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.
952
datasets available to search
ShareScore release 0.9.0
Dataset results
952 results for “Noise”
Spin Noise Data
<p>This is the raw spin noise data used in the paper:</p> <p>"Nuclear spin noise tomography in three dimensions with<br> iterative simultaneous algebraic reconstruction<br> technique (SART) processing"</p>
Spin Noise Data
<p>This is the raw spin noise data used in the paper:</p> <p>"Nuclear spin noise tomography in three dimensions with<br> iterative simultaneous algebraic reconstruction<br> technique (SART) processing"</p>
Traffic noise data at Qianjiangxincheng
<p>Traffic noise data recorded at Qianjiangxincheng, Hangzhou, China in 2019. </p> <p>NE193-198 & SE1-197</p>
Data from: Measurement artefacts lead to false positives in the study of birdsong in noise
1: Numerous studies over the past decade have reported correlations between elevated levels of anthropogenic noise and a rise in the minimum frequency of acoustic signals of animals living in noisy habitats. This pattern appears to be occurring globally, and higher pitched signals have been hypothesized to be adaptive changes that reduce masking by low-frequency traffic noise. However, the sound analysis methods most often used in these studies are prone to measurement errors that can result in false positives. In addition, the commonly used method of measuring frequencies visually from spectrograms might also lead to observer-expectancy biases that could exacerbate measurement errors. 2: We conducted an experiment to (a) quantify the size and type of errors that result from "eye-balling" frequency measurements with cursors placed manually on spectrograms of signals recorded in noise and no-noise conditions, and (b) to test whether observer expectations lead to significant errors in frequency measurements. We asked 54 volunteers, blind to the true intention of our study, to visually measure the minimum frequency of a variety of natural and synthesized bird sounds, recorded either in noise, or no-noise conditions. Test subjects were either informed or uninformed about the hypothesized results of the measurements. 3: Our results demonstrate that inappropriate methodology in acoustic analysis can yield false positives with effect sizes as large, or even larger, than those reported in published studies. In addition to these measurement artefacts, psychological observer biases also led to false positives – when observers expected signals to have higher minimum frequencies in noise, they measured significantly higher minimum frequencies than uninformed observers, who had not been primed with any expectation. 4: The use of improper analysis methods in bioacoustics can lead to the publication of spurious results. We discuss alternative methods that yield unbiased frequency measures and we caution that it is imperative for researchers to familiarize themselves both with the functions and limitations of their sound analysis programs.. In addition, observer expectancy biases are a potential source of error not only in the field of bioacoustics, but in any situation where measurements can be influenced by human subjectivity.
Data from: Males increase call frequency, not intensity, in response to noise, revealing no Lombard effect in the little torrent frog
Noise is one of the main factors that can influence the processes of sound communication across a wide range of animal groups. Although the effects of ambient noise on animal communication, including anthropogenic noise, have received increasing attention, few studies have examined changes in the fine structure of acoustic signals produced by vocalizing species in constantly noisy environments. Here, we used natural recordings to determine the associations between stream noise and call parameters in the little torrent frog (Amolops torrentis). We also used playbacks of stream noise recorded in natural habitats and playbacks of white noise to examine how male vocal signals change with increasing noise levels. The results show that noise intensity has a significant effect on male call frequency, but not on call amplitude or other call characteristics. Based on this evidence, we suggest that in streamside species stream noise drives males to alter call frequency and call as loudly as possible in order to improve discriminability. These findings provide insights into the role played by ecological selection in the evolution of noise‐dependent anuran vocal plasticity
The perception of emotional cues by children in artificial background noise
<p>This repository contains the necessary resources to replicate the experimental results presented in the article entitled "The perception of emotional cues by children in artificial background noise" published in the International Journal on Speech Technology on January 22, 2020.</p>
Causal relationship between air pollution and noise pollution exposure and brain cortical structure: a two-sample Mendelian randomization study
Open the record for dataset details and reuse information.
