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5,247 results for “Catalogues”
Catalogue of Bayesian SZNet's spectroscopic redshift predictions
<p>The "dr16q_superset_redshift.csv" file provides a catalogue of spectroscopic redshift predictions for spectra from the <a href="https://www.sdss.org/dr16/algorithms/qso_catalog/">16th data release of the Sloan Digital Sky Survey (SDSS) quasar superset catalogue</a> <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..250....8L/abstract">(Lyke et al., 2020)</a>. Redshifts are predicted by a Bayesian convolutional neural network named Bayesian SZNet with associated predictive uncertainties in the form of predictive variances. The catalogue is released in the CSV format with the following columns:</p> <ul> <li><em>plate</em>: spectroscopic plate number;</li> <li><em>mjd</em>: modified Julian day of the spectroscopic observation;</li> <li><em>fiberid</em>: fiber identification number;</li> <li><em>z_pred</em>: redshift from Bayesian SZNet;</li> <li><em>variance</em>: predictive variance associated with redshift from Bayesian SZNet;</li> <li><em>z</em>: primary redshift;</li> <li><em>source</em><em>_z</em>: origin of the reported redshift in <em>z;</em></li> <li><em>is_qso_final</em>: flag indicating quasars included in the DR16Q <a href="https://ui.adsabs.harvard.edu/abs/2020ApJS..250....8L/abstract">(Lyke et al., 2020)</a>;</li> <li><em>z_vi</em>: redshift from visual inspection;</li> <li><em>z_pipe</em>: redshift from the SDSS pipeline;</li> <li><em>zwarning</em>: quality flag on the redshift from the SDSS pipeline;</li> <li><em>z_dr12q</em>: redshift from the DR12Q catalogue <a href="http://ui.adsabs.harvard.edu/abs/2017A%26A...597A..79P/abstract">(Pâris et al., 2017)</a>;</li> <li><em>z_dr7q_sch</em>: redshift from the DR7Q catalogue <a href="https://ui.adsabs.harvard.edu/abs/2010AJ....139.2360S/abstract">(Schneider et al., 2010)</a>;</li> <li><em>z_dr6q_hw</em>: redshift from the DR6 catalogue <a href="https://ui.adsabs.harvard.edu/abs/2010MNRAS.405.2302H/abstract">(Hewett and Wild, 2010)</a>;</li> <li><em>z_10k</em>: redshift from the random visual inspection of 10000 spectra in the DR16Q superset;</li> <li><em>z_pca</em>: redshift from the <a href="https://ascl.net/2106.017">redvsblue algorithm</a>;</li> <li><em>z_qn</em>: redshift from QuasarNET <a href="https://arxiv.org/abs/1808.09955">(Busca and Balland, 2018)</a>;</li> <li><em>z_pred_1</em> to <em>z_pred_256</em>: sampled redshifts from Bayesian SZNet;</li> </ul> <p>where columns <em>z</em>, <em>source_z</em>, <em>is_qso_final</em>, <em>z_vi</em>, <em>z_pipe</em>, <em>zwarning</em>, <em>z_dr12q</em>, <em>z_dr7q_sch</em>, <em>z_dr6q_hw</em>, <em>z_10k</em>, <em>z_pca</em>, and <em>z_qn</em> are taken from the 16th data release of the SDSS quasar superset catalogue.</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>
The SDSS Peculiar Velocity Catalogue
<p>Data, randoms and mock galaxy catalogues for the <em>Sloan Digital Sky Survey Peculiar Velocity Catalogue</em>; Howlett et. al., 2022, MNRAS, in press. See arXiv:2201.03112 for more details.</p> <p>Changelog:</p> <ul> <li>1.1.0: <ul> <li>Fixed error in v1.0.0 in specObjID column caused (at some point in the pipeline) due to rounding errors when reading/writing large numbers. v1.1.0 has the correct specObjIDs. Note that 'objid' is correct in both versions of the SDSS PV catalogue, and our recommended method to crossmatch to the spectroscopic SDSS data (wherein the corresponding column to match with is 'bestobjid').</li> </ul> </li> </ul>
Earthquake Catalogues for DWARFS (Dense Westland Arrays Researching Fault Segmentation)
<p>This dataset contains earthquake hypocentral information catalogued as part of the DWARFS (Dense Westland Arrays Researching Fault Segmentation) broadband seismometer networks along New Zealand's Alpine Fault, between April 2019-April 2020.</p> <p>'Preferred Lat/Lon/Depth' refers to origin determined by method under 'Method'. HypoDD is the preferred method, but some origins could not be relocated and so we present the NonLinLoc derived origin instead. All magnitudes are Local magnitudes calculated using displacements on the vertical channel (MLv). All times are in UTC time. </p> <p>This dataset accompanies a publication recently submitted (July 2022) to the AGU journal 'Journal of Geophysical Research: Solid Earth' entitled 'Heterogeneity in microseismicity and stress near rupture-limiting section boundaries along the late interseismic Alpine Fault'. </p>
