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.
145
datasets available to search
ShareScore release 0.9.0
Dataset results
145 results for “Gaia”
PM_024651_E_Santa_Perpetua_de_Gaia
<u>File Name</u>: PM_024651_E_Santa_Perpetua_de_Gaia.jpg <br><u>Sublocation</u>: Torre de Pontils <br><u>Location</u>: Santa Perpètua de Gaià <br><u>Province</u>: Catalunya, Tarragona <br><u>Country</u>: Spain <br><u>Header</u>: El lloc; L'església i la torre del castell; <br><u>Description</u>: Torre de Pontils The site The church and the castle tower <br><u>Keywords</u>: Castle, Catalunya, Cultural heritage, Europe, Monuments, Romanesque, Romanesque Catalonia, Ruins, Santa Perpètua de Gaià, Spain, Styles, Tarragona <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_024674_E_Santa_Perpetua_de_Gaia
<u>File Name</u>: PM_024674_E_Santa_Perpetua_de_Gaia.jpg <br><u>Sublocation</u>: Torre de Pontils <br><u>Location</u>: Santa Perpètua de Gaià <br><u>Province</u>: Catalunya, Tarragona <br><u>Country</u>: Spain <br><u>Header</u>: El lloc; L'església; <br><u>Description</u>: Torre de Pontils The site The church <br><u>Keywords</u>: Castle, Catalunya, Cultural heritage, Europe, Monuments, Romanesque, Romanesque Catalonia, Ruins, Santa Perpètua de Gaià, Spain, Styles, Tarragona <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_024661_E_Santa_Perpetua_de_Gaia
<u>File Name</u>: PM_024661_E_Santa_Perpetua_de_Gaia.jpg <br><u>Sublocation</u>: Torre de Pontils <br><u>Location</u>: Santa Perpètua de Gaià <br><u>Province</u>: Catalunya, Tarragona <br><u>Country</u>: Spain <br><u>Header</u>: El lloc; La torre del castell; Segle 10; <br><u>Description</u>: Torre de Pontils The site The castle tower 10th century <br><u>Keywords</u>: Castle, Catalunya, Cultural heritage, Europe, Monuments, Romanesque, Romanesque Catalonia, Ruins, Santa Perpètua de Gaià, Spain, Styles, Tarragona <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_024645_E_Santa_Perpetua_de_Gaia
<u>File Name</u>: PM_024645_E_Santa_Perpetua_de_Gaia.jpg <br><u>Sublocation</u>: Torre de Pontils <br><u>Location</u>: Santa Perpètua de Gaià <br><u>Province</u>: Catalunya, Tarragona <br><u>Country</u>: Spain <br><u>Header</u>: El lloc; <br><u>Description</u>: Torre de Pontils The site <br><u>Keywords</u>: Castle, Catalunya, Cultural heritage, Europe, Monuments, Romanesque, Romanesque Catalonia, Ruins, Santa Perpètua de Gaià, Spain, Styles, Tarragona <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_024658_E_Santa_Perpetua_de_Gaia
<u>File Name</u>: PM_024658_E_Santa_Perpetua_de_Gaia.jpg <br><u>Sublocation</u>: Torre de Pontils <br><u>Location</u>: Santa Perpètua de Gaià <br><u>Province</u>: Catalunya, Tarragona <br><u>Country</u>: Spain <br><u>Header</u>: El lloc; L'església; <br><u>Description</u>: Torre de Pontils The site The church <br><u>Keywords</u>: Catalunya, Cultural heritage, Europe, Monuments, Romanesque, Romanesque Catalonia, Ruins, Santa Perpètua de Gaià, Spain, Styles, Tarragona <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_027704_E_Santa_Perpetua_de_Gaia
<u>File Name</u>: PM_027704_E_Santa_Perpetua_de_Gaia.jpg <br><u>Sublocation</u>: Torre de Pontils <br><u>Location</u>: Santa Perpètua de Gaià <br><u>Province</u>: Catalunya, Tarragona <br><u>Country</u>: Spain <br><u>Header</u>: El lloc; L'església; Interior; La nau; Ruïnes; Barroc; <br><u>Description</u>: Torre de Pontils The site The church Interior The nave Ruins Baroque <br><u>Keywords</u>: Baroque, Catalunya, Cultural heritage, Europe, Monuments, Ruins, Santa Perpètua de Gaià, Spain, Styles, Tarragona <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_027701_E_Santa_Perpetua_de_Gaia
