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952 results for “noise”
The influence of boating noise on the parental care behaviors of smallmouth bass (Micropterus dolomieu) during the summer of 2024 at Douglas Lake, Michigan, USA.
Anthropogenic noise is on the increase and in aquatic systems one of the major sources of noise is boat traffic. For organisms in lakes, rivers, and oceans that are capable of hearing, anthropogenic noise may alter behavior in a number of different ways. Here we did a combination of field and experimental work by locating smallmouth bass nests that were actively being guarded by males. Using an underwater drone, we monitored nest guarding behavior before and after a boat ran by the nest. In addition, we monitored behavior during this period while simultaneously recording boat motor noise. The results showed that the sequence of behavior performed by bass was altered during and after the boat ran by the nest.
PsPM-HRM1-2: SCR and ECG measurements in response to white noise sounds and an auditory oddball task
<p>This dataset includes skin conductance response (SCR) and electrocardiogram (ECG) for each of 61 healthy unmedicated participants (28 males, 32 females, 1 unassigned, aged 25.8 +/- 4.6 years) in response to 20 broadband white noise sounds (HRM1) or 10 oddball tones in a oddball task (HRM2). Some participants did not did not complete HRM1 or HRM2 or were excluded from analysis such that there are 56 recordings for HRM1 and 58 recordings for HRM2. White noise sounds in HRM1 were 1 s long with 10 ms on- and offset ramp and presented at ~85 dB. Oddball and standard sounds in HRM2 were 50 ms long, with 10 ms on- and offset ramp, and presented at ~75 dB. Sound frequency was 440 Hz or 460 Hz, randomly balanced per participant to oddballs and standards. SOA between white noise sounds and oddball tones was selected randomly on each trial from 30 s, 35 s or 40 s. All stimuli were presented in one block. There is a marker for each sound onset (including standard tones) in the windaq files.</p>
Acoustic noise radiation measurements of three disel-electric ferries
<p>This dataset contains measured noise radiation for three diesel-electric hybrid ferries, both in air and in water. The ferries have been measured in fully electric battery powered propulsion as well as in hybrid propulsion with the on-board diesel generator running.</p>
PAN-AR: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures
<h1>PAN-AR</h1> <p>This is <strong>PAN-AR</strong> (Panoramas, Ambient Noise & Ambisonics RIRs), a dataset described in the following <a href="https://doi.org/10.1145/3678299.3678332" target="_blank" rel="noopener">paper</a>:</p> <blockquote> <p>Filippo Denti, Davide Fantini, Federico Avanzini and Giorgio Presti. PAN-AR: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures. In <em>Proceedings of the 19th International Audio Mostly Conference</em>, Milan, Italy, September 2024.</p> </blockquote> <p>The dataset includes Spatial Room Impulse Responses (SRIRs) in second-order Ambisonics format, ambient noise recordings, and spherical photos. These data have been captured in four environments with different configurations of the source and listener positions:</p> <ol> <li>Printer room</li> <li>Meeting room</li> <li>Classroom</li> <li>Underground parking area</li> </ol> <p>Panoramas and planimetries are provided in a temporary version. The final version with post-processed panoramas and complete planimetries will be available soon. An example of the final panoramas is provided for position A of the printer room, while an example of complete planimetry is provided for the printer and the meeting rooms.</p> <h2>SOFA</h2> <p>The SRIRs are also provided in SOFA format <a href="https://sofacoustics.org/data/database/pan-ar/" target="_blank" rel="noopener">here</a>.</p> <h2>How to cite</h2> <p>If you use the PAN-AR dataset, please cite the following <a href="https://doi.org/10.1145/3678299.3678332" target="_blank" rel="noopener">paper</a>:</p> <pre><code>@inproceedings{denti2024panar,</code><br><code> title = {{PAN-AR}: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures},</code><br><code> author = {Denti, Filippo and Fantini, Davide and Avanzini, Federico and Presti, Giorgio},</code><br><code> year = {2024},</code><br><code> month = {September},</code><br><code> booktitle = {Proceedings of the 19th International Audio Mostly Conference (AM '24)},</code><br><code> location = {Milan, Italy},</code><br><code> publisher = {ACM},</code><br><code> isbn = {979-8-4007-0968-5/24/09},</code><br><code> doi = {10.1145/3678299.3678332}</code><br><code>}</code></pre>
Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study (Supplementary Data)
<p>The zip file contains supplementary data for the publication - Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study, accepted for publication in Environmental Health Perspectives (DOI: 10.1289/EHP6174).</p> <p>The description of the files are noted below:</p> <p><strong>1. Readme File for SAPALDIA Noise and Air Pollution EWAS Single Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_SingleExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_SingleExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p> </p> <p><strong>General footnote for all files:</strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from single exposure epigenome-wide linear mixed models, with random intercept at the level of participant. Each model was adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator (for Lden models) and leukocyte composition. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites.</p> <p>Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p> <p> </p> <p><strong>2. Readme File for SAPALDIA Noise and Air Pollution EWAS Multi Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_MultiExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_MultiExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p><strong>General table footnotes: </strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from multi-exposure epigenome-wide linear mixed models, with random intercept at the level of participant, and were adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator and leukocyte composition. Multi-exposure models included all five exposures (Aircraft, railway, road traffic Lden and respective truncation indicators, NO<sub>2</sub> and PM<sub>2.5</sub>) at the same time. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites. Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p>