Figure Sets and Data Associated with AJ Publication: "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M"
<p>Images for Figure Sets 4, 5, 6, 7, and 9 and data behind the figure for Figure 15 from the AJ publication "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M" (Currently accepted and in press.). Figure sets and file names are described in the fsREADME file. Data behind the figure is described in the dbfREADME file.</p>
HANZE catalogue of modelled and historical floods in Europe, 1950-2020
<p>The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries. It contains:</p> <ul> <li>2521 historical floods with impact data (1870-2020);</li> <li>237 further historical floods with significant impacts, but without precise impact data (1950-2020)</li> <li>Nearly 15,000 modelled floods with a potential to cause significant impacts, classified by actual historical occurrence or non-occurrence impacts (1950-2020).</li> </ul> <p>Historical floods and the classification of modelled floods was completed by extensive data-collection from more than 900 sources ranging from news reports through government databases to scientific papers. Impact data collected or modelled include area inundated, fatalities, persons affected or economic loss. Economic losses were inflation- and exchange-rate adjusted to 2020 value of the euro. The historical catalogue (lsit A) also includes losses in the original currencies and price levels. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: <a href="https://naturalhazards.eu">https://naturalhazards.eu</a>. </p> <p>The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders):</p> <p>HANZE_historical_floods_catalogue_listA.csv - historical floods with impact data (1870-2020)</p> <p>HANZE_historical_floods_catalogue_listB.csv - historical floods without impact data (1950-2020)</p> <p>HANZE_potential_flood_catalogue_all.csv - modelled potential floods (1950-2020)</p> <p>HANZE_list_of_references.csv - List of all references used in the catalogues</p> <p>HANZE_model_completness_analysis.csv - Comparison between modelled and reported footprints of historical floods</p> <p>Regions_v2010_simplified.zip - Map of subnational regions (v2010)</p> <p>Regions_v2021_simplified.zip - Map of subnational regions (regions v2021)</p> <p> </p> <p>v1.2: corrected NUTS regions v2021 for a few events, which were accidently coded with v2010 regions.</p> <p>v1.1: errors in two records in "HANZE_historical_floods_catalogue_listB.csv" (wrong country code in event ID 8227 and wrong start date in event ID 8237) were corrected.</p>
Deliverable 1.3 -Vision Catalogue - Encompassing the visions from all 30 countries
<p>This deliverable presents an English translation of the 179 visions elaborated by more than 1000 citizens during the National Citizen Vision Workshops (NCVs), arranged as a part of the CIMULACT project.</p> <p>The main objective of CIMULACT is to add to the relevance and accountability of the European Research and Innovation (R&I) agenda by engaging citizens and multi-actors in the actual formulation of the European Union’s R&I agenda. The NCVs contributed to this process by engaging citizens in formulating their visions for desirable and sustainable futures.</p> <p>Over a three month period (November 2015 until January 2016) 30 NCVs were held in 30 European countries (28 EU member states, as well as Switzerland and Norway). At each NCV 25-42 (36 on average) citizens met for a full day to formulate and debate their visions for a desirable and sustainable future.</p> <p>The visions represent the final product of the NCVs and are the result of a comprehensive and intensive vision building process in each of the participating countries. The visions were originally formulated in the citizens’ national language, but for simplicity all visions have been translated into English. The original visions and national reports from each NCV are to be found elsewhere (Deliverable 1.2 - Collection of national reports on the citizens’ future visions).</p> <p>The present deliverable documents the European citizens’ wishes, needs and demands for a desirable future. The visions enable dialogue between the citizens and the European policy- and decision makers, hereby enhancing Responsible Research and Innovation (RRI) in the European Union.</p> <p>CIMULACT is a three-year project funded by the Horizon 2020 Framework Program of the European Union. The project was kicked-off in June 2015</p> <p> </p>
