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768 results for “reinforcement”
X-ray computed tomography aided engineering approach for non-crimp fabric reinforced composites [Data set]
<p>The finite element models behind the publication</p> <p>Auenhammer, R.M., Jeppesen, N., Mikkelsen, L.P., Dahl, V.A., Blinzler, B.J., Asp, L.E. Robust numerical analysis of fibrous composites from X-ray computed tomography image data enabling low resolutions, <em>Composites Science and Technology, </em><strong>224</strong>, 109458, <a href="https://doi.org/10.1016/j.compscitech.2022.109458">https://doi.org/10.1016/j.compscitech.2022.109458</a>, 2022. </p> <p>The x-ray scan data which the model is based on can be found in the following publication:</p> <p>Jeppesen, N., V.A. Dahl, A.N. Christensen, A.B. Dahl, L.P. Mikkelsen, Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor. IOP Conf. Ser.: Mater. Sci. Eng. 942, 012037, <a href="https://doi.org/10.1088/1757-899X/942/1/012037">https://doi.org/10.1088/1757-899X/942/1/012037</a> 2020</p> <p>and data-set</p> <p>Jeppesen N, Dahl V A, Christensen A N, Dahl A B and Mikkelsen L P 2020 Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor [data set] Zenodo. <a href="http://dx.doi.org/10.5281/zenodo.3877522">http://dx.doi.org/10.5281/zenodo.3877522.</a></p> <p> </p>
RL-MLZerD: Multimeric protein docking using reinforcement learning
<p>This is the dataset used in the publication:<strong> RL-MLZerD: Multimeric protein docking using reinforcement learning by Tunde Aderinwale, Charles Christoffer and Daisuke Kihara</strong>, in submission 2022.</p> <p>The dataset included 30 protein complexes targets from 3 to 5 chains used for docking. Each target in the dataset are randomly rotated and shifted three times to remove bias to the starting conformation.</p> <p>We also include the docking results for each target in the corresponding directory for all the methods reported in the manuscript.</p> <p>For each target, 1500 to 12,000 models were provided that were generated by RL-MLZerD, 1700 to 12,000 models for Multi-LZerD, about 100 to 300 models by Combdock, and 5 models each by Alphafold-Multimer, Alphafold-Multimer (no template), and ColabFold respectively.</p> <p>Directory description:</p> <p>Target: contains all the files related to all experiment for that target e.g 1A0R<br> Target/*_mod_run_? contain files for all random rotation experiments and combdock results. e.g 1A0R/1A0R_mod_run_1/<br> Target/*_mod_combine_athird contains files for combined rotation experiment for RL-MLZerD and Multilzerd. e.g 1A0R/1A0R_mod_combine_athird/<br> Target/alphafold_multimer contains prediction files for AlphaFold-Multimer, both regular and no template run. e.g alphafold_multimer/1a0r and alphafold_multimer/1a0r_nodb<br> Target/colabfold contains prediction files for ColabFold results. e.g colabfold/prediction_1a0r</p>
Supplemental Material to Journal Article "Determination of as-built properties of fiber reinforced polymers in a wind turbine blade using scanning electron and high-resolution X-ray microscopy"
<p>This set supplements the figure data to the article "Determination of as-built properties of fiber reinforced polymers in a wind turbine blade using scanning electron and high-resolution X-ray microscopy", DOI: <a href="https://doi.org/10.1016/j.jcomc.2022.100310">https://doi.org/10.1016/j.jcomc.2022.100310</a></p>
Data for: Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma using reinforcement learning
<p>This contains data used for a paper titled: Evolution Reinforces Cooperation with the Emergence of Self-Recognition Mechanisms: an empirical study of the Moran process for the iterated Prisoner's dilemma using reinforcement learning.</p> <p>Numerous data sets are included, the main being `main.csv` which includes the fixation counts for a number of Moran processes between pairs of players from the Axelrod library.</p> <p>The source code and explanation of the data is here: https://github.com/Axelrod-Python/axelrod-moran. </p> <p> </p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (parameters, trajectories)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7. <br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p>
Deep Gradient Reinforcement learning for Music Improvisation in cloud computing framework
<p><span>The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG) are leveraged to assess the quality of the improvised music.</span></p>
Electromagnetic Wave Dataset for Strength Degradation Detection in Reinforced Concrete Structures Using RFID Measurements and CNN Model
