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2,555 results for “catalogs”
NLL-SSST-coherence earthquake relocation catalogs for the Parkfield and Lone Pine, California earthquake sequence.
<p>CSV tables of the final, NLL-SSST-coherence earthquake relocation catalogs for the Parkfield and Lone Pine, California earthquake sequence.</p> <p>These datasets are from relocations presented in the article<br> High-precision, earthquake location using source-specific station terms and inter-event waveform similarity<br> submitted to Journal of Geophysical Research Solid Earth</p> <p> </p> <p> </p>
Catalog of synthetic seismic records from mineral physics and travel-time tables from Waszek et al., 2021, Nature Geoscience
<p>This release is associated with the accepted publication in Nature Geoscience:</p> <p>Waszek L., Tauzin B., Schmerr N., Ballmer M. and Afonso J.C. A poorly mixed mantle transition zone and its thermal state inferred from seismic waves. Nature Geoscience, 2021.</p> <p>This dataset must be used in conjunction with the NoLimit software package (https://zenodo.org/record/5512805).</p> <p>Both the software and dataset allow the prediction of synthetic seismic waveforms for SS and PP-precursors from mineral physics models, as well as their processing for reconstructing the surface of seismic boundaries associated with major mineralogical phase transitions in the Earth’s mantle (namely, the 410-km and 660-km depth discontinuities).</p> <p>For technical reasons (storage and quick access), the catalog is downsampled with respect to the one in Waszek et al. (2021), and it is provided with the HDF5 format. For more advanced applications such as changing mantle composition, or generating waveforms for deeper earthquakes, please contact Benoit Tauzin (benoit.tauzin@univ-lyon1.fr) and Lauren Waszek (lauren.waszek@jcu.edu.au).</p> <p>The dataset includes:</p> <p>* A fixed mantle composition, which is a mechanical mixture of basalt and harzburgite with a fraction of basalt f=0.2.<br> * A downsampled catalog of synthetic waveforms for event depths between 0 and 80 km by step of 10 km (enough for reproducing the processing of observed SS and PP precursors waveforms).<br> * Adiabatic temperature gradients with potential temperature Tpot between 1200 and 2100 K by step of 100 K.</p> <p>This catalog and associated travel-time tables will allow any user to generate synthetic waveforms for any moment tensor, and events within the pre-defined depth interval.<br> </p> <p><strong>How to cite this material?</strong></p> <p>Any use of the datasets or software must refer to:</p> <p>The reference paper: Waszek L., Tauzin B., Schmerr N., Ballmer M., Afonso J.C. A poorly mixed mantle transition zone and its thermal state inferred from seismic waves. Nature Geoscience. 2021.<br> <br> Software: Tauzin, Benoit, & Waszek, Lauren. (2021). NoLiMit MATLAB package v1.0. Non-Linear Bayesian partition Modeling of the Earth's Mantle Transition zone (Version 1). Zenodo. https://doi.org/10.5281/zenodo.5512805<br> <br> Datasets: Tauzin, Benoit, Waszek, Lauren, & Afonso, Juan Carlos. (2021). Catalog of synthetic seismic records from mineral physics and travel-time tables from Waszek et al., 2021, Nature Geoscience (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5512035</p>
Catalogs of Eclipsing Binaries with Pulsating Components, δ Scuti stars and γ Doradus stars
<p>I present a comprehensive, up-to-date catalog of <strong>3324</strong> <strong>eclipsing binary star systems containing pulsating components </strong>(not updated in this version). The initial compilation builds upon existing lists of `oscillating Algol-type eclipsing binaries' (oEA) harboring δ Scuti stars. However, the catalog expands upon this foundation to encompass a broader range of pulsating binary systems identified in recent years. This new catalog is valuable for researchers studying binary stars' evolution and pulsating stars. It incorporates various pulsating variable types across the Hertzsprung-Russell diagram, including δ Scuti stars, γ Doradus stars, β Cephei stars, Cepheids, and red giants exhibiting solar-like oscillations. However, this catalog is NOT an exhaustive list of eclipsing binaries with pulsating components of the above types and it is subject to updates. </p> <p>Many stars in this catalog are potentially interesting for further studies. Due to potential blending and contamination in TESS photometry, the binarity of a few pulsating stars could be attributed to neighboring eclipsing binaries. Follow-up studies of individual systems are necessary to resolve contamination issues and accurately identify the true source of variability. If you use part of the catalog in your research, please cite: Zhou, A.