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841 results for “vibrations”
Disentangling the percepts of illusory movement and sensory stimulation during tendon vibration in the EEG
Open the record for dataset details and reuse information.
Vibration-based Monitoring of a Small-scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark
<p>This repository contains all publicly available data related to the experimental part of <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2660">Sonkyo-Benchmark</a>. The data of each experimental case (R, A, B, C, D, E, F, G, H, I, J, K, L) and temperature point (-15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40) are stored in a zip file named "Case_<em>X</em>_(<em>T</em>)", where <em>X</em> denotes the case label and <em>T</em> refers to the temperature value. Each file "Case_<em>X</em>_(<em>T</em>).zip" contains two folders "Case_<em>X</em>_(<em>T</em>)_1" and "Case_<em>X</em>_(<em>T</em>)_2", wherein the test results from the two sensor layouts are stored. </p>
Effects of Sinusoidal Vibrations on the Motion Response of Honeybees - datasets
<p>data sets on the effects of sinusoidal stimuli on the motion activity of honeybees. For more details please refer to </p> <p>Stefanec, M., Oberreiter, H., Becher, M. A., Haase, G., & Schmickl, T. (2021). Effects of Sinusoidal Vibrations on the Motion Response of Honeybees. <em>Frontiers in Physics</em>, <em>9</em>, 318.</p> <p>amplitude_experiments.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different amplitudes at different frequencies.</p> <p>amplitude_experiments_with_velocity.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different amplitudes at different frequencies as well as a post-hoc derived intensity measurement at a certain amplitude. This intensity measurement was detected by laser vibrometer on the surface of the honeycomb and represents the measurement at the point in the region of interest that had the highest intensity. This measurement could not be made during the experiments on the animals, but had to be made post-hoc, since a laser vibration measurement was only possible without animals passing through the laser point.<br> <br> frequency_experiments.csv contains the data of measured motion activity according to the pixel-based motion index in a certain region of interest in regards to different frequency stimuli.</p>
Dataset accompanying the article: Exploring the Effects of Additional Vibration on the Perceived Quality of an Electric Cello
<p>Dataset accompanying the article: Exploring the Effects of Additional Vibration on the Perceived Quality of an Electric Cello. </p>
Crowdsourcing vibration data stemming from different transportation usages
<p> </p> <p>Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.</p>
Database for RailRad calculation method for simulating sound radiated by railway track vibrations
<p>This dataset contains precalculated acoustic transfer functions for efficiently calculating the sound radiated by railway track vibrations.</p> <p>The transfer functions contained in each file describe the complex sound pressure produced at a number of receiver locations given a unit velocity at a source element on the railway track surface, per frequency and at a fixed wavenumber along the track.</p> <p>Four different acoustic geometries are included: (1) a standard UIC60 rail in free space, (2) the rail in an acoustic half space, (3) the rail located above a slab track surface, and (4) identical geometry to (3) but including an acoustically hard hull of a passenger train geometry above the track.</p> <p>More information about the exact location of source and receiver coordinates can be found in the .hdf5 files, in the subgroup 'info'. The transfer functions themselves are located in the dataset 'tfs', which are matrices of size (Number of frequency lines x number of sources x number of receivers).</p> <p>More information can be found here https://github.com/janniktheyssen/railrad</p> <p>This collection of databases is part of ongoing work at CHARMEC / Chalmers University of Technology, Gothenburg, Sweden (https://www.charmec.chalmers.se/). Parts of the study have been funded from the European Union's Horizon 2020 research and innovation programme in the In2Track3 project under grant agreements No 101012456. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.</p>
Vibration-based smart sensor for high flow dust measurement
<p><strong>Abstract:</strong> Drying process of aggregates needed for asphalt manufacturing involves a high quantity of dust or filler that needs to be heated and extracted with the aid of a baghouse. A sensor that is able to measure the amount of filler aspirated will be a relevant innovation as the current state of the art for drying of aggregates involves a high amount of energy to heat all the aggregates so the highest amount of dust or filler is extracted. The final step of asphalt production is to mix all the components like bitumen, aggregates and cold filler itself. In the context of European project CAPRI [1,2], it is presented a prototype for measurement of filler flow based on vibration analysis, inside the pipe with an accelerometer in the insulator of an existing thermocouple subjected to the hard conditions of temperature and pressure. The paper shows the laboratory prototype results together with preliminary onsite evaluation previously to final demonstration. The paper provides also open access to all the data and results used as part of the commitment of CAPRI project with open science.</p> <p><strong>Keywords:</strong> Sensors, Innovation, Process Industry, Automation, Industry 4.0, Digital Transformation, Industrial Plants, Filler, Dust, Vibration, Signal processing, Smart sensing.</p>
