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21 results for “Seismic Source”
Malawi probabilistic seismic hazard analysis (PSHA) using the Malawi Seismogenic Source Model (MSSM). Supplementary Files v1.1
<p>Updated (October 2022) version of supplementary files for running probabilistic seismic hazard analysis (PSHA) MATLAB codes for Malawi. The PSHA codes themselves (v1.0) are available at: https://doi.org/10.5281/zenodo.7265781and the most recent version will be available on GitHub at: https://github.com/jack-williams1/Malawi_PSHA. Note the variables stored here are not stored on GitHub due to the file size.</p> <p>Includes both input files for performing PSHA and output ground motions for plotting PSHA results.</p> <p>Files are:</p> <ul> <li>malawi_Vs30_active.txt: Input USGS slope-based Vs30 values for Malawi (Wald and Allen 2007)</li> <li>EQCAT_comb.mat: MSSM Direct catalog for all possible rupture weightings (stored as MATLAB variable)</li> <li>GM_MSSM_em_20221027: Ground motions for plotting PSHA maps (stored as MATLAB variable)</li> <li>GM_MSSM_20221021.mat: Ground motions needed for plotting PSHA-site analysis figures (stored as MATLAB variable)</li> <li>mssm_comb.mat: Matlab file for combined MSSM Direct and Adapted MSSM catalogs (stored as MATLAB variable)</li> <li>MSSM_Catalog_Adapted_em.mat: Adapated MSSM event catalog (stored as MATLAB variable)</li> <li>syncat_bg.mat: Areal source stochastic event catalog (stored as MATLAB variable)</li> </ul> <p>Further descriptions of these files and how to use them are provided on Github. An open-access manuscript describing the PSHA is available at: </p> <p>Williams J. N., Werner M. J., Goda K., Wedmore L. N. J., De Risi R., Biggs J., Mdala H., Dulanya Z., Fagereng Å, Mphepo F., Chindandali P. (2023). Fault-based probabilistic seismic hazard analysis in regions with low strain rates and a thick seismogenic layer: a case study from Malawi, Geophysical Journal International, Volume 233, Issue 3, June 2023, Pages 2172–2206, <a href="https://doi.org/10.1093/gji/ggad060">https://doi.org/10.1093/gji/ggad060</a></p> <p>Please reference this publication along with this repository when using these data.</p> <p>USGS vs30 value compilation described in:</p> <p>Allen, T. I., and Wald, D. J., 2009, On the use of high-resolution topographic data as a proxy for seismic site conditions (Vs30), Bulletin of the Seismological Society of America, 99, no. 2A, 935-943.</p> <p> </p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.</p>
LASSO coherent seismic wavefield reconstruction and source imaging
<p>Coherent wavefield reconstruction and source imaging has been performed for 4 cataloged seismic events recorded with the Large-N Seismic Survey in Oklahoma (LASSO). The array consists of almost 2,000 densely spaced seismic stations and the corresponding raw time sries data have been made freely accessible by the Incorporated Research Institutions for Seismology (IRIS). The results for the 4 seismic events are accompanied with results gained for controlled seismic simulations for two of these events Reconstruction results and source images are provided in HDF5 and MAT file formats, respectively. File names were giving according to the following pattern: <br> <br> "LASSO_<<em>event name>_<reconstruction mode>_<result type>"</em></p> <p>where <<em>reconstruction mode</em>> refers either to "enhancement" (reconstruction performed for the original station layout) or "regularization" (reconstruction perfomed for a new, sense and regular station layout). <<em>result type</em>> denotes either reconstructed waveforms ("wavefield"), waveform coherence ("coherence"), or spatial source images. For the HDF5 files, mportant meta information like spatial coordinates and temporal sampling parameters are stored in a symbolic dictionary named "META", whereas the time series data is saved as a 2D matrix. Important META fields include "ntrac" (number of traces), "nt" (number of time samples), "dt" (dampling interval), "gx" (stations x coordinates), "gy" (stations y coordinates).<br> <br> The MAT files (result type "images") contain raw waveform and STA/LTA images, which are stored as 3D regular arrays named "recm1z_Enh_5_raw" (enhancement) / "recm1z_Reg5_5_raw" (regularization) and "recm1z_Enh_5_slta" (enhancement) / "recm1z_Reg5_5_slta" (regularization), respectively. For comparison, source images generated for the raw field data (without reconstruction are included in every MAT file and can be accessed through fields "recm1z_Raw_raw" and "recm1z_Raw_slta".</p>
