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216 results for “gravitation”
Simulating relic gravitational waves from inflationary magnetogenesis
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Simulating relic gravitational waves from inflationary magnetogenesis" by Axel Brandenburg and Ramkishor Sharma. If anything turns out to be incomplete, please email brandenb@nordita.org. See also the notes.pdf file in the paper directory with information about the importance of the f'/f term in the expression for the E field.</pre>
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: H1 detector
<p>Gravitational Wave signals and random noise datasets, generated with the PyCBC library (https://pycbc.org). </p> <p>Data consists of 8 sec time sequences for a single detector (the H1 detector, mimicking LIGO Hanford), sampled at 2048 Hz and represented as a one-dimensional array with 16,384 entries. </p> <p>Signal samples are provided, corresponding to Binary Black Hole and Binary Neutron Star mergers overlapped to noise. </p>
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: L1 detector
<p>Gravitational Wave signals and random noise datasets, generated with the PyCBC library (https://pycbc.org). </p> <p>Data consists of 8 sec time sequences for a single detector (the L1 detector, mimicking LIGO Livingston), sampled at 2048 Hz and represented as a one-dimensional array with 16,384 entries. </p> <p>Signal samples are provided, corresponding to Binary Black Hole and Binary Neutron Star mergers overlapped to noise. </p>
NSNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>NSNS </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E) and "unstable case BB + optimistic CE" (model F) files.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates: <ul> <li><strong>csvFilesForFigure1_DCOpaper.zip </strong># contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names: <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Details of how to use the data (a readme), as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
BHNS simulations from: Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers
<p>The data for all <strong>BHNS </strong>simulations shown in<em><strong> "Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Mergers". </strong>Broekgaarden et al. (2021, submitted, preprint: <a href="https://arxiv.org/abs/2112.05763">https://arxiv.org/abs/2112.05763</a>)</em></p> <p> </p> <p><strong>Contents: </strong></p> <ul> <li><strong>18 zip files that each contain an hdf5 file with the raw data for one of the simulations from Table 1 in the paper. The only exception is the fiducial.zip file and the unstableCaseBB.zip file, which contain both the fiducial (model A) and 'optimistic CE' (model K) data file and the "unstable case BB" (model E) and "unstable case BB + optimistic CE" (model F) files.</strong><br> <strong>These zip files are: </strong> <ul> <li><em>fiducial.zip, </em> the Fiducial model (A) and Optimistic CE model (K)</li> <li><em>massTransferEfficiencyFixed_0_25.zip, </em>the <span class="math-tex">\(\beta\)</span> = 0.25 model (B) </li> <li><em>massTransferEfficiencyFixed_0_5.zip</em>, the <span class="math-tex">\(\beta\)</span> = 0.5 model (C) </li> <li><em>massTransferEfficiencyFixed_0_75.zip,</em> the <span class="math-tex">\(\beta\)</span> = 0.75 model (D)</li> <li><em>unstableCaseBB.zip, </em>the unstable case BB mass transfer model (E) and unstable case BB & optimistic CE model (F) </li> <li><em>alpha0_1 zip</em>, the <span class="math-tex">\(\alpha = 0.1\)</span> model (G) </li> <li><em>alpha0_5.zip</em>, the <span class="math-tex">\(\alpha = 0.5\)</span> model (H) </li> <li><em>alpha2_0.zip</em>, the <span class="math-tex">\(\alpha = 2.0\)</span> model (I) </li> <li><em>alpha10_0.zip</em>, the <span class="math-tex">\(\alpha = 10.0\)</span> model (J) </li> <li><em>rapid.zip</em>, the rapid SN model (L) </li> <li><em>maxNSmass2_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 2\, \rm{M}_{\odot}\)</span> model (M) </li> <li><em>maxNSmass3_0.zip, </em>the max <span class="math-tex">\(m_{\rm{NS}} = 3\, \rm{M}_{\odot}\)</span> model (N)</li> <li><em>noPISN.zip</em>, the no PISN model (O) </li> <li><em>ccSNkick_100km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 100 km/s model (P) </li> <li><em>ccSNkick_30km_s.zip, </em>the <span class="math-tex">\(\sigma_{\rm{cc}}\)</span>= 30 km/s model (Q)</li> <li> <em>noBHkick.zip, </em>the no BH SN kick model (R)</li> <li><em>wolf_rayet_multiplier_0_1.