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951 results for “Data release”
SCEC Broadband Platform Release 22.4.0 Validation Data
<p>This package includes the set of validation plots generated with the Broadband Platform Release 22.4.</p> <p>Fabio Silva, Kevin Milner, & Philip Maechling. (2022). SCECcode/bbp: Broadband Platform Release v22.4.0 (v22.4.0). Zenodo. https://doi.org/10.5281/zenodo.7062972</p> <p>The following folders include:</p> <p>2022-08-30-bbp-part-a - Broadband validation runs using ground motions from 17 historical events.</p> <p>2022-08-30-bbp-part-a-all - Broadband validation runs using ground motions from 17 historical events (same as above, but includes additional GoF plots such as maps and distance)</p> <p>2022-08-30-bbp-part-b - Broadband verification runs against NGA-West 2 GMPEs.</p> <p>2022-08-30-bbp-converge - Convergence plots for each method and event</p> <p>2022-08-30-bbp-tables - Summary tables for each method, along with aggregate results from all methods/events per distance and period range. Also includes Dreger figure 3 plots (see references below for more information)</p>
Data from randomized control trials released hatchery salmon treated with anti-parasitic treatment
<p>Data used in the article "<strong>Parasite spillback from fish farms reduce return rate of wild salmon"</strong></p> <p> </p> <p>Each release group has been used as a randomized control trials (RCT) of hatchery reared salmon smolts where half of the fish has been treated with an antiparasitic drug. Description of this method has been given in various other publications (Vollset et al. 2014, Vollset et al 2016, Skilbrei et al. 2013). The method involves rearing salmon eggs originating from the national Gene Bank to smolt size in hatchery facilities during one year, and then treating the salmon smolts with fish feed pellets coated with emamectin benzoate (SLICE®). These fish are then released into the river or transported in tanks or mobile net pens further out in the fjord before release. The fish are tagged with either coded-wire-tags (CWT; years 2000-2017) or Passive Integrated Transponders (PIT; 2015-2019) so that it is possible to identify them as they are recaptured or registered on an antenna upon their return as adults. In a few trials, another antiparasitic treatment (Substance EX) has been used, but in most cases the EB has been the only available treatment. Releases of hatchery reared salmon in freshwater have not been successful in this system, i.e. very few fish have returned from any group released in the river, lakes or estuary of Vosso. Since the release groups are also a part of a restoration effort of the Vosso salmon, some years fish have only been released in the fjord. There has been some variation in the release sites in the fjords, but for the purpose of this study we group the release groups in either group that has been released in the outer fjord (70-105 km from the river mouth) and the inner fjord (15-70 km from the river mouth), and freshwater (approx -10 to 15 km from the river mouth). The two most prevalent locations are at Manger (WGS84; 60.63918, 4.92149) and Arna (WGS84; 60.50812, 5.37777).</p> <p> </p> <p><em>Sea lice surveillance</em></p> <p> </p> <p>Sea lice surveillance on sea trout has been conducted at Herdla, the northern peninsula of the island Askøy (WGS84; 60.568972, 4.963010) since 2009. Here, trout have been caught using a trap net that has been developed specifically to capture and treat trout while minimizing sea lice loss during handling (Barlup et al. 2013). From an earlier study by Vollset et al. (2018), it has been shown that the lice numbers on sea trout on this site correlate with the infestation pressure of fish farms in the outer region of the fjord. This area is also one of the largest fish farm zones with coordinated production and fallowing in the outer fjord system where all the released salmon smolts must migrate (see Vollset et al. 2018). This is also the area where surface salinity layers permit salmon lice to overlap with out-migrating salmon smolts (Vollset et al. 2016).</p> <p> </p> <p>The number of trout caught during the monitoring season has varied with weather conditions, sampling intensity, and number of traps operated. The way that trout are handled is described in more detail in Vollset et al. (2018), but in brief, the trap chambers are checked daily, and individual trout are transferred from the trap using a hand held dip net and are either euthanized and placed in zip-lock bag or transported in a large bucket with aerated water to land. Euthanized samples are kept cold and frozen when at land, and later thawed and counted in the lab, while live samples are counted after being sedated with half dose (0.05 g/L) of MS222 and then assessed for salmon lice in a high-contrast bucket using a headlamp by trained personnel. Since 2015 the sea lice surveillance at Herdla is also operated as a part of the Norwegian national sea lice monitoring program.</p> <p>We aimed to use a standardized time period from which to assess sea lice numbers on sea trout that can be representative of the lice infestation pressure from when the tagged hatchery salmon smolts are released. When counting sea lice on sea trout, the most observable lice are large chalimus and mobile stages, while recently attached copepods are more likely to be missed. Therefore, we use total lice counts on sea trout from Julian day 135 to 165 as an assessment of the infestation pressure the salmon smolts must experience. This corresponds to approximately 15 May to 15 of June, and is based on a study on progression rate of salmon smolts from hatchery smolt in this area (Vollset et al. 2016). To account for the fact that larger fish will attract more parasites, we use parasites per gram fish per individual and average data to get one index per year. This method is expected to provide a fair index of interannual variation of the infestation pressure.