Seismic release ratio of 2012 Emilia seismic source during the Earthquake sequances
<p>Seismology data of 2012 seismic sequence for calculate the seismir release ratio model along main seismic source. Data are download from INGV and DISS INGV catalogs. </p>
Non-Poissonian Forecast and Hazard source files - New Zealand National Seismic Hazard Model 2022
<h3>This repository contains:</h3><ul><li>The forecast's files for the Distributed Seismicity Model of the NZNSHM2022, as well as figures, and the Paraview files to explore them in the software interactively. (https://www.paraview.org/)</li><li>The Openquake source files (https://github.com/gem/oq-engine) to run the NZ-NSHM2022 model using the non-Poisson forecasts as single branches.</li></ul><h3>Installation instructions</h3><p>For reproducibility, this package should install OpenQuake (https://github.com/gem/oq-engine) in its version v3.16.4. However, Openquake should remain backward compatible for the Negative Binomial formulation in the future. To install the version 3.16.4, a virtual environment can be created used Anaconda/Miniconda/Micromamba (the latter is recommended, see installation instructions https://mamba.readthedocs.io/en/latest/installation.html) by using:</p><blockquote><p><i>conda env create -f environment.yml</i></p></blockquote><p>This environment should already contain the Openquake version. If the Openquake software should be installed manually into an environment created by the user:</p><blockquote><p><i>source activate {user_env}</i></p><p><i>git clone https://github.com/gem/oq-engine --depth=1 --branch=v3.16.4</i></p><p><i>cd oq-engine</i></p><p>pip install -e .</p></blockquote><p>For additional information, please see the README.md file, or visit <a href="https://github.com/pabloitu/nz_nshm2022_nonpoisson">https://github.com/pabloitu/nz_nshm2022_nonpoisson</a></p>
Seismic source location with a match field processing approach during the RESOLVE dense seismic array experiment on the Glacier d'Argentiere
<p>This deposit contains the data set we used in our paper ‘<em>Dynamic imaging of glacier structures at high-resolution using source localization with a dense seismic array</em>’. The paper is in review for GRL and a preprint can be found here: <a href="http://dx.doi.org/10.1002/essoar.10507953.1">10.1002/essoar.10507953.1</a>.</p> <p>The dataset present here contains 34 files named ‘<strong>beam_15423_jd***.h5</strong>’. These files correspond to the output of the matched field processing for each day. They are in .h5 format and we provide a matlab code (<strong>read_MFP_data.m</strong>) to read these files. These files can be read with any other language since they are in . h5.</p> <p>In linux you can use <strong>h5dump –A filename.h5</strong> and you can see the content of each files.</p> <p> </p> <p>More information on how the MFP process is conducted can be found in on the <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>dedicated to this aspect or on our paper. The whole procedure and associated codes is provided on the <a href="http://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/</a>.</p> <p>We also deliver with this deposit one day of seismic data <strong><a href="https://zenodo.org/api/files/873ccbe8-814d-4202-90ae-e115aab1942d/ZO_2018_121.h5?versionId=c59d2014-a6e1-43c5-96a6-91ee9a6b89ce">ZO_2018_121.h5 </a></strong>that can be used to test our MFP process. The data corresponds to the signal measured for 24 hours at each of the 98 sensors with a sampling rate of 500 Hz. More information on these seimsic signals can be found on our <a href="https://lecoinal.gricad-pages.univ-grenoble-alpes.fr/resolve/">website </a>and the whole seimsic dataset can be found here <a href="https://seismology.resif.fr/networks/#/ZO__2018">https://seismology.resif.fr/networks/#/ZO__2018</a>. Detailed for downloading the dataset should be search on our website.