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994 results for “2d”
Database on available 2D materials
<p>This deliverable is a database of exfoliable three-dimensional (3D) layered materials available for 2D-PRINTABLE, and the corresponding two-dimensional (2D) materials produced by project partners by means of various exfoliation methods in liquid media, including liquid-phase exfoliation method (LPE), electrochemical exfoliation (EE) and chemical exfoliation (CE). Exfoliable 3D layered materials are those synthesized and currently available at VSCHT facilities, while LPE-produced 2D materials are those produced by BeD, UKa, TCD TUD and VSCHT. The database includes the main specifications for exfoliable 3D layered materials, including their (physical) form (e.g., powder/crystal and corresponding dimension), stoichiometry and doping, as well as the material amount that can be supplied within the consortium. For 2D materials, the database reports the references to public documents (e.g., paper in international peer-reviewed journal or public repositories) showing material characterizations. </p> <p>This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101135196. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p>
Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements
<p>Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements</p> <p>DOI: 10.1021/acsami.3c19254</p> <p><span><span>I. Brotons-Alcázar, Jason. S. Terreblanche, S. Giménez-Santamarina, G. M. Gutiérrez-Finol, K. S. Ryder, A. Forment-Aliaga, E. Coronado, <em>ACS Appl. Mater. Interfaces</em> <strong>2024</strong>, <em>16</em>, 19711.</span> </span></p>
Dataset of the publication: Hybrid Heterostructures of a Spin Crossover Coordination Polymer on MoS2: Elucidating the Role of the 2D Substrate. Small 2023, 19, e2304954.
<p><span> Dataset of the publication: Hybrid Heterostructures of a Spin Crossover Coordination Polymer on MoS2: Elucidating the Role of the 2D Substrate.</span></p> <p><span><span>A. Núñez-López, R. Torres-Cavanillas, M. Morant-Giner, N. Vassilyeva, R. Mattana, S. Tatay, P. Ohresser, E. Otero, E. Fonda, M. Paulus, V. Rubio-Giménez, A. Forment-Aliaga, E. Coronado, <em>Small</em> <strong>2023</strong>, <em>19</em>, e2304954.</span> </span></p> <p><span><span>doi: 10.1002/smll.202304954</span></span></p> <p><span><span><span>10.1002/smll.202304954</span><span>10.1002/smll.202304954<span>10.1002/smll.202304954</span></span></span></span></p>
Dataset of the publication: Magnon Straintronics in the 2D van der Waals Ferromagnet CrSBr from First-Principles
<p>Dataset of the publication: Magnon Straintronics in the 2D van der Waals Ferromagnet CrSBr from First-Principles</p> <p>DOI: 10.1021/acs.nanolett.2c02863</p> <p>D. L. Esteras, A. Rybakov, A. M. Ruiz, J. J. Baldoví</p> <p>Nano Lett. 2022, 22, 21, 8771–8778</p>
Dataset of the publication: Liquid‐Phase Fabrication of Janus 2D Materials: Defect‐Rich MoS2 Ultrathin Layers Asymmetrically Decorated with Au Nanoparticles. Small 2024, 2406599
<p>Dataset of the publication: Liquid‐Phase Fabrication of Janus 2D Materials: Defect‐Rich MoS2 Ultrathin Layers Asymmetrically Decorated with Au Nanoparticles</p> <p>N. V. Vassilyeva, A. Forment-Aliaga, E. Coronado, <em>Small</em> <strong>2024</strong>, e2406599.</p> <p>doi: 10.1002/smll.202406599</p>
Dataset of the publication: Chemical design and magnetic ordering in thin layers of 2D MOFs
<p>Dataset of the publication: Chemical design and magnetic ordering in thin layers of 2D MOFs</p> <p>DOI: 10.1021/jacs.1c07802</p> <p>López-Cabrelles, J; Mañas-Valero, S; Vitórica-Yrezábal, IJ; Siskins, M; Lee, M; Steeneken, PG; van der Zant, HSJ; Espallargas, GM; Coronado, E<br>J. Am. Chem. Soc. 2021, 143, 44, 18502–18510</p>
Dataset of the publication: Tailoring spin waves in 2D transition metal phosphorus trichalcogenides via atomic-layer substitution
<p>Dataset of the publication: Tailoring spin waves in 2D transition metal phosphorus trichalcogenides via atomic-layer substitution</p> <p>DOI: 10.1039/d2dt02482a</p> <p>A. M. Ruiz, DL. Esteras, A. Rybakov, J. J. Baldov </p> <p>Dalton Trans., 54, 44, 16816-16823 (2022)</p>
Dataset of the publication: Spin-crossover tuning of the luminescence in 2D Hofmann-type compounds in bulk and exfoliated flakes
