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952 results for “Noise”
Hectar grid of noise, NDVI and Thermal radiation in Geneva
<p>Different indexes (mean, median, std) of NDVI, day and night noise and Thermal outgoing radiation in Geneva</p>
RPA simulated dataset containing noisy I-V and noise-free I-V
<p>Dataset contains information for the simulated ideal and noisy I-V curves used in the paper "A bootstrap based approach for improving measurements by retarding potential analyzers", submitted JGR-Space Physics.</p>
Crustal and upper mantle structure of the Tien Shan orogenic belt from full-wave ambient noise tomography
<p>Empirical Green’s functions, phase delays and shear wave velocity model of Tien Shan</p>
Supplementary File S2: Plasmids for independently tunable, low-noise gene expression
<p>Supplementary File S2 README</p> <p>2019-April-26</p> <p>"Plasmids for independently tunable, low-noise gene expression" (Version 2)</p> <p>João P. N. Silva, Soraia Vidigal Lopes, Diogo J. Grilo, Zach Hensel</p> <p>This file describes the contents of the supplementary file for this manuscript. Python scripts were run in a Python 3 environment on OSX with various scientific python packages updated as of April 2019. With minor modifications for any similar environment it should be possible to generate Figures 1, 2, and 4 in the manuscript from these scripts and raw data.</p> <p>Contents:</p> <p>\dna sequences<br> \pDG101.gb Annotated DNA sequence in genbank format of plasmid pDG101<br> \pJS101.gb Annotated DNA sequence in genbank format of plasmid pJS101 (AddGene #118280)<br> \pJS102.gb Annotated DNA sequence in genbank format of plasmid pJS102 (AddGene #118281)<br> \pZH501.gb Annotated DNA sequence in genbank format of plasmid pZH501<br> \pZH509.gb Annotated DNA sequence in genbank format of plasmid pZH509 (AddGene #102664)<br> \pZH713.gb Annotated DNA sequence in genbank format of plasmid pZH713<br> \ZHX99.gb Annotated DNA sequence in genbank format for E. coli MG1655 chromosome insertion mutant ZHX99<br> <br> \data<br> \DG FCS Data: Raw flow cytometry data from BioRad S3 sorted by day and experimental condition; file name format: plasmid_inducer-concentration_inducer-units_inducer.fcs<br> \pJS101 pDG101 independence<br> \"quick scope intensity.ijm" Fiji macro used to extract average fluorescence intensities<br> \"Microscope Data\" Microscope data analyzed using the above Fiji macro; directory names indicate ATc and IPTG concentrations for each experimentation condition/replicate<br> \"intensity analysis\"<br> X_nM_ATc_Y_uM_IPTG.csv files: Exported CSV data from Fiji for each condition<br> backgrounds.csv: Average intensity for every condition, image frame, and color for background subtraction<br> \"images for Figure 4": Image stack with raw images used to generate Fig 4A and 4B</p> <p>\code<br> \fcsAnalysis_final_181228.py step-wise script for generating Figures 1 and 2 from FCS data<br> \fcsCalcDirectory181228.py script containing functions for FCS analysis and figure generation<br> \fcsImages PDF figures output by FCS analysis scripts<br> \FlowCal-master Distribution of the FlowCal library used in analysis for this manuscript; this is distributed under the MIT license<br> \scopeAnalysisWorkflow190430.py step-wise script for generating Figures 4C and 4D from cell fluorescence microscopy data in CSV format exported from Fiji<br> \scopeCalcDirectory190430.py script containing functions for microscopy data analysis and figure generation</p>
Projecting boreal bird responses to climate change: the signal exceeds the noise
