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.
69
datasets available to search
ShareScore release 0.7.1
Dataset results
69 results for “LISA”
LISA Sensitivity to Gravitational Waves from Sound Waves
<p>Supplemental material for the paper of the same name, consisting of LISA's (1) strain noise power spectrum and (2) peak-integrated sensitivities for all the different spectral shapes of the signal and observing times presented in this paper.</p>
Immagini relative al corso di Concertmaster Programme con Lisa Schatzaman
<p>Queste immagini rappresentano le lezioni all'Accademia Stauffer del corso Concertmaster Programme con Lisa Schatzman </p>
LISA Data Challenge Sangria (LDC2a)
<p><strong>Sangria</strong> includes two main datasets: each contains Gaussian instrumental noise and simulated waveforms from 30 million Galactic white dwarf binaries, from 17 verification Galactic binaries, and from merging massive black-hole binaries with parameters derived from an astrophysical model. The first dataset includes the full specification used to generate it: source parameters, a description of instrumental noise with the corresponding power spectral density, LISA's orbit, etc. We also release noiseless data for each type of source, for waveform validation purposes. The second dataset is <em>blinded</em>: the level of istrumental noise and number of sources of each type are not disclosed (except for the known parameters of the verification binaries).</p> <p>See <a href="https://lisa-ldc.lal.in2p3.fr/challenge2a">LDC website</a> for more details.</p> <p> </p>
Galactic Populations of LISA DWD Binaries
<p>These COSMIC simulation outputs and assembled galactic populations of<br> Double White Dwarf (DWD) binaries<br> represent the work published by V. Delfavero et al,<br> in https://arxiv.org/abs/2409.15230</p> <p>The COSMIC version used in these simulations is 3.4.10</p> <p>The git repository for the accompanying code which built this dataset<br> will be available at https://gitlab.com/xevra/basil-cosmic<br> upon the publication of our article.</p> <p>Also useful is the fork of ldasoft used in this pipeline:<br> https://github.com/xevra/ldasoft</p> <p>Please cite this work if you use the included data in a publication.</p> <p>-------------------------------------------------------------------------------<br>Summary<br>-------------------------------------------------------------------------------<br>Params_[MODEL].ini<br> This initialization file was used for COSMIC, <br> for the runs tagged with [MODEL].</p> <p>[MODEL]_[KSTAR1]_[KSTAR2]_[METALLICITY_BIN].tar.gz<br> This tarball contains the outputs from one run of COSMIC,<br> using Params_[MODEL].ini, for a type of binary indicated by<br> [KSTAR1] and [KSTAR2] according to COSMIC syntax,<br> and for the [METALLICITY_BIN]'th metallicity bin.</p> <p>[MODEL]_COSMIC.hdf5<br> This hdf5 database holds the DWD population outputs for all 60<br> COSMIC simulations in a compressed format.<br> It only holds information about binary systems at the end<br> of evolution (conv, according to COSMIC syntax).<br> It does not contain the evolutionary history of individual binaries<br> (the bpp information, according to COSMIC syntax).</p> <p>[MODEL]_m12i-000_LISA.nal.hdf5<br> This hdf5 database holds the LISA "resolved" population of DWD binaries<br> for a single realization of a Milky-Way-like galaxy.<br> It also stores many attributes indicating assumptions and various<br> population statistics.<br> The "nal" extension indicates that this database contains<br> truncated Gaussian likelihood models for the resolved population<br> of LISA DWDs,<br> which can be loaded by the gwalk code (https://gitlab.com/xevra/gwalk).