<p>This dataset comprises 1,800 electromagnetic wave (EM-wave) images collected from three different reinforced concrete beams subjected to varying levels of corrosion. Each image is classified into 'normal' or 'reduced strength' categories based on the beam's structural integrity. Generated through a non-destructive RFID-based monitoring technique, this dataset integrates advanced analyses like 2-D Fourier transforms and fractal dimensions. It is specifically designed to train and validate Convolutional Neural Networks (CNNs) for detecting strength degradation in reinforced concrete structures.</p>
Benchmarking Study of Deep Generative Models for Inverse Polymer Design: Reinforcement Learning
<p>Well-trained models and generation results for reinforcement learning part of <a href="https://github.com/ytl0410/Polymer-Generative-Models-Benchmark">ytl0410/Polymer-Generative-Models-Benchmark: Well-trained models and generative outcomes for the paper "Benchmarking Study of Deep Generative Models for Inverse Polymer Design" (github.com)</a></p>
A multimodal data-set of a unidirectional glass fibre reinforced polymer composite
<p>Please cite the following article when using the data-sets hereby shared:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. A multimodal data-set of a unidirectional glass fibre reinforced polymer composite. <em>Data in brief</em>, <em>18</em>, pp.1388-1393.</p> <p>These data-sets were used for validating the use of X-ray tomography and our dictionary-based probabilistic method for detection of individual fibres, for more information see the following article:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. Statistical validation of individual fibre segmentation from tomograms and microscopy. <em>Composites Science and Technology</em>, <em>160</em>, pp.208-215.</p>
Uniaxial Cyclic Tests on Reinforced Concrete Members with Lap Splices
<p>This data paper presents the quasi-static uniaxial cyclic tests of 24 RC members, of which 22 feature lap splices and two are reference units with continuous reinforcement. The objective of the experimental programme is to investigate the influence of lap splice length, confining reinforcement and loading history on the behaviour of lap splices. Particular attention is placed on the measurement of local deformation quantities such as lap splice strains and rebar-concrete slip. Details on the geometry and reinforcement layout of the specimens as well as on the employed test setup, instrumentation and loading protocols are provided. The global behaviour of the test units including the observed crack pattern and failure modes are discussed. The organization of the experimental data is explained.</p>
A combined experimental/numerical investigation on hygrothermal aging of fiber-reinforced composites
<p>The x-ray scan data and videos used in the following paper to where the references should be given:</p> <p>Rocha, I.B.C.M., van der Meer, F.P., Raijmaekers, S., Lahuerta, F., Nijssen, R.P.L., Mikkelsen, L.P., Sluys, L.J., A combined experimental/numerical investigation on hygrothermal aging of fiber-reinforced composites, European Journal of Mechanics / A Solids, doi: <a href="https://doi.org/10.1016/j.euromechsol.2018.10.003">https://doi.org/10.1016/j.euromechsol.2018.10.003</a>, 2018.</p> <p>More details on the scans can be found in the paper.</p> <p>The following relations is between the files and the figures from the paper</p> <p>Fig 7: YouTube:<a href="https://youtu.be/o4NLVGRgTW8">https://youtu.be/o4NLVGRgTW8</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/LFoV-Ref-036_036-LFOV-2.5mm-x4_recon.mpg?versionId=d8c523f6-9cf7-4e38-8e8c-1e6fe0bd2876">LFoV-Ref-036_036-LFOV-2.5mm-x4_recon.mpg</a><br> Fig 8a: YouTube:<a href="https://youtu.be/hbxVToJD7Mc">https://youtu.be/hbxVToJD7Mc</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-Ref-Surf-165b-side-0.5-20x_recon.mpg?versionId=edb0f8c2-6eb8-42c1-a968-ed44f247f53a">SFoV-Ref-Surf-165b-side-0.5-20x_recon.mpg </a><br> Fig 8b: YouTube:<a href="https://youtu.be/keVhRX78m7o">https://youtu.be/keVhRX78m7o</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-Ref-Center-165b-center-0.5-20x_recon.mpg?versionId=ec6fb064-14f2-40f4-9aba-e3eb9416e427">SFoV-Ref-Center-165b-center-0.5-20x_recon.mpg </a><br> Fig 9: YouTube:<a href="https://youtu.be/N-ji_cDbEXc">https://youtu.be/N-ji_cDbEXc</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/LFoV-65C-179a-2863-179a_recon.mpg?versionId=527e02a2-9a69-4ad5-bc51-a6096dd0ecc6">LFoV-65C-179a-2863-179a_recon.mpg </a><br> Fig 10a: YouTube:<a href="https://youtu.be/T-Yq3fkGxzA">https://youtu.be/T-Yq3fkGxzA</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-65C-Surf-179a-center-and-side_179a-side-0.5mm-x20_recon.mpg?versionId=9a15a8ad-c55b-44d0-bece-6ee8f093179c">SFoV-65C-Surf-179a-center-and-side_179a-side-0.5mm ...