-Y., 2010, arXiv e-prints (DOI: 10.48550/arXiv.1002.2729) (ADS: https://ui.adsabs.harvard.edu/abs/2010arXiv1002.2729Z/abstract)</p> <p>In addition, I have also included the up-to-date catalogs of <strong>118,410 δ Scuti stars</strong> and <strong>41,622 γ Doradus stars</strong>, featuring thousands of unpublished discoveries. For these two catalogs, please cite: </p> <p>Zhou, Ai-Ying, 2024, New Astronomy, Volume 105, 102081 (Published: January 2024)</p> <p>Paper in ADS: https://ui.adsabs.harvard.edu/abs/2024NewA..10502081Z/abstract</p> <p>Paper in Publisher web: https://www.sciencedirect.com/science/article/pii/S1384107623000829</p> <p>Thanks for your reading. Your comments are more than welcome!</p>
pop-cosmos: Galaxy property and redshift catalog for COSMOS2020
<p>This record (v≥2.0.0) contains data products associated with the paper "<em>pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population</em>" by Thorp et al. (2025). Earlier versions of this record (v<2.0.0) contain data products associated with the paper "<em>pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model</em>" by Thorp et al. (2024), which are superseded by the contents of v2.0.0. In v≥2.0.0, we include results for all COSMOS2020 galaxies with $\textit{Ch.1}<26$ or $r<25$.</p> <p>The included products are derived from spectral energy distribution (SED) fits to 26-band COSMOS2020 photometry, using the 16-parameter SPS model described in Thorp et al. (2024, 2025), and the <code>pop-cosmos</code> prior from Thorp et al. (2025). All results are based on Markov Chain Monte Carlo (MCMC) runs using the configuration described in Thorp et al. (2024, 2025). Results correspond to v2.1 of the COSMOS2020 catalog.</p> <p>The current release includes the following files:</p> <ul> <li><strong>README_v2_2_0.txt</strong>: Detailed information about how to read the other files in the record.</li> <li><strong>mcmc_summaries.h5.gz</strong>: Zipped HDF5 file with summaries (percentiles) of the posteriors.</li> <li><strong>mcmc_samples_pop_cosmos.h5.gz</strong>: Zipped HDF5 file with posterior samples (using <code>pop-cosmos</code> prior).</li> <li><strong>mcmc_samples_Prospector.h5.gz</strong>: Zipped HDF5 file with posterior samples (using <code>Prospector</code>-$\alpha$ prior).</li> </ul> <p>If you make use of any of these products, please cite this repository and Thorp et al. (2024, 2025). Please also cite the <code>pop-cosmos</code> overview paper by Alsing et al. (2024), and the paper by Deger et al. (2025). If you make use of any COSMOS data products, please cite Weaver et al. (2022) and any other relevant publications. If you make use of COSMOS spectroscopic data, please cite Khostovan et al. (2025) and references therein.</p> <p>If you spot any issues or have any requests, please contact the corresponding author (Stephen Thorp) using the details in the README.</p> <p>If you want to access <code>pop-cosmos</code> mock galaxy catalogs, these can be found on <a href="https://doi.org/10.5281/zenodo.15622324">Zenodo</a>.</p> <p>Related software, including a demo notebook for working with the data in this record, can be found on <a href="https://github.com/Cosmo-Pop/pop-cosmos">GitHub</a>. </p> <p>References:</p> <ol> <li>Alsing et al. (2024). ApJS 274, 12. [<a href="https://arxiv.org/abs/2402.00935">arXiv:2402.00935</a>][<a href="https://doi.org/10.3847/1538-4365/ad5c69">doi</a>]</li> <li>Deger et al. (2025). MNRAS, submitted. [<a href="https://arxiv.org/abs/2509.20430">arXiv:2509.20430</a>]</li> <li>Khostovan et al. (2025). ApJ, submitted. [<a href="https://arxiv.org/abs/2503.00120">arXiv:2503.00120</a>]</li> <li>Thorp et al. (2024). ApJ 975, 145. [<a href="https://arxiv.org/abs/2406.19437">arXiv:2406.19437</a>][<a href="https://doi.org/10.3847/1538-4357/ad7736">doi</a>]</li> <li>Thorp et al. (2025). ApJ, accepted. [<a href="https://arxiv.org/abs/2506.12122">arXiv:2506.12122</a>]</li> <li>Weaver et al. (2022). ApJS 258, 11. [<a href="https://arxiv.org/abs/2110.13923">arXiv:2110.13923</a>][<a href="https://doi.org/10.3847/1538-4365/ac3078">doi</a>]</li> </ol>
Narrative Acts Catalog