Catalogue of Stone Vessels
<p>This open dataset lists, describes, and provides relevant bibliography to all known archaeological sites in Galilee with evidence for stone vessels. It forms part of the dataset used in the monograph <em>Being Jewish in Galilee, 100–200 CE: An Archaeological Study</em> (Brepols). The dataset is available in both PDF and CSV formats. The PDF file provides a detailed description of and bibliography for the evidence of stone vessels at each archaeological site. The CSV file contains the raw data that can be easily imported into spreadsheets and databases.</p>
Catalogue of Stepped Pools
<p>This dataset lists, describes, and provides relevant bibliography to all known stepped pools in Galilee from the Hellenistic to Byzantine periods that have been exposed through archaeological excavations or field surveys. It forms part of the dataset used in the book <em>Being Jewish in Galilee, 100–200 CE: An Archaeological Study</em> (Brepols). The dataset is available in both PDF and CSV format. The PDF file provides a detailed description of and bibliography for each stepped pool. The CSV file contains the raw data that can be easily imported into spreadsheets and databases.</p>
Automatic TEI encoding of manuscripts catalogues with GROBID-Dictionaries
<p>Manuscript Sales Catalogues (MSC) are highly important for authenticating documents and studying the reception of authors. Their regular publication throughout Europe since the beginning of the 19th c. has consequently raised the interest around scaling up the means for automatically structuring their contents. </p> <p>Following successful first encoding tests with <em>GROBID-Dictionaries</em> on a single MSC collection, we aim in this paper to present the results of more advanced tests of the system’s capacity to handle a larger corpus with MSC of different dealers, and therefore multiple layouts. Four different types of catalogues published between the middle of the 19th c. and the beginning of the 20th c. have been tested.</p>
Chilean Seismic Catalogue - 1982 - mid-2020
<p>This repository contains the relocated catalogue of the <em>Centro Sismológico Nacional</em> (CSN, Universidad de Chile, <a href="http://www.sismologia.cl" target="_blank" rel="nofollow noreferrer noopener">http://www.sismologia.cl</a>).</p> <p>Contact:</p> <ul> <li>Bertrand Potin, <em>DGF, University of Chile</em> (<a href="mailto:bertrand.potin@uchile.cl">bertrand.potin@uchile.cl</a>)</li> <li>Sergio Ruiz, <em>DGF, University of Chile</em> (<a href="mailto:sruiz@uchile.cl">sruiz@uchile.cl</a>)</li> </ul> <p>Catalogues for both the seismicity and the clusters are formatted into CSV archives.</p> <h1>How to cite this material</h1> <h2>Material doi</h2> <p><a href="https://doi.org/10.5281/zenodo.13146436" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.5281/zenodo.13146436</a></p> <h2>Related article</h2> <p>Potin, B., S. Ruiz, F. Aden-Antoniow, R. Madariaga, and S. Barrientos (2024). A Revised Chilean Seismic Catalog from 1982 to Mid-2020, <em>Seismol. Res. Lett.</em>. <a href="https://doi.org/10.1785/0220240047" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1785/0220240047</a></p> <h1>Files format</h1> <h2>CHILE_SEISMICITY_RELOCATED.csv</h2> <p>The file contains 1 header line and 118004 event lines (1 line per event) corresponding to all events between 1982 and mid-2020 relocated for this study.