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952 results for “Noise”
Input data for the exposure assessment case study on air pollution and noise for the province of Utrecht, the Netherlands
<p>Input datasets for the case study described in the manuscript "A computational framework for agent-based assessment of multiple environmental exposures".<br>Use the free 7-Zip to uncompress. Uncompressed size ca 103 GiB.</p> <p>The datasets are licensed under a Creative Commons license (CC-BY 4.0). Contact: o.schmitz@uu.nl</p>
Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"
<p>Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"</p> <p>Includes scripts for generating raw results, postprocessing scripts, as well as scripts/notebooks for generating plots for the manuscript</p>
High-resolution lithospheric shear velocity structure of the Suqian segment of the Tanlu fault zone from ambient noise tomography
<p>This file is the phase velocity dispersion curve manually picked up of Rayleigh wave in Suqian segment of the Tanlu Fault Zone. Only these Rayleigh wave dispersion curve are used for tomographic inversion.</p> <p>Rayleigh Wave Phase Dispersion Data (TL_SQ_Dispersion.zip):<br>Format:<br> Lon (station A) Lat (station A)<br> Lon (station B) Lat (station B)<br> Period (s) Vs (km/s)</p>
High-resolution lithospheric shear velocity structure of the Suqian segment of the Tanlu fault zone from ambient noise tomography
<p>Cross-correlation Functions Data(TL_SQ_CCFs.zip):<br>Format:<br> Lon (station A) Lat (station A) Elevation (A)<br> Lon (station B) Lat (station B) Elevation (B)<br> Time (t=0) GAB(t) GBA(t)<br> Time (t=dt) GAB(t) GBA(t) <br> Time (t=2dt) GAB(t) GBA(t)</p>
Data from: Acoustic risk balancing by marine mammals: anthropogenic noise can influence the foraging decisions by seals
<p class="MsoCommentText">Avoidance of anthropogenic sounds has been measured in many species. The results, which are typically based on observations in limited exposure contexts, are frequently used to inform policy and the regulation of industrial activities. However, the occurrence and magnitude of avoidance may be a consequence of complex risk-balancing decisions made by animals. The importance of the factors in decision-making, such as perceived risks associated with the sounds or prey quantity and quality during sound exposure, is unknown.</p> <p class="MsoCommentText">Here we address this knowledge gap by measuring the relative influence of perceived risk of a sound (silence, <span>pile driving, and a tidal turbine)</span> and prey patch quality on decision making and foraging success in grey seals <span>(<i>Halichoerus grypus</i>). </span>Seals were given access to two <span>underwater 'prey patches' </span>in an experimental pool <span>where fish were delivered at controlled rates to simulate a </span>low density (LD) and a high density (HD) prey patch. Acoustic playbacks were made using an underwater speaker above one of the prey patches (randomised during the study), and three decision and foraging metrics (foraging duration, foraging effort allocation between the prey patches, and foraging success) were measured.</p> <p>Foraging success was highest during silent controls and was similar regardless of speaker location (LD/HD). Under the tidal turbine and pile driving treatments, foraging success was similar to the controls when the speaker was located at the HD prey patch but was significantly reduced (~16-28% lower) when the speaker was located at the LD prey patch. Foraging decisions by the seals were consistent with a risk/profit balancing approach. Avoidance rates depend on the quality of the prey patch as well as the perceived risk.</p> <p>Policy implications: The results suggest that foraging context is important when interpreting avoidance behaviour and should be considered when predicting the effects of anthropogenic activities. For example, sound exposure in different prey patch qualities may result in markedly different avoidance behaviour, potentially leading to contrasting predictions of impact in Environmental Assessments. We recommend future studies explicitly consider foraging context, and other contextual factors such as behavioural state (e.g. foraging or travelling) and habitat quality.</p>
Impacts of additional noise on the social interactions of a cooperatively breeding fish
<p>Anthropogenic noise is a global pollutant known to affect the behaviour of individual animals in all taxa studied. However, there has been relatively little experimental testing of the effects of additional noise on social interactions between conspecifics, despite these forming a crucial aspect of daily life for most species. Here we use established paradigms to investigate how white-noise playback affects both group defensive actions against an intruder and associated within-group behaviours in a model fish species, the cooperatively breeding cichlid <i>Neolamprologus pulcher</i>. Additional noise did not alter defensive behaviour, but did result in changes to within-group behaviour. Both dominant and subordinate females, but not the dominant male, exhibited less affiliation and showed a tendency to produce more submissive displays to groupmates when there was additional noise compared to control conditions. Our experimental results therefore indicate the potential for anthropogenic noise to affect social interactions between conspecifics and that there can be intraspecific variation in the impacts of this global pollutant.</p>
