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768 results for “reinforcement”
Dataset for effects of shear zone thickness in root-reinforced soils: Fallow P X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Fallow P X-ray CT scans and digital volume correlation data</p>
Dataset for effects of shear zone thickness in root-reinforced soils: Gorse A X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Gorse A X-ray CT scans and digital volume correlation data</p>
Dataset for effects of shear zone thickness in root-reinforced soils: Willow C X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Willow C X-ray CT scans and digital volume correlation data</p>
Dataset for effects of shear zone thickness in root-reinforced soils
<p>Dataset for paper titled: <strong><em>Modelling of stress transfer in root reinforced soils informed by 4D X-ray computed tomography and digital volume correlation data</em></strong></p> <ul> <li><strong>Rood Area Ratios.zip</strong> - Root area ratio data for figure 6 and figure 7, and table 5.</li> <li><strong>All_load_data_with_reducing_area.xlsx</strong> - Shear Stress vs shear displacement plot for figure 4.</li> <li><strong>Local_shear_Zone_Thickness_vs_x_displacement.xlsx</strong> - Change in shear zone thickness for figure 13.</li> <li><strong>Depth_vs_vx_plots_local_regions.xlsx</strong> - Depth <em>vs</em> <em>x</em>-displacement line profiles for figure 11.</li> <li><strong>Estimate of change in length.xlsx</strong> - Comparison between changes in root length derived from equations and measured directly from the root path geometry for figure 14.</li> <li><strong>Artificial_root.xlsx</strong> - Shear stress-displacement curves for specimens containing an artificial root, and a fallow specimen for figure 17.</li> </ul> <p> </p>
Dataset for effects of shear zone thickness in root-reinforced soils: Gorse G X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Gorse G X-ray CT scans and digital volume correlation data</p>
Dataset for effects of shear zone thickness in root-reinforced soils: Fallow D X-ray CT scans and digital volume correlation data
<p>Dataset for effects of shear zone thickness in root-reinforced soils: Fallow D X-ray CT scans and digital volume correlation data</p>
Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning"
<p><strong>Data Release for Article: <em>Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning</em></strong></p> <p>This package releases a Python notebook reproducing the quantitative<br> figures featured in the research article "Mastering the Game of Stratego with <br> Model-Free Multiagent Reinforcement Learning".</p> <p><strong>Usage</strong></p> <p>The notebook can be uploaded to and executed using the<br> [Colab](https://colab.research.google.com) runtime service. <br> The Python notebook is tested against Python `3.7`.</p> <p><strong>License and disclaimer</strong></p> <p>Copyright 2022 DeepMind Technologies Limited</p> <p>All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you<br> may not use this file except in compliance with the Apache 2.0 license. You may<br> obtain a copy of the Apache 2.0 license at:<br> https://www.apache.org/licenses/LICENSE-2.0</p> <p>All other materials are licensed under the Creative Commons Attribution 4.0<br> International License (CC-BY). You may obtain a copy of the CC-BY license at:<br> https://creativecommons.org/licenses/by/4.0/legalcode</p> <p>Unless required by applicable law or agreed to in writing, all software and<br> materials distributed here under the Apache 2.0 or CC-BY licenses are<br> distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,<br> either express or implied. See the licenses for the specific language governing<br> permissions and limitations under those licenses.</p> <p>This is not an official Google product.</p>
Scientific internship - Influence of fire conditions on the behaviour of reinforced concrete columns
<p>Data collected during the scientific internship (photos and videos, research materials, infoCAD files, presentations).</p>
Load-bearing hydrogels ionically reinforced through competitive ligand exchanges
<p>Fast advances in soft robotics and tissue engineering demand for new soft materials whose mechanical properties can be interchangeably and locally varied, thereby enabling, for example, the design of soft joints within an integral material. Inspired by nature, we introduce a competitive ligand-mediated approach to selectively and interchangeably reinforce metal-coordinated hydrogels. This is achieved by reinforcing carboxylate-containing hydrogels with Fe<sup>3+</sup> ions. Key to achieving a homogeneous, predictable reinforcement of the hydrogels is the presence of weak complexation agents that delay the formation of metal-complexes within the hydrogels, thereby allowing a homogeneous distribution of the metal ions. The resulting metal-reinforced hydrogels show a compressive modulus of up to 2.5 MPa, while being able to withstand pressures as high as 0.6 MPa without appreciable damage. Competitive ligand exchanges offer an additional advantage: they enable non-linear compositional changes that, for example, allow the formation of joints within these hydrogels. These features open up new possibilities to extend the field of use of metal reinforced hydrogels to load-bearing applications that are omnipresent for example in soft robots and actuators.</p>
Data of "Experimental investigation on the bond behaviour of steel reinforcement in self-healing concrete"
