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4,578 results for “Assistance”
video_assistant_blowing_1
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_assistant_marvelling
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
video_assistant_blowing_5
This is the process of making a carafe Bontemps. The step depicted is called "Annealing"(from the Mingei project).
Assessing the Influence of Zeolite Composition on Oxygen-Bridged Diamino Dicopper(II) Complexes in Cu-CHA DeNOx Catalysts by Machine Learning-Assisted X‑ray Absorption Spectroscopy
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements and related elaboration from Figures 1-4 of the corresponding article</li> <li>Files are with filename extensions: <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <p>In situ XANES and EXAFS data were collected at the BM23 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) in a Microtomo reactor cell; measured Cu-CHA samples are indicated in the following with “Cu/Al”-“Si/Al” labels</p> <ul> <li><strong>fig_01_XANES:</strong> Normalized Cu K-edge XANES for Cu-CHA samples 0.1-5; 0.5-15; 0.6-29, collected at 200 °C after pretreatment in O<sub>2</sub>, reduction in NO+NH<sub>3</sub> and subsequent oxidation in O<sub>2</sub>.</li> <li><strong>fig_02_Conversion:</strong> NOx conversion in the 150−500 °C temperature range for Cu-CHA samples 0.1-5, 0.5-15, 0.6-29; TOF at 200 °C versus fraction of Cu(I) from XANES LCF after oxidation and fraction of Cu(I) from XANES LCF after oxidation versus Cu density for the same catalysts.</li> <li><strong>fig_03_EXAFS_FT_WT:</strong> Magnitude of experimental EXAFS spectra, obtained by Fourier transforming k<sup>2</sup>χ(k) spectra in the 2.4−12.0 Å<sup>−1</sup> range for Cu-CHA samples 0.1-5, 0.5-15, 0.6-29 after reduction in NO+NH<sub>3</sub> and subsequent oxidation in O<sub>2</sub>; corresponding EXAFS WT maps magnified in high-R range (2-4 Å), obtained using a Morlet WT with parameters (σ=1, η=7).</li> <li><strong>fig_04_EXAFS_MLfit:</strong> Magnitude of experimental and best fit EXAFS spectra, obtained by Fourier transforming k<sup>2</sup>χ(k) spectra in the 2.4−12.0 Å<sup>−1</sup> range for Cu-CHA samples 0.1-5, 0.5-15, 0.6-29 after oxidation in O<sub>2</sub>. Scaled components 1 ([Cu<sup>I</sup>(NH<sub>3</sub>)<sup>2</sup>]<sup>+</sup>), 2 and 3 (planar and bent μ-η<sup>2</sup>,η<sup>2</sup>-peroxo diamino dicopper(II)) isolated by ML-assisted EXAFS fitting are also reported, vertically translated.</li> <li><strong>Information on</strong>:</li> <li>specialized abbreviations: <strong>CHA</strong>– chabazite; <strong>XANES</strong>– X-ray absorption near edge structure, <strong>EXAFS</strong> – Extended X-ray absorption fine structure; <strong>LCF</strong> – Linear Combination Fit;<strong> FT</strong>: Fourier Transform; <strong>WT</strong> – Wavelet Transform; <strong>ML</strong> – Machine Learning; <strong>TOF</strong> – Turn Over Frequency;</li> </ul>
Raw Data for the Protocol: Antibody-Assisted Selective Isolation of Purkinje Cell Nuclei
<p>Sun1/sfGFP+, Pcp2-Cre+ and Sun1/sfGFP+, Pcp2-Cre- cryosectioned cerebella immunostained for the Myc tag (files 3037, 3046), which is fused to the GFP protein, Calbindin (files 3038, 3047) and Hoechst (files 3036, 3045). </p> <p> </p> <p>Original uncropped images from western blot analysis of TOM20, Histone H3, and GAPDH.</p>
Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach
<p>This is the dataset used in the analysis "Identification of Southeast Asian <em>Anopheles </em>mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach". It consists in 3584 raw mass spectra (mzXML file format) of the head of 359 <em>Anopheles </em>mosquito specimens collected in Karen (Kayin state) in Myanmar between 2020 and 2022 and associated metadata (Rdata file format) including sample information (taxonomy.Rdata) and spectra information (metadata.Rdata).</p>
Supporting data for "Fundamental limitations of cavity-assisted atom interferometry"
<p>Supporting data with code to generate Fig. 2 Cavity-induced deformation of a Gaussian input. Publication: DOI:https://doi.org/10.1103/PhysRevA.96.053820</p> <p>arXiv:1710.02448</p> <p>This dataset contains a zip file with raw data sets of all relevant measurements to plot figure 2.</p> <p>Figure 2. Envelope functions of the intracavity field for a 1 m cavity injected with<br> a 1μs pulse for different cavity finesses. All areas are normalized<br> to the input pulse area for comparison. When the pulse duration is<br> comparable to the photon lifetime of the cavity, its envelope function<br> is elongated. Inset: Envelopes without normalization.</p> <p> </p> <p>Further data and information are available from Miguel Dovale <mdovale@star.sr.bham.ac.uk> at reasonable request.</p> <p>School of Physics and Astronomy and Institute of Gravitational Wave Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom</p>
