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FIGURE 3 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 3 Schematic drawing of the ear of Gadus morhua (anterior is to the left): (a) top view of the body showing the location of the ears in the cranial cavity as well as the proximity of the rostral end of the swim bladder to the ear; (b) lateral and (c) top view of the same ear. Each ear is set at an angle relative to the midline of the fish., The otolith organs,, the semicircular canals (enlarged areas are the ampullae regions that contain the sensory cells);, the dense calcarious otolith lying in close proximity to the sensory epithelium (). Also see Figure 4. Fig. © 2018 Anthony D. Hawkins, all rights reserved
FIGURE 5 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 5 The sensory epithelia of the end organs of the inner ear have numerous mechanoreceptive sensory hair cells. The apical ends of these cells, directed into the lumen of the epithelia, have ciliary bundles (inserts in the figure) consisting of a single kinocilium (longest of the cilia) and graded stereocilia. Bending of the ciliary bundle during sound stimulation results in neurotransmitter release to stimulate the 8th cranial nerve. The sensory cells on the otolith maculae are organized into orientation groups, with all of the cells in each group having their kinocilia in the same general direction. In this typical saccular epithelium (anterior to the left, dorsal to the top), the cilia on the rostral end are oriented rostrally or caudally, while the cells on the caudal end are oriented dorsally and ventrally., The approximate dividing lines between orientation groups)
FIGURE 2 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 2 Masking in the Gadus morhua and Salmo salar by ambient noise. The thresholds were determined using a pure tone signal at a frequency of 160 Hz. The ambient noise (natural sea noise, augmented by white noise from a loudspeaker) is expressed as the spectrum level at that same frequency (dB re 1 μPa/Hz). Closed symbols, thresholds to natural levels of ambient noise; open symbols, thresholds to anthropogenic noise. n.b., The thresholds in S. salar were only influenced by high noise levels, above the natural ambient levels of noise (data from Hawkins, 1993). Fig. © 2018 Anthony D. Hawkins, all rights reserved
FIGURE 4 A in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 4 A frontal view of the head of Gadus morhua showing a section of the saccule (). The saccular chamber is filled with perilymph and contains the otolith (), which lies close to the sensory hair cells of the epithelium (macula). The hair cells are innervated by the eighth cranial nerve. Fig. © 2018 Anthony D. Hawkins, all rights reserved
FIGURE 1 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 1 Fish hearing sensitivity (thresholds) obtained under open sea, free-field, conditions in response to pure tone stimuli at different frequencies. The lower the thresholds (y-axis), the more sensitive the fish is to a sound. Thus, Clupea harengus has best hearing of all of these species over a wider range of frequencies. Note that the thresholds in Gadus morhua and C. harengus obtained under quiet conditions may be below natural ambient noise levels, especially at their most sensitive frequencies. In the presence of higher levels of noise, the thresholds would be raised, a phenomenon referred to as masking. Gadus morhua and C. harengus are sensitive to both sound pressure and particle motion, whereas Limanda limanda and Salmo salar are only sensitive to particle motion. The reference level for the particle velocity is based on the level that exists in a free sound field for the given sound pressure level. n.b., For the particle velocity levels in this figure to match the sound pressure levels in a free sound field it is necessary to calculate an appropriate particle velocity reference level. If the standard reference levels are used, then the curves will not match one another and so they are not included here to keep the figure relatively simple. Fig. © 2018 Anthony D. Hawkins, all rights reserved
Presentation in ARO2024: Middle-ear sound transmission in cadaveric temporal bones
<p>This is Bastian Baselt's presentation in ARO 2024. This data include presentations and related data.</p> <p>A subfolder "Data" includes categorized raw data and explanation for the data.</p>
DATASET Stridulation sounds of the pacamã Lophiosilurus alexandri Steindachner, 1876 (Pseudopimelodidae), a threatened endemic Brazilian catfish
<p>This dataset contains acoustic files with sounds produced by the pacamã Lophiosilurus alexandri Steindachner, 1876. The details can be found in the related publication Raick et al. 2025.</p>
Vehicle Interior Sound Dataset
