled Cluster Monte Carlo"
<p>We consider a new formulation of the stochastic coupled cluster method in terms of the similarity transformed Hamiltonian. We show that improvement in the granularity with which the wavefunction is represented results in a reduction in the critical population required to correctly sample the wavefunction for a range of systems and excitation levels and hence leads to a substantial reduction in the computational cost. This development has the potential to substantially extend the range of the method, enabling it to be used to treat larger systems with excitation levels not easily accessible with conventional deterministic methods.</p>
Data from ``Developments in Stochastic Coupled Cluster Theory: The initiator approximation and application to the Uniform Electron Gas''
<p>We describe further details of the Stochastic Coupled Cluster method and a diagnostic of such calculations, the shoulder height, akin to the plateau found in Full Configuration Interaction Quantum Monte Carlo. We describe an initiator modification to Stochastic Coupled Cluster Theory and show that initiator calculations can be extrapolated to the unbiased limit. We apply this method to the 3D 14-electron uniform electron gas and present complete basis set limit values of the CCSD and previously unattainable CCSDT correlation energies for up to $r_s=2$, showing a requirement to include triple excitations to accurately calculate energies at high densities.</p>
CFHTLenS 3D-MF Galaxy Cluster Catalog
<p>These catalogs contain the publically available 3D-Matched-Filter (3D-MF) Galaxy Cluster candidates in the 4 fields of CFHTLenS.</p> <p>Each catalog contains 5 columns: right ascension (RA), declination (DEC), redshift (<em>z</em>), 3D-MF detection significance (sig), and richness (n200).</p> <p>As discussed in the references below, sig has been found to scale well with mass. While these catalogs contain all clusters detected at sig > 3.5, we expect there to be significant false detections at the lower end of this. Depending on your application, you may find it useful to make a cut at perhaps sig > 5 or 10. The position of the cluster (RA, DEC) is coincident with the peak in the 3D-MF likelihood map, and does NOT necessarily coincide with a member galaxy. Redshift <em>z</em> is the center of the 3D-MF redshift bin that maximizes a cluster detection, and has not been refined to more precise values than the binning employed by the 3D-MF algorithm. For more details, please consult the references given below.</p> <p>Details of the 3D-MF algorithm can be found in Milkeraitis et al. 2010 (http://arxiv.org/abs/0912.0739).</p> <p>The specifics of the 3D-MF application to the CFHTLenS fields can be found in Ford et al. 2014 (http://arxiv.org/abs/1310.2295) and Ford et al. 2015 (http://arxiv.org/abs/1409.3571).</p> <p>If you have any questions about these catalogs, please contact jesfordphd@gmail.com</p>
FIGURE 3 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 3. Numbers of OCTUs formed at 99% within-OCTU similarity for each recovered nematode species. Brackets group species by the persistence (Always, Often, Never) of Head-Tail structure of OCTUs. Error bars indicate standard deviation across 7 metagenetic datasets.
FIGURE 5 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 5. Estimated (using the Head-Tail guidelines) number of nematode species from OCTUs generated at 99% within-OCTU similarity for sequencing reads recovered from soil, litter, and canopy habitats at La Selva Biological Station in Costa Rica.
FIGURE 2 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 2. Number of nematode species recovered using different levels of within-OCTU (operational clustered taxonomic units) similarity. Bars represent means across 7 metagenetic datasets. Error bars indicate standard deviation. Note: out of 41 used nematode species, three did not amplify (Porazinska et al. 2009a).
FIGURE 4 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 4. An example of OCTUs generated at 99% within-OCTU similarity matching Bursaphelenchus seani illustrating Head-Tail structure associated with the presence of within-species/individual variation of the SSU repeats. OCTUPUS generates a series of indices allowing OCTU ranking. When ranked by "Bit Score" and Reads, OCTUs can be divided into two categories: 1. Head – a single OCTU (27) that contains most abundant (63) and most common sequencing reads and matches a database (DB match) sequence (B. seani 175) at the highest value of bit score (525), Evalue and % Similarity (100) across the OCTU length. 2. Tail – many OCTUs each with fewer than the Head OCTU reads and lower values of bit score and % similarity.
FIGURE 1 in Linking operational clustered taxonomic units (OCTUs) from parallel ultra sequencing (PUS) to nematode species
FIGURE 1. Nematode OCTUs generated at different levels of within-OCTU similarity. Points represent the means across 7 metagenetic datasets, bars represent standard deviation.
FIGURES 4−6. Araucoderus gloriosus eggs. 4. Egg cluster. 5. Single egg with chorion. 6 in The Neotropical tanyderid Araucoderus gloriosus (Alexander) (Diptera, Tanyderidae), with description of the egg, larva and pupa, redescription of adults, and notes on natural history
FIGURES 4−6. Araucoderus gloriosus eggs. 4. Egg cluster. 5. Single egg with chorion. 6. Single egg without chorion. Scale bars = 0.20 mm.
FIGURES 26–30. 26–29 in Revision of the cluster flies of the Pollenia haeretica species-group (Diptera, Calliphoridae)
FIGURES 26–30. 26–29. Pollenia ibalia Séguy, male (from holotype of Pollenia rungsi Séguy in MNHN). 26. Aedeagus, left lateral view. 27. Aedeagus, dorsal (posterior) view. 28. Tip of cerci, slightly oblique view. 29. Three original labels, determination label and holotype label by K.R. 30. Right pregonite and tip of cercus of P. ibalia (left) and P. haeretica (right) [reproduced from Séguy 1930: 148, figs. 96 and 97, by permission].