</p> <p> </p> <p>Table 1 Description of column names in csv file</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>release_year</td> <td>Year of release as smolts</td> </tr> <tr> <td>release_place</td> <td>Name of release place location</td> </tr> <tr> <td>release_date</td> <td>Date of release as smolts</td> </tr> <tr> <td>Released</td> <td>Number of hatchery smolt released</td> </tr> <tr> <td>Recaptured</td> <td>Number of hatchery smolt recaptured as adults</td> </tr> <tr> <td>treat</td> <td>Treatment (either treatment or control)</td> </tr> <tr> <td>tag</td> <td>Tag type (either CWT or PIT)</td> </tr> <tr> <td>release_category</td> <td>Release place (either river, outer fjord or inner fjord)</td> </tr> <tr> <td>lpg</td> <td>Lice per gram fish on trout during surveillance from 15 of May to 15 of June the year of release</td> </tr> <tr> <td>pr</td> <td>Percent (%) recaptures as adults</td> </tr> </tbody> </table> <p> </p>
OCO-2 v11.1 10-second average data, early release
<p>The file archived here, "OCO2_b11.1_10sec_GOOD_r0.nc4", contains dry air column CO2 mixing ratio (XCO2) data from the Orbiting Carbon Obervatory (OCO-2) averaged over 10-second spans. It is a preliminary version of files that will be used in the upcoming OCO-2 v11 flux inversion model intercomparison project (MIP), a study designed to quantify various error sources and analysis differences that lead to different surface CO2 flux estimates when the OCO-2 data are used in global CO2 flux inversions. In particular, this file is the source of the OCO-2 XCO2 10-second average data used in the recently-completed study "An Error Model for Evaluating satellite-based XCO2 products" by Yadav et al.</p> <p>The Orbiting Carbon Observatory (OCO-2) is a satellite that measures solar radiance reflected from the Earth's surface in two CO2 absorption bands (1.6 and 2.0 um), as well as in the O2 A-band. By comparing the CO2 / O2 absorption ratio, the dry air mixing ratio of CO2 may be estimated along the observed path, i.e. averaged across the full atmospheric column, though with sensitivity peaking near the Earth's surface (where the impact of surface CO2 fluxes is the greatest). A radiative transfer model that accounts for the scattering effects of thin clouds and aerosols, water vapor, surface albedo variations, is used, and the vertical profile of CO2 mixing ratio is estimated from the radiance data. This CO2 profile is then collapsed to a scalar vertical average (XCO2), which is then bias corrected post-hoc against Earth-based Fourier spectrometer data from the Total Carbon Column Observation Network (TCCON), which itself is tied to CO2 measurement standards using in situ aircraft CO2 profile measurements.</p> <p>OCO-2 takes measurements in a thin swath (up to 10-km wide) underneath the satellite, with a 3 Hz scan rate. Each cross-scan is divided into 8 individual fields of view (FOVs) of size 2.25km x 1.25km, though the shape of the FOVs is distorted due to the pirouetting of the satellite to keep the sensor slit oriented perpendicular to the Sun-Earth-satellite plane. These small FOVs increase the chances of seeing through clouds and can reveal details of point-source CO2 emissions, but are generally much finer-scale than can be modeled by the atmospheric transport models used in global CO2 flux inversions, which typically use grid boxes 100s of km on a side. Rather than assimilating each fine-scale FOV XCO2 value individually and comparing them to modeled XCO2 values that change much more slowly, it is convenient to average the original OCO-2 data to coarser scales beforehand, then assimilating these averaged values in the flux inversions. This averaging has the beneficial side-effect of reducing the OCO-2 data volume considerably. In the file presented here, the data have been averaged across 10-second spans, equivalent to an along-track distance of ~67 km on the Earth's surface.</p> <p>The source of the data averaged in the attached file is the data collection "OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V11.1r (OCO2_L2_Lite_FP)", available at NASA's Goddard Earth Sciences (GES) Data and Information Services Center (DISC): https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_11.1r/summary?keywords=OCO2_L2_Lite_FP. The data span for this preliminary version ('Release 0') of the 10-second average file is 20140906-20230430. The original XCO2 values contained in this data collection, along with other auxiliary variables provided for analysis purposes, have been averaged in the same manner across each 10-second span, with the approach also used with the previous (Version 10) release of the OCO-2 XCO2 data, as described in Section 3.2.1 of Byrne et al. (2023) and Section 3.1.1 of Baker et al. (2022): each value in the span is weighted with the inverse square of its retrieved XCO2 uncertainty value, taken from variable 'xco2_uncertainty' from the v11.1r 'Lite" file. Data inside each 10-second span are averaged separately based on viewing mode and surface type, as indicated by variable 'data_type'. Only data that pass the retrieval quality flag (variable 'xco2_quality_flag' in the 'Lite' file equal to zero) are included in the average; the number of such 'good' data values (1-240) included in each average value is indicated in variable 'N_total_shots'. It has been found that 10-second spans with fewer 'good' retrievals tend to be affected more by the unwanted effects of aerosols and undetected clouds: these may be mitigated somewhat by not using 10-sec averages for spans with low 'N_total_shots' values. Variable 'assimilate_flag' separates