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96 results for “material properties”
Dataset for "The influence of the amount of recycled material on the microstructure and properties of the second generation of single-domain YBCO bulks"
<p>The development of a recycling process for various REBCO materials is crucial considering both environmental sustainability and economic efficiency, particularly in light of the upcoming large-scale applications. In this paper, a novel general recycling process based on chemical dissolution was employed to grow REBCO bulks; recycled material obtained by recycling defective YBCO single-domain bulks was added (15 wt. %, 30 wt. % and 45 wt. %) to raw materials to prepare recycled YBCO precursor powder. Subsequently, recycled single-domain YBCO bulks were produced using Top-Seeded Melt Growth. The waste recycling related to of single-domain bulks growth was chosen, as it represents the most challenging form of waste in the context of REBCO superconductor production. The properties and microstructure of recycled bulks were further analyzed to determine the influence of the amount of recycled material used and compared to commercially produced bulks. Single-domain YBCO bulks were grown successfully from the recycled precursor powder. Furthermore, it was found that their properties could be tuned by varying the amount of the added recycled powder, allowing the use of vast amounts of REBCO waste for the preparation of bulks, when achieving the best possible properties is not essential for a given application. Given that the underlying recycling process is designed to work for all REBCO systems and any form of waste, it has significant implications for the sustainability and cost-effectiveness of REBCO superconductor production. </p>
Geochemical characterization and material properties of coastal permafrost near Drew Point, Alaska
Permafrost cores (4.5-7.5 m long) were collected April 10th-19th, 2018, along a geomorphic gradient near Drew Point, Alaska to characterize active layer and permafrost geochemistry and material properties. Cores were collected from a young drained lake basin, an ancient drained lake basin, and primary surface that has not been reworked by thaw lake cycles. Measurements of total organic carbon (TOC) and total nitrogen (TN) content, stable carbon isotope ratios (δ13C) and radiocarbon (14C) analyses of bulk soils/sediments were conducted on 45 samples from 3 permafrost cores. Porewaters were extracted from these same core sections and used to measure salinity, dissolved organic carbon (DOC), total dissolved nitrogen (TDN), anion (Cl-, Br-, SO4 2-, NO3 -), and trace metal (Ca, Mn, Al, Ba, Sr, Si, and Fe) concentrations. Radiogenic strontium (87Sr/86Sr) was measured on a subset of porewater samples. Cores were also sampled for material property measurements such as dry bulk density, water content, and grain size fractions.
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Research data for Investigation of coatings and metallic materials for icephobic properties, dataset
<p>This dataset is used in deliverable 3.5, 'Investigation of coatings and metallic materials for icephobic properties', where you can get more information.</p> <p>The Dataset includes:</p> <table> <tbody> <tr> <td>Coating Data</td> </tr> <tr> <td>Metalic materials data</td> </tr> <tr> <td>Coating Freezing spike</td> </tr> <tr> <td>Coating Atmospheric freezing</td> </tr> <tr> <td>Coating contact angle</td> </tr> <tr> <td>Coating Ice adhesion</td> </tr> <tr> <td>Submerged freeze depression</td> </tr> <tr> <td>Metalic materials droplet freezing</td> </tr> <tr> <td>Metalic materials Droplet contact angle</td> </tr> <tr> <td>Coating freeze depression brine test</td> </tr> <tr> <td>Coating freeze depression CFT</td> </tr> <tr> <td>Metalic amorphous materials freeze depression</td> </tr> <tr> <td>Metalic pure materials freeze depression</td> </tr> </tbody> </table>
Database for machine learning of hydrogen storage materials properties
<p><strong>Database for machine learning of hydrogen storage materials properties</strong></p> <p>Matthew Witman<sup>a</sup>, Mark Allendorf<sup>a</sup>, Vitalie Stavila<sup>a</sup></p> <p><sup>a</sup>Sandia National Laboratories, Livermore, CA</p> <p> </p> <p><strong>Description</strong></p> <p>This ML-HydPARK dataset provides a csv file of metal hydride compositions, capacities, and thermodynamic values that can be used as target properties for building, training, and testing machine learning models. It has been parsed and cleaned from the DOE’s original publicly available HydPARK database according to the procedure in [1] to make it more suitable for immediate use with data-driven models. Generally, this removed duplicate entries, removed entries missing critical data, and attempted to fix various entries with obvious errors in the data. It is continuously updated under version control as new metal alloy hydrides are published in the open literature. Most entries contain data on the enthalpy and entropy of the hydriding reaction, as well the maximum hydrogen capacity, for which compositional machine learning models can be trained [1,2].