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416 results for “Acoustic data”
SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"
<p>This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309). </p> <p>A Github repository containing code for using the SQLite database also accompanies this paper at: <a href="https://github.com/cbalantic/false-positive-mitigation">http://github.com/cbalantic/false-positive-mitigation</a></p>
Data and code related to the paper: "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation"
<p>This archive contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Kyriacos Yiannacou, Vipul Sharma and Veikko Sariola, "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation", <em>Langmuir</em> 2022, 38, 38, 11557–11564.</p> <p><a href="https://doi.org/10.1021/acs.langmuir.2c01061">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>The acoustofluidic controller software is the same as in our previous paper and is archived <a href="https://doi.org/10.5281/zenodo.4593021">here</a>.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure(s) and/or movie(s). Within each folder, the raw data files are under the folder `data/`. Once ran, the scripts produce another folder called `output/`, to which they place the created plots and movies. Most folder contain a script name `plot_*.m` that makes the figure(s) and `video_*.m` that generates the video(s). You will need `ffmpeg` installed to convert the serial images into a video.<br> </p>
Rainfall data monitored by acoustic sensors in Zurich and Milan during spring and summer 2022
<p>The database contains rainfall information obtained from acoustic sensors and rain gauges (meteoblue AG) in the cities of Zurich (Switzerland) and Milan (Italy) during field work conducted in spring and summer 2022.</p> <p>Zurich:</p> <p>Continuous rainfall data is provided at 15 min intervals for April 2022; data_acoustic_Zurich.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm).</p> <p>Milan:</p> <p>Data is provided for 5 rain events in June 2022 at 1 min intervals; data_acoustic_Milan.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm).</p> <p>The locations of the acoustic sensors and rain gauges can be find in the metadata files: Metadata_acoustic.xlsx and Metadata_meteoblue.xlsx</p> <p>The presented-data passed only a primilinary quality control.</p> <p>Further infromation about the senor networks in Milan and Zurich can be found here: https://doi.org/10.5194/nhess-2022-257</p>
INFORE22 Acoustic data from SLim Towed Array (SLiTA)
<p>Acoustic data stem from hydrophone array SLiTA (SLim Towed Array [*]) towed by marine robots during INFORE22 experiments on 28<sup>th</sup> February and 1<sup>st</sup> March 2022. Two series of acquisitions are shared, one for each OEX.</p> <p>Data have been generated during the INFORE22 sea trial, run by CMRE from 21st February to 1st March 2022 in the Gulf of La Spezia to experiment and validate the CMRE hybrid robotic network in support of the INFORE maritime use case [**]. See also https://zenodo.org/record/637272</p> <p>Acoustic data are shared in binary format (.dat), and are complemented by Matlab scripts (.m) and header textual descriptors (sliva-header-specification.2.x0.txt). </p> <p>The archive (ACOUSTIC.zip) is split in multiple (60) files of 650MB each, which can me joined/combined with archive software (e.g., 7zip). </p> <p>For a full description of the dataset, see [***]</p> <p><strong>Conditions for use and distributions</strong></p> <p>This dataset is provided by NATO STO CMRE within the condition stated in the H2020 INFORE Grant and Consortium Agreement (GA. no. 825070) . The creation of derived products, as well the use in scientific publications must be pre-approved by CMRE and acknowledged. </p> <p><strong>Non liability clause</strong></p> <p>These data and software are provided in the scope of INFORE by NATO STO CMRE, in compliance with the INFORE open data strategy. Data and software are provided as they are. NATO and NATO STO CMRE decline any responsibility for bugs and any damage or accidental issue that the use of those data and software could cause. </p> <p><strong>References</strong></p> <p>[*] Alain Maguer, Rodney Dymond, Piero Guerrini, Luigi Troiano, Vittorio Grandi, Alberto Figoli, Claudio Olivero, Alessandro Sapienza, Stefano Fioravanti, John Potter Receiving and transmitting acoustic systems for AUV/gliders. Proceedings of the 3rd International Conference and Exhibition on Underwater Acoustic Measurements: Technologies and Results, 21-26 June, 2009, Nafplion, Greece. </p> <p>[**] Gabriele Ferri, Raffaele Grasso, Elena Camossi, Francesca de Rosa, Alessandro Faggiani, Kevin LePage, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Antonis Kontaxakis, Nikos Giatrakos, Antonios Deligiannakis, Maritime Use Case: Final Evaluation Report Work Package 3 Tasks 3.3 INFORE Deliverable D3.3</p> <p>[***] Nikos Giatrakos, Antonios Deligiannakis, Arnau Montagud, Miguel Ponce de León, Thaleia Ntiniakou, Holger Arndt, Stefan Burkard, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Raffaele Grasso, Gabriele Ferri, Arjan Vermeij, Alessandro Faggiani, Elena Camossi, Kevin Le Page: Data Management Plan V3 Work Package 8 Task 8.3 INFORE Deliverable 8.6</p> <p> </p> <p> </p> <p> </p>
Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform data