<p>The used dataset is collected from the point of view (PoV) driving of different vehicle types from YouTube ("https://www.youtube.com/," 2020). These are only vehicle interior sounds. There is no driver or any human voice.5980 sounds were recorded with 8 classes. These vehicles were driven on asphalt roads in open-air. We didn’t prefer to collect interior vehicle sounds on unpaved roads in rainy weather.</p> <p>The file format of these data is wav. The length of the used sounds is in the range of 3-5 seconds with 48 kHz frequency. The chosen vehicle types are bus, minibus, pickup, sports car, jeep, truck, crossover, and car (automobile). The attributes of the collected vehicle interior sound (VIS) dataset are summarized in Table.</p> <table align="center"> <tbody> <tr> <td> <p><strong>No</strong></p> </td> <td> <p><strong>Class name</strong></p> </td> <td> <p><strong>Number of Samples</strong></p> </td> <td> <p><strong>No</strong></p> </td> <td> <p><strong>Class name</strong></p> </td> <td> <p><strong>Number of Samples</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p><strong>Bus </strong></p> </td> <td> <p><strong>850</strong></p> </td> <td> <p><strong>5</strong></p> </td> <td> <p><strong>Jeep</strong></p> </td> <td> <p><strong>600</strong></p> </td> </tr> <tr> <td> <p><strong>2</strong></p> </td> <td> <p><strong>Minibus</strong></p> </td> <td> <p><strong>600</strong></p> </td> <td> <p><strong>6</strong></p> </td> <td> <p><strong>Truck</strong></p> </td> <td> <p><strong>900</strong></p> </td> </tr> <tr> <td> <p><strong>3</strong></p> </td> <td> <p><strong>Pickup</strong></p> </td> <td> <p><strong>680</strong></p> </td> <td> <p><strong>7</strong></p> </td> <td> <p><strong>Crossover</strong></p> </td> <td> <p><strong>800</strong></p> </td> </tr> <tr> <td> <p><strong>4</strong></p> </td> <td> <p><strong>Sports Car</strong></p> </td> <td> <p><strong>800</strong></p> </td> <td> <p><strong>8</strong></p> </td> <td> <p><strong>Car (C Class – 4K)</strong></p> </td> <td> <p><strong>750</strong></p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>5980</strong></p> </td> </tr> </tbody> </table> <p>This dataset was used in the article given below. Researchers who want to use the DataSet should cite the specified article.</p> <p>Akbal, E., Tuncer, T., & Dogan, S. (2022). Vehicle Interior Sound Classification Based on Local Quintet Magnitude Pattern and Iterative Neighborhood Component Analysis. <em>Applied Artificial Intelligence</em>, <em>36</em>(1), 2137653.</p>
Fig. 3 in Sound production and pectoral spine locking in a Neotropical catfish (Iheringichthys labrosus, Pimelodidae)
Fig. 3. Sonogram (top) and oscillogram (below) swimbladder drumming sounds recorded underwater illustrating sound characteristics measured. TD = train durations; SD = sound duration; PP = pulse period; PD = pulse durations.
Fig. 1 in Sound production and pectoral spine locking in a Neotropical catfish (Iheringichthys labrosus, Pimelodidae)
Fig. 1. Sonogram (top) and oscillogram (below) of pectoral sounds illustrating sound characteristics measured. TD = train durations; SD = sound duration; PP = pulse period; PD = pulse durations.
Fig. 2 in Sound production and pectoral spine locking in a Neotropical catfish (Iheringichthys labrosus, Pimelodidae)
Fig. 2. Oscillogram of pectoral sound (all pulses in the oscillogram) emitted by Iheringichthys labrosus caught in the net in field.
Sound examples of an Impulse Pattern Formulation model synchronizing to different click tracks
<p>In the publication, supplemented by these sound examples, the Impulse Pattern Formulation is used to model the synchronization of musicians to a collective tempo. Several click tracks are numerically created, representing eighth notes played by a musician or a metronome for different tempo changes.<br> By replacing every beat with a sound sample, audio files are created for a more musical evaluation of the results. The IPF is represented by a cowbell and the underlying click track with claves. Those sound files are in stereo, whereby the click track is at the left channel, and the IPF's signal is at the right channel. Thus, e.g., the balance potentiometer of a stereo system can be used to blend both sounds freely. The practical examples are:</p> <p><strong>Fig.2:</strong><br> IPF reacts to four different step changes in tempo.</p> <p><strong>Fig.5:</strong><br> The IPF reacting to the same changes in tempo as shown in Figure 2, when changing the tempo linear during 24 beats instead of step changes.</p> <p><strong>Fig.8:</strong><br> IPF adapting to a noisy click track: the upper line (a) and b)) shows white noise, and the lower line (c) and d)) Brownian noise. On the left (a) and c)), the fluctuation is <span class="math-tex">\(\pm1~\%\)</span>, and on the right (b) and d)) <span class="math-tex">\(\pm 5~\%\)</span>.</p> <p><strong>Fig.9:</strong><br> IPF adapting to a sinusoidally modulated click track: the upper line (a) and b)) shows a modulation period of 32 eighth notes, and the lower line (c) and d)) shows a modulation period of 8 eighth notes. On the left (a) and c)), the amplitude is 36 bpm, and on the right (b) and d)) 6 bpm, both centered around 113 bpm.