<u>File Name</u>: PM_027701_E_Santa_Perpetua_de_Gaia.jpg <br><u>Sublocation</u>: Torre de Pontils <br><u>Location</u>: Santa Perpètua de Gaià <br><u>Province</u>: Catalunya, Tarragona <br><u>Country</u>: Spain <br><u>Header</u>: El lloc; L'església; Interior; La nau; Ruïnes; Barroc; <br><u>Description</u>: Torre de Pontils The site The church Interior The nave Ruins Baroque <br><u>Keywords</u>: Baroque, Catalunya, Cultural heritage, Europe, Monuments, Ruins, Santa Perpètua de Gaià, Spain, Styles, Tarragona <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_024667_E_Santa_Perpetua_de_Gaia
<u>File Name</u>: PM_024667_E_Santa_Perpetua_de_Gaia.jpg <br><u>Sublocation</u>: Torre de Pontils <br><u>Location</u>: Santa Perpètua de Gaià <br><u>Province</u>: Catalunya, Tarragona <br><u>Country</u>: Spain <br><u>Header</u>: El lloc; La torre del castell; Segle 10; <br><u>Description</u>: Torre de Pontils The site The castle tower 10th century <br><u>Keywords</u>: Castle, Catalunya, Cultural heritage, Europe, Monuments, Romanesque, Romanesque Catalonia, Ruins, Santa Perpètua de Gaià, Spain, Styles, Tarragona <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_024662_E_Santa_Perpetua_de_Gaia
<u>File Name</u>: PM_024662_E_Santa_Perpetua_de_Gaia.jpg <br><u>Sublocation</u>: Torre de Pontils <br><u>Location</u>: Santa Perpètua de Gaià <br><u>Province</u>: Catalunya, Tarragona <br><u>Country</u>: Spain <br><u>Header</u>: El lloc; La torre del castell; Segle 10; <br><u>Description</u>: Torre de Pontils The site The castle tower 10th century <br><u>Keywords</u>: Castle, Catalunya, Cultural heritage, Europe, Monuments, Romanesque, Romanesque Catalonia, Ruins, Santa Perpètua de Gaià, Spain, Styles, Tarragona <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
Gaia Photometric Science Alerts Crossmatch with Gaia DR3 - Feb. 2024
<p>The <a href="http://gsaweb.ast.cam.ac.uk/alerts/home">Gaia Photometric Science Alerts</a> were crossmatched to the Gaia DR3 catalog on <strong>February 23rd, 2024</strong>. We assumed a 1" separation for each crossmatch. The crossmatch was conducted using the <a href="https://lsdb.readthedocs.io/en/latest/">Large Survey Database</a> (LSDB). </p>
An empirical model of the Gaia DR3 selection function
<p>Precomputed maps of the M10 parameter used to predict the completeness of the Gaia DR3 source catalogue.</p> <p><strong>allsky_M10_hpx7.hdf5 </strong>is a tessellation of the whole sky in Galactic nested healpix scheme of order 7.</p> <p><strong>allsky_uniq_10.fits</strong> uses an adaptive resolution and is based on the 'uniq' numbering of healpix tiles. Regions of higher density use a finer resolution, up to order 10, chosen so that every tile contains a minimum of twenty sources to compute M10 from.</p> <p>Paper: https://ui.adsabs.harvard.edu/abs/2023A%26A...669A..55C/abstract</p> <p>Tutorial using the GaiaUnlimited python package to query these maps: https://github.com/gaia-unlimited/gaiaunlimited/blob/main/docs/notebooks/dr3-empirical-completeness.ipynb</p> <p>Documentation for the GaiaUnlimited package: https://gaiaunlimited.readthedocs.io/en/latest/</p>
Empirical relationship between calcium triplet equivalent widths and [Fe/H] using Gaia photometry
