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2,326 results for “clusters”
Asteroseismology of the young open cluster NGC 2516 I: Photometric and spectroscopic observations
<p>A column-to-column explaination is here:</p> <p>gaia_dr3_source_id: Gaia DR3 source ID</p> <p>TIC: TIC number</p> <p>gaia_RA: RA by Gaia</p> <p>gaia_DEC: DEC by Gaia</p> <p>gaia_G_apparent_mag: Gaia G band magnitude</p> <p>gaia_G_apparent_mag_err: Gaia G band magnitude uncertainty</p> <p>gaia_G_absolute_mag: Gaia G band absolute magnitude, without the correction of extinction</p> <p>gaia_G_absolute_mag_err: uncertainty of gaia_G_absolute_mag</p> <p>log_Luminosity: log of luminosity, calculated by Gaia effective temperature, with the bolometric correction and extinction correction. Use with caution.</p> <p>log_Luminosity_err: uncertainty of log_Luminosity</p> <p>Gaia_Teff: effective temperature provided by Gaia. Use with caution.</p> <p>Gaia_Teff_err: uncertainty of Gaia_Teff. Use with caution. A uncertainty value of '100' means the temperature is absent by Gaia, so we use the temperature from the TIC input catalog.</p> <p>BP-RP: Gaia colour index.</p> <p>BP-RP_err: uncertainty of BP-RP</p> <p>Teff_by_spectra: effective temperature by FEROS spectra, better than Gaia_Teff, only available for nine stars. "9999" means no data available.</p> <p>Teff_by_spectra_err: uncertainty of Teff_by_spectra. "9999" means no data available.</p> <p>log_L_by_Teff_spectra: log of luminosity calculated by Teff_by_spectra, with the bolometric correction and extinction correction, better than log_Luminosity.</p> <p>log_L_by_Teff_spectra_err: uncertainty of log_L_by_Teff_spectra</p> <p>spectra_logg: log g by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_logg_err: uncertainty of spectra_logg</p> <p>spectra_vsini: projected equatorial velocity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_vsini_err: uncertainty of spectra_vsini</p> <p>spectra_matellicity: matellicity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_matellicity_err: uncertainty of spectra_matellicity</p> <p>spectra_radial_velocity: radial velocity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_radial_velocity_err: uncertainty of spectra_radial_velocity</p> <p>spectra_microturbulent: microturbulent velocity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_microturbulent_err: uncertainty of spectra_microturbulent</p> <p>spectra_SNR: signal-to-noise ratio of the FEROS spectra</p> <p>Pi0: asymptotic spacing of g modes, measured by g modes, only available for 11 stars. '9999' means no data available.</p> <p>Pi0_err: uncertainty of Pi0</p> <p>core_rotation_g_mode: near-core rotation rate in unit of days^{-1}, measured by g modes, only available for 11 stars. '9999' means no data available.</p> <p>surface_modulation_period: surface rotation period measured by surface modulations. '9999' means no data available.</p> <p>surface_modulation_period_err: uncertainty of surface_modulation_period</p> <p> </p>
Clustering of Librarian Knowledge in Sidrap Regency
<p>Using K-means, this research attempts to determine the best grouping and number of librarians on Sidenreng Rappang for some training.</p>
Dataset of structural and energetic descriptors for optimized geometries of the pyrene dimer in the S1 state and Jupyter Notebbok used for the unsupervised clustering, analysis and visualization
<p>Geometrical and energy data extracted from a set of 188 optimized geometries for the pyrene dimer in the first excited singlet state at the TD-CAMB3LYP + D3BJ / 6-31G* / C-CPCM(Cyclohexane) level. </p> <p>Coordinates (in xyz format) of the 188 optimized geometries considered.</p> <p>Jupyter notebook used to perform unsupervised clustering and analysis of the available structures.</p>
Multiwavelength Constraints on the Origin of a Nearby Repeating Fast Radio Burst Source in a Globular Cluster (Public Data Release)
<p>This Zenodo dataset contains the data for radio bursts B1-B9 from FRB 20200120E, as described in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) (see: https://doi.org/10.1038/s41550-024-02386-6).</p> <p>The following data products are included:</p> <ul> <li>Channelized total intensity (Stokes I) data containing radio bursts B1-B5 from FRB 20200120E, recorded using the Effelsberg radio telescope during Pinpointing Repeating CHIME Sources with the EVN (PRECISE) VLBI observations. These data have a time resolution of 8 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b1_8us_burst_data.npy</li> <li>frb20200120e_b2_8us_burst_data.npy</li> <li>frb20200120e_b3_8us_burst_data.npy</li> <li>frb20200120e_b4_8us_burst_data.npy</li> <li>frb20200120e_b5_8us_burst_data.npy</li> </ul> </li> <li>Channelized total intensity (Stokes I) data containing radio bursts B6-B9 from FRB 20200120E, recorded using the Effelsberg radio telescope. These data have a time resolution of 64 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b6_64us_burst_data.npz</li> <li>frb20200120e_b7_64us_burst_data.npz</li> <li>frb20200120e_b8_64us_burst_data.npz</li> <li>frb20200120e_b9_64us_burst_data.npz</li> </ul> </li> <li>Frequency-summed total intensity (Stokes I) burst profiles of radio burst B4. The frequency range and time resolution of the data are listed below. These data were used in Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).