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73 results for “Data Catalog”
Tremor catalogs produced (output data) in the paper "Dynamics of the 2021 Fagradalsfjall eruption (Iceland) revealed by volcanic tremor patterns"
<p>This repository contains tremor catalogs produced as output data in the paper "Dynamics of the 2021 Fagradalsfjall eruption (Iceland) revealed by volcanic tremor patterns" submitted to Journal of Geophysical Research - Solid Earth by Soubestre J., Caudron C., Melnik O., Lecocq T., Jaupart C., Shapiro N.M., Journeau C., Çubuk-Sabuncu Y., and Jónsdóttir K.</p> <p>In particular, it contains three catalogs :<br>+ time and 3-D location of <strong>tremor sources</strong> shown in Figure 5 ;<br>+ time and duration of <strong>tremor bursts</strong> associated with pulsating lava fountains shown in Figure 6 ;<br>+ time and duration of <strong>repose times</strong> associated with pulsating lava fountains shown in Figure 6 ;</p>
GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Candidate Data Release
<p>Data associated with candidates in <a href="https://dcc.ligo.org/LIGO-P2100063/public">GWTC-2.1</a>. These are gravitational-wave candidates from the first half of the third observing run (O3a) of the Advanced LIGO and Virgo detectors that pass a false alarm rate threshold of 2/day. We upload a tar file (search_data_GWTC2p1.tar.gz) containing all the data and a python notebook (search_data.ipynb) which provides description on how to use the files contained in the dataset.</p>
Data release associated with ``Search for Coincident Gravitational Wave and Long Gamma-Ray Bursts from 4-OGC and the Fermi-GBM/Swift-BAT Catalog"
<p>This is associated data release for the paper https://arxiv.org/abs/2208.03279. It contains the skymaps from potential gravitational-wave candidates from binary neutron star or neutron star-black hole merger. The notebook showcases how to use it. More information can be found in the github repository: https://github.com/gwastro/gw-longgrb</p> <pre> </pre> <pre> </pre>
Regional Moment Tensor Catalog (Declustered-Shallow Depth) for Northern Banda Arc Region-Indonesia (2009 to 2020) with Additional 3D Synthetic Data
<p>This dataset is produced using an innovative automated procedure that enhances the accuracy and reliability of moment tensor solutions, as described in Halauwet et al. (2024). The dataset includes RMT solutions for the period from 2009 to 2020 in the Northern Banda Arc Region. Additionally, synthetic data, test results and setup files used in the testing and validation of this procedure are included.<br><br>When using this data, please cite the following references:</p> <ul> <li>Halauwet, Y., Afnimar, Triyoso, W., Vackář, J., Daryono, Supendi, P., Daniarsyad, G., Simanjuntak, A. V. H., Pranata, B., Narwadan, H. A. A. M., & Hakim, M. L., Regional moment tensor catalog (declustered-shallow depth) for northern Banda Arc region-Indonesia (2009 to 2020) with additional 3D synthetic data [Data set]. <em>Zenodo</em>, 2024;, <a href="https://doi.org/10.5281/zenodo.10212539">https://doi.org/10.5281/zenodo.10212539</a></li> <li>Halauwet, Y., Afnimar, Triyoso, W., Vackář, J., Daryono, Supendi, P., Daniarsyad, G., Simanjuntak, A. V. H., Pranata, B., Narwadan, H. A. A. M., & Hakim, M. L., A new automated procedure to obtain reliable moment tensor solutions of small to moderate earthquakes (3.0 ≤ M ≤ 5.5) in the Bayesian framework, <em>Geophysical Journal International</em>, 2024;, ggae309, <a href="https://doi.org/10.1093/gji/ggae309">https://doi.org/10.1093/gji/ggae309</a></li> </ul> <p>Email: yehezkiel.halauwet@bmkg.go.id</p>
Data set, Model, and Catalog for: Data-driven stellar intrinsic colors and dust reddenings for spectro-photometric data
