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90 results for “vibration data”
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>
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>
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>
Data Associated with the paper "Bell correlations between light and vibration"
<p>Data Associated with the <a href="http://doi.org/10.1126/sciadv.abb0260">following paper</a></p> <blockquote> <p>S. Tarrago Velez, V. Sudhir, N. Sangouard, C. Galland, Bell correlations between light and vibration at ambient conditions. Sci. Adv. 6, eabb0260 (2020).</p> </blockquote> <p>A thorough explanation of the experiment performed is available there.</p> <p> </p> <p>The file <strong>RawData_DelaySweep.zip</strong> contains the raw count information as obtained during the experiment. It is organized in different folders, each containing the data associated with one particular position of the delay stage. The file naming convention has the position, followed by the angles in detection (thA corresponds to the Stokes detection arm and thB corresponds to the anti-Stokes detection arm), and the acquisition time. As explained in the Supplementary Information Sec. 2.1, there are multiple measurements for each combination of delay and detection settings. The zip file also contains the file AnalysisBellv3.py, which was used to analyze the data. Running the file produces 'resultsBell.txt' and 'results_g2.txt', which contain the results for the CHSH parameter and the second order cross correlation, respectively.</p> <p> </p> <p>The file <strong>RawData_Visibility.zip</strong> contains the raw count information obtained for the visibility curves, as expained in the Supplementary Information Sec. 2.1. The files follow the same naming convention as those in RawData_DelaySweep.zip. It also contains the file AnalysisCounts.py, which analyzes the data to obtain the correlation parameters.</p> <p> </p> <p>The file <strong>BellCorrelations_CompiledData.xlsx</strong> is a spreadsheet containing the results of the analysis of the previous two sets of data.</p>
Data set from ambient vibration monitoring and static loading of a steel stringer bridge subject to imposed damage
<p>This data set contains structural response measurements acquired from the Route 345 Bridge over Big Sucker Brook in Waddington, NY prior to demolition and replacement. This steel stringer bridge was instrumented with dual-axis accelerometers and strain transducers and response measurements were obtained prior to and following mechanically imposed damages. Accelerometer measurements were obtained under vehicle passes and strain measurements were acquired during static loading of the span with a truck positioned at three prescribed locations. All measurement data is contained in a single h5-file (hierarchical data format version 5). The data is shared with the intent of promoting the advancement of structural health monitoring and vibration-based damage detection. </p> <p><strong>Version 2 Note: </strong>This version corrects the strain measurement data. Due to an index counter error in the script that compiled the strain measurement data into the h5-file, the strain measurement data in the Version 1 data were incorrectly sourced from a single scenario. The strain measurements are corrected in this new version. No other changes were made in the h5-file.</p>
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
<p>Energy harvesting from flow induced vibrations (FIV) in flexible bodies offer opportunities for power generation in biomimicking robotic devices and is an active area of research. The focus of this study is on investigating the underlying physics and qualitatively analysing the energy extraction scenarios in similar structural systems, comprising of a flexible piezoelectric flapper in a low Reynolds number flow regime. A high-fidelity three-way fully coupled fluid-structure-electric energy solver is developed in-house to study the energy harvesting capabilities of such a flapper, its hydrodynamic characteristics and the associated unsteady flow-field. The results indicate that the flapper deformation profiles at the most efficient harvesting regimes, resemble the propulsion gaits of natural swimmers. Investigations on the effects of a sinusoidal heaving actuation reveal no significant impact on the harvested power at the high yield (high power output) regime, identified under the passive condition showing biomimetic gait. This study provides mechanics based insights that is expected to be useful for bio-inspired designs of FIV based harvesters.</p>
Data from: Biomechanical properties of non-flight vibrations produced by bees
