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.
669
datasets available to search
ShareScore release 0.9.0
Dataset results
669 results for “ATOM”
Dataset related to "Atomic Force Microscope with an Adjustable Probe Direction and Integrated Sensing and Actuation"
<p>These original measurement data relate to the publication: J. Schaude, T. Hausotte: Atomic Force Microscope with an Adjustable Probe Direction and Integrated Sensing and Actuation, Nanomanufacturing and Metrology 5, pp. 519-148, <a href="https://doi.org/10.1007/s41871-022-00143-9">https://doi.org/10.1007/s41871-022-00143-9</a>. Please refer to this open access publication for a detailed description of the measurement setup and procedure.</p> <p>All data are in ASCII-format. Each file contains six columns, where column one to three are the <em>x</em>, <em>y</em>, and <em>z</em>-coordinates of the positioning system, column four is the demodulated signal of the AFM (<em>R</em><sub>AFM</sub>) and columns five and six are the raw signals of the <em>z</em>-interferometer (<em>Q</em><sub>A</sub> and <em>Q</em><sub>B</sub>).</p> <p><strong>Content of the folders</strong></p> <p>10_Calibrations: Repeated calibration of the AFM against the <em>z</em>-interferometer of the NMM-1.</p> <p>20_Standstill-Measurements: Three standstill measurements for 90 s each.</p> <p>30_Long-Term-Precision: Standstill measurements for 15 s every 10 min for in sum 18 hours.</p> <p>40_Scans: Repeated closed-loop scans on a calibration grating.</p> <p>50_SINCOS: Repeated movement of the stage in z-direction for 2 µm with the cantilever being in free air just before the sample.</p> <p><strong>Acknowledgement</strong></p> <p>This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) –TRR 285 -Project-ID 418701707, subproject C05</p>
Droplet-Based Microfluidics Platform for the Synthesis of Single-Atom Heterogeneous Catalysts
<p>Data set supporting the publication of : "Droplet-Based Microfluidics Platform for the Synthesis of Single-Atom Heterogeneous Catalysts" (<a href="https://doi.org/10.1002/sstr.202200284">https://doi.org/10.1002/sstr.202200284</a>) by T. Moragues, S. Mitchell, D. Faust Akl, J. Pérez-Ramírez, and A. deMello.</p>
A photonic entanglement filter with Rydberg atoms
<p>Devices capable of deterministically manipulating the photonic entanglement are of paramount importance, since photons are the ideal messengers for quantum information. However, due to the non-interacting nature of photons, many photonic quantum operations have only been demonstrated using probabilistic linear-optical approaches, which lead to overwhelming resource overhead and poor scalability. Here, we report a novel entanglement filter that transmits the desired photonic entangled state and blocks the unwanted ones. In contrast to prior probabilistic approaches, our experiment exploits strong and controllable photon-photon interaction enabled by Rydberg atoms, so the filtering of undesired states succeeds in a fully deterministic way. Photonic entanglement with near-unity fidelity can be extracted from an input state with an arbitrarily low initial fidelity. The protocol is inherently robust, and succeeds both in the Rydberg blockade regime and in the interaction-induced dissipation regime. Such an entanglement filter opens new routes toward scalable photonic quantum information processing with multiple ensembles of Rydberg atoms.</p>
Dataset belonging to the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper"
<p>This repository contains the raw data of the experimental (S)TEM imaging and the data corresponding to the simulations and theoretical calculations of the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper",. A pre-print version of the paper is available on arXiv: <a href="http://doi.org/10.48550/arXiv.2212.01180">http://doi.org/10.48550/arXiv.2212.01180</a></p> <p>See the file README.md for a detailed description.</p>
Modular Synthesis of (Borylmethyl)silanes through Orthogonal Functionalization of a Carbon Atom
<p>Raw data for the characterization of the compounds in the research paper with the same name.</p>
Colossal optical anisotropy from atomic-scale modulations: manuscript data