Data from: Distinguishing noise from signal in patterns of genomic divergence in a highly polymorphic avian radiation
Recently diverged taxa provide the opportunity to search for the genetic basis of the phenotypes that distinguish them. Genomic scans aim to identify loci that are diverged with respect to an otherwise weakly differentiated genetic background. These loci are candidates for being past targets of selection because they behave differently from the rest of the genome that has either not yet differentiated or that may cross species barriers through introgressive hybridization. Here we use a reduced-representation genomic approach to explore divergence among six species of southern capuchino seedeaters, a group of recently radiated sympatric passerine birds in the genus Sporophila. For the first time in these taxa, we discovered a small proportion of markers that appeared differentiated among species. However, when assessing the significance of these signatures of divergence, we found that similar patterns can also be recovered from random grouping of individuals representing different species. A detailed demographic inference indicates that genetic differences among Sporophila species could be the consequence of neutral processes, which include a very large ancestral effective population size that accentuates the effects of incomplete lineage sorting. As these neutral phenomena can generate genomic scan patterns that mimic those of markers involved in speciation and phenotypic differentiation, they highlight the need for caution when ascertaining and interpreting differentiated markers between species, especially when large numbers of markers are surveyed. Our study provides new insights into the demography of the southern capuchino radiation and proposes controls to distinguish signal from noise in similar genomic scans.
Flywing (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Flywing (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Flywing (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Structured noise Convallaria dataset for structured noise removal benchmark as used in StructN2V paper
<p>Structured Convallaria dataset for structured noise removal benchmark as used in StructN2V paper (https://www.frontiersin.org/articles/10.3389/fcomp.2020.00005/full)</p>
Narwhals react to ship noise and airgun pulses embedded in background noise
<p>Anthropogenic activities are increasing in the Arctic posing a threat to species with high seasonal site-fidelity, such as the narwhal Monodon monoceros. In this controlled sound exposure study, six narwhals were live-captured and instrumented with animal-borne tags providing movement and behavioural data, and exposed to concurrent ship noise and airgun pulses. All narwhals reacted to sound exposure by reduced buzzing rates, where the response was dependent on the magnitude of exposure defined as 1/distance to ship. Halving of buzzing rate, compared with undisturbed behaviour, and cessation of foraging occurred at 12 and ~7-8 km from the ship, respectively. The effect of exposure could be detected > 40 km from the ship. At distances > 5 km, the received high-frequency cetacean weighted sound exposure levels were below background noise indicating sensitivity of narwhals towards sound disturbance and demonstrating their ability to detect signals embedded in noise. Further studies are needed to evaluate the energetic costs of disrupted foraging due to sustained disturbance but the observed sensitivity should be considered in the management of anthropogenic activities in the Arctic. The results of this study emphasize the importance of controlled sound exposure studies in the wild to explore the auditory capabilities of odontocetes.</p>
Fine seismic imaging of the Lianhuashan fault zone, South China, and tectonic implications – Constrained by ambient noise adjoint tomography
<p>The Rayleigh wave group velocity dispersion and the cross-correlation functions used in the study "Fine seismic imaging of the Lianhuashan fault zone, South China, and tectonic implications – Constrained by ambient noise adjoint tomography".</p>
Data files 2: A plasmid-based E. coli gene expression system with cell-to-cell variation below the extrinsic noise limit
<p>This zip archive contains flow cytometry data used to generate the figures in the submitted manuscript "A plasmid-based Escherichia coli gene expression system with cell-to-cell variation below the extrinsic noise limit" (PONE-D-17-11810). Text files within the archive describe how to open files and which data corresponds to manuscript figures.</p>
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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.