<p>Bees use thoracic vibrations produced by their indirect flight muscles for powering wingbeats in flight, but also during mating, pollination, defence, and nest building. Previous work on non-flight vibrations has mostly focused on acoustic (airborne vibrations) and spectral properties (frequency domain). However, mechanical properties such as the vibration's acceleration amplitude are important in some behaviours, e.g., during buzz pollination, where higher amplitude vibrations remove more pollen from flowers. Bee vibrations have been studied in only a handful of species and we know very little about how they vary among species. Here, we conduct the largest survey to date of the biomechanical properties of non-flight bee buzzes. We focus on defence buzzes as they can be induced experimentally and provide a common currency to compare among taxa. We analysed 15,000 buzzes produced by 306 individuals in 65 species and six families from Mexico, Scotland, and Australia. We found a strong association between body size and the acceleration amplitude of bee buzzes. Comparison of genera that buzz-pollinate and those that do not suggests that buzz-pollinating bees produce vibrations with higher acceleration amplitude. We found no relationship between bee size and the fundamental frequency of defence buzzes. Although our results suggest that body size is a major determinant of the amplitude of non-flight vibrations, we also observed considerable variation in vibration properties among bees of equivalent size and even within individuals. Both morphology and behaviour thus affect the biomechanical properties of non-flight buzzes.</p>
The Politecnico di Torino rolling bearing test rig: description of the open-access data for vibration monitoring and diagnostics
<p>Accelerometric measurements from the rolling bearing test rig of the Dynamic and Identification Research Group (DIRG), Department of Mechanical and Aerospace Engineering, Politecnico di Torino.</p> <p>Goals:</p> <p> • Vibration Monitoring, Bearing Diagnostics, Damage detection, Damage localization, Damage classification, Damage assessment.</p> <p>Features:</p> <p> • high-speed spindle driving a hollow shaft supported by a couple of identical roller bearings B1 and B3. B1 is the bearing under analysis and features various damages.</p> <p> • two damage types (indentations on a roller and on the inner ring) and severities (0, 150, 250, 450 µm).</p> <p> • a central, larger roller bearing (B2) is loaded through a sledge generating a controlled radial force measured by a load cell.</p> <p> • lubrication is obtained by oil injection into the hollow shaft.</p> <p> • a K-type thermocouple is used to monitor the temperature (manually recorded).</p> <p> • two triaxial accelerometers are mounted on the supports of bearings B1 and B2.</p> <p>Dataset:</p> <p> • stationary acquisitions at different speed & load combinations (speed: 0, 100, 200, 300, 400, 500 Hz; load: 0, 1000, 1400, 1800 N).</p> <p> • endurance acquisitions of the bearing featuring the 450µm roller indentation. Monitoring of the damage evolution for about 230 hours under the same speed and load condition.</p> <p> </p> <p>The extended description of the dataset can be found in the attached pdf "Description and analysis of open access data" or in:</p> <p>A.P. Daga, A. Fasana, S. Marchesiello, L. Garibaldi, The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data, Mechanical Systems and Signal Processing 120 (2019) 252–273. doi:10.1016/j.ymssp.2018.10.010.</p>
Supporting Data for: The V30 Benchmark Set for Anharmonic Vibrational Frequencies of Molecular Dimers
<p>Intermolecular vibrations are extremely challenging to describe but are the most crucial part for determining entropy and hence free energies, and enable for instance the distinction between different crystal-packing arrangements of the same molecule via THz spectroscopy. Herein, we introduce a benchmark data set - V30 - containing 30 small molecular dimers with intermolecular interactions ranging from exclusively van-der-Waals dispersion to systems with hydrogen bonds. All calculations are performed with the gold standard of Quantum Chemistry CCSD(T). We discuss vibrational frequencies obtained via different models starting with the harmonic approximation over independent Morse oscillators up to second-order vibrational perturbation theory (VPT2), which allows a proper anharmonic treatment including coupling of vibrational modes. However, large amplitude motions present in many low-frequency intermolecular modes are problematic for VPT2. In analogy to the often used treatment for internal rotations, we replace such problematic modes by a simple one-dimensional hindered rotor model. We compare selected dimers with available experimental data or high-level calculations of potential energy surfaces and show that VPT2 in combination with hindered rotors can yield a very good description of fundamental frequencies for the discussed subset of dimers involving small and semi-rigid molecules.<br><br>This supporting dataset includes the calculated force constants, harmonic frequencies, Morse frequencies, VPT2 frequencies, and the optimized structures for the V30 dataset. See the included README.md file for more details. The related preprint can be found at <a href="https://doi.org/10.48550/arXiv.2209.04392">https://doi.org/10.48550/arXiv.2209.04392</a>.</p>
GNSS data of ambient vibrations and artificial excitations of the Aaresteg bridge.