<p>Intrinsic colors of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the stellar intrinsic colors with the atmospheric parameters, Teff , log g, and [M/H], which is an improvement of the widely used blue-edge method. The dust reddening toward each line-of-sight can then be calculated by the observed colors minus the derived intrinsic colors.</p> <p>Here we provide the related data sets:</p> <ul> <li>xgb_model.pkl: the trained XGBoost model.</li> <li>use_xgb.py: a simple script showing how to use the XGBoost model to predict intrinsic colors.</li> <li>data_set.fits: this fits file contains the training and test sets, separated into four data arrays: <ul> <li>X_train: X-data (teff,logg,mh) of the training set.</li> <li>X_test: X-data (teff,logg,mh) of the test set.</li> <li>y_train: y-data (BP-RP, BP-Ks, J-Ks) of the training set.</li> <li>y_test: y-data (BP-RP, BP-Ks, J-Ks) of the test set.</li> </ul> </li> <li>IC_catalog.csv: a catalog containing a representative set of intrinsic colors at three bands ('BPRP0', 'BPK0', and 'JK0' in columns) as a function of typical Teff, logg, and [M/H] ('teff', 'logg', and 'mh' in columns).</li> </ul> <p>With the above data and model, users can apply the trained XGBoost to new sources to predict their intrinsic colors and calculate their dust reddenings. One can further control the quality of the prediction by selecting training-like sources with the training set and estimating the <a>generalization error by the test set.</a></p>
Dog gut gene catalog. Supplemental data for "Similarity of the dog and human gut microbiomes in gene content and response to diet"
<p>Gene catalogue for the dog gut microbiome including</p> <ol> <li>FASTA file of nucleotide sequences (including padding, see coords file for exact coordinates)</li> <li>FASTA file of amino-acid sequences</li> <li>coords file (gene coordinates)</li> <li>Taxonomic predictions</li> <li>Functional predictions</li> </ol> <p>See the paper "<em>Similarity of the dog and human gut microbiomes in gene content and response to diet</em>" by Coelho et al. in Microbiome for details. We ask that you cite that publication when using this dataset in published literature</p>
Data for: PerchPicker classifier model v7: A catalog of American silver perch (Bairdiella chrysoura) calls for machine learning
<p>This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep - 2023), <em>Automated cataloging of American silver perch (Bairdiella chrysoura) calls using machine learning</em>. The software accompanying this paper is known as PerchPicker (<a href="https://github.com/drbohnen/PerchPicker" rel="noopener">GitHub - drbohnen/PerchPicker)</a>, and the classifier model presented in the paper is v7. It consists of more than 6000 labeled perch and 6000 labeled other signals. Labeled scalogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 90 ms long. The center 30 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'perch' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the PerchPicker model or develop alternative classifiers. </p>
Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
<p>This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep – 2023), <span>Automated cataloging of oyster toadfish (<em>Opsanus</em> <em>tau</em>) calls using template matching and machine learning</span>. The software accompanying this paper is known as ToadFishFinder, and the classifier model presented in the paper is v4. It consists of more than 10000 labeled toadfish and 10000 labeled other signals. Labeled spectrogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 1350 ms long. The center 850 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'boatwhistle' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the ToadFishFinder model or develop alternative classifiers. </p>
Data for: PerchPicker classifier model v7: A catalog of American silver perch (Bairdiella chrysoura) calls for machine learning
Open the record for dataset details and reuse information.
Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
Open the record for dataset details and reuse information.