<br> <ul> <li>frb20200120e_b4_8us_1254-1510mhz_burst_profile.npz; (frequency range, time resolution) = (1254-1510 MHz, 8 μs)</li> <li>frb20200120e_b4_1us_1302-1478mhz_burst_profile.npy; (frequency range, time resolution) = (1302-1478 MHz, 1 μs)</li> <li>frb20200120e_b4_31.25ns_1398-1414mhz_burst_profile.npy; (frequency range, time resolution) = (1398-1414 MHz, 31.25 ns)</li> </ul> </li> </ul> <p>We also provide the following Python code containing functions that can be used to load and plot the radio data. The plots generated by this code are similar to those shown in Figure 1 and Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).</p> <ul> <li>plot_frb20200120e_radio_data_pearlman+2024_nature_astronomy.py</li> </ul> <p>The X-ray data (from <em>NICER</em>, <em>XMM-Newton</em>, <em>Chandra</em>, and <em>NuSTAR</em>) used in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) are publicly available and can be accessed through NASA's High Energy Astrophysics Science Archive Research Center (HEASARC) archive.</p> <p>If the data or Python code included in this Zenodo repository are used, please include the following two citations in your work:</p> <ol> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster. <em>Nature Astronomy</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.1038/s41550-024-02386-6</a></li> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster (public data release). <em>Zenodo</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.5281/zenodo.13359005</a></li> </ol> <p>If you have questions about the contents of this Zenodo repository, please contact the lead author: Dr. Aaron B. Pearlman (<a href="mailto:aaron.b.pearlman@physics.mcgill.ca">aaron.b.pearlman@physics.mcgill.ca</a>)</p>
Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected by spacecraft 1 of the Cluster mission in the foreshock of Earth
<p>Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected in the foreshock of Earth by spacecraft 1 of the Cluster mission between the years 2002-2012.</p> <p>An automated algorithm has been used for SLAMS identification followed by a manual verification process to remove bow shock oscillations and other false detections. SLAMS have been defined to have an amplitude of at least two times the background magnetic field. </p> <p>More details on the creation of the database are given in the following publication:</p> <p><span><span lang="EN-US">Bergman, S.</span></span><span lang="EN-US">, Karlsson, T., Wong Chan, T. K., & Trollvik, H. (2025). Statistical properties of Short Large</span><span lang="EN-US">‐</span><span lang="EN-US">Amplitude Magnetic Structures (SLAMS) in the foreshock of Earth from Cluster measurements. <em>Journal of Geophysical Research: Space Physics</em>, 130. </span><a href="https://doi.org/10.1029/2024JA033568"><span lang="EN-US">https://doi.org/10.1029/2024JA033568</span></a></p> <p>Contact: S. Bergman, sofiabergmanphd@gmail.com </p>
Dataset of the publication: Influence of Fe-clustering on the water oxidation performance of two-dimensional layered double hydroxides
<p>Dataset of the publication: Influence of Fe-clustering on the water oxidation performance of two-dimensional layered double hydroxides</p> <p>DOI: 10.1039/d1dt03737d</p> <p>Seijas-Da Silva, A; Oestreicher, V; Coronado, E; Abellán, G</p> <p>Dalton Trans., 2022,51, 4675-4684</p> <p> </p>
Dataset of the publication: A Novel Banana-Shaped Mixed-Metal Co/Fe Polyoxometalate Cluster
<p>Dataset of the publication: A Novel Banana-Shaped Mixed-Metal Co/Fe Polyoxometalate Cluster</p> <p>DOI: 10.1002/cplu.202400473</p> <p>J. Quirós-Huerta, J. Troya, M. Clemente-León, J. M. Clemente-Juan, E. Coronado, J. Soriano-López </p> <p>ChemPlusChem, e202400473 (2024)</p>
Intra-cluster summed galaxy colors
<p>Inter-cluster summed galaxy colors and other cluster properties for TNG300-1, SDSS NYU VAGC, and Buzzard Flock low redshift galaxies. </p> <p> </p> <pre>'Hmass': log10(Cluster mass/M_sun) TNG: M_200c, BZZ: M_vir, SDSS: M_vir 'N_gal': Number of galaxies in Cluster 'Z': Redshift 'rich_cts','rich_dsc': Richness, continuous and discrete 'mag_gap': Magnitude gap 'g_cen','r_cen','i_cen','z_cen': Magnitudes for central galaxies 'sum_g','sum_r','sum_i','sum_z': Summed Magntiudes for total cluster population 'sum_g_sat', 'sum_r_sat', 'sum_i_sat', 'sum_z_sat': Summed Magntiudes for sattelite cluster population </pre>
Table S1. Geographical localization of Pinus pseudostrobus Lindley. specimens used in the study and concordance of morphological identification with real-time PCR-HRM assay for Pinus pseudostrobus varieties pseudostrobus, apulcensis, oaxacana and coatepecensis, based on the cluster pattern.