</p> <h3>Columns description:</h3> <ul> <li><strong>#</strong>: number of the event from 0 to 118003, in chronological order,</li> <li><strong>year</strong>: event origin time year (YYYY),</li> <li><strong>month</strong>: event origin time month (MM),</li> <li><strong>day</strong>: event origin time day (DD),</li> <li><strong>hour</strong>: event origin time hours (hh, [0,23]),</li> <li><strong>minute</strong>: event origin time minutes (mm, [0,59]),</li> <li><strong>second</strong>: event origin time seconds (ss.ss, [0.00,59.99]),</li> <li><strong>longitude</strong>: hypocentre longitude (xxxx.xxxx, [-76.2294,-64.8110]),</li> <li><strong>latitude</strong>: hypocentre latitude (xxx.xxxx, [-45.9796,-17.8178]),</li> <li><strong>depth</strong>: hypocentre geographical depth (xxx.xxxx, [-4.9540,336.5503]),</li> <li><strong>RMS</strong>: Root-mean-square of data adjustment in location process (see below for details),</li> <li><strong>magnitude</strong>: magnitude value, either a number [0.20,8.80] or empty when no magnitude was determined,</li> <li><strong>magnitude_type</strong>: <ul> <li><code>l</code>: local magnitude,</li> <li><code>c</code>: coda magnitude,</li> <li><code>w</code>: moment magnitude determined following Brune's spectral approach,</li> <li><code>ww</code>: moment magnitude determined following the W-phase approach,</li> <li><code>xx</code>: no magnitude.</li> </ul> </li> </ul> <h3>RMS computation:</h3> <p>See the gitlab link for more details <a href="https://gitlab.com/bertrand.potin/chilean_seismic_catalogue-1982-2020.git">https://gitlab.com/bertrand.potin/chilean_seismic_catalogue-1982-2020.git</a></p> <h3>file format example:</h3> <div> <pre><code>... 100870,2018,1,26,6,57,12.06,-71.403518,-29.438705,57.5149,0.26,2.8,l 100871,2018,1,26,9,50,6.16,-67.257799,-23.912037,190.2663,0.32,3.0,l 100872,2018,1,26,12,54,38.73,-71.32775,-31.009157,50.2409,0.31,4.1,l 100873,2018,1,26,13,27,5.3,-74.226795,-37.488853,21.4607,0.3,5.1,ww 100874,2018,1,26,15,4,52.48,-72.550192,-29.705678,16.8856,0.43,3.2,l 100875,2018,1,26,15,26,23.56,-73.808079,-37.575196,18.9708,0.6,5.0,ww 100876,2018,1,26,15,50,17.33,-68.686646,-21.865756,117.7657,0.27,3.2,l ...</code></pre> </div> <h2>CHILE_CLUSTERS_RELOCATED.csv</h2> <p>The file cointains 1 header line and 29263 event lines (1 line per event).</p> <h3>Columns description:</h3> <ul> <li><strong>year</strong>: event origin time year (YYYY),</li> <li><strong>month</strong>: event origin time month (MM),</li> <li><strong>day</strong>: event origin time day (DD),</li> <li><strong>hour</strong>: event origin time hours (hh, [0,23]),</li> <li><strong>minute</strong>: event origin time minutes (mm, [0,59]),</li> <li><strong>second</strong>: event origin time seconds (ss.ss, [0.00, 59.99]),</li> <li><strong>longitude</strong>: hypocentre longitude (xxxx.xxxx, [-76.2294,-64.8110]),</li> <li><strong>latitude</strong>: hypocentre latitude (xxx.xxxx, [-45.9796,-17.8179]),</li> <li><strong>depth</strong>: hypocentre geographical depth (xxx.xxxx, [-4.9540, 336.5503]),</li> <li><strong>RMS</strong>: Root-mean-square of data adjustment in location process (see above for details),</li> <li><strong>magnitude</strong>: magnitude value, either a number [2.43, 8.80] or empty when no magnitude was determined,</li> <li><strong>magnitude_type</strong>: <ul> <li><code>l</code>: local magnitude,</li> <li><code>c</code>: coda magnitude,</li> <li><code>w</code>: moment magnitude determined following Brune's spectral approach,</li> <li><code>ww</code>: moment magnitude determined following the W-phase approach,</li> <li><code>xx</code>: no magnitude.</li> </ul> </li> <li><strong>label</strong>: number between 0 and 48 used to identify clusters. Label order is random and do not indicate clasification of clusters. Some numbers are missing from the list because they correspond to deep clusters that where not included in the article.</li> </ul> <h3>file format example:</h3> <div> <pre><code>... 2017,3,8,13,33,55.53,-33.862827,-71.447657,58.5711,0.35,3.6,l,23 2017,7,16,8,19,31.55,-33.914015,-71.28985,49.62,0.19,4.0,l,23 2017,7,19,8,6,19.82,-33.871165,-71.348819,58.1362,0.23,3.7,l,23 2017,9,23,22,2,2.37,-33.761952,-71.504656,49.2679,0.43,4.9,ww,23 2017,10,27,2,8,18.04,-33.89703,-71.421792,50.3903,0.55,2.6,l,23 ...</code></pre> </div>
SERPENTINE CME catalogue