</a><br> Fig 10b: YouTube:<a href="https://youtu.be/4fZqXQY2VOs">https://youtu.be/4fZqXQY2VOs</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-65C-Center-179a-center-and-side_179a-center-0.5mm-x20_recon.mpg?versionId=33951d51-6763-4cf1-a88b-8e793813d4be">SFoV-65C-Center-179a-center-and-side_179a-center-0 ...</a><br> Fig 11a: YouTube:<a href="https://youtu.be/4L8BCa6tgFA">https://youtu.be/4L8BCa6tgFA</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-50C-Surf-2863-165a-side-0.5-20x_recon.mpg?versionId=147fb3ff-3b7a-4aed-971d-8085bcc821da">SFoV-50C-Surf-2863-165a-side-0.5-20x_recon.mpg </a><br> Fig 11b: YouTube:<a href="https://youtu.be/uBWqHxYG3GU">https://youtu.be/uBWqHxYG3GU</a> File:<a href="https://zenodo.org/api/files/f43d3d15-fc47-492f-b805-572e70aae812/SFoV-50C-Center-Spec-2863-165a-2863-165a-middle-0.5-20x_recon.mpg?versionId=f09a97f4-91a2-40a4-9109-4d2ef518933b">SFoV-50C-Center-Spec-2863-165a-2863-165a-middle-0. ...</a></p> <p> </p> <p> </p>
Additional data for "In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load"
<p>The following document is an extension of the <em>In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load</em> publication. It contains guidelines for the experimental results for the single fiber experiments and bundle of carbon fiber ones. The detailed data from the experiments is provided with this document as <em>CSV </em>files.</p>
FreeCiv games for the experiment on comparing Knowledge-Based Reinforcement Learning and Neural Networks in Strategic Games
<p>Dataset provides played FreeCiv games. The Tournament subset of them was played fully by two Artificial Intelligence (AI) agents against each other and one more computer player. The Human games subset was played by humans for demonstration/teaching purposes. The rest of the games were played 120 turns and stopped.</p>
A Comprehensive Reliability-Based Framework for Corrosion Damage Monitoring and Repair Design of Reinforced Concrete Structures
<p>Corresponding data set for Tran-SET Project No. 17STLSU03. Abstract of the final report is stated below for reference:</p> <p>"In this work, we developed a comprehensive framework for corrosion management of reinforced concrete (RC) structures. This framework includes critical steps of an effective approach to quantify the damage evolution as well as providing the timeframe for effective maintenance/repair strategies for corrosion assessment in RC structures. The framework included several activities including the use of indirect and direct inspection tools, theoretical development for damage prediction, experimental measurements and theoretical development of repair time based on reliability. The uniqueness of the framework is the integration of deterministic modeling of corrosion damage evolution by using mechanistic analysis with statistical modeling on corrosion of RC structures by using measurements from the field (or natural environment) and experimental testing (in the laboratory). The framework includes a simple algorithm that relies in each development and task generated from this work to monitor and estimate the status of the reinforced concrete structures and the most suitable strategy to extend the life of the system while maximizing the reliability."</p>
Integrated Health Monitoring and Reinforcement of Transportation Structures with Optimized Low-Cost Multifunctional Braided Cables
<p>Corresponding data set for Tran-SET Project No. 17STTAM04. Abstract of the final report is stated below for reference:</p> <p>"The objective of this research study is to design, fabricate, and characterize multifunctional high strength and self-sensing braided cables and structures using novel Fe-based shape memory alloys (SMAs). The system exploits unique properties of recently developed low-cost super-elastic FeMnAlNi SMAs, which enables excellent super-elastic properties, high strength, and self-sensing in structural health monitoring (SHM) systems. This novel material technology can be coupled with modeling efforts that allow for accurate prediction of both the materials and structural response during sensing. At the conclusion of the project, we have demonstrated that with careful design of processing parameters, it is possible to control the yield strength and superleastic properties of FeMnAlNi SMAs. We have fabricated, for the first time in the world, a large diameter wires and bars from these inexpensive iron based SMAs which are expected to help with scaling up the fabrication of these materials. In addition, we were able to develop a method for fabricating braided cables from the fabricated Fe-SMA wires and a lab scale experimental setup has been designed and built. Prototype design for the braiding weave was created and tested. Finally, an experimental setup has been designed and manufactured