those data that are not taken in glint viewing mode over either land or water, or in nadir mode over land, from data taken in other modes (target mode, or in transition to/from target mode, or nadir mode over water, or mixed land/water scenes) that are generally not assimilated in flux inversions; it also flags 5% of the assimilable data by orbit for possible use as withheld evaluation data. The uncertainty on the 10-sec average XCO2 value is given in variable 'xco2_uncertainty'; this accounts for correlations in error between individual scenes (+0.3 over land, +0.6 over ocean) as described in Section 3.2.1 of Baker et al (2022), as well as variability in the averaged XCO2 values not captured by the retrieval uncertainties. Finally, variable 'model_error' provides an example of errors incurred in attempting to model the computed 10-second average XCO2 value: this could be added in quadrature to the uncertainty from 'xco2_uncertainty' to get the value used in the flux inversions.</p>
A Rescaled Subset of the Alternative Data Release 1 of the TIFR GMRT Sky Survey
<p>The catalogue in FITS format from this paper: http://adsabs.harvard.edu/abs/2017arXiv170306635H</p> <p>This Rescaled Subset of the Alternative Data Release 1 to the Tata Institute of Fundamental Physics Giant Metrewave Radio Telescope Sky Survey (TGSS-RSADR1) modifies the initial data release of TGSS-ADR1 (Intema et al. 2017) to bring that catalogue to the same flux scale as the extragalactic catalogue from the GaLactic and Extragalactic All-sky Murchison Widefield Array survey (GLEAM: Wayth et al. 2015; Hurley-Walker et al. 2017). In this paper we motivate the derivation of correct and complementary flux density scales, introduce a methodology for correction based on radial basis functions, apply it to TGSS-ADR1, and create a modified catalogue, TGSS-RSADR1. This catalogue comprises 383,589 TGSS-ADR1 sources with updated flux density and flux density uncertainty values, and covers $\mathrm{Declination}\leq+30^\circ$, $|b|\geq10^\circ$, a sky area of 18,800 deg$^2$.</p>
COHERENT Collaboration data release from the first observation of coherent elastic neutrino-nucleus scattering
<p>Release of COHERENT Collaboration data associated with the first observation of coherent elastic neutrino-nucleus scattering (CEvNS), as published in Science (DOI: <a href="http://dx.doi.org/10.1126/science.aao0990">10.1126/science.aao0990</a>) and also available as arXiv:1708.01294[nucl-ex].</p> <p>This data set should enable researchers to extend the study of CEvNS as desired. Future COHERENT Collaboration results will have similar data releases.</p> <p>Example code can be accessed at https://code.ornl.gov/COHERENT/codeExamples_dataRelease_april2018.<br> The full data-release package, including data, code examples, and a descriptive accompanying document can be found at http://coherent.ornl.gov/data.</p>
CLAMATO2017: IGM Lyman-alpha Forest Tomography Survey Public Data Release of Spectra and Maps
<p><strong>CLAMATO 2017 Data Release 1 </strong></p> <p>Public release: 2017 October 9</p> <p>Uploaded to Zenodo on 2018 June 19th after acceptance for publication in ApJS</p> <p>By Khee-Gan Lee (kglee@lbl.gov) and collaborators</p> <p>Supporting paper: https://arxiv.org/abs/1710.02894</p> <p>These are data products associated with the first data release of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field.</p> <p>The following is the summary of the main products:<br> - Source catalog (CL2017_VALUEADDED_RELEASE_20171009.TXT)<br> - Reduced spectra, in /spec_v0/ (blue) and /spec_v0_red (red) sub-directories<br> - Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data.bin)<br> - Wiener-reconstructed 3D absorption map (map_2017_v3.bin)</p> <p>Versions:<br> v0 (not public): Initial rough extraction for 2.15<z<2.55 <br> v1 (not public): Extended redshift range to 2.05<z<2.55 <br> v2 (not public): Caught bug that caused wrong [RA,Dec] for ~4-5 objects<br> v3 (released 2017 Oct 9): Fixed bug that caused wrong aspect ratio in output map<br> v4 (released 2018 Mar 29): Fixed bug that caused negative continua in some spectra</p> <p><br> <strong>Redshift Catalog and Spectra</strong> </p> <p>We provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS</p> <p>The source catalog is provided in the ASCII file CL2017_VALUEADDED_RELEASE_20171009.TXT, with the following columns:</p> <p>- BLUE_SPEC: Blue spectrum filename (in /spec_v0/ sub-directory)<br> - TOMO_ID: CLAMATO ID number<br> - GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog<br> - CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894<br> - ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum<br> - QSO: QSO flag (1 if QSO, 0 if non-QSO)<br> - RA: R.A. in degrees (J2000)<br> - DEC: Dec in degrees (J2000)<br> - S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate<br> - S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate<br> - S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate<br> - S/N_RED: Estimated S/N over restframe 1250 ang < lambda < 1350 ang, -9.0 denotes no estimate<br> - TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes)<br> - EXPTIME: Exposure time on the spectrum, in seconds (aggregate)<br> - RED_SPEC: Red spectrum filename (in /spec_v0_red/ sub-directory), 'NA' if doesn't exist</p> <p>The tarballs spec_v0.tar.gz and spec_v0_red.tar.gz include all the reduced spectra from LRIS-Blue and LRIS-Red, respectively.</p> <p>The individual LRIS spectra are provided in FITS format, with the following HDU Extensions:<br> - HDU0: Object spectral flux density, in units of 10^{-17} ergs/s/cm^2/angstrom<br> - HDU1: Noise standard deviation<br> - HDU2: Pixel Wavelengths in angstroms</p> <p><strong>Pixel Data </strong></p> <p>The binary file PIXEL_DATA_v4.