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746 results for “Powder”
Data for a publication "Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques"
<p><span><span>These data are published as part of the paper: “</span><span>Exploring the microst</span><span>ructure, mechanical properties, </span><span>and corrosion resistance of innovative bioabsorbable Zn-Mg-(S</span><span>i) alloys fabricated via powder </span><span>metallurgy techniques</span><span>” published in journal: “</span><span>Journal of Materials Research and Technology</span><span>”.</span></span><span> </span></p>
Hyperspectral X-ray CT datasets of an aluminium phantom containing three metal-based powders
<p><strong>General Data description:</strong></p> <p>This is a set of two hyperspectral (energy-resolved) X-ray CT projection datasets of a multi-phase phantom. It was acquired in a custom-built, laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction, following two hyperspectral scans of a metal, multi-phase phantom. The phantom consists of an external aluminium cylinder, with three holes, each filled with a different metal-based powder (CeO<sub>2</sub>, ZnO, Fe). Each powder provides a unique attenuation signal, with CeO<sub>2</sub> in particular producing a distinct spectral marker which can be measured by an energy-sensitive detector. Two identical scans were acquired, with only the exposure time per projection changed.</p> <p>Note: Zenodo Version 2 of this dataset contains the incorrect version of the 180s, 180 projection phantom dataset, if wishing to analyse the dataset used in the associated hyperspectral paper. This version (Version 3) contains the correct dataset from the paper.</p> <p><strong>File descriptions:</strong></p> <p>Contained is an image (.jpg) of the sample, along with five MATLAB (.mat) data files, as well as a single text (.txt) file. Where necessary, the files have been named to match the dataset they belong to, based on the different exposure times used for each dataset.</p> <p>Phantom_design_measurements.jpg contains a photograph of the physical phantom, combined with a diagram showing full sample measurements.</p> <p>Powder_phantom_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections for both scans.</p> <p>Powder_phantom_30s_30Proj_sinogram.mat contains the 4D sinogram constructed following flatfield normalisation of the raw projection data, where an exposure time of 30 s was used for each projection. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired during scanning. The total number of channels in the file is 200.</p> <p>Powder_phantom_180s_180Proj_sinogram.mat is the 4D sinogram for the dataset, when exposure times of 180 s were used for each projection, following flatfield normalisation. A discontinuity occurs at projection 137 due to an interruption in the scan procedure. The total number of channels in the file is 200.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning. This is the same for both datasets.</p> <p>FF_30s.mat contains the 4D flatfield data acquired when no sample was present, in the case of 30 s exposure times. This data was used to normalise the projection datasets, as the sinogram was constructed. The first 200 channels are included.</p> <p>FF_180s.mat contains the 4D flatfield data for the dataset where 180 s exposure times were used. The first 200 channels are included.</p>
XCT data of metallic feedstock powder with pore size analysis
<p><strong>X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)<sub>2</sub>(P,Si) alloy</strong></p> <p>The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction.<br> The powder material is an (Mn,Fe)<sub>2</sub>(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2].</p> <p><strong>Data acquisition</strong></p> <p>The data was acquired using a Zeiss Xradia 620 Versa X-ray microscope which provides the opportunity of optical magnification.</p> <table> <caption><strong>Tomographic imaging parameters</strong></caption> <tbody> <tr> <td>XCT system</td> <td>Zeiss Xradia 620 Versa</td> </tr> <tr> <td>Voltage</td> <td>80</td> <td>kV</td> </tr> <tr> <td>Power</td> <td>10</td> <td>W</td> </tr> <tr> <td>Source filtering</td> <td>"<em>LE2</em>" (system specific)</td> <td>-</td> </tr> <tr> <td>Source-object distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Object-detector distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Geom. magnification</td> <td>2</td> <td>-</td> </tr> <tr> <td>Optical magnification</td> <td>20</td> <td>-</td> </tr> <tr> <td>Native pixel size</td> <td>13.5</td> <td>µm</td> </tr> <tr> <td>Binning</td> <td>2x2</td> <td>px</td> </tr> <tr> <td>Voxel