<p>I present a new empirical relationship for red giant branch stars between the overall metallicity of the star and the sum of equivalent widths of the near-infrared calcium triplet (CaT) spectral lines. This method takes advantage of the all-sky photometry and astrometry of the Gaia mission, and the archival spectra from 2050 red giant branch stars from 18 globular clusters (-0.69>[Fe/H]>-2.44) acquired with the Anglo-Australian Telescope's AAOmega spectrograph.</p> <ul> <li><cluster_name>.tar.gz <ul> <li>Raw and reduced spectra for 18 globular clusters (NGC104, NGC6752, NGC6809, NGC288, NGC7099, NGC362, NGC6218, NGC4590, IC4499, NGC1904, ESO452-SC06, ESO280-SC12, NGC1851, NGC6624, NGC2298, Pal 5, NGC5024, Terzan 8, NGC5053)</li> <li>Lists of likely members</li> <li>Measured equivalent widths and radial velocities</li> </ul> </li> <li>code.tar.gz <ul> <li>running.py – calculates the equivalent widths and radial velocities of the stars from the spectra</li> <li>cat_new_params.py – calculates the best fitting empirical relationship</li> </ul> </li> <li>paper.tar.gz <ul> <li>The LaTeX source for the submitted RNAAS paper.</li> </ul> </li> </ul> <p> </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>
UniDAM results with Gaia eDR3 parallaxes
<p>Results of UniDAM run with Gaia eDR3 parallaxes included.</p> <p>+------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | Survey | Input catalogue size | Stars with estimates | Reference |<br> | | | done using | |<br> | | | Gaia eDR3 parallaxes | |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | APOGEE (DR16) | 473307 | 326884 | Ahumada et al. (2020) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | Bensby | 714 | 547 | Bensby et al. (2014) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | Gaia-ESO (DR3) | 25533 | 20127 | Gilmore et al. (2012) G.Gilmore& S.Randich (2016) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | GALAH (DR3) | 564620 | 505403 | Buder et al. (2020) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | GCS | 13565 | 7633 | Casagrande et al. (2011) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | LAMOST (DR6) | 5581266 | 4377103 | Luo et al. (2015) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | LAMOST MRS (DR6) | 328187 | 223407 | Luo et al. (2015) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | RAVE (DR6) | 491349 | 347211 | Steinmetz et al. (2020) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | SEGUE | 235595 | 180012 | Yanny et al. (2009) |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+<br> | Total (unique sources) | 5856273 | 4616931 | |<br> +------------------------+----------------------+-----------------------+---------------------------------------------------+</p>
Gaia EDR3 Catalogs of Machine-Learned Radial Velocities
<p><strong>Gaia EDR3 Catalogs of Machine-Learned Radial Velocities</strong></p> <p>Spatially complete Test-Set and Machine-Learned Radial Velocity (ML-RV) Catalogs described in Dropulic et al., arXiv:<a href="https://arxiv.org/abs/2205.12278">2205.12278</a>. The spatially complete Test-Set Catalog contains a total of 4,332,657 stars, while the spatially complete ML-RV Catalog contains 91,840,346 stars. We provide Gaia EDR3 Source IDs, the network-predicted line-of-sight velocity in km/s, and the network-predicted uncertainty in km/s. </p> <p>We have included a simple Jupyter notebook demonstrating how to import the data, and make a simple histogram with it.</p> <p>If you find this catalog useful in your work, please cite Dropulic et al. arXiv:<a href="https://arxiv.org/abs/2205.12278">2205.12278</a>, as well as Dropulic et al. <a href="https://doi.org/10.3847/2041-8213/ac09ef">ApJL 915, L14 (2021)</a> arXiv:<a href="https://arxiv.org/abs/2103.14039">2103.14039</a>. </p>
