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768 results for “reinforcement”
Dynamic testing of a four-storey building with reinforced concrete and unreinforced masonry wall: Data set
<p>This paper presents a publically available data set recorded during the shake-table test of a structure with reinforced concrete (RC) and unreinforced masonry (URM) walls. The shake-table test, performed at the TREES laboratory of EUCENTRE (Pavia, Italy), was part of a larger research initiative at EPFL (Lausanne, Switzerland) that addresses the seismic behaviour of mixed RC-URM structures. The half-scale test unit was subjected to several shakings of different intensity levels. The paper presents the geometry of the test unit, the properties of the construction materials, the instrumentation, and outlines the organization of the recorded data. Two sets of data are available: the unprocessed data and a second set of processed data where conventional and optical measurements are synchronised. This second set contains also some derived data, which allows to quickly plot key quantities such as base shear and top displacement. The aim of the paper is to provide all information required by the reader for analysing the test data and using it for validation purposes of numerical and mechanical models. The performance of the test unit is described in a companion paper.</p>
Data for: New approach for processing recycled carbon staple fiber yarns to unidirectional reinforced recycled carbon staple fiber tape
<p>This data set was generated at Leibniz-Institut für Verbundwerkstoffe GmbH in the project "Process analysis of the pseudo-plastic deformation behavior of unidirectional reinforced staple fiber organo sheets" is funded by the German Research Foundation (DFG) – funding reference 471480678. The goal was to develop a novel process approach for the further processing of staple fiber yarns made from long recycled carbon fibers (rCF) and polyamide 6 (PA6) to highly aligned staple fiber tapes. For this purpose, the input material as well as the manufactured tapes were characterized and examined in thermal analyses. In addition, the influence of the process parameters in the tape manufacturing process on the width and thickness of the tapes and the mechanical properties were investigated. The flexural and tensile properties were then compared with a laminate wound from the staple fiber yarns. The data set is divided in the results of the TGA, DSC, laser profile scans, tensile tests and flexural tests.</p>
Mesh dataset for the paper "Bending-Reinforced Grid Shells for Free-form Architectural Surfaces"
<p>If using this dataset, please cite the paper: </p> <p>Laccone, F., Pietroni, N., Cignoni, P., Malomo, L.: Bending-Reinforced Grid Shells for Free-form Architectural Surfaces. Computer-Aided Design. Available online 23 December 2023, 103670.</p> <p>https://doi.org/10.1016/j.cad.2023.103670</p>
Datasets: Enhancing Anger Management via Reinforcement Learning: A Comparative Analysis of the PPO Algorithm with Optimised Hyperparameters
Open the record for dataset details and reuse information.
Data from: Genomic and bioacoustic variation in a midwife toad hybrid zone: a role for reinforcement?
<p>This data package includes the following datasets and scripts used in the corresponding publication: </p> <ul> <li>An alignment (fasta format) of the 16S sequences obtained fromt the 221 new <em>A. obstetricans</em>/<em>almogavarii </em>samples barcoded in this study + the <em>A. cisternasii</em> sequence used as outgroup (16S_alignment.fas).</li> <li>A matrix of 1,642 SNPs genotyped in 89 <em>A. obstetricans</em>/<em>almogavarii </em>samples used for ancestry analyses (n89p41r0.5wrs_1642SNP_STRUCTURE.str).</li> <li>The R script used to compute geographic clines with HZAR and their graphical displays (Cline_analyses.r) and the input files it uses (Transect_Q_mtDNA_HZAR.csv; Transect_n57p19r0.5_diag_loci_HZAR.csv; dist_transect.txt; clines_diag_SNPs_1perlocus.csv).</li> <li>The R script used for the bioacoustic analyses (Alytes_FR_Bioacoustics.r) and the corresponding input data extracted from 71 mating calls of <em>A. obstetricans</em>/<em>almogavarii </em>(Alytes_FR_Bioacoustics.csv) the R script used for their analysis.</li> </ul>
Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"
<pre># Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"<br>Author: Naoto Yoshida<br><br>How to use:<br>1. Clone https://github.com/ugo-nama-kun/journalpaper_robot_2024 from github.<br>2. Extract data_20241119.zip in the cloned repository.<br>3. Run each plot_Fig*.py</pre>
Data and Code for 'Increased generalization in a peak procedure after delayed reinforcement'
<p>This dataset comes from the paper</p> <p>Buritica, J., & Alcala, E. (2019). Increased generalization in a peak procedure after delayed reinforcement. <em>Behavioural processes</em>, 169, 103978.</p> <p>The data comes in raw files and processed files. Commented scripts are also provided. For information about its structure see the Readme file.</p> <p><strong>License</strong></p> <p>CC-BY-4.0</p>
Mechanical behavior of textile reinforced alkali-activated mortar based on fly ash, metakaolin and ladle furnace slag