size</td> <td>0.68</td> <td>µm</td> </tr> <tr> <td>No. of projections per scan</td> <td>801</td> <td>1</td> </tr> <tr> <td>No. of scans</td> <td>4</td> <td>-</td> </tr> <tr> <td>Exposure time per projection</td> <td>5</td> <td>s</td> </tr> </tbody> </table> <p> </p> <p><strong>Projection data</strong> (801 single TIFF-files each):</p> <ul> <li>proj_00</li> <li>proj_01</li> <li>proj_02</li> <li>proj_03</li> </ul> <p><strong>Reconstructed data</strong>:</p> <ul> <li>raw-volume (MnFePSi-Powder_80kV_10W_LE2_20x_5s_801_0p68_BHC=2_Stitch_U16_966x1020x2916.raw + header.txt)</li> <li>analyzed data as Volume Graphics Studio MAX 3.4.5 project</li> </ul> <p><strong>Stitched 2D data</strong> (images stitched with ImageJ-Plugin described in [3]<strong>:</strong></p> <ul> <li>Stitched_0deg_Projections.tif</li> <li>Pores+Particles_Analysis.tif</li> </ul> <p> </p> <p>[1] G.-R. Jaenisch, U. Ewert, A. Waske, and A. Funk, “Radiographic Visibility Limit of Pores in Metal Powder for Additive Manufacturing,” Metals, vol. 10, no. 12, p. 1634, Dec. 2020. https://doi.org/10.3390/met10121634</p> <p>[2] X. Miao et al., “Printing (Mn,Fe)2(P,Si) magnetocaloric alloys for magnetic refrigeration applications,” J. Mater. Sci., vol. 55, no. 15, pp. 6660–6668, May 2020. https://doi.org/10.1007/s10853-020-04488-8</p> <p>[3] S. Preibisch, S. Saalfeld, and P. Tomancak, “Globally optimal stitching of tiled 3D microscopic image acquisitions,” Bioinformatics, vol. 25, no. 11, pp. 1463–1465, Jun. 2009.</p>
SEM images of SiO2 and juniper charcoal powders (pure samples and intimate binary mixtures).
<p><strong>Summary:</strong><br>These images are those from SiO2 and juniper charcoal (JChc) powder samples observed with a SEM. The powders were obtained from commercial sources and these samples were prepared at the Bern University (Switzerland) as part of the D-A-CH/CoPhyLab project (https://www.cophylab.space/index.php?id=home)<br><br><strong>Details:</strong><br>¤ SEM images from the sample of pure SiO2:<br> 001-St5607t0_00.tif<br> 002-St5607t0_01.tif<br> 003-St5607t0_03.tif:<br><br>¤ SEM images from the sample of pure juniper charcoal powder:<br> 004-St5607t6_00.tif<br> 005-St5607t6_01.tif<br> 006-St5607t6_04.tif<br> 007-St5607t6_05.tif<br> 008-St5607t6_07.tif:<br><br>¤ SEM images from the sample of the intimate mixture with 90% SiO2 - 10% JChc by mass:<br> 009-St5606t1_00.tif<br> 010-St5607t1_01.tif:</p><p>¤ SEM images from the sample of the intimate mixture with 70% SiO2 - 30% JChc by mass:<br> 011-St5607t2_00.tif<br> 012-St5607t2_02.tif<br><br>¤ Zoom-in on the 70% SiO2 - 30% JChc sample at lignin fragment peppered with smaller JChc and SiO2 particles and agglomerates:<br> 013-St5607t2_03.tif:<br><br>¤ Zoom-in on the 70% SiO2 - 30% JChc sample with apparent large fragment of lignin structure:<br> 014-St5607t2_07.tif:<br><br>¤ SEM images from the sample of the intimate mixture with 50% SiO2 - 50% JChc by mass:<br> 015-St5607t3_00_St5606t3_00.tif<br> 016-St5607t3_03.tif<br><br>¤ SEM images from the sample of the intimate mixture with 30% SiO2 - 70% JChc by mass:<br> 017-St5607t4_00.tif<br> 018-St5607t4_01.tif, <br><br>¤ SEM images from the sample of the intimate mixture with 10% SiO2 - 90% JChc by mass:<br> 019-St5607t5_00.tif<br><br><strong>Addendum:</strong><br>These SEM images are associated with the spectroscopic and photometric data available at the following addresses:<br> https://doi.org/10.26302/SSHADE/EXPERIMENT_CF_20200723_000<br> https://doi.org/10.26302/SSHADE/EXPERIMENT_CF_20200813_000</p>
1.3A (Ge337) calibration data for new Ge115 monochromator installed on Echidna Neutron Powder Diffraction Instrument
<p>In early October 2024 the Echidna neutron powder instrument located at the OPAL reactor, ANSTO, installed a new monochromator with Ge115 cut. The present calibration data were collected shortly afterwards from a standard LaB6 sample in a 6mm diameter Vanadium can. The instrument was set to 140 degrees takeoff angle and monochromator angle 85.08 degrees, corresponding to the Ge337 reflection. Raw data in NeXus format are contained in <strong>ECH0034261.nx.hdf</strong>. These data were corrected for variable detector response using the information in <strong>eff_2024-10-06.cif</strong> and pixel vertical positions adjusted according to the table in <strong>vertical_offsets_2024-10-06.txt. </strong>Deviations from the ideal detector 1.25 degree angular spacing were applied using <strong>echidna-Apr2018.ang</strong>. The detector response was then recorrected based on overlapping measurements using the algorithm described in <a href="https://doi.org/10.1107/S1600576718014048">Avdeev and Hester (2018)</a> resulting in a 1D pattern suitable for fitting wavelength and peak shapes. This 1D pattern is provided here as a plain table (<strong>ECH0034261_LaB6.xyd</strong>) and as a pdCIF file (<strong>ECH0034261_LaB6.cif</strong>) including metadata on data collection and reduction. Details of data reduction are described in the above paper, and the data reduction routines used are included in the <a href="https://github.com/Gumtree/Echidna_scripts">Gumtree package as python code</a>.</p>