Simulated EIT circular targets with noise and blur
<p>Companion data for the paper "Post-processing electrical impedance tomography reconstructions with incomplete data using convolutional neural networks".</p> <p>See <a href="https://github.com/robert-abc/KTC2023-ABC1">https://github.com/robert-abc/KTC2023-ABC1</a> for more information. </p> <p>Files:</p> <ul> <li>CNN_input.mat: CNN input, the noisy data</li> <li>CNN_output.mat: CNN output, the clean data</li> <li><em>cnn_training.py: </em>Describes the CNN training using Keras</li> <li><em>CNN_training.ipynb: </em>Notebook after the CNN training</li> <li><em>ultimate_cnn1.h5: </em>CNN (keras) file after training</li> </ul> <p>We uploaded the files to Google Drive and executed the code using Google Colab.</p> <p>Before executing the code, one should change the current working directory to the folder where the files are.</p>
Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR
<p>Dataset for demographic, psychological and physiological data obtained for RESTORE (Experiment 1 WP1). Study results are published in the article titled "Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR" in the Journal of Environmental Psychology. Explanations on all variables (column names) in the datasets are given either in the second spreadsheet in each Excel file or in the csv files appended with _legend.csv (see latest version of the dataset). File 'Psychophysiological_participant_data_aggregated' is aggregated per participant (single or mean values), and the file 'Restoration_EDA_baseline-corrected_aggregated' contains EDA data aggregated per time point per restoration setting (Nature vs Urban) and prior cognitive demand condition. Methodological details on how the data was obtained and processed are given in the Open Access article.</p>
Chemical and Random Additive Noise Elimination (CRANE)
<p><strong>Improved identification and quantification of peptides in mass spectrometry data via chemical and random additive noise elimination (CRANE)</strong></p> <p><strong>Availability and implementation</strong></p> <p>The software is available on Github (<a href="https://github.com/CMRI-ProCan/CRANE">https://github.com/CMRI-ProCan/CRANE</a>). The datasets were obtained from ProteomeXchange (Identifiers—PXD002952 and PXD008651). Preliminary data and intermediate files are available via ProteomeXchange (Identifiers—PXD020529 and PXD025103).</p>
Experimental data for Quantum noise limited microwave amplification using a graphene Josephson junction
<p>This dataset was used in our study of "Quantum noise limited microwave amplification using a graphene Josephson junction".</p>
UPWARDS - Høgjaeren noise prediction benchmark
<p>The benchmark consists in a reduced layout of nine wind turbines located in the Høgjaeren wind farm in Norway for two wind conditions of similar wind speed amplitude but having opposite wind directions. The necessary information for the user to produce the noise footprint on an observer grid are detailed in the Upwards_D4_6_v1.pdf document.</p>
PIBE project- Experimental characterization of stall noise in static and dynamic regimes using a NACA 63(3)418 airfoil
<p>Dynamic stall noise is one of the potential sources of amplitude modulations associated with wind turbine noise. This phenomenon is related to the periodic separation and reattachment of the boundary layer on the wind turbine blade suction side during its rotation. Within the framework of the PIBE project (Predicting the Impact of Wind Turbine Noise - <a href="https://www.anr-pibe.com/en">https://www.anr-pibe.com/en</a>), experiments were conducted in the anechoic wind tunnel of the École Centrale de Lyon in order to characterize stall noise on a pitching airfoil in both static and dynamic conditions.</p> <p>In version 1.0.0 of the database, <span>data from the second campaign using an instrumented NACA63(3)418 airfoil in static and dynamic conditions are provided. The static data can be found in the file static_data_NACA63418.h5 that contains:</span></p> <ol> <li>static wall pressure data : lift and pressure coefficients;</li> <li>dynamic wall pressure data : Power Spectral Density (PSD) of fluctuating wall pressure;</li> <li>far-field acoustic data : Power Spectral Density (PSD) of acoustic pressure.</li> </ol> <p><span>The structure of the file is described in Tree_structure_static_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_static_Matlab.zip can be used.</span></p> <p><span>The dynamic data can be found in the file dynamic_data_NACA63418.h5 that contains:</span></p> <ol> <li><span>static wall pressure data : phase-averaged lift coefficients;</span></li> <li><span>dynamic wall pressure data : phase-averaged spectrograms of fluctuating wall pressure;</span></li> <li><span>far-field acoustic data : phase-averaged spectrograms of acoustic pressure.</span></li> </ol> <p><span>The structure of the file is described in Tree_structure_dynamic_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_dynamic_Matlab.zip can be used. Only the results for a mean angle of attack of 15° and an amplitude of 15° are provided in this file.</span></p>