<br> This database can also be explored by any hdf5 library (such as h5py).</p> <p>[MODEL]_m12i-000_LISA.hdf5<br> This hdf5 database holds all of the LISA-band binaries (f_GW > 10^-4 Hz)<br> for a single galaxy realization.</p> <p>[MODEL]_m12i-000_LISA.txt.gz<br> This text file stores the inputs for ldasoft gbfisher analysis,<br> for a specific galaxy realization under assumptions<br> broadly described by [MODEL]<br><br>See README.txt for full description<br><br>-------------------------------------------------------------------------------<br>Funding<br>-------------------------------------------------------------------------------<br>VD is supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center administered by Oak Ridge Associated Universities under contract NPP-GSFC-NOV21-0031. ROS gratefully acknowledges support from NSF awards NSF PHY-1912632, PHY-2012057, PHY-2309172, AST-2206321, and the Simons Foundation. JB is supported by the NASA LISA Project Office.<br><br>We acknowledge software packages used in this publication, including NUMPY (Harris et al. 2020), SCIPY (Virtanen et al. 2020), MATPLOTLIB (Hunter 2007), ASTROPY (Astropy Collaboration et al. 2013, 2018), H5PY (Collette 2013), LEGWORK (Wagg et al. 2022), and ldasoft Littenberg et al. (2020). This research was done using resources provided by the Open Science Grid (Pordes et al. 2007; Sfiligoi et al. 2009), which is supported by the National Science Foundation awards #2030508 and #1836650, and the U.S. Department of Energy’s Office of Science</p>
The TDI data and PSD/sensitivity-related files for PyCBC LISA documentation example
<p>The TDI data and PSD/sensitivity-related files for PyCBC LISA documentation example, most of them are generated from LDC-Sangria<em> </em>dataset.</p>
LISA Data Challenge Spritz (LDC2b)
<p>The purpose of this challenge is to address for the first time the realistic instrumental and environmental noise. We have two datasets with merging MBHBs. (i) Dataset with a loud (SNR ~2000) GW signal, lasting for about 31 days. The signal is expected to be detectable a few weeks before the merger and, therefore, is suitable for testing low-latency algorithms. We have added three short loud glitches distributed in the inspiral, late inspiral and near merger parts of the signal. (ii) Dataset with a quiet (SNR ~100) GW signal lasting for one week, with a several-hour-long glitch placed near the merger. A third 1-year long dataset contains 36 verification binaries. We have placed glitches according to a Poisson distribution with a rate of 4 glitches per day, whose model is described in the Spritz documentation.</p> <p>See <a href="https://lisa-ldc.lal.in2p3.fr/challenge2a">LDC website</a> for more details.</p>
Dataset [Amazônia and Amazon: domain analysis with IRaMuTeQ in Scopus and LISA databases]
<p>The study reports the comparative analysis between the results of the search queries for the terms<em> Amazônia</em> and <em>Amazon</em> in Scopus and LISA databases, in the period from 2008 to 2018. Concept Theory and Domain Analysis were used in conjunction with IRaMuTeQ software in order to identify, quantify and analyse semantic distances in a sample consisting of 80 abstracts from retrieved articles.</p> <p>DOI: <a href="https://doi.org/10.5771/9783956507762-522">https://doi.org/10.5771/9783956507762-522</a></p> <p> </p>
Fig. 94. Cissidium lisae Darby, 2015. A. Habitus. B. Pronotum, C in A revision of Cissidium Motschulsky (Coleoptera: Ptiliidae) with seventy seven new species
Fig. 94. Cissidium lisae Darby, 2015. A. Habitus. B. Pronotum, C. Mesoventrite showing median process of collar, mid-keel and keel, × 550.