An Effective Activation Method for Industrially Produced TiFeMn Powder for Hydrogen Storage [Dataset related to publication]
<p>Data type: XRD patterns; SEM micrographs and EDX maps; particle size distributions; atomic concentrations; hydrogen loading profiles; kinetic models; volume expansions. </p> <p>Data format: *.opj; *.tif.</p> <p>Origin of the data: laboratory equipment from Hereon (XRD, SEM, PSD Analyzer, BET, XPS, Sievert apparatus) and UniPV (SEM).</p> <p>Software needed to plot the data: folders need to be unzipped, Origin.</p>
Propagation Measurements and Analyses at 28GHz on NSF POWDER
<p><strong>IEEE ICC 2023: </strong>Propagation Measurements and Analyses at 28GHz via an Autonomous Beam-Steering Platform</p> <p> </p><blockquote> <p>This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28-GHz V2X propagation modeling. A pan-and-tilt subsystem facilitates uninhibited rotational mobility along the yaw and pitch axes, driven by open-loop servo units and orchestrated via inertial motion controllers. A geo-positioning subsystem augmented in accuracy by real-time kinematics enables navigation events to be shared between a transmitter and receiver over an Apache Kafka messaging middleware framework with fault tolerance. Herein, our system demonstrates a 3D geo-positioning accuracy of 17 cm, an average principal axes positioning accuracy of 1.1 degrees, and an average tracking response time of 27.8 ms. Crucially, fully autonomous antenna alignment and tracking facilitates continuous series of measurements, a unique yet critical necessity for millimeter wave channel modeling in vehicular networks. The power-delay profiles, collected along routes spanning urban and suburban neighborhoods on the NSF POWDER testbed, are used in pathloss evaluations involving the 3GPP TR38.901 and ITU M.2135 standards. Empirically, we demonstrate that these models fail to accurately capture the 28-GHz pathloss behavior in urban foliage and suburban radio environments. In addition to RMS direction-spread analyses for angles-of-arrival via the SAGE algorithm, we perform signal decoherence studies wherein we derive exponential characteristics of the spatial autocorrelation coefficient under distance and alignment effects.</p> </blockquote> <p></p> <p><strong>Note</strong>: <em>This is a smaller version of our dataset. The original dataset collected on the NSF POWDER testbed is approximately 400 GB. Due to Zenodo's size restrictions, the data uploaded here contains only a few of our calibration (USRP 76 dB gain) and measurement logs (fully-autonomous V2X routes onsite). To gain access to our complete dataset, please contact the authors at <bkeshav1@asu.edu> or <zhan1472@purdue.edu>. Additional measurements in our full dataset include USRP 0 dB calibration results; fully-autonomous urban-stadium-van, urban-campus-cart, and urban-presidents-circle-full-van routes; and semi-autonomous (and manual) urban-garage-cart and urban-campus-cart routes.</em></p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
The intermittency regions of powder snow avalanches [Data-set]
<p>This data repository contains the data-sets presented in the publication:</p> <p>Sovilla, B., McElwaine, J. N., & Köhler, A. (2018). The intermittency regions of powder snow avalanches. Journal of Geophysical Research: Earth Surface, 123, <a href="https://doi.org/10.1029/2018JF004678">https://doi.org/10.1029/2018JF004678</a>.</p> <p>This data set should be cited, together with the publication, as a:</p> <p>B. Sovilla, J. N. McElwaine, and A. Köhler (2018), The intermittency regions of powder snow avalanches [Data set]. Zenodo. <a href="https://doi.org/10.5281/">https://doi.org/10.5281/</a> zenodo.1415456.</p> <p>Information on the data can be found in the Readme file or can be obtained by writing an e-mail at: avalanche.data@slf.ch.</p>
Thermal decomposition data of uranium containing microspheres produced via internal gelation and ammonium diuranate powder