<p><strong>What is a Narrative Act?</strong></p> <p><br> A narrative act is a linguistic form that represents a complex action which has a secondary action.</p> <p>For example : Marc encourages Mary to study hard. «To encourage» is the complex action and «To study hard» is the secondary action. </p> <p><br> A narrative act is characterized by :</p> <ul> <li>A predicate form : this is very useful because when a narrative act is coded, it's coded with its predicate form (for more information, see predicate form). For example : Encourage(X, Y, a)</li> <li>Number of involved characters : this attribute represents the number of characters that are involved when the action is executed. It represents the number of active subjects. Example : Marc tells Mary that July is beautiful.Here Marc and Mary are the only involved characters</li> <li>A type : the type defines if the narrative act is an action about an action (AA), an action about a state (AS), a state about an action (SA) or a state about a state (SS)</li> <li>A sequence position : the sequence position corresponds to the position of the principal action related to the secondary action. It can be before, during or after.</li> <li>A level of abstraction : this attribute corresponds to the complexity of the sentence.</li> <li>Valence : valence is a linguistic characteristic of a verb. It corresponds to the number of elements that are connected to that verb. The element can be active or passive. A verb can be avalent (for example : to rain) and it can have a different valence according to how it is used (for example : He gives a gift (v=1). He gives a gift to her (v=2))</li> <li>A domain and a Class : a narrative act is classified in domains and classes. </li> </ul>
Planet Four Data Catalog
<p>This is the data catalog for the paper:</p> <p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0019103518301039">Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits</a></p> <p>The catalog can be automatically retrieved from here using the Python package <a href="https://pypi.org/project/p4tools/">p4tools</a></p>
PITS Apparent Depth Profiles for Mars Global Cave Candidate Catalog (MGC3) Features
<p>Apparent depth profiles calculated by the Pit Topography from Shadows (PITS) tool for the majority of the features in the Mars Global Cave Candidate Catalog (MGC<sup>3</sup>). PITS is a Python framework for automatically calculating apparent depth profiles for Martian and Lunar pits from just a single cropped satellite image. These images can also be single- or multi-band, such as in the case of the Mars Reconnaissance Orbiter (MRO) HiRISE camera. You can learn more about PITS by reading its <a href="https://academic.oup.com/rasti/article/2/1/492/7241547">journal article</a> in RAS Techniques and Instruments, going to its <a href="https://github.com/dlecorre387/Pit-Topography-from-Shadows/">GitHub repository</a> or reading the following <a href="https://www.danlecorre.com/post/first-paper-published">post</a>.</p> <p>Since not all catalogued cave candidates on Mars will be pits, PITS has so far been applied to the following MGC<sup>3</sup> subcategories:</p> <ul> <li>Atypical Pit Craters (APCs).</li> </ul> <p>With plans to extend this to:</p> <ul> <li>Lava tube skylights,</li> <li>small rimless pits,</li> <li>generic, amorphous pits,</li> <li>and polar pits.</li> </ul> <p>This totals 123 apparent depth profiles in CSV format, which have been derived automatically by PITS for 88 APCs. Therefore, these profiles can be plotted as the user prefers, and/or used in combination with other data to reveal more about this particular APC on the surface of Mars.</p> <p>Each depth profile's CSV file is named according to the HiRISE Reduced Data Record Version 1.1. (RDRV11) that it was calculated upon (e.g. ESP_011386_2065_RED_profile.csv for the red-band version of the HiRISE image ESP_011386_2065). Where there are multiple MGC3 APCs contained within a single image, the file names are numbered generally from the most northern to southernmost, or most westerly to easterly. ESRI shapefiles for the location of all APCs in each HiRISE image have been provided in polygon (containing the extents used to crop the larger HiRISE product) and point format in order to give context in these intances. </p> <p>As the headers suggest, the first four columns represent the shadow length (<em><span class="math-tex">\(L\)</span></em>), apparent depth (<em><span class="math-tex">\(h\)</span></em>), and the upper/lower bounds of <span class="math-tex">\(\Delta h\)</span>, respectively, before they have been corrected for non-zero emission angles (<span class="math-tex">\(\varepsilon\)</span>) at the time of image acquisition. Whereas the latter four columns represent the same quantities after <span class="math-tex">\(\varepsilon\)</span>-correction. How this correction is derived and applied is explained in the PITS journal article linked above.</p>
Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013
Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is jus
Catalog of GenBank sequence read archive (SRA) entries of metagenomic DNA sequence analyses of bacterial and archaeal water column communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2012