Vibrational signals produced by wing buzzing in Cacopsylla pyrisuga males (Hemiptera: Psyllidae)
<p>High-speed camera (video files) and laser vibrometer (audio files) recordings of Cacopsylla pyrisuga males producing vibrational signals - a dataset accompanying the publication</p> <p>Polajnar J., Kvinikadze E., Harley A.W., Malenovský I. (2024) Wing buzzing as a mechanism for generating vibrational signals in psyllids (Hemiptera: Psylloidea). Insect Science. See the publication for details about the methodology used.</p> <p>The dataset additionaly includes tracked points at wing and abdomen tips from two videos, and an R script with instructions to read this data.</p>
SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Skin Vibrations Across the Upper Limb
<p>The repository contains the data for the toolbox released as part of the publication “SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb.” The toolbox and installation and usage instructions can be found on GitHub here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>. If you use these data or our toolbox please cite our publication: <a href="https://doi.org/10.1109/HAPTICS59260.2024.10520852">https://doi.org/10.1109/HAPTICS59260.2024.10520852</a>.</p> <p>Full citation: “Tummala, N., Reardon, G., Fani, S., Goetz, D., Bianchi, M., and Visell, Y. (2024) SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb. IEEE Haptics Symposium 2024. DOI: 10.1109/HAPTICS59260.2024.10520852” </p> <p> </p> <p><strong>Abstract From Manuscript</strong></p> <p>Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a data-driven toolbox for predicting skin vibrations across the upper limb in response to user-specified input forces. The toolbox leverages impulse response measurements that encode the physics of vibration transmission across the hands and arms of four participants and provides software tools for analyzing the predicted skin responses. We show that the SkinSource predictions closely match experimental measurements and confirm the underlying assumption of linear vibration transmission in the skin. We also demonstrate through several usage examples how SkinSource can act as a versatile computational platform for haptic research applications, such as characterizing vibrotactile transmission in the skin, engineering haptic interfaces, and investigating touch perception.</p> <p><strong> </strong></p> <p><strong>Dataset Description</strong></p> <p>This dataset comprises experimental data of 3-axis surface acceleration at 72 locations on the skin in response to unit impulsive forces supplied at 20 different input locations on the palmar hand surface. For details on our experimental procedure, please see our publication. This data is intended to be used as part of the SkinSource toolbox, which can be found here: <a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>.</p> <p><strong> </strong></p> <p><strong>Data Fields</strong></p> <p>The data is provided as a .mat file. This file contains a single variable “dataTable” of variable type “table.” The table contains 80 rows, each corresponding to a unique experimental condition (4 participants x 20 input locations), and contains the following fields:</p> <p><strong>Data </strong>(522x72x3) - 3D array containing the 3-axis skin acceleration at 522 time points (impulse responses) for each of 72 accelerometers. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for the accelerometer locations on the dorsal surface of the upper limb.</p> <p><strong>Model </strong>- The upper limb model number. This number specifies the participant that data was taken on.</p> <p><strong>Location</strong> -<strong> </strong>Number designating which input location on the palmar hand surface the data corresponds to. Please see the GitHub code and documentation (<a href="https://github.com/neelitummala/skinsource">https://github.com/neelitummala/skinsource</a>) for input location number mapping.</p>
Piezomagnetic vibration energy harvester with an amplifier
<p>This repository contains results of simulation of the effect of an amplification mechanism in a nonlinear vibration energy harvesting system where a ferromagnetic beam resonator is attached to the vibration source through an additional linear spring with a damper. The beam moves in the nonlinear double-well potential caused by interaction with two magnets. The piezoelectric patches with electrodes attached to the electrical circuit support mechanical energy transduction into electrical power. The results show that the additional spring can improve energy harvesting. By changing its stiffness, we observed various solutions. At the point of the optimal stiffness of the additional spring, the power output is amplified a few times depending on the excitation amplitude.</p>
Datasets for the paper "High-frequency voltage-driven vibrations in dielectric elastomer membranes" by G. Moretti et al.