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 0.1\)</span> (S)</li> <li><em>wolf_rayet_multiplier_5.zip, </em>the model with Wolf-Rayet wind factor <span class="math-tex">\(f_{\rm{WR}} = 5\)</span> (T)<br> <br> </li> </ul> </li> <li>2 more zip files containing csv files with the summarized rates to create Figures 1, 2 and 3, which do not require downloading the entire dataset, but instead use these csv files with the summarized rates: <ul> <li><strong>csvFilesForFigure1_DCOpaper.zip </strong># contains the files to recreate figure 1 with the merger rates per metallicity for BH-BH, BH-NS and NS-NS: <ul> <li>formationRatesTotalAndPerChannel_BHBH_.csv</li> <li>formationRatesTotalAndPerChannel_BHNS_.csv</li> <li>formationRatesTotalAndPerChannel_NSNS_.csv</li> </ul> </li> <li><strong>csvFilesForFigure2_and_3_DCOpaper.zip </strong># contains the files to recreate figure 2 with the merger rates for intrinsic and GW detection weighted, containing the csv files with names: <ul> <li>rates_MSSFR_Models_BHBH_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_NSNS_AllDCOsimulation.csv</li> <li>rates_MSSFR_Models_BHNS_AllDCOsimulation.csv</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Details of how to use the data (a readme), as well as scripts to reproduce all results, plots, and figures from the paper are given in the accompanying Github repository <a href="https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers">https://github.com/FloorBroekgaarden/Double-Compact-Object-Mergers</a> </p> <p>If you use this data, please cite </p> <p>Broekgaarden et al. (2021): see <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv211205763B/abstract</a></p>
Ray Tracing-Based Delay Model for Compensating Gravitational Deformations of VLBI Radio Telescopes (Data Set)
<p>The precision and the reliability of very long baseline interferometry (VLBI) depend on several factors. Apart from fabrication discrepancies or meteorological effects, gravity-induced deformations of the receiving unit of VLBI radio telescopes are identified as a crucial error source biasing VLBI products and obtained results such as the scale of a realized global geodetic reference frame. Gravity-induced deformations are systematical errors and yield signal path variations (SPVs). In 1988, Clark and Thomsen derived a VLBI delay model, which was adopted by the International VLBI Service for Geodesy and Astrometry (IVS) to reduce these systematic errors. However, the model parametrizes the SPV by a linear substitute function and considers only deformations acting rotationally symmetrically. The aim of this investigation is to derive the signal path variations of a legacy radio telescope and a modern broadband VGOS-specified radio telescope and to study the effect of nonrotationally symmetric deformation patterns. For that purpose, SPVs are obtained from a nonlinear spatial ray tracing approach. For the first time, a tilt and a displacement of the subreflector perpendicular to the optical axis of the feed unit is taken into account. The results prove the commonly used VLBI delay model as a suitable first-order delay model to reduce gravity-induced deformations.</p>
Reproduction package for the paper "Prospects of Gravitational Wave Follow-up Through a Wide-field Ultra-violet Satellite: a Dorado Case Study"
<p>This is a basic reproduction package for the paper "Prospects of Gravitational Wave Follow-up Through a Wide-field Ultra-violet Satellite: a Dorado Case Study" by [Dorsman et al. (2022)](https://doi.org/10.48550/arXiv.2206.09696). This package provides (1) data and software to reproduce the results from the Bayesian inference, (2) the pre-computed results from the Bayesian inference for the purpose of reproducing the plots, and (3) the plots.</p>
Abell 1201: Detection of an Ultramassive Black Hole in a Strong Gravitational Lens
<p>A preprint of the paper "Abell 1201: Detection of an Ultramassive Black Hole in a Strong Gravitational Lens".</p> <p>This repository also contains lens analysis scripts, plotting routines and dynesty chains of the paper.</p>
Precision Ephemerides for Gravitational-wave Sources+
<p>Zenodo repository containing datasets and ephemeris predictions from the Precision Ephemerides for Gravitational-wave Searches project. New ephemerides will be published here, and updated incrementally as new data is acquired.</p> <p><strong>Version history</strong></p> <ul> <li><strong>1.0.0-release</strong>: first data release based on the PEGS IV MNRAS publication</li> <li><strong>1.0.1: t_asc </strong>column now correctly corresponds to the neutron star time of ascending node.</li> <li><strong>1.0.2: </strong>now provide files tabulating our measured velocities, and velocity errors for each dataset.</li> </ul>