Dataset: Utilization of Novel (KNbO3)1-x(Ba2FeNbO6)x (x = 0.1, 0.2, 0.3) Solid Solutions for Efficient Photo-assisted Fenton Degradation of Methylene Blue Dye
<p>Supplemental information containing the inputs and outputs of all DFT calculations performed as part of this work.</p> <p>This archive contains the following scripts:</p> <ul> <li>defects_workup.py: a Python script for processing all calculations in a given folder. It extracts the total energy, estimated SCF accuracy (for non-converged results), and convergence status (true/false) for all cases found in each subfolder. For converged calculations, the mean Ba-Ba distance and its standard deviation as well as the mean Ba-Fe distance and its standard deviation is calculated. </li> <li>bands_plotter.ipynb: a Jupyter notebook for band structure analysis.</li> <li>ase_rdf.ipynb: a Jupyter notebook for bond distance vs energy analysis</li> </ul> <p>Furthermore, the following data is included:</p> <ul> <li>3x2x2.json: the output json file generated for the 3x2x2 dataset using defect_workup.py</li> <li>3x2x2.7z: a compressed folder containing the 3x2x2 dataset with QE input and output files.</li> <li>3x2x2-v2.7z: a compressed dataset containing some supplementary calculations used in band plotting.</li> </ul>
Structure and composition and carbon Stocks of woody plant community in assisted and unassisted ecological succession in a Tamaulipan thornscrub, Mexico
<p>In November of 2017, the structure and composition of woody plant communities were investigated through a floristic composition and diversity evaluation on three areas: a control area, an assisted ecological succession area and an unassisted ecological succession area.</p>
Supplementary material for "AR-assisted timber drilling with smart retrofitted tools"
<p>The dataset contains point clouds from 3D scans, reconstructed 3D data, and statistical data analysis, which are intended as complementary material for the publication "AR-assisted timber drilling with smart retrofitted tools". </p>
Initial Evaluation Data for SimIMA: A Virtual Simulink Intelligent Modeling Assistant
<p>The following is our initial dataset and evaluation materials corresponding to our development and evaluation of the <a href="https://zenodo.org/record/5123570">Simulink Intelligent Modeling Assistant (SimIMA)</a>. </p> <p>We evaluate SimGestion and SimXample separately. </p> <ul> <li>The directory SimGestion-evaluation contains datasets, evaluation scripts, and log files involved in the evaluation of SimGestion. </li> <li>The directory SimXample-evaluation contains datasets, evaluation scripts, and log files involved in the evaluation of SimXample. </li> </ul> <p>This is v1.0, which is the evaluation associated with the thesis "INTELLIGENT SIMULINK MODELING ASSISTANCE VIA MODEL CLONES AND MACHINE LEARNING" by Bhisma Adhikari @ Miami University , 2021. </p>
NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH
<p>This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009).</p> <p>The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm<sup>-1</sup> and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances.</p> <p>In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior.</p> <p>The file format is netcdf and the file naming conventions are</p> <p>NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p> </p> <p>The time stamp of all data is in UTC.</p> <p>Selected basic variables are (many more provided):</p> <p> </p> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Dimension</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>base_time</p> </td> <td> <p>Single value</p> </td> <td> <p>Seconds (since 00 UTC 1 Jan 1970)</p> </td> </tr> <tr> <td> <p>time_offset</p> </td> <td> <p>Time</p> </td> <td> <p>Second (since base_time)</p> </td> </tr> <tr> <td> <p>hour</p> </td> <td> <p>Time</p> </td> <td> <p>Hours since 00UTC this day</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p>Height</p> </td> <td> <p>km AGL</p> </td> </tr> <tr> <td> <p><strong>temperature </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, temperature</p> </td> </tr> <tr> <td> <p><strong>waterVapor </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, water vapor mixing ratio</p> </td> </tr> <tr> <td> <p>theta</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, potential temperature</p> </td> </tr> <tr> <td> <p>pressure</p> </td> <td> <p>Time, Height</p> </td> <td> <p>hPa, pressure</p> </td> </tr> <tr> <td> <p>rh</p> </td> <td> <p>Time, Height</p> </td> <td> <p>%, relative humidity</p> </td> </tr> <tr> <td> <p>dewpt</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, dew point temperature</p> </td> </tr> <tr> <td> <p>thetae</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, equivalent potential temperature</p> </td> </tr> <tr> <td> <p>sigma_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, 1-sigma uncertainty temperature</p> </td> </tr> <tr> <td> <p>sigma_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, 1-sigma uncertainty water vapor</p> </td> </tr> <tr> <td> <p>cdfs_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for temperature</p> </td> </tr> <tr> <td> <p>cdfs_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for water vapor</p> </td> </tr> </tbody> </table> <p>Bold variables are the main retrieved profiles, from which the other variables are derived.</p> <p>Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model.</p> <p>Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. </p> <p><strong>References: </strong></p> <p>Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17.</p> <p>Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1.</p> <p>Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.</p> <p>Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.</p>
