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.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “Seismic catalogues”
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>
Two global ensemble M5.95+ seismicity models obtained from the combination of interseismic strain rates and earthquake-catalogue data
<p>Contains two global earthquake-rate forecasts developed by Bayona et al. (2021) to be prospectively evaluated by the Collaboratory for the Study of Earthquake Predictability (CSEP). The Tectonic Earthquake Activity Model (TEAM) is a geodetic-based model using Version 2.1 of the Global Strain Rate Map (GSRM2.1; Kreemer et al., 2014), while the World Hybrid Earthquake Estimates based on Likelihood scores (WHEEL) is a model obtained from a multiplicative log-linear combination of TEAM with the Smoothed Seismicity (KJSS) model of Kagan and Jackson (2011).</p> <p>Earthquake densities are expressed as number of M5.95+ events per unit 0.1<sup>o</sup> cell per year. The forecasts are stored in tab separated value files, with the following fields (the first row of data is shown as an example):</p> <table> <tbody> <tr> <td><sub>lon_min</sub></td> <td><sub>lon_max</sub></td> <td><sub>lat_min</sub></td> <td><sub>lat_max</sub></td> <td><sub>depth_min</sub></td> <td><sub>depth_max</sub></td> <td><sub>5.95</sub></td> <td><sub>6.05</sub></td> <td>...</td> </tr> <tr> <td><sub>-180.0</sub></td> <td><sub>-179.9</sub></td> <td><sub>-90.0</sub></td> <td><sub>-89.9</sub></td> <td><sub>0.0</sub></td> <td><sub>70.0</sub></td> <td><sub>4.95e-11</sub></td> <td><sub>3.97e-11</sub></td> <td>...</td> </tr> </tbody> </table> <p>Data and forecasts are described in detail in the following publications:</p> <p>Bayona, J.A., Savran, W., Strader, A., Hainzl, S., Cotton, F. and Schorlemmer, D., 2021. Two global ensemble seismicity models obtained from the combination of interseismic strain measurements and earthquake-catalogue information. <em>Geophysical Journal International</em>, <em>224</em>(3), pp.1945-1955.</p> <p>Kreemer, C., Blewitt, G. and Klein, E.C., 2014. A geodetic plate motion and Global Strain Rate Model. <em>Geochemistry, Geophysics, Geosystems</em>, <em>15</em>(10), pp.3849-3889.</p> <p>Kagan, Y.Y. and Jackson, D.D., 2011. Global earthquake forecasts. <em>Geophysical Journal International</em>, <em>184</em>(2), pp.759-776.</p>
Earthquake Catalogue for: Illuminating the pre-, co-, and post-seismic phases of the 2016 M7.8 Kaikoura earthquake with 10 years of seismicity
<p><strong>0.2: Correction</strong></p> <p>This version corrects the original dataset which had some incorrect focal mechanisms. These mechanisms had incorrect rakes as a result of an error in the uncertainty calculation algorithm. The remainder of the catalogue is unchanged.</p> <p><strong>Code:</strong></p> <p>If you are looking for the code used for this project this is here: <a href="https://zenodo.org/record/5047794#.Y22Mvn5By-Y">https://zenodo.org/record/5047794#.Y22Mvn5By-Y</a> - the link in the paper appears to be wrong as of 11/11/2022.</p> <p><strong>Description</strong></p> <p>Earthquake catalogue generated around the rupture area of the 2016 M7.8 Kaikoura earthquake. For a full description see the associated paper submitted to JGR 2021.</p> <p>The catalogue is here in two forms:</p> <ol> <li>A QuakeML file with all picks, magnitudes, and locations included. This is quite a large file. When reading using ObsPy this will take a long time to read (>10 minutes), and expand in memory to > 8GB.</li> <li>A CSV file with the preferred origin and magnitude information for all events. Relocated events can be identified because they have a station count of 0: GrowClust does not return the number of stations used, whereas NonLinLoc (used for absolute locations) does.</li> </ol>
Preliminary catalogue of High Agri Valley seismicity (southern Italy) recorded by the temporary INSIEME network.
