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.
74
datasets available to search
ShareScore release 0.9.0
Dataset results
74 results for “Channel modelling”
Supplementary Data: Global fits if simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of simplified models for dark matter with GAMBIT II. Vector dark matter with an s-channel vector mediator".</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi. Plotting scripts (*.pip) are designed to work with either the original version of pippi 2.1 or the forked unreleased version. The provided scripts do not reproduce all the figures in the paper exactly.</p> <p>To save storage space, all samples have been compressed using <code>tar</code>. To inflate each dataset after downloading run <code>tar -zxvf <samples>.hdf5.gz</code>.</p> <p>To facilitate uploading to Zenodo, several of the data files have been thinned to only include enough points to reproduce plots.</p>
Measurement-based MIMO channel model at 140GHz
<p><strong>1. Introduction</strong></p> <p>The file “gen_dd_channel.zip” is a package of a wideband multiple-input multiple-output (MIMO) stored radio channel model at 140 GHz in indoor hall, outdoor suburban, residential and urban scenarios. The package consists of 1) measured wideband double-directional multipath data sets estimated from radio channel sounding and processed through measurement-based ray-launching and 2) MATLAB code sets that allows users to generate wideband MIMO radio channels with various antenna array types, e.g., uniform planar and circular arrays at link ends.</p> <p><strong>2. What does this package do?</strong></p> <p><em>Outputs of the channel model</em></p> <p>The MATLAB file “ChannelGeneratorDD_hexax.m” gives the following variables, among others. The .m file also gives optional figures illustrating antennas and radio channel responses.</p> <table> <tbody> <tr> <td> <p>Variables</p> </td> <td> <p>Descriptions</p> </td> </tr> <tr> <td> <p><em>CIR</em></p> </td> <td> <p>MIMO channel impulse responses</p> </td> </tr> <tr> <td> <p><em>CFR</em></p> </td> <td> <p>MIMO channel frequency responses</p> </td> </tr> </tbody> </table> <p><em>Inputs to the channel model</em></p> <p>In order for the MATLAB file “ChannelGeneratorDD_hexax.m” to run properly, the following inputs are required.</p> <table> <tbody> <tr> <td> <p>Directory</p> </td> <td> <p>Descriptions</p> </td> </tr> <tr> <td> <p>data_030123_double_directional_paths</p> </td> <td> <p>Double-directional multipath data, measured and complemented by ray-launching tool, for various cellular sites.</p> </td> </tr> </tbody> </table> <p><em>User’s parameters</em></p> <p>When using “ChannelGeneratorDD_hexax.m”, the following choices are available.</p> <table> <tbody> <tr> <td> <p>Features</p> </td> <td> <p>Choices</p> </td> </tr> <tr> <td> <p>Channel model types for transfer function generation</p> </td> <td> <ul> <li> <p>'<em>snapshot</em>': single time sample per link = static, random phase for each path, amplitude from measurements</p> </li> <li>'<em>virtualMotion</em>': Doppler shifts & temporal fading, static propagation parameters, random phase for each path, amplitude from measurements, Doppler frequency per path from AoA and velocity vector</li> </ul> </td> </tr> <tr> <td> <p>Antenna / beam shapes</p> </td> <td> <ul> <li> <p>'<em>single3GPP</em>': single antenna element with power pattern shape defined in 3GPP, adjustable HPBW etc.</p> </li> <li> <p>'<em>URA</em>': uniform rectangular array, omni-directional elements</p> </li> <li>'<em>UCA</em>': uniform circular array, omni-directional elements</li> </ul> </td> </tr> </tbody> </table> <p><strong>List of files in the dataset</strong></p> <p><em>MATLAB codes that implement the channel model</em></p> <p>The MATLAB files consist of the following files.</p> <table> <tbody> <tr> <td> <p>File and directory names</p> </td> <td> <p>Descriptions</p> </td> </tr> <tr> <td> <p>readme_100223.txt</p> </td> <td> <p>Readme file; please read it before using the files</p> </td> </tr> <tr> <td> <p>ChannelGeneratorDD_hexax.m</p> </td> <td> <p>Main code to run; a code to integrate antenna arrays and double-directional path data to derive MIMO radio channels. No need to see/edit other files.