</p> <p><strong>Fig.12:</strong><br> Several scenarios shown in Figures 2, 5, 8, and 9 applied to an extended IPF which considers phase differences: a) step change from 120 to 100 bpm, b) linear change from 120 to 130 bpm, c) <span class="math-tex">\(\pm 5~\%\)</span> Brownian noise added to a 120 bpm click track and d) sinusoidal modulation with a period length of 32 eight notes varied <span class="math-tex">\(\pm 6~bpm\)</span> around 113 bpm.</p> <p><strong>Fig.13:</strong><br> Several scenarios shown in Figures 2, 5, 8, and 9 applied to an extended IPF optimized for polyrhythms: a) step change from 120 to 140 bpm, b) linear change from 100 to 120 bpm, c) <span class="math-tex">\(\pm 5~\%\)</span> Brownian noise added to a 90 bpm click track and d) sinusoidal modulation with a period length of 32 eight notes varied <span class="math-tex">\(\pm 6~bpm\)</span> around 113 bpm.</p> <p>In all Figures, blue lines refer to the tempo of the click track, and red lines correspond to the tempo of the IPF. The single crosses represent single beats.</p> <p>A more in-depth description of how these sounds were synthesized can be found in the publication supplemented by these examples:</p> <p>Linke, S., Bader, R., & Mores, R. (2021). Modeling synchronization in human musical rhythms using Impulse Pattern Formulation (IPF). http://arxiv.org/pdf/2112.03218v1</p>
INTAROS CTD mooring data from Young Sound, NE Greenland (2018-2021)
<p>Greenland fjords are currently undergoing significant ecosystem change due to unprecedented melting of the Greenland Ice Sheet (GrIS).The rapidly increasing discharge of GrIS meltwater not only influences circulation patterns and stratification of the water column, but it also introduces large fluxes of inorganic sediments and organic material that are suspended in the water column. These inputs can limit light availability to primary producers. However, data is still limited for most Greenland fjord systems and there is an especial paucity of data showing yearly cycles. The Integrated Arctic Observation System (INTAROS) project funded by the European Commission’s H2020 programme allowed for the deployment of two moorings equipped with CTDs in the Young Sound fjord system––one in the inner fjord closest to GrIS meltwater discharge and another in the outer fjord region. This dataset reports the temperature and salinity recorded on these two moorings over 3 yearly cycles from August 2018 to August 2021. The moored CTDs were RBR Maestro<sup>3</sup> and Concerto<sup>3</sup> and were deployed at ~10 and ~20m in the inner and outer fjord respectively. Data were recorded at 2 Hz with measurements taken at 5min–1h intervals and raw data were processed using RBR Ruskin software. This dataset is made up of comma separated CSV files separated by mooring and year.</p> <p>We would like to thank Carl Isaksen and MarineBasis, Greenland Ecosystem Monitoring Programme (<a href="https://g-e-m.dk/">https://g-e-m.dk</a>) for assistance during deployment. The moorings were funded by the EU Horizon2020 funded project INTAROS (grant no. 727890). </p>
Emergence and function of cortical offset responses in sound termination detection
<p>Offset responses in auditory processing appear after a sound terminates. They arise in neuronal circuits within the peripheral auditory system, but their role in the central auditory system remains unknown. Here we ask what the behavioral relevance of cortical offset responses is and what circuit mechanisms drive them. At the perceptual level, our results reveal that experimentally minimizing auditory cortical offset responses decreases the mouse performance to detect sound termination, assigning a behavioral role to offset responses. By combining <i>in vivo</i> electrophysiology in the auditory cortex and thalamus of awake mice, we also demonstrate that cortical offset responses are not only inherited from the periphery but also amplified and generated <i>de novo</i>. Finally, we show that offset responses code more than silence, including relevant changes in sound trajectories. Together, our results reveal the importance of cortical offset responses in encoding sound termination and detecting changes within temporally discontinuous sounds crucial for speech and vocalization.</p>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
IHTApark. Multi-detailed 3D architectural model for sound perception research in Virtual Reality
<p><strong>IHTApark – Multi-detailed 3D architecture model</strong></p> <p>This dataset describes visual and acoustic 3D architectural models of the park next to the IHTA.</p> <p>Institute of Hearing Technology and Acoustics (IHTA), RWTH Aachen, 52056 Aachen, Germany</p> <p>Files are stored in FBX format for geometry, JPEG format for visual textures, and Unreal Engine for the virtual reality scenes.</p> <p><strong>VERSION 1: Visual photogrammetry + Acoustic model</strong></p> <p>As used in the publication:</p> <p>[1] Llorca-Bofí, J. and Vorländer, M. (2021). Multi-Detailed 3D Architectural Framework for Sound Perception Research in Virtual Reality. Front. Built Environ. 