Supporting Data for the paper titled "The Intensity, Directionality and Statistics of Underwater Noise from Melting Icebergs"
<p>The dataset contains:</p> <p>a) 13 audio files, with names including date, track number and channel; format: WAV files</p> <p>b) data from magnetic compass used to calculate noise directionality; format: txt files with lines containing date, time and magnetic direction (degrees)</p> <p>c) GPS tracks of the boat and attached acoustic buoy; format: txt files with NMEA codes</p> <p>d) GPS tracks around each iceberg tracked; format: txt files with UTM coordinates</p> <p>The study was founded by National Science Centre Poland grant no. 2013/11/N/ST10/01729 and partially supported within statutory activities No 3841/E-41/S/2018 of the Ministry of Science and Higher Education of Poland. Partial support for this work was also provided by US Office of Naval Research, Grant No. N00014-17-1-2633.</p> <p>Corresponding author: Oskar Glowacki, oglowacki@igf.edu.pl</p>
Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing (code, data and scripts to reproduce paper results)
<p>This repository contains the data, code, and scripts required to reproduce the results of the paper "Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing" by Daniele De Sensi, Salvatore Di Girolamo and Torsten Hoefler, presented at the 2019 International Conference for High Performance Computing, Networking, Storage, and Analysis. </p> <p>This repository does not contains the code of the library used to automatically tune the routing algorithm, which can be found at http://doi.org/10.5281/zenodo.3372785</p>
NDVI, noise and land temperature of Geneva
<p>Statistical data set (mean, standard deviation, median and sum) of</p> <p>- Land temperature (Landsat 8 satellite : band 11 thermal)</p> <p>- NDVI (Landsat 8 satellite : band 4 red and 5 NIR)</p> <p>- Road traffic noise (day and night)</p> <p>- Hectometric vector grid representing inhabited hectares of the canton of Geneva.</p>
SOUNDSCAPE North Adriatic Underwater Noise Sound Pressure Levels
<p>Within the Interreg Italy-Croatia <strong>SOUNDSCAPE</strong> <strong>project</strong> a basin-scale, cross-national, long-term underwater monitoring in the Northern Adriatic Sea was carried out (https://www.italy-croatia.eu/web/soundscape). A broad network of nine monitoring stations, characterized by different natural conditions and anthropogenic pressures, ensured acoustic data collection from March 2020 to June 2021, including the first full lockdown period related to the COVID-19 pandemic (March–April 2020). Calibrated stationary recorders featured with an omnidirectional Neptune Sonar D60 Hydrophone recorded continuously 24h a day (48 kHz, 16 bit).</p> <p>Data were analysed to Sound Pressure Levels (SPLs, dB re 1 uPa) that are here released as a dataset composed of 20 and 60 seconds averaged SPL output files for each station. Data are archived using structured hdf5 files, each one containing metadata and SPL data, according to ICES (International Council for the Exploration of the Sea) continuous noise data specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx).</p> <p>A research object with a jupyter notebook developed to post process SPL data is available at https://doi.org/10.24424/hrhm-8849</p> <p>If data are used, please cite also Petrizzo, A., Barbanti, A., Barfucci, G. <em>et al.</em> Author Correction: First assessment of underwater sound levels in the Northern Adriatic Sea at the basin scale. <em>Sci Data</em> <strong>10</strong>, 179 (2023). https://doi.org/10.1038/s41597-023-02099-x</p>
Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem - Data
<p>Figures and corresponding data associated with the manuscript 'Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem' by Herbst et al.</p>
Data - Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop
<p>This dataset contains measurement data and processing code for the results published in "Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop". The Pyrpl code change used in the work is also attached.</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Noise exposure at ultrasound-related industrial workplaces and public sites
<p>The dataset contains single measurements at different public sites and workplaces in Europe. The data has been used or obtained in the context of the project 15HLT03 “EarsII” from the EMPIR-programme.</p> <p>For each measurement metadata is available. This includes the measurement circumstances and involved machinery, a description of the measurement location and noise reduction measures, the microphone position during measurement, and the measurement procedure used to obtain the measurement data.</p> <p>A detailed description of all the quantities contained in the dataset is documented in the accompanying pdf-file.</p>
MCR LTER: Coral Reef: Finding Signals in the Noise of Coral Recruitment, data for Edmunds 2021 Coral Reefs
These data, looking at coral recruitment were collected in Moorea, French Polynesia, measured over 13 years, and tested for associations with environmental conditions. Recruitment of spawning pocilloporid corals was recorded using settlement tiles immersed for ~ 6 months at 10 m and 17 m depth, biannually, and the environment was quantified through seawater clarity (Kd490), surface and bottom flow speeds, coral cover, and temperature. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2021). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Final models for "Global-Scale Full-Waveform Ambient Noise Inversion" by Sager et al. (2020)
<p>The exodus model contains the inverted structure model and the source distribution can be found in the HDF5 file. Both can be visualized in ParaView. For the source model, we recommend opening it with the correspoding XDMF file (select "XDMF Reader" in the dialogue box).</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.