<p>Data corresponding to all figures and tables presented in the publication</p>
Imaging data of mechanically loaded, micro-patterned, silk-reinforced cellulose films with gold coating for flexible electrodes in medical implants
<p>Neurodegenerative diseases can be treated using a functional interface between the physically soft tissue such as brain and the man-made electrodes. The orders of magnitude harder neural probes cause local injuries, due to periodic micromovements owing to breathing and pulsatile blood flow leading to encapsulation and related collapsing signals. An alternative to the currently used neural implant films including polyimide, poly(p-xylylene), SU-8 - epoxy-based negative photoresist, liquid crystal polymer, and benzocyclobutene is the natural polymer cellulose with an elastic modulus between 100 and 200 MPa. This article elucidates the measurement of the mechanical properties of bare as well as mono- and double-layer silk-reinforced cellulose in phosphate-buffered saline using a universal testing machine. In addition, the article contains electron microscopy data of these micro-structured, gold-coated films subsequent to peel-off tests to access the impact of micro-structures on gold adhesion on cellulose. These imaging data were completed by electron micrographs of mechanically loaded gold-coated cellulose films to demonstrate the impact of micro-structures on crack formation. Finally, the phosphate-buffered saline-induced swelling of the micro-structure was visualized by electron micrographs obtained before and after two-month storage in air and phosphate-buffered saline, respectively.</p>
Source codes and datasets for the paper "DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning"
<p>This contains the<strong> reproducible source code and dataset </strong>for the paper "DRLComplex : Reconstruction of protein quaternary structures using deep reinforcement learning paper"</p>
Conflict over fertilization underlies the transient evolution of reinforcement
<p>When two species meet in secondary contact, the production of low-fitness hybrids may be prevented by the adaptive evolution of increased prezygotic isolation, a process known as reinforcement. Theoretical challenges to the evolution of reinforcement are generally cast as a coordination problem, i.e. "how can statistical associations between traits and preferences be maintained in the face of recombination?" However, the evolution of reinforcement also poses a potential conflict between mates. For example, the opportunity costs to hybridization may differ between the sexes or species. This is particularly likely for reinforcement based on postmating prezygotic (PMPZ) incompatibilities, as the ability to fertilize both conspecific and heterospecific eggs is beneficial to male gametes, but heterospecific mating may incur a cost for female gametes. We develop a population genetic model of interspecific conflict over reinforcement inspired by "gametophytic factors'', which act as PMPZ barriers among <em>Zea mays</em> subspecies. We demonstrate that this conflict results in the transient evolution of reinforcement––after females adaptively evolve to reject gametes lacking a signal common in conspecific gametes, this gamete signal adaptively introgresses into the other population. Ultimately the male gamete signal fixes in both species, and isolation returns to pre-reinforcement levels. We interpret geographic patterns of isolation among <em>Z. mays</em> subspecies considering these findings and suggest when and how this conflict can be resolved. Our results suggest that sexual conflict over fertilization may pose an understudied obstacle to the evolution of reinforcement. </p>
Estimation of reinforced urn processes under left-truncation and right-censoring
<p>We propose a nonparametric estimator for bivariate left-truncated and right-censored (LTRC) observations that combines the Expectation-Maximization (EM) algorithm and the Reinforced Urn Process (RUP). The resulting Expectation-Reinforcement (ER) algorithm allows for the inclusion of experts' knowledge in the form of a prior distribution, thus belonging to the class of Bayesian models. This can be relevant in applications where the data is incomplete, due to biases in the sampling process, as in the case of left-truncation and right-censoring. With this new approach, the distribution of the truncation variables is also recovered, granting further insight into those biases, and playing an important role in applications like prevalent cohort studies. The estimators are tested numerically using artificial and empirical datasets and compared with other methodologies such as copula models and the Kaplan-Meier estimator.</p>
Structure of single-walled carbon nanotube reinforced polymer matrix composites
