Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,555
datasets available to search
ShareScore release 0.7.1
Dataset results
2,555 results for “catalogs”
Relocated earthquake catalog using DiTing
<p>Relocated earthquake catalog from 2021/08/01 to 2022/09/20 using the DiTingPicker method</p>
Catalog of Cool Host Stars with Established Rotation Periods
<p>Catalog of 249 late K- and M-type exoplanet host stars with rotation periods obtained from the literature or new analysis of space- or ground-based time-series photometry as of August 2022. Model-based, metallicity-dependent corrections are provided but not included in gyrochronological age estimates. Please cite the reference paper if any information from this table is used. Table 1 in Gaidos et al. 2023, in press, in CDS format.</p>
Earthquake catalogs for: A specific earthquake processing workflow for studying long-lived explosive volcanic eruptions with application to the 2008 Okmok eruption
<p>Repository for the seismic catalogs from Garza-Giron et al. (2023a,b). These include the catalog with absolute locations using NonLinLoc (Lomax et al., 2001; Lomax and Curtis, 2001), and the relocated catalogs using hypoDD (Waldhauser and Ellsworth, 2000) and GrowClust (Trugman and Shearer, 2017).</p> <p>The header of the CSV files is as follows:</p> <p><strong>Date</strong> (year/month/day), <strong>Time</strong> (hr:min:sec:msec), <strong>Latitude</strong> (decimal degrees), <strong>Longitude</strong> (decimal degrees), <strong>Depth</strong> (km), <strong>Magnitude</strong> (Ml calculated for this study), <strong>Event_type</strong> (VT:vulcano-tectonic;LP:long-period), <strong>Number of stations</strong> where the event was detected, <strong>ID</strong></p> <p>References:</p> <div>Garza‐Giron, R., Brodsky, E. E., Spica, Z. J., Haney, M. M., & Webley, P. W. (2023a). A specific earthquake processing workflow for studying long‐lived, explosive volcanic eruptions with application to the 2008 Okmok Volcano, Alaska, eruption. <em>Journal of Geophysical Research: Solid Earth</em>, e2022JB025882.</div> <div> </div> <div> <div>Garza‐Girón, R., Brodsky, E. E., Spica, Z. J., Haney, M. M., & Webley, P. W. (2023b). Earthquakes record cycles of opening and closing in the enhanced seismic catalog of the 2008 Okmok Volcano, Alaska, eruption. <em>Journal of Geophysical Research: Solid Earth</em>, <em>128</em>(7), e2023JB026893.</div> <div> </div> </div> <p>Lomax A, Curtis A (2001) Fast, probabilistic earthquake location in 3-D models using oct-tree importance sampling. Geophys Res Abstracts, 3:955.</p> <p>Lomax, A., Zollo, A., Capuano, P., and Virieux, J. (2001). Precise, absolute earthquake location under Somma‐Vesuvius volcano using a new 3D velocity model.Geophysical Journal International,146, 313–331.</p> <p>Trugman, D. T., and Shearer, P. M. (2017). GrowClust: A hierarchical clustering algorithm for relative earthquake relocation, with application to the Spanish Springs and Sheldon, Nevada, earthquake sequences. Seismological Research Letters, 88(2A), 379-391.</p> <p>Waldhauser, F., and Ellsworth, W. L. (2000). A double-difference earthquake location algorithm: Method and application to the northern Hayward fault, California. Bulletin of the Seismological Society of America, 90(6), 1353-1368.</p>
game_es_9th_generation_catalog
<p>This dataset, created for educational purposes, contains video games related information extracted from a Spanish company called GAME. </p>
SERVS (Spitzer Extragalactic Representative Volume Survey) IRAC12 Catalogs