<p>Data files used in the publication: "Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform", submitted to Physics of Plasma August 2022. To be used in conjunction with analysis software KVIST.</p> <p>KVIST can be found at:</p> <ul> <li>https://doi.org/10.5281/zenodo.7017043</li> <li>https://github.com/Planetary-Surfaces-and-Spacecraft-Lab/KVIST</li> </ul> <p>Data files generated with:</p> <p>Truitt, A. (2020). Simulation of Forced Korteweg De Vries Equation as Applied to Small Orbital Debris. Digital Repository at the University of Maryland. https://doi.org/10.13016/FOR0-XJYD</p>
Segmentations of the core of the acoustic radiation in HCP data
<p><strong>Description of the repository</strong>:</p> <p>The goal of our paper (<a href="https://doi.org/10.3389/fneur.2022.934650">https://doi.org/10.3389/fneur.2022.934650</a>) was to segment the acoustic radiation (AR), one of the most important white matter fiber bundles of the hearing system. This repository contains the segmentations masks of the AR we created from 105 subjects of the Human Connectome Project (HCP) young adult dataset (<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>). These subjects are exactly the same used by Wasserthal et al. (2018) <a href="https://doi.org/10.1016/j.neuroimage.2018.07.070">https://doi.org/10.1016/j.neuroimage.2018.07.070</a>.</p> <p>In the file "data_training.tar.gz", one directory was created per HCP subject. Every directory contains the file "bundle_masks_AR.nii.gz" that contains the binary masks for the left and right AR.</p> <p>In our paper, we used these masks to train TractSeg. The file best_weights_ep110.npz contains the weights after training TractSeg that can be used in inference for targeting the AR. For using these weights on new data, one can use TractSeg with the option "--exp_name best_weights_ep110.npz". Please read the documentation of TractSeg and our paper for more information.</p> <p> </p> <p>If you use the training data or the pre-trained network, please cite our publication:</p> <p>Malin Siegbahn, Cecilia Engmér Berglin, Rodrigo Moreno. Automatic segmentation of the core of the acoustic radiation in humans. Frontiers in Neurology (2022) 13:934650. doi: 10.3389/fneur.2022.934650</p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
Temporal study of Santa Cruz Mountain bats using environmental DNA and acoustic data
<p>Data and R scripts for a study of niche partitioning in a bat community in California's Santa Cruz Mountains using environmental DNA and bioacoustic data collected over a roosting season.</p> <p>Associated with the publication "Temporal study of environmental DNA and acoustic data reveals coexistence of sympatric bat species in a North American ecosystem" in <em>Environmental DNA. </em></p>
Geophysical and physical oceanography data and acoustic facies mapping results of the Central Basin in the northwestern Ross Sea margin, Antarctica
<p>Multi-channel seismic (MCS), sub-bottom profiler (SBP), multi-beam echosounder (MBES) and expandable conductivity-temperature-depth (XCTD) data and acoustic facies mapping results of the Central Basin in the northwestern Ross Sea margin, Antarctica. The Coordinate Reference System (CRS) for the MCS, SBP, MBES data and acoustic mapping results is WGS 84 / Antarctic Polar Stereographic (EPSG:3031). ). The geophysical data (MCS, SBP, MBES) and oceanographic measurements (XCTD) collected by the RV <em>Araon</em> are provided by the Korea Polar Data Center (<a href="https://kpdc.kopri.re.kr">https://kpdc.kopri.re.kr</a>).</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>
Airborne Infrasound Data from The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct
<p>Airborne infrasound data including waveform recordings from two payloads attached to a single 6 m heliotrope that was launched at dawn (~0700 local) out of Belen Regional Airport, NM, USA. Balloon trajectory is also included. This data accompanies the publication titled, "The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct" submitted to Earth & Space Science.</p>
Acoustic Data for Endotracheal Intubation Simulation with Machine Learning Feedback
<p>This dataset contains raw acoustic data collected during endotracheal intubation simulations, utilized for developing a machine learning-based performance feedback system. The data includes .wav audio recordings sampled at 192 kHz, organized by buzzer and microphone location and intubation states.</p><p>The data is associated with the following paper:</p><p>Steffensen, T. L., Bartnes, B., Fuglstad, M. L., Auflem, M., & Steinert, M. (2023). Playing the pipes: Acoustic sensing and machine learning for performance feedback during endotracheal intubation simulation. <i>Frontiers in Robotics and AI</i>, <i>10–2023</i>. https://doi.org/10.3389/frobt.2023.1218174</p>
Fig. 3 in New acoustic and molecular data shed light on the poorly known Amazonian frog Adenomera simonstuarti (Leptodactylidae): implications for distribution and conservation
Fig. 3. Preserved male of nominal Adenomera simonstuarti (Angulo & Icochea, 2010) (= genetic lineage 3): call voucher INPA-H 40967 (SVL = 23.4 mm) from the upper Juruá River, in Tarauacá, Brazilian state of Acre. This specimen corresponds to a call voucher (see Fig. 5). A−B. Body in dorsal and ventral views, not to scale. C−D. Detail of the ventral surface of right foot and hand, respectively. Note the nearly solid, dark-colored stripe along the underside of the forearm. Photographs by J. Magnusson. Scale bar = 5 mm.