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted. </p> <p>The first value in the binary is a 32-bit integer specifying the number of pixels (64332), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels.</p> <p>An example python script to read pixel_data is as follows:<br> import numpy as np<br> with open('CLAMATO2017_public/pixel_data_v4.bin','r') as f:<br> npix = np.fromfile(f, dtype=np.int32, count=1)<br> f.seek(4)<br> pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5))</p> <p>LIST_TOMO_INPUT_2017.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list.</p> <p><strong>Tomographic Map</strong></p> <p>The Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the dachshund algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund). </p> <p>The reconstructed map is MAP_2017_V4.BIN, which is a 60x48x876 = 2552880 pixel double-precision binary with. The dimension that changes fastest is the z-dimension (876 pixels per dimension), followed by the y-dimension (48 pixels per dimension) and x-dimension (60 per dimension).</p> <p>Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift].</p> <p>The file MAP_2017_V4_SM2.0.BIN is the same map, but smoothed with a R=2Mpc/h Gaussian kernel.</p>
SURVEY OF IONIZED GAS OF THE GALAXY, MADE WITH THE ARECIBO TELESCOPE (SIGGMA): INNER GALAXY DATA RELEASE
<p>The Survey of Ionized Gas of the Galaxy, Made with the Arecibo telescope (SIGGMA) provides a fully-sampled view of the radio recombination line (RRL) emission from the portion of the Galactic plane visible by Arecibo. Observations use the Arecibo L-band Feed Array (ALFA), which has a FWHM beam size of 3 0 .4. Twelve hydrogen RRLs from H163α to H174α are located within the<br> instantaneous bandpass from 1225 MHz to 1525 MHz. We provide here cubes of average (“stacked”) RRL emission for the inner Galaxy region 32 ◦ ≤ ` ≤ 70 ◦ , |b| ≤ 1.5 ◦ , with an angular resolution of 6 0 . The stacked RRL rms at 5.1 km s<sup>−1</sup> velocity resolution is ∼ 0.65 mJy beam<sup>−1</sup> , making this the most sensitive large-scale fully-sampled RRL survey extant. We use SIGGMA data to catalogue 319 RRL detections in the direction of 244 known H ii regions, and 108 new detections in the direction of 79 HII region candidates. We identify 11 Carbon RRL emission regions, all of which are spatially coincident with known H ii regions. We detect RRL emission in the direction of 14 of the 32 supernova remnants (SNRs) found in the survey area. </p>
Data, multiscale dataset and supplementary information for 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process'
<p>This repository contains the analytical Supplementary Information, the data, the multiscale dataset and the codes used to construct the dataset and plot figures used in the manuscript 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process' (accepted in Earth and Planetary Science Letters). </p> <p> </p> <p> </p>
On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations - data release
<p>This release contains all data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2405.05765"><em>On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations</em> [2405.05765].</a> </p> <p>If you encounter difficulties downloading the large files, we recommend using <a href="../records/11142962">zenodo-get</a>. This provides a command-line downloader for any Zenodo record. For unstable connections we recommend using it with the -w flag to generate a list all files in this Zenodo record. This can then be used with tools such as <a href="https://www.gnu.org/software/wget/">wget</a> to resume partial downloads as</p> <p><code>zenodo_get RECORD_ID_OR_DOI -w - | xargs wget -c<br>zenodo_get RECORD_ID_OR_DOI </code></p> <p>(The second line ensures that the downloads completed correctly, and that the md5 hashes match)<br><br>Further details are given in the file README.md.</p> <p>The work of EB and BL is supported in part by the EPSRC ExCALIBUR programme ExaTEPP (project EP/X017168/1). The work of EB, BL, MP, and FZ has been supported by the STFC Consolidated Grant No. ST/X000648. The work of EB has also been supported by the UKRI Science and Technology Facilities Council (STFC) Research Software Engineering Fellowship EP/V052489/1. The work of NF has been supported by the STFC Consolidated Grant No. ST/X508834/1. The work of DKH was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B06033701). The work of DKH was further supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A4A5031460). The work of JWL is supported by IBS under the project code, IBS-R018-D1. The work of HH and CJDL is supported by the Taiwanese MoST grant 109-2112-M-009-006-MY3 and NSTC grant 112-2112-M-A49-021-MY3. The work of CJDL is also supported by Grants No. 112-2639-M-002-006-ASP and No. 113-2119-M-007-013. The work of BL and MP has been further supported in part by the STFC Consolidated Grant No. ST/T000813/1.<br>BL and MP received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under Grant Agreement No.~813942. The work of DV is supported by STFC under Consolidated Grant No. ST/X000680/1.</p> <p>Numerical simulations have been performed on the DiRAC Extreme Scaling service at the University of Edinburgh, and on the DiRAC Data Intensive service at Leicester.<br>The DiRAC Extreme Scaling service is operated by the Edinburgh Parallel Computing Centre on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BEIS capital funding via STFC capital grant ST/R00238X/1 and STFC DiRAC Operations grant ST/R001006/1. DiRAC is part of the National e-Infrastructure</p>
SU(2) gauge theory with one and two adjoint fermions towards the continuum limit—data release