<p>A combination of simultaneous thermal analysis, evolved gas analysis and non-ambient XRD techniques was used to characterise and investigate the thermal decomposition behaviour in the NH<sub>3</sub> − UO<sub>3</sub> − H<sub>2</sub>O class of materials.</p> <p>One compound was prepared according to a typical ammonium diuranate precipitation reaction, and could be identified as 3UO<sub>3</sub>·NH<sub>3</sub>·5H<sub>2</sub>O. Microspheres prepared by the sol-gel method via internal gelation were associated to the composition 3UO<sub>3</sub>·2NH<sub>3</sub>·4H<sub>2</sub>O under the specified conditions.</p> <p>The products were analysed using the techniques listed below, the resulting data are part of this dataset.</p> <ul> <li>TGA, combined with EGA-MS (<em>T<sub>max</sub></em> = 1300 °C, heating rate = 2 °C/min)</li> <li>TG-DSC, combined with EGA-MS (<em>T<sub>max</sub></em> = 1300 °C, heating rate = 10 °C/min)</li> <li>ambient XRD (dried products after synthesis)</li> <li><em>in-situ</em> high temperature XRD (including initial and final scans, taken at 35 °C) <ul> <li><em>T<sub>max</sub></em> for 3UO<sub>3</sub>·NH<sub>3</sub>·5H<sub>2</sub>O = 1300 °C; <em>T<sub>max</sub></em> for 3UO<sub>3</sub>·2NH<sub>3</sub>·4H<sub>2</sub>O = 650 °C</li> <li>Samples measured directly on a Pt/Rh heating strip (Pt/Rh phase visible in patterns, blank scan included)</li> </ul> </li> </ul>
Data set for "Quantification of amorphous siliceous fly ash in hydrating blended cement pastes by X-ray powder diffraction"
<p>The main data is XRD patterns originally collected as xrdml and converted into rd format.</p> <p>The data set for the manuscript:</p> <p>Quantification of amorphous siliceous fly ash in hydrating blended cement pastes by X-ray powder diffraction</p> <p>Xuerun Li<sup>a</sup>, Ruben Snellings<sup>b</sup> and Karen L. Scrivener<sup>a</sup></p> <p><sup>a</sup>Laboratory of Construction Materials, Swiss Federal Institute of Technology in Lausanne (EPFL), Station 12, CH-1015 Lausanne, Switzerland</p> <p><sup>b</sup>Sustainable Materials Management, Flemish Institute of Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium<br> </p>
Zinc-doped Zeolite 13X, Partially Zinc-doped Zeolite 13X, and pure Zeolite 13X X-Ray Powder Diffraction
<p>This repository holds X-Ray Powder Diffraction data for three different Zeolite 13X samples to allow for characterisation of the diffraction pattern for zinc-doped Zeolite 13X to perform accurate phase-based diffraction-tomography reconstructions using the data from 10.5281/zenodo.13329639.</p> <p>The three samples are fully Zinc-doped Zeolite 13X, partially Zinc-doped 13X, and pure Zeolite 13X. An empty borosilicate glass capillary is provided to remove scattering from the capillary the samples were housed in.</p> <p>Data in all instances is provided in ASCII format as a .asc file. A basic jupyter notebook is provided to perform the analysis used to determine powder peaks.</p> <p>Data was collected on a Rigaku SmartLab Diffractometer with a copper x-ray source of wavelength 1.5406 angstroms at the ISIS Neutron & Muon Source Materials Characterisation Lab.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p>
Nitrite content in powders from plasma-activated egg whites
<p>These are source data collected to determine the effects of three quantitative independent variables—plasma treatment time, the distance of the plasma source from the surface of egg whites, and drying temperature—on the nitrite concentration (mg·kg⁻¹) in powdered plasma-treated egg whites sourced from both hens and ostriches. The experimental ranges for these variables were as follows: plasma treatment time (20-180 minutes), plasma source distance (10-30 cm), and drying temperature (40-50 °C).</p> <p>The analysis of nitrite content in all samples was conducted according to the method of Lee et al. (2018) with some modifications.</p> <p>A design comprising 20 experimental runs was generated using Design Expert (version 11) software (Stat-Ease, Inc., USA).</p>
Flow behaviour of magnetic steel powder
<p>These are the data used in the article "Flow behaviour of magnetic steel powder". This dataset includes:</p> <ul> <li>Optical micrographs used to determine the mean size and circularity in each size class after sieving <ul> <li>Scale bars are included for two images and may be used to determine the scale of all micrographs included - all images were taken using the same microscope, camera and software. All images are as-recorded.</li> </ul> </li> <li>X-ray diffractograms used to determine the fraction of martensite present in each sample <ul> <li>Calibration data for the diffraction instrument are included</li> </ul> </li> <li>Vibrating sample magnetometry data, used to determine the saturation and remanent magnetisation.</li> <li>Shear cell metrics derived automatically form the shear cell tests</li> <li>Hall flow times, both with and without drying</li> <li>Angle of repose dat</li> <li>Videos of all angle of repose tests</li> </ul> <p>Other data can be provided on request.</p>
Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties
<p>This dataset contains the data from the publication</p> <p><strong>"Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties"</strong></p> <p> </p> <p> </p>
Background optimization of powder electron diffraction to implement e-PDF technique and study the local structure of iron oxide nanocrystals
<p>The local structural characterization of iron oxide nanoparticles is explored using a total scattering analysis method known as Pair Distribution Function (PDF) (also known as Reduced Density Function) profiles derived from background corrected powder electron diffraction patterns. Due to the strong coulombic interaction between the electron beam and the sample, electron diffraction generally leads to multiple scattering, causing redistribution of intensities towards higher scattering angles and an increased background in the diffraction profile. In addition to this, the electron-specimen interaction gives rise to an undesirable inelastic scattering signal that contributes primarily to the background. The present work demonstrates the efficacy of a pre-treatment of the underlying complex background function, which is a combination of both incoherent multiple and inelastic scatterings that cannot be identical for different electron beam energies. Therefore, two different background subtraction approaches are proposed for the electron diffraction patterns acquired at 80 kV and 300 kV beam energies. From the least square refinement (small-box modelling), both approaches are found to be very promising, leading to a successful implementation of the e-PDF technique to study the local structure of the considered nanomaterial.</p>
Data for: Processability of Mg-Gd powder via friction extrusion
<p>This dataset contains measurement data, machine logs as well as microstructure and overview images for the publication "Processability of Mg-Gd powder via friction extrusion".</p>
All-inorganic micrometric CsPbBr3:Yb3+ powder as a multifunctional material for photovoltaics and optical thermometry: structural and optical characterization
<p>Halide perovskites have been studied very intensively by researchers during the last decade. Development of these materials has improved their unique optoelectrical properties reaching even higher standards making them promising candidates for photovoltaic applications. It should be noted that most inorganic halide perovskites obtained to date are synthesized using organic solvents in the form of nanosized colloids. Here, a low-temperature synthesis protocol for the preparation of microcrystalline CsPbBr<sub>3</sub> perovskite powder doped with Yb<sup>3+</sup> ions is proposed. The structural and photoluminescence features of the studied material have been thoroughly investigated and described. It turned out that the excitation of the CsPbBr<sub>3</sub>:Yb<sup>3+</sup> perovskite with a 375 nm wavelength leads to spontaneous luminescence of excitons and Yb<sup>3+ </sup>ions. Hence, the use of CsPbBr<sub>3</sub>:Yb<sup>3+</sup> as a luminescent thermometer or an additional absorbing layer on a solar cell surface is possible. The latter application may result in an increase in the conversion efficiency of the cell. In order to verify this, such a layer was prepared and installed on a commercial silicon solar cell. Its photovoltaic properties have been investigated by the measurements of current-voltage characteristics with 1-sun illumination and spectral characteristics of external quantum efficiency.</p>
Figure 7 in An illustrated key to powder post beetles (Coleoptera, Bostrichidae) associated with rubberwood in Thailand, with new records and a checklist of species found in Southern Thailand
Figure 7. Heterobostrychus aequalis (Waterhouse, 1884). Dorsal view of female a and male b lateral view of male elytral declivity c intercoxal process of the first abdominal ventrite d.
Figure 3 in An illustrated key to powder post beetles (Coleoptera, Bostrichidae) associated with rubberwood in Thailand, with new records and a checklist of species found in Southern Thailand
Figure 3. Dorsal views of Minthea reticulata Lesne, 1931 a Minthea rugicollis (Walker, 1858) b and Lyctoxylon dentatum (Pascoe, 1866) c.
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.