Noise Intensity and Its Impact on The Perception and Concentration Level Among Forest Harvesting Workers in Industrial Forest Plantation, North Sumatera, Indonesia
<p>Abstract</p> <p><strong>Background:</strong> Noise has the potential to affect the comfort and health of workers. The objective of this research was to find out the effect of noise caused by timber harvesting process using chainsaw on the concentration of workers in an industrial forest plantation.</p> <p>Methods: This cross-sectional study included 20 respondents which consisted of 10 chainsaw operators/helpers and 10 non-chainsaw operators. All respondents were exposed to the sound of chainsaw in three different conditions (idle, half gas and racing conditions) with and without personal protective equipments (PPE). The sound intensity produced by the chainsaw and the noise received by the respondents were measured using sound level meter. Respondents’ perception toward noise was recorded using Likert scale. Respondents’ concentration level was assessed by giving 25 math-questions that must be answered within 10 minutes. Wilcoxon sign rank test was used to analysed paired data.</p> <p>Results: the average sound intensity received by respondents’ left and right ear were lower than the average of sound intensity produced by the chainsaw. The average sound intensity produced by the chainsaw in idle condition was 101.3 dB, while that received by the right ear was 81.36 dB and the left ear was 82.86 dB. When the chainsaw was in half gas condition, the average sound intensity produced was 111 dB while that received by the right ear was 95.83 dB and the left ear was 97.11 dB. In racing condition, the average sound intensity produced by the chainsaw was 124.7 dB while that received by the right ear was 106.2 dB and the left ear was 107.7 dB, respectively. The use of earmuff leads to better perception towards noise when compared with the use of earplug and the absent of any personal protective equipment (PPE). Wilcoxon test result showed that noise did not have significant impact on the concentration level of chainsaw operators, whilst the contratry result is true for non-chainsaw operators.</p> <p>Conclusion: The research indicated that although the noise produced by the chainsaw machine was considered as noisy for both chainsaw operators and non-chainsaw operator, it did not give significant effect on the concentration level of chainsaw operators and only affects the non-chainsaw operators. Therefore, given that the non-chainsaw operators were still affected by the noise, noise control measures are still needed to ensure occupational safety and health for the workers.</p> <p>Keywords: Chainsaw; forest harvesting; industrial forest plantation; noise</p>
Low-noise frequency-agile photonic integrated lasers for coherent ranging
<p>Data and source code used in the manuscript "Low-noise frequency-agile photonic integrated lasers for<br> coherent ranging"</p>
Data for ambient noise analysis around Tristan da Cunha in the South Atlantic from OBS data
<p>This file includes:</p> <p>1. Files of the ambient noise cross-correlation functions between all the station pairs in SAC format.</p> <p>2. Files of probabilistic power spectral densities (PPSDs) of 4-components recordings (two horizontal components, one vertical component and one hydrophone component) of all the OBSs and 3-components recordings (two horizontal components and one vertical component) of two island stations during deployment. </p> <p> </p>
Data for "The 20-year highest tropical cyclone-generated waves associated with the maximum energy of seismic noises" (Subset 3)
<p>This is dataset of ocean wave simulations used in the paper "The 20-year highest tropical cyclone-generated waves associated with the maximum energy of seismic noises" by Shimura et al. (submitted).</p> <p>The dataset contains </p> <ul> <li>significant wave height (Subset 1),</li> <li>wave induced surface pressure (Subset 2)</li> <li>long-period componet of wave heights (Subset 3)</li> <li>long-period component of surface pressure (Subset 4)</li> </ul> <p>during 2004 from 2023 summer. </p>
Noise samples for Costantino et al., Seismic Source Characterization From GNSS Data Using Deep Learning (2023)
Open the record for dataset details and reuse information.
High-resolution lithospheric shear velocity structure of the Suqian segment of the Tanlu fault zone from ambient noise tomography
<p>Our dataset includes S-wave velocity tomography model of of the Suqian segment of the Tanlu fault zone.</p>
Data from: The effect of mood state on visual search times for detecting a target in noise: an application of smartphone technology
The study of visual perception has largely been completed without regard to the influence that an individual's emotional status may have on their performance in visual tasks. However, there is a growing body of evidence to suggest that mood may affect not only creative abilities and interpersonal skills but also the capacity to perform low-level cognitive tasks. Here, we sought to determine whether rudimentary visual search processes are similarly affected by emotion. Specifically, we examined whether an individual's perceived happiness level affects their ability to detect a target in noise. To do so, we employed pop-out and serial visual search paradigms, implemented using a novel smartphone application that allowed search times and self-rated levels of happiness to be recorded throughout each twenty-four-hour period for two weeks. This experience sampling protocol circumvented the need to alter mood artificially with laboratory-based induction methods. Using our smartphone application, we were able to replicate the classic visual search findings, whereby pop-out search times remained largely unaffected by the number of distractors whereas serial search times increased with increasing number of distractors. While pop-out search times were unaffected by happiness level, serial search times with the maximum numbers of distractors (n = 30) were significantly faster for high happiness levels than low happiness levels (p = 0.02). Our results demonstrate the utility of smartphone applications in assessing ecologically valid measures of human visual performance. We discuss the significance of our findings for the assessment of basic visual functions using search time measures, and for our ability to search effectively for targets in real world settings.
