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1,392 results for “Match”
Waveforms, relocated earthquake and matched filter catalog of seismic swarm preceding the 2017 Mount Agung eruption
<p>Datasets for the manuscript:</p> <p>Sianipar, D., Ulfiana, E., and Sipayung, R. (2020), Seismic swarm preceding the 2017 Mount Agung eruption in Bali (Indonesia) enhanced by the matched filter approach (submitted) (preprint is available at EarthArxiv: <a href="https://eartharxiv.org/a7yx2/">https://eartharxiv.org/a7yx2/</a>)</p> <p>by Dimas Sianipar, Emi Ulfiana, and Renhard Sipayung (STMKG, BMKG, Indonesia).</p> <p>Files including:</p> <p>1) List of continuous waveforms</p> <p>2) HypoDD files: dt.cc, dt.ct, event.dat, hypoDD.reloc, phase.dat</p> <p>3) Processed (filtered) 407 template waveforms</p> <p>4) BMKG catalog</p> <p>5) MFT catalog in ZMAP format</p> <p>6) Table S1: template candidates</p> <p>7) Table S2: MFT catalog</p> <p>The compressed file (*.rar) has been successfully extracted in Ms. Windows OS using WinRAR.</p>
Fig. 6 in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes
Fig. 6. Ceratitis serrata De Meyer, 1996. a. Head, frontal view. b. Head and thorax, anterodorsal view. c. Thorax, lateral view. d. Thorax, dorsal view. e. Abdomen, dorsal view. Scale bars: A = 0.5 mm; B–D = 1 mm.
Fig. 3 in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes
Fig. 3. Ceratitis taitaensis De Meyer & Copeland sp. nov. a. Head and thorax, anterodorsal view. b. Thorax, lateral view. c. Thorax, dorsal view. d. Abdomen, dorsal view. e. Male wing. f. Female wing. g. Female aculeus. h. Aculeus tip. Scale bars: A–F = 1 mm; G–H = 0.1mm.
Fig. 4 in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes
Fig. 4. Ceratitis sawahilensis De Meyer & Virgilio sp. nov. a. Head and thorax, anterodorsal view. b. Thorax, lateral view. c. Thorax, dorsal view. d. Abdomen, dorsal view. e. Wing. f. Habitus image (credit: S.A. Marshall). g. Female aculeus. h. Aculeus tip. Scale bars: A–F = 1 mm; G–H = 0.1 mm.
Fig. 2 in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes
Fig. 2. Ceratitis pallidula De Meyer, Mwatawala & Virgilio sp. nov. a. Head and thorax, anterodorsal view. b. Head and thorax, lateral view. c. Thorax, dorsal view. d. Abdomen, dorsal view. e. Wing. Scale bars = 1 mm.
Fig. 1. — a–f in Description of new Ceratitis species (Diptera: Tephritidae) from Africa, or how morphological and DNA data are complementary in discovering unknown species and matching sexes
Fig. 1. — a–f. Ceratitis quilicii De Meyer, Mwatawala & Virgilio sp. nov. Ƌ. a. Head and thorax, anterodorsal view. b. Head and thorax, lateral view. c. Thorax, dorsal view. d. Midleg, anterior view. e. Wing. f. Midtibia, anterior view. — g. C. rosa Karsch, 1887 s.str. midtibia, anterior view. Scale bars = 1 mm.