SPECTRAL PROPERTIES OF QUASARS FROM SLOAN DIGITAL SKY SURVEY DATA RELEASE 14: THE CATALOG
<p>We present measurements of the spectral properties for a total of 526,265 quasars, out of which 63% have continuum S/N> 3 pixel<sup>−1</sup>, selected from the fourteenth data release of the Sloan Digital Sky Survey (SDSS-DR14) quasar catalog. We performed a careful and homogeneous analysis of the SDSS spectra of these sources, to estimate the continuum and line properties of several emission lines such as Hα, Hβ, Hγ, Mg ii, C iii], C iv and Lyα. From the derived emission line parameters, we estimated single-epoch virial black hole masses (M<sub>BH</sub>) for the sample using Hβ, Mg ii, and C iv emission lines. The sample covers a wide range in bolometric luminosity (log L<sub>bol;</sub> erg s<sup>−1</sup> ) between 44.4 and 47.3 and log M<sub>BH</sub> between 7.1 and 9.9 M. Using the ratio of L<sub>bol</sub> to the Eddington luminosity as a measure of the accretion rate, the logarithm of the accretion rate is found to be in the range between −2.06 and 0.43. We performed several correlation analyses between different emission line parameters and found them to match with that known earlier using smaller samples. We noticed that strong Fe ii sources with large Balmer line width, and highly accreting sources with large M<sub>BH</sub> are rare in our sample. We make available an extended and complete catalog that contains various spectral properties of 526,265 quasars derived in this work along with other properties culled from the SDSS-DR14 quasar catalog.</p> <p> </p> <p><strong>Here, you will find the entire spectral catalog in the FITS forma</strong>t.</p> <ul> <li>The main catalog (“ <strong>dr14q_spec_prop.fits</strong>”) is based on the spectral information from this study consisting of 274 columns, which are described in Table B4.</li> <li>An extended catalog (“ <strong>dr14q_spec_prop_ext.fits</strong>”) where all the columns of DR14Q (Paris et al. 2018) are appended after the main catalog (ie, after column # 274). The extended catalog has a total of 380 columns.</li> </ul> <p><strong>We also make available all the model components (in FITS format) for each quasar and the spectral decomposition plots (PDF format):</strong> <strong><a href="https://seafile.utu.fi/d/9866433c200743119e92/">HERE</a></strong></p> <ul> <li><em>Each model FITS file has the following 8 columns:</em></li> </ul> <blockquote> <ol> <li>wave : rest-frame wavelength</li> <li>flux : rest-frame spectral flux</li> <li>err : rest-frame spectral flux uncertainty</li> <li>model_host : decomposed host-Galaxy flux</li> <li>model_pl_bc : a sum of the model power law and Balmer continuum</li> <li>model_feii : a sum of the Fe II UV and Fe II optical</li> <li>model_line_bc : total broad line model</li> <li>model_line_nc : total narrow line model</li> </ol> </blockquote> <ul> <li><strong>A dedicated website has been created for Supplementary material:</strong> <strong><a href="http://www.utu.fi/sdssdr14">WEBSITE</a></strong></li> </ul>
Data for: "A partial near infrared guide star catalog for Thirty Meter Telescope Operations"
<p>This .zip file contains the infrared guide star catalogs (IRGSC) generated using optical data (in g, r, i, z, and y bands) from the PANSTARRS DR2 for twenty test fields selected across the TMT's observable sky. The IRGSC is generated by computing the near infrared (J, H and K band) magnitudes of the stellar sources using the Kurucz and Phoenix stellar atmospheric models. Each source in the IRGSC is likely to be stellar and contains the respective PANSTARRS and GAIA object identifiers , their astrometric information from Gaia DR3, the optical and computed NIR magnitudes, the model parameters, the scale factor and the metric d_dev that determines the goodness-of-fit of the model to the data. In addition, the test fields also contain the infrared guide star catalog validated using the UKIDSS DR11 data and plots that show the comparison between the computed NIR magnitudes and observed UKIDSS NIR magnitudes.</p> <p>Inside this .zip file, there is also another directory that contains the IRGSC generated for additional PANSTARRS Medium Deep Survey fields with readily available PANSTARRS 3pi survey data. Some plots are included that show the efficiency of star-galaxy classification and completeness of the 3-pi survey data for these fields.</p> <p>More details and the code to generate IRGSC can be found <strong><a href="https://github.com/sshah1502/irgsc/tree/main">here</a></strong></p>
