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.
1,300
datasets available to search
ShareScore release 0.9.0
Dataset results
1,300 results for “Sounds”
Figure 1 in Bioacoustic study of a sound produced with forced air by larvae of Phileurus didymus (Linnaeus) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figure 1. Phileurus didymus third instar larvae.
BioAcoustica: Wildlife Sound Database: BioAcoustica
Welcome to BioAcoustica. This site is an online repository and analysis platform for scientific recordings of wildlife sounds. <p></p>http://bio.acousti.ca/<p></p>Welcome to BioAcoustica. This site is an online repository and analysis platform for scientific recordings of wildlife sounds. <p></p>http://bio.acousti.ca/
Sounds Like Music - Glasgow
<p>Data generated during the project and workshop series Sounds Like Music which Dr Christian Ferlaino and Prof Raymond MacDonald led in Glasgow in November-December 2023. </p> <p>Sounds Like Music is a workshop for people with every musical experience, including none. It is an inclusive music-making platform that values participation, creativity and interaction over technical skills. We strongly believe that everybody is musical and can take part in music-making regardless of their skills. Every participant brings an object or an instrument that makes a sound with a special meaning to them. We explore those sounds in new and fun ways and make music using improvisation, interaction strategies, games and verbal and graphic scores. The workshop series aims at increasing access to musical participation for people who would be otherwise excluded from making music. It challenges established concepts and practices around music and musicality and promotes social innovation by initiating new ways of relating with the others and the environment through sound. We believe that unlinking music-production abilities from specialised instrumental training enables everybody to make music without requiring mastery of an instrument, or specific musical idioms and techniques, and lead to the emergence of new types of musicality.</p> <p>The files contained in this dataset are part of the EU Funded research LoMus - Local Sound for a New Musicality: Enhancing Musical Participation Through a Local Sonic Practice. The data have been purged of unnecessary personal information and any information that goes beyond the scope of the research.<br>The data are protected under the Creative Common Attribution 4.0 International licence.</p>
Sound Absorption Coefficients of Natural Materials Database
<p>This database is a working compilation of the sound absorption coefficients (α) of various natural materials for the octave band frequencies (125, 250...4000 Hz), when data is available. Additional physical properties of these materials, including thickness, density, airflow resistivity, and noise reduction coefficient (NRC) are included when available. Examples of materials in the database include kenaf, fibers, wood, food waste products (corn husk, fruit stone wastes, etc.), and animal byproducts (chicken feathers, sheep wool, etc.). Additionally, reference links are also included for each material, for easy reference to the original work/data.</p> <p>The data was gathered from published results in peer-reviewed scientific journals, whether through the papers' graphs/figures, which were then analyzed using the PlotDigitizer tool, or the papers' data tables. The aim of this database is to provide a central location of acoustical data for sustainable materials, in the hopes that researchers, scientists, teachers, students, or interested members of the public can easily access and utilize this data, whether for acoustical, environmental, sustainability, engineering, physics, mathematical, computational, or personal purposes. </p>
Auditory Scene Analysis dataset (Multichannel universal sound separation & polyphonic audio classification)