ASSIST-IOT Open Call Project RAZOR DATASETS (INSIGHIO)
<p>Example datasets for road anomaly detection produced in the context of RAZOR Open Call ASSIST-IoT Project, carried out by INSIGHIO.</p>
Supplementary Data for "Substrate-Assisted Mechanism for the Degradation of N-glycans by a Gut Bacterial Mannoside Phosphorylase"
<p>This dataset contains atomic coordinates of the molecular dynamics simulations described in "Substrate-Assisted Mechanism for the Degradation of N-glycans by a Gut Bacterial Mannoside Phosphorylase" by M. Alfonso-Prieto, I. Cuxart, G. Potocki-Véronèse, I. André and C. Rovira, published in ACS Catalysis (https://doi.org/10.1021/acscatal.3c00451). Further details on the setup of the simulations can be found in the Supplementary Information of the article. </p> <p>If you use this dataset, please cite this zenodo upload (https://doi.org/10.5281/zenodo.7704778), as well as the the original journal article (https://doi.org/10.1021/acscatal.3c00451). </p> <p>This dataset is organized in the following folders:</p> <p><strong>Snapshots_Figures_Main_Text.zip</strong>, that contains a README.txt file and:</p> <p><strong>- Figure_3</strong> contains representative structures (atomic coordinates) of the hexameric form of UhgbMP in complex with 3 different disaccharide molecules, Man-b-(1,4)-GlcNAc, Man-b-(1,4)-Glc and Man-b-(1,4)-Man.</p> <p><strong>- Figure_4</strong> contains representative structures (atomic coordinates) of the hexameric form of UhgbMP at the three minima observed along the reaction coordinate corresponding to phosphorolysis of the disaccharide Man-b-(1,4)-GlcNAc: Michaelis complex (MC), transition state (TS) and product (P) complex.</p> <p>Files in this dataset are in PDB format. For all structures, the solvation box (water and ions) has been stripped to reduce file size. See README.txt inside <a href="https://zenodo.org/api/files/f3836540-b7b6-4820-87b3-7fa5dff7840c/Snapshots_Figures_Main_Text.zip">Snapshots_Figures_Main_Text.zip </a>for more information.</p>
Photonic crystals with rainbow colors by centrifugation-assisted assembly of colloidal lignin nanoparticles
<p>Source data (CSV files) associated with the publication titled <strong>Photonic crystals with rainbow colors by </strong><strong>centrifugation-assisted assembly </strong><strong>of colloidal lignin nanoparticles</strong>.</p>
Supplementary codes and datasets for "Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials"
<p>This repository provides data and codes for manuscript “Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials” by M. Doškář, M. Somr, R. Hlůžek, J. Havelka, J. Novák, and J. Zeman, published first as a preprint <a href="https://arxiv.org/abs/2305.09280">arXiv:2305.09280</a> at arXiv.org; see the actual description of the Zenodo entry for the latest reference.</p> <p>This repository contains:</p> <ol> <li>MATLAB and C++ source codes for combinatorial design and numerical analyses (folder <code>./numerics/</code>),</li> <li>experimental data (folder <code>./experiments/</code>),</li> <li>3D models of parts used in robotic-assembly (folder <code>./models/</code>),</li> <li>a control script for robotic assembly (folder <code>./robotics/</code>).</li> </ol> <p><strong>Numerics</strong></p> <p>All simulations were performed with an in-house MATLAB code, which extends the finite element toolbox for finite strain calculations accompanying the work of <a href="https://doi.org/10.1016/j.cma.2020.113333">van Bree, S. E. H. M., Rokoš, O., Peerlings, R. H. J., Doškář, M., & Geers, M. G. D. (2020). A Newton solver for micromorphic computational homogenization enabling multiscale buckling analysis of pattern-transforming metamaterials. Computer Methods in Applied Mechanics and Engineering, 372, 113333</a>. In particular, this snapshot corresponds to a cleaned-up version (excluding files unrelated to the publications) of commit <code>21cfc2e9</code>.</p> <p>The MATLAB codebase contains MEX files written in C++ to accelerate selected procedures. In order to run any code, these MEX files must be compiled first. We use CMake build automation, with the main <code>CMakeLists.txt</code> located in <code>./numerics/mex</code>.