<p>This catalogue contains the information of coronal mass ejections (CMEs) related to multi-spacecraft solar energetic particle (SEP) events detected during solar cycle 25. It has been created within the European Union’s Horizon 2020 project <em>SERPENTINE</em> (Solar energetic particle analysis platform for the inner heliosphere). The multi-spacecraft SEP events are documented in the <a href="../doi/10.5281/zenodo.10732268">catalogue</a> build during SERPENTINE project and described in detail in <a href="https://www.aanda.org/articles/aa/pdf/forth/aa49831-24.pdf">Dresing et al., doi:10.1051/0004-6361/202449831, 2024.</a></p> <p>The catalogue lists the occurrence of CMEs and some of their key parameters as extracted from the SOHO <a href="https://cdaw.gsfc.nasa.gov/CME_list/">LASCO CME catalogue</a> and STEREO automated <a href="http://spaceweather.gmu.edu/seeds/secchi.php">SEEDS catalogue</a>.</p> <p>For all events we have listed the information on the CME occurrence time, kinematics, size and location. We have included only CMEs with angular width larger than 60 degrees (at least from one of the observation location). In addition, for those events that were clear enough in white-light coronagraph images and when the LASCO and STEREO-A spacecraft were far enough from each other (> 30 degrees) were fitted with Graduated Cylindrical Shell (GCS) approach (<a href="https://link.springer.com/article/10.1007/s11207-009-9346-5">Thernisien, Vourlidas and Howard, 2009</a>; <a href="https://iopscience.iop.org/article/10.1088/0067-0049/194/2/33/pdf">Thernisien, 2011</a>) to obtained 3D information on the CME kinematics, location and geometrics. </p> <p>The parameters extracted from the SOHO/LASCO and STEREO/SEEDS CME catalogues include</p> <ul> <li>Plane of the sky speed [km/s]</li> <li>Acceleration [m/s^2]</li> <li>Angular width [degrees]* (for halo CMEs, the angular width is 360 degrees)</li> <li>Central position angle [degrees] (the position angle is measured from solar north clockwise)</li> </ul> <p>The GCS parameters include</p> <ul> <li>time of the fit [UTC]</li> <li>height of the leading edge [solar radii]</li> <li>half-angular width [degrees]</li> <li>aspect ratio</li> <li>propagation longitude [degrees]</li> <li>propagation latitude [degrees]</li> <li>tilt angle with respect to the solar equator [degrees]</li> </ul> <p>The GCS fitting is performed for multiple time steps.</p> <p> </p>
The Catalogue of Endangered Languages as CLDF dataset
<p>Cite the source of the dataset as:</p> <blockquote> <p>Catalogue of Endangered Languages. 2023. University of Hawaii at Manoa. http://www.endangeredlanguages.com</p> </blockquote>
Catalogue of XMM-SUSS variable sources
<p>Catalogue of variable XMM-Newton Optical Monitor (XMM-OM) Serendipitous Ultraviolet Sky Survey (XMM-SUSS) sources, identified as variable in one or more passbands from lightcurves derived from SUSS data. </p>
Nature-Based Solutions Tools Catalogue
<p>The Adaptive Cities Through Integrated Nature-Based Solutions (ACT on NBS), EIT Climate KIC project made an inventory and assessment of NBS tools for climate resilient cities. The objectives of the catalogue are: 1) to increase organizational accessibility to data on NBS and climate resilience tools that have been developed and used in Europe and worldwide; 2) that different end-users become aware of several tools that already exist to plan, design, and implement NBS and make use of them to address specific environmental challenges and adaptation measures in their cities, neighborhoods or even regions.</p> <p>The NBS tools catalogue contains 70 tools collected. For this research, a “tool” is understood to be either a methodology, software, catalogue, repository, e-platform, guideline or handbook. The tools were identified through interviews with EU municipalities and workshops organized by ACT on NBS, and by additional desk research.</p> <p>The desk study entailed a combination of reviewing the websites from international organizations and EU granted projects related to cities dealing with NBSs, ecosystem services (ESs), green infrastructure, urban resilience and climate change, and reviewing peer-reviewed scientific journals, reports and grey literature.</p> <p>The latter search was implemented through Google search, Google scholar and Scopus in August 2020. This implies that tools that were developed and published before that date were collected. The search strategy was implemented using combinations of search terms such as: nature-based solutions, NBS, ecosystem-based adaptation, green infrastructure, climate adaptation, climate resilience, ecosystem services, climate hazards, urban biodiversity, urban nature, water and land management “AND” urban areas, cities “AND” tools, software, methodology, catalogue, repository, platform, handbook and guideline.</p> <p>For the assessment, tools were labeled on their descriptive characteristics and potential fields of application. The use of pre-defined indicators was chosen to support the characterization of the tools and to allow for their comparison. The categories and indicators were formulated based on the current literature and refined through expert judgements. The indicators were also, in some cases, further adapted through an iterative process of tools’ analysis.</p> <p>For more information on the NBS tools analysis please read our academic publication on “Nature-Based Solutions Tools for Planning Urban Climate Adaptation: State of the Art”.</p> <p><strong><a href="https://doi.org/10.3390/su13116381">https://doi.org/10.3390/su13116381</a></strong> </p>