<p>The preliminary catalogue is a comma-separated values (CSV) file which lists the preliminary location and magnitude estimation of 852 local natural and induced earthquakes occurred between September 2016 and March 2019. The catalogue has been produced with the Origin Locator Viewer (scolv) tool of the software SeisComP3 (<a href="https://www.seiscomp3.org">https://www.seiscomp3.org</a>) running on the server of the INSIEME seismic network (<a href="https://doi.org/10.7914/SN/3F_2016">https://doi.org/10.7914/SN/3F_2016</a>). The information included in this catalogue should be considered preliminary both in terms of earthquake location and local magnitude (ML) estimation.</p> <p>Each row of the CSV file indicates the following source parameters:</p> <p><em>ORIGIN TIME (UTC), LATITUDE </em>˚<em>N, LONGITUDE </em>˚<em>E, DEPTH (KM), MAGNITUDE (ML)</em></p> <p>---</p> <p>This file belongs to the Supplement of the article: Stabile, T. A., Serlenga, V., Satriano, C., Romanelli, M., Gueguen, E., Gallipoli, M. R., Ripepi, E., Saurel, J.-M., Panebianco, S., Bellanova, J., and Priolo, E.: The INSIEME seismic network: a research infrastructure for studying induced seismicity in the High Agri Valley (southern Italy), Earth Syst. Sci. Data, <a href="https://doi.org/10.5194/essd-2019-113">https://doi.org/10.5194/essd-2019-113</a>, 2020.</p>
An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times
<p>Catalog of 440,697 earthquakes of the 2016-2017 Central Italy seismic sequence semi-automatically generated by Spallarossa et al. (2020). The catalogue covers one year of aftershocks following the first mainshock of the sequence (from 08242016 to 08312017).</p> <p>The catalog has been generated using the Complete Automatic Seismic Processor (CASP) procedure (Scafidi et al., 2019) to detect the events and an advanced picker engine (RSNI-Picker<sub>2</sub>; Scafidi et al., 2018; Spallarossa et al., 2014) to determine their phase arrival times. The final set of about 7 million P- and 10 million S-wave arrival times have been used to locate the events using a non-linear location algorithm (NonLinLoc; Lomax et al. 2000), with a 1D velocity model calibrated for the area (De Luca et al., 2009) and station corrections. For each event, also local magnitudes (M<sub>L</sub>) has been calculated as well as a locations quality.</p> <p>Earthquake locations quality has been classified by means of the procedure proposed by Michele et al., (2019) consisting of the combination of diverse uncertainty parameters provided by the NonLinLoc location code. Locations quality is provided in terms of a unique numeric normalized value, named quality factor, varying between qf=0 (best quality location) and qf=1 (worst quality location). Then locations have been assigned to a quality class depending on the qf parameter value according to the following scheme: A-class (0 < qf ≤ 0.25), B-class (0.25 < qf ≤ 0.50), C-class (0.50 < qf ≤ 0.75), and D-class (0.75 < qf < 1.00). The earthquake locations are distributed between the quality classes as A-30.6%, B-31.4%, C-18.6%, and D-19.4% (details in Spallarossa et al., 2020).</p> <p>We accompanied the catalogue with the 30 events with M>3.5 missed by our procedure (bring the total number of events to 440,727), including the first Amatrice mainshock (M<sub>W</sub>6.0; see Spallarossa et al., 2020). These 30 missing events recognisable by the ID starting with ISI), have been taken from INGV bulletin (<a href="http://terremoti.ingv.it">http://terremoti.ingv.it</a>; ISIDe Working Group., 2007), manually generated. These additional events report INGV locations and magnitude parameters while are missing related quality factors and quality class, being generated by a different procedure.</p> <p>We added to the larger events, the available moment magnitudes (M<sub>W</sub>) from Time Domain Moment Tensor catalogue (<a href="http://terremoti.ingv.it/tdmt">http://terremoti.ingv.it/tdmt</a>; Scognamiglio et al., 2006).