<p>This upload contains the numerical datasets used for the numerical plots reported in the paper "High-frequency voltage-driven vibrations in dielectric elastomer membranes" by G. Moretti et al., Mechanical Systems and Signal Processing, Elsevier, 2022 (https://doi.org/10.1016/j.ymssp.2021.108677).</p> <p>Please refer to the readme file for information on the files structures and content. </p>
Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit and Effects on the Point Cloud
<p>This data repository contains vibration tests of an Ouster OS1-64 lidar including a docker based python environment and documentation.</p> <p>It consists of movement data, Fotos, IMU data, pointclouds and a ground truth measurements with an Riegl VZ6000 laser scaner.</p> <p>For further details see the linked publication and the example.ipynb notebook.</p> <p><strong>Quick start</strong></p> <ul> <li> <p>download the repo</p> </li> <li> <p>unzip the archive</p> </li> <li> <p>install VS code with the remote development extension</p> </li> <li> <p>install docker desktop</p> </li> <li> <p>open the folder in a new VS code window</p> </li> <li> <p>Say "yes" to open the folder inside a docker container</p> </li> <li> <p>wait for the container to start</p> </li> <li> <p>open the example jupyter notebook</p> </li> </ul> <p><strong>Structure</strong></p> <blockquote> <pre>├── .devcontainer .... VS code devcontainer (arm64 and amd64)</pre> <pre>├── Acceleromenter_A_z_deflection .... Accelerometer data</pre> <pre>├── Foto .... Fotos of the setup</pre> <pre>│ ├── VZ6000 .... Fotos of the ground truth measurements</pre> <pre>│ └── test_setup .... Fotos of the test setup</pre> <pre>├── Notebook .... A jupyter notebook with examples</pre> <pre>├── OS1_64_IMU .... Ouster IMU data</pre> <pre>├── OS1_64_pointcloud .... point cloud data</pre> <pre>├── VZ6000_groundtruth .... Ground truth data from Riegl VZ6000</pre> <pre> └── targets .... contains 2 different sets of reference</pre> <pre>│ ├── scene_aligned_by_reflectors</pre> <pre>│ └── targets_aligned</pre> <pre>└── files.csv .... an overview of the files and meta data</pre> </blockquote>
Electric Motor Vibrations Dataset
<p><strong>Scope:</strong></p> <p>This repository contains data provided by vibrations sensor that can be used in designing and testing ML algorithms for general classification problems or more specific one such as predictive maintenance.</p> <p><strong>Source of the data:</strong></p> <p>Data were obtained in the framework of CHIST-ERA SOON project with the aim of testing machine learning predictive maintenance algorithms.</p> <p><strong>Special remarks:</strong></p> <p>Each file name codes how the data was obtained and and implicitly the data label.</p> <p>In experiment were used two electrical motors named m1 and m2, where <em>m1</em> is the tested motor and <em>m2</em> is a second motor for obtaining a more complex testing environment (eg. supplemental noise source).</p>
Dataset on substrate-borne vibrations of Constrictotermes cyphergaster (Blattodea: Isoptera) termites
<p>Here we present data on distinct stimuli as elicitors of substrate-borne vibrations performed by worker and soldier termites belonging to the species<em> Constrictotermes cyphergaster</em> (Blattodea: Isoptera: Termitidae: Nasutitermitinae). The study consisted of assays where groups of termites were exposed to different air-borne stimuli and the vibrations thereby elicited were captured by an accelerometer attached under the floor of the arena in which the termites were confined. A video camera was also used as a visual complement. The data provided here contribute to fill a gap currently existing in published datasets on termite communication. </p>
First-principles prediction of the Co-Al phase diagram including configurational, vibrational and magnetic contributions
<p>Documentation for the Dataset used in the publication entitled "First-principles prediction of the Co–Al phase diagram including configurational, vibrational and magnetic contributions" <br>** These datasets comprise all configurations used in Co-Al system and their formation enthalpies at different temperatures, where configurational, vibrational and magnetic contributions were considered. Hcp Co and fcc Al were used as reference states. **<br>** More details about the methodology can be found in the paper "First-principles prediction of the Co-Al phase diagram including configurational, vibrational and magnetic contributions, Journal of Materials Research and Technology, 2024" **</p> <p>1. bcc-Co-Al.zip<br>- Description: bcc-Co-Al.zip is a compressed folder. It contains Al1-xCox configurations with bcc lattice used to fit the cluster expansion (CE). Each folder contains a POSCAR file that correspons to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p>2. fcc-Co-Al.zip<br>- Description: fcc-Co-Al.zip is a compressed folder. It contains Al1-xCox configurations with fcc lattice used to fit the CE. Each folder contains a POSCAR file that correspons to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p>3. hcp-Co-Al.zip<br>- Description: hcp-Co-Al.zip is a compressed folder. It contains Al1-xCox configurations with hcp lattice used to fit the CE. Each folder contains a POSCAR file that correspons to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>4. Formation enthalpies of bcc-Co-Al.xlsx<br>- Description: Formation enthalpies of bcc lattice in Co-Al system at different temperatures, which includes the effect of lattice vibration and magnetic excitation. Fcc Al and hcp Co were used as reference states.