<p>Dataset of the publication: Spin-crossover tuning of the luminescence in 2D Hofmann-type compounds in bulk and exfoliated flakes</p> <p>DOI: 10.1039/d3tc03693f</p> <p>V. García-López, F. Marques-Moros, J. Troya, J. Canet-Ferrer, M. Clemente. León, E. Coronado</p> <p>J. Mater. Chem. C, 12, 161-169 (2024)</p>
Movies: herringbone flows and flapping waves, 2d inclined inviscid Saint-Venant equations
<p>Supplementary movies for <em><strong>Multidimensional stability and transverse bifurcation of hydraulic shocks and roll waves in open channel flow</strong>.</em></p> <p>See <a href="https://github.com/zyang-pde/Multi-d_inviscid_Saint-Venant_eqs">https://github.com/zyang-pde/Multi-d_inviscid_Saint-Venant_eqs</a> for source codes used to generate these movies.</p> <p><strong>dam_break_F_equals_2_point_25.mp4</strong></p> <p>Dam-break initial data with left fluid height 1, right fluid height 0.7, and Froude number 2.25. The discontinuous hydraulic shock is 2d convectively unstable. Herringbone flows are seen at the end.</p> <p><strong>flat_F_equals_2_point_25.mp4</strong></p> <p>Flat initial data with co-moving speed set to be that of discontinuous hydraulic shock with left limiting fluid height 1, right limiting fluid height 0.7, and Froude number 2.25. Herringbone flows, parabola, and roll waves are seen at the end.</p> <p><strong>dam_break_comparison.mp4</strong></p> <p>Comparison of simulations with dam break initial data and with Froude numbers 2.13,2.14,2.15.</p> <p><strong>flat_comparison.mp4</strong></p> <p>Comparison of flat simulations with flat initial data and with Froude numbers 2.13,2.14,2.15.</p> <p> </p> <p>Movies below are simulated with perturbed roll waves initial data. The Froude number is set to be 6 and the minimum fluid height is set to be 0.28 and the channel width and the y-boundary condition are varied.</p> <p><strong>roll_width_point15.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.15</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point16.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.16</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point17.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.17</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point18.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.18</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic.</p> <p><strong>roll_width_point18_refined.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.18</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic. Finer mesh grid is used compared with roll_width_point18.py. Raw data files are used to generate figures in the paper.</p> <p><strong>roll_width_point18_periodic.mp4</strong></p> <p>With <strong>periodic</strong> y-boundary condition, the width of the channel is set to be <strong>0.18</strong>. No flapping front is seen at the end. Channel roll waves are stable.</p> <p><strong>roll_width_point36_periodic.mp4</strong></p> <p>With <strong>periodic</strong> y-boundary condition, the width of the channel is set to be <strong>0.36</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic.</p> <p><strong>roll_width_point19.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.19</strong>. Wave fronts start to flap after a while. The flapping waves are persistent and do not become chaotic.</p> <p><strong>roll_width_point2.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>0.2</strong>. Wave fronts start to flap after a while. The flapping waves are also unstable, transitioning to chaotic flow at the end.</p> <p><strong>roll_width_1.mp4</strong></p> <p>With <strong>wall</strong> boundary condition, the width of the channel is set to be <strong>1</strong>. Wave fronts start to flap after a while. The flapping waves are also unstable, transitioning to chaotic flow at the end.</p> <p> </p>
Tensile2d: 2D quasistatic non-linear structural mechanics solutions, under geometrical variations