</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-event – ID</li> <li>Latitude (°) expressed in decimal degrees - LAT</li> <li>Longitude (°) expressed in decimal degrees - LON</li> <li>Depth(km) hypocentral depth expressed in kilometres - DEP</li> <li>Year of origin time in the format yyyy - YR</li> <li>Month of origin time in the format mo - MON</li> <li>Day of origin time in the format dd - DY</li> <li>Hour of origin time in the format hh - HR</li> <li>Minute of origin time in the format mi - MIN</li> <li>Second of origin time in the format XX.XXX s - SEC</li> <li>Local Magnitude - ML</li> <li>Standard deviation of the Local Magnitude – STD</li> <li>Moment Magnitude – Mw (from TDMT)</li> <li>Horizontal Error (from NLL output) (km) expressed in kilometres - ERH</li> <li>Vertical Error (from NLL output) (km) expressed in kilometres - ERZ</li> <li>RMS (from NLL output) (s) expressed in seconds - RMS</li> <li>Number of Phases – NPHS</li> <li>Stations Azimuthal GAP (°) expressed in decimal degrees - GAP</li> <li>Quality factor - Qf</li> <li>Quality class - Qc</li> </ul> <p> </p> <p>De Luca G., M. Cattaneo, G. Monachesi and A, Amato (2009). Seismicity in the Umbria-Marche region from the integration of national and regional seismic networks. Tectonophysics, 476(1), 219-231. doi: 10.1016/j.tecto.2008.11.032.</p> <p>ISIDe Working Group. (2007). Italian Seismological Instrumental and Parametric Database (ISIDe). Istituto Nazionale di Geofisica e Vulcanologia (INGV); https://doi.org/10.13127/ISIDE.</p> <p>Lomax, A., J. Virieux, P. Volant, and C. Berge-Thierry (2000). Probabilistic earthquake location in 3D and layered models: introduction of a Metropolis–Gibbs method and comparison with linear locations. In: Advances in seismic event location, ed. C. H. Thurber and N. Rabinowitz, 101–134. Dordrecht and Boston: Kluwer Academic Publishers.</p> <p>Michele, M., Latorre, D., Emolo, A. (2019). An Empirical Formula to Classify the Quality of Earthquake Locations. Bulletin of the Seismological Society of America. Vol. 109, No. 6, pp. 2755–2761, December 2019, doi: 10.1785/0120190144.</p> <p>Scafidi, D., Viganò A., Ferretti G., and Spallarossa D. (2018). Robust picking and accurate location with RSNI-Picker2: real-time automatic monitoring of earthquakes and non-tectonic events, Seismol. Res. Lett, Vol. 89 (4), pp. 1478-1487, doi: 10.1785/0220170206.</p> <p>Scafidi D, Spallarossa D, Ferretti G, Barani S, Castello B, Margheriti L (2019). A complete automatic procedure to compile reliable seismic catalogs and travel-time and strong-motion parameters datasets. Seismol Res Lett 90(3):1308–1317.</p> <p>Scognamiglio, L., Tinti, E., Quintiliani, M. (2006). Time Domain Moment Tensor [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/TDMT.</p> <p>Spallarossa, D., G. Ferretti, D. Scafidi, C. Turino, and M. Pasta (2014). Performance of the RSNI-Picker, Seismol. Res. Lett. 85, 1243–1254.</p> <p>Spallarossa D., Cattaneo M., Scafidi D., Michele M., Chiaraluce L., Segou M. and I. G. Main (2020). An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times. Geophys. J. Int. doi: 10.1093/gji/ggaa604.</p>
WindSightNet: Catalogue of wind speed and direction data from NASA InSight lander on Mars using seismic data
<p>Dataset associated with the publication "WindSightNet: the inter-annual variability of Martian winds retrieved from InSight's seismic data with machine learning" submitted to JGR: Planets.</p> <p>Authors:</p> <p>A. E. Stott, R. F. Garcia, N. Murdoch, D. Mimoun, M. Drilleau, C. Newman, A. Spiga, D. Banfield, M. Lemmon, S. Navarro, L. Mora-Sotomayor, C. Charalambous, W. T. Pike, P. Lognonné, W. B .Banerdt</p> <p>Files containing catalogue of winds produced from the seismic data on the NASA InSight mission using machine learning algorithm produced in above publication. Please refer to this publication for technical details.</p> <p> </p> <p>Contents:</p> <p>WindSightNet.csv - file containing wind speed and direction produced from the WindSightNet neural network based on seismic data</p> <p>TWINS.csv - comparitive wind speed and direction from TWINS wind sensor when available. </p> <p>TWINS data originally available from:</p> <p>J A Manfredi, Insight Auxiliary Payload Sensor Subsystem (APSS) Temperatures and Wind Sensor for Insight (TWINS) Archive Bundle, (2019), https://doi.org/10.17189/1518950</p> <p> </p> <p>Each file contains values for:</p> <p>Wind Speed</p> <p>Wind dir.</p> <p>Sol - number of sol of InSight mission </p> <p>UTC - Coordinated Universal Time of sample</p> <p>LTST - Local True Solar Time of sample</p> <p>L_s - Solar longitude value of sample</p> <p>Time - seconds since UNIX epoch</p> <p>Data is considered to be sampled at a rate of 0.01 Hz when there are no gaps.</p> <p> </p> <p>Example code for plotting paper figures can be found:</p> <p>https://doi.org/10.5281/zenodo.14267939</p>