<p>File encloses geographical location of collected Pinus pseudostrobus samples in México, as well as haplotype grouping obtaines from HRM analysis</p>
Research data for "Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure"
<p>This dataset supports the paper: "Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure". The paper is online here: https://doi.org/10.1002/adma.202107515. </p> <p>The following .xyz and .zip files are provided:</p> <ul> <li>"LDA_structure_final.xyz": the atomic structure of the LDA model generated in this work.</li> <li>"slow_melt_quench.zip": the trajectory of the slow melt-quench process in (extended) XYZ format. </li> <li>"compress_decompress.zip": the trajectory of the ambient-pressure compression and the subsequent decompression processes in (extended) XYZ format. </li> <li>"Structure_factor.zip": atomic structures in (extended) XYZ format at different pressures (used to calculate the structure factors). </li> </ul> <p> </p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: Calibration Data
<p>Calibration data accompanying our work, "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse.</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data A
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part A</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data C
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part C.</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data B
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part B.</p>
Copernicus Global Land Service: Global biome cluster layer for the 100m global land cover processing line
<p><strong>A map of 73 global biome clusters, geographic areas that were grouped to optimize the global 100m land cover processing.</strong></p> <p>In order to group Earth Observation data for faster processing or adaptation of algorithms to specific regions, the 100m global land cover (CGLS-LC100) algorithm uses a Global Biome Cluster layer. The term <em>biome cluster</em> hereby refers to a geographic area which has similar bio-geophysical parameters and, therefore, can be grouped for processing. In other words, the biome cluster layer can be seen as an ecological regionalisation which outlines areas of similar environmental conditions, ecological processes, and biotic communities (Coops et al., 2018). There are already several global regionalisation layers existing, e.g. Ecoregions 2017 global dataset (Dinerstein et al., 2017), Geiger-Koeppen global ecozones after Olofsson update (Olofsson et al., 2012), Global ecological zones for FAO forest reporting with update 2010 (FAO, 2012). But several tests in the CGLS-LC100 workflow have shown that the existing layers did not provide the required global and continental classification accuracy. These findings go along with Coops et al. (2018) who stated that "<em>Most regionalisations are made based on subjective criteria, and cannot be readily revised, leading to outstanding questions with respect to how to optimally develop and define them."</em></p> <p>Therefore, we decided to develop a customized ecological regionalisation layer which performs best with the given PROBA-V remote sensing data and the specifications of the CGLS-LC100 product. It groups spectral similar areas and helps to optimize the later classification/regression to regional patterns. Input into the layer creation were well-known existing datasets which were combined, re-grouped and advanced based on prior CGLS-LC100 classification results and local mapping knowledge of the workflow developer. To ensure that this layer is clearly separable from other existing regionalisations and not mistakenly interpreted as an eco-region layer, we decide to call it <em>biome clusters</em> <em>layer</em>.</p> <p>The following steps outline the global biome clusters layer generation:</p> <ul> <li>Spatial union of Ecoregions 2017 dataset (Dinerstein et al., 2017), Geiger-Koeppen dataset (Olofsson et al., 2012) and Global FAO eco-regions datasets (FAO, 2012);</li> <li>Regrouping and dissolving by using experience from first global CGLS-LC100 mapping results and subjective mapping experience of the developer;</li> <li>Refinement of the biome clusters in the High North latitudes via incorporation of a Global tree-line layer (Alaska Geobotany Center, 2003);</li> <li>Manual improvement of borders between biome clusters to reduce classification artefacts by using a DEM and mapping experience from previous projects and continental test runs;</li> <li>Usage of a global land/sea mask, the Sentinel-2 tiling grid and PROBA-V imaging extent to extend the borders of the biome clusters into the sea to make sure that also small islands on the coastline are correctly processed.</li> </ul> <p>When developing a regionalisation, the definition of the clusters and the boundaries that delineate them in time and space is the key challenge. Overall, the map distinguishes <strong>73 global biome clusters</strong>.</p>