<p>Current and projected future potential boreal bird densities (4-km resolution)</p> <p>Citation for journal article associated with this dataset:<br> --------------------<br> Stralberg, D., S. M. Matsuoka, A. Hamann, E. M. Bayne, P. Sólymos, F. K. A. Schmiegelow, X. Wang, S. G. Cumming, and S. J. Song. 2015. Projecting boreal bird responses to climate change: the signal exceeds the noise. Ecological Applications 25:52-69. http://dx.doi.org/10.1890/13-2289.1</p> <p>Coordinate System<br> ------------------<br> Projection: Lambert Conformal Conic<br> False Easting: 0.00000000<br> False Northing: 0.00000000<br> Central Meridian: -95.00000000<br> Standard Parallel 1: 49.00000000<br> Standard Parallel 2: 77.00000000<br> Latitude Of Origin: 0.00000000<br> Linear Unit: Meter<br> Datum: D WGS 1984</p> <p>Summary<br> -------<br> The boreal forest biome provides a resource-rich environment for breeding birds, supporting high species diversity and bird numbers. These birds are likely to shift their distributions northward in response to rapid climate change over the next century. We used a comprehensive dataset of avian point-count surveys from across boreal Canada and Alaska, combined with interpolated climate data, to develop bioclimatic niche models of current avian distribution and density for 80 boreal-breeding songbird species. We then used a downscaling of projected future climates to assess the potential for these species to change their distribution and abundance in response to climate change. Note that projections represent potential densities based on climatic conditions, land use and topography. They do not account for physiographic barriers such as the northern extent of the Rocky Mountains that may prevent colonization of otherwise suitable habitat. Therefore current species’ distributions may be over-estimated in certain regions, particularly in Alaska.</p> <p>Boosted regression tree models of species distribution were averaged across two sets of covariates (climate-only and climate + land use + topography), 11 bootstrap samples, and four global climate models. Mean projections and uncertainty estimates (coefficient of variation) are available for the current period (based on climate data from 1961-1990) and three future time periods (2011–2040, 2041­–2070, 2071–2100). Climate data layers available at tinyurl.com/ClimateNA.</p> <p>Contact<br> -------<br> Diana Stralberg, University of Alberta (stralber@ualberta.ca)<br> Boreal Avian Modelling Project (borealbirds.ca)</p> <p>Project sponsors<br> ----------------<br> Boreal Avian Modelling (BAM) Project<br> Alberta Biodiversity Management and Climate Change Adaptation Project</p> <p>Avian data providers<br> --------------<br> http://www.borealbirds.ca/index.php/data_partners<br> USGS Breeding Bird Survey<br> Breeding Bird Atlases of Canada</p> <p>BAM founding organisations and funders<br> --------------------------------------<br> Environment Canada<br> University of Alberta<br> Canadian BEACONs Project</p> <p>Financial supporters<br> --------------------<br> USFWS Neotropical Migratory Bird Conservation Act<br> Vanier Canada Graduate Scholarships</p> <p>Alberta Biodiversity Monitoring Institute<br> Alberta Innovates Technology Futures<br> Alberta Pacific Forest Industries Inc.<br> Climate Change and Emissions Management Corporation<br> Joint Canada-Alberta Implementation Plan for Oil Sands Monitoring<br> Killam Trusts<br> Landscape Conservation Cooperatives<br> National Fish and Wildlife Foundation<br> Université Laval</p> <p>Species code definitions<br> ------------------------<br> Code Common name (Scientific name)<br> ALFL Alder Flycatcher (Empidonax alnorum) ‡<br> AMCR American Crow (Corvus brachyrhynchos)<br> AMGO American Goldfinch (Spinus tristis)<br> AMPI American Pipit (Anthus rubescens) ‡<br> AMRE American Redstart (Setophaga ruticilla)<br> AMRO American Robin (Turdus migratorius) ‡<br> ATSP American Tree Sparrow (Spizella arborea) ‡<br> BAWW Black-and-white Warbler (Mniotilta varia)<br> BBWA Bay-breasted Warbler (Setophaga castanea)<br> BCCH Black-capped Chickadee (Poecile atricapillus) ‡<br> BHCO Brown-headed Cowbird (Molothrus ater)<br> BHVI