Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
26
datasets available to search
ShareScore release 0.9.0
Dataset results
26 results for “acoustic emission”
Dataset from: Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions
<p>Measures of functionality, proprioception, self reported status and joint acoustic emissions (AE) were recorded for a sample of general population. Specifically, threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded using two sensors in different frequency ranges and three modes of AE event detection were investigated during cycling with 30 and 60 rpm cadences.</p>
Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study
<p>This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in <em>IEEE Transactions on Biomedical Engineering</em>, doi: 10.1109/TBME.2023.3263388.</p> <p>Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. </p> <p><em><strong>Stages of artificially inflicted cartilage damage:</strong></em> the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment.</p> <p><strong><em>Sensor positions</em></strong>: medial and lateral knee</p>
[Data] Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction
<p>The dataset for this study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncovering notable discrepancies within the 50-750 kHz frequency range. Leveraging Machine Learning (ML) methodologies, we accurately predict composition for particular processing conditions. These fluctuations in AE signals primarily arise from unique microstructural alterations linked to martensitic phase transformation, corroborated by operando synchrotron X-ray diffraction and post-mortem SEM and EBSD analysis. Moreover, cracks are evident at the periphery of the printed parts, stemming from local inadequate heat input during the blending of Ti6Al4V with added Fe powder. These cracks are discerned via AE signals subsequent to the cessation of the laser beam, correlating with the presence of brittle intermetallics at their junction. This study highlights for the first time the potential of AE monitoring in reliably detecting footprints of martensitic transformations during the LPBF process. Additionally, AE is shown to prove valuable for assessing crack formations, particularly in scenarios involving premixed powders and necessitating precise selection of processing parameters, notably at part edges.</p>
Acoustic Emission dataset for impact localization: numerical and experimental case studies
<h1>Acoustic Emission dataset for Defect Detection in Aluminum plates</h1> <h2>Simulated data</h2> <h3>File name: Simulation.zip</h3> <p>Simulated AE signals based on a ray-tracing algorithm taking into consideration reflections with the mechanical boundaries of the medium (reflection up to the 4th order), which corresponds to a 1x1x0.003 m square aluminum plate.</p> <p>Each signal has been created by simulating the propagation between a transmitter (Tx, index from 1 to 40) and a Receiver (Rx, index from 1 to 25), grouped by Tx position and saved as a .mat struct ('data') containing the following fields:</p> <ul> <li>data.Rx = 2 x 25 matrix containing in the first and second row the x and y axis of the Rx position</li> <li>data.Rx = 2 x 25 matrix containing in the first and second row the x and y axis of the Tx position</li> <li>data.data = 8000 x 25 matrix containing the transmitted, propagated AE signal from Tx to Rx organized by column. Each AE instance constitutes of 8000 samples acquired at a sampling frequency of 2 MHz (indicated in the file name), one for each Rx given that Tx position.</li> <li>data.Label = 1x25 vector containing the ToA labels associated with each of the 25 Tx-rx pairs (per Tx position) computed by means of the Akaike Information Criterion. </li> </ul> <h2>Experimental data</h2> <h3>File name: Test_x0.xx_y0.yyFs2MHz_1x1x0.003_Al.csv</h3> <p>Experimental data collected with custom AE instrumentation as described in <a href="https://www.mdpi.com/1424-8220/22/3/1091">Ref 1.</a> </p> <p>One single file is a collection of 3 tests (three repetitions of the impact event at the same position), each of them containing three signals acquired simultaneously by three sensors located in proximity of three corners of a 1x1x0.003 aluminum plate having the same geometrical and numerical characteristics of the numerical one. The sensors acquire 5000 samples at a rate of 2 MHz (indicated in the file name) with a pre-trigger window of 1500 samples. The specific coordinates of the sensors are:</p> <ul> <li>s1 [x = 0.05, y = 0.95] m</li> <li>s2 [x = 0.05, y = 0.05] m</li> <li>s3 [x = 0.95, y = 0.05] m</li> </ul> <p>There are 9 files associated with as many impact positions, indicated by the "x0.xx_y0.yy" entry in the file name, with 0.xx and 0.yy corresponding to the x and y coordinate, respectively. Excitation has been provided by means of a waveform generator exciting a 3-cycle sinusoidal wave with central frequency of 250 kHz. More deatils about the electronics and the full setup are provided in the same reference above. </p> <p> </p> <p><em>This research work has been carried out within the Intelligent Sensor Systems Lab@University of Bologna, Italy. </em></p> <p><em>For any needs, warning or curiosities, please contact federica.zonzini@unibo.it</em></p>