Datasets for "Relic gravitational waves from the chiral plasma instability in the standard cosmological model"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Relic Gravitational Waves from the Chiral Plasma Instability in the Standard Cosmological Model". If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
The second data release from the European Pulsar Timing Array III. Search for gravitational wave signals
<p>We present the results of the search for an isotropic stochastic gravitational wave background (GWB) at nanohertz frequencies using the second data release of the European Pulsar Timing Array (EPTA) for 25 millisecond pulsars and a combination with the first data release of the Indian Pulsar Timing Array (InPTA). A robust GWB detection is conditioned upon resolving the Hellings-Downs angular pattern in the pairwise cross-correlation of the pulsar timing residuals. Additionally, the GWB is expected to yield the same (common) spectrum of temporal correlations across pulsars, which is used as a null hypothesis in the GWB search. Such a common-spectrum process has already been observed in pulsar timing data. We analysed (i) the full 24.7-year EPTA data set, (ii) its 10.3-year subset based on modern observing systems, (iii) the combination of the full data set with the first data release of the InPTA for ten commonly timed millisecond pulsars, and (iv) the combination of the 10.3-year subset with the InPTA data. These combinations allowed us to probe the contributions of instrumental noise and interstellar propagation effects. With the full data set, we find marginal evidence for a GWB, with a Bayes factor of four and a false alarm probability of 4%. With the 10.3-year subset, we report evidence for a GWB, with a Bayes factor of 60 and a false alarm probability of about 0.1% (≳ 3σ significance). The addition of the InPTA data yields results that are broadly consistent with the EPTA-only data sets, with the benefit of better noise modelling. Analyses were performed with different data processing pipelines to test the consistency of the results from independent software packages. The latest EPTA data from new generation observing systems show non-negligible evidence for the GWB. At the same time, the inferred spectrum is rather uncertain and in mild tension with the common signal measured in the full data set. However, if the spectral index is fixed at 13/3, the two data sets give a similar amplitude of (2.5 ± 0.7) × 10−15 at a reference frequency of 1 yr−1 . Further investigation of these issues is required for reliable astrophysical interpretations of this signal. By continuing our detection efforts as part of the International Pulsar Timing Array (IPTA), we expect to be able to improve the measurement of spatial correlations and better characterise this signal in the coming years.</p>
Data of Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media
<p>Fully data of the paper "Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media" in the Astrophysical Journal.</p> <p>The Astrophysical Journal, 911:119 (18pp), 2021 April 20.</p>
Fermi-GBM and Swift-BAT Data Release Related to Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run
<p>This material contains the data products associated with A Joint Fermi-GBM and Swift-BAT Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run [1]. It is based, in part, on data products associated with GWTC-2.1 [2][3] and GWTC-3 [4][5] 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 provided under a Creative Commons Attributional 4.0 International license. For more information, see the paper (<a href="https://arxiv.org/abs/2308.13666">https://arxiv.org/abs/2308.13666</a>) and the included README.txt.</p> <p>The data release includes the following directories:</p> <ul> <li><strong>3sigma_upper_limit_maps:</strong> 3 sigma flux upper limits reported by GBM as a function of sky position over a 10-1000 keV energy range.</li> <li><strong>5sigma_upper_limit_maps: </strong>5 sigma flux upper limits reported by GBM and BAT as a function of sky position over a 15-350 keV energy range.</li> <li><strong>gbm_targeted_results: </strong>GRB candidates from the GBM Targeted Search.</li> <li><strong>gbm_temporal_offset_analysis: </strong>input files to the time offset analysis applied to GBM on-board triggers and candidates from the GBM Untargeted Search.</li> <li><strong>bbh_model_fluxes: </strong>predicted gamma-ray fluxes over the 10-1000 keV energy range for likely BBH mergers.</li> <li><strong>gw_localizations:</strong> GW localization files used to overlay the 90% credible area onto GBM upper limits as a function of sky position.</li> <li><strong>m1_m2_contours: </strong>90% credible region paths for the GW component masses m1, m2.