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416 results for “Acoustic data”
Data for: Large-scale long-term passive-acoustic monitoring reveals spatiotemporal activity patterns of boreal bats
<p class="MsoNormal"><span>The distribution ranges and spatio-temporal patterns in the occurrence and activity of boreal bats are yet largely unknown due to their cryptic lifestyle and lack of suitable and efficient study methods. We approached the issue by establishing a permanent passive-acoustic sampling setup spanning the area of Finland to gain an understanding on how latitude affects bat species composition and activity patterns in northern Europe. The recorded bat calls were semi-automatically identified for three target taxa; <em>Myotis</em> spp., <em>Eptesicus nilssonii</em> or <em>Pipistrellus nathusii</em> and the seasonal activity patterns were modeled for each taxa across the seven sampling years (2015–2021). We found an increase in activity since 2015 for <em>E. nilssonii</em> and <em>Myotis </em>spp. For <em>E. nilssonii</em> and <em>Myotis</em> spp. we found significant latitude -dependent seasonal activity patterns, where seasonal variation in patterns appeared stronger in the north. Over the years, activity of <em>P. nathusii</em> increased during activity peak in June and late season but decreased in mid season. We found the passive-acoustic monitoring </span><span>network to be an effective and cost-efficient method for gathering b</span><span>at activity data to analyze spatio-temporal patterns. Long-term data on the composition and dynamics of bat communities facilitates better estimates of abundances and population trend directions for conservation purposes and predicting the effects of cli</span><span>mate change.</span></p>
Acoustic optical survey data for snapper survey in shark bay July 2020
<p>Dataset to accompany Scoulding et al. 2023. Estimating abundance of fish associated with structured habitats by combining<strong> </strong>acoustics and optics. Journal of Applied Ecology.</p> <p>The dataset includes:</p> <p>1. Acoustic integration outputs from Echoview</p> <p>2. Snapper lengths from RUV deployments</p> <p>3. Snapper lengths from commercial catch</p> <p>4. Fish species length-weight relationships</p> <p>5. Habitat validation determined from camera deployments</p> <p>6. Proportions of fish species determined per RUV deployment</p> <p>Raw data files (acoustic and optics) are too large for inclusion in this repository but can be provided on request. The Python code used to analysis the data is being packaged and will be added to the repository once complete.</p>
Surface acoustic wave resonators on thin film piezoelectric substrates in the quantum regime - data archive
<p>The Archive contains all raw and processed (fitted) data that is used in the manuscript "surface acoustic wave resonators on thin film piezoelectric substrates in the quantum regime" submitted to IOP Materials for quantum technology.</p>
Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data
<p>This is the data required to reproduce the results of the manuscript "Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data", including measurements of tree canopy cover extracted from the Global Forest Cover Change dataset (Townshend 2016).</p> <p> </p> <p><strong>Reference</strong></p> <p>Gillard, G. L. & Rowley, J. J. L. (2023). Assessment of the acoustic adaptation hypothesis in frogs using large-scale citizen science data. <em>Journal of Zoology</em>. [In publication].</p> <p> </p> <p><strong>Global Forest Cover Change Dataset</strong></p> <p>Townshend J. 2016. Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30 m V003 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed June 22, 2022. doi:10.5067/MEaSUREs/GFCC/GFCC30TC.003.Townshend J. 2016. Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30 m V003 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed June 22, 2022. doi:10.5067/MEaSUREs/GFCC/GFCC30TC.003.</p>
Data from: Narwhal (Monodon monoceros) echolocation click rates to support cue counting passive acoustic density estimation
<p class="MsoNormal"><span>The datasets correspond to the data used to obtain the results shown in the manuscript "Narwhal (<em>Monodon monoceros</em>) echolocation click rates to support cue counting passive acoustic density estimation".</span></p> <p class="MsoNormal"><span>When the manuscript is accepted we will also edit and add here the full reference including the DOI of the publication.</span></p>
Data of Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media
<p>Fully data of the paper "Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media" in the Astrophysical Journal.</p> <p>The Astrophysical Journal, 911:119 (18pp), 2021 April 20.</p>
Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks
<p>This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. </p> <p>The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis.</p> <p>The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.</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>
Figure 8 Relationship between host parasitism rate and mean parasitoid load per host. Each data point represents 1 in Infection behavior, life history, and host parasitism rates of Emblemasoma erro (Diptera: Sarcophagidae), an acoustically hunting parasitoid of the cicada Tibicen dorsatus (Hemiptera: Cicadidae)
Figure 8 Relationship between host parasitism rate and mean parasitoid load per host. Each data point represents 1 year of host population sampling data for a single study site. The solid line (blue in the color figure) represents the linear regression model for the data.
Data from: Optimizing passive acoustic monitoring (PAM) for Biodiversity Studies: using species-area relationship (SAR) to predict species richness
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Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
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Data and code supporting acoustic and environmental factors driving digging behavior in the early life of a freshwater turtle
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Data from: Acoustic surveillance of bats along the Green and Colorado Rivers
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Data from: Evaporation induced acoustic emissions in microfluidic vessels
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Data for: Large-scale long-term passive-acoustic monitoring reveals spatiotemporal activity patterns of boreal bats
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Data for: Hit2flux: A machine learning framework for boiling heat flux prediction using hit-based acoustic emission sensing
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Data from: Koe: Web-based software to classify acoustic units and analyse sequence structure in animal vocalisations
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Acoustic data of calls of Manx shearwater on Lundy Island
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Data from: Innovative microphone transmitter reveals differences in acoustic structure between broadcast and whisper songs of Myadestes obscurus (ʻŌmaʻo)
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Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
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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.