In contrast to temperate systems, Arctic lagoons that span the Alaska Beaufort Sea coast face extreme seasonality. Nine months of ice cover up to ∼1.7 m thick is followed by a spring thaw that introduces an enormous pulse of freshwater, nutrients, and organic matter into these lagoons over a relatively brief 2–3 week period. Prokaryotic communities link these subsidies to lagoon food webs through nutrient uptake, heterotrophic production, and other biogeochemical processes, but little is known about how the genomic capabilities of these communities respond to seasonal variability. This study characterizes the metabolic capabilities of microbial communities across three seasons in two lagoons and one open coastal site along the eastern Alaska Beaufort Sea coast. We used metagenomic DNA sequence data of bacterial and archaeal water column communities to identify genes of relevant biogeochemical pathways. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA642637 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA642637. This data package is associated with the following publication: Baker, Kristina D., Colleen T. E. Kellogg, James W. McClelland, Kenneth H. Dunton, and Byron C. Crump. “The Genomic Capabilities of Microbial Communities Track Seasonal Variation in Environmental Conditions of Arctic Lagoons.” Frontiers in Microbiology 12 (2021). https://doi.org/10.3389/fmicb.2021.601901. Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provi
Catalog of Coronal Mass Ejections Observed in Conjunction between Radially Aligned Spacecraft in the Inner Heliosphere
<p>This catalog lists 47 CME events observed in a longitudinal conjunction between MESSENGER, Venus Express, STEREO, and Wind/ACE. We list the onset date and time of the probable CME candidate. If the CME was observed by LASCO onboard the SOHO<br> spacecraft, we report the average CME onset time as calculated in the CDAW catalog (average between first-order-constant speed and second-order-constant acceleration onset times). Otherwise, we report the time of the first STEREO/COR image containing the<br> CME. We then list the arrival times of the shock/discontinuity, magnetic ejecta leading edge and trailing edge at spacecraft 1 and 2. Arrival times at MESSENGER are listed from Winslow et al. (2015, 2017), Venus Express from Good and Forsyth (2016), STEREO from Jian, Russell, Luhmann, and Galvin (2018), and L1 from Richardson and Cane (2010). We also list the heliocentric distances of the spacecraft at the CME onset time, the longitudinal separation between the spacecraft when the discontinuity/ejecta arrives<br> at spacecraft 1, and the maximum magnetic field strength observed in the CME (including both the sheath and the ejecta) at each spacecraft. The maximum magnetic field strength measured in the CME at MESSENGER are listed from Winslow et al. (2015, 2017), the maximum magnetic field strength measured in the ejecta at Venus Express are listed from Good and Forsyth (2016). The longitudinal separations are in Heliographic Inertial (HGI) coordinates. We also list the initial CME speed. For the speed, we select the coronagraph which observed the CME closest to a limb event. Limb views signicantly minimize projection effects as compared to halo views and provide a better estimate of CME speeds. When LASCO observed the CME as a limb event, we report the second-order CME speed at 20 Rs (solar radius) listed in the CDAW catalog. For STEREO observations, we report the maximum<br> speed as listed in the CACTus catalog. We also list the average impact speeds at spacecraft 1 from the DBM (Vrsnak et al., 2013) and either the average impact speeds (when spacecraft 2 is Venus Express) or the maximum CME speed (when spacecraft 2 is<br> STEREO/Wind/ACE) measured at spacecraft 2. The maximum CME speeds measured at STEREO are listed from Jian et al. (2018) and at L1 listed from Richardson and Cane. (2010). We list the average transit speeds as well between the Sun and spacecraft 1,<br> spacecraft 1 and spacecraft 2, and the Sun and spacecraft 2.</p>
iMGMC - integrated Mouse Gut Metagenomic Catalog
<p><em>Creation of an new mouse gut gene catalog with special features:</em></p> <ul> <li>more diverse samples from different studies (12 Vendors incl. wild mice and various gut locations)</li> <li>clustering-free approach: all-in-one assembly, keeping track of each ORF to contigs to bins</li> <li>higher taxonomic resolution and more accuracy by using contigs for annotation</li> <li>16S rRNA gene integration via linkage to bins</li> <li>expansion by 20,927 MAGs from sample-wise assembly of 871 mouse gut metagenomic samples, representing 1,296 species</li> </ul> <p>Code used: <a href="https://github.com/tillrobin/iMGMC">https://github.com/tillrobin/iMGMC</a></p> <p>The vast complexity of host-associated microbial ecosystems requires