Dataset from: Gravitational wave sources in our Galactic backyard - Predictions for BHBH, BHNS and NSNS binaries in LISA
<p>The data from all simulations used in "<em><strong>Gravitational wave sources in our Galactic backyard: Predictions for BHBH, BHNS and NSNS binaries in LISA</strong></em>"</p> <p>Contents:</p> <ul> <li><strong>detections_and_totals.zip</strong> <ul> <li>Contains for .npy files that contain tables of the detections and total DCOs in Milky Way. These were calculated with <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA/blob/main/simulation/postprocessing_notebooks/get_detection_rates.ipynb">this</a> and <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA/blob/main/simulation/postprocessing_notebooks/get_total_DCOs_in_MW.ipynb">this</a> notebook and are included for convenience so you don't have to re-run these notebooks</li> </ul> </li> <li><strong>simulations_4yr.zip</strong> <ul> <li>Contains 60 .h5 files that contain the main simulations for a 4-year LISA mission. Each file contains the results for a single DCO type (BHBH, BHNS or NSNS) and model variation (20 variations) are labeled as <em>{DCO_type}_{variation}_all.h5</em><strong><em>. </em></strong></li> </ul> </li> <li><strong>simulations_10yr.zip</strong> <ul> <li>As simulations_4yr.zip but for a 10-year LISA mission</li> </ul> </li> <li><strong>simple_mw_simulations.zip</strong> <ul> <li>As simulations_4yr.zip but using a simple model for the Milky Way (discussed in Appendix D) and only for models A and F (hence only contains 6 .h5 files)</li> </ul> </li> </ul> <p>For a description of how to use these files to reproduce figures and results see the README.md in the associated GitHub repository: <a href="https://github.com/TomWagg/detecting-DCOs-in-LISA">https://github.com/TomWagg/detecting-DCOs-in-LISA</a></p> <p>Version 0.0.1 - Changes model E to E' as discussed in paper (now we allow HeHG donors to survive common-envelopes)</p> <ul> </ul>
LISA visualizations
<p><strong>LISA-visualization</strong></p> <p>LISA consists of three satellites orbiting the sun, trailing earth completing one cartwheel-like motion during the course of one year. the three satellites are pictured as orange, green and blue dots. The sizes of earth and LISA are not to scale, but the distances between all objects and the size of the sun are at scale.</p> <p><strong>Videos</strong></p> <p>The videos show LISA orbiting the sun while a Gravitational wave is traveling perpendicular to the plane of LISA's and earth's orbit. The amplitude of the wave is vastly exaggerated. For visualizations purposes a low frequency wave is chosen which is below LISA's sensitivity. Gravitational waves in the LISA's sensitivity spectrum would oscillate to fast for visualizing a one year LISA orbit in 20 seconds.</p> <p>In the corner we show the change of distance between the three satellites of LISA with a view locked to the center of LISA and perpendicular to the plane of the three satellites. This change of distance is LISA's measurement of the Gravitational wave.</p> <p>It is recommended to download the videos for proper display since viewing the videos in the browser are zoomed in.</p> <p><strong>Picture</strong></p> <p>LISA’s constellation is pictured at eight different points in time with 1.5 month intervals.</p>
Supplementary data PhD of Lisa Joos
<p>Here you can find the following supplementary material of my Phd:</p> <p>- Chapter 2 - Table S2.1</p> <p>- Supplementary excel of Chapter 2</p> <p>- Supplementary excel of Chapter 5</p>
Video Intervista alla Concertmaster Lisa Schatzman e l'allieva Dora Merelli
<p>Questo video rappresenta l'intervista al primo violino dell'orchestra di Lucerna Lisa Schatzman e l'allieva Dora Merelli</p>
LISA XAS Database
<p>A <a href="https://lisa.iom.cnr.it/xasdb/" target="_blank" rel="noopener">database of XAS spectra </a>of reference compounds collected at BM08 - LISA beamline. </p>
Neutron Star - White Dwarf Binaries: Probing Formation Pathways and Natal Kicks with LISA