<p>*** SERVS DR2 - SERVS IRAC12 "Bandmerge" - Last Updated on 13 Jan 2017 *** </p> <p>*** Mattia Vaccari - University of Cape Town - <a href="https://www.mattiavaccari.net">https://www.mattiavaccari.net</a> ***</p> <p><a href="https://www.mattiavaccari.net/df">https://www.mattiavaccari.net/df</a> - <a href="https://doi.org/10.5281/zenodo.7929150">https://doi.org/10.5281/zenodo.7929150</a></p> <p>Mauduit et al. 2012 - <a href="https://ui.adsabs.harvard.edu/abs/2012PASP..124..714M/abstract">https://ui.adsabs.harvard.edu/abs/2012PASP..124..714M/abstract</a></p> <p>Vaccari 2015 - <a href="https://ui.adsabs.harvard.edu/abs/2012PASP..124..714M/abstract">https://ui.adsabs.harvard.edu/abs/2015fers.confE..27V/abstract</a></p> <p>We produced single-band IRAC1 and IRAC2 catalogs, measured aperture and integrated fluxes/magnitudes and corrected aperture fluxes/magnitudes using the correction factors adopted in SWIRE DR2 by</p> <p>Surace et al. 2005 - <a href="https://ui.adsabs.harvard.edu/abs/2005AAS...207.6301S/abstract">https://ui.adsabs.harvard.edu/abs/2005AAS...207.6301S/abstract</a></p> <p>SWIRE DR2 docs - <a href="https://irsa.ipac.caltech.edu/data/SPITZER/SWIRE/docs/delivery_doc_r2_v2.pdf">https://irsa.ipac.caltech.edu/data/SPITZER/SWIRE/docs/delivery_doc_r2_v2.pdf</a></p> <p>IRAC aperture radii [arcsec] - ap1 = 1.4 / ap2 = 1.9 / ap3 = 2.9 / ap4 = 4.1 / ap5 = 5.8</p> <p>We did merge single-band IRAC1 and IRAC2 catalogs into an IRAC12 catalog using a search radius of 1.0 arcsec</p> <p>We did add three columns at the beginning of the catalog</p> <p>- ID_12 index to track sources (single-band indices are NUMBER_1 and NUMBER_2)</p> <p>- ALPHA_J2000_12 and DELTA_J2000_2 which are average values for sources detected in both IRAC1 and IRAC2</p>
Trinidad Zone 2008-2022 Template-Detected Earthquake Catalog & 2011-2012 Aftershock Array Catalog
<p>This dataset contains two earthquake catalogs for the Trinidad zone in the Raton Basin on the Colorado-New Mexico, USA border. The excel spreadsheet has three tabs and each has a descriptor. The "aftershock_catalog" tab is an earthquake catalog following the 2011 Mw 5.3 mainshock earthquake in the Trinidad zone. It spans from August 2011 to May 2012. The "template" tab contains the earthquakes used as templates for the template-detection earthquake catalog. The "template_detected_catalog" is an earthquake catalog spanning May 30, 2008 to April 1, 2022 built using Transportable Array seismometer T25A.</p>
ValEqt: A high-resolution Earthquake and Repeating earthquakes catalog of the 2017 Valparaiso sequence
<p><strong>The ValEqt earthquake catalog</strong></p> <p>Description:</p> <p>Catalog of earthquakes detected near the 2017 Mw=6.9 Valparaiso (Chile) earthquake from 01/01/2016 to 01/01/2021. We also include a catalog of ValEqt's repeating earthquakes.</p> <p>Methods used to build this dataset are extensively described in this paper : <em>Upcomming paper Doi</em></p> <p>Files:</p> <ol> <li><em>ValEqt.txt </em>: Earthquake catalog</li> <li><em>Repeater.txt</em> : Repeating earthquake catalog</li> </ol> <p> </p>
Number of publications in French for the winners of the Nobel Prize for Literature (extraction of the BnF catalog)
<p>Python script and dataset.</p> <p>The dataset contains the number of publications in French one can find in the catalog of the Bibliothèque nationale de France (BnF) for every winner of the Nobel Prize of Literature.</p> <p>Idea from LIBER : <a href="https://twitter.com/kiru/status/1677413039132352512">https://twitter.com/kiru/status/1677413039132352512</a></p>
Catalog of Sagittarius Stream Stars with Gaia BP/RP Metallicities
<p>This catalog contains 34,240 stars selected as high probability Sagittarius stream members (selected in Vasiliev et al. 2021) with reported metallicities inferred from Gaia BP/RP spectra (measured in Andrae et al. 2023), as well as their Sagittarius/disk/outlier membership probabilities under the model described in Cunningham et al. (2023).</p>
Global long period earthquake catalog
<p>This file contains information about long period earthquakes recorded in between 2010 and end of 2022</p>
OntoUML/UFO Catalog
<p>The FAIR Model Catalog for Ontology-Driven Conceptual Modeling Research, commonly referred to as <strong>OntoUML/UFO Catalog</strong>, is a structured and open-source catalog that contains OntoUML and UFO ontology models. It was conceived to allow collaborative work and to be easily accessible to all its users.</p> <p>The goal of the OntoUML/UFO Catalog is to support empirical research in OntoUML and UFO, as well as for the general conceptual modeling area, by providing high-quality curated, structured, and machine-processable data on <em>why</em>, <em>where</em>, and <em>how</em> different modeling approaches are used.</p> <p>The catalog offers a diverse collection of models, created by modelers with varying modeling skills, for a range of domains and different purposes. The models are available in machine-readable formats (JSON and Turtle) and are accessible via permanent identifiers.</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>
