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639 results for “spin”
Data Release: "No evidence that the majority of black holes in binaries have zero spin"
<p>This dataset contains the results presented in "<em>No evidence that the majority of black holes in binaries have zero spin</em>".</p> <p>In this paper, we systematically explored the effective and component spin distributions of binary black holes among the LIGO/Virgo GWTC-3 catalog. In particular, we tried to answer the following core questions, which have been the subject of active exploration and some debate in the literature:</p> <p><em>1. Is there an excess of binary black holes with vanishing spin, as predicted by some theories of angular momentum transport in stellar cores?</em></p> <p><strong>We find no evidence for an excess of vanishing spin systems.</strong> This finding is confirmed by three complementary analyses: one relying only on the Bayes factors between spinning and non-spinning priors for each BBH observation, one that seeks to model the distribution of effective aligned spins, and one modeling the distribution of component spin magnitudes and misalignment angles. Instead, we find BBH spin magnitudes to be consistent with a single, continuous distribution that remains finite at magnitude zero.</p> <p><em>2. Do there exist binaries with component spins misaligned by more than 90 degrees relative to their orbits?</em></p> <p><strong>We find a strong preference for the existence of such strongly misaligned spins.</strong> Our analysis of the BBH component spin distribution indicates that at least some component spins are misaligned from their orbits by more than 90 degrees. This result is robust under a variety of modeling choices regarding both the distribution of component spin magnitudes and tilts.</p> <p>The code used to generate this data can be found in the repository <a href="https://github.com/tcallister/gwtc3-spin-studies/">https://github.com/tcallister/gwtc3-spin-studies/</a>. This repository includes <a href="https://github.com/tcallister/gwtc3-spin-studies/tree/main/data">jupyter notebooks</a> that can be used to open, explore, and plot the files contained in this data set. Additional information about reproducing and/or using this dataset can be found in <a href="https://tcallister.github.io/gwtc3-spin-studies/build/html/index.html">our associated documentation</a>.</p> <p>Further notes:</p> <ul> <li>The files <em>sampleDict_FAR_1_in_1_yr.pickle</em> and <em>injectionDict_FAR_1_in_1.pickle</em>, used as inputs to our analyses, are created via code in the repository <a href="https://github.com/tcallister/get-lvk-data">https://github.com/tcallister/get-lvk-data</a> (see also <a href="https://zenodo.org/record/6505409">https://zenodo.org/record/6505409</a>).</li> <li>The file <em>posteriors_gaussian_spin_samples_FAR_1_in_1.json</em>, used for figure generation, was published by the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration in support of the paper "<a href="https://arxiv.org/abs/2111.03634">The population of merging compact binaries inferred using gravitational waves through GWTC-3</a>" (see <a href="https://zenodo.org/record/5655785">https://zenodo.org/record/5655785</a>).</li> </ul>
Neutron spin echo and intramolecular FRET and DEER-EPR measurements on hGBP1 (human guanylate binding protein 1)
<p>Neutron spin echo (NSE), double electron–electron resonance (<em>DEER</em>) <em>EPR</em>, ensemble time-correlated single photon counting (eTCSPC) fluorescence, and single-molecule detection (SMD) fluorescence spectroscopy data of the human guanylate binding protein 1 (hGBP1).</p> <p>CSH prepared samples for smFRET and performed protein activity assays. TV prepared sampled for EPR measurements. TOP, CSH, and AV performed the smFRET measurements under the supervision of CAMS. TOP analyzed the smFRET measurements. JPK performed and analyzed the EPR measurements.</p>
Supporting Information for Disclosing Spin-Polarized Bonds on Isolable Molecules
<p>The file corresponds to the Bachelor Thesis of Ms. Elena Paulus. It contains the xyz coordinates of all optimized structures and their corresponding electronic energy in Hartree.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>covid</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>counterfactual</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - A. Code and data for 2016-2019