host-specific reference catalogs to survey the functions and diversity of these communities. We generated a comprehensive resource, the integrated mouse gut metagenome catalog (iMGMC), comprising 4.6 million unique genes and 660 metagenome-assembled genomes (MAGs) with many of them (485 MAGs, 73%) linked to reconstructed full-length 16S rRNA gene sequences. iMGMC enables unprecedented coverage and taxonomic resolution of the mouse gut microbiota, i.e. more than 92% of MAGs lack species-level representatives in public repositories (<95% ANI match). The integration of MAGs and 16S rRNA gene data allows a more accurate prediction of functional profiles of communities than based on 16S rRNA amplicons alone. Accompanying iMGMC we provide a set of MAGs representing 1,296 gut bacteria obtained through complementary assembly strategies. We envision that integrated resources such as iMGMC together with MAG collections will enhance the resolution of numerous existing and future sequencing-based studies.</p> <p>Genecatalog:</p> <p>Description Size Filename<br> Catalog ORF sequences 1 GB iMGMC-GeneID.fasta.gz<br> Full assembly contigs 1.3 GB iMGMC-ConitgID.fasta.gz<br> Mapping File (GeneID->ContigID->BinID) 30 MB iMGMC-map-Gene-Contig-Bin.tab.gz<br> Taxonomic annotations 40 MB iMGMC_map_taxonomy.tar.gz<br> Functional annotations 36 MB iMGMC_map_functionality.tar.gz<br> 16S rRNA sequences 2 MB iMGMC-16SrRNAgenes.fasta</p> <p>Metagenome-assembled genomes (MAGs) :</p> <p>Description Size Filename<br> integrated MAGs 0.5 GB iMGMC_MAGs.tar.gz<br> representave mMAGs (n=1296) 1 GB iMGMC-mMAGs-dereplicated_genomes.tar.gz<br> representave hqMAGs (n=830) 0.7 GB iMGMC-hqMAGs-dereplicated_genomes.tar.gz<br> all mMAGs (n=20,927) 15 GB iMGMC-mMAGs.tar.gz<br> Annotations by CheckM, dRep-Clustering, GTDB-Tk 2 MB MAG-annotation_CheckM_dRep_GTDB-Tk.tar.gz<br> Functional annotations (hqMAGs by eggNOG mapper v2) 187 MB hqMAGs.emapper.annotations.gz</p> <p> </p>
A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog - Supporting Data
<p><strong>Description </strong></p> <p>The following files are available : </p> <ul> <li>Simulated non-redundant Gene Catalog (SGC) composed of 128267 genes;</li> <li>Gene abundance profiles across 40 samples: raw read counts, gene length normalized base counts, depth file computed by the jgi_summarize_bam_contig_depth script provided by MetaBAT;</li> <li>Gold Standard (GS) and Gold Standard Single Assignment (GS_SA) binning results;</li> <li>Binning results obtained on the SGC with nine binning methods: MSPminer, MGS-canopy, DAS Tool, MaxBin2, MetaBAT2, SolidBin, CONCOCT, COCACOLA and MyCC.</li> </ul> <p><strong>License</strong></p> <p>These files are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
Catalog of GAIA eDR3 sources within 20 arcsec of pulsars
<p>Catalog of GAIA eDR3 sources within 20 arcsec of pulsars. Details on how the catalog was created can be found in <a href="https://arxiv.org/abs/2011.08075">Antoniadis (2021)</a></p> <p>The source code can be found <a href="https://zenodo.org/record/4378294#.X9-rR-lKjOQ">here</a></p>
A join of the Huber et al. (2014) catalog of stellar parameters and the Kepler Input Catalog
<p>This a join of the <a href="http://arxiv.org/abs/1312.0662">Huber et al. (2014)</a> and the <a href="http://arxiv.org/abs/1102.0342">Kepler Input Catalog</a></p>
RCSED - A Value-Added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-Infrared Bands: Morphologies, Colors, Ionized Gas and Stellar Populations Properties
<p>We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low and intermediate redshift galaxies (0.007 < z < 0.6) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with the state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution during the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up-to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results by Simard et al. We construct the color-magnitude, Faber-Jackson, mass-metallicity relations, compare them with the literature and discuss systematic errors of galaxy properties presented in our catalog. RCSED is accessible from the project web-site and via VO simple spectrum access and table access services using VO compliant applications. We describe several SQL query examples against the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospectives for the catalog extension to higher redshifts and different wavelengths.</p>
Supplementary material 3: World Spider Catalog Bibliographic Data: Treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
List of journal/publisher by ranked by treatment count exported from the World Spider Catalog 14 October 2014 with total treatments by source, cumulative treatments, and cumulative proportion of treatments.