</p> <p>- Variable description by columns:<br> 1-(Folder name) - type: numerical (integer)<br> Description: Each folder name in the bcc-Co-Al.zip corresponds to a configuration.<br> 2- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 3- (H_f^(conf)(DFT) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by density functional theory (DFT) following eq.(18) in the paper.<br> 4- (H_f^(conf)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K fitted by CE. <br> 6- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 7- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K calculated by DFT, the bond length vs. bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 8- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K fitted by CE. <br> 10- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 11- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K calculated by DFT, the bond length vs. bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 12- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K fitted by CE. <br> 14- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 15- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K calculated by DFT, the bond length vs.bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 16- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K fitted by CE.<br> 18- (at. fraction of Co (%)) - type: numerical (float)<br> Description: The atomic fraction of Co in each configuration.<br> 19- (H_f^(conf+vib+mag)(Cal.) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1600 K calculated by DFT, the bond length vs.bond stiffness relationship and Monte Carlo simulation of the Heisenberg Hamiltonian following eq.(20) in the paper.<br> 20- (H_f^(conf+vib+mag)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1600 K fitted by CE.</p> <p><br>5. Formation enthalpies of fcc Co-Al.xlsx<br>- Description: Formation enthalpies of fcc lattice in Co-Al system at different temperatures, which includes the effect of lattice vibration and magnetic excitation. Fcc Al and hcp Co were used as reference states.</p> <p>- Variable descriptions by columns are the same as those of Formation enthalpies of bcc-Co-Al.xlsx.</p> <p><br>6. Formation enthalpies of hcp-Co-Al.xlsx<br>- Description: Formation enthalpies of hcp lattice in Co-Al system at different temperatures, which includes the effect of lattice vibration and magnetic excitation. Fcc Al and hcp Co were used as reference states.</p> <p>- Variable descriptions by columns are the same as those of Formation energies of bcc-Co-Al.xlsx.</p> <p><br>7. ECIs of bcc-Co-Al at different temperatures.txt<br>- Description: ECIs of bcc lattice in Co-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that configurational, vibrational and magnetic contributions were considered.</p> <p><br>8. ECIs of fcc-Co-Al at different temperatures.txt<br>- Description: ECIs of fcc lattice in Co-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that configurational, vibrational and magnetic contributions were considered.</p> <p><br>9. ECIs of hcp-Co-Al at different temperatures.txt<br>- Description: ECIs of hcp lattice in Co-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that configurational, vibrational and magnetic contributions were considered.</p> <p><br>10. Clusters of bcc-Co-Al.txt<br>- Description: Cluster information of bcc lattice in Co-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>11. Clusters of fcc-Co-Al.txt<br>- Description: Cluster information of fcc lattice in Co-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>12. Clusters of hcp-Co-Al.txt<br>- Description: Cluster information of hcp lattice in Co-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p>
First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions
<p>Documentation for the Dataset used in the publication entitled "First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions" <br>** These datasets comprise all configurations used in Al-Li system and their formation enthalpies at different temperatures, where both configurational and vibrational contribution were considered. Bcc Li and fcc Al were used as reference state. **<br>** More details about the methodology can be found in the paper "Wei Shao, Sha Liu, Javier LLorca, First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions, Computational Materials Science, 2023"**</p> <p>1. bcc-Al-Li.zip<br>- Description: bcc-Al-Li.zip is a compressed folder. It contains Al1-xLix configurations with bcc lattice used to fit the cluster expansion (CE). Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>2. fcc-Al-Li.zip<br>- Description: fcc-Al-Li.zip is a compressed folder. It contains Al1-xLix configurations with fcc lattice used to fit the CE. Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p>3. Formation enthalpies of bcc-Al-Li.xlsx<br>- Description: Formation enthalpy of each configuration in bcc Al-Li system at different temperatures, which includes the effect of lattice vibration. Bcc Li and fcc Al were used as reference state.