FIGURES 16–25 in Revision of the cluster flies of the Pollenia haeretica species-group (Diptera, Calliphoridae)
FIGURES 16–25. Pollenia ibalia Séguy, male (16–23, 25 from holotype in MNHN; 24 from specimen labelled "Asni ..." in BMNH). 16. Aedeagus, left lateral view. 17. Tip of paraphallic process (large magnification). 18. Pre- and postgonites. 19. Cerci and surstyli, posterior view. 20. Cerci, surstyli, epandrium and bacilliform sclerites, left lateral view. 21. Tip of cerci (large magnification). 22. Head, dorsal view. 23. Facial region, from in front. 24. Abdomen, oblique view. 25. Three original labels. Scale = 0.25mm (Figure 16).
FIGURES 11–15 in Revision of the cluster flies of the Pollenia haeretica species-group (Diptera, Calliphoridae)
FIGURES 11–15. Pollenia haeretica Séguy male and female (11 from lectotype of Pollenia haeretica Séguy in MNHN, slide 289; 12 from male labelled "… Villacidro...14.XI.2006…" in CNBF; 13–14 from slide "G. pr. 314" made from dissected female paralectotype labelled "Philippeville..."in MNHN; 15 from glycerol preparation of the internal genitalia of the same female paralectotype in MNHN). 11. Wing tip. 12. Wing tip. 13. Ovipositor, flat mount. 14. Tip of ovipositor, flat mount. 15. Uterus with lateral sacs and spermathecae; arrow points to lumen of a lateral sac. Scale = 0.25mm (Figure 15).
FIGURES 1–10 in Revision of the cluster flies of the Pollenia haeretica species-group (Diptera, Calliphoridae)
FIGURES 1–10. Pollenia haeretica Séguy, male (1–5 from "Tunisia, 10 km N Korba …" specimen in ZMUC; 6, 9 from "Sorgono, Sardegna …" specimen in BMNH; 7–8, 10 from lectotype of Pollenia haeretica Séguy in MNHN). 1. Aedeagus, left lateral view. 2. Tip of paraphallic process (large magnification). 3. Pre- and postgonites. 4. Cerci and surstyli, posterior view. 5. Cerci, surstyli, epandrium and bacilliform sclerites, left lateral view. 6. Tip of cerci (large magnification). 7. Head, dorsal view. 8. Facial region, from in front. 9. Abdomen, oblique view. 10. Four original labels and K.R.'s label referring to MNHN slides.
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Clustering Simulations
<p>Simulation files (Gromacs 4.6.7 format) for the "Clustering" simulations in Ref. [1]. There are two replicas marked with "_1" and "_2".</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
TimeSeries_Clustering_ML_data
<p>extended database upload for Time Series Clustering Machine Learning framework</p> <p>(this version includes all the initial large dataset files >10 GB that were created with Time Series Clustering Machine Learning)</p> <p> </p> <p> </p>
GWAS Summary data generated for study: disease clusters and their genetic determinants following a diagnosis of depression
<p>Second version of GWAS summary data</p> <p>A1: effect allele</p> <p>A2: other allele</p> <p>Project github page: https://github.com/HZcohort/3D-Disease-Network</p>
BASCULE: Bayesian inference and clustering of mutational signatures leveraging biological priors
<p>In the preprint available at https://doi.org/10.1101/2024.09.16.613266 we present BASCULE, a new method to perform Bayesian signatures deconvolution and to cluster patients from the inferred exposures. The method can deconvolve any kind of mutational signature types (SBS, DBS, ID, etc.) including as input a reference catalogue of known signatures (i.e., COSMIC), and cluster the samples joinltly from the exposures of all signature types. BASCULE is available as an R package (https://github.com/caravagnalab/bascule.git). Here we release the data and code to reproduce the analysis on synthetic and real datasets presented in the preprint, in the "synthetic_data_validation.zip" and "real_data_validation.zip", respectively.</p>
Hydraulic Tomography Estimates Improved by Zonal Information from the Clustering of Geophysical Survey Data
<p>The spreadsheet contains the information of hydraulic conductivity fields, drawdown calibration and validation data sets, and tracer data sets used to accomplish the research article with the same title.</p>
AFM data of ice clusters on Cu(111) and Au(111) in paper "Structure discovery in Atomic Force Microscopy imaging of ice"
<p>Frequency shift CO-tip atomic force microscopy data of small ice clusters on Cu(111) and Au(111) surfaces as they appear in the paper "Structure discovery in Atomic Force Microscopy imaging of ice".</p><p>The data are saved in a compressed .tar.gz archive. The unpacked archive contains each experiment as a Numpy .npz file. Each file contains the measurement data as a 3D array in the key 'data' and the physical extent of the scan region in the x and y directions in Ånströms in the keys 'lengthX' and 'lengthY'.</p>
Behavioral Data Cluster Analysis
<p>The data set includes the main behavioral readouts (mean % prepulse inhibition, social preference index, % alternation in the Y-maze, and total distance moved in the open field) used for cluster analyses in the poly(I:C)-based mouse model of maternal immune activation. </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.