<p>We constructed a new dataset for <strong>multichannel universal sound separation</strong> and <strong>polyphonic audio classification</strong> tasks.</p> <p>We constructed a new dataset for multichannel USS and polyphonic audio classification tasks. The proposed dataset is designed to reflect various conditions, including moving sources with temporal onsets and offsets. For foreground sound sources, signals from 13 audio classes were selected from open-source databases (Pixabay and FSD50K, Librispeech, MUSDB18, Vocalsound). These signals were resampled to 16 kHz and pre-processed by either padding zeros or cropping to 4 seconds. Each sound source has a 75% probability of being a moving source, with speeds ranging from 0 to 3 m/s. The dataset features between 2 to 4 foreground sound sources, along with one background noise from the diffused TAU-SNoise dataset with a signal-to-noise ratio (SNR) ranging from 6 to 30 dB. The simulations were conducted using gpuRIR. Room dimensions were set to a width and length between 5 and 8 meters, and a height between 3 and 4 meters, with reverberation times ranging from 0.2 to 0.6 seconds. These parameters were sampled from uniform distributions. We simulated spatialized sound sources using a 4-channel tetrahedral microphone array with a radius of 4.2 cm. The procedure for dataset generation and details about class configuration and durations of audio clips are provided in the paper. This dataset poses a significant challenge for separation tasks due to the inclusion of moving sources, onset and offset conditions, overlapped in-class sources, and noisy reverberant environments.</p> <p>The procedure for dataset generation and details about class configuration and durations of audio clips are provided in the paper. This dataset poses a significant challenge for separation tasks due to the inclusion of moving sources, onset and offset conditions, overlapped in-class sources, and noisy reverberant environments.</p>
Sounding Data from SKYDEW hygrometer
<p>Sounding Data from the chilled-mirror hygromter SKYDEW. </p> <p>These data include the temperature, humidity, presssure(height) from the radiosonde RS-11G (or RS41) in addtion to dewpoint from SKYDEW.</p>
Data from: Stressful city sounds: glucocorticoid responses to experimental traffic noise are environmentally dependent
A major challenge in urban ecology is to identify the environmental factors responsible for phenotypic differences between urban and rural individuals. However, the intercorrelation between the factors that characterise urban environments, combined with a lack of experimental manipulations of these factors in both urban and rural areas, hinder efforts to identify which aspects of urban environments are responsible for phenotypic differences. Among the factors modified by urbanisation, anthropogenic sound, particularly traffic noise, is especially detrimental to animals. The mechanisms by which anthropogenic sound affects animals are unclear, but one potential mechanism is through changes in glucocorticoid hormone levels. We exposed adult house wrens, Troglodytes aedon, to either traffic noise or pink noise. We found that urban wrens had higher initial (pre-restraint) corticosterone than rural wrens before treatment, and that traffic noise elevated initial corticosterone of rural, but not urban, wrens. By contrast, restraint stress-induced corticosterone was not affected by noise treatment. Our results indicate that traffic noise specifically contributes to determining the glucocorticoid phenotype, and suggest that glucocorticoids are a mechanism by which anthropogenic sound causes phenotypic differences between urban and rural animals.
Data from: Open notes sounds great, but will a provider's documentation change?
<p><strong>Background</strong>: The effects of shared clinical notes on patients, care partners, and clinicians ("open notes") were first studied as a demonstration project in 2010. Since then, multiple studies have shown clinicians agree shared progress notes are beneficial to patients, and patients and care partners report benefits from reading notes. To determine if implementing open notes at a hematology/oncology practice changed providers' documentation style, we assessed the length and readability of clinicians' notes before and after open notes implementation at an academic medical center in Boston, MA.</p> <p><strong>Methods</strong>: We analyzed 143,888 notes from 60 hematology/oncology clinicians before and after the open notes debut at Beth Israel Deaconess Medical Center, from January 1, 2012, to September 1, 2016. We measured the providers' (medical doctor/nurse practitioner) documentation styles by analyzing character length, the number of addenda, note entry mode (dictated vs. typed) and note readability. Measurements used five different readability formulas and were assessed on notes written before and after the introduction of open notes on November 25, 2013.