Codes and catalogs for: Parametric testing of EQTransformer's performance against a high-quality, manually-picked catalog for reliable and accurate seismic phase picking
<p><strong>Codes and Catalogs for:</strong> "Parametric Testing of EQTransformer's Performance Against a High-Quality, Manually-Picked Catalog for Reliable and Accurate Seismic Phase Picking."</p> <p><strong>Codes:</strong></p> <ol> <li><strong>overlap_check.py:</strong> This script tests the overlap parameter of EQTransformer to help minimize detection inconsistencies.</li> <li><strong>picks_comparison_other_networks.py:</strong> This code evaluates the probability threshold of EQTransformer by obtaining the time differences between picks from a catalog and EQTransformer.</li> <li><strong>test_seisbench_eq_eqt.py:</strong> A comparative analysis between the native EQTransformer and its implementation in SeisBench.</li> </ol> <p><strong>Catalogs:</strong></p> <ol> <li><strong>picks_differences_all_years_0.01_mag_cat.csv:</strong> This catalog presents pick differences for the central Alpine Fault using the SAMBA network and manual picks from Michailos et al. (2019).</li> <li><strong>sed_picks.csv:</strong> A catalog that showcases pick differences derived from data obtained from the Swiss Seismological Service (SED).</li> </ol> <p><strong>Note:</strong> Versions <1.0 represent pre-acceptence files and should not be used.</p>
CIV absorption catalog in SDSS DR12
<p>For each sight-line, identified by Column 1 and 2, we report the absorber’s redshift (Column 3), column density in log(cm −2 ) (Column 4), Doppler velocity dispersion in km s −1 (Column 5), rest equivalent width for 1548 Å W𝑟 ,1548 (Column 6), rest equivalent width for 1550 Å W𝑟 ,1550 (Column 7), the posterior probability of the C iv absorber P(M𝐷 ) (Column 8), and the posterior probability of the singlet absorber P(M𝑆 ) (Column 9). We show only absorbers with P(M𝐷 )≠NaN. This table demonstrates a portion of the full table for the first ten rows. Note that those measurements with large errors are uncertain (i.e. low absorption model posterior probability).</p>
Catalog of seismicity for the Kerguelen Islands (1999-2020)
<p>This catalog of the seismicity of the Kerguelen Islands is related to the article: 'Recent seismicity on the Kerguelen Islands' by O. Lengliné, J. Rimpôt, A. Maggi and D. Zigone, Seismica, 2023.<br> </p>
Red_Giants Catalog
<p>This dataset is provided by a red giant search method based on convolutional neural networks. This data set is located in the sub-area of the SMSS survey project. After data cleaning and search through the CBAMResNets model, and comparison with models such as CVGG16 and TPC, a relatively complete and reliable red giant star catalog-provided area for this sub-area was obtained.</p>
Metatranscriptomic unigenes catalog of MICROSTORE project
<p><strong>Extracted from Monjot et al., 2023 </strong></p> <p><em>Sequencing data are archived at ENA under accession number PRJEB61527.</em></p> <p>The metatranscriptome derived unigene catalog and the assessment of their expression were obtained as described in Carradec <em>et al.</em> (2018). Paired-ends reads from each metatranscriptomic sample were assembled using velvet (v1.2.07) with a kmer size of 89 as described in Carradec <em>et al.</em> (2018). Isoform detection was performed using oases (v0.2.08). Contigs smaller than 150 bp were removed from further analysis. Contig redundancy was removed using CD-HIT-EST (v4.6.1), with the following parameters: <em>-id 95 -aS 90</em> (95% of nucleic identity over 90% of the length of the smallest sequence). For each cluster of contigs, the longest sequence was kept as reference for the unigene catalog. In order to estimate the expression of each unigene in each sample, cleaned reads were mapped against the reference catalog using the bwa tool (v0.7.15). The following parameters were used: <em>bwa aln -l 30 -O 11 -R 1; bwa sampe -a 20000 -n 1 –N; samtools; rmdup</em>. Low complexity reads were removed. Reads covering at least 80% of read length with at least 95% of identity were retained for further analysis. In the case of several possible best matches, a random one was picked.