<ul> <li>Relevant data files for Main Text figures of "Colossal optical anisotropy from atomic-scale modulations"</li> <li>Relevant data files for Supplementary Information figures of "Colossal optical anisotropy from atomic-scale modulations"</li> </ul>
(new version data) Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions
<p><strong>Deciphering the three-dimensional atomic structure of solid-solid interfaces in core-shell nanomaterials is the key to understand their remarkable catalytical, optical and electronic properties. Here, we probe the three-dimensional atomic structures of palladium-platinum core-shell nanoparticles at the single-atom level using atomic resolution electron tomography. We successfully quantify the rich structural variety of core-shell nanoparticles with heteroepitaxy in 3D at atomic resolution. Instead of forming an atomically-sharp boundary, the core-shell interface is atomically diffuse with an average thickness of 4.2 Å, irrespective of the particle's morphology or crystallographic texture. We observed dissolved free Pd and Pt single atoms and sub-nanometer clusters using cryogenic electron microscopy. The high concentration of Pd in the diffusive interface is highly related to the free Pd atoms dissolved from the Pd seeds. These results advance our understanding of core-shell structures at the fundamental level, providing potential strategies into precise nanomaterial manipulation and chemical property regulation.</strong></p> <p> </p> <p>The data and source codes for the paper "Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions" are posted below.</p> <p><strong># Repositary Contents</strong></p> <p><strong>### 1. Experiment Data</strong></p> <p>Folder: [Measured_data](./1_Measured_data)</p> <p>This folder contains denoised and aligned ADF-STEM projections and corresponding finalized tilt angles for three Pd@Pt core-shell nanoparticles. Three particles are named PB (pentagonal bipyramid shaped), EPB (elongated pentagonal bipyramid shaped) and TO (truncated octahedron shaped), respectively.</p> <p><strong>### 2. Reconstructed 3D Volume</strong></p> <p>Folder: [Final_reconstruction_volume](./2_Final_reconstruction_volume)</p> <p>This folder contains 3D tomographic reconstruction volumes of three particles. For the source code of RESIRE algorithm used in these reconstructions, please see the [source code](https://github.com/AET-MetallicGlass/Supplementary-Data-Codes/tree/master/2_RESIRE_package) of Yao Yang's paper on github.</p> <p><strong>### 3. Atom Tracing and Classification</strong></p> <p>Folder: [Tracing_and_classification](./3_Tracing_and_classification)</p> <p>This folder contains the source code to trace and classify atoms in the 3D volume.</p> <p><strong>### 4. Experimental Atomic Models</strong></p> <p>Folder: [Final_coordinates](./4_Final_coordinates)</p> <p>This folder contains the final coordinates of three nanoparticles.</p> <p><strong>### 5. Analysis of core-shell interface and others</strong></p> <p>Folder: [Analysis_of_interface](./5_Analysis_of_interface)</p> <p>This folder contains the codes to analyse the pair distribution function (PDF), the core-shell interface, the local coordination structure (PTM), the displacement and strain map of three nanoparticles.</p>
Silica in Silico: a Molecular Dynamics Characterization of the Early Stages of Protein Embedding for Atom Probe Tomography
<p>The .zip archive contains the trajectories of all the simulations performed and analysed within the manuscript. The water molecules were removed for control systems.</p>
Research data supporting: "Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments"
<p>This repository contains the set of data shown in the paper <strong>"Innate dynamics and identity crisis of a metal surface unveiled by machine learning of atomic environments"</strong>, published on The Journal of Chemical Physics (DOI:10.1063/5.0139010)</p>
Atomic-scale environment of niobium in minerals as revealed by X-ray absorption spectroscopy at the Nb K-edge