<p><strong>Structure of single-walled carbon nanotube reinforced polymer matrix composites</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>A composite material, also called a composite, is a solid material that results when two or more different substances, each with its own characteristics, are combined to create a new substance whose properties are superior to those of the original components in a specific application. The term composite more specifically refers to a structural material within which a fibrous material is embedded. The remarkable properties of composites are achieved by embedding fibers of one substance in a host matrix of another. In materials science, a polymer matrix composite is a composite material composed of a variety of short or continuous fibers bound together by a matrix of organic polymers. Polymer matrix composites are designed to transfer loads between fibers of a matrix. Some of the advantages with polymer matrix composites include their light weight, high resistance to abrasion and corrosion, and high stiffness and strength along the direction of their reinforcements. The function of the matrix in polymer matrix composites is to bond the fibers together and transfer loads between them. Polymer matrix composites matrices are typically either thermosets or thermoplastics. Thermosets are by far the predominant type in use today. Thermosets are subdivided into several resin systems including epoxies, phenolics, polyurethanes, and polyimides. Of these, epoxy systems currently dominate the advanced composite industry. Unlike fiber-reinforced polymer matrix composites, nanomaterials reinforced polymer matrix composites are able to achieve significant improvements in mechanical properties at much lower loadings. Carbon nanotubes in particular have been intensely studied due to their exceptional intrinsic mechanical properties and low densities. In particular carbon nanotubes have some of the highest measured tensile stiffnesses and strengths of any material due to the strong covalent bonds between carbon atoms. However, in order to take advantage of the exceptional mechanical properties of the nanotubes, the load transfer between the nanotubes and matrix must be very large. Like in fiber-reinforced composites, the size dispersion of the carbon nanotubes significantly affects the final properties of the composite. Long carbon nanotubes lead to an increase in tensile stiffness and strength due to the large-distance stress transfer and crack propagation prevention. On the other hand, short carbon nanotubes do not lead to any enhancement of properties without any interfacial adhesion. However once modified, short carbon nanotubes are able to further improve the stiffness of the composite, however there is still very little crack propagation countering. In general, long and high aspect ratio carbon nanotubes lead to greater enhancement of mechanical properties, but are more difficult to process. Aside from size, the interface between the carbon nanotubes and the polymer matrix is of exceptional importance. In order to achieve better load transfer, a number of different methods have been used to better bond the carbon nanotubes to the matrix by functionalizing the surface of the carbon nanotube with various polymers. These methods can be divided into non-covalent and covalent strategies. Non-covalent carbon nanotube modification involves the adsorption or wrapping of polymers to the carbon nanotube surface, usually via van der Waal's or π-stacking interactions. In contrast, covalent functionalization involves direct bonding onto the carbon nanotube. This can be achieved in a number of ways, such as oxidizing the surface of the carbon nanotube and reacting with the oxygenated site, or using a free radical to directly react with the carbon nanotube lattice. Covalent functionalization can be used to directly attach the polymer to the carbon nanotube, or to add an initiator molecule which can then be used for further reactions.</p>
Fluid mechanics of carbon nanotube reinforced polymer composites
<p><strong>Fluid mechanics of carbon nanotube reinforced polymer composites</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>Fluid mechanics is the science concerned with the response of fluids to forces exerted upon them. It is a branch of classical physics with applications of great importance in hydraulic and aeronautical engineering and chemical engineering. Fluid mechanics is a subject with almost endless ramifications, and the account that follows is necessarily incomplete. Some knowledge of the basic properties of fluids will be needed. Fluids are not strictly continuous media in the way that all the successors of Euler and Bernoulli have assumed, for they are composed of discrete molecules. The molecules, however, are so small and, except in gases at very low pressures, the number of molecules per milliliter is so enormous that they need not be viewed as individual entities. There are a few liquids, known as liquid crystals, in which the molecules are packed together in such a way as to make the properties of the medium locally anisotropic, but the vast majority of fluids are isotropic. In fluid mechanics, the state of an isotropic fluid may be completely described by defining its mean mass per unit volume, or density, its temperature, and its velocity at every point in space, and just what the connection is between these macroscopic properties and the positions and velocities of individual molecules is of no direct relevance. A number of phenomena of considerable physical interest can be discussed using little more than the law of