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> Data for years 2020 to 2026 are stored in the corresponding repositories:</p> <ul> <li><em>covid</em>: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></li> <li><em>counterfactual: </em><a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></li> </ul> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>From 2020 to 2026, the dataset includes two diverging scenarios. The <em>covid</em> scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as <em>hist</em>.</p> <p>The <em>Projections</em> folder includes the generated tables for years from 2016 to 2019 (<em>hist</em> scenario) and the corresponding labels.<br> The <em>Sources </em>folder contains the data records from the IFS and WEO databases. The <em>Method data</em> contains the data files used to generate the tables with the SPIN method and the following Python scripts:</p> <ul> <li><em>SPIN_covid19_MRIO_files_preparation.py</em> generates the data files from the source data.</li> <li><em>SPIN_covid19_RMRIO runs.py</em> is the command to run the SPIN method and generate the dataset.</li> <li><em>figures.py</em> is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.</li> </ul> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario
<p>The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.</p> <p>This repository includes data for years from 2020 to 2026 (<em>counterfactual</em> scenario).<br> Code, method material and data for years 2016-2019 are stored in the following repository: <a href="http://doi.org/10.5281/zenodo.5713811">10.5281/zenodo.5713811</a><br> Data for the <em>covid</em> scenario are stored in the following repository: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></p> <p>Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>.</p> <p>The<em> counterfactual</em> scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19.</p> <p>All tables are labelled in 2015 US$ and valued in basic prices.</p>
High resolution spectra of the spinning-top Be star Achernar
<p>Achernar, the closest and brightest classical Be star, presents rotational flattening, gravity darkening, occasional emission lines due to a gaseous disk, and an extended polar wind. It is also a member of a close binary system with an early A-type dwarf companion. We aim to determine the orbital parameters of the Achernar system and to estimate the physical properties of the components. We monitored the relative position of Achernar B using a broad range of high angular resolution instruments of the VLT/VLTI over a period of 13 years (2006-2019). These astrometric observations are complemented with a series of more than 700 optical spectra for the period from 2003 to 2016. The present dataset contains the high resolution spectra of Achernar that were included in our study. They were collected using the BESO, BeSS, CHIRON, CORALIE, FEROS, HARPS, PUCHEROS, and UVES instruments. The spectra are provided in the form of standard FITS files, with the continuum flux normalized to unity.</p>
Data for "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning"
<p>Datasets and material for replicating plots and results from the paper "Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning" <a href="https://scipost.org/SciPostPhys.15.1.018">SciPost Phys. 15, 018 (2023)</a>.</p> <p>You will find three data files and a ReadMe.txt:</p> <ul> <li><strong>couplings.tar.gz </strong>contains the random couplings of the system's Hamiltonian <span class="math-tex">\(H = \sum_{\langle ij \rangle}{J_{ij} \sigma_i \sigma_j}\)</span>;</li> <li><strong>datasets.tar.gz </strong>contains all the datasets generated by the <a href="https://www.dwavesys.com/">D-Wave</a> quantum computer. They are already split into train and validation and divided for the type of model and annealing time;</li> <li><strong>data_for_fig.tar.gz </strong>contains files for reproducing the plots of the article, almost all of them are saved in double format, .csv and .npy or .npz.</li> </ul> <p>We encourage you to download the GitHub code linked below to open all the listed data.</p> <p>All the data are zip, so to unzip them using</p> <pre><code class="language-bash">tar -xvf datasets.tar.gz</code></pre> <p>The code for training the Neural Networks and reproducing all the results is open access at <a href="https://doi.org/10.5281/zenodo.7118502">zenodo.7118502</a>.</p>
Dataset for the manuscript "Collective spin waves in RKKY interlayer-coupled Ni80Fe20/Ru/ Ni80Fe20 nanowire arrays"
<p>These are the dataset relative to paper entitled "<strong><span>Collective spin waves in RKKY interlayer-coupled </span></strong><strong><span>Ni</span></strong><strong><sub><span>80</span></sub></strong><strong><span>Fe</span></strong><strong><sub><span>20</span></sub></strong><strong><span>/Ru/</span></strong><strong><span> </span></strong><strong><span>Ni</span></strong><strong><sub><span>80</span></sub></strong><strong><span>Fe</span></strong><strong><sub><span>20 </span></sub></strong><strong><span>nanowire arrays</span></strong><strong>"</strong></p>
Supplementary data for the article "Spin reorientation in premartensite and austenite Ni-Mn-Ga".