</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors gratefully acknowledge research support from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Fuel Cell Technologies Office through the Hydrogen Storage Materials Advanced Research Consortium (HyMARC). This work was supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy<br> or the United States Government.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Witman, M.; Ling, S.; Grant, D. M.; Walker, G. S.; Agarwal, S.; Stavila, V.; Allendorf, M. D. Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning. <em>J. Phys. Chem. Lett</em>. <strong>2020</strong>, 11, 40–47.</li> <li>Witman, M.; Ek, G.; Ling, S.; Chames, J.; Agarwal, S.; Wong, J.; Allendorf, M. D.; Sahlberg, M.; Stavila, V. Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability. <em>Chem. Mater</em>. <strong>2021</strong>, 33, 4067–4076.</li> </ol> <p> </p> <p><strong>Contact</strong></p> <p>Please email <a href="mailto:mwitman@sandia.gov">mwitman@sandia.gov</a> , <a href="mailto:mdallen@sandia.gov">mdallen@sandia.gov</a>, or <a href="mailto:vnstavi@sandia.gov">vnstavi@sandia.gov</a> for questions or to request addition of recent data from the literature to this dataset.</p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Dataset for the publication: First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries
<p>This dataset contains the input and output files from the calculation of the atomistic properties of metallic magnesium, such as bulk, surface, adsorption, and diffusion properties.</p> <p>The discussion of the results were published in the ChemSusChem article: 'First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries' (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>). A preprint of the publication is further available under: <a href="http://doi.org/10.26434/chemrxiv-2022-qz055">https://doi.org/10.26434/chemrxiv-2022-qz055</a>.</p> <p>All calculations were performed using the density function theory code Vienna <em>ab initio</em> simulation package (VASP).</p> <p>The dataset contains all raw data for the performed convergence studies and calculated bulk-, surface-, adsorption-, and diffusion properties. An overview of the folder structure of the Zip archive, more precisely in which folders the data for the respective figures or tables of the underlying publication (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>) are stored, is provided in the following table:</p> <table> <tbody> <tr> <td>Convergence_study</td> <td>Figure S1</td> </tr> <tr> <td>Bulk_properties</td> <td>Table S3</td> </tr> <tr> <td>Surface_properties</td> <td>Table 1, Table 2, Figure 1, Table S5</td> </tr> <tr> <td>Adsorption_properties</td> <td>Monomer: Table S6; Dimer: Table 4, Table 5, Table 6; Islands: Figure S5, Table S9</td> </tr> <tr> <td>Diffusion_properties</td> <td>Table 3, Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Figure 13, Figure 14, Table S7, Table S8, Table S10 Table S11, Figure S4, Figure S7, Figure S9</td> </tr> </tbody> </table> <p> </p>
Material Property Database of Organic Liquids, Ices, and Hazes on Titan
<p>Titan has a diverse range of materials in its atmosphere and on its surface: the simple organics that reside in various phases (gas, liquid, ice) and the solid complex refractory organics that form Titan's haze layers. These materials all actively participate in various physical processes on Titan, and many material properties are found to be important in shaping these processes. Future in-situ exploration on Titan would likely encounter a range of materials, and a comprehensive database to archive the material properties of all possible material candidates will be needed.</p> <p>Here we archive several important material properties of the organic liquids, ices, and the refractory hazes on Titan that are available in the literature and/or that we have computed. These properties include thermodynamic properties (phase change points, sublimation and vaporization saturation vapor pressure, and latent heat), physical property (density), and surface properties (liquid surface tensions and solid surface energies).</p> <p>We have archived all the data involved in our first paper (https://arxiv.org/abs/2210.01394 for the Arxiv version and https://doi.org/10.3847/1538-4365/acc6cf for the publisher version) here to make them available to the science community. These data can be used as inputs for various theoretical models to interpret current and future remote sensing and in-situ atmospheric and surface measurements on Titan. The material properties of the simple organics may also be applicable to giant planets and icy bodies in the outer solar system, interstellar medium, and protoplanetary disks.</p> <p>The "Summary of Data Tables and Jupyter Notebook Files" summarizes the names of all the data files (.csv) and Jupyter Notebook files (.ipynb) and their corresponding Tables in the paper.</p> <p><strong>Please cite our paper in your use of the data: Yu et al. (2023), https://doi.org/10.3847/1538-4365/acc6cf</strong></p> <p><strong>Yu, X., Yu, Y., Garver, J., Li, J., Hawthorn, A., Sciamma-O’Brien, E., ... & Barth, E. (2023). Material Properties of Organic Liquids, Ices, and Hazes on Titan. The Astrophysical Journal Supplement Series, 266(2), 30.</strong></p>