Blue-headed Vireo (Vireo solitarius)<br> BLBW Blackburnian Warbler (Setophaga fusca)<br> BLJA Blue Jay (Cyanocitta cristata)<br> BLPW Blackpoll Warbler (Setophaga striata) ‡<br> BOCH Boreal Chickadee (Poecile hudsonicus) ‡<br> BRBL Brewer’s Blackbird (Euphagus cyanocephalus)<br> BRCR Brown Creeper (Certhia americana) ‡<br> BTNW Black-throated Green Warbler (Setophaga virens)<br> CAWA Canada Warbler (Cardellina canadensis)<br> CCSP Clay-colored Sparrow (Spizella pallida)<br> CEDW Cedar Waxwing (Bombycilla cedrorum)<br> CHSP Chipping Sparrow (Spizella passerina) ‡<br> CMWA Cape May Warbler (Setophaga tigrina)<br> COGR Common Grackle (Quiscalus quiscula)<br> CONW Connecticut Warbler (Oporornis agilis)<br> CORA Common Raven (Corvus corax) ‡<br> CORE Common Redpoll (Acanthis flammea) ‡<br> COYE Common Yellowthroat (Geothlypis trichas)<br> CSWA Chestnut-sided Warbler (Setophaga pensylvanica)<br> DEJU Dark-eyed Junco (Junco hyemalis) ‡<br> EAKI Eastern Kingbird (Tyrannus tyrannus)<br> EAPH Eastern Phoebe (Sayornis phoebe)<br> EVGR Evening Grosbeak (Coccothraustes vespertinus)<br> FOSP Fox Sparrow (Passerella iliaca) ‡<br> GCKI Golden-crowned Kinglet (Regulus satrapa) ‡<br> GCTH Gray-cheeked Thrush (Catharus minimus) ‡<br> GRAJ Gray Jay (Perisoreus canadensis) ‡<br> HETH Hermit Thrush (Catharus guttatus) ‡<br> HOLA Horned Lark (Eremophila alpestris) ‡<br> LCSP Le Conte's Sparrow (Ammodramus leconteii)<br> LEFL Least Flycatcher (Empidonax minimus)<br> LISP Lincoln's Sparrow (Melospiza lincolnii) ‡<br> MAWA Magnolia Warbler (Setophaga magnolia)<br> MOWA Mourning Warbler (Geothlypis philadelphia)<br> NAWA Nashville Warbler (Oreothlypis ruficapilla)<br> NOWA Northern Waterthrush (Parkesia noveboracensis) ‡<br> OCWA Orange-crowned Warbler (Oreothlypis celata) ‡<br> OSFL Olive-sided Flycatcher (Contopus cooperi) ‡<br> OVEN Ovenbird (Seiurus aurocapilla)<br> PAWA Palm Warbler (Setophaga palmarum)<br> PHVI Philadelphia Vireo (Vireo philadelphicus)<br> PIGR Pine Grosbeak (Pinicola enucleator) ‡<br> PISI Pine Siskin (Spinus pinus) ‡<br> PUFI Purple Finch (Carpodacus purpureus)<br> RBGR Rose-breasted Grosbeak (Pheucticus ludovicianus)<br> RBNU Red-breasted Nuthatch (Sitta canadensis) ‡<br> RCKI Ruby-crowned Kinglet (Regulus calendula) ‡<br> REVI Red-eyed Vireo (Vireo olivaceus)<br> RUBL Red-winged Blackbird (Agelaius phoeniceus) ‡<br> RWBL Rusty Blackbird (Euphagus carolinus) ‡<br> SAVS Savannah Sparrow (Passerculus sandwichensis) ‡<br> SOSP Song Sparrow (Melospiza melodia)<br> SWSP Swamp Sparrow (Melospiza georgiana)<br> SWTH Swainson's Thrush (Catharus ustulatus) ‡<br> TEWA Tennessee Warbler (Oreothlypis peregrina)<br> TRES Tree Swallow (Tachycineta bicolor) ‡<br> VATH Varied Thrush (Ixoreus naevius) ‡<br> VESP Vesper Sparrow (Pooecetes gramineus)<br> WAVI Warbling Vireo (Vireo gilvus)<br> WCSP White-crowned Sparrow (Zonotrichia leucophrys) ‡<br> WETA Western Tanager (Piranga ludoviciana)<br> WEWP Western Wood-Pewee (Contopus sordidulus) ‡<br> WIWA Wilson's Warbler (Cardellina pusilla) ‡<br> WIWR Winter Wren (Troglodytes hiemalis)<br> WTSP White-throated Sparrow (Zonotrichia albicollis)<br> WWCR White-winged Crossbill (Loxia leucoptera) ‡<br> YBFL Yellow-bellied Flycatcher (Empidonax flaviventris)<br> YRWA Yellow-rumped Warbler (Setophaga coronata) ‡<br> YWAR Yellow Warbler (Setophaga petechia) ‡</p> <p>‡ symbols denote the 38 species currently breeding in the Alaskan boreal region.<br> </p>
Parametric bump noise data (from Varnet et al. 2019, Trends in Hearing)
<p>Data collected by Léo Varnet and Chloé Langlet<br> Parametric bump noise experiment (main experiment)</p> <p>See description of the methods and results in 'High-frequency sensorineural hearing loss alters cue-weighting strategies when discriminating stop consonants in noise' (Varnet et al., 2019, Trends in Hearing)</p>
Bump noise ACI data (from Varnet et al. 2019, Trends in Hearing)