<p>We present supplementary datasets accompanying our publication<em> </em><a href="https://arxiv.org/abs/2310.06559">Neutron Star - White Dwarf Binaries: Probing Formation Pathways and Natal Kicks with LISA</a>.<em> </em>These catalogues reperesent the Galactic population of double white dwarf (DWD) and neutron star - white dwarf (NSWD) binaries emitting gravitational waves (GWs) in the <em>Laser Interferometer Space Antenna</em> (LISA) frequency band (0.1 mHz - 1 Hz). The catalogues have been constructed based on binary evolution models <a href="https://arxiv.org/abs/1208.6446">Toonen et al. 2012</a> for DWDs and <a href="https://arxiv.org/abs/1804.01538">Toonen et al. 2018</a> for NSWD binaries, obtained using SeBa binary population synthesis code.</p> <p><strong>Data contents</strong></p> <p>The dataset consists of <strong>12 catalogues </strong>representing Galactic populations of NSWD and/or DWD binaries, which are expected to be the most numerous types of binaries amongt LISA's Galactic sources. Each catalogue is distinguished by its model ID, which specifies the presence of NSWD and/or DWD binaries, the CE model used, CE efficiency values, and the NS natal kick prescription applied (see table below).</p> <p>Each catalogue is structured to describe a binary systems with the following attributes:</p> <ul> <li><strong>Name*</strong>: binary identifier; this consist of a prefix indicating the binary type (<code>'MW_DWD'</code> for a DWD binary, <code>'MW_NSWD_ecc0'</code> for a circular NSWD bianry, or <code>'MW_NSWD_ecc1'</code> for an eccentric NSWD binary) followed by a unique ID number. For example, <code>'MW_DWD 28713637'</code>.</li> <li><strong>Frequency</strong>: present-day GW frequency (Hz).</li> <li><strong>Frequency Derivative</strong>: rate of change of GW frequency over time (Hz^2).</li> <li><strong>Ecliptic Latitude</strong>: in radians (rad).</li> <li><strong>Ecliptic Longitude</strong>: in radians (rad).</li> <li><strong>Amplitude</strong>: GW amplitude (dimensionless).</li> <li><strong>Inclination</strong>: angle between the binary's orbital plane and our line of sight, in radians (rad).</li> <li><strong>Polarization</strong>: Orientation of the GW's polarization, in radians (rad).</li> <li><strong>Initial Phase</strong>: initial phase (rad).</li> <li><strong>Eccentricity</strong>: orbital eccentricity (dimensionless).</li> </ul> <p><strong>*</strong>Note that the <strong>Name </strong>field for eccentric NS+WD binaries (staring with <code>'MW_NSWD_ecc1'</code>) is not unique because these binaries are represented by multiple harmonics sharing the same name ID. The number of harmonics included varies for each binary to ensure that at least 99% of the binary's total GW power is represented. Thus, for each binary, we added harmonics incrementally until this threshold is reached.</p> <table> <tbody> <tr> <td>Model ID</td> <td>WD+WD</td> <td>NS+WD</td> <td>CE model</td> <td>CE efficiency</td> <td>NS natal kick</td> </tr> <tr> <td>1_0</td> <td>Yes</td> <td>No</td> <td>αα</td> <td>αλ=2.00</td> <td>N/A</td> </tr> <tr> <td>1_1</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Verbunt</td> </tr> <tr> <td>1_2</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Arzoumanian</td> </tr> <tr> <td>1_3</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Hobbs</td> </tr> <tr> <td>1_4</td> <td>Yes</td> <td>Yes</td> <td>αα</td> <td>αλ=2.00</td> <td>Blaauw</td> </tr> <tr> <td>2_0</td> <td>Yes</td> <td>No</td> <td>αα2</td> <td>αλ=0.25</td> <td>N/A</td> </tr> <tr> <td>2_1</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Verbunt</td> </tr> <tr> <td>2_2</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Arzoumanian</td> </tr> <tr> <td>2_3</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Hobbs</td> </tr> <tr> <td>2_4</td> <td>Yes</td> <td>Yes</td> <td>αα2</td> <td>αλ=0.25</td> <td>Blaauw</td> </tr> <tr> <td>3_0</td> <td>Yes</td> <td>No</td> <td>αγ</td> <td>αλ=2.00, γ=1.75</td> <td>N/A</td> </tr> <tr> <td>3_1</td> <td>Yes</td> <td>Yes</td> <td>αγ</td> <td>αλ=2.00, γ=1.75</td> <td>Verbunt</td> </tr> </tbody> </table> <p> </p> <h4><strong>Citing the Dataset</strong></h4> <p>When utilising these catalogues in your research, please cite <a href="https://arxiv.org/abs/2310.06559">Korol et al. 2024.</a> We also note our companion data-analysis-focused paper <a href="https://arxiv.org/abs/2310.06568">Moore et al. 2024</a>.</p>
Jacqueline Grosjean, Entomologin, sammelt mit Vorliebe Hautflügler und Zweiflügler. Foto: Lisa Schäublin. in Erstfunde von Trichopoda pennipes (FABRI- CIUS, 1781) (Diptera, Tachinidae) in der Schweiz, und eine Würdigung einer Amateurentomologin.