<p>GNSS Data:</p> <p>- Instrument: Java GrAnt-G3T antenna and Septentrio PolaRx receiver</p> <p>- sampling rate: 20 Hz</p> <p>- Bandwidth of loop filter: auto adjust</p> <p>- Date: 2021-09-27</p> <p>Location:</p> <p>Instrument was set up on the eastern railing of the Aaresteg bridge, 1.125 m height above the wooden planks. The bridge was excited by jumping, twisting, running, walking, impulse hammering and combinations thereof. </p>
Data and code associated with the paper 'Measurement-induced collective vibrational quantum coherence under spontaneous Raman scattering in a liquid'
<p>Data and code associated with the following paper <a href="https://doi.org/10.1038/s41467-023-38483-9">V. Vento, S. Tarrago-Velez et al., Nat. Commun. (2023)</a></p> <p>A thorough explanation of the experiment performed is available there.</p> <p>The name of each sub-folder and file in <strong>CS2_data_code.zip</strong> indicates the corresponding figure number ("FIG #") and the type of content ("Data", "Analysis" or "Model").</p> <p> </p>
Data for Transient 2D IR spectroscopy and multiscale simulations reveal vibrational couplings in the Cyanobacteriochrome Slr1393-g3
<p>Data used in the Manuscript Transient 2D IR spectroscopy and multiscale simulations reveal vibrational couplings in the Cyanobacteriochrome Slr1393-g3</p>
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
Open the record for dataset details and reuse information.
Data from: Biomechanical properties of non-flight vibrations produced by bees
Open the record for dataset details and reuse information.
Data and code from: Life under leaves: Substrate-borne vibrations provide a window into the behavior and ecology of two miniaturized geckos
Open the record for dataset details and reuse information.
Fine-Grained Activities of Daily Living Data with Structural Vibration and Electrical Load Sensing
<p>Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. We utilize structural vibration sensing and electrical load sensing to acquire multiple fine-grained kitchen activities under a lab structure setting.</p> <p>Each file contains the following values:<br> -RawData: time series of data for each channel (vibration on the table, vibration on the floor, load)<br> -Label: manually fine-grained labels of events<br> -Table: detected events start/stop index for vibration sensor on the table<br> -Floor: detected events start/stop index for vibration sensor on the floor<br> -Load: detected events start/stop index for load sensor<br> <br> Label notation:<br> 1 -- operating the kettle<br> 2 -- kettle on<br> 3 -- operating the microwave<br> 4 -- microwave on<br> 5 -- put things on the stove<br> 6 -- operating with stove<br> 7 -- stove on<br> 8 -- operating vacuum<br> 9 -- sweep floor<br> 10 -- walking/step<br> 11 -- miscellaneous<br> 12 -- synchronization signal (knock on the floor)<br> 13 -- vacant<br> 14 -- microwave door open</p>
Data and Code from Pritchard & Vallejo-Marin (2020) "Floral vibrations by buzz-pollinating bees achieve higher frequency, velocity and acceleration than flight and defence vibrations"
<p>Data and Code from Pritchard & Vallejo-Marin (2020) "Floral vibrations by buzz-pollinating bees achieve higher frequency, velocity and acceleration than flight and defence vibrations" Journal of Experimental Biology. doi: 10.1242/jeb.220541</p>
Dataset and Data analysis "Multimodal vibrational studies of drug uptake in vitro: Is the whole greater than the sum of their parts?"