</p> <p> </p> <p>Other dataset linked to this project are:</p> <ul> <li>Nanni, Ugo, Gimbert, Florent, Roux, Phillipe, & Lecointre, Albanne. (2020). DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations." [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4024660">https://doi.org/10.5281/zenodo.4024660 </a></li> <li>Nanni, Gimbert, Roux, Helmstetter, Garambois, Lecointre, Walpersdorf, Jourdain, Langlais, Laarman, Lindner, Sergenat, Vincent, & Walter. (2020). DATA of the RESOLVE Project (https://resolve.osug.fr/) [Data set]. In Seismological Research Letters (Version v0). Zenodo. <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815 </a></li> </ul> <p>This dataset is also linked to two other study:</p> <p><em>Observing the subglacial hydrology network and its dynamics with a dense seismic array: </em></p> <p><a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a></p> <p><em>A Multi‐Physics Experiment with a Temporary Dense Seismic Array on the Argentière Glacier, French Alps: The RESOLVE Project</em></p> <p><a href="https://doi.org/10.1785/0220200280">https://doi.org/10.1785/0220200280</a></p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Do not hesitate to contact us if you would like to try this approach an another dataset.</p>
Dataset for paper "Mitigating the effect of errors in source parameters on seismic (waveform) inversion"
<p>Dataset corresponding to the journal article "Mitigating the effect of errors in source parameters on seismic (waveform) inversion" by Blom, Hardalupas and Rawlinson, accepted for publication in Geophysical Journal International. In this paper, we demonstrate the effect or errors in source parameters on seismic tomography, with a particular focus on (full) waveform tomography. We study effect both on forward modelling (i.e. comparing waveforms and measurements resulting from a perturbed vs. unperturbed source) and on seismic inversion (i.e. using a source which contains an (erroneous) perturbation to invert for Earth structure. These data were obtained using Salvus, a state-of-the-art (though proprietary) 3-D solver that can be used for wave propagation simulations (Afanasiev et al., GJI 2018).</p> <p>This dataset contains:</p> <ul> <li>The entire Salvus project. This project was prepared using Salvus version 0.11.x and 0.12.2 and should be fully compatible with the latter.</li> <li>A number of Jupyter notebooks used to create all the figures, set up the project and do the data processing.</li> <li>A number of Python scripts that are used in above notebooks.</li> <li>two conda environment .yml files: one with the complete environment as used to produce this dataset, and one with the environment as supplied by Mondaic (the Salvus developers), on top of which I installed basemap and cartopy.</li> <li>An overview of the inversion configurations used for each inversion experiment and the name of hte corresponding figures: inversion_runs_overview.ods / .csv .</li> <li>Datasets corresponding to the different figures. <ul> <li>One dataset for Figure 1, showing the effect of a source perturbation in a real-world setting, as previously used by Blom et al., Solid Earth 2020</li> <li>One dataset for Figure 2, showing how different methodologies and assumptions can lead to significantly different source parameters, notably including systematic shifts. This dataset was kindly supplied by Tim Craig (Craig, 2019).</li> <li>A number of datasets (stored as pickled Pandas dataframes) derived from the Salvus project. We have computed: <ul> <li>travel-time arrival predictions from every source to all stations (df_stations...pkl)</li> <li>misfits for different metrics for both P-wave centered and S-wave centered windows for all components on all stations, comparing every time waveforms from a reference source against waveforms from a perturbed source (df_misfits_cc.28s.pkl)</li> <li>addition of synthetic waveforms for different (perturbed) moment tenors. All waveforms are stored in HDF5 (.h5) files of the ASDF (adaptable seismic data format) type</li> </ul> </li> </ul> </li> </ul> <p>How to use this dataset:</p> <ul> <li>To set up the conda environment: <ol> <li>make sure you have anaconda/miniconda</li> <li>make sure you have access to Salvus functionality. This is not absolutely necessary, but most of the functionality within this dataset relies on salvus. You can do the analyses and create the figures without, but you'll have to hack around in the scripts to build workarounds.</li> <li>Set up Salvus / create a conda environment. This is best done following the instructions on the Mondaic website. Check the changelog for breaking changes, in that case download an older salvus version.