conservation of energy. However, the argument has so far been restricted to cases of steady flow. To discuss cases in which the flow is not steady, an equation of motion for fluids is needed, and one cannot write down a realistic equation of motion without facing up to the problems presented by viscosity, which have so far been deliberately set aside. Thermodynamics is the science of the relationship between heat, work, temperature, and energy. In broad terms, thermodynamics deals with the transfer of energy from one place to another and from one form to another. The key concept is that heat is a form of energy corresponding to a definite amount of mechanical work. Although thermodynamics developed rapidly during the 19th century in response to the need to optimize the performance of steam engines, the sweeping generality of the laws of thermodynamics makes them applicable to all physical systems. In particular, the laws of thermodynamics give a complete description of all changes in the energy state of any system and its ability to perform useful work on its surroundings. Classical thermodynamics does not involve the consideration of individual atoms or molecules. Such concerns are the focus of the branch of thermodynamics known as statistical thermodynamics, or statistical mechanics, which expresses macroscopic thermodynamic properties in terms of the behavior of individual particles and their interactions. It has its roots in the latter part of the 19th century, when atomic and molecular theories of matter began to be generally accepted. The application of thermodynamic principles begins by defining a system that is in some sense distinct from its surroundings. In general, systems are free to exchange heat, work, and other forms of energy with their surroundings. A particularly important concept is thermodynamic equilibrium, in which there is no tendency for the state of a system to change spontaneously. For example, the gas in a cylinder with a movable piston will be at equilibrium if the temperature and pressure inside are uniform and if the restraining force on the piston is just sufficient to keep it from moving. The system can then be made to change to a new state only by an externally imposed change in one of the state functions, such as the temperature by adding heat or the volume by moving the piston. A sequence of one or more such steps connecting different states of the system is called a process.</p>
Data publication: Virtual experiments for steel fiber reinforced high performance concrete (HPC)
<p>This data set contains all necessary inputs for the virtual experiments using an ellipsodal RVE for steel fiber reinforced high performance concrete (HPC), including discretization data, boundary conditions, material parameters and numerical results. The discretization is realized in terms of the finite element method. </p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (inputs, outputs, analysis)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt: contains raw data that are used for analysis, also conatin folder for GaMD testing.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. GaMD-testing :</p> <p> 1. Input file of GaMD used to run testing and output gamd.log files for multiple run of σOP 1.2 - 1.4 and σOD 2.5.</p> <p> 4. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant: contains raw data that are used for analysis.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant: contains raw data that are used for analysis.</li> </ul> <p><br> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>03_TT_analysis.tar.gz - TransportTools: contains config file and all the raw data from all set and subset of reclustered (using in-house python script) caver calculations used for running TT.</li> </ul> <p> 1. Caver input data for comparison between 500ns, 1 us, 2.5 us and 5us between LinB-Wt and it’s mutants.<br> 2. TransportTools log file.<br> 3. Main statistics result of comparative analysis.</p> <ul> <li>04_reweighting.tar.gz: directory contains reweighted .csv files after running in-house reweighting protocol.<br> <br> 1. GaMD log files from each simulation of LinB-Wt and it’s mutants.<br> 2. CSV files from TT result folder.<br> 3. Result *.csv file contained reweighted tunnel properties in folder reweighted_filtered_new.</li> <li>05_caverdock.tar.gz: contains raw data for caverdock calculations uisng 100 best tunnels with four ligands 2-bromoethanol (be), 1,2-dibromoethane (dbe), Bromide ion (br-) and water (h2o).</li> </ul> <p> 1. Top 100 tunnels present in tunnel folder for all three tunnels ST, p1b and p3 with subdirectory containing three variants and four ligand, whichare used for running caverdock.<br> 2. Ligand *.pdbqt file and receptor *.pdbqt are present in each 100 tunnel folder of respective caverdock calculation.<br> 3. Inside each variant and each ligand, there is respective result of migration analysis with energy barrier calculation of respective tunnels *energy_barriers-new.log* and further simplied *.csv files that was used for preparing figure in manuscript.</p> <p> </p>
Monotonic Tensile Testing of Corroded Reinforcing Steel Bars Database