Gaia Catalogue of Synthetic Photometry - White Dwarfs (GCSP-WD)
<p>GSPC-WD catalogue </p> <p>This catalogue contains objects described in detail in Gaia Collaboration, Montegriffo et al., 2022, A&A, in press. The description of the catalogue from the paper is given below.</p> <p>We have made the GSPC-WD synthetic photometry available<br> as a stand-alone catalogue27, including SDSS, JKC and JPLUS<br> XPSP and the DA classification probability. The photometry<br> of the individual J-PAS bands, used in the random forest<br> analysis, is not included due to their low signal-to-noise. For<br> WDs classified in SDSS, a subset of which were used in the<br> training/validation of the random forest algorithim, we also include<br> the full SDSS classifications as a separate column in the<br> GSPC-WD catalogue table. When the synthetic spectral bands are very narrow a<br> significant number of sources will have low signal-to-noise. Furthermore,<br> at the edges of the Gaia spectral range, away from<br> the peak of the effective area, this is also true for some stars<br> in the wider bands included in the catalogue. In some extreme<br> cases, there is no significant detection of the object. The random<br> forest algorithm is only able to classify a WD when valid<br> flux measurements are available for every photometric band we<br> include in the analysis. Therefore, no classification is recorded<br> in the catalogue when data for one or more bands is "missing".<br> In total 15,003 WDs from the total sample of 101,783 are not<br> classified. For completeness, we have made all the flux measurements<br> and corresponding magnitudes available for all objects in<br> the GSPC-WD. Hence magnitude/fluxes with very large errors,<br> up to several times the flux itself, are included. However, where<br> fluxes are negative, the magnitudes are not defined. When using<br> the catalogue, appropriate signal-to-noise cuts are advisable for<br> the specific work in-hand, to ensure data quality.</p> <p>Total number of objects = 101,786; Format 1 object per row, 73 columns of data as listed below.</p> <p>Column Description of contents<br> 1 Gaia source_id<br> 2 ra<br> 3 ra_error<br> 4 dec<br> 5 dec_error<br> 6 JohnsonStd_mag_U<br> 7 JohnsonStd_mag_B<br> 8 JohnsonStd_mag_V<br> 9 JohnsonStd_mag_R<br> 10 JohnsonStd_mag_I<br> 11 JohnsonStd_flux_U<br> 12 JohnsonStd_flux_B<br> 13 JohnsonStd_flux_V<br> 14 JohnsonStd_flux_R<br> 15 JohnsonStd_flux_I<br> 16 JohnsonStd_flux_error_U<br> 17 JohnsonStd_flux_error_B<br> 18 JohnsonStd_flux_error_V<br> 19 JohnsonStd_flux_error_R<br> 20 JohnsonStd_flux_error_I<br> 21 SdssStd_mag_u<br> 22 SdssStd_mag_g<br> 23 SdssStd_mag_r<br> 24 SdssStd_mag_i<br> 25 SdssStd_mag_z<br> 26 SdssStd_flux_u<br> 27 SdssStd_flux_g<br> 28 SdssStd_flux_r<br> 29 SdssStd_flux_i<br> 30 SdssStd_flux_z<br> 31 SdssStd_flux_error_u<br> 32 SdssStd_flux_error_g<br> 33 SdssStd_flux_error_r<br> 34 SdssStd_flux_error_i<br> 35 SdssStd_flux_error_z<br> 36 Jplus_mag_uJAVA<br> 37 Jplus_mag_J0378<br> 38 Jplus_mag_J0395<br> 39 Jplus_mag_J0410<br> 40 Jplus_mag_J0430<br> 41 Jplus_mag_gJPLUS<br> 42 Jplus_mag_J0515<br> 43 Jplus_mag_rJPLUS<br> 44 Jplus_mag_J0660<br> 45 Jplus_mag_iJPLUS<br> 46 Jplus_mag_J0861<br> 47 Jplus_mag_zJPLUS<br> 48 Jplus_flux_uJAVA<br> 49 Jplus_flux_J0378<br> 50 Jplus_flux_J0395<br> 51 Jplus_flux_J0410<br> 52 Jplus_flux_J0430<br> 53 Jplus_flux_gJPLUS<br> 54 Jplus_flux_J0515<br> 55 Jplus_flux_rJPLUS<br> 56 Jplus_flux_J0660<br> 57 Jplus_flux_iJPLUS<br> 58 Jplus_flux_J0861<br> 59 Jplus_flux_zJPLUS<br> 60 Jplus_flux_error_uJAVA<br> 61 Jplus_flux_error_J0378<br> 62 Jplus_flux_error_J0395<br> 63 Jplus_flux_error_J0410<br> 64 Jplus_flux_error_J0430<br> 65 Jplus_flux_error_gJPLUS<br> 66 Jplus_flux_error_J0515<br> 67 Jplus_flux_error_rJPLUS<br> 68 Jplus_flux_error_J0660<br> 69 Jplus_flux_error_iJPLUS<br> 70 Jplus_flux_error_J0861<br> 71 Jplus_flux_error_zJPLUS<br> 72 probability DA<br> 73 SDSS WD type</p>