Jacqueline Grosjean, Entomologin, sammelt mit Vorliebe Hautflügler und Zweiflügler. Foto: Lisa Schäublin.
The Large Magellanic Cloud Revealed in Gravitational Waves with LISA: Population Release
<p>The Large Magellanic Cloud (LMC)’s binary populations for study by the <em>Laser Interferometer Space Antenna (LISA)</em> as generated by Keim et al. in a paper submitted to MNRAS (Keim, M. A., Korol, V., Rossi, E. M. The Large Magellanic Cloud Revealed in Gravitational Waves with LISA. <em>Monthly Notices of the Royal Astronomical Society</em>, 2022, submitted). The files include all current double white dwarfs in the LISA band (‘LISABand’), all which will be detectable with a S/N>7 after 4 yrs (‘Detect’), and all which are detached/non-accreting, i.e. sure LISA sources (‘Detached’). This release represents a 2.7*10^9 stellar mass LMC, and includes distribution models based on observation (‘M1’) and simulation (‘M3’). For more information, please refer to Keim et al. (2022). We request that researchers utilising any of these populations cite Keim et al. (2022).</p> <p>The data columns are as follows:</p> <p>Column 1 = Right Ascension (Degrees)</p> <p>Column 2 = Declination (Degrees)</p> <p>Column 3 = Age (Myr, since formation of Main Sequence Pair)</p> <p>Column 4 = Mass of White Dwarf One (Msun)</p> <p>Column 5 = Mass of White Dwarf Two (Msun)</p> <p>Column 6 = Radius of White Dwarf One (Rsun)</p> <p>Column 7 = Radius of White Dwarf Two (Rsun)</p> <p>Column 8 = Orbital Radius (Rsun)</p> <p>Column 9 = Frequency (Hz)</p> <p>Column 10 = Chirp (Hz^2)</p> <p>Column 11 = Latitude (Radians)</p> <p>Column 12 = Longitude (Radians)</p> <p>Column 13 = Amplitude (Defined with a prefactor of 2)</p> <p>Column 14 = Inclination (Radians)</p> <p>Column 15 = Polarization (Radians)</p> <p>Column 16 = Orbital Phase (Radians)</p> <p>Column 17 = Distance (kpc)</p> <p>Column 18 = Mass Transfer (1= Yes, i.e. Roche Lobe Overfill, 0= No)</p>
Applying the metallicity-dependent binary fraction to double white dwarf formation: Implications for LISA -- COSMIC + Ananke data
<p>This dataset contains all data required to run the pipeline which produces the results and figures in Thiele+2022 including:</p> <p>- results of all COSMIC simulations for the fiducial, alpha25, alpha5, and q3 models for each double white dwarf type and binary fraction assumption</p> <p>- metallicities, ages, positions and star particle kernel lengths from the Ananke framework of galaxy m12i in the Latte suite of the FIRE-2 simulations.</p>
Data release for "Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries"
<p>Posterior samples and code to reproduce all figures associated with <em>Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries</em>.</p> <p>The <code>parameter_estimation</code> folder contains the following:</p> <ul> <li><code>campaigns</code>: Analyses of eccentric quasi-monochromatic binaries, gridding over gravitational-wave frequency, eccentricty, and SNR. See the <code>README</code> inside for more information. The resulting posteriors are used in Figure 3, and the fitting formula Eq. 21. </li> <li><code>fiducial_source_checks</code>: Analyses that vary parameters other than SNR and frequency to investigate the effect on the minimum eccentricity that can be recovered. Used in Figure A1. Posteriors used for Figure 4 are also found in the <code>golden_binary</code> folder. </li> <li><code>nhat_runs</code>: Various analyses used for Figures 5, 6, B1, and C1. See the <code>README</code> inside for more information. Also see the <code>README</code> in <code>eccentric_gb_scripts</code> and links therein.