</p> </td> </tr> <tr> <td> <p>gen_pathDD.m, randl.m, randLoc.m</p> </td> <td> <p>Sub-routines used in ChannelGeneratorDD_hexax.m; no need of modifications.</p> </td> </tr> <tr> <td> <p>Hexa-X channel generator DD_presentation.pdf</p> </td> <td> <p>User manual of ChannelGeneratorDD_hexax.m.</p> </td> </tr> </tbody> </table> <p> </p> <p><em>Measured multipath data</em></p> <p>The directory "data_030123_double_directional_paths" in the package contains the following files.</p> <table> <tbody> <tr> <td> <p>Filenames</p> </td> <td> <p>Descriptions</p> </td> </tr> <tr> <td> <p>readme_100223.txt</p> </td> <td> <p>Readme file; please read it before using the files</p> </td> </tr> <tr> <td> <p>RTdata_[<em>scenario</em>]_[<em>date</em>].mat</p> </td> <td> <p>Containing double-directional multipath parameters at 140 GHz in the specified scenario, estimated from radio channel sounding and ray-tracing.</p> </td> </tr> <tr> <td> <p>description_of_data_dd_[<em>scenario</em>].pdf</p> </td> <td> <p>Explaining data formats, the measurement site and sample results.</p> </td> </tr> </tbody> </table> <p><strong>References</strong></p> <p>Details of the data set are available in the following two documents:</p> <p><em>The stored channel models</em></p> <p>A. Nimr (ed.), "Hexa-X Deliverable D2.3 Radio models and enabling techniques towards ultra-high data rate links and capacity in 6G," April 2023, available: https://hexa-x.eu/deliverables/</p> <p>@misc{Hexa-XD23,<br> author = {{A. Nimr (ed.)}},<br> title = {{Hexa-X Deliverable D2.3 Radio models and enabling techniques towards ultra-high data rate links and capacity in 6G}},<br> year = {2023},<br> month = {Apr.},<br> howpublished = {https://hexa-x.eu/deliverables/},<br>}</p> <p><em>Derivation of the data, i.e., radio channel sounding and measurement-based ray-launching</em></p> <p>M. F. De Guzman and K. Haneda, "Analysis of wave-interacting objects in indoor and outdoor environments at 142 GHz," IEEE Transactions on Antennas and Propagation, vol. 71, no. 12, pp. 9838-9848, Dec. 2023, doi: 10.1109/TAP.2023.3318861</p> <p>@ARTICLE{DeGuzman23_TAP,<br> author={De Guzman, Mar Francis and Haneda, Katsuyuki},<br> journal={IEEE Transactions on Antennas and Propagation}, <br> title={Analysis of Wave-Interacting Objects in Indoor and Outdoor Environments at 142 {GHz}}, <br> year={2023},<br> volume={71},<br> number={12},<br> pages={9838-9848},<br>}</p> <p>Finally, the code “randl.m” are from the following MATLAB Central File Exchange.</p> <p>Hristo Zhivomirov (2023). Generation of Random Numbers with Laplace Distribution (https://www.mathworks.com/matlabcentral/fileexchange/53397-generation-of-random-numbers-with-laplace-distribution), MATLAB Central File Exchange. Retrieved February 15, 2023.</p> <p><strong>Data usage terms</strong></p> <p>Any usage of the data must be upon consent on the following conditions:</p> <ul> <li>The file “ChannelGeneratorDD_hexax.m” is owned by OUL. Contact: Dr. Pekka Kyösti, Pekka.Kyosti@oulu.fi.</li> <li>The other files and those in the directories, except for “randl.m”, are owned by AAU. Contact: Mr. Mar Francis de Guzman, francis.deguzman@aalto.fi.</li> <li>When a scientific paper is published that exploits the data and code, please cite this data set; the citation can be downloaded from the zenodo page of this data set.</li> </ul>
Examples of ALD saturation profiles in rectangular channel LHAR structures simulated with a diffusion-reaction model
<p>Examples of atomic layer deposition ALD saturation profiles in rectangular channel LHAR structures simulated with a diffusion-reaction model by Ylilammi et al. (Journal of Applied Physics <strong>123</strong>, 205301 (2018); <a href="https://doi.org/10.1063/1.5028178">https://doi.org/10.1063/1.5028178</a>) as function of (a) Reactant A pulse time <em>t</em>, (b) Reactant A partial pressure <em>p</em>, and (c) sticking coefficient <em>c</em>. Parameters used in the simulation, if not otherwise stated: channel height <em>H</em> 500 nm, temperature 250°C, 250 cycles, inert carrier gas partial pressure 500 Pa, Reactant A molar mass 100 g/mol, inert carrier gas molar mass 28 g/mol, Reactant A diameter 0.600 nm, inert gas diameter 0.374 nm, adsorption capacity 4 metal atoms per nm<sup>2</sup>, density of the material grown 3.5 g/cm<sup>3</sup>, pulse time 0.1 s, Reactant A partial pressure 100 Pa, sticking coefficient 0.01. </p> <p>Abbreviations: ALD = atomic layer deposition, LHAR = lateral high aspect ratio</p>