Polar / Plasma Waves Investigation processed dataset and ephemeris used to produced the Smith et al. (2022) catalogue (doi:10.5281/zenodo.7260994 )
<p>This data set contains Polar / Plasma Waves Investigation processed using the SPACE Labelling Tool (Louis et al., 2022, doi:10.5281/zenodo.6886528). It also contains the Polar ephemeris in the geocentric solar ecliptic (GEO) coordinate system (from https://sscweb.gsfc.nasa.gov/cgi-bin/Locator.cgi)</p> <p>This processed dataset contains Auroral Kilometric Radiation (AKR) observations and was used to produced the Smith et al. (2022) catalogue of AKR (doi:10.5281/zenodo.7260994)</p> <p>This work has been funded by Science Foundation Ireland Grant 18/FRL/6199, and by a 2022 SCOSTEP/PRESTO<br> Grant.</p>
Catalogue of Wide Binaries from GAIA EDR3
<p>This dataset contains candidate wide binary systems from Gaia EDR3, used to perform tests of Modified Gravity theories in the low acceleration regime; as shown in the paper "Wide Binaries from GAIA EDR3: preference for GR over MOND ?" by Charalambos Pittordis & Will Sutherland. Accepted by Open Journal of Astrophysics, 31 Jan 2023.</p> <p>There are two files:</p> <p><strong>CleanedWB_EDR3_Prlx300pc_Gmag20_20230111_Size73087_ZenodoSample.csv</strong>: .CSV table with 73087 rows and 230 columns.</p> <p><br> <strong>00README_WideBinaries_EDR3_PS2023.txt : </strong>README file, describing the columns within the above dataset.</p>
Precessing binary-black-hole numerical relativity catalogue (minimal data release)
<p>This page contains the minimal data release associated with the catalogue presented in <a href="https://dcc.ligo.org/DocDB/0186/P2300054/001/catalogue.pdf">A catalogue of precessing black-hole-binary numerical-relativity simulations</a>. This catalogue contains 80 single-spin precessing black-hole-binary configurations. </p> <p>The content of the data release is described <a href="https://data.cardiffgravity.org/bam-catalogue/">here</a>, along with instructions on how to parse the data.</p>
Raw data for the computation of ExPaNDS facilities maturity wrt FAIR data catalogues
<p>Raw data for the computation of ExPaNDS facilities maturity wrt FAIR data catalogues.</p> <p>The method is explained in the <a href="https://doi.org/10.5281/zenodo.4146819">report on status, gap analysis and roadmap towards harmonised and federated metadata catalogues for EU national Photon and Neutron RIs</a>.</p>
Extended catalogue of infant and adult gut phageome shows high prevalence of lysogeny
<p>Leveraging metagenomes from the Finnish HELMi birth cohort, a large collection of 6,186 MAGs from infant and adult gut microbiota was obtained and screened for integrated prophages, allowing the identification of 7,165 proviral sequences longer than 10kb. Strikingly, more than 70% of the near-complete MAGs were identified as lysogens. The prevalence of prophages in MAGs varied across bacterial families, with a lower prevalence observed in Coriobacteriaceae, Eggerthellaceae, Veillonellaceae and Burkholderiaceae, while a very high prevalence of lysogen MAGs was observed for Oscillospiraceae, Enterococcaceae, Enterobacteriaceae. Interestingly for several bacterial families such as Bifidobacteriaceae and Bacteroidaceae, the prevalence of proviruses in MAGs was higher in early infant time point (3 weeks and 3 months) than in later sampling points (6 and 12 months) and in adults. The proviral sequences were clustered into 5,616 species-like vOTUs, 77% of which were novel.</p> <p>This repository contains the fasta files for the MAGs collection and the proviral sequences retrieved in this study.</p>
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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.