<p>This package contains all data generated in preparing the publication <a href="https://arxiv.org/abs/2408.00171">SU(2) gauge theory with one and two adjoint fermions towards the continuum limit</a>. It includes four classes of data:</p> <ol> <li>Raw data, as generated from the measurement code running on HPC, in their native formats (raw_data.zip).</li> <li>Metadata around the analysis of the ensembles, in YAML format (ensembles.yaml).</li> <li>Data obtained by analysing the above data and presented in <a href="https://arxiv.org/abs/2408.00171">arXiv:2408.00171</a>, for specific ensembles, in sqlite3 format (su2.sqlite).</li> <li>The above data in (3), and additional data obtained by further analysing them, in CSV format (ensemble_results.csv and gammastar_results.csv).</li> <li>For convenience, the data in (1) above, repackaged in HDF5 format (package.h5).</li> </ol> <p>Each of these is documented in more detail in the file README.md.</p> <p>Due to their size, raw gauge configurations are not included in this package.</p> <p> </p>
Catalog for The MDW Hα Sky Survey: Data Release 0
<p>Here, we upload the source catalog for Data Release 0 (DR0) of the MDW Hα Sky Survey. This catalog of ~1.9 million sources is catalog-matched to the Pan-STARRS DR1 catalog, with a subset of 160k sources matched to the IGAPS survey of the Galactic plane. </p> <p>This catalog is related to the AJ manuscript titled "The MDW Hα Sky Survey: Data Release 0", currently undergoing review (submission ID: AAS55752R1).</p> <p>More data from DR0 (e.g. images, QA) can be accessed at <a href="https://mdw.astro.columbia.edu" target="_blank" rel="noopener">https://mdw.astro.columbia.edu</a>. Any use of DR0 data (including the source catalog) should include the following acknowledgement:</p> <blockquote> <p>Funding for the MDW Survey Project has been provided by the Michele and David Mittelman Family Foundation. David R. Mittelman, Dennis di Cicco, and Sean Walker are founding members of the survey and made possible the acquisition and reduction of the data. Columbia University Astronomy Department is responsible for the final data reduction, calibration, and dissemination of the survey data. All commercial rights for the use of the data are reserved.</p> </blockquote>
Public Utility Data Liberation Project (PUDL) Data Release
<h2><strong>v2025.10.0 (2025-10-14)</strong></h2> <p>This is a regular monthly data release, primarily intended to ensure that PUDL has the most up-to-date EIA-860M data. It also happens to include final EIA-860 data for 2024, and some newly integrated EIA-923 financial data and PHMSA natural gas data. See below for details.</p> <h3>Expanded Data Coverage</h3> <h4>EIA-860</h4> <ul> <li> <p>Updated EIA-860 with final release data from 2024. See issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4616">#4616</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4617">#4617</a>.</p> </li> </ul> <h4>EIA-860M</h4> <ul> <li> <p>Updated EIA-860M monthly generator report with newly published data for August of 2025. See issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4639">#4639</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4638">#4638</a>.</p> </li> </ul> <h3>New Data</h3> <h4>PHMSA</h4> <ul> <li> <p>Added eight transformed table containing annual data from PHMSA natural gas distributors from 1970 to the present. Note that these containing mostly numeric values are named as <code><span>_core</span></code> - indicating that these tables have not been fully cleaned and validated. We’ve published these tables to make the 50+ years of PHMSA data we’ve extracted and mapped available for others to use and for contributors to more easily improve incrementally. See <a href="https://github.com/catalyst-cooperative/pudl/issues/3770">#3770</a> and <a href="https://github.com/catalyst-cooperative/pudl/pull/4005">#4005</a>.</p> </li> <li> <p>The first cleaned table, <code><span>core_phmsagas__distribution_operators</span></code> has been added to our PUDL database. Thanks to <a href="https://github.com/sponsors/seeess1">@seeess1</a> for all of your work on this!</p> </li> </ul> <h4>EIA 923</h4> <ul> <li> <p>Thanks to contributions from <a href="https://github.com/sponsors/alexclippinger">@alexclippinger</a>, we’ve added cleaned EIA923 Schedule 8B Financial Information to the PUDL database as <a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html#i-core-eia923-yearly-byproduct-expenses-and-revenues"><span>_core_eia923__yearly_byproduct_expenses_and_revenues</span></a>. Once harvested, this table will be replaced with a well-normalized version of the same data, but it is being published in this form until then. See <a href="https://github.com/catalyst-cooperative/pudl/issues/4099">#4099</a> and <a href="https://github.com/catalyst-cooperative/pudl/issues/2448">#2448</a>, and <a href="https://github.com/catalyst-cooperative/pudl/pull/4636">#4636</a>.</p> </li> </ul> <h3>Documentation</h3> <ul> <li> <p>Added data source pages for:</p> <ul> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_sources/censuspep.html"><span>Population Estimates Program's (PEP) Federal Information Processing Series (FIPS) Codes</span></a>; see issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4375">#4375</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4622">#4622</a>.</p> </li> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_sources/sec10k.html"><span>U.S. Securities and Exchange Commission (SEC) Form 10-K</span></a>; see issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4329">#4329</a>, <a href="https://github.com/catalyst-cooperative/pudl/issues/4347">#4347</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4562">#4562</a>.