</li> </ul> <p>Within each parameter estimation output folder there are <code>.dat</code> files for quantities such as the source SNR, log evidence, and posterior. There are also configuration <code>.yaml</code> files which are used by the BALROG code. These contain:</p> <ul> <li><code>lisa_config</code>: Parameters describing the LISA mission, including the duration in seconds. </li> <li><code>nessai_opts</code>: Settings used by nessai (the sampler used in this work). </li> <li><code>priors</code>: Lower and upper limits used for each source parameter. </li> <li><code>sources</code>: Injected values for each source parameter.</li> </ul> <p>The <code>notebooks</code> folder contains code to produce Figures 2, 3, 4, 6, and A1. Also included are notebooks to produce the fitting formula Eq. 21 (<code>emin_grid.ipynb</code>), and to inspect analyses in the <code>campaigns</code> and <code>fiducial_source_checks</code> folders.</p> <p>The <code>eccentric_gb_scripts</code> folder contains code to produce Figures 1, 5, B1 and C1. See the <code>README</code> inside for more information.</p>
for lisa ASH setup
Open the record for dataset details and reuse information.
Catalogues of LISA-detectable sBHB inspirals and confusion backgrounds following the GWTC-3 fiducial model posterior
<h2>Data set</h2> <p>The contents of <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> consist of approximately 10k folders, each corresponding to each of the samples of the public GWTC-3 fiducial sBHB population model posterior, and containing the following files:</p> <ul> <li><code>population.yaml</code>: population parameters of this sample.</li> <li><code>background.txt</code>: frequencies and characteristic strain squared of the sBHB confusion noise in the LISA band for this population.</li> <li><code>population_detector_frame_SNR.h5</code>: subset of loud sBHB sources, including but not limited to those with LISA SNR larger than 4 (missing in some samples). </li> </ul> <p>For a description of the population parameters in <code>population.yaml</code> and the individual source parameters in <code>population_detector_frame_SNR.h5</code>, see the contents of the <code>Demo.ipynb</code> notebook.</p> <p>To be able to run the notebooks described below, uncompress the <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> file inside a <code>data/</code> subfolder under that of the notebook.</p> <h2>Demo notebook</h2> <p>For examples of how to load and process the catalogues, see the <code>Demo.ipynb</code> notebook.</p> <p>This notebook requires the following Python packages:</p> <p> <code>numpy, scipy, pandas, matplotlib, pyyaml, tqdm</code><br> <br>Some of the plots in the notebook require the <a href="https://github.com/JesusTorrado/extrapops" target="_blank" rel="noopener">extrapops</a> simulation package:</p> <p> <code>$ git clone git@github.com:JesusTorrado/extrapops.git</code><br> <code>$ cd extrapops</code><br> <code>$ pip install .</code></p> <h2>References</h2> <p>For detailed descriptions of the generation of the data set, see the references mentioned in the Zenodo page.</p> <h2>Questions and comments</h2> <p>Please use the <a href="https://github.com/JesusTorrado/LISA_sBHB_catalogues/issues">GitHub issue tracker</a> or contact the corresponding authors of the papers cited under the "described by" header of the Zenodo entry.</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.