Structural modeling of hERG channel: Drug interactions using Rosetta
<p>Human Ether-a-go-go-Related Gene (hERG) encodes a potassium-selective voltage-gated ion channel essential for normal electrical activity in the heart but is also a major drug anti-target. Genetic hERG mutations and blockage of the channel pore by drugs can cause long QT syndrome (LQTS), which predisposes individuals to potentially deadly arrhythmias. However, not all hERG blocking drugs are pro-arrhythmic, and their differential affinities to discrete channel conformational states have been suggested to contribute to arrhythmogenicity. We used Rosetta electron density refinement and homology modeling to build structural models of open-state hERG channel wild-type (WT) and mutant variants (Y652A, F656A, and Y652A/F656A), and a closed state WT channel based on cryo-electron microscopy structures of hERG and EAG1 channels. These models were used as protein targets for molecular docking of charged and neutral forms of amiodarone, nifekalant, dofetilide, d/l-sotalol, flecainide, and moxifloxacin. We selected these drugs based on their different arrhythmogenic potentials and abilities to facilitate hERG current. Our docking studies and clustering provided atomistic structural insights into state-dependent drug–channel interactions l that play a key role in differentiating safe and harmful hERG blockers and can explain hERG channel facilitation through drug interactions with its open-state hydrophobic pockets.</p>
CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
<p>We present a cellular microscopic image dataset for investigating channel-adaptive models. We collected and pre-processed images from three publicly available sources: 1) the WTC-11 hiPSC dataset from the Allen Institute (Viana et al., 2023), 2) the Human Protein Atlas dataset (Thul et al., 2017), and 3) a combined Cell Painting dataset from the Broad Institute (Gustafsdottir et al., 2013; Bray et al., 2017; Way et al., 2021). These images contain 3, 4, or 5 channels with different cellular structures highlighted in each channel. The goal of this dataset is to facilitate the creation and evaluation of novel computer vision models that are invariant to channel numbers.</p>
Calcium channel model
<p>This dataset contains two data tables, include "channel_model.mat", "U_potential_3D_e(60).mat". The first data table is used to construct 2D and 3D calcium channel models. The model is constructed based on the model in the Ref. (Corry et al., 2001). The second data table gives the potential distribution in 3D calcium ion channels for Brownian dynamics calculation of ion transport in calcium ion channels.</p>
All data of the manuscript "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process" submitted to Geophysical Research Letters
<p>The data supports the manuscript entitled "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process”. Microsoft Notepad can open the *.txt files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at the first fork of positive or negative leader channels. A normal video player software can open Movies S1.avi, and it shows the entire development process of IC1 discharge.</p> <p>The data can be used freely for scientific purposes with the appropriate citation.</p>
Figure S4 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S4. – Spatial-temporal correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure S2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S2. – Spatial hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
Figure 2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 2. – Spatial correlation matrix at a 522 km2 scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure 11 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 11. – Scophthalmus rhombus from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.