</p> <p>Combinatorial search was performed by the <code>RUN_modular_exploration.m</code> script; see definition of problems with the script. The results of the enumerations, stored in <code>./dat/exploration</code>, were analysed with <code>POST_modular_S_v3.m</code>, identifying layouts leading to the extreme (min/max) tilt angles.</p> <p>Comparison against experimental measurements was facilitated by a series of scripts <code>POST_DIC_{...}.m</code>. First, run <code>POST_DIC_step1_extract_points_in_mesh.m</code> to identify locations.mat. Next, post-process extensometer data with <code>POST_DIC_step2_merge_extensometer_data.m</code>, and use <code>POST_DIC_step3_impose_extracted_BC.m</code> to parse DIC results in a format suitable for imposing BC later in this script. Finally, comparison between experimental and computed displacements is provided by <code>POST_DIC_step4_modular_comparison_experiments.m</code>. (Note that the particular files need to be manually provided in the “Compute deformation process” part of <code>POST_DIC_step4_modular_comparison_experiments.m</code>.)</p> <p><strong>Experimental data</strong></p> <p>This folder contains data from (i) an unixaial tension test of a dogbone specimen (both from a MTS loading machine and DIC data) and (ii) two measurement sessions extracting the L-shape domain responses using DIC (<code>20_11_30 - Hluzek_Elka_newassemblyplan</code> and <code>21_04_12 - Hluzek_ Elka_quarters</code> with lower loading threshold). For post-processing, see the above-mentioned <code>POST_DIC_{...}.m</code> scripts. <code>*.mat</code> files present directly in <code>./experiments/</code> folder were obtained and are need by those scripts.</p> <p><strong>3D models</strong></p> <p>The folder contains geometrical models for individual parts needed for robot-assisted assembly of module molds for casting. This includes:</p> <ol> <li>a silo extension to store more tiles (file <code>silo_extension.stl</code>),</li> <li>formwork modules around the main structure for the purpose of casting silicone (file <code>tile_formwork.stl</code>),</li> <li>all types of tiles for the inside structure (file <code>tile_inside_types.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_1.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_2.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_3.stl</code>),</li> <li>connection for spacers (file <code>yumi_base_4.stl</code>),</li> <li>spacer holding a silo and the build plate (file <code>yumi_base_5.stl</code>),</li> <li>YuMi grippers with extensions to hold the tiles (file <code>grippers_extend.st</code>).</li> </ol> <p><strong>Robotics</strong></p> <p>The folder contains a single file with a script created in RobotStudio (RobotWare Version: 6.08.01.00, SmartGripper Version: 3.55.0000.00) to assemble the plan with YuMi IRB 14000-0.5/0.5 left hand.</p> <p><strong>Acknowledgement</strong></p> <p>The related research, experiments, and code development were supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</p>
User Study Data for Paper "A case study in designing trustworthy interactions: implications for socially assistive robotics"
<p>Experimental data collected for the user study described in Frontiers paper "A case study in designing trustworthy interactions: implications for socially assistive robotics" by Mengyu Zhong et al. Citation: <i>Zhong, Mengyu, et al. "A case study in designing trustworthy interactions: implications for socially assistive robotics." Frontiers in Computer Science 5.1152532 (2023). </i></p>
LOFAR dataset for deep learning assisted data Inspection for radio astronomy
<p>This dataset is used for the training of the magnitude and phase-based VAE in the paper entitled "Deep learning assisted data inspection for radio astronomy".</p> <p>For uploading purposes the dataset has been separated into 4 different .zip files. In order to use this dataset each of the zip files should be extracted into a single directory so that the training can be performed on all files at the same time. <br> <br> More information can be found on <a href="https://github.com/mesarcik/DL4DI">the project github repository</a>. </p>
Dataset of the scientific paper "A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients" (Front. Aging Neurosci.)
<p> There are three files with the following information:<br> - data_2d.bin, binary file with information of the different parameters of the nine subjects during 2d tasks<br> - data_3d.bin, binary file with information of the different parameters of the nine subjects during 3d tasks<br> - survey.bin, binary file with the score of the System Usability Scale (SUS) survey of each subject</p>
Machine learning and multi-layer molecular network-assisted screening uncovers unknown compounds in the fentanyl family
<p>These LC-HRMS data was collected in study of Fentanyl-Hunte. All source codes along with a user manual are available for scientific research purposes at https://github.com/FangLabNTU/Fentanyl-Hunter.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.