Relation between seismic noise levels and soil fauna
<p><span><span><span><span><span><span><span><span><span><span><span>Human activities often impact the sensory environment of organisms. Wind energy turbines are a fast-growing potential source of anthropogenic vibrational noise that can affect soil animals sensitive to vibrations and thereby alter soil community functioning. Larger soil animals, such as earthworms (macrofauna, > 1 cm in size), are particularly likely to be impacted by the low-frequency turbine waves that can travel through soils over large distances. Here we examine the effect of wind turbine-induced vibrational noise on the abundance of soil animals. We measured vibrational noise generated by seven different turbines located in organically-farmed crop fields in the Netherlands. Vibratory noise levels dropped by an average of 23 ± 7 dB over a distance of 200 m away from the wind turbines. Earthworm abundance showed a strong decrease with increasing vibratory noise. When comparing the nearest sampling points in proximity of the wind energy turbines with the points furthest away, abundance dropped on average by 40% across all seven fields. The abundance of small-sized soil animals (mesofauna, < 10 mm in size) differed between crop fields, but was not related to local noise levels. Our results suggest that anthropogenic vibratory noise levels can impact larger soil fauna, which has important consequences for soil functioning. Earthworms, for instance, are considered to be crucial ecosystem engineers and an impact on their abundance, survival and reproduction may have knock-on effects on important processes such as water filtration, nutrient cycling and carbon sequestration.</span></span></span></span></span></span></span></span></span></span></span></p>
Dataset and 3D Vs Model for "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography"
<p>Final 3D Vs model and dispersion data for the paper entitled "Crustal velocity images of north-western Türkiye along the North Anatolian Fault Zone from transdimensional Bayesian ambient seismic noise tomography".</p> <p>In the vel_files folder, there are 10 files for each depth for 1-15 km. The format of each velocity file is as follows:</p> <p>Column Value<br> 1 Lattitude (°)<br> 2 Longitude (°)<br> 3 Vs (km/s)</p> <p>The format of the dispersion data is as follows (See <a href="https://www.eas.slu.edu/eqc/eqc_cps/TUTORIAL/EMPIRICAL_GREEN/example1.html">Computer Programs in Seismology Tutorials - do_mft</a> for more information on the format):</p> <p>Column Value<br> 1 Type of file, MFT96<br> 2 Wave type: R for Rayleigh <br> 3 Dispersion type: U for group velocity<br> 4 Mode: 0 represents the fundamental mode<br> 5 Filter period, T, in seconds<br> 6 Dispersion value, either group or phase<br> 7 Error in dispersion. This is just a place holder since there is no way to estimate an error from a single trace. The group velocity error is determined from the ratio of the filter period to travel time<br> 8 Distance in km<br> 9 Azimuth from the source to the receiver<br> 10 Spectral amplitude. <br> 11 Epicenter latitude <br> 12 Epicenter longitude<br> 13 Station latitude<br> 14 Station longitude<br> 15 control flag<br> 16 control flag<br> 17 Instantaneous period if this is preferred. This differs from the ilter period because the signal spectram is not flat.<br> 18 Comment: keyword<br> 19 Station <br> 20 Component<br> 21 Year<br> 22 Day of year<br> 23 Hour<br> 24 Minute - these identify the event origin time </p>
Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01
<p>This repository contains the following data sets related to Detecting Plumes in Mobile Air Quality Monitoring Time Series with DBSCAN published in . Please cite the following: .</p> <p>Validated_Data.csv: A .csv file containing the validation set used in the study. Column headings are the following:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been manually flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p> <p>Labeled_DBSCAN_Anomalies.csv: A .csv file containing points labeled as anomalies by the DBSCAN algorithm described in the manuscript. Columns are the following.</p> <p>"BC": Black carbon measurement (ng/m^3)<br> "CO2": Carbon dioxide measurement (ppm)<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2 (ppb)<br> "UFP": Ultrafine particle count (p/cc)<br> "Anomaly": Whether the DBSCAN algorithm has labeled this point as "Anomaly" (2) or "Normal" (1)<br> "Uniq_Fac": Factor spanning from 1-277 grouping measurements taken on separate days by car. E.g. all measurements with Uniq_Fac=1 were grouped and analyzed together.<br> "LST": Timestamp (US/Central)<br> "Road_Class": TigerLINE census road class designation for the given point. Possible road classes are S1100 - Primary Road, S1200 - Secondary Road, S1400 - Local Road, S1630 - Ramps, S1640 - Service Drives, S1730 - Private Roads<br> "X": Universal Transverse Mercator Easting for Zone 15N (m).<br> "Y": Universal Transverse Mercator Northing for Zone 15N (m).</p> <p>*_To_Be_Validated.csv: A series of files where * denotes the following.</p> <p>"DB": DBSCAN Algorithm<br> "QOR": QOR Algorithm<br> "QAND": QAND Algorithm<br> "Drew": Drewnick Algorithm</p> <p>Each file contains the following columns:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p>
cross-correlations of seismic ambient noise - Liupan Shan
<p>Stacked cross-correlation functions of the continuous seismic data recorded at the stations around the Liupan Shan area. Data in Text format, named "LPS.stationA_stationB.ZZ".</p> <p>The format of the data is as follows:</p> <p>Lon (stationA) Lat (stationA) Elevation (stationA)</p> <p>Lon (stationB) Lat (stationB) Elevation (stationB)</p> <p>Time (t=0) CF<sub>AB</sub>(t) CF<sub>BA</sub>(t)</p> <p>Time (t=dt) CF<sub>AB</sub>(t) CF<sub>BA</sub>(t)</p> <p>Time (t=2dt) CF<sub>AB</sub>(t) CF<sub>BA</sub>(t)</p> <p>……</p>
Readout of quantum devices with a sideband microwave interferometer immune to systematic noise
<p>supporting data from paper 'Readout of quantum devices with a sideband microwave interferometer immune to systematic noise' by N. Crescini, E.G. Kelly, G. Salis and A. Fuhrer</p>
Notched Noise Therapy for Suppression of Tinnitus
ClinicalTrials.gov study NCT04661995. IPD Sharing: NO. Countries: 1. Publications: 0.
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.