Dataset for the optical properties of tilted surfaces in material jetting
<p>Dataset for the optical properties of tilted surfaces in material jetting:</p> <p>Including dataset for gloss, haze, scattering, specular BRDF, reflectance, transmittance, and statistical analysis</p>
Material properties of bovine intervertebral discs across strain rates - dataset
<p>Raw experimental data from axial compression tests of each of the bovine intervertebral disc specimens and the .dat files from each of the subject specific FE models at each strain rate.</p>
Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"
<p><span><span><span>This dataset contains supplementary code, images and models for the publication „Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.</span></span></span></p> <p> </p> <p><span><span><span>The content will be updated and additionally linked to the corresponding git repositories.</span></span></span></p>
Supplementary material from: Prediction of the Cold Flow Properties of Biodiesel using the FAME Distribution and Machine Learning Techniques
<p><span>The dataset is divided into three sections within the worksheet.</span></p> <p><span> </span><span>The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors.</span></p> <p><span> </span><span>The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs.</span></p> <p><span><span>The third and final section describes the measured properties Cloud Point (CP), Cold Filter Plugging Point (CFPP) and Pour Point (PP).</span></span></p>
Selected properties and microstructure of concrete with tire rubber granulate as recycled material in construction industry
<p><span>The paper explores the use of recycled materials in the construction industry to promote sustainable development. There is a growing demand for recycling and innovative materials in engineering. The study specifically investigates the potential of tire rubber recyclate as a recycled raw material, comparing two different mixtures in an experimental program. These mixtures highlight the importance of utilizing local resources, aligning with the principles of the circular economy. The experimental program focuses on evaluation of mechanical properties in addition to specialized tests. Findings indicate that higher proportions of rubber granulate not only impact mechanical properties but also significantly affect durability when exposed to environmental factors. </span></p>
Data for the publication "Impact of Enzymatic Degradation on the Material Properties of Poly(ethylene terephthalate)"
<p><strong>Background</strong></p> <p>The data set contains raw data of fatigue crack propagation resistance measurements (da/dN), differential scanning calorimetry (DSC), atomic-force microscopy (AFM), and ultra-high performance liquid chromatography (UHPLC) of PET samples incubated with PETase. The experiments were done in the laboratory at the Department of Polymer Engineering and Department of Biochemistry, University of Bayreuth, Germany in 2020 and 2021.</p> <p>The data set was analysed in the publication: Menzel, T.; Weigert, S.; Gagsteiger, A.; Eich, Y.; Sittl, S.; Papastavrou, G.; Ruckdäschel, H.; Altstädt, V.; Höcker, B. Impact of Enzymatic Degradation on the Material Properties of Poly(Ethylene Terephthalate). <em>Polymers</em> <strong>2021</strong>, 13(22), 3885. https://doi.org/10.3390/polym13223885</p> <p><strong>Disclaimer</strong></p> <p>The data are provided without any warranty. Details on the experimental setup are given in the publication.</p> <p><strong>References</strong></p> <p>Menzel, T.; Weigert, S.; Gagsteiger, A.; Eich, Y.; Sittl, S.; Papastavrou, G.; Ruckdäschel, H.; Altstädt, V.; Höcker, B. Impact of Enzymatic Degradation on the Material Properties of Poly(Ethylene Terephthalate). <em>Polymers</em> <strong>2021</strong>, 13(22), 3885. https://doi.org/10.3390/polym13223885</p> <p> </p> <p> </p> <p> </p> <p> </p>
TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images
<p>We present TransProteus, a dataset, for predicting the 3D structure and properties of materials, liquids, and objects inside transparent vessels from a single image without prior knowledge of the image source and camera parameters. Manipulating materials in transparent containers is essential in many fields and depends heavily on vision. This work supplies a new procedurally generated dataset consisting of 50k images of liquids and solid objects inside transparent containers. The image annotations include 3D models and material properties (color/transparency/roughness...) for the vessel and its content. The synthetic (CGI) part of the dataset was procedurally generated using 13k different objects, 500 different environments (HDRI), and 1450 material textures (PBR) combined with simulated liquids and procedurally generated vessels. In addition, we supply 104 real-world images of objects inside transparent vessels with depth maps of both the vessel and its content.