</p> <p><strong>Results</strong>: After the introduction of open notes, the mean length of progress notes increased from 6,174 characters to 6,648 characters (P<0.001), and the mean character length of the "assessment and plan" (A&P) increased from 1,435 characters to 1,597 characters (P<0.001). The Average Grade Level Readability of progress notes decreased from 11.50 to 11.33, and overall readability improved by 0.17 (P=0.01). There were no statistically significant changes in the length or readability of "Initial Notes" or Letters, inter-doctor communication, nor in the modality of the recording of any kind of note.</p> <p><strong>Conclusions</strong>: After the implementation of open notes, progress notes and A&P sections became both longer and easier to read. This suggests clinician documenters may be responding to the perceived pressures of a transparent medical records environment.</p>
TAU-SEBin Binaural Sound Events 2021
<p><strong>TAU-SEBin Binaural Sound Events 2021 </strong>is a dataset of synthetic binaural audio recordings, which consist of sound events spaced in simulated shoebox rooms. The data is suitable for experiments with several acoustic scene analysis tasks such as sound source localization, sound distance estimation or sound event detection.</p> <p> </p> <p>Data is created using isolated sound events derived from several datasets: NIGENS [1], DESED [2] and TUT Rare Sound Events 2017 [3], containing 18 total sound classes, namely: alarm, baby, blender, cat, crash, dishes, dog, engine, fire, footsteps, glassbreak, gunshot, knock, phone, piano, scream, speech, water. The data is split into two subsets, one of which (bin_prox_dir) contains up to two overlapping sound events, whereas the other one consists of single sources only (bin_prox_dir_one). Each subset contains 400 audio files, divided into 4 equal splits for fold-wise cross-validation.</p> <p> </p> <p>The metadata provides the following information:</p> <p><strong>sound_event_recording</strong> - sound event class</p> <p><strong>start_time, end_time - </strong>onset and offset times of the sound events (in seconds)</p> <p><strong>azi, ele - </strong>the azimuth and elevation angle of the sound source (in degrees)</p> <p><strong>dist</strong> - sound source to receiver distance (in metres)</p> <p> </p> <p> </p> <p>References:</p> <p>[1] I. Trowitzsch, J. Taghia, Y. Kashef, and K. Obermayer, NIGENS general sound events database. Zenodo, 2019.<br> [2] N. Turpault, R. Serizel, A. Parag Shah, and J. Salamon, “Sound event detection in domestic environments with weakly labeled data and soundscape synthesis,” in Workshop on Detection and Classification of Acoustic Scenes and Events, 2019.<br> [3] A. Mesaros, T. Heittola, A. Diment, B. Elizalde, A. Shah, E. Vincent, B. Raj, and T. Virtanen, “DCASE 2017 challenge setup: Tasks, datasets and baseline system,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), 2017, pp. 85–92.</p>
SLoClas: A Database for Joint Sound Localization and Classification
<p>We present a new database namely Sound Localization and Classification (SLoClas) corpus, for studying and analyzing sound localization and classification. The corpus contains a total of 23.27 hours of data recorded using a 4-channel microphone array. 10 classes of sounds are played over a loudspeaker at 1.5 meters distance from the array by varying the DoA from 1 degree to 360 degree at an interval of 5 degree. To facilitate the study of noise robustness, 6 types of outdoor noise are recorded at 4 DoAs, using the same devices.</p> <p>We release this database for research purpose only. If you use this corpus, cite the following paper</p> <p>Qian Xinyuan, Bidisha Sharma, Amine El Abridi, and Haizhou Li. "SLoClas: A Database for Joint Sound Localization and Classification." <em>arXiv preprint arXiv:2108.02539</em> (2021).</p> <p> </p>
fUS imaging of ferret auditory cortex during passive listening of natural sounds
<p>Source data for paper: Distinct higher-order representations of natural sounds in human and ferret (BiorXiv, 2020), Landemard A, Bimbard C, Demené C, Shamma S, Norman-Haigneré S, Boubenec Y.</p> <p>This data repository contains several folders:<br>- <em>fUSData </em>contains raw data for all recording sessions. Information on data formatting can be found in README_fUSData text file.<br>- <em>Analysis</em> contains processed and denoised data. This data can be readily used to produce our figures using our publicly available scripts. This data can also be re-generated using data from <em>fUSData </em>folder using our denoising scripts. <br>- <em>AdditionalData </em>contains additional files necessary to run some of the analyses. </p> <p>Code implementing our denoising procedure and reproducing figures from the paper can be found on <a href="http://github.com/agneslandemard/naturalsounds_analysis">https://github.com/agneslandemard/naturalsounds_analysis </a></p> <p> </p>