<p>Supplementary data for the article “Spin reorientation in premartensite and austenite Ni-Mn-Ga” by Alexej Perevertov, Ross H. Colman and Oleg Heczko.</p> <p>Dataset - worksheet, M(H) loop at T = 252K. Columns - time (seconds); Field (A/m), Magnetic polarization, J(T); dJ/dt (T/s); dH/dt (A/(m*s))</p> <p>Supplementary video 1. The DS curves and J(H) hysteresis loops were displayed on the graphs in the post-processing LabView graphs for every temperature for 0.1sec and the screen recording was done using the VLC player/recorder (the capture device – desktop).</p> <p>Supplementary Video 2 - we have cut a 6x0.15mm disk, perpendicular to the long side of our sample with the face of the disk in the [100] – [110] plane. The disk was placed in a constant field of 50mT. On the heating cycle, immediately after martensitic transformation the disk [100] axis is parallel to the field. Close to the premartensitic transformation the disk spontaneously rotates by 45º, with the field then parallel to the [110] direction. With further heating, close to the room temperature, it rotates back to [100].</p>
Data of publication All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles
<p>Data corresponding to the figures of the publication "All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles" by D. Serrano et al. (https://www.nature.com/articles/s41467-018-04509-w). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Replication Data for: Probing magnetism in 2D materials at the nanoscale with single spin microscopy
<p>Data repository for: <strong>Probing magnetism in 2D materials at the nanoscale with single spin microscopy</strong></p> <p><em>Data description.pdf </em>describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>MzFromBNV.m</em>, <em>kvalues.m</em>, <em>NVZeemanShiftFromMagnetizedSampleEdge.m</em> are Matlab code files used to transform and fit the data.</p> <p> </p>
The complex non-collinear magnetic orderings in Ba2YOsO6: A new approach to tuning spin-lattice interactions and controlling magnetic orderings in frustrated complex oxides
<p><strong>Project abstract</strong>: Frustrated magnets are one class of fascinating materials that host many intriguing phases such as spin ice, spin liquid and complex long-range magnetic orderings at low temperatures. In this work we use first-principles calculations to find that in a wide range of magnetically frustrated oxides, at zero temperature a number of non-collinear magnetic orderings are more stable than the type-I collinear ordering that is observed at finite temperatures. The emergence of non-collinear orderings in those complex oxides is due to higher-order exchange interactions that originate from second-row and third-row transition metal elements. This implies a collinear-to-noncollinear spin transition at sufficiently low temperatures in those frustrated complex oxides. Furthermore, we find that in a particular oxide Ba2YOsO6, experimentally feasible uniaxial strain can tune the material between two different non-collinear magnetic orderings. Our work predicts new non- collinear magnetic orderings in frustrated complex oxides at very low temperatures and provides a mechanical route to tuning complex non-collinear magnetic orderings in those materials. <br> <br> <strong>About this entry</strong>: We provide the input files of our DFT calculations for the studied complex oxides. The structures in POSCAR format and the INCAR files for all stabilized magnetic orderings in our study are all included. These files can be directly used into DFT calculations with VASP. Only the versions of PAW potentials are included in POT.info files owing to the VASP license restrictions.</p>
Replication Data for: Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins
<p>Data repository for: <strong>Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins</strong></p> <p><em>Data description.pdf</em> describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>Esrfit_Npeak.m</em>, <em>Esrfit_xN.m</em>, <em>GaussianFunc.m</em>, <em>Gaussian_xN_Func.m</em>, <em>Lorentz_Func.m</em>, <em>Lorentz_xN_Func.m</em>, <em>Rabifit_xN.m</em>, <em>Rabi_xN_Func.m</em>, <em>FourierTransformRabi.m</em> are Matlab code files to transform and fit the data.</p>
Data of publication Coherent optical and spin spectroscopy of nanoscale Pr3+ : Y2O3
<p>Data corresponding to the figures of the publication " Coherent optical and spin spectroscopy of nanoscale Pr3+: Y2O3" by D. Serrano et al. (file:///C:/Users/diana.serrano/Zotero/storage/86IZ7H73/PhysRevB.100.html). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Dataset supporting the paper "Single-Spin Sensing: A Molecule-on-Tip Approach. ACS Nano 18, 13829 (2024)"
<p>Dataset corresponding to theoretical calculations in the paper "Single-Spin Sensing: A Molecule-on-Tip Approach" ACS Nano 18, 13829 (2024) DOI: https://doi.org/10.1021/acsnano.4c02470</p> <p>Please cite as:</p> <p>Alex Fétida, Olivier Bengone, Michelangelo Romeo, Fabrice Scheurer, Roberto Robles, Nicolás Lorente, and Laurent Limot. Dataset supporting the paper "Single-Spin Sensing: A Molecule-on-Tip Approach. ACS Nano 18, 13829 (2024)" DOI: 10.5281/zenodo.13774118</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <p>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).</p> <p>.agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</p> <p>Image files in png format.</p>
Electron-spin decoherence in trityl radicals in the absence and presence of microwave irradiation