</li> <li>Additionally in your conda env, install basemap and cartopy: <pre><code class="language-bash">conda-env create -n salvus_0_12 -f environment.yml conda install -c conda-forge basemap conda install -c conda-forge cartopy</code></pre> </li> <li> <p>Install LASIF (https://github.com/dirkphilip/LASIF_2.0) and test. The project uses some lasif functionality.</p> </li> <li> <p> </p> </li> <li> <p> </p> </li> </ol> </li> <li>To recreate the figures: This is extremely straightforward. Every figure has a corresponding Jupyter Notebook. Suffices to run the notebook in its entirety. <ul> <li>Figure 1: separate notebook, Fig1_event_98.py</li> <li>Figure 2: separate notebook, Fig2_TimCraig_Andes_analysis.py</li> <li>Figures 3-7: Figures_perturbation_study.py</li> <li>Figures 8-10: Figures_toy_inversions.py</li> </ul> </li> <li>To recreate the dataframes in DATA: This can be done using the example notebook Create_perturbed_thrust_data_by_MT_addition.py and Misfits_moment_tensor_components.M66_M12.py . The same can easily be extended to the position shift and other perturbations you might want to investigate.</li> <li>To recreate the complete Salvus project: This can be done using: <ul> <li>the notebook Prepare_project_Phil_28s_absb_M66.py (setting up project and running simulations)</li> <li>the notebooks Moment_tensor_perturbations.py and Moment_tensor_perturbation_for_NS_thrust.py</li> <li>For the inversions: using the notebook Inversion_SS_dip.M66.28s.py as an example. See the overview table inversion_runs_overview.ods (or .csv) as to naming conventions.</li> </ul> </li> </ul> <p> </p> <p>References:</p> <ul> <li>Michael Afanasiev, Christian Boehm, Martin van Driel, Lion Krischer, Max Rietmann, Dave A May, Matthew G Knepley, Andreas Fichtner, Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophysical Journal International</em>, Volume 216, Issue 3, March 2019, Pages 1675–1692, <a href="https://doi.org/10.1093/gji/ggy469">https://doi.org/10.1093/gji/ggy469</a></li> <li>Nienke Blom, Alexey Gokhberg, and Andreas Fichtner, Seismic waveform tomography of the central and eastern Mediterranean upper mantle, <em>Solid Earth</em>, Volume 11, Issue 2, 2020, Pages 669–690, 2020, <a href="https://doi.org/10.5194/se-11-669-2020">https://doi.org/10.5194/se-11-669-2020</a></li> <li>Tim J. Craig, Accurate depth determination for moderate-magnitude earthquakes using global teleseismic data. <em>Journal of Geophysical Research: Solid Earth</em>, 124, 2019, Pages 1759– 1780. <a href="https://doi.org/10.1029/2018JB016902">https://doi.org/10.1029/2018JB016902</a></li> </ul> <p> </p>
Supplementary Datasets and Movies for the Paper "Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change"
<p>Supplementary Datasets and Movies for the Paper <br><strong>Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax</p> <p>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2404.05437</a></p> <p> </p> <p><strong>Movie S1 Animation of the 2018, Mw 7.1 Anchorage, Alaska sequence and background seismicity 2014-2022.</strong> Relocated seismicity shown for: 2014 – 2018 mainshock (light blue), 2018 mainshock – 1 month after mainshock (green), 1 month after mainshock through 2022 (light orange); large black dot indicates the Mw 7.1 mainshock hypocenter. See figure caption in main paper for more details.</p> <p><strong>Movie S2 Animation of seismicity-stress, 3D finite-faulting potential slip results the 2018 Mw 7.1 Anchorage, Alaska earthquake sequence.</strong> The high-potential portion of the seismicity-stress finite-faulting field is shown in red for west-dipping reciever faults inferred from the first 1 day of aftershocks (blue dots) after the 2018 mainshock (large black dot). See figure caption in main paper for more details.</p> <p> </p> <p><strong>CSV (.csv) and NLL-Hypocenter (.hyp) format catalogs of NLL-SSST-coherence relocations used in this study:</strong></p> <p>Parkfield_2022_NLL-SSST-coherence_20231201A.csv<br>Parkfield_2022_NLL-SSST-coherence_20231201A.hyp</p> <p>AntelopeValley_2021_NLL-SSST-coherence_20231223A.csv<br>AntelopeValley_2021_NLL-SSST-coherence_20231223A.hyp</p> <p>Anchorage_2018_NLL-SSST-coherence_20231125A.csv<br>Anchorage_2018_NLL-SSST-coherence_20231125A.hyp</p> <p> </p>
Kinematic and Paleoseismic Investigation of an Upper-Plate Fault on Chirikof Island: A Potential Tsunami-Seismic Hazard Source within the Alaska Subduction Zone
Open the record for dataset details and reuse information.