</p> </li> </ul> </li> </ul> <h3>New Data Tests & Data Validations</h3> <ul> <li> <p>After investigating some modest discrepancies between our imputed hourly electricity demand and prior work by <a href="https://github.com/sponsors/truggles">@truggles</a> & <a href="https://github.com/sponsors/awongel">@awongel</a>, we’re removing the “EXPERIMENTAL” warning label that we had on those tables. See <a href="https://github.com/catalyst-cooperative/pudl-examples/pull/10">our discussion about the imputation results in the PUDL Examples repo</a>. The <a href="https://www.kaggle.com/code/catalystcooperative/06-pudl-imputed-electricity-demand">associated notebook is available on Kaggle</a></p> <p>This relates to the PUDL imputed demand values in following tables:</p> <ul> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html#out-eia930-hourly-operations"><span>out_eia930__hourly_operations</span></a></p> </li> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html#out-eia930-hourly-subregion-demand"><span>out_eia930__hourly_subregion_demand</span></a></p> </li> <li> <p><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html#out-eia930-hourly-aggregated-demand"><span>out_eia930__hourly_aggregated_demand</span></a></p> </li> </ul> </li> </ul> <h3>Deprecations</h3> <ul> <li> <p>We have finally shut down our long-suffering <a href="https://datasette.io">Datasette</a> deployment, but are still working on achieiving feature parity in the new <a href="https://viewer.catalyst.coop">PUDL Data Viewer</a>. We have <a href="https://github.com/catalyst-cooperative/eel-hole/issues/36">an epic tracking our progress</a>. See issue <a href="https://github.com/catalyst-cooperative/pudl/issues/4481">#4481</a> and PR <a href="https://github.com/catalyst-cooperative/pudl/pull/4605">#4605</a> for the removal of Datasette references within the main PUDL repo.</p> </li> </ul> <h2><strong>Other PUDL v2025.10.0 Resources</strong></h2> <ul> <li><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/data_dictionaries/pudl_db.html">PUDL v2025.10.0 Data Dictionary</a></li> <li><a href="https://catalystcoop-pudl.readthedocs.io/en/v2025.10.0/">PUDL v2025.10.0 Documentation</a></li> <li><a href="https://registry.opendata.aws/catalyst-cooperative-pudl/">PUDL in the AWS Open Data Registry</a></li> <li>PUDL v2025.9.1 in a free, public AWS S3 bucket: s3://pudl.catalyst.coop/v2025.10.0/</li> <li>PUDL v2025.9.1 in a requester-pays GCS bucket: gs://pudl.catalyst.coop/v2025.10.0/</li> <li><a href="https://doi.org/10.5281/zenodo.17352325">Zenodo archive of the PUDL GitHub repo for this release</a></li> <li><a href="https://github.com/catalyst-cooperative/pudl/releases/tag/v2025.10.0">PUDL v2025.10.0 release on GitHub</a></li> <li><a href="https://pypi.org/project/catalystcoop.pudl/2025.10.0">PUDL v2025.10.0 package in the Python Package Index (PyPI)</a></li> </ul> <h2><strong>Contact Us</strong></h2> <p><strong>If you're using PUDL, we would love to hear from you!</strong> Even if it's just a note to let us know that you exist, and how you're using the software or data. Here's a bunch of different ways to get in touch:</p> <ul> <li><a href="https://github.com/catalyst-cooperative">Follow us on GitHub</a></li> <li>Use the <a href="https://github.com/catalyst-cooperative/pudl/issues">PUDL Github issue tracker</a> to let us know about any bugs or data issues you encounter</li> <li><a href="https://github.com/orgs/catalyst-cooperative/discussions">GitHub Discussions</a> is where we provide user support.</li> <li>Watch our <a href="https://github.com/orgs/catalyst-cooperative/projects/9">GitHub Project</a> to see what we're working on.</li> <li>Email us at <a href="mailto:hello@catalyst.coop">hello@catalyst.coop</a> for private communications.</li> <li>On Mastodon: <a href="https://mastodon.energy/@catalystcoop">@CatalystCoop@mastodon.energy</a></li> <li>On BlueSky: <a href="https://bsky.app/profile/catalyst.coop">@catalyst.coop</a></li> <li>On Twitter: <a href="https://twitter.com/CatalystCoop">@CatalystCoop</a></li> <li>Connect with us <a href="https://www.linkedin.com/company/catalyst-cooperative/">on LinkedIn</a></li> <li>Play with our data and notebooks <a href="https://www.kaggle.com/catalystcooperative">on Kaggle</a></li> <li>Combine our data with ML models <a href="https://huggingface.co/catalystcooperative">on HuggingFace</a></li> <li>Learn more about us on our website: <a href="https://catalyst.coop">https://catalyst.coop</a></li> <li>Subscribe to our announcements list for <a href="https://catalyst.coop/updates">email updates</a>.</li> </ul>
Data Release of Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations
<p>Dataset of the paper "Cosmic evolution of the incidence of Active Galactic Nuclei in massive clusters: Simulations versus observations".</p> <p> </p> <p>All the necessary code to deal with these data can be found in: https://github.com/IvanMuro/agn_frac_data_release</p>
The Young Supernova Experiment Data Release 1 (YSE DR1) Light Curves
<p>This is the official Zenodo data release of the Young Supernova Experiment Public Data Release 1 (YSE DR1) light curves associated with the paper, <em>"The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae</em><em>"</em>.<em> </em>YSE DR1 is comprised of processed multi-color Pan-STARRS1 (PS1)-<em>griz</em> and Zwicky Transient Facility (ZTF)-<em>gr </em>photometry lightcurve files in the SNANA data format of 1975 transients with host galaxy associations, redshifts, spectroscopic/photometric classifications, and additional data products from November 24th, 2019 to December 20, 2021. See Aleo et al. (2022) for details. </p> <p>"yse_dr1_zenodo.tar.gz" -- All lightcurve data with no cut on signal to noise (S/N).</p> <p>"yse_dr1_zenodo_snr_geq_4.tar.gz" -- All lightcurve data with S/N >= 4. This can be used to recreate the analysis in Aleo et al. (2022).</p> <p>"parsnip_results_for_ysedr1_table_A1_full_for_online" -- The full version of Table~C2 in Aleo et al. (2022). The full ParSNIP (tertiary classification) results for YSE DR1.</p> <p>NOTE: An example tutorial on how to download the YSE DR1 data (full sample, spec sample, phot sample), grab metadata, and recreate a plot from the paper can be found <a href="https://github.com/patrickaleo/ysedr1_data_demos/blob/main/ysedr1_quick_tutorial.ipynb">on Github</a>. </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>