<p>Experimental data sets on bare-spin and dressed-spin decoherence of some trityl radicals, DFT-predicted hyperfine couplings and atom coordinates, simulation scripts, and simulated data. </p>
Reconstructing dust provenance from quartz optically stimulated luminescence (OSL) and electron spin resonance (ESR) signals: Preliminary results on loess from around the world
<p>Dataset for publication</p> <p><strong>Reconstructing dust provenance from quartz optically stimulated luminescence (OSL) and electron spin resonance (ESR) signals: </strong></p> <p><strong>Preliminary results on loess from around the world</strong></p> <p> </p> <p>Quantitative provenance analysis studies are instrumental in understanding the tectonic and climatic processes that shape the earth’s landscape. Although the most abundant mineral in the sedimentary system is quartz, almost all studies in provenance analysis investigate accessory minerals. Quartz crystals contain a vast number of point defects, intrinsic or due to impurities. For a signal to be an accurate indicator of provenance one needs to show that it is either dose independent or reaches a quantifiable steady state characteristic of the source rock. For signals used by trapped charge dating methods (optically stimulated luminescence (OSL) and electron spin resonance (ESR)), the latter option is the feasible one. By using quartz samples collected from the Chinese Loess Plateau (Luochuan loess-paleosol section), we show that the laboratory and natural dose response curves of E`<sub>1</sub> and and peroxy electron spin resonance signals of quartz (as defined later) overlap and reach a steady state for doses over about 1000 Gy. For E’<sub>1</sub> signals we attribute this steady state to reaching an equilibrium state between diamagnetic oxygen vacancies (the oxygen deficiency centre (ODC), Si=Si<em>)</em> and paramagnetic oxygen vacancies (E’<sub>1</sub>). For sedimentary quartz irradiated naturally or artificially in this dose range we show a strong linear relationship with zero intercept between E’<sub>1</sub> and peroxy signals for samples worldwide, supporting the hypothesis that these defects are Frenkel pairs. Further, we show significant correlations between the optically stimulated (OSL) sensitivity and the above two mentioned ESR signals. The very strong correlations (Pearson`s r ˃0.9) between E’<sub>1</sub>, peroxy and OSL sensitivity remain valid after the samples have been heated for 15 min to 350 ˚C for E’<sub>1</sub> to reach its maximum value, believed to be a result of the conversion of diamagnetic oxygen vacancies to E’<sub>1</sub>, clearly suggesting a relationship between OSL sensitivity and oxygen vacancies in general. Samples collected from different loess sites around the world can be distinguished based on both these OSL and ESR properties. An empirical increase in OSL sensitivity as well as oxygen related defect concentrations is observed in areas where the source material has components with older detrital zircon U-Pb ages, inferring a positive correlation between OSL sensitivity, as well as the signal intensity for E<sub>1</sub>` and peroxy defects and the age of the source rocks.</p>
Spin wave dispersion of ultra-low damping hematite (α-Fe2O3) at GHz frequencies
<p>Raw data associated to the manuscript ‘’Spin wave dispersion of ultra-low damping hematite (α-Fe<sub>2</sub>O<sub>3</sub>) at GHz frequencies‘’,</p> <p>Physical Review Materials 7, 054407(2023); doi: 10.1103/PhysRevMaterials.7.054407<br> Information about file formats and measurement parameters are described in text files in the specific folders.</p> <p>Paper abstract:<br> Low magnetic damping and high group velocity of spin waves (SWs) or magnons are two crucial parameters for functional magnonic devices. Magnonics research on signal processing and wave-based computation at GHz frequencies focused on the artificial ferrimagnetic garnet Y<sub>3</sub>Fe<sub>5</sub>O<sub>12</sub> (YIG) so far. We report on spin-wave spectroscopy studies performed on the natural mineral hematite (α-Fe<sub>2</sub>O<sub>3</sub>) which is a canted antiferromagnet. By means of broadband GHz spectroscopy and inelastic light scattering, we determine a damping coefficient of 1.1×10<sup>−5</sup> and magnon group velocities of a few 10 km/s, respectively, at room temperature. Covering a large regime of wave vectors up to k≈24 rad/μm, we find the exchange stiffness length to be relatively short and only about 1 Å. In a small magnetic field of 30 mT, the decay length of SWs is estimated to be 1.1 cm similar to the best YIG. Still, inelastic light scattering provides surprisingly broad and partly asymmetric resonance peaks. Their characteristic shape is induced by the large group velocities, low damping and distribution of incident angles inside the laser beam. Our results promote hematite as an alternative and sustainable basis for magnonic devices with fast speeds and low losses based on a stable natural mineral.</p>
Dataset supporting the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold. ACS Nano 17, 10608 (2023)"
<p>Dataset corresponding to theoretical calculations in the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold" ACS Nano 17, 10608 (2023), https://pubs.acs.org/doi/10.1021/acsnano.3c01595</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:<br> .siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (https://doi.org/10.5281/zenodo.3581159).<br> CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).<br> .agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</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.