Data from: Evaporation induced acoustic emissions in microfluidic vessels
<p>Fluid flow processes such as drainage and evaporation in porous media are crucial in geological and biological systems. The motion of the displacement front of a moving fluid through multi-phase interfaces is often associated with abrupt mechanical energy release, detectable as acoustic emissions. The exact origin of these pulses and their damping mechanisms are still subjects of debate. Here, we study the characteristics of such acoustic emissions during evaporation of water from artificial microfluidic vessels, inspired by the physiology of vascular water-transport in plants. From the extracted settling times of the recorded acoustic emissions, we identify three pulse types and attribute their origins to bubble formation, snap-off events and rapid pore invasion. We also show that the resonance frequencies between 10 and 70 kHz present in specific pulse types decrease with increasing vessel radius (ranging from 0.25 to 1.0 mm) and length (ranging from 2.5 to 10.0 mm). Our findings provide insight into evaporation-induced acoustic emissions from microfluidic systems, both natural and artificial, and their potential use in non-invasive inspection or vascular health monitoring.</p>
Data from: Evaporation induced acoustic emissions in microfluidic vessels
Open the record for dataset details and reuse information.
Data for: Hit2flux: A machine learning framework for boiling heat flux prediction using hit-based acoustic emission sensing
Open the record for dataset details and reuse information.
Dielectric Loss due to Charged-Defect Acoustic Phonon Emission
Open the record for dataset details and reuse information.
MATLAB Codes for: Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation
<p>The following MATLAB codes belong to the paper following paper which has been publication in MDPI Machines. This repository includes all the necessary MATLAB scripts and functions used in the research for diagnosing faults in drones using advanced acoustic emission analysis and optimization techniques. The dataset used in this paper is referenced in the article. Please check the publication for the dataset reference. Download the dataset, decompress it, and place it in the same repository as these codes to ensure proper functionality. For any queries or further information, please refer to this paper.</p> <p>Berghout, Tarek, and Mohamed Benbouzid. 2024. "Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation" <em>Machines</em> 12, no. 8: 504. https://doi.org/10.3390/machines12080504 </p> <p> </p>
Data for: Nonintrusive heat flux quantification using acoustic emissions during pool boiling
Open the record for dataset details and reuse information.
Acoustic emission characterization of coral sands during drained triaxial shearing
<p>These dataset was derived from saturated coral sands subjected to drained triaxial shearing, combing with a high-performance AE measurement system.</p>
Data from: Acoustic emissions of Sorex unguiculatus (Mammalia: Soricidae): assessing the echo-based orientation hypothesis
Shrew species have been proposed to utilize an echo-based orientation system to obtain additional acoustic information while surveying their environments. This system has been supported by changes in vocal emission rates when shrews encounter different habitats of varying complexity, although detailed acoustic features in this system have not been reported. In this study, behavioral experiments were conducted using the long-clawed shrew (Sorex unguiculatus) to assess this orientation system. Three experimental conditions were set, two of which contained obstacles. Short-click, noisy, and different types of tonal calls in the audible-to-ultrasonic frequency range were recorded under all experimental conditions. The results indicated that shrews emit calls more frequently when they are facing obstacles or exploring the experimental environment. Shrews emitted clicks and several different types of tonal calls while exploring, and modified the use of different types of calls for varying behavior. Furthermore, shrews modified the dominant frequency and duration of squeak calls for different types of obstacles, i.e., plants and acrylic barriers. The vocalizations emitted at short interpulse intervals could not be observed when shrews approached these obstacles. These results are consistent with the echo-based orientation hypothesis according to which shrews use a simple echo-orientation system to obtain information from their surrounding environments, although further studies are needed to confirm this hypothesis.