<p>This dataset contains 2D quasistatic non-linear structural mechanics solutions, under geometrical variations. </p> <p>A Description is provided in <a href="https://arxiv.org/pdf/2305.12871.pdf">the MMGP paper</a> Sections 4.1 and A.2.</p> <p>The file format is PLAID, see <a href="https://plaid-lib.readthedocs.io/ ">the plaid documentation</a>.</p> <p>The variablity in the samples are 6 input scalars and the geometry (mesh). Outputs of interest are 4 scalars and 6 fields.</p> <p>Seven nested training sets of sizes 8 to 500 are provided, with complete input-output data. A testing set of size 200, as well as two out-of-distribution sample, are provided, for which outputs are not provided. </p> <p> </p> <p>Tips to access the data:</p> <p>After decompressing the downloaded file:</p> <p>from plaid.containers.dataset import Dataset<br>from plaid.problem_definition import ProblemDefinition</p> <p>dataset = Dataset()<br>problem = ProblemDefinition()</p> <p>problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))<br>dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)</p> <p>print("problem =", problem)<br>print("dataset =", dataset)</p> <p>sample = dataset[0]<br>print("sample =", sample)</p> <p>for fn in sample.get_field_names():<br> print(f"{fn} =", sample.get_field(fn))<br>for sn in sample.get_scalar_names():<br> print(f"{sn} =", sample.get_scalar(sn))</p> <p>print("nodes =", sample.get_nodes())<br>print("elements =", sample.get_elements())<br>print("nodal_tags =", sample.get_nodal_tags())</p> <p> </p>
Data set for "Superconducting 2D NbS2 Grown Epitaxially by Chemical Vapor Deposition "
<p>Data set for the paper "Superconducting 2D NbS<sub>2</sub> Grown Epitaxially by Chemical Vapor Deposition"</p>
2D P-wave velocity model of the northern Hikurangi margin.
<p>You should find three files attached that include (1) a table that maps x-coordinates to longitude/latitude, (2) the Vp model with columns for x, depth, and Vp, and (3) a table of interfaces that include the topography (value 1) and the Moho (value 2). The zero x coordinate is arbitrarily the location of the westernmost shot in the Bay of Plenty from the SHIRE seismic survey of 2017, so x values range from ~190 to 400 km.</p> <p>This P-wave velocity model is presented in: Gase, Andrew C., et al. "Crustal structure of the northern Hikurangi margin, New Zealand: Variable accretion and overthrusting plate strength influenced by rough subduction." <em>Journal of Geophysical Research: Solid Earth</em> 126.5 (2021): e2020JB021176.</p>
LiftWEC deliverable D4.4: Dataset from 2D experimental test campaign, with calculated hydrodynamic forces
<p><em>This dataset contains 2-dimensional wave tank testing data for a wave-driven rotating hydrofoil model. The model tested is composed of one or two hydrofoils rotating around a horizontal axis, perpendicular to the wave direction. The model was tested in a range of regular and irregular seas. The data contains measurements of the model in the wave tank including; wave measurement, rotor position, forces on the hydrofoils, and torque on the power take off. </em> <em>This data is the first of two sets of wave tank data generated for the LiftWEC H2020 research project. This first set consists of results for the device tested in 2D, while the second set will contain results for tests conducted in 3D. </em><em>This new version contains all data from version 1 of the first set, which consists of measurement from the experimental testing, plus results from the data analysis calculating the hydrodynamic forces. These forces are calculated by removing the static force and the centrifugal force. For a complete description of the test campaign, readers are directed to "LiftWEC Deliverable D4.4. </em> Report on physical modelling of 2D LiftWEC concepts <em>"</em></p>
2D Sound Navigation - Tutorial Materials
<p>Materials presented to the experiment participants to familiarize them with the navigation controls and auditory guidance.</p>
2d adaptive extinction maps for the VVV footprint
<p><strong>2d adaptive resolution extinction maps for the VVV footprint</strong></p> <p>Extinction maps associated with the publication <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220510378S">Sanders et al. (2022, MNRAS)</a> </p> <p>Three maps are provided: E(J-Ks), E(H-Ks) and E(H-[4.5]). The E(J-Ks) and E(H-Ks) maps have been computed over the entire VVV footprint whilst the E(H-[4.5]) map is only computed for the inner -1.5<l<1.5, -1.5<b<1.5. To convert to AKs multiply by 0.449, 1.293, 0.700 respectively (or otherwise for other extinction laws).</p> <p>The files *.csv.gz give the colour excess (e*) and its spread (sigma_e*) at a set of Healpix labelled by their unique index. A series of Healpix resolutions have been used to provide higher resolution where needed (except for the E(H-[4.5]) map that is only at level=13). The indices are using the nested scheme given the Galactic coordinates (l,b). In this way, it is simple to handle the varying resolution (see <a href="https://ivoa.net/documents/MOC/">https://ivoa.net/documents/MOC/</a>).