</p> <p>- Variable descriptions by columns:<br> 1-(Folder nam) - type: numerical (integer)<br> Description: Each folder name in the bcc-Al-Li.zip corresponds to a configuration.<br> 2- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 3- (H_f^(conf)(DFT) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by density functional theory (DFT).<br> 4- (H_f^(conf)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration fitted by CE at 0 K. <br> 6- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 7- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 100 K calculated by DFT and bond length vs. bond stiffness relationship (L-S).<br> 8- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 100 K fitted by CE. <br> 10- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 11- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 200 K calculated by DFT and L-S.<br> 12- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 200 K fitted by CE. <br> 14- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 15- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K calculated by DFT and L-S.<br> 16- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K fitted by CE.<br> 18- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 19- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K calculated by DFT and L-S.<br> 20- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 400 K fitted by CE.<br> 22- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 23- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 500 K calculated by DFT and L-S.<br> 24- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 500 K fitted by CE. <br> 26- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 27- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K calculated by DFT and L-S.<br> 28- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K fitted by CE.<br> 30- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 31- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 700 K calculated by DFT and L-S.<br> 32- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 700 K fitted by CE.<br> 34- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 35- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K calculated by DFT and L-S.<br> 36- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 800 K fitted by CE. <br> 38- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 39- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K calculated by DFT and L-S.<br> 40- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K fitted by CE.<br> 42- (at. fraction of Li (%)) - type: numerical (float)<br> Description: The atomic fraction of Li in each configuration.<br> 43- (H_f^(conf+vib)(DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1000 K calculated by DFT and L-S.<br> 44- (H_f^(conf+vib)(CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1000 K fitted by CE.</p> <p>4. Formation enthalpies fcc-Al-Li.xlsx<br>- Description: Formation enthalpy of each configuration in fcc Al-Li system at different temperatures, which includes the effect of lattice vibration. Bcc Li and fcc Al were used as reference state.</p> <p>- Variable descriptions by columns are the same as those of Formation enthalpies of bcc-Al-Li.xlsx.</p> <p><br>5. ECIs of bcc-Al-Li at different temperatures.txt<br>- Description: ECIs of bcc lattice in Al-Li system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>6. ECIs of fcc-Al-Li at different temperatures.txt<br>- Description: ECIs of fcc lattice in Al-Li system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>7. Clusters of bcc-Al-Li.txt<br>- Description: Cluster information of bcc lattice in Al-Li system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>8. Clusters of fcc-Al-Li.txt<br>- Description: Cluster information of fcc lattice in Al-Li system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p>
Observation of a regular structure formation on the surface of vibrated ball beds started from random lose packing
<p>Near 4,000 2-mm diameter plastic balls were poured 88 times into plexiglass cylinder of internal diameter 26 mm. Then, such initially random loose-packing systems/beds were vibrated vertically with 100 Hz frequency using the vibration table Vibrax (Renfert GmbH, Germany) working in sinusoidal mode until a regular structure was observed on the cylinder surface. The power levels of the vibrations in the recorded ordering of balls were selected to represent all four levels (1, 2, 3 or 4) of vibrations available in the table, where number 1 means the weakest vibration and 4 means the strongest one.</p> <p>Locations of the balls on all sides of a vibrated cylindrical bed were simultaneously recorded on one video frame thanks to the use of two perpendicular mirrors, which enables observation of four images: one of the real cylinder and three of its mirror reflections. View of the table with the attached cylinder containing balls and two mirrors is presented in Fig. 1, while an explanation of the scene, as seen by the recording camera, is given in the scheme in Fig. 2. Video names were given in a standard form explained in the README.txt file.</p>
Movement of plastic balls in a long-vibrating cylinder: from disorder to structure formation with examples of its instability