Normalized subject indexing data of K10plus library union catalog
<p>This dataset contains <strong>normalized subject indexing data of K10plus library union catalog</strong>. It includes links between bibliographic records in K10plus and concepts (subjects or classes) from controlled vocabularies:</p> <ul> <li>kxp-subjects.tsv.gz: TSV format</li> <li>kxp-subjects.nt.gz: RDF format (in form of NTriples)</li> <li>vocabularies.json: information about vocabularies</li> <li>stats.json: statistics (number of records, subjects per vocabulary etc.)</li> </ul> <p>The dataset is based on a K10plus database dump at 2024-12-02.</p> <p><strong>K10plus</strong></p> <p>K10plus is a union catalog of German libraries, run by library service centers BSZ and VZG since 2019. The catalog contains bibliographic data of the majority of academic libraries in Germany. Bibliographic records in K10plus are uniquely identified by a PPN identifier.</p> <p>Several APIs exist to retrieve more data for a record via its PPN, e.g. link into K10plus OPAC:</p> <p>https://opac.k10plus.de/PPNSET?PPN={PPN}</p> <p>Retrieve full record in MARC/XML format:</p> <p>https://unapi.k10plus.de/?format=marcxml&id=opac-de-627:ppn:{PPN}</p> <p>Get formatted citation for display:</p> <p>https://ws.gbv.de/suggest/csl2?citationstyle=ieee&language=en&database=opac-de-627&query=pica.ppn=${PPN}</p> <p>APIs to look up more data from a notation or identifier of a vocabulary can be found in <a href="https://bartoc.org/">https://bartoc.org/</a>. For instance BK class 58.55 can be retrieved via DANTE API:</p> <p>https://api.dante.gbv.de/data?uri=http%3A%2F%2Furi.gbv.de%2Fterminology%2Fbk%2F58.55</p> <p>See vocabularies.json for mapping of vocabulary symbol to BARTOC URI and additional information.</p> <p><strong>Statistics</strong><br>See stats.json for number of records, links, triples and subjects per vocabulary.</p> <p><strong>TSV</strong></p> <p>The .tsv file contains three tab-separated columns:</p> <ol> <li>Bibliographic record identifier (PPN)</li> <li>Vocabulary symbol</li> <li>Notation or identifier in the vocabulary</li> </ol> <p>An example:</p> <p>010000011 bk 58.55<br>010000011 gnd 4036582-7</p> <p>Record 010000011 is indexed with class 58.55 from Basic Classification and with authority record 4036582-7 from Integrated authority file.</p> <p><strong>RDF</strong></p> <p>The NTriples file contains the same information as given in TSV file but identifiers are mapped to URIs. An example:</p> <p><http://uri.gbv.de/document/opac-de-627:ppn:010000011> <http://purl.org/dc/terms/subject> <http://d-nb.info/gnd/4036582-7> .<br><http://uri.gbv.de/document/opac-de-627:ppn:010000011> <http://purl.org/dc/terms/subject> <http://uri.gbv.de/terminology/bk/58.55> .</p> <p><strong>Changelog</strong></p> <ul> <li>2024-12-11: new dump based on K10plus title records extracted by a slightly different workflow.</li> <li>2024-04-05: new dump from end of February 2024. Total number of records decreased by merging of duplicates.</li> <li>2024-01-11: New dump from end of 2023. Added fivs and fivr classification</li> <li>2023-11-01: New dump from end of September.</li> <li>2023-05-07: New dump. Number of records slightly reduced because K10plus cleaned up duplicate records.</li> <li>2023-04-13: New dump, added stats.json</li> <li>2023-01-20: New dump</li> <li>2022-09-11: New dump, fixed PPN URIs and broken UTF-8 encoding</li> <li>2022-08-24: Fixed GND URIs, added LCC and KAB (<a href="https://doi.org/10.5281/zenodo.7018350">https://doi.org/10.5281/zenodo.7018350</a>)</li> <li>2022-08-24: First version (<a href="https://doi.org/10.5281/zenodo.7016626">https://doi.org/10.5281/zenodo.7016626</a>)</li> </ul> <p><strong>License and provenance</strong></p> <p>All data is public domain but references are welcome. See <a href="https://coli-conc.gbv.de/">https://coli-conc.gbv.de/</a> for related projects and documentation.</p> <p>This dataset has been created with public scripts from git repository <a href="https://github.com/gbv/k10plus-subjects">https://github.com/gbv/k10plus-subjects</a>.</p>
Data presented in Stress regimes and dynamic implications of the southeastern margin of the Tibetan Plateau: Insights from a refined focal mechanism catalog