</p> <p>The colour excesses have been found from Gaussian fits to the red clump colours for (J-Ks) and (H-Ks) and an average over all giant stars for (H-[4.5]) (accounting for the weak gradient of the giant branch in the (H-[4.5]) vs. Ks colour-magnitude space). The spreads in extinction come from the width of the Gaussian peak for (J-Ks) and (H-Ks) and the width of the full distribution for (H-[4.5]) after subtracting the average photometric uncertainties and accounting for a (0.05,0.02,0.00) intrinsic colour width for (J-Ks, H-Ks, H-[4.5]) respectively.</p> <p>The provided file extinction_maps.py provides a class for reading in all extinction maps (version=JK,HK,H45 allows one to pick the required map) and querying the colour excess and its spread for large numbers of Galactic coordinates. Also there is functionality for finding the resolution of the map at a given location.</p> <p>The queries will throw a warning but return a value if the coordinate is outside the reliable footprint (the entire VVV footprint for JK and HK and the inner -1.5<l<1.5, -1.5<b<1.5 for H45).</p> <p>The example.ipynb notebook shows an example of querying the extinction map and plotting the result.</p> <p> </p> <p> </p>
The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations: Data
<pre>This repository accompanies the paper ``` The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations by Martina Monaco, Juliane Dannberg, Rene Gassmoeller, Stephen Pugh ``` The global models presented in the manuscript were run using the following dependencies: ``` ----------------------------------------------------------------------------- -- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion. -- . version 2.3.0-pre (master, 74e48be) -- . using deal.II 9.3.0 -- . with 32 bit indices and vectorization level 2 (256 bits) -- . using Trilinos 12.10.1 -- . using p4est 2.2.0 ----------------------------------------------------------------------------- ``` This repository contains: - The 'all_model_series' folder with the files used to analyze the depth averages. Each series (100, Aoki, Hefesto) has its own subfolder; - The 'plugins' folder, with the required plugin to run the models. To compile the plugin, navigate into this directory and follow the steps: 1. `cmake -D Aspect_DIR=PATH_TO_ASPECT` (replace `PATH_TO_ASPECT` with the directory where you compiled ASPECT). 2. `make` - The 'run_series' bash script, with the command to run multiple models at once. The user should modify: ° The input file name: INPUT_FILE=heterogeneity-several-blobs-INSERT-SERIES.prm ° The directory where ASPECT is located: srun --mpi=pmix_v2 $HOME/aspect/aspect-build/aspect --" echo -e $COMMAND | sbatch --job-name gs_${viscosity}_spacing_${blob_spacing} -p hpg2-compute -N 1 -n 32 -t 3-23:59:00 -o output_gs_${viscosity}_spacing_${blob_spacing}.%j -e error_gs_${viscosity}_spacing_${blob_spacing}.%j --constraint 'haswell|skylake' --mem-per-cpu '3gb' --distribution block; - The three parameter files (.prm), one per series; - Two .py files containing the scripts necessary to plot all the figures in the paper</pre>
SPAAM Summer School 2022: Introduction to Ancient Metagenomics - 2d Introduction to nf-core/eager
<p>Teaching data for practical session: "2d Introduction to nf-core/eager" of the 2022 SPAAM Summer School: Introduction to Ancient Metagenomics (Aug. 1-5 2022).</p> <p>See: <a href="https://spaam-community.github.io/wss-summer-school/#/2022/">https://spaam-community.github.io/wss-summer-school/#/2022/</a> or <a href="https://doi.org/10.5281/zenodo.6976711">https://doi.org/10.5281/zenodo.6976711</a> for slides.</p> <p>Once downloaded, run:</p> <pre><code>tar xvfz <session>.tar.gz</code></pre> <p> to decompress the data directory for the session.</p>
2D MoS2/carbon/polylactic acid filament for 3D printing: Photo and electrochemical energy conversion and storage
<p>Raw data of published journal article "2D MoS2/carbon/polylactic acid filament for 3D printing: Photo and electrochemical energy conversion and storage", DOI: 10.1016/j.apmt.2021.101301</p>
Code and Velocity Data for Sustained indentation in 2D models of continental collision involving whole mantle subduction