<p>Dataset for the fresh and hardened properties of self-healing concrete, and the bond properties of steel reinforcement in self-healing concrete</p>
Elastic properties of fibre reinforced composites
<pre><code class="language-markdown">## Structure of the datasets The data set contains the transverse elastic properties of uni-directional composite materials with the following details. ### Datasets: D1, D2, D3 and D4 | | Dataset: D1 | Dataset: D2 | Dataset: D3 | Dataset: D4 | |----------------- |------------------------|-----------------------|------------------------|------------------------| | **vf** range | [25, 75] | [25, 75] | [10, 25] | [10, 25] | | **Ecr** range | [5, 250] | [250, 500] | [250, 500] | [5, 250] | | **X_train** shape| (80, 250, 256, 256, 1) | - | - | - | | **y_train** shape| (80, 250, 8) | - | - | - | | **X_test** shape | (40, 250, 256, 256, 1) | (20, 250, 256, 256, 1)| ( 6, 250, 256, 256, 1) | (15, 200, 256, 256, 1) | | **y_test** shape | (40, 250, 8) | (20, 250, 8) | ( 6, 250, 8) | (15, 200, 8) | Here, + vf: fibre volume fraction + Ecr: Elastic moduli contrast, the ratio of fibre elastic moduls to that of the matrix + Each of the label arrays contain 8 columns, which are + column index => label `0 => Vf`, Fibre volume fraction `1 => Em`, Matrix elastic modulus `2 => NUm`, Matrix Poisson's ratio `3 => Ef`, Fibre elastic modulus `4 => NUf`, Fibre Poisson's ratio `5 => E22/Em` `6 => E33/Em` `7 => G23/Gm` </code></pre> <p>rse elastic properties of uni-directional composite materials with the following details.</p>
Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution: Data
<p>The data was simulated in GATEv8.0. The macro's and other files for the simulation can be found in the folders: Sim_50_40, Sim80_60 and Sim90_70. Here, the different files to perform the simulation are given, as well as the output data, found in the folders Flats and Projs (flatfields and projections) in the form of a csv-file. The csv-file contains 5000 rows (one for each image), and each row contains a flattened 450x450 image.</p> <p>The files for visualization of the fiber directions are given in the folder MRtrix. This folder contains the reconstructed scatter magnitudes and their respective directions in a mat-file. On this set of directions the constrained spherical deconvolution is applied using the sf_dirs.mif, sf_mask.mif, sf_response.mif and data.mif files. This returns the orientation density function in each voxel as output (odf_csd_14.mif). An alternative approach is performing a Funk-Radon transform on the coefficients of the scatter function (data_coeffs_10.mif) to obtain odf_frt_10.mif.</p> <p>Using a peak finding algorithm the fiber orientations are extracted from these odf-files and written to peaks_x.mif files, with or without a threshold (10% of largest amplitude).</p>
Data of "Optimization of concrete mix designs toward the bond properties of steel reinforcement in self-healing concrete by Taguchi method"
<p>Dataset for the hardened properties of self-healing concrete and the bond properties of steel reinforcement in self-healing concrete used for the optimization via Taguchi method</p>
Champion-Level Drone Racing using Deep Reinforcement Learning: Supplementary Data
<p>Supplementary data for the publication "Champion-Level Drone Racing using Deep Reinforcement Learning".</p> <p>racing_data.zip: The data includes the recorded position of human and autonomous drones during head-to-head races and time-trial runs. The included code provides plotting and statistical analysis of the data.</p> <p>pseudocode.zip: Pseudocode that illustrates gate detection, VIO drift estimation, policy training, and residual model identification.</p>
Out-of-plane performance of structurally and energy retrofitted masonry walls: Geopolymer versus cement-based textile-reinforced mortar combined with thermal insulation
<p>Data corresponding to all figures and tables presented in the publication</p>
Supplementary information to "Material intensity and embodied CO2 benchmark for reinforced concrete structures in Brazil"
<p>This Excel file contains the electronic supplementary information to the manuscript " Material intensity and embodied CO<sub>2</sub> benchmark for reinforced concrete structures in Brazil", including detailed structural design data for the 53 analyzed buildings and the calculation of the structural material quantity and embodied CO2 indicators.</p>
Reinforcement learning prioritizes general applicability in reaction optimization - optimization data logs
<p>A zipped file of all optimization data logs for publication "Reinforcement learning prioritizes general applicability in reaction optimization". Included in the files are: acquisition logs for simulation testing with synthetic data, simulation testing with various chemistry datasets and a real time amide coupling optimization</p>
Graphene-Reinforced CAD/CAM Restorations for MIH-Affected Molars in Adolescents: A Prospective Clinical Study
ClinicalTrials.gov study NCT07329244. IPD Sharing: NO. Countries: 1. Publications: 2.
Impact of Reinforced Education by Video on the Quality of Bowel Preparation
ClinicalTrials.gov study NCT03185299. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
The Role of the Glutamatergic System in the Extinction of Conditioned Reinforcement Processes
ClinicalTrials.gov study NCT00926900. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.