Figure S5 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S5. – Spatial-temporal hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
Channel Measurements from Sitarjevec Mine for Propagation Modelling in a Cave Environment
<p>The dataset contains data collected during a measurement campaign using the <a href="https://www.qorvo.com/products/p/DW1000">DecaWave1000 </a>UWB pulse radio module in the <a href="https://rudniksitarjevec.si/en">Sitarjevec</a> mine in Litija, Slovenia. </p> <p>The measurements include complex channel impulse responses (CIRs) collected for 40 predefined positions with the following system parameters:</p> <ul> <li><strong>CONF1:</strong> Channel 4, DataRate 110, PRFR 16, PreambleLenght 4096, PreambleCode 7;</li> <li><strong>CONF2: </strong>Channel 4, DataRate 110, PRFR 64, PreambleLenght 4096, PreambleCode 17;</li> <li><strong>CONF3: </strong>Channel 7, DataRate 110, PRFR 16, PreambleLenght 4096, PreambleCode 7;</li> <li><strong>CONF4: </strong>Channel 7, DataRate 110, PRFR 64, PreambleLenght 4096, PreambleCode 17.</li> </ul> <p><strong>Measurement setup</strong></p> <p>Measurements were performed using the DecaWave1000 UWB pulse radio module in Segment 1 of the mine. Four points (Point A, Point B, Point C, and Point D) were selected for defining the positions of the nodes as shown in Figure 1. Point A denotes the main entrance, the entrance to the measurement segment is represented by Point B, and the beginning and the end of the measurement environment are denoted by Point C, and Point D, respectively. The length of the mine between Point A and Point D is approximately 60 m. </p> <p>The nodes were mounted on stands 1.4 m above the ground, which is not flat. They were placed on a straight line connecting Point C and Point D. The ANCHOR node was positioned in Point C, and the TAG node was moved along the reference line with a step of 1 m up to a maximum distance of 40 m. The nodes were always positioned so the antenna was centred above the reference line. </p> <p><strong>Folder structure</strong></p> <p>The measurements collected for each of the TAG node positions are stored in the corresponding folder. The folders are named m_n, with n=1, ..., 40 corresponding to the TAG's distance to the ANCHOR. Each folder contains four CSV files holding the CIR data measured using the four pre-defined configurations of the UWB system. Each CSV file stores CIRs of approximately 200 repeated measurements as separate records. The CIRs hold complex values of the 156 strongest multipath components corresponding to the taps in the DecaWave1000 accumulator, each of which represents a 1 ns sample interval. </p> <p><strong>Authors</strong></p> <p>Teodora Kocevska, Aleš Simončič, Grega Morano, Tomaž Javornik, and Andrej Hrovat</p> <p>Department of Communication Systems</p> <p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p> <p>teodora.kocevska@ijs.si</p>
Detection of a Timing Channel in an UPPAAL Model of a Cyber-Manufacturing System
<p>Model of a cyber-manufacturing system for the UPPAAL model checker, including a mitigation of a timing channel.</p>
Seismic modeling of bedload transport in a gravel-bed alluvial channel
<p>This repository publishes data for 4 flow events at the Arroyo de Los Pinos on the paper "Seismic modeling of bedload transport in a sandy gravel-bed alluvial channel".</p>
UNSUPERVISED MACHINE LEARNING AND VECTOR MODELS IN DESIGNING AND OPTIMIZATION OF TELECOM RETAIL CHANNELS
<p>This paper examines the use of unsupervised machine learning and vector models in the design and optimization of retail channels for telecommunications services. Unsupervised machine learning allows you to analyze and identify hidden patterns in large volumes of untagged data, which is especially important in a dynamically changing consumer market. Vector models, in turn, provide high accuracy of demand forecasting and inventory management, contributing to an increase in the efficiency of trading channels. The synergy of these technologies allows companies to improve customer experience, optimize operational processes and increase competitiveness in the market. The main focus of the work is on data processing methods, including correlation analysis, the use of the support vector machine (SVM) method and its adaptation to solve problems related to predicting customer behavior and optimizing logistics processes.</p>
ChannelLeveeModel: Decoupled Channel-levee Evolution Model for MATLAB
<h1>ChannelLeveeModel v1.0.1</h1> <p>This is the archive of a numerical model that decoupled channel bed and levee evolution with associated simulations, dataset, and figures using MATLAB</p> <p><strong>Features</strong></p> <ul> <li>Generate random weekly hydrographs and solve the divided channel method to compute the flooded water surface elevation (Lotter, 1933)</li> <li>Identify two flood styles: Front loading and Back loading</li> <li>Run advection-settling model (Han and Kim, 2022) for Front loading events and Ponded water model (Nicholas and Walling, 1996) for Back loading events</li> <li>Visualizes results with MATLAB plotting functions</li> </ul> <p><strong>File lists</strong></p> <ul> <li><strong>Main file:</strong> <code>DecoupledCLM.m</code></li> <li><strong>Function files:</strong> <code>HydraulicGeometry.m</code>, <code>Hydrograph.m</code>, <code>OverflowLevel.m</code>, <code>LeveeBimodal.m</code>, <code>Backloading_wellmix.m</code></li> <li><strong>Plotting files:</strong> <code>plot_BE.m</code>, <code>plot_ConfinedRelease.m</code>, <code>plot_figures.m</code></li> <li><strong>Output folder:</strong> <code>simulation_code/output/</code> contains example model simulations in <code>.m</code> format. and figures in <code>.pdf</code> format.</li> <li><code>README.md</code> and <code>LICENSE</code></li> </ul>