Gaia Data Release 3: Basis function configuration for internally calibrated BP/RP spectra
<p>This XML file contains the basis function configuration adopted for the internally calibrated BP and RP spectra published in Gaia Data Release 3. The same file is included in the GaiaXPy (https://gaia-dpci.github.io/GaiaXPy-website/index.html) python package offering some useful functions to use the spectra.</p> <p>The content of the file and its basic usage are described in detail in Appendix C in the paper "Gaia Data Release 3: Processing and validation of BP/RP low-resolution spectral data", De Angeli, F. et al. A&A (2022).</p>
Datenaufbereitung zum State-of-the-Art und Fortschritt europäischer Gaia-X sowie Datenraum Initiativen mit einem Schwerpunkt auf Industrie 4.0 Anwendungsfälle
<p><span>In seiner Gesamtheit umfasst der Datensatz eine Sammlung von Initiativen und Projekten, die mit </span><span>Gaia-X sowie Data Spaces im Allgemeinen in Verbindung stehen</span><span>.</span> Ziel der Datensammlung war es, eine detaillierte Übersicht über bestehende Projekte zu erhalten und diese systematisch zu kategorisieren, um anschließend spezifische industrielle Anwendungsfälle herauszuarbeiten und diese zu analysieren. Der Zeitraum dieser Sammlung erstreckte sich von April 2023 bis März 2024. Anzumerken ist, dass sämtliche zur Verfügung stehenden Informationen innerhalb dieses Zeitraums in den Datensatz aufgenommen wurden. Ab März 2024 wurden keine weiteren Daten erfasst, wodurch der Datensatz den aktuellen Stand bis zu diesem Zeitpunkt widerspiegelt.</p> <p>Insgesamt wurden 211 Initiativen aus 281 Quellen zusammengetragen und ausgewertet. Diese unterteilen sich (über alle Domänen hinweg) in 102 Data Spaces und 93 Anwendungsfälle. Die 93 Anwendungsfälle beinhalten 47 Industrie 4.0 relevante Anwendungsfälle.</p>
Various Gaia DR2 subsets
<p>This dataset contains various subsets of DR2 data;</p> <p>- within 20 pc<br> - within 25 pc<br> - within 100 pc<br> - within 125 pc<br> - within 200 pc<br> - between 200 and 300 pc<br> - "clean" version of 100 pc (several astrometric quality flag filters applied for cleaner color-mag diagrams)<br> - "clean" version of 200 pc<br> - All white dwarfs within 100 pc<br> - All white dwarfs within 300 pc<br> - All white dwarfs in the Limoges et al. 2015 sample<br> - Rough cut around NGC 6774<br> - Rough cut around M67</p> <p>The file names should be self-explanatory</p>
Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS: The Sample
<p>Sample from "Exploring the age dependent properties of M and L dwarfs using Gaia and SDSS". We present a sample of 74,216 M and L dwarfs constructed from two existing catalogs of cool dwarfs spectroscopically identified in the Sloan Digital Sky Survey (SDSS). We cross-matched the SDSS catalog with Gaia DR2 to obtain parallaxes and proper motions and modified the quality cuts suggested by the Gaia Collaboration to make them suitable for late-M and L dwarfs. </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.