Detection of very low frequency earthquakes based on matched-filter technique
<p>List of origin times of detected Very low frequency earthquakes based on Baba et al. (2020, Journal of Geophysical Research).</p> <p>Analysis period: January 2003-June 2019</p> <p>The content of this analysis is published in Geophysical Research Letters: <a href="https://doi.org/10.1029/2020GL088089">https://doi.org/10.1029/2020GL088089</a></p> <p>First column: year, second column: month, third column: day, forth column: hour, fifth column: minute, sixth column: second, seventh column: longitude, eighth column: latitude, ninth column: depth, and tenth column: region name. Times are described in JST (UT+9).</p> <p>Data Set S1 of supporting information of GRL corresponds to version 2. We updated the catalog because the information on Line 19035 in version 2 was incomplete.</p>
GBIF Backbone matches - changes
<p>All GBIF occurrence records with changed species matches due to an improved matching service. See http://gbif.blogspot.com/2015/03/improving-gbif-backbone-matching.html for details</p>
Classification of Matching Molecular Series on the Basis of SAR Phenotypes and Structural Relationships
<p>A database comprising a total of 13,236 pairs of MMS with different SAR characteristics is provided. For each pair the corresponding MMS-cores are provided as SMILES. In addition, for each MMS-core the number of compounds and the SAR phenotype are given. ChEMBL target IDs (CHEMBLID_Target) designate target sets from which the MMS pairs originate. </p>
APOGEE DR13 matched to Gaia DR1
<p><strong>APOGEE DR13</strong></p> <p>from Jessica Birky.</p> <p>Documentation https://arxiv.org/pdf/1608.02013.pdf</p> <p> </p> <p>the download link for the catalog of all of the APOGEE sources:</p> <p>https://data.sdss.org/sas/dr13/apogee/spectro/redux/r6/allStar-l30e.2.fits</p> <p> </p> <p>In addition, Jessica's list of specific M star sources with their RA and DEC values `APOGEE_Mstars.csv`</p> <p> </p> <p>crossmatch with Gaia DR1 TGAS by M. Fouesneau provided in</p> <p>`xmatch_TGAS_allStar-l30e.2.fits` median angular matched distances 0.08 arcsec.</p> <p>`xmatch_TGAS_APOGEE_Mstars.csv` median angular matched distances 0.073 arcsec.</p> <p> </p> <p>crossmatch with Gaia DR1 (all) by M. Fouesneau provided in</p> <p>`xmatch_DR1_allStar-l30e.2.fits` median angular matched distances 0.12 arcsec.</p> <p>`xmatch_DR1_APOGEE_Mstars.csv` median angular matched distances 0.108 arcsec.</p>
Pan Arctic Species List (PASL) matching script
<p>This script matches a provided species list with the Panarctic Species List (PASL) (Raynolds et al, 2013) to obtain the commonly accepted scientific names of the plants, including short names and full names.</p> <p>Inputs:</p> <ul> <li>Species list: CSV file containing a list of species names, as provided by the geobotanist;</li> <li>PASL: a text file with the Panarctic Species List names, including the short and full scientific names of the plants (with synonyms).</li> </ul> <p>Outputs:</p> <ul> <li>A new file with the matched species list, including the short and full scientific names of the plants as found in the PASL.</li> </ul> <p><span>References:</span></p> <ul> <li> <span>Raynolds, M.K., Breen, A.L., Walker, D.A., Elven, R., Belland, R., Konstantinova, N., Kristinsson, H. & Hennekens, S. (2013). The Pan-Arctic Species List (PASL). </span>Arctic Vegetation Archive (AVA) Workshop.</li> </ul>
PUBG - Match Data
<p>This dataset was generated from skihikingkevin/pubg-match-deaths. It only consists of matches where at least one player has player more than 1 game (in different matches). The data was processed using polars and converted from CSV to Parquet files. A random sample was performed (groupwise) to produce an 80/20 split.</p>
Data from: Background matching can reduce responsiveness of jumping spiders to stimuli in motion