to measure changes in the magnetic response of the SMA braided cables under load in order to directly correlate the magnetic response with deflection and strain. Clearly, the successful fabrication of wires and braided cables of the inexpensive iron based SMAs with superelastic strains comparable to nickel-titanium SMAs and with favorable magnetic sensing capabilities have significant implications for transportation infrastructure as these materials can provide structural health monitoring capability while also mitigating large shape changes during natural disasters. Future work needs to focus on revealing the coupling between mechanical properties (in particular damage) and the changes in magnetic properties in order to provide guidance on how these materials can be utilized in structural health monitoring."</p>
Self-Healing Microcapsules as Concrete Aggregates for Corrosion Inhibition in Reinforced Concrete
<p>Corresponding data set for Tran-SET Project No. 17CLSU08. Abstract of the final report is stated below for reference:</p> <p>"Reinforced Concrete (RC) structures are vital to the US’s civil infrastructure for their strength and versatility. Unfortunately, RC elements deteriorate rapidly when exposed to corrosive environments. One possible solution is to extend the life of RC elements and systems using microencapsulated corrosion inhibitors to reduce the rebar corrosion rate. The capsules house an anodic corrosion inhibitor agent including calcium nitrate (CN) and triethanolamine (TEA). The integration of such microencapsulated materials will enhance the durability and extend the useful life by controlling the corrosion precursors and the corrosion process during damage evolution. Therefore, this work aims to develop and characterize the performance of microcapsules containing corrosion inhibitors (CN-C and TEA-C) in comparison to those introduced as admixtures (CN-A and TEA-A) for reinforced concrete applications. For the corrosion tests, all samples were subjected to continuous ponding, wet/dry cycles, and fog chamber exposure to simulate different environments. The results showed that TEA-C is more effective in giving a corrosion protection than TEA-A and the Control. In contrast, the corrosion protection performance of both CN-A and CN-C was alike. The corrosion kinetics was slightly reduced on inhibited rebars compared to unprotected rebars (the Control). When comparing TEA-C and CN-C, in the presence of each stimulus (pH changes for TEA-C, cracks for CN-C), TEA-C protected the rebar better than CN-C. For the admixture samples (TEA-A and CN-A) that do not need stimuli in the concrete, a stable and better corrosion protection was provided by CN-A. The outcome of this proof-of-concept study in the laboratory validates the merit of the proposed technology for corrosion control in RC structures."</p>
Use of Ultra‐High‐Performance Fiber‐Reinforced Concrete (UHP‐FRC) for Fast and Sustainable Repair of Pavements
<p>Corresponding data set for Tran-SET Project No.17STUTA03. Abstract of the final report is stated below for reference:</p> <p>"This research presents a new methodology, which enables streets, roads, highways, bridges, and airfields to use an advanced fiber-reinforced concrete material, which can delay or prevent the deterioration of these transportation infrastructure when subjected to traffic and environmental loadings. The major problem of concrete is its considerable deterioration and limited service life due to its brittleness and limited durability. As a result, it requires frequent repair and eventual replacement, which consumes more natural resources. Ultra-high-performance fiber-reinforced concrete (UHP-FRC) introduces significant enhancement in the sustainability of concrete structures due to its dense microstructure and damage-tolerance characteristics. These characteristics can significantly reduce the amount of repair, rehabilitation, and maintenance work, thereby giving the transportation infrastructure a longer service life. This research addresses the strong need to develop fast and sustainable UHP-FRC materials for pavement repair that can be easily cast onsite without special treatments. This avoids any major changes to current concrete production practice and accelerates the use of UHP-FRC materials. This research investigated a new method for concrete repair by combining precast UHP-FRC panels with a small quantity of cast-in-place UHP-FRC for pavement repair without any dowel bars. In this method, a precast UHP-FRC panel is used along with cast-in-place UHP-FRC. The vertical repair surfaces of the existing concrete are roughened on site. The outer edges of the UHP-FRC precast panel are roughened before they are brought to the site (no dowel bars are needed). The depth of the precast UHP-FRC panel is the same as the existing pavement thickness. Only a small cast-in-place UHP-FRC joint (one to two inches wide) is done onsite. The roughened precast UHP-FRC panel is placed in the repair area and cast-in-place UHP-FRC is cast into the joint. Experimental results showed that using a roughened surface (up to about CSP 5) provides a very large bond resistance, which is enough to prevent faulting."</p>