Data used in: Low Velocity Zones in the Martian Upper Mantle Highlighted by Sound Velocity Measurements
<p>Data used in</p> <p> </p> <p><strong>Low Velocity Zones in the Martian Upper Mantle Highlighted by Sound Velocity Measurements</strong></p> <p>F. Xu<strong><sup>1,</sup></strong><sup>†<strong>,</strong>‡</sup><strong>,</strong>, N. C. Siersch<sup>1<strong>,</strong>‡</sup>, S. Gréaux<sup>2</sup>, A. Rivoldini<sup>3</sup>, H. Kuwahara<sup>2,4</sup>, N. Kondo<sup>2</sup>, N. Wehr<sup>5</sup>, N. Menguy<sup>1</sup>, Y. Kono<sup>2</sup>, Y. Higo<sup>6</sup>, A.-C. Plesa<sup>7</sup>, J. Badro<sup>5</sup>, D. Antonangeli<sup>1</sup></p> <p><sup>1</sup> Sorbonne Université, Muséum National d‘Histoire Naturelle, UMR CNRS 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC, Paris, France</p> <p><sup>2</sup> Geodynamics Research Center, Ehime University, Matsuyama, Japan</p> <p><sup>3</sup> Royal Observatory of Belgium, Brussels, Belgium</p> <p><sup>4</sup> Institute for Planetary Materials, Okayama University, Misasa, Tottori, Japan</p> <p><sup>5</sup> Université de Paris, Institut de physique du globe de Paris, CNRS, Paris, France</p> <p><sup>6</sup> Japan Synchrotron Radiation Research Institute, SPring-8, Hyogo, Japan</p> <p><sup>7</sup> DLR Institute of Planetary Research, Berlin, Germany</p> <p> </p> <p>Corresponding author: Daniele Antonangeli (<a href="mailto:email@address.edu)">daniele.antonangeli@upmc.fr)</a></p> <p><sup>†</sup> Current address: Department of Earth Sciences, University College London, London, United Kingdom</p> <p><sup>‡</sup> Equal contributing authors</p> <p> </p> <p> </p>
Dataset: Effect of water withdrawal on the appearance and sound level of waterfalls
<p>This dataset includes discharge, water-covered area, sound intensity, and extent values of water withdrawal of 15 waterfalls in Switzerland, Austria, and Norway.</p>
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Data: Acoustic disturbance in blue mussels: sound-induced valve closure varies with pulse train speed but does not affect phytoplankton clearance rate
<p>Data abstract:</p> <p>Data on mussels' valve gape behaviour and phytoplankton clearance during sound exposure trials. We provide the raw data, processed data, scripts to process the raw data, make plots, and run the statistics.</p> <p> </p> <p>Paper abstract:</p> <p>Anthropogenic sound has increasingly become part of the marine soundscape and may negatively affect animals across all taxa. Invertebrates, including bivalves, received limited attention even though they make up a significant part of the marine biomass and are very important for higher trophic levels. Behavioural studies are critical to evaluate individual and potentially population-level impact of noise and can be used to compare the effects of different sounds. In the current study, we examined the effect of impulsive sounds with different pulse rates on the valve gape behaviour and phytoplankton clearance rate of blue mussels (<em>Mytilus</em> spp.<em>)</em>. We monitored the mussels’ valve gape using an electromagnetic valve gape monitor, and their clearance rate using spectrophotometry of phytoplankton densities in the water. We found that the mussels’ valve gape was positively correlated with their clearance rate, but the sound exposure did not significantly affect the clearance rate or reduce the valve gape of the mussels. They did close their valves upon the onset of a pulse train, but the majority of the individuals recovered to pre-exposure valve gape levels during the exposure. Individuals that were exposed to faster pulse trains returned to their baseline valve gape faster. Our results show that different sound exposures can affect animals differently, which should be taken into account for noise pollution impact assessments and mitigation measures.</p> <p> </p> <p>Paper reference:</p> <p>Hubert, J., Moens, R., Witbaard, R., Slabbekoorn, H. (2022). Acoustic disturbance in blue mussels: sound-induced valve closure varies with pulse train speed but does not affect phytoplankton clearance rate. <em>ICES Journal of Marine Science</em>. DOI: 10.1093/icesjms/fsac193.</p>
MEMEX_9d_Sounds_v1.0