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
Data and code used in analyses for Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity
<p>Evaluation datasets, Python scripts, and computation environments used to conduct analyses for Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity. <br><br>transfer_learning_project.zip also contains a vignette describing the use of a generalized script for adapting these methods to novel acoustic classification tasks. </p> <p> </p>
Data from: movement or plasticity: acoustic responses of a torrent frog to stream geophony
<p>Vocalization is the main form of communication in many animals, including frogs, which commonly emit advertisement calls to attract females and maintain spacing. In noisy environments such as streams, mechanisms to maximize signaling efficiency may include vocal plasticity and/or movement of individuals to quieter sections, but which strategy is used is still uncertain. We investigated the influence of stream geophony on the advertisement call of the torrent frog <em>Hylodes perere</em> in the Atlantic Rainforest, southeastern Brazil. In a mark-recapture study, we tested if males remain in their territories and thus adjust their advertisement calls to maximize their communication. We ran mixed linear and generalized models to verify the relation of call parameters and stream geophony, body size and environmental temperature. We found that males remained in the same location across time, increased call intensity in noisier environments but did not reduce call effort. Males also increased the dominant frequency in these situations, suggesting a modulation in this parameter. Our results indicate that territoriality is an important factor to males to increase call intensity to surpass stream noise instead of repositioning along the stream. However, because call effort was maintained, we suggest that sexual selection is crucial in this system, favoring males that better detect others and adjust their call efficiency. This is the first study to evaluate simultaneously frog movements and adaptations to geophony, which contributes to the investigation of the concomitant environmental and sexual selective pressures in species that communicate in noisy environments.</p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
<p>Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics, and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally, and identifies aspects of information processing in complex environments that remain to be investigated. </p>
Data from: Acoustic surveillance of bats along the Green and Colorado Rivers
<p><em>Aim</em>: Emerging research shows how bioindicators, specifically bats, can serve as a means for monitoring conservation and management of riparian corridors for multiple taxonomic groups. To track changes in composition or abundance of bioindicator species, researchers must attain a baseline in species presence and relative activity. We examined the spatial and temporal patterns of bat community composition and activity along a 1,000-mile river corridor to determine species diversity trends by latitude and habitat.</p> <p><em>Location</em>: Colorado River Basin</p> <p><em>Methods</em>: Here we describe the results from an acoustic bat survey conducted opportunistically on the 2019 Sesquicentennial Colorado River Exploring Expedition. This broad, 1,000-mile survey provides a baseline for species distributions over a large geographic range.</p> <p><em>Results</em>: In total, we collected 63 nights of acoustic data over 70-days and recorded over 59,000 files equating to 45,363 call files (≥2 pulses). 18,490 (41% of call files) were identified to species (n = 19 bat species). We applied non-metric multidimensional scaling to characterize spatiotemporal patterns of activity between species, as well as compared bat activity among river features and local environmental conditions (i.e., temperature and time since sunset) using an information theoretic approach.</p> <p><em>Conclusion</em>: Species composition varied by physiographic region and adjacent river habitat, thus providing a quantifiable measure of determining habitat quality along this major river system and providing baseline information for using bats as bioindicators of habitat quality</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>
Experimental data on the effects of an azimuthal mean flow on the (thermo)acoustic modes in the annular electroacoustic feedback setup at TU Berlin
<p>Experimental data obtained in the presence of an azimuthal mean flow on the acoustic/thermoacoustic response in the annular electroacoustic feedback setup at TU Berlin. This dataset was used for the published article<br> S. C. Humbert, J. P. Moeck, A. Orchini, C. O. Paschereit, "Effect of an Azimuthal Mean Flow on the Structure and Stability of Thermoacoustic Modes in an Annular Combustor Model With Electroacoustic Feedback", J. Eng. Gas Turbines Power. June 2021, 143(6): 061026. Experimental data as well as Matlab scripts to use them are provided. Useful information is contained in "readme" files.</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.