Model setup and output for 'Tidal conversion and dissipation at steep topography in a channel poleward of the critical latitude'
<p><strong>Data supplement to Hughes and Klymak 2019</strong></p> <p>Model input and output in a reduced form associated with the following paper:</p> <p><strong>Tidal conversion and dissipation at steep topography in a channel poleward of the critical latitude<br></strong><em>Journal of Physical Oceanography.</em> <a href="http://dx.doi.org/10.1175/JPO-D-18-0132.1">doi:10.1175/JPO-D-18-0132.1</a></p> <p><strong>Inputs</strong></p> <p>As described in Table 1 of the associated paper, there are three main sets of simulations. The input files for these sets are contained in their respective directories (`vary_width`, `vary_forcing`, and `vary_freq`). The python script that creates all of the necessary files is `gendata.py`. A fourth directory is titled `baroclinic_terms` and includes that simulation in which <em>u'</em> and <em>p'</em> are output at high temporal resolution.</p> <p>A key point regarding the input files is that for the vary width and vary forcing cases, a single simulation involves multiple channels. This lets me compile a single executable `mitgcmuv` with a Nx × Ny grid of 600 × 1280, which I divide up into the necessary number of channels by putting vertical walls in appropriate places. For the vary width cases, the 'narrow' simulations are all channels from 0.2 to 32 km and the 'wide' simulations are all wider channels. Once the simulation has run, I use netcdf tools (`ncks`) to extract the individual channels using the scripts in the `extract_scripts` directory.</p> <p>Most of the files in the `code` directories will be familiar to anyone that uses the MITgcm. An exception is the `energy_diagnostics_fill.F` (and `diagnostics_main_init.F` and `do_statevars_diags.F`, which have minor additions). The original, from `https://github.com/jklymak/MITgcmcode`, was modified slightly to suit this project.</p> <p><strong>Outputs</strong></p> <p>The results directory contains five subdirectories to be described in turn.</p> <p>Notes that in all cases, energy terms in the netCDF files do not include a factor of ρ. This was added in at the plotting stage.</p> <p>All simulations used Checkpoint67b and were run on Graham: https://docs.computecanada.ca/wiki/Graham.</p> <p><strong>vary_width</strong></p> <p>The majority of the files are of the form `obstacle_FFF_YY.nc` where `FFF` is $1000 ω/f$ and `YY` is the channel width in kilometres. These files contain the tidally averaged, depth-integrated energy diagnostics for the seventh tidal cycle at all points (<em>x, y</em>) within the energy control volume.</p> <p>There are also three files entitled `tophat_995_YY.nc`, which contain fields of <em>U</em>, <em>V</em>, and <em>T</em> (which gives density with α = 0.0002) at two levels. These fields are used as examples for weakly and strongly responding channels.</p> <p><br><strong>vary_forcing</strong></p> <p>These files are of the form `forcing_UU.nc` where `UU` is the deep-water tidal current amplitude <em>U_</em>0 in cm/s. They contain the same energy terms as for the vary width simulations.</p> <p><strong>vary_freq</strong></p> <p>These files are of the form `freq_FFF_fields.nc` and contain fields of <em>U</em>, <em>V</em>, and <em>T</em> at two levels. Energy terms are not included because the vary frequency simulations were only run to get estimates of the along-ridge wavelength.</p> <p><strong>baroclinic_terms</strong></p> <p>The single file within this directory contains <em>u'</em> and <em>p'</em> at a single <em>x</em> position every five minutes for four tidal cycles.</p> <p><strong>gaussian_26</strong></p> <p>This directory, named for its obstacle and width, contains <em>U</em>, <em>V</em>, and <em>T</em> at every grid point for a snapshot in time and another file with the corresponding snapshots of all energy terms.</p>
Numerical model and natural river data for the timescale analysis of meandering channel migration
<p>This is the archive of the numerical model and river centerline data used for analyzing the timescale related to meandering channel migration, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers.</p> <p>Running this model needs a MATLAB® software environment. The model can be launched by the wrapper scripts saved under the folder "software code/example wrappers". The wrapper script called "wrapper01a_channelOnly_runModel.m" is used to generate all model simulations in this study.</p>
A selective small-molecule agonist of G protein-gated inwardly-rectifying potassium channels reduces epileptiform activity in a mouse model of tumor associated epilepsy - Thy1-GCaMP Tumor Electrophysiology
Open the record for dataset details and reuse information.
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.