</p> <p>Proteins were predicted from all unigenes with <em>Transdecoder.LongOrfs</em> followed by <em>TransDecoder.Predict</em> (v5.5.0) using the default parameters. Then, unigenes without predicted protein were used for a second run with a minimum protein length of 70 <em>(-m</em>). Finally, the predicted proteins were tested against the AntiFam database (v7.0) (Eberhardt <em>et al.,</em> 2012) with <em>hmmsearch</em> using the <em>--cut_ga</em> parameter (Eddy, 2011).</p> <p>The KEGG Orthology (KO) identifiers were assigned by KoFamScan (v1.3.0) with the KO’s HMM profiles (2022-01-03 release). For proteins without significant hit, the best hit with an e-value <1e-5 was retained as described in Hu <em>et al.</em>(2018).</p> <p>Taxonomic affiliation was performed on proteins with the MMseqs2 suite (v407b315) (Steinegger & Söding, 2017), against the MetaEuk database (Levy Karin <em>et al.,</em> 2020). Taxonomy was assigned with mmseqs taxonomy and the parameters <em>--tax-lineage 1 --lca-mode 2 --max-seqs 100 -e 0.00001 -s 6 --max-accept 100</em>. The unigene catalog was cleaned of contaminants by excluding proteins and unigenes affiliated to Human, Bacteria, Archaea, Virus and Metazoans. </p> <p> </p> <p><strong>References</strong></p> <p>Carradec, Q., Pelletier, E., Da Silva, C., Alberti, A., Seeleuthner, Y., Blanc-Mathieu, R., et al. (2018) A global ocean atlas of eukaryotic genes. <em>Nat Commun</em> <strong>9</strong>: 373.</p> <p>Eddy, S.R. (2011) Accelerated Profile HMM Searches. <em>PLoS Comput Biol</em> <strong>7</strong>: e1002195.</p> <p>Hu, S.K., Liu, Z., Alexander, H., Campbell, V., Connell, P.E., Dyhrman, S.T., et al. (2018) Shifting metabolic priorities among key protistan taxa within and below the euphotic zone. <em>Environmental Microbiology</em> <strong>20</strong>: 2865–2879.</p> <p>Levy Karin, E., Mirdita, M., and Söding, J. (2020) MetaEuk—sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. <em>Microbiome</em> <strong>8</strong>: 48.</p> <p>Steinegger, M. and Söding, J. (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. <em>Nat Biotechnol</em> <strong>35</strong>: 1026–1028.</p>
High frequency moonquake catalog
<p><strong>Catalog csv file for the Apollo 14, 15, and 16 stations</strong></p><p><strong>== File names ==</strong></p><ul><li>S14_New_MQ_SP.csv</li><li>S15_New_MQ_SP.csv</li><li>S16_New_MQ_SP.csv</li></ul><p><strong>== Format ==</strong></p><ul><li>1st column: Event ID</li><li>2nd column: Year</li><li>3rd column: Day of Year (DOY)</li><li>4th column: Station (14, 15, or 16)</li><li>5th column: Start time of a window used in coherence analysis</li><li>6th column: End time of a window used in coherence analysis</li><li>7th column: Event Type (see the main text)</li></ul><p><strong>== Note ==</strong></p><p>The detailed information on new shallow moonquakes is summarized in the main text (Table A1 in Appendix A). You may refer to it for a particular analysis of shallow moonquakes.</p>
A Deep Learning-Based Low-Frequency Earthquake Catalog in Southern Vancouver Island
<p>A deep learning-based LFE catalog in southern Vancouver Island. Refer to the paper "<strong>Detection of Hidden Low-Frequency Earthquakes in Southern Vancouver Island with Deep Learning</strong>" by J.-T. Lin, A. M. Thomas, L. Bachelot, D. R. Toomey, J. Searcy, D. Melgar for details. The columns in the data are as follows: starttime: start of the detection window; OT: event origin time; lon: longitude; lat: latitude; depth: source depth in km; residual: averaged travel time residual; dt: shifted time from starttime to OT; N: number of stations used. </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.
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.