Data release for paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method"
<p>This is the data release for the paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method", which is available on https://arxiv.org/abs/2306.12908.</p> <p>These results are derived from the gravitational-wave parameter-estimation results by the LIGO-Virgo-KAGRA Collaboration, released with the GWTC-1, GWTC-2, GWTC-2.1, and GWTC-3 catalogs under the following links:</p> <ul> <li> https://dcc.ligo.org/P1800370-v5/public</li> <li> https://dcc.ligo.org/P2000223-v7/public</li> <li> https://doi.org/10.5281/zenodo.6513631</li> <li> https://doi.org/10.5281/zenodo.5546663</li> </ul> <p>For the lensed-unlensed hypothesis test posterior overlap Bayes factors, we provide the following files for event pairs from within each observing run:</p> <ul> <li> blu_all_pairs_O1.txt</li> <li> blu_all_pairs_O2.txt</li> <li> blu_all_pairs_O3.txt</li> </ul> <p>In each file, the column "event_pair" contains the names of the two events from the pair sorted chronologically, the column "data_releases" contains the names of the data releases from which the posterior samples of each event were taken, the column "waveform" contains the name of the waveform model used in the parameter estimation for both sets of posteriors, and the column "log10blu" contains the log10 of the Bayes factors.</p> <p>The differences between runs for the same event pair, only including O1-O1, O2-O2, O3-O3 pairs, where at least one run gave log10blu>0, are also given in the file "blu_differences_pairs_with_log10blu_pos.txt". The column "event_pair" contains the event pairs, the columns "waveform_{1,2}" contain the names of the waveform models used in the parameter estimation for both sets of posteriors, the columns "data_releases_{1,2}" contain the the data releases from which the posterior samples of each event were taken, the columns "log10blu_{1,2}" contain the log10 Bayes factors, and the column "difference" contains the difference between "log10blu_1" and "log10blu_2".</p> <p>We also provide the following files corresponding to the appendix of the paper, analyzing overlaps between posterior samples for individual events:</p> <ul> <li> overlap_different_runs.txt</li> <li> overlap_same_run.txt</li> <li> rescaled_difference_single_event.txt</li> </ul> <p>The file "overlap_different_runs.txt" contains Bayes factors for a single event, but comparing the posteriors from different runs. The file "overlap_same_run.txt" contains Bayes factors for the overlap of a single run on a single event with itself. The file "rescaled_difference_single_event.txt" contains the difference between the results contained in the file overlap_different_runs.txt and the results in overlap_same_run.txt, taking the ones that produce the biggest difference, as per equation (A.1) in the paper.</p> <p>In these files, the column "event_name" is the name of the event, the column "data_release" or "data_releases" contains the name(s) of the data release(s) from which the posterior samples of each run were taken, the column "waveform" or "waveform_pair" contains the name(s) of the waveform model(s) used, and the column "log10blu" is the log10 Bayes factor obtained. In the file "rescaled_difference_single_event.txt", the columns "max_run_waveform" and "max_run_data_release" identify an entry from the "overlap_same_run.txt" file from which we use the "log10blu" to compute the value listed in the "difference" column using equation (A.1).<br> </p>
Majorana Demonstrator Data Release for AI/ML Applications
<p>The enclosed data release consists of a subset of the 228Th calibration data from the Majorana Demonstrator<br> experiment. Each Majorana event is accompanied by raw Germanium detector waveforms, pulse shape discrimina-<br> tion cuts, and calibrated final energies, all shared in an HDF5 file format along with relevant metadata. This release<br> is specifically designed to support the training and testing of Artificial Intelligence and Machine Learning (AI/ML)<br> algorithms upon our data. Please read the following ArXiV posting before using this dataset: https://arxiv.org/abs/2308.10856. Please direct questions about the material provided within this release to liaobo77@ucsd.edu (A. Li).</p>
Data Release: "LIGO-Virgo-KAGRA's Oldest Black Holes: Probing star formation at cosmic noon with GWTC-3"
<p>This repository contains the data behind the figures presented in v2 of "LIGO-Virgo-KAGRA's Oldest Black Holes: Probing star formation at cosmic noon with GWTC-3" (<a href="https://ui.adsabs.harvard.edu/link_gateway/2023arXiv230715824F/arxiv:2307.15824">arXiv:2307.15824</a>), to appear in ApJL.</p><p>The csv files (in Output.zip) and the h5 files contain the data products. The three Jupyter notebooks include code for plotting the figures and calculating the summary statistics that appear in the paper. </p><p> </p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Parameter estimation data release