<p>Data collected by Leo Varnet<br> Bump noise ACI experiment (pilot experiment)</p> <p>See description of the methods and results in 'High-frequency sensorineural hearing loss alters cue-weighting strategies when discriminating stop consonants in noise' (Varnet et al., 2019, Trends in Hearing)</p>
Data for publication 'Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape' (Scientific Reports 2019)
<p>Data on vessel tracks and underwater noise levels presented in the publication Hermannsen, L., Mikkelsen, L., Tougaard, J., Beedholm, K., Johnson, M. and P. T. Madsen, "Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape", Scientific Reports 9:15477 (<a href="https://doi.org/10.1038/s41598-019-51222-9">https://doi.org/10.1038/s41598-019-51222-9</a>).</p>
Mid-crustal low-velocity zones beneath Southeastern Coastal China revealed by multimodal ambient noise tomography: insights into Mesozoic Magmatic Activities
<p><span>It contains Rayleigh dispersion data </span><span>and</span><span> a three</span><span>-</span><span>dimensional crustal shear</span><span>-</span><span>wave velocity model of the Southeastern Coastal China </span><span>(</span><span>SCC</span><span>)</span><span>.</span></p>
Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - CALIBRATED MOVEMENT DATA AND VARIABLES SUPPORTING ANALYSES
<p><strong>Description of the data and file structure<br></strong>This record contains data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:</p> <p> Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. <em>Global Change Biology</em>.<strong> </strong>In press.</p> <p>The data include the following: (1) calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13333019 - all 2009 & 2010 audio data; 10.5281/zenodo.13328931 - all 2011 & 2014 audio data.</p> <p>These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p> </p> <p><strong>Description of the movement data files<br></strong>The movement files have been calibrated from the raw data and are ready to use. The files contain the .mat extension, and need to be opened using Matlab and the tagtools tool kit available at https://github.com/animaltags . Tutorials for working with the toolkit are available at animaltags.org . These files contain several vector and matrix variables. We define those used in our analyses below. For questions about how to work with these files, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p>Aw: calibrated triaxial accelerometer data (converted from tag frame to whale frame)</p> <p>fs: sample rate (50 Hz)</p> <p>head: animal's circular heading (rotation about the dorsal-ventral axis, in radians)</p> <p>Mw: calibrated triaxial magnetometer data (converted from tag frame to whale frame)</p> <p>p: depth (in meters)</p> <p>pitch: animal's pitch (rotation about the left-right axis, in radians)</p> <p>roll: animal's roll (rotation about the anterior-posterior axis, in radians)</p> <p>tempr: temperature recorded on tag (in Celsius)</p> <p>TT: time cues for the start and end of every analyzed dive within a deployment. This matrix contains 6 columns:<br>-col 1: start cue (in sec)<br>-col 2: end cue (in sec)<br>-col 3: maximum depth of dive (m)<br>-col 4: time cue at max depth (in sec)<br>-col 5: mean depth (m)<br>-col 6: mean compression</p> <p> </p> <p><strong>Description of the analyzed variables<br></strong>The data are provided column-wise in a spreadsheet, whereby each column contains one of several variables used to build the corresponding models listed in Table 2 in the above article. Model details are provided in the above article, including the statistical packages needed to run the models. </p> <p><em>The following is a list of variable names (column headers) and their corresponding definitions:<br></em><strong>bzsounds:</strong> binary presence (1)/absence (0) of buzz bouts within a dive. Buzzing is defined as the