Integrated Analysis of Seismic Sources and Structures: Understanding Earthquake Clustering during Hydraulic Fracturing
<p>The uploaded files include the 3D velocity model, 2D seismic reflection profiles, and horizontal slice utilized in this study.</p>
Microseismic source parameters from induced seismicity in the Horn river basin (British Columbia) [Dataset]
<p>This is a database of results linked to the associated manuscript (Klinger and Werner, 2021) which has been submitted to Geophysical Journal International and is currently in review. </p> <p>We report magnitudes, corner frequencies and stress drops of microseismic events linked to fault reactivation during hydro-fracturing operations in the Horn river basin (British Columbia), as well as the corresponding uncertainties for these parameters. Stress drops are calculated using a Brune model (Brune, 1970) and uncertainties are calculated using a bootstrapping technique. </p> <p>Prior to publication please cite this database using the following two references:</p> <p>Klinger, A.G., Werner, M.J. (2021). Stress drops of hydraulic fracturing induced microseismicity in the Horn River basin: Challenges at high frequencies recorded by borehole geophones. <em>Manuscript submitted to Geophysical Journal International . </em></p> <p>Klinger, A.G., Werner, M.J. (2021). Microseismic source parameters from induced seismicity in the Horn river basin (British Columbia) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5603835.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data for Urban Seismic Source Mapping
<p>In this study, we explore the innovative use of existing and ubiquitous urban infrastructure--telecommunication optical fibers--to map urban seismic sources. We integrate seismic interferometry and beamforming algorithms to overcome the proximity limitations of previous urban seismology approaches. <br>Our method models the propagation of seismic surface waves to estimate the spatio-temporal distribution of seismic source power (SSP; average seismic energy per unit of time), enabling the detection and localization of urban seismic sources occurring remotely from optical fibers.</p> <p><code>Put these pickle files into '/urban_das/data/das_data/' and run the scripts at https://github.com/jingxiaoliu/urban_das</code></p> <p>If you use this implementation, please cite our papers:</p> <blockquote> <p>[1] Liu, J., Li, H., Noh, H. Y., Santi, P., Biondi, B., & Ratti, C. (2025). Urban sensing using existing fiber-optic networks. <em>Nature Communications</em>, <em>16</em>(1), 3091.</p> <p>[2] Yuan, S., Liu, J., Noh, H. Y., Clapp, R., & Biondi, B. (2024). Using vehicle‐induced DAS signals for near‐surface characterization with high spatiotemporal resolution. <em>Journal of Geophysical Research: Solid Earth</em>, <em>129</em>(4), e2023JB028033.</p> <p>[3] Liu, J., Yuan, S., Dong, Y., Biondi, B., & Noh, H. Y. (2023). TelecomTM: A fine-grained and ubiquitous traffic monitoring system using pre-existing telecommunication fiber-optic cables as sensors. <em>Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies</em>, <em>7</em>(2), 1-24.</p> </blockquote>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>This dataset contains waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo sampling algorithm and a 3-D Earth model of the Japanese islands. Specifically, it includes processed observed waveforms from the Full Range Seismograph Network of Japan (F-Net, http://www.fnet.bosai.go.jp) and synthetic waveforms for the maximum-likelihood solutions as well as Global Centroid Moment Tensor (GCMT) solutions for all study events inverted at different periods. Detailed description of the dataset is included in the README file. </p>
Supplementary Movies: High-rate very-long-period seismicity at Yasur volcano, Vanuatu: Source mechanism and decoupling from surficial explosions and infrasound
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Seismic dataset in "Source-Independent Passive Seismic Reverse-time Structure Imaging with Grouping Imaging Condition: Method and Application to Microseismic Events Induced by Hydraulic Fracturing"
<p>This dataset contains the seismic data and the velocity model used in the manuscript entitled "Source-Independent Passive Seismic Reverse-time Structure Imaging with Grouping Imaging Condition: Method and Application to Microseismic Events Induced by Hydraulic Fracturing" submitted to Journal of Geophysical Research-Solid Earth.</p>
Plots source data for the Communications Earth & Environment research article titled: Diurnal expansion and contraction of englacial fracture networks revealed by seismic shear wave splitting
<p>The source data for plotting figures presented in the main text of article titled: Diurnal expansion and contraction of englacial fracture networks revealed by seismic shear wave splitting.</p>
Receiver-function datasets of the passive-source seismic profiles in the northeastern Tibetan plateau contributed by LRC
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Synthetic and real datasets for "Seismic source tracking with six degree-of-freedom ground motion observations"
<p>Synthetic datasets for the 2D and 3D rupture tracking and real datasets for the traffic noise tracking used in the manuscript "Seismic source tracking with six degree-of-freedom ground motion observations". The README file describes the data structures.</p>
Seismic dataset in "General Dislocation Source Model and its Application to Microseismic Focal Mechanism Inversion"
<p>This dataset contains the seismic data and the velocity model used in the manuscript entitled "General dislocation source model and its application to microseismic focal mechanism inversion" submitted to Geophysical Journal International.</p>
Seismic dataset in "General Dislocation Source Model and its Application to Microseismic Focal Mechanism Inversion"
<p>This dataset contains the seismic data and the velocity model used in the manuscript entitled "General dislocation source model and its application to microseismic focal mechanism inversion" submitted to Geophysical Journal International.</p>
ScienceDex guides
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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.