Microseismic catalogue from seismicity on the Jericho Fault (Dead Sea) [Dataset]
<p>This is a database of results linked to the associated manuscript (Klinger et al., 2024) which has been submitted to Geophysical Journal International and is currently in review. </p> <p>We report locations, magnitudes, timing and location errors for microseismic events linked to seismic acitivity on the Jericho Fault.</p> <p>Prior to publication please cite this database using the following two references:</p> <p>Klinger, A.G., Kurzon, I., Sagy, A. (2024). Microseismic and damage-zone characteristics of a fully locked fault segment on the Dead Sea Transform. <em>Manuscript submitted to Geophysical Journal International . </em></p> <p>Klinger, A.G., Werner, M.J. (2021). Microseismic catalogue from seismicity on the Jericho Fault (Dead Sea) [Data set]. Zenodo. https://zenodo.org/uploads/11653666.</p>
Codes, Catalogues and Data for "Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"
<p><strong>Codes, Catalogues and Data available for:</strong> <br>"Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"</p> <p><strong>Catalog</strong> folder: Contains the CMM (beam-forming based) event catalogue as well as event and station information for the PNR-1z site.</p> <p><strong>Classification Test</strong> folder: Jupyter notebooks that run the classification tests and mseed input data of isolated phases (P, S, Noise).</p> <p><strong>DL_model_catalogues</strong> folder: Contains full catalogues for each DL phase picker (GPD, U-GPD, EQT and PhaseNet) and the LinMEF-filtered catalogues.</p> <p><strong>Model_run_docs</strong> folder: Util/core files for PhaseNet and EQTransformer to read data with different sampling frequencies (i.e., not 100 Hz)</p> <p><strong>Data</strong> folder: Contains one hour of continuous downhole data (11th December 2018, 9am-10am) from the PNR-1z dataset.</p>
Seismic catalogue Campi Flegrei 1982-1984
<p>This is a temporary database, an updated database will be created within the INGV database </p>
Seismic Catalogue for Hengill 2017-2022: Handpicked earthquakes
<p>Earthquake catalogue from the Hengill high-temperature geothermal area, SW Iceland, 2017-2022: Manually refined earthquakes.</p> <p>Iceland GeoSurvey (https://isor.is/) operates a local seismic network in the Hengill area (https://www.fdsn.org/networks/detail/OR/) for ON Power (https://www.on.is/), including streaming of real time data, automatic processing and manual refining of earthquake locations. The purpose of the network is to monitor ON Power’s geothermal operations and natural geothermal activity. All waveform data and earthquake locations made with the seismic network are owned by ON Power. The network is complemented with nearby stations from the SIL national seismic network of the Icelandic Meteorological Office (https://www.vedur.is/). </p> <p>For the purpose of the study “Scattering and absorption imaging of the Hengill high-temperature geothermal area, southwest Iceland” by Napolitano et al., ON Power shares earthquake locations of ML > 0.5 between latitude: 64.02°N - 64.15°N and longitude: 21.1°W - 21.4°W. The earthquake catalogue consists of 2240 events from 2017-2022. ÍSOR manually refined both P and S phases for all 2240 events in the catalogue used for the attenuation tomography. The initial automatic SeisComP picks were generated using the SIL velocity model (Bjarnason et al., 1993), with a constant Vp/Vs ratio of 1.78. </p> <p> </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.