Solute-solvent clusters
Changed <ul> <li>Reorganized <code>solute</code> and <code>solute.solvent</code> to <code>heterogeneous</code> and <code>homogeneous</code>.</li> <li>Moved selected ABCluster structures into the <code>abc</code> folders instead of having them separate.</li> </ul> Added <ul> <li>Methanol 20mers from Pires & Deturi (DOI: <a href="https://doi.org/10.1021/ct600348x">10.1021/ct600348x</a>) and Yao et al (DOI: <a href="https://doi.org/10.1063/1.4973380">10.1063/1.4973380</a>).</li> <li>Packmol generated structures of 30h2o, 112h2o, 140h2o, 30mecn, 39mecn, 30meoh, 50meoh, and 62meoh.</li> <li>ORCA MP2/def2-TZVP energy+gradient calculation of Yoo et al. boat-b 16mer.</li> <li>Methanol 4-6mer minima from Boyd et al. (DOI: <a href="https://doi.org/10.1021/ct6002912">10.1021/ct6002912</a>) with MP2/def2-TZVP engrads.</li> <li>Acetonitrile 4-6mer minima from Malloum et al. (DOI: <a href="https://doi.org/10.1002/qua.26222">10.1002/qua.26222</a>) with MP2/def2-TZVP engrads.</li> <li>Water 16mer minima from Yoo et al. (DOI: <a href="https://doi.org/10.1021/jz101245s">10.1021/jz101245s</a>) with RI-MP2/def2-TZVP engrad.</li> <li>Water 4-6mer minima from Temelso et al. (DOI: <a href="https://doi.org/10.1021/jp2069489">10.1021/jp2069489</a>) with MP2/def2-TZVP engrads.</li> <li>20 Angstrom box of water from GROMACS solvate.</li> </ul> Removed <ul> <li>Moved 12h2o.su.etal monomers, dimers, and trimers to <a href="https://github.com/keithgroup/mbgdml-h2o-meoh-mecn-engrads">another repository</a>.</li> </ul>
The impact of galaxy selection on the splashback boundaries of galaxy clusters (Data)
<p>Data from O'Neil et al. (2022), The impact of galaxy selection on the splashback boundaries of galaxy clusters.</p>
Laser scan and polarization resolved Fourier-plane measurements of nanoparticle clusters
<p><strong>meas_00 - meas_09: measurements of particle ensembles</strong></p> <p><strong>meas_10 - meas_11: measurements of excitation beam</strong></p> <p><strong>meas_12: measurement of camera background</strong></p> <p>See "meas_readme.pdf" for more details on how to use and understand the data.</p>
Dataset for "Structural Linkage Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies"
<p>Dataset relating to the publication "Structural Linkage Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies".</p> <p>Raw input and output data as well as pre- and postprocessing scripts and figures.</p>
REALISE: Enabling full-chain CCUS for refineries through cluster-based strategies
<p>Refineries play a major role in our modern lifestyles, creating an almost infinite range of everyday products. Alongside other industries, they face the challenge to decarbonise as part of Europe’s wider efforts to meet climate targets by 2030. </p> <p>This webinar introduced the REALISE CCUS project, an international partnership of industry and scientists working to support the delivery of carbon capture, utilisation and storage (CCUS) technology for the refinery sector. </p> <p>Our research, funded by the European Union's Horizon 2020 programme, focuses on the full CCUS chain – from CO<sub>2</sub> capture, transport and geological CO<sub>2</sub> storage to CO<sub>2</sub> reuse – for clusters which include refineries and other industries. </p> <p>Specifically, we aim to demonstrate CCUS technology, enable sizeable cost-reductions, undertake public engagement and assess financial, political and regulatory barriers. </p> <p>Programme:</p> <ul> <li>Welcome & intro – Inna Kim, SINTEF (5 mins) </li> <li>Optimising and validating technologies for refineries (WP1) – Solrun Vevelstad, SINTEF (10 mins) </li> <li>Demonstrating pilot-scale CO<sub>2</sub> capture with optimised solvent (WP2) – Juliana Monteiro, TNO (10 mins) </li> <li>Assessing potential for CCUS at oil refineries within clusters (WP3) – Pádraig Fleming, Ervia (10 mins) </li> <li>Social, political and commercial context for CCS deployment (WP4) – Niall Dunphy, UCC (10 mins) </li> <li>Q&A (15 mins) </li> </ul>
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.