occurrence of echolocation clicks with an inter-click interval < 11 ms<br><strong>code:</strong> categorical identifier of the numerical week of year in which the tag was deployed (e.g., week 33 of 2009 is different than week 33 of 2011)<br><strong>deployment:</strong> the event whereby a tag was affixed to an individual killer whale and data were collected via tag sensors; each deployment was assigned a unique deployment ID, consisting of the first letter of the Genus and species names (“oo” for Orcinus orca), followed by two digits corresponding to the year (“09” = 2009), followed by the Julian day of the year (e.g. “234”), followed by a letter indicating the deployment order of the day. NRKW deployments were assigned a through l, and SRKW deployments were assigned m through z (e.g. “a” = first deployment of the day for NRKW, “m” = first deployment of the day for SRKW)<br><strong>durwho: </strong>duration of a whole dive, in seconds. Dives were defined as all departures from the surface, to at least 1 m or deeper, followed by a return to within 0.5 m of the surface<br><strong>divenum: c</strong>hronological identifier for dive position within a deployment (e.g., for the 10<sup>th</sup> dive within a deployment, divenum = 10)<br><strong>kindet: </strong>binary presence (1)/absence (0) of a prey capture event within a dive. Prey capture was informed by the occurrence of stereotyped movement signatures in sensor data indicative of prey capture, following an established method validated with visual and acoustic confirmation of predation events. Prey capture is defined as the occurrence of three movement variables indicative of prey capture (peak jerk, roll and heading variance) each exceeding pre-determined thresholds (see Tennessen et al. 2019b in above article for details)<br><strong>maxdep:</strong> maximum depth of a dive, in meters<br><strong>NLmax: </strong>the maximum noise level received during a dive, measured as the root-mean-square sound pressure level (dB re 1 mPa) within one second bins over the 15-45 kHz frequency band<br><strong>population:</strong> population to which the tagged whale belongs (NRKW = Northern Resident killer whale; SRKW = Southern Resident killer whale)<br><strong>sex:</strong> sex of tagged whale (F = female, M = male, NA = unknown)<br><strong>sc:</strong> binary presence (1)/absence (0) of slow-click sounds within a dive. Slow-clicking is defined as the occurrence of echolocation clicks with an inter-click interval >100 ms<br><strong>tagID:</strong> identifier for the individual tag used for each deployment<br><strong>year:</strong> year of deployment</p>
Dataset accompaning "Variational inference for correlated gravitational wave detector network noise"
<p>Dataset accompaning the paper "<strong>Variational inference for correlated gravitational wave detector network noise</strong>"</p> <p> </p> <h3>Raw data files</h3> <ul> <li>ET_caseA_noise.h5 (correlated noise)</li> <li>ET_caseB_noise.h5 (uncorrelated noise)</li> </ul> <p>These contain:</p> <ul> <li>raw_XYZ (the XYZ channels of ET noise)</li> <li>time (time in seconds, corresponding to raw_XYZ)</li> <li>periodogram: <ul> <li>pdgrm (of the above channels, truncated to 5-128 Hz)</li> <li>freq (in Hz)</li> </ul> </li> <li>true_psd <ul> <li>psd </li> <li>freq</li> </ul> </li> </ul> <p><em>Note</em>: case C from the manuscript utilised the case B dataset, (but with a model that does not account for the cross-spectrum). It does not have a separate dataset. </p> <p> </p> <h3><strong>Result file</strong></h3> <ul> <li>ET-CaseA-SGVB-PSD.h5</li> <li>ET-CaseB-SGVB-PSD.h5</li> <li>ET-CaseC-SGVB-PSD.h5</li> </ul> <p>These contain:</p> <ul> <li>psd_quantiles (the lower 0.05, median 0.50, upper 0.95 quantiles of 500 PSD samples)</li> <li>freq (in Hz, associated with the psd_quantiles)</li> </ul>
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.