<p>The database presented here provides a collection of 1,387 monotonic tensile tests performed on corroded reinforcing steel bars from 26 experimental programs available in the literature. The database includes 1,255 corroded reinforcing bars, 132 uncorroded control samples, 19 independent variables, and 17 dependent variables. Nine of the independent variables are categorical, and ten are numerical. 274 samples were naturally corroded, 271 were corroded under environmentally-induced conditions (typically salt-spray exposure), and 707 were corroded artificially under an impressed-current regime. All observations (individual bar tests) are statistically independent, as each data entry represents one independent test. Empty cells indicate non-reported variables.</p> <p>This database was compiled as part of the primary author's Ph.D. research for the purpose of predictive machine learning and empirical modeling. Published articles applying the database can be accessed at:</p> <ul> <li><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.conbuildmat.2024.137023" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.conbuildmat.2024.137023</a></li> <li><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dibe.2024.100527" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dibe.2024.100527</span></span></a></li> </ul> <p>Please see the accompanying User's Manual PDF for a complete description of the input and response variables, nomenclature, abbreviations, and formulations included in this database. </p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 6. The general conception of our asynchronous system BCI (offline - online) for reinforcement of a joystick movement
<p>Once the motor imagery is identified, a command may be associated to this mental task in order to control a machine (Prataksita et al., (2014)) (Guger et al., 1999). In this work, we constructed a new Simuhnk/MathWork model to translate on-line the EEG signals into low-level commands. Fig. 6 shows our experimental EEG-based BCI System </p>
Interlocked thin-ply reinforcements for improved fracture toughness and compression after impact
<p>This dataset contains data showing the effect of interlocked thin-ply reinforcement units on the fracture toughness and compression after impact strength of CFRP laminates. For both the fracture toughness and the CAI experiments, tests were conducted on non-reinforced baseline specimens, as well as reinforced specimens. These experiments were conducted to test a reinforcement concept, consisting of creating reinforcement units by interlocking two thin-ply prepreg layers with a tab-and-slit geometry. These reinforcement units are then inserted between the plies of a regular composite lay-up.</p> <p>This reinforcement concept was first presented at the 18th European Conference on Composite Materials (2018, Athens, Greece). More information on the concept can be found at: http://wwwf.imperial.ac.uk/aeronautics/research/pinholab/interlock-tabs/. </p> <p>The fracture toughness section of this dataset contains both mode I and mode II data. The mode I data was acquired using DCB specimens, and the mode II data was obtained from 4-point end notch flexure tests. Force, displacement, crack length, and strain energy release rate during the test are provided for all specimens.</p> <p>The CAI section of the dataset contains post-impact C-scans, as well as force, displacement, and strain gauge data from the subsequent compression tests.</p> <p><br> </p>
Dataset of article: Residual capacity of RC beams subjected to impact loading: influence of reinforcement ductility. Part 4: Series I20
<p><strong>Part 4: Series I20</strong></p> <p>Results in this dataset are presented in Johansson et. al. (2024). Any results used from this open access data shall be cited to:<br><br><span>Johansson M., Leppänen J., Andersson M. and Pettersson E. (2024). Residual Capacity of RC Beams Subjected to Impact Loading: Influence of reinforcement ductility. <em>International Journal of Protective Structures</em>. </span><a href="https://doi.org/10.1177/20414196241277221"><span>https://doi.org/10.1177/20414196241277221</span></a></p> <p>The presented data is based on Andersson and Pettersson (2019):</p> <p>Andersson M. and Pettersson E. (2019). Reinforced concrete beams subjected to drop-weight impact: Experimental study of the influence of reinforcement properties on the structural response. Department of Architecture and Civil Engineering. Gothenburg, Sweden: Chalmers University of Technology. Master’s Thesis ACEX30-19-27.</p> <p>---</p> <p>This dataset consists of the following records:</p> <p><strong>Part 1: General information</strong></p> <p>Andersson and Pettersson 2019 - Reinforced Concrete Beams Subjected to Drop-Weight Impact</p> <p><strong>Part 2: Series S</strong></p> <p>Static tests – COM Correlate files</p> <p><strong>Part 3: Series I10</strong></p> <p>Static tests – COM Correlate files</p> <p>Impact tests – High speed images</p> <p>Impact tests – COM Correlate files</p> <p><strong>Part 4: Series I20</strong></p> <p>Static tests – COM Correlate files</p> <p>Impact tests – High speed images</p> <p>Impact tests – COM Correlate files</p> <p><strong>Part 5: Result files</strong></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.