7:687237.doi: https://doi.org/10.3389/fbuil.2021.687237</p> <p>Data is available separately for each definition, and for each visual and acoustic cue. The level of detail for each definition is shown here:</p> <ul> <li>Visual cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> <li>Acoustic cues <ul> <li>Geometries <ul> <li>HighLOD</li> </ul> </li> </ul> </li> </ul> <p>This version of the model includes only the modules used for the description of the referenced paper. The authors reserve the right to complete other levels of detail if future applications require them.</p> <p>An additional data file contains a unique file in [IHTApark_UnrealEngine] Unreal Engine format, with the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_UnrealEgine] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_UnrealEngine]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark</strong> map under the folder <strong>Content > Maps</strong> by double clicking on it.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p><strong>VERSION 2: Object-based visualization in three different weather conditions</strong></p> <p>As used and described in the publication:</p> <p>[2] Submitted to journal.</p> <p>The file [IHTApark_3weath_comp] Unreal Engine format contains the set up scenario. The instructions to open the final scenario are described here:</p> <ol> <li>Download the [IHTApark_3weath_comp] file, and save it in your working space.</li> <li>Extract the content of the [IHTApark_3weath_comp]. The folder naming and arrangement are prepared for the scenario.</li> <li>Run the .uproject file.</li> <li>Open a <strong>Content Browser</strong> tab to navigate through the folder hierarchy. You can open the <strong>Content Browser</strong> under the tabs <strong>Window > Content Browser</strong></li> <li>Open the <strong>IHTApark_warm</strong>, <strong>IHTApark_wet </strong>or<strong> IHTApark_snowy</strong> maps under the folder <strong>Content > Maps</strong> by double clicking on it to visualize each weather condition.</li> <li>The scenario will be visible in the <strong>Viewport 1</strong> tab. Go to <strong>Window > Viewports > Viewport 1</strong> to open the tab.</li> <li>Press key <strong>G</strong> to hide or unhide the helpers and editor actors.</li> <li>Press keys <strong>0,</strong> <strong>1</strong>, <strong>2</strong>, <strong>3</strong>… <strong>9</strong> to jump into different saved view positions.</li> <li>Drag the mouse while pressing right click to rotate the viewer direction</li> <li>While pressing right click, press key <strong>W</strong> to navigate through the scenario.</li> </ol> <p>The folder [IHTApark_3weathers_audio] contains the sound signals, as .wav files, in fist order ambisonics format (B-format).</p> <p> </p>
EVEX rocket experiment: sounding rocket and C/NOFS satellite observations in equatorial regions of the ionosphere at sunset
<p>These data files support the JGR: Space Physics article by Pfaff, R. et al. (2022) entitled "Dual sounding rocket and C/NOFS satellite observations of DC electric fields and plasma density in the equatorial E and F region ionosphere at sunset".</p>
Size data for Chinook salmon caught in the Tengu Derby and Puget Sound commercial purse seine fisheries
<p>The tengu_derby_size.csv file contains information on the following fields (columns):</p> <ol> <li>year</li> <li>members (number of anglers who participated in the derby; not all anglers fished each day the derby was open)</li> <li>n_over_10 (total number of Chinok salmon greater than 10 pounds)</li> <li>n_over_5 (total number of Chinok salmon greater than 5 pounds)</li> <li>size_1 (mass in kg of the largest fish landed)</li> <li>size_2 (mass in kg of the second largest fish landed)</li> <li>size_3 (mass in kg of the third largest fish landed)</li> <li>size_4 (mass in kg of the fourth largest fish landed)</li> <li>size_5 (mass in kg of the fifth largest fish landed) </li> </ol> <p>The wdfw_size.csv file contains the following fields (columns):</p> <ol> <li>year</li> <li>mass (mean mass in kg of natural- and hatchery-origin Chinook salmon combined)</li> </ol>
Plymouth sound and surroundings surface temperature and salinity from FVCOM model
<p>Output of surface temperature and salinity from a high-resolution model of the Plymouth sound and surrounding areas, monthly files covering from May - October 2016. The model setup is as for the operational model (https://www.plymouthmarineforecasts.org/) except with observed river flows and boundary forcing was bias corrected against observations from the L4 buoy timeseries (https://www.westernchannelobservatory.org.uk/). This dataset was used in calculations of coastal CO2 flux in:</p> <p>bg-2021-166<br> Title: Tidal mixing of estuarine and coastal waters in the Western English Channel is a control on spatial and temporal variability in seawater CO2<br> Author(s): Richard Peter Sims et al.</p> <p>The code and model mesh data required to create plots therein are also included in plot_data.zip</p>
2D Sound Navigation - Tutorial Materials
<p>Materials presented to the experiment participants to familiarize them with the navigation controls and auditory guidance.</p>
ScienceDex guides
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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.