<p>Contains the Python code for all models and resolution tests using the <a href="https://www.underworldcode.org/intro-to-underworld">underworld geodynamics code</a> in "Sustained indentation in 2D models of continental collision involving whole mantle subduction" submitted to GJI</p>
Nesting Tasks Dataset for 2D-Nesting Efficiency Estimation
<p>Nesting efficiency dataset</p> <p>This is the raw dataset associated with the paper “Graph Neural Networks Comparison for 2D-Nesting Efficiency Estimation”, by C.Lallier, L. Vézard, B. Pinaud and G. Blin, 2022. Consisting of 100,000 nesting tasks.</p> <p><strong>Usage:</strong></p> <p>The files are: <em>tasks.gz, parts.gz, constraints.gz, </em>and<em> shapes.gz</em>. They are in PICKLE file format version 5 with a gzip compression. Example to load a file :</p> <pre><code class="language-python">import pandas as pd tasks = pd.read_pickle('tasks.gz')</code></pre> <p> </p> <p><strong>Description:</strong></p> <p><em>Tasks.gz</em> file contains nestings high-level descriptors. It is composed of the following columns:</p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <th scope="row">efficiency</th> <td>float</td> <td>The variable to predict (label). Given in % </td> </tr> <tr> <th scope="row">duration</th> <td>integer</td> <td>input data. The nesting algorithm convergence time. Given in s.</td> </tr> <tr> <th scope="row">sheet_width</th> <td>integer</td> <td>input data. The width of the nesting area. Given in m<sup>-4</sup></td> </tr> <tr> <th scope="row">sheet_length</th> <td>integer</td> <td>input data. Facultative. The height of the nesting area. Given in m<sup>-4</sup></td> </tr> <tr> <th scope="row">sheet_type</th> <td>integer</td> <td>input data. Kind of the nesting.</td> </tr> <tr> <th scope="row">tasks_index</th> <td>integer</td> <td>Generated data. Join key between tables.</td> </tr> <tr> <th scope="row">is_train, is_val, is_test</th> <td>boolean</td> <td>Generated data. Can be used as mask for the train, val and test subsets.</td> </tr> </tbody> </table> <p><em>Parts.gz</em> contains description of the parts to be nested : </p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><strong>tasks_index</strong></td> <td>integer</td> <td>Reference to the join key from the <em>Task</em> table. </td> </tr> <tr> <td><strong>parts_id</strong></td> <td>integer</td> <td>Generated part id.</td> </tr> <tr> <td><strong>shape_hash</strong></td> <td>integer</td> <td>Reference to the hash of the part's shape, join key from the <em>Shape </em>table.</td> </tr> </tbody> </table> <p><em>Shapes.gz</em> is the description of the shapes of the parts to be nested : </p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><strong>shape_hash</strong></td> <td>integer</td> <td>Generated data. Join key between tables.</td> </tr> <tr> <td><strong>raw</strong></td> <td>list of integers</td> <td>List of x, y tuples for each point. Unit is m<sup>-4</sup></td> </tr> <tr> <td><strong>sizes</strong></td> <td>list of integers</td> <td>List of sub-shapes sizes. </td> </tr> </tbody> </table> <p><em>Constraints.gz</em> describes constraints and their parameters:</p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><strong>type</strong></td> <td>string</td> <td>Generated constraint type.</td> </tr> <tr> <td><strong>tasks_index</strong></td> <td>integer</td> <td>Reference to the join key from the <em>Task</em> table.</td> </tr> <tr> <td><strong>parts_1, parts_2</strong></td> <td>list of integers</td> <td>References to the <em>parts_id</em> from the <em>Parts</em> table. </td> </tr> <tr> <td><strong>p1_x, p1_y and p2_x, p2_y</strong></td> <td>list of floats</td> <td>Input data. Origin position (x, y) of the constraint on parts. For each part of the constraint.</td> </tr> <tr> <td><strong>r1_start, r1_end, r1_flip_x</strong></td> <td>list of floats</td> <td>Input data. Rotation (start, end, and flip_x) parameters of the constraint. Multiple ranges accepted.</td> </tr> <tr> <td><strong>y_min, y_max</strong></td> <td>list of floats</td> <td>Input data. Range from (y_min, y_max). Multiple ranges accepted.</td> </tr> <tr> <td> <p><strong>x_offset, y_offset, motif_order, x_alignment_type, y_alignment_type, proximity_type, max_distance, groups_relative_orientation, is_frozen</strong></p> </td> <td>float</td> <td>Input data. Other constraint parameters.</td> </tr> </tbody> </table>
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.