<p>The dataset includes thewaveform data required for source mechanism inversion presented in the paper Stress regimes and dynamic implications of the southeastern margin of the Tibetan Plateau: Insights from a refined focal mechanism catalog</p>
Sichuan GNSS data and earthquake catalog
<p>The GNSS data is provided by GNSS data product service platform of China Earthquake Administration. </p>
Data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows"
<p>These are the data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows" by J. F. Crenshaw, et. al. This includes the input catalog and the outputs of the workflow described here https://github.com/jfcrenshaw/pzflow-paper, as well as a gzip of the github repo.</p>
Cataloging Distant Galactic Open Clusters: Identification of 739 New Star Clusters Beyond 5 kpc Utilizing GAIA DR3 Data
<p><span>The figures of the 739 open clusters that report in our paper (Cataloging Distant Galactic Open Clusters: Identification of 739 New Star Clusters Beyond 5 kpc Utilizing GAIA DR3 Data)</span></p> <p><span> complete King's model<span> </span>profile fitting, sky charts and 5-panels ( spatial distribution, proper-motion distribution,</span></p> <p><span><span>parallax statistics, parallax distribution, and CMD)</span> .</span></p>
Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs [DATA]
<p>Catalogs for Nature Communications article: Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs.</p> <p> </p> <p>Update: In DataVariableExplanation, two catalogs from University of Minnesota</p> <p>Mixed mode and mode I bending data description needs to show that the third column is in seconds.</p>
Supplemental data for: Longitudinal, multi-platform metagenomics yields a high-quality genomic catalog and guides an in vitro model for cheese communities
<p><span>Microbiomes are intricately intertwined with human health, geochemical cycles, and food production. While many microbiomes of interest are highly complex and experimentally intractable, cheese rind microbiomes have proven powerful model systems for the study of microbial interactions. To provide a more comprehensive view of the genomic potential and temporal dynamics of cheese rind communities, we combine longitudinal, multi-platform metagenomics of three ripening washed-rind cheeses with whole genome sequencing of community isolates. Sequencing-based approaches revealed a highly reproducible microbial succession in each cheese, co-existence of closely related <em>Psychrobacter</em> species, and enabled the prediction of plasmid and phage diversity and their host associations. Combined with culture-based approaches, we established a genomic catalog and a paired 16-member in vitro washed rind cheese system. The combination of multi-platform metagenomic time-series data and an <em>in vitro</em> model provides a rich resource for further investigation of cheese rind microbiomes both computationally and experimentally. </span></p>
Migration of mechanical perturbations estimated by seismic coda wave interferometry during the 2018 pre-eruptive period at Kīlauea volcano, Hawaii : Noise Cross-correlation Functions, Seismic catalog, and GNSS data
<p>ARCHIVE_NCFs_KILAUEA_2018.zip : Compress folder with (1) the daily noise cross-correlation functions (in MSEED format) of the station pairs used in the paper and (2) the one hour noise cross-correlation functions (in H5 format) of the station pairs used in the figure 9 of the paper.</p> <p>Code_Data_HVO.ipynb : Code to download the seismic data, available on IRIS, used in this paper.</p> <p>GPS_data_AHUP.zip : Compress folder with the daily GPS data of the station AHUP used in the paper [Year, Month, Day, Day_of_the_year, Second_of_the_day, East_comp(mm), North_comp(mm), Vertical_comp(mm), Sig_East_comp, Sig_North_comp, Sig_Vertical_comp].</p> <p>Radial_tilt_UWD.txt : Daily radial tilt measurement of the tiltmeter UWD [Year, Month, Day, Radial_tilt(µrad)].</p> <p>Seismic_stations_Kilauea.txt : Name code and location of the seismic stations used in the paper [Station_code, Longitude, Latitude].</p> <p>Seismicity_Catalog_Kilauea_2018_USGS.txt : Seismic catalog from USGS used in the paper [Date_Time, Latitude, Longitude, Depth, Magnitude].</p>
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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.