<p>Many plastic balls, made from PolyOxyMethylene (POM) and of three different diameters 2, 3 and 4 mm, were used to observe the formation and stability of the structure in ball beds when, after pouring into a plexiglass cylinder, they were subjected to long-term vertical vibrations with frequency 100 Hz. The vibration table Vibrax (Renfert GmbH, Germany), used in the experiment and shown in Fig. 1, can function in two modes: sinusoidal (s) and nonsinusoidal (ns). The vibration table can act at four power levels of the vibrations, selected by an operator using the right knob of the table (see Fig. 1). In the presented series of 43 vibration experiments, always the highest power level was used. </p> <p>Locations of surface balls on all sides of a vibrated cylindrical bed were simultaneously recorded on one video frame thanks to the use of two perpendicular mirrors (see Fig. 1), which enables observation of four images: one of the real cylinder and three of its mirror reflections. An explanation of the scene, as seen by the recording camera, is given in the scheme in Fig. 2. Video names were given in a standard form to inform the user about the most important parameters (more in README.txt).</p> <p> </p>
Vibration Sensor and Process Data from Ferrosilicon Production
<p><strong>Elkem Facility</strong></p> <p>The facility specializes in producing ferrosilicon (FeSi) and ferrosilicon magnesium (FSM) master alloys. Elkem Bjølvefossen is among the world’s largest producers of FSM. Three reduction furnaces deliver the base metal which is then alloyed and refined to the right quality of FeSi or FSM. These alloys are important additives in the manufacturing of steel products. Silicon in the form of FeSi is used to remove oxygen from the steel and as an alloying element to improve the final quality of the steel. Silicon increases strength and wear resistance, elasticity, i.e., spring steels, scale resistance, and heat resistant steels and lowers electrical conductivity and magnetostriction.</p> <p>After tapping and refining, the ferro-alloys are crushed to grains ranging from 1 mm to 25 mm in size. Consumers of FeSi and FSM have strict requirements for particle size, related mainly to the chemical kinetics of their refining and alloying processes. For this reason, the crushed material is separated in sieves and packaged by particle size before shipment. Two lattice gratings inside the Mogensen shaker separate the material according to required particle size. </p> <p>The subject of the present study is a mechanical shaker platform containing one or more such sieves. The shaker is a <a href="https://www.mogensen.se/">Mogensen S0556</a> that was installed in 1996 in Bjølvefossen and is no longer produced in this type. This device is powered by two counter-rotating 1.2-horsepower AC motors operating at 960 RPM. Together with the spring suspension, these cause an elliptical motion that both transports and scatters the incoming material across the sieve. The shaker is engineered so that the motion transitions from a slanted ellipse at the in-feed to nearly linear at the output.</p> <p><strong>Vibration Data</strong></p> <p>Vibration data from two sensors. Each sensor measures acceleration in three axes with three different ADCs. </p> <p><strong>ERP and MES data</strong></p> <p>Manufacturing Execution System (MES) data as well as process data from the Enterprise Resource Planning (ERP) is given. It is providing information about the material that is currently being produced as well as the data from the scales from the material packing station where the bags with completed production were packed. MES data has a resolution of 5 seconds and the process data from ERP has a resolution of roughly 10 minutes. This operational data is meant to provide insight into the current state and throughput of the facility and will serve as labels for the correlation analysis with the vibration data. </p>
Dataset used in manuscript Tailored Nanoscale Plasmon-Enhanced Vibrational Electron Spectroscopy
<p>This file contains the raw dataset used in the manuscript "Tailored Nanoscale Plasmon-Enhanced Vibrational Electron Spectroscopy" published in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659)</p> <p><br> Data has been acquired using Nion Swift (https://nionswift.readthedocs.io/en/stable/). Experimental details can be found in L. H. G. Tizei et al Nano Letters, 2020 (doi: 10.1021/acs.nanolett.9b04659).<br> <br> The dataset has been analyzed using the following Python libraries:</p> <p>Numpy, Scipy, Hyperspy, Matplotlib</p> <p>EELS hyperspectral images have been aligned using the Hyperspy "align1D" method. Aligned EELS hyperspectral images are saved in files finished with "_Aligned.hspy":</p> <p>For the strong coupling experiments:<br> Tip 1 is on hBN<br> Tip 2 is on vacuum</p> <p>For each of the nanowires tips, a file with the fitted coefficients are available, as well as a plot of the data and the fitted curve.</p> <p>Datasets have been fitted with gaussian and/or lorentizan functions, as described in the published text.</p> <p>Any question can be forwarded to the corresponding authors of the published text.</p> <p> </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.