Supplementary material 2: World Spider Catalog Bibliographic Data: Publications from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Ranked list of journal/publisher exported from the World Spider Catalog 14 October 2014 with total articles by source, cumulative articles, and cucmulative proportion of articles.
High-resolution earthquake catalog obtained through template-matching in the Southern Apennine (Italy)
<p>This is an enhanced, high-resolution earthquake catalog obtained through template-matching (TM). It covers the area of the Southern Apennines (Italy), for the period 2009-2014</p> <p>Starting from about 4000 events used as templates, TM allowed to detect the hidden, small-magnitude seismicity in the 0-1 magnitude range, allowing a significant decrease of the magnitude of completeness in the resulting earthquake catalog.</p> <p>The catalog contains:</p> <ul> <li>templates (events catalogued by INGV and used as templates)</li> <li>template-matching detections (i.e. newly detected events by TM)</li> <li>events catalogued by INGV that are also found through template-matching</li> </ul> <p>All events are located with the same 1-D velocity model obtained by averaging several models that have been proposed in the literature, covering different portion of the Southern Apennines. </p> <p><strong>DATA STRUCTURE</strong></p> <p><strong>id</strong>: id of event. Events detected by template-matching start with 'TM', otherwise the id is the same as in the official INGV catalog.</p> <p><strong>lon</strong>: longitude (degrees)</p> <p><strong>lat</strong>: latitude (degrees)</p> <p><strong>depth</strong>: depth in km</p> <p><strong>time</strong>: origin time</p> <p><strong>M_l</strong>: local magnitude</p> <p><strong>lon_error</strong>: error on longitude (degrees)</p> <p><strong>lat_error</strong>: error on latitude (degrees)</p> <p><strong>depth_error</strong>: error on depth (km)</p> <p><strong>RMS</strong>: root-mean-square (sec)</p> <p><strong>az_gap</strong>: azimuthal gap</p> <p><strong>n_phases</strong>: total number of P and S arrivals </p> <p><strong>n_stations</strong>: total number of station recording the event</p> <p><strong>mag_diff</strong>: difference in magnitude between detection and its template</p> <p><strong>dt</strong>: difference in origin time between template and detected event (sec)</p> <p><strong>templ_id</strong>: id of the template event</p> <p><strong>as_template</strong>: =1 if the event was used as template, 0 otherwise</p> <p><strong>matched_TM</strong> (for events already catalogued by INGV): =1 if the events matched a detection made by template matching, =0 otherwise</p> <p><strong>matched_BSI</strong>: ==id of the corresponding event catalogued by INGV. For newly detected events (thus never catalogued before) this field is 'NA'</p>
Extracted Source Properties Catalog for "Monitoring the X-ray Variability of Bright X-ray Sources in M33"
<p>Supplemental data to the article "Monitoring the X-ray Variability of Bright X-ray Sources in M33" accepted for publication in ApJ. Contains all extracted source properties for the 56-source final catalog, including single-ObsID extractions and merged values. See ReadMe for column descriptions and additional comments.</p>
Review of redshift values of bright AGNs with hard spectra in 4LAC catalog v3
<p>Review of redshift values of bright AGNs with hard spectra in 4LAC catalog</p> <p>FITS table and description file (pdf)</p>
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