<p>This data set provides the raw EXAFS and XRD data of the manuscript '<strong>Atomic-scale </strong><strong>environment of niobium in minerals as revealed b</strong><strong>y </strong><strong>X-ray absorption spectroscop</strong><strong>y at the Nb </strong><strong>K</strong><strong>-edge'</strong> submitted to European Journal of Mineralogy.</p> <p>- EXAFS data files were converted into .txt for more accessibility. <em>Pcl</em> is the abbreviation for <em>pyrochlore. </em>The files are accoridngly labelled.</p> <p>- XRD data files for synthetic Nb-doped compounds start with 'XRD_...'.</p> <p>- The refined crystal structure of hydropyrochlore is 'hydropcl_01.cif' file.</p> <p> </p> <p> </p>
Data for "Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size"
<p>This record contains <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE databases</a> in sqlite format that contain the minimum energy structures of nanoparticles of Ag, Au, Cu, and Pd as determined in the paper "<em>Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size</em>", Rahm and Erhart, Nano Letters <strong>17</strong>, 5775 (2017); <a href="http://doi.org/10.1021/acs.nanolett.7b02761">doi: 10.1021/acs.nanolett.7b02761</a></p> <p>The <code>access-database.py</code> script included in this record demonstrates how to access the databases. It requires the <a href="https://wiki.fysik.dtu.dk/ase/">ASE package</a> to be installed.</p>
Data, scripts and simulations for ProxyOH-[OH] analysis using ATom data and F0AM and AM3 simulations
<p>This dataset provides the simulations and analysis code used in Baublitz et al., An observation-based, reduced-form model for oxidation in the remote marine troposphere, <em>Proceedings of the National Academy of Sciences</em>, <strong>120</strong>.</p> <p><strong>Code, package versions</strong></p> <p>The code is generally written in Python and saved to a Jupyter Notebook (.ipynb) format, except for the component developing the Bayesian regressions, which is written in R. For improved accessibility, the code has also been printed to PDF format so that it may be readable without requiring access to Jupyter. The code used to create the main text figures is specified in the file names. When the primary focus of a script is to create supplemental figures, the figure names have also been specified in the script file name. The code for creating other supplemental figures is also available in the script corresponding to the section where that figure is referenced. </p> <p>The following packages and package versions were used to develop this analysis:</p> <p><em><strong>Python </strong></em>(v3.10.0)</p> <ul> <li>anaconda 4.13.0 <ul> <li>collections (native to anaconda installation)</li> <li>datetime</li> <li>os<span> </span></li> <li>random</li> </ul> </li> <li>jupyter 1.0.0, jupyter-core 4.9.1</li> <li>matplotlib (visualization) 3.5.1</li> <li>notebook 6.4.6</li> <li>numpy 1.21.4</li> <li>pandas 1.3.4</li> <li>scipy 1.7.3</li> <li>seaborn (figure formatting) 0.11.2</li> </ul> <p>The full environment is specified in the YAML file "atom_env.yml." Anaconda users (not tested, potentially restricted to Windows) may load this environment with this file and the following command:</p> <p><em>$ conda env create -f atom_env.yml</em></p> <p><em><strong>R </strong></em> (v4.1.2, includes package parallel)</p> <ul> <li>tidyverse 1.3.1</li> <li>rjags 4-12</li> <li>runjags 2.2.0-3</li> <li>lattice 0.20-45</li> <li>lme4 1.1-31</li> <li>loo 2.4.1</li> <li>ggpubr 0.4.0</li> <li>matrixStats 0.61.0</li> </ul> <p><strong>Zipped directory contents</strong></p> <p>The full set of global, hourly AM3 model simulations developed for this project are included in this repository (AM3_hourly_simulations_global_ATom1-4.zip) <em>for reference and potential future application, though they are not used in the code</em>. They are described here (vs listed) and span the dates for each campaign leg and are broken into four variable categories, concentrations and met fields ('stp_conc_v2'), individual reaction rates ('ind_rate'), integrated reaction rates ('all_rate') and deposition velocities or photolysis rates ('dep_jval'). Some of these files include all days in the range, while others include only the days that the campaign took measurements.