Error, noise and bias in de novo transcriptome assemblies
<p><i>De novo</i> transcriptome assembly is a powerful tool, widely used over the last decade for making evolutionary inferences. However, it relies on two implicit assumptions: that the assembled transcriptome is an unbiased representation of the underlying expressed transcriptome, and that expression estimates from the assembly are good, if noisy approximations of the relative abundance of expressed transcripts. Using publicly available data for model organisms, we demonstrate that, across assembly algorithms and data sets, these assumptions are consistently violated. Bias exists at the nucleotide level, with genotyping error rates ranging from 30-83%. As a result, diversity is underestimated in transcriptome assemblies, with consistent under-estimation of heterozygosity in all but the most inbred samples. Even at the gene level, expression estimates show wide deviations from map-to-reference estimates, and positive bias at lower expression levels. Standard filtering of transcriptome assemblies improves the robustness of gene expression estimates but leads to the loss of a meaningful number of protein-coding genes, including many that are highly expressed. We demonstrate a computational method, length-rescaled CPM, to partly alleviate noise and bias in expression estimates. Researchers should consider ways to minimize the impact of bias in transcriptome assemblies.</p>
Noise to void model and results of denoising + segmentation using VollSeg
<p>Noise to void model and results of denoising + segmentation using VollSeg</p>
Noise data for the study of consonant-in-noise discrimination using an auditory model with different speech-based decision devices
<p>The data stored in this repository correspond to two sets of 5000 speech-shaped noises (SSN) that were used in the conference paper titled "Consonant-in-noise discrimination using an auditory model with different speech-based decision devices" by the same authors, presented in the DAGA conference in Vienna, Austria, on 17/08/2021. </p> <p>The two zip files (<strong>osses2021c_S01</strong> and <strong>osses2021c_S02</strong> for participants S01 and S02, respectively) have following structure:</p> <ul> <li><strong>NoiseStim-SSN</strong>: Folder containing the 5000 noises</li> <li><strong>Results</strong>: Results of the listening experiment collected using the fastACI toolbox (https://github.com/aosses-tue/fastACI).</li> </ul> <p>To obtain similar results for other participants the same experiment has to be run using the fastACI toolbox. For instance, to collect new data for participant 'S03', you have to input the following command in MATLAB:</p> <pre><code class="language-bash">fastACI_experiment('speechACI_varnet2013','S03','SSN');</code></pre>
Pied tamarins go multimodal in response to anthropogenic noise
<p><span>Sounds produced by human activities are often loud and may mask acoustic signals used by other species for communication. To circumvent this, animals may use various strategies, including shifting modality completely or complementing acoustic information by also using another modality of communication. Here we tested the overlooked multimodal shift hypothesis using pied tamarins (<i>Saguinus</i> <i>bicolor</i>) as models. We predicted that in noisier areas the species would exhibit more scent marking behaviour (i.e., olfactory communication), while reducing the emission of long calls (i.e., acoustic communication). We collected information on vocal and scent marking behaviour in nine groups of wild pied tamarins in Manaus, Brazil. We found that scent marking occurrence increased with noise amplitude, though the number of long calls did not change. Thus, our results do not suggest a shift between channels but complementation of information, where scent marking may counteract against the compromising of the acoustic channel by the urban environment. This is an interesting result from a conservation perspective and supports ongoing efforts to create ecological corridors in Manaus, Brazil; once habitat connection is established, pied tamarins may be capable of coping with city noise to communicate with conspecifics, a key tenet of species survival.</span></p>