<p>Audios produced by Paris Pilot participants of the MEMEX project, resulting from group and individual dynamics. Collected during the facilitated workshop sessions with the pilot group participants, and stored in an anonymized format. </p>
Dataset for "Coulomb-mediated antibunching of an electron pair surfing on sound"
<p>*********************************************************<br> This repository contains the raw experimental data associated with the manuscript<br> "Coulomb-mediated antibunching of an electron pair surfing on sound"<br> by Junliang Wang et al.<br> See <a href="http://doi.org/10.48550/arXiv.2210.03452">arXiv:2210.03452</a> for more details.<br> *********************************************************</p> <p>***************************************<br> Folder organization<br> ***************************************<br> Each figure in the manuscript which contains experimental data has assigned an unique folder.<br> In each folder, you will find:<br> - a 'data' folder containing the data files<br> - the figure in pdf format<br> - the jupyter notebook employed to generate the figure.</p> <p>There are two types of data files:<br> - .txt with comma separated values where the header contains the information for each column.<br> - .xlxs: standard Excel format.</p> <p> </p>
Spatial learning overshadows learning odors and sounds in both predatory and frugivorous bats
<p>To forage efficiently, animals should selectively attend to and remember the cues of food that best predict future meals. One hypothesis is that animals with different foraging strategies should vary in their reliance on spatial versus feature cues. Specifically, animals that store food in dispersed caches or that feed on spatially stable food, like fruit or flowers, should be relatively biased to learning a meal's location, whereas predators that hunt mobile prey should instead be relatively biased towards learning feature cues such as odor or sound. Several authors have predicted that nectar-feeding and fruit-feeding bats would rely relatively more on spatial cues, whereas closely related predatory bats would rely more on feature cues, yet no experiment has compared these two foraging strategies under the same conditions. To test this hypothesis, we compared learning in the frugivorous bat, <em>Artibeus jamaicensis</em>, and the predatory bat, <em>Lophostoma silvicolum</em>, which hunts katydids using acoustic cues. We trained bats to find food paired with a unique and novel odor, sound, and location. To assess which cues each bat had learned, we then dissociated these cues to create conflicting information. Rather than finding that the frugivore and predator clearly differ in their relative reliance on spatial versus feature cues, we found that both species used spatial cues over sounds or odors in subsequent foraging decisions. We interpret these results alongside past findings on how foraging animals use spatial cues versus feature cues and explore why spatial cues may be fundamentally more rich, salient, or memorable.</p>
Raw data and stimuli for assessing the perceived reverberation in different rooms for a set of musical instrument sounds
<p>This set of data and sound stimuli was used in the study by Osses, McLachlan, and Kohlrausch (2020) to assess the perceived reverberation --including measurements and simulations-- for different instrument sounds in eight different rooms. The following are the directories that are provided:</p> <ul> <li><strong>00-Experiment-WAE_GM_201712</strong>: Web Audio Evaluation tool (WAE) used to run the listening experiment with 24 participants. Follow the instructions in README.txt to get the experiment running.</li> <li><strong>01-Stimuli</strong>: Sound stimuli as exactly used during the listening experiments.</li> <li><strong>02-Raw-data</strong> and <strong>03-Results-summary</strong>: Outputs from WAE for each of the participants. The raw data contained in these XML files were extracted and stored in '03-Results-summary'</li> <li><strong>04-Stimuli-9s-for-simulations</strong>: Same sounds as in '01-Stimuli' but truncated to have a duration of 9 s. These sounds were used as input to an implementation (Osses et al. 2017, 2020) of the model by van Dorp et al. (2013).</li> </ul> <p>The paper figures can be reproduced in two MATLAB toolboxes: fastACI (script: publ_osses2020a_JASA_EL_figs.m) and AMT (script: exp_osses2020.m, availability as of 2023).</p>
waggle run phase sound during honeybee waggle dance
<p>Acoustic files of waggle run phase sound produce during the honeybee waggle dance</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.