<p><span>Motion and camouflage were previously considered to be mutually exclusive, as sudden movements can be easily detected.</span> <span>Background matching, for instance, is a well-known, effective camouflage strategy where the color and pattern of a stationary animal match its surrounding background. However, background matching may lose its efficacy when the animal moves, as the boundaries of the animal become more defined against its background. Recent evidence shows otherwise, as camouflaged objects can be less detectable than uncamouflaged objects even while in motion.</span><span> Here, we explored if the detectability of computer-generated stimuli varies with the speed of motion, background (matching and unmatching) and size of stimuli in six species of jumping spiders </span><span>(Araneae: Salticidae)</span><span>. Our results showed that in general, the responsiveness of all six salticid species tested decreased with increasing stimulus speed regardless of whether the stimuli were conspicuousness or camouflaged. Importantly, salticid responses to camouflaged stimuli were significantly lower compared to conspicuous stimuli. There were significant differences in motion detectability across species when the stimuli were conspicuous, suggesting differences in visual acuity in closely related species of jumping spiders. Furthermore, small stimuli elicited significantly lower responses than large stimuli across species and speeds. </span><span>Our results thus suggest that background matching is effective even when stimuli are in motion, reducing the detectability of moving stimuli.</span></p>
Diagnostic plots of cross-matches between Fermi-LAT (4FGL-DR4) and SRG/eROSITA sources (eRASS:4)
<p>This repository provides diagnostic plots used for visual inspection accompanying the paper "Searching for X-ray counterparts of unassociated Fermi-LAT sources and rotation-powered pulsars with SRG/eROSITA" by Martin G. F. Mayer and Werner Becker, published in Astronomy & Astrophysics in 2024. </p> <p>The attached tarball contains ~ 500 PDF files, one per Fermi source with at least one possible X-ray counterpart. In each file, the top row of panels displays the eROSITA (eRASS:4) X-ray image of the Fermi-LAT uncertainty region in the energy bands 0.2-0.6, 0.6-2.3, and 2.3-5.0 keV, overlaid with the 1 sigma (blue dashed line) and 95% error ellipse of the 4FGL source (blue solid line), and the position of the detected X-ray sources (red). The pixel size is 12 arcsec.</p> <p>For each X-ray source, the bottom panels show the local distribution of optical sources from Gaia DR3 (left), mid-infrared sources from CatWISE2020 (center), as well as SIMBAD entries and radio sources (right) around the respective X-ray source position (error bars). In the left and center panels, the size of the markers is proportional to the source brightness, and their color reflects the photometric colors (with bluer markers indicating bluer colors). In the left panel, pentagonal (triangular) markers correspond to sources with statistically significant (insignificant) proper motion or parallax, circular markers indicate the lack of a complete astrometric solution for a source. </p>
Data for High-confidence 3D template matching for cryo-electron tomography
<p>This repository contains supporting data to the manuscript: "High-confidence 3D template matching for cryo-electron tomography" by Sergio Cruz-León, et al. </p> <p>It contains an example to run high-confidence template matching with GAPSTOP-TM, supporting raw data to the manuscript and a jupyter notebook for data visualization. </p> <p> </p> <p>Contact information:<br>Name: Sergio Cruz-León, PhD<br>Institution: Department of Theoretical Biophysics, Max Planck Institute of Biophysics<br>Address: Max-von-Laue-Str. 3, 60438 Frankfurt am Main, Germany<br>Email: sergio.cruz@biophys.mpg.de</p>
Prospective with historical control, case-matched cohort study of the tertiary survey beneficial in critically severe trauma patients.