Earthquake records and ambient noise cross correlations from ENAP Magallanes seismographs, Jan 2019- Mar 2020
<p>This archive contains selected data and data products from 8 seismic monitoring stations operated by Empresa Nacional del Petroleo, Magallanes (ENAP). The data are selected earthquake records from January 2019 through March 2020, and the data products are time-frequency phase-weighted stacks of ambient noise cross correlations between ENAP stations and nearby permanent and temporary seismographs for the period from January 2019 through March 2020. These data and data products can be used, along with other seismic data available from the IRIS DMC (including some data that is embargoed as of the time of writing), to reproduce the authors' model for Vsv in Patagonia.</p>
Dataset for: Vermilion flycatchers avoid singing during sudden peaks of anthropogenic noise
<p>Dataset from a playback experiment with free-living vermilion flycatchers where we show that they stop singing when traffic noise levels suddently increase (as would occur when a car passes by). This behavior may be a strategy to increase the probability of detection by singing in periods when noise levels are lower. Males' responses toward the noise playbacks were also related with the level of artificial light at night (ALAN) in males' territories, where males living in brighter territories shortened their song bout length to a lesser extent during noise exposure than those males living in territories with lower levels of ALAN, suggesting ALAN may decrease the extent at which traffic noise affects male singing behavior.</p>
Data from: Anthropogenic noise impairs cooperation in bottlenose dolphins
<p><span>Understanding the impact of human disturbance on wildlife populations is of societal importance</span><span>, with anthropogenic noise known to impact a range of taxa, including mammals,</span><span> birds</span><span>, fish</span><span>, and invertebrates</span><span>.</span><span> While animals are known to use acoustic and other behavioural mechanisms to compensate for increasing noise at the individual level, our understanding of how noise impacts social animals working together remains limited. Here, we investigated the effect of noise on coordination between two bottlenose dolphins performing a cooperative task. We previously demonstrated that the dolphin dyad can use whistles to coordinate their behaviour, working together with extreme precision</span><span>. By equipping each dolphin with a sound-and-movement recording tag (DTAG-3</span><span>) and exposing them to increasing levels of anthropogenic noise, we show that both dolphins nearly doubled their whistle durations and increased whistle amplitude in response to increasing noise. While these acoustic compensatory mechanisms are the same as those frequently used by wild cetaceans</span><span>, they were insufficient to overcome the effect of noise on behavioural coordination. Indeed, cooperative task success decreased in the presence of noise, dropping from 85% during ambient noise control trials to 62.5% during the highest noise exposure. To our knowledge, this is the first study to demonstrate in any non-human species that noise impairs communication between conspecifics performing a cooperative task. Cooperation facilitates vital functions across many taxa and our findings highlight the need to account for the impact of disturbance on functionally important group tasks in wild animal populations.</span></p>
Swift BAT Mosaic Pattern Noise Maps
<p>This dataset is the noise pattern maps that are needed to analyze Swift BAT data and produce "time integrated" mosaic images using the <a href="https://github.com/parsotat/BatAnalysis">BatAnalysis</a> python package. </p> <p>The file can be untarred and placed into a directory accessible by python such that these files can be used in the analysis of BAT data. </p> <p>These pattern maps are from 2004 day 350 (2004-12-15) to 2019 day 212 (2019-07-31). </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.