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Parameter estimation data release</strong></p> <p>This data release contains posterior samples (*.h5) for gravitational-wave candidates from the second part of the third observing run (O3b).We provide results for the 35 candidates that have a probability of astrophysical origin of over 0.5, plus <a href="https://doi.org/10.3847/2041-8213/ac082e">GW200105_162426</a>, which is a clear outlier from the noise background. There are two .h5 files per event</p> <ul> <li>Cosmologically reweighted (*cosmo.h5)</li> <li>Not cosmologically reweighted (*nocosmo.h5)</li> </ul> <p>The cosmologically reweighted posteriors are reweighted to have a luminosity-distance prior that has a uniform merger rate in the source's comoving frame. See the <a href="http://dcc.ligo.org/LIGO-P2000318/public">paper</a> appendices for further information. In addition to containing the posterior samples, the .h5 files also contain metadata about the analyses including the configuration files (which specify details such as the detector data analysed), noise power spectral densities (potentially for a superset of the detectors used in the analysis) and calibration uncertainty envelopes.</p> <p>The inference of the source parameters were performed with <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby</a>, <a href="https://lscsoft.docs.ligo.org/parallel_bilby/">Parallel Bilby</a> and <a href="https://git.ligo.org/richard-oshaughnessy/research-projects-RIT/tree/temp-RIT-Tides">RIFT</a>. The results are formatted using <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a>.</p> <p><strong>A note about mixed samples:</strong> The samples provided here are produced using different waveform approximants. The Mixed label indicates that equal numbers of samples have been included from two different waveform approximants. For the binary black holes, these are IMRPhenomXPHM and SEOBNRv4PHM (for more details, see GWTC3p0PEDataReleaseExample.ipynb included in this data release and the paper). As different waveforms were analysed with different codes, there are sometimes differences in some parameters due to conventions in the codes. For example:</p> <ul> <li>As RIFT does not sample over time of coalescence as Bilby does, the RIFT time of coalescence results have a posterior distribution with a single spike, whereas the Bilby results have a distribution of peaks representing different sky positions for the source.</li> <li>There are different conventions for the range of the polarization angle (either 0 to π or 0 to 2 π). The parameter psi_wrapped maps all results to the range 0 to π, should consistency be important.</li> <li>The likelihood may show small differences when different sampling rates were used for Bilby and RIFT. The log-likelihood is expected to have a relative shift between the two runs of a few nats.</li> </ul> <p>Due to these differences, care must be taken when using Mixed samples, which will contain results using both codes' conventions. This should not impact the most interesting quantities, such as the masses, and so should only be rarely an issue.</p> <p>A <a href="https://doi.org/10.5281/zenodo.5117702">similar parameter-estimation release has been made to accompany GWTC-2.1</a> for results from the first part of the third observing run.</p> <p><strong>Sky localization data release</strong></p> <p>The sky localization tar file (IGWN-GWTC3p0-v2-PESkyLocalizations.tar.gz) contains candidate sky localizations corresponding to different parameter estimation configurations (.fits). Two waveforms are used for the majority of targets (IMRPhenomXPHM and SEOBNRv4PHM) and additional waveforms are used for possible neutron star--black hole mergers (see the <a href="https://dcc.ligo.org/LIGO-P2000318/public">paper</a> for further information). If you do not mind which waveform, the sky localizations labelled "Mixed" include posterior samples from both waveforms used. A machine readable list (skyLocalizationFileList.csv) of sky localization files is included within the .tar.gz file for ease of use, where the Mixed results are indicated as Default=True.</p> <p><strong>Contour data release</strong></p> <p>The contour tar file (IGWN-GWTC3p0-v2-PEContours.tar.gz) contains the contour files used to produce Figures 8 and 9 in the <a href="http://dcc.ligo.org/LIGO-P2000318/public">paper</a>. The python notebook (GWTC3p0PEPlotContourData.ipynb) explains how to reproduce these figures (and an interactive version of these plots can be accessed at <a href="https://gwtc3-contours.streamlit.app/">gwtc3-contours.streamlit.app/</a>).</p> <p><strong>Python notebook</strong></p> <p>The Python notebook (GWTC3p0PEDataReleaseExample.ipynb) explains how to read and use the posterior samples with a selection of examples.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5546662 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave parameter estimation, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
Gene family data from the PhyloGenes (release version 1.2, phylogenes.org)
<p>The compressed file contains: </p> <p><br> 1. PhyloXML_files </p> <p>This folder has family trees in PhyloXML format, one file per family (e.g. <family_ID>.xml).</p> <p>The following information is provided for each node of a tree:<br> 1) leaf node:<br> branch length<br> name <gene_id><br> taxonomy scientific_name<br> sequence accession <UniProt ID></p> <p>2) non-leaf node:<br> branch length<br> events <duplication or speciation></p> <p><br> 2. phylogenes_csv.tar.xz</p> <p>This tar file has gene information of family members in CSV format, one file per family (e.g. <family_ID>.csv). </p> <p>A CSV file includes the following columns:<br> Uniprot ID<br> Gene <Gene name. If none then Gene ID><br> Gene ID<br> Gene name<br> Organism<br> Subfamily name</p> <p>Any columns displayed after 'Subfamily name' are 'Known functions'. Each 'Known function' is a GO molecular function term that is annotated to at least one member of the gene family AND that the annotation is supported by an experimental evidence. Number 1 or 0 indicates the presence or absence of a particular function in a gene.</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.