</li> </ul> <p>The data release also includes a set of example scripts to show how the contents of the data files are used. Refer to the included README.txt for more details.</p> <p><strong>References:</strong></p> <p><a href="https://arxiv.org/abs/2308.13666">[1] Fletcher, C. et al 2023, arXiv, 2308.13666</a><br> <a href="https://doi.org/10.5281/zenodo.6513631">[2] LIGO Scientific Collaboration and Virgo Collaboration. 2022, Zenodo, 6513631</a><br> <a href="https://doi.org/10.5281/zenodo.5759108">[3] LIGO Scientific Collaboration and Virgo Collaboration. 2021, Zenodo, 5759108</a><br> <a href="https://doi.org/10.5281/zenodo.5546663">[4] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration. 2021, Zenodo, 5546663</a><br> <a href="https://doi.org/10.5281/zenodo.5546665">[5] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration 2021, Zenodo, 5546665</a></p> <p><br> </p>
A Unified pastro for Gravitational Waves: Consistently Combining Information from Multiple Search Pipelines
<p>This repository is made to contain the data products released with the paper " <a href="https://arxiv.org/abs/2305.00071">A Unified p-astro for Gravitational Waves: Consistently Combining Information from Multiple Search Pipelines</a>". In this paper, we showed how information from multiple GW pipelines can be combined to calculate a single probability of astrophysical origin - p-astro for GW triggers. This repository contains the joint probability distributions trained using injection (simulated signal) and noise triggers from the PyCBC and gstLAL pipelines from the O3 observing run of LIGO and Virgo. Using these distributions, a unified p-astro can be calculated for the on-source triggers from GWTC-2.1, as we show in the paper. </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><p>pip install zenodo_get zenodo-get RECORD_ID_OR_DOI</p><p> </p><p>There are two data products in this repository.</p><p>1. A text file "pastro_calcs.txt" which has the unified p-astro of all triggers from GWTC-2.1</p><p>2. A pickle file containing the injection KDEs and the noise distribution files, along with the values of the normalization constants for the various cases.</p><p> </p><p>The pickle file can be read in python by,</p><p>import pickle pdf = pickle.load(open('zenodo_pdf.pickle', 'rb'))</p><p> </p><p>The injection KDEs were created using search pipelines run on simulated signals done by the LIGO-Virgo-KAGRA collaboration. The injection files can be found in <a href="https://zenodo.org/record/5546676">this zenodo</a></p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event S191205ah
<p>Attached in a .json file is the completed pointing information for 12 observation(s) for the EM counterpart search associated with the gravitational wave event S191205ah.</p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event S191216ap
Attached in a .json file is the completed pointing information for 30 observation(s) for the EM counterpart search associated with the gravitational wave event S191216ap. These observations were taken on the Sinistro, and Spectral instruments.
Datasets for ``Numerical Simulations of Gravitational Waves from Early-Universe Turbulence''
<pre>The tar archive GW.tar contains an index.html file with links to the run directories for each run in Table 1 of the paper "Numerical Simulations of Gravitational Waves from Early-Universe Turbulence" by A. Roper Pol, S. Mandal, A. Brandenburg, T. Kahniashvili, &amp; A. Kosowsky with the temporary URL http://norlx55.nordita.org/~brandenb/tmp/GW. Corrections and updates are available on the active URL to this tar archive: https://www.nordita.org/~brandenb/projects/GW/</pre>
Picture Gravitational Lensing and new phenomena
<p>Gravitational Lensing object marked as red dot on the screenshot picture. All 9 images shwo gravitational Lensing at same location as screenshot mark location. If one carefuly see all 9 images then he/she find the time evolution of the gravitational lensing object.</p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event TEST_EVENT
Attached in a .json file is the completed pointing information for 2 observation(s) for the EM counterpart search associated with the gravitational wave event TEST_EVENT. These observations were taken on the DLT40 instrument.
Submitted Completed pointings to the Gravitational Wave Treasure Map for event TEST_EVENT
Attached in a .json file is the completed pointing information for 2 observation(s) for the EM counterpart search associated with the gravitational wave event TEST_EVENT. These observations were taken on the DECam instrument.
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.