[Data] Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
<div> <div> <div> <p>Different Laser Powder Bed Fusion (LPBF) process spaces were deliberately introduced by employing two distinct 316L stainless steel powder distributions (with particle sizes >45 μm and < 45 μm) and processing them with two sets of laser parameters, resulting in the creation of four datasets [D1, D2, D3, and D4]. These datasets encompass LoF pores, conduction mode, and keyhole formations, each associated with three LPBF regimes denoted as D1, D2, D3, and D4. The experiments utilized a Sisma MYSINT 100 commercial LPBF printer and an airborne AE sensor system with a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the course of fabricating a cube using a powder bed and laser, data acquisition from an AE sensor was triggered when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the ensuing continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were then segmented into a 12.5 ms window comprising 5000 data points. To eliminate any noise, an offline application of a low-pass Butterworth filter with a 150 kHz cut-off frequency was employed, aligned with the frequency response specification of the AE sensor. Each dataset has two files against it [raw/groundtruth label].</p> </div> </div> </div>
Permeability, acoustic emission and wave velocities during compaction of porous sandstone
Open the record for dataset details and reuse information.
A study on Acoustic Emission signal Characteristics of the Larvae of Coscinesthes salicis
<p>The data acquisition device used in this experiment was the AE signal acquisition instrument DS5-8B manufactured by Beijing Soft Island Times Technology Co., Ltd. with a maximum sampling rate of 10M (the sampling rate shown in the system is processed by the acquisition card); to ensure the accuracy and data integrity, The larva of Coscinesthes salicis and the poplar wood used in this experiment were both collected from the campus of Southwest Forest University, Kunming City, Yunnan province, PRC.</p>
Laboratory visualization of fault asymmetry formation via acoustic emission and digital imaging correlation
Open the record for dataset details and reuse information.
Acoustic Emission Dataset for Multi-Laser LPBF Systems: Supporting DUAL DISCO Signal Processing Technique
<p>This dataset contains acoustic emission (AE) signals collected during experiments with multi-laser Laser Powder Bed Fusion (LPBF) systems. The data supports the research presented in the paper titled "DUAL DISCO: A Novel Approach to Acoustic Emission Monitoring in Multi-Laser LPBF Systems." The dataset is designed to facilitate the development and validation of advanced signal processing techniques, specifically the DUAL DISCO method, which aims to disentangle and analyze AE signals from simultaneous laser operations.</p> <p><strong>Contents:</strong></p> <ul> <li> <p><strong>Raw_data.zip:</strong> This file contains the AE signals used for training. The data was recorded using two condenser microphones positioned around the LPBF build plate, capturing signals from both sequential and simultaneous laser operations across various melting regimes (conduction and keyhole modes).</p> </li> <li> <p><strong>Raw_data_test.zip:</strong> This file includes the AE signals used for testing, recorded under different experimental conditions to evaluate the generalization capabilities of signal processing algorithms.</p> </li> <li> <p><strong>params.xlsx:</strong> This spreadsheet provides the ground truth labels for the training data, indicating the melting regime (conduction or keyhole) for each signal in Raw_data. </p> </li> <li> <p><strong>params_test.xlsx:</strong> This spreadsheet contains the ground truth labels for the test data, similarly indicating the melting regime for each signal in Raw_data_test.</p> </li> </ul> <p><strong>Applications:</strong></p> <p>This dataset is intended for researchers and practitioners in the field of additive manufacturing and signal processing. It can be used to:</p> <ul> <li>Develop and test new algorithms for AE signal processing in multi-laser LPBF systems.</li> <li>Explore the acoustic characteristics of different melting regimes.</li> <li>Enhance the understanding of process monitoring techniques in additive manufacturing.</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>The dataset was collected using the AddUp FormUp 350 machine and is part of a research project supported by the Bern Economic Development Agency. Special thanks to Thomas Rytz for technical support.</p>
Structural control within flawed rock specimens under external loading as visualized through repeating nucleation on multiple sites by acoustic emission (AE) [DATA]
<p>Data for article: Structural control within flawed rock specimens under external loading as visualized through repeating nucleation on multiple sites by acoustic emission (AE).</p>
Acoustic Emission Biomarkers for the Detection and Monitoring of Early Knee Osteoarthritis
ClinicalTrials.gov study NCT06351059. IPD Sharing: NO. Countries: 1. Publications: 7.
Data from: Acoustic emissions of Sorex unguiculatus (Mammalia: Soricidae): assessing the echo-based orientation hypothesis
Open the record for dataset details and reuse information.
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.