<p>Raw data, Tertiary survey record form, and STROBE checklist of "Prospective with historical control, case-matched cohort study of the tertiary survey beneficial in critically severe trauma patients" study.</p>
Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis — exact and related matches
<p>Results of the 'Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis' papers. There are 2 datasets:</p> <ul> <li>'exact_matches.csv': contains detailed information about the concepts present both in OpenAlex and OpenAIRE.</li> <li>'related_matches.csv': contains detailed information about the concepts from OpenAlex and OpenAIRE that were not present in both KGs but got aligned following the algorithm presented in the paper.</li> </ul> <p>The detailed information refers to the following column:</p> <ul> <li>Category1: name of the first category</li> <li>Source1: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Category2: name of the second category</li> <li>Source2: source of the first category ('OpenAlex' or 'OpenAIRE')</li> <li>Similarity: semantic similarity value of the two categories</li> <li>PapersInC1: number of papers from the collected dataset belonging to the first category</li> <li>PapersInC2: number of papers from the collected dataset belonging to the second category</li> <li>PapersInBoth: number of papers from the collected dataset belonging to both of the categories</li> <li>Agreement: the value of the agreement of the categories in the tw KGs (Intersection over Union)</li> </ul>
SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2
<p>Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.</p> <p>For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/</p> <p>For 2024 edition, see: https://sem-tab-challenge.github.io/2024/</p> <p>Note on License: This data includes data from the following sources. Refer to each source for license details:<br>- Wikidata https://www.wikidata.org/</p> <p>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
MS2Query pre-trained datasets needed for library matching
<p>The models, embeddings, sqlite file with metadata and classifiers identifiers needed for running MS2Query (https://github.com/iomega/ms2query)</p>
Seismic source location with a match field processing approach during the RESOLVE dense seismic array experiment on the Glacier d'Argentiere
<p>This deposit contains the data set we used in our paper ‘<em>Dynamic imaging of glacier structures at high-resolution using source localization with a dense seismic array</em>’. The paper is in review for GRL and a preprint can be found here: <a href="http://dx.doi.org/10.1002/essoar.10507953.1">10.1002/essoar.10507953.1</a>.</p> <p>The dataset present here contains 34 files named ‘<strong>beam_15423_jd***.h5</strong>’. These files correspond to the output of the matched field processing for each day. They are in .h5 format and we provide a matlab code (<strong>read_MFP_data.m</strong>) to read these files. These files can be read with any other language since they are in . h5.</p> <p>In linux you can use <strong>h5dump –A filename.h5</strong> and you can see the content of each files.</p> <p> </p> <p>More information on how the MFP process is conducted can be found in on the <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>dedicated to this aspect or on our paper. The whole procedure and associated codes is provided on the <a href="http://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/</a>.</p> <p>We also deliver with this deposit one day of seismic data <strong><a href="https://zenodo.org/api/files/873ccbe8-814d-4202-90ae-e115aab1942d/ZO_2018_121.h5?versionId=c59d2014-a6e1-43c5-96a6-91ee9a6b89ce">ZO_2018_121.h5 </a></strong>that can be used to test our MFP process. The data corresponds to the signal measured for 24 hours at each of the 98 sensors with a sampling rate of 500 Hz. More information on these seimsic signals can be found on our <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>and the whole seimsic dataset can be found here <a href="https://seismology.resif.fr/networks/#/ZO__2018">https://seismology.resif.fr/networks/#/ZO__2018</a>. Detailed for downloading the dataset should be search on our website.</p> <p> </p> <p>Other dataset linked to this project are:</p> <ul> <li>Nanni, Ugo, Gimbert, Florent, Roux, Phillipe, & Lecointre, Albanne. (2020). DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations." [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4024660">https://doi.org/10.5281/zenodo.4024660 </a></li> <li>Nanni, Gimbert, Roux, Helmstetter, Garambois, Lecointre, Walpersdorf, Jourdain, Langlais, Laarman, Lindner, Sergenat, Vincent, & Walter. (2020). DATA of the RESOLVE Project (https://resolve.osug.fr/) [Data set]. In Seismological Research Letters (Version v0). Zenodo. <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815 </a></li> </ul> <p>This dataset is also linked to two other study:</p> <p><em>Observing the subglacial hydrology network and its dynamics with a dense seismic array: </em></p> <p><a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a></p> <p><em>A Multi‐Physics Experiment with a Temporary Dense Seismic Array on the Argentière Glacier, French Alps: The RESOLVE Project</em></p> <p><a href="https://doi.org/10.1785/0220200280">https://doi.org/10.1785/0220200280</a></p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Do not hesitate to contact us if you would like to try this approach an another dataset.</p>
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.