A Comprehensive Framework for Life-Cycle Cost Assessment of Reinforced Concrete Bridge Decks
<p>Corresponding data set for Tran-SET Project No. 18STOKS02. Abstract of the final report is stated below for reference:</p> <p>"Various environmental and mechanical stressors cause deterioration of concrete bridge decks. Normal wear and tear, freeze and thaw cycles, and chloride penetration due to deicing salts can cause aggressive deterioration that usually require frequent interventions during the life-cycle of the bridge. These interventions include deck maintenance and repairs (e.g., application of sealers or overlay placement) as well as bridge deck replacement. The quantification of the life-cycle cost of bridge decks considering maintenance and repair activities represents a significant challenge facing local and state transportation agencies. The life-cycle maintenance activities not only increase the direct life-cycle cost of the bridge, but they also lead to significant indirect user costs due to increasing traffic delays, work zone crashes, and operating cost. Moreover, these traffic delays increase the carbon footprint of the bridge and adversely affect the life-cycle bridge sustainability. Accordingly, the proper quantification of indirect costs associated with life-cycle bridge management activities including maintenance, repair, and rehabilitation activities is of paramount importance. The research attempts to fill in the knowledge gaps in quantifying the indirect costs associated with bridge deck maintenance activities and their impact on the overall bridge life-cycle cost."</p>
Data for a publication "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys"
<p>Dataset contains data that has been used within the manuscript entitled: "Microstructure and mechanical properties of in-situ SiO2-reinforced mechanically alloyed CoCrFeNiMnX (X= 5, 20, 35 at.%) high-entropy alloys". For more information, please read the README.txt file.</p>
Mechanical Properties, Workability, and Experiments of Reinforced Composite Beams with Alternative Binder and Aggregate
<p><span>Arguably the most important element in the sustainability of concrete development is the discovery of an optimal sustainable binder and substitution for the increasingly depleted reserves of natural aggregates. Considerable interest has been shown in alkali-activated materials, which possess good characteristics and could be considered environmentally friendly because of their use of secondary materials in production. The aim of this study was the determination of the mechanical properties of three different mixtures based on the same locally accessible raw materials. The reference mixture contained Portland cement, the second mix contained a finely ground granulated blast furnace slag instead of cement, and the third mixture contained a portion of light artificial aggregate. The experiments focused on the testing and mutual comparison of the processability of the fresh mixture and mechanical characteristics (like compressive and flexural strength, as well as resistance to high temperatures and surface layer tear strength tests). Reinforced concrete beams without shear reinforcement and with three levels of reinforcement were also tested with a three-point bend test. The results show that, overall, the mechanical properties of all the tested mixtures were similar, but each had its own disadvantages. For example, the blast furnace slag-based mixture had a more vulnerable surface layer or a debatable loss of bulk density in the light aggregate mix at the expense of the mechanical properties. One of the main results of the research is that it was possible to technologically produce beams from the alkali-activated concrete (AAC) mixture. Then, the performed beam experiments verified the mechanism of damage, collapse, and load capacity. The obtained results are essential because they present the use of AAC not only in laboratory conditions but also for building elements. In beams without shear reinforcement, the typical tensile cracks caused by bending and shear cracks appeared under loading, where their character was affected depending on the degree of beam reinforcement and loading.</span></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.