<p>Data Analysis for the publication 10.1002/jbio.202000264.</p> <p>It is divided in three different folders describing three different part of the data analysis:</p> <p><strong>A. DATA TREATMENT RAMAN (Folder 1)</strong></p> <p><em>1. Import data using the Import_Raman script.<br> 2. Plot Spectra and integrate DOX band<br> Figure 1A<br> Figure 1B<br> 3. PCA<br> Figure 1D<br> Figure 1C<br> SM 1<br> 4. PLS<br> Figure 1F<br> Figure 1E</em></p> <p><strong>B. ANALYSIS OF IR DATA AND MULTIMODAL IR-RAMAN OF DOX UPTAKE (Folder 2)</strong></p> <p><em>1 Load Data IR<br> 2 Exploratory Analysis IR<br> Figure 2A<br> 3 PCA <br> SM 2<br> 4. Partial Least Squares vs time<br> Figure 2C<br> Figure 2B<br> 5. Partial Least Squares vs Raman Signal<br> Figure 2E<br> Figure 2D<br> 6. Make Averages and clean up Data for DATA Fusion<br> IR<br> Raman<br> 7. 2DCORR<br> Figure 3B<br> 8. MCR_ALS WITH DATA FUSION<br> Fitting of the concentration of Raman using the method in [9].<br> MCR-ALS<br> Figures 4 A, B and C</em></p> <p> </p> <p><strong>C. SIMULATION (Folder 3)</strong></p> <p><em>1. Load Raman DATA<br> 2. Simulate Raman DAta<br> 3. Load and simulate IR Data<br> 4. 2D corr<br> Figure 3A</em></p> <p> </p> <p>. Each folder contains a .mlx with the data analysis performed. Figures numbering corresponds to the one found in the article.</p>
Data from: Entangling the vibrational modes of two massive ferromagnetic spheres using cavity magnomechanics
<p>Source data for Figures 2 and 3.</p>
Data Set "Benchmarking of Vibrational Exciton Models Against Quantum-Chemical Localized-Mode Calculations"
<p>This data set accompanies the publication "Benchmarking of Vibrational Exciton Models Against Quantum-Chemical Localized-Mode Calculations" <br>by Anna M. van Bodegraven, Kevin Focke, Mario Wolter, and Christoph R. Jacob <br>(TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory '01_AIM':</p> <p> - input (structure.pdb, topol.top and *_input.txt) and results (*.log and<br> Hamiltonian/AtomPos/Dipole/Parameters.txt) from frequency calculations<br> with the Amide-I-maps (AIM) program for six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip), <br> each in vacuo or water with three different maps (Jansen, Skinner, <br> Tokmakoff) for 11 snapshots based on a MD run.<br> To rerun the calculations, you will have to change the paths in <br> the *_input.txt files (topfile, trjfile, sourcedir) accordingly<br> <br>Directory '02_SNF':</p> <p> - results (*.dat, *.out, coord and control) from frequency calculations <br> using Turbomole, SNF, and LocVib for six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip) <br> each in vacuo or water for snapshots based on an MD run.<br> <br>Directory '03_NMA':</p> <p> - coordinates and results from pyADF for NMA molecules each alligned <br> with a peptide bond from the six polypeptide test cases<br> (1gpB_310, ala_310, ala_hairpin, ala_helix, ala_strand, and trpzip) <br> each in water for 11 snapshots based on a MD run and input <br> (*_input.txt) and results (*.log and Hamiltonian/AtomPos/Dipole/Parameters.txt) <br> from calculations with the Amide-I-maps (AIM) program<br> <br>Directory '04_Handling_Data':</p> <p> - contains all used notebooks to extract the data, plot the figures <br> and calculate the errors<br> - RMSD_Error_vacuo/water.ipynb is used to calculate the overall shifts <br> and generates a map-dependent mean value to shift the frequencies of AIM<br> - Frequencies.ipynb and Couplings.ipynb are used to plot the figures <br> - RMSD_values_vacuo/water.ipynb show the calculations for the statistical <br> analysis<br> To use the notebooks, start with the Dictionary_setup_for_data_for_paper.ipynb <br> to set up the main dictionary from the calculated data</p>
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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.