</p> <p>In addition, a subset of the AM3 simulations that specifically include variables used in the manuscript analysis that have been sampled along the ATom flight is included, along with the 10 s ATom merge data (AM3_model_simulations_sampled.zip). <em>This is the file that should be downloaded for reproducing the manuscript in the analysis.</em></p> <ul> <li>AM3_model_simulations_sampled.zip <ul> <li>atom1_10s_ss_030122.csv</li> <li>atom2_10s_ss_030122.csv</li> <li>atom3_10s_ss_030122.csv</li> <li>atom4_10s_ss_030122.csv</li> </ul> </li> <li>bayes_data.zip <ul> <li>bayes_atom_10s_model_122022.csv</li> <li>bayes_ats_10s_remNOlsth2sigma_highlogNO_emulate_122022.csv</li> <li>bayes_ats_10s_remNOlsth2sigma_highlogNO_emulate_allPOH_030723.csv</li> <li>base/ <ul> <li>.Rhistory</li> <li>atom_jags_010723.R</li> <li>atom_lmer_model_122122.R</li> <li>atom_sens_030723.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>gelman_list_base.csv</li> <li>levels.csv</li> <li>log_pd.csv</li> <li>model_b0.csv</li> <li>model_b1.csv</li> <li>p.fit.csv</li> <li>p.mu.csv</li> <li>p.sd.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CH4_CO_HCHO_MHP/ <ul> <li>.Rhistory</li> <li>atom_altCH4_CO_HCHO_MHP_031423.R</li> <li>atom_jags_altCH4_CO_HCHO_MHP_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_ch4_co_hcho_mhp.csv</li> <li>levels.csv</li> <li>log_pd.csv</li> <li>p.fit.csv</li> <li>p.mu.csv</li> <li>p.sd.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_ch4_co_hcho_mhp.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CO_HCHO/ <ul> <li>.Rhistory</li> <li>atom_altCO_HCHO_031423.R</li> <li>atom_jags_altCO_HCHO_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_CO_HCHO.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_co_hcho.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>CO_HCHO_MHP/ <ul> <li>atom_altCO_HCHO_MHP_031423.R</li> <li>atom_jags_altCO_HCHO_MHP_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_CO_HCHO_MHP.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_CO_HCHO_MHP.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>H2O2_O3_CH4_CO_HCHO_MHP/ <ul> <li>.Rhistory</li> <li>atom_altH2O2_O2_CH4_CO_HCHO_MHP_122122.R</li> <li>atom_jags_altH2O2_O3_CH4_CO_HCHO_MHP_030723.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_h2o2_o3_ch4_co_hcho_mhp.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_h2o2_o3_ch4_co_hcho_mhp.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>HCHO/ <ul> <li>atom_altHCHO_031423.R</li> <li>atom_jags_altHCHO_031423.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_HCHO.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_HCHO.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> <li>MHP/ <ul> <li>atom_altMHP_122122.R</li> <li>atom_jags_altMHP_122122.R</li> <li>dat1_bins.csv</li> <li>dat1_OH.csv</li> <li>dat1_proxy_MHP.csv</li> <li>levels.csv</li> <li>r_prx_ytrue.pkl</li> <li>rjmt_B0.csv</li> <li>rjmt_B1.csv</li> <li>rjmt_proxy_MHP.csv</li> <li>rjmt_y_true.csv</li> </ul> </li> </ul> </li> <li>F0AMv3.2.zip <ul> <li>mean_ratio_OH_loss_bins_oce.npy</li> <li>mean_ratio_OH_prod_bins_oce.npy</li> <li>mean_ratio_OH_prod_loss_bins_oce.npy</li> <li>Data/ <ul> <li>atom1/ <ul> <li>atom1_output_alt.cs</li> <li>atom1_output_CO.csv</li> <li>atom1_output_H2O.csv</li> <li>atom1_output_lat.csv</li> <li>atom1_output_lon.csv</li> <li>atom1_output_lossOH_ppt_lump15.csv</li> <li>atom1_output_M.csv</li> <li>atom1_output_NO.csv</li> <li>atom1_output_OH.csv</li> <li>atom1_output_prodOH_ppt_lump15.csv</li> <li>atom1_output_startTime.csv</li> <li>atom1_output_sza.csv</li> </ul> </li> <li>atom2/ <ul> <li>atom2_output_alt.csv</li> <li>atom2_output_CO.csv</li> <li>atom2_output_H2O.csv</li> <li>atom2_output_lat.csv</li> <li>atom2_output_lon.csv</li> <li>atom2_output_lossOH_ppt_lump15.csv</li> <li>atom2_output_M.csv</li> <li>atom2_output_NO.csv</li> <li>atom2_output_OH.csv</li> <li>atom2_output_prodOH_ppt_lump15.csv</li> <li>atom2_output_startTime.csv</li> <li>atom2_output_sza.csv</li> </ul> </li> <li>atom3/ <ul> <li>atom3_output_alt.csv</li> <li>atom3_output_CO.csv</li> <li>atom3_output_H2O.csv</li> <li>atom3_output_lat.csv</li> <li>atom3_output_lon.csv</li> <li>atom3_output_lossOH_ppt_lump15.csv</li> <li>atom3_output_M.csv</li> <li>atom3_output_NO.csv</li> <li>atom3_output_OH.csv</li> <li>atom3_output_prodOH_ppt_lump15.csv</li> <li>atom3_output_startTime.csv</li> <li>atom3_output_sza.csv</li> </ul> </li> <li>atom4/ <ul> <li>atom4_output_alt.cs</li> <li>atom4_output_CO.csv</li> <li>atom4_output_H2O.csv</li> <li>atom4_output_lat.cs</li> <li>atom4_output_lon.cs</li> <li>atom4_output_lossOH_ppt_lump15.csv</li> <li>atom4_output_M.csv</li> <li>atom4_output_NO.csv</li> <li>atom4_output_OH.csv</li> <li>atom4_output_prodOH_ppt_lump15.csv</li> <li>atom4_output_startTime.csv</li> <li>atom4_output_sza.csv</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>For any further questions on the model simulations or code included here, please contact the corresponding author (Colleen Baublitz, cbb2158@columbia.edu). </p>
Dataset for "Computer vision assisted decomposition analysis of atom probe tomography data"
<p>Dataset for the article "Computer vision assisted decomposition analysis of atom probe tomography data". APT measurements were performed by Marcus Hans at Materials Chemistry (RWTH Aachen University) using a CAMECA LEAP 4000X HR. Training data was created by Janis A. Sälker.</p> <p>Content:</p> <p>- 13 (V,Al)N and 3 (Ti,Al)N APT reconstructions (.epos file format) and the corresponding range file (.rrng file format).</p> <p>- Training data (images & masks) for 9 labeled (V,Al)N APT samples (h5 file format). Image data with key "image" of shape (2, number_of_slices, 608, 192), where 2 corresponds to the V- and Al-contribution/channel and 608/192 to the height/width of the images. Masks/labels with key "label" of shape (number_of_slices, 608, 192)</p> <p> </p>
A Novel Backtracing Model to Study the Emission of Energetic Neutral Atoms at Titan
<p>Data for the manuscript "A Novel Backtracing Model to Study the Emission of Energetic Neutral Atoms at Titan" by Tippens et al., (2023). See README.txt for a description of the data files included here.</p>
All-atom MD simulations of POPC/SSM/CHOL mixture
<p>All-atom MD simulation of lipid bilayer in water, with composition POPC/SSM/CHOL, at 321.15 K, NPT conditions.</p> <p>Force field: CHARMM36. Water model: TIP3P. Number of molecules: 100 POPC + 100 SSM + 100 CHOL + 9000 SOL + 18 Na+ + 18 Cl-.</p> <p>SSM is (d18:1/18:0) sphingomyelin.</p> <p>Duration of the simulation: 300 ns.</p>
Research data of "Quantum resonant optical bistability with a narrow atomic transition: bistability phase diagram in the bad cavity regime"
<p>The data set includes the matlab programs, measured data and drawings used for the figures in D Rivero et al 2023 New J. Phys. 25 093053</p>
Data for: "Parallel implementation of CNOT^N and C_2NOT^2 gates via homonuclear and heteronuclear Forster interactions of Rydberg atoms"
<p>Datasets for “Parallel implementation of CNOT^N and C_2NOT^2 gates via homonuclear and heteronuclear Forster interactions of Rydberg atoms”,</p> <p>by Ahmed M. Farouk, I.I. Beterov, Peng Xu, S. Bergamini, and I.I. Ryabtsev</p> <p>This repository contains files, each representing data plotted in the manuscript in .csv format. </p> <p>For more details please see manuscript.</p> <p>preprint url:</p> <p>https://arxiv.org/abs/2206.12176 </p> <p>Contact details: </p> <p>ahmed.farouk@azhar.edu.eg</p> <p> </p>
Deconstruction of tropospheric chemical reactivity using aircraft measurements: the Atmospheric Tomography Mission (ATom) data
Open the record for dataset details and reuse information.
Pool size and 15N atom % of nydrolyzable N in natural and enriched soils in Imnavait watershed
Hydrolyzable N pool size and 15N atom % of natural and enriched soils collected from Imnavait watershed in summer of 2005.
Research data supporting "Atomic-Scale Patterning of Arsenic in Silicon by Scanning Tunneling Microscopy"
<p>Research data supporting the publication: Stock, T. J. Z, <em>et. al.</em>, <strong>2020</strong>, Atomic-Scale Patterning of Arsenic in Silicon by Scanning Tunneling Microscopy, <em>ACS Nano</em>, https://dx.doi.org/10.1021/acsnano.9b08943</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.