</p> <p>Note that there are two files here:</p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z</a></p> <p>and</p> <p><br> <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z </a>, contain subset of the virtual CGI data set.</p> <p>https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z</p> <p><a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TransProteus_RealSense_RealPhotos.7z">TransProteus_RealSense_RealPhotos.7z </a>: Contain real-world photos scanned with real sense with depth map of both the vessel and its content</p> <p>See ReadMe file in side the downloaded files for more details</p> <p>The full dataset (>100gb) can be found here:</p> <p><a href="https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV">https://e.pcloud.link/publink/show?code=kZfx55Zx1GOrl4aUwXDrifAHUPSt7QUAIfV</a></p> <p>https://<a href="http://icedrive.net/1/6cZbP5dkNG">icedrive.net/1/6cZbP5dkNG</a></p> <p>See: <a href="https://arxiv.org/pdf/2109.07577.pdf"> https://arxiv.org/pdf/2109.07577.pdf</a> for more details</p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">**This dataset is complementary to LabPics dataset with 8k real images of materials in vessels in chemistry labs, medical labs, and other settings. The LabPics dataset can be downloaded from here:</a></strong></p> <p><strong><a href="https://zenodo.org/record/4736111#.YVOAx3tE1H4">https://zenodo.org/record/4736111#.YVOAx3tE1H4</a></strong></p> <p> </p> <p><strong>************************************************************************************</strong></p> <p><a href="https://zenodo.org/api/files/12b013ca-36be-4156-afd4-c93b5fa22093/Tansproteus_SimulatedLiquids2_New_No_Shift.7z">Transproteus_SimulatedLiquids2_New_No_Shift.7z </a>and <a href="https://zenodo.org/api/files/2b833de0-4007-4682-ad5b-5e08bd63597e/TranProteus2.7z?versionId=f16e7126-8750-41f7-99e6-d35ca60399cc">TranProteus2.7z</a></p> <p>The two folders contain relatively similar data styles.<br> The data in No_Shift contain images that were generated with no camera shift in the camera paramters. If you try to predict 3d model from an image as a depth map, this is easier to use (Otherwise, you need to adapt the image using the shift). For all other purposes, both folders are the same, and you can use either or both. In addition, a real image dataset for testing is given in the RealSense file.</p> <p> </p> <p> </p> <p> </p>
The data on the optical properties of kombucha and kombucha proteinoids, as well as chaos calculations are provided for the paper titled "Kombucha Mats as Responsive Materials."
<p>The data on the optical properties of kombucha and kombucha proteinoids, as well as chaos calculations are provided for the paper titled "Kombucha Mats as Responsive Materials."</p>
Common material properties
<p>Common material properties and scatter plot for comparison.</p>
Dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7
<p>Open dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7. https://doi.org/10.1186/s40069-022-00568-y</p> <p>File 1 - X-ray diffractogram and particle size distribution (laser granulometry) data - *.opju (Origin)<br> File 2 - Rheological test results - *.opju (Origin)<br> File 5 - Mechanical peformance (early strength - ultrasounds and compressive strength) and density test results - *.opju (Origin)<br> File 4 - Mercury intrusion porosimetry test data - *.opju (Origin)</p>
The Influence of Optical Material Properties on the Perception of Liquids
<p>Dataset relative to the following publication:</p> <p>Jan Jaap R. van Assen, Roland W. Fleming (2016). Influence of optical material properties on the perception of liquids. Journal of Vision, 16(15):12, 1–20. doi: 10.1167/16.15.12.</p> <p>One zip file contains the datasets of the various experiments.</p> <p>The second zip file contains the liquid stimuli used during the experiments.</p>
Radionuclide, organic matter, geochemical and colorimetric properties of potential source material and target sediment for conducing sediment fingerprinting approaches in the Dzoumogné reservoir, Mayotte Island, France
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Colorimetric properties analysed with a portable diffuse reflectance spectrophotometer (Konica Minolta CM-700d) and geochemical contents obtained with an energy dispersive X-ray fluorescence spectrometer (ED-XRF Epsilon 4), organic matter and stable isotopes were analysed by EA-IRMS and radionuclides using coaxial N- and P- type HPGe detectors (Canberra/Ortec). These properties were analysed in potential source material that may supply sediment to the Dzoumogné reservoir, Mayotte island, France. Three potential soil source materials (n = 57) were considered: cropland (n = 29), forest (n = 13) and subsurface material originating from channel bank collapse, landslides, badlands (n = 16). A sediment core was collected in the Dzoumogné reservoir (Target) on the 8th October 2021 and 20 layers were sampled.</p><p>The current dataset comprises two Excel files including the metadata description and the data itself.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.