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952 results for “Noise”
Weekly noise estimate of the residual of the LDC2a data set
<p>Weekly noise estimate of the residual of the LDC2a data set. The recovered Galactic binaries and massive black hole binaries are subtracted for each week.</p>
Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston
<p>Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and quiet locations such as parks. This project demonstrates that changes in human activity can reduce noise pollution and that simple smartphone technology can be used to make highly detailed maps of noise pollution that identify sources of high sound levels potentially harmful to humans in urban environments.</p>
Characterising underwater noise and changes in harbour porpoise behaviour during the decommissioning of an oil and gas platform
<p>Many man-made marine structures (MMS) will have to be decommissioned in the coming decades. While studies on the impacts of the construction of MMS on marine mammals exist, no research has been done on the effects of their decommissioning. The complete removal of an oil and gas platform in Scotland in 2021 provided an opportunity to investigate the response of harbour porpoises to decommissioning. Arrays of broadband noise recorders and echolocation detectors were used to describe noise characteristics produced by decommissioning activities and assess porpoise behaviour. During decommissioning, sound pressure levels in the frequency range 100 Hz to 10 kHz were 30-40 dB higher than baseline, with the presence of vessels being the main source of noise. The study detected small-scale (< 2 km) and short-term levels of porpoise displacement during decommissioning, with porpoise occurrence increasing immediately after this. These findings can inform the consenting process of future decommissioning projects.</p>
Fig 1 in Effect of traffic noise on Scinax nasicus advertisement call (Amphibia, Anura)
Fig 1. Noise backgrounds and Scinax nasicus (Cope, 1862) call parameters at reference (Site A) and noisy environments (Site B). Amplitude (oscillograms A1, B1) and frequency (spectrograms A2, B2) of the environments. Amplitude (oscillograms A3, B3) and frequency (spectrograms A4, B4) of the advertisement frog's call.
Fig 2 in Effect of traffic noise on Scinax nasicus advertisement call (Amphibia, Anura)
Fig 2. Non-metric multidimensional scaling (NMDS) ordination of call variables of Scinax nasicus (Cope, 1862) adult males in natural (filled circles, Site A) and noisy environments (empty circles, Site B) Sites. The minimum frequency and duration as well as the displayed note pulse of the frogs call were different between the Sites in the first dimension.
Time-frequency maps of gravitational waves embeded in noise
<p>This data set constains the time-frequency maps of gravitational waves embeded in noise. </p>
Pre- and postnatal noise directly impairs avian development, with fitness consequences
<p><span>Noise pollution is expanding at an unprecedented rate and </span><span>is</span><span> increasingly associated with impaired reproduction and development across taxa. However, whether noise soundwaves are intrinsically harmful for developing young – or merely disturb parents – and the fitness consequences of early exposure </span><span>remains</span><span> unknown. Here, </span><span>by only manipulating the offspring</span><span>, we </span><span>show</span><span> that sole exposure to noise in early-life </span><span>in zebra finches </span><span>has fitness consequences, </span><span>and causes</span><span> embryonic death during exposure. </span><span>Exposure to </span><span>pre- and postnatal traffic noise cumulatively impaired nestling growth and physiology, and </span><span>aggravated telomere shortening </span><span>across life stages </span><span>until adulthood</span><span>. Consistent with a long-term somatic impact, early-life noise exposure, especially prenatally, decreased individual offspring production throughout adulthood. Our findings </span><span>suggest the </span><span>effects of noise pollution </span><span>are more pervasive </span><span>than previously realized.</span></p>
Signal-to-Noise Ratios, DYNAMITE Posteriors, and Combined Detectability Plots for 173 TESS Multi-Planet Systems
<p>Collection of figures for 173 TESS Multi-Planet Systems. Each figure (titled with the system's TOI number) has 2 rows of planets, corresponding to results from the clustered period model (upper) and period ratio model (lower) predictions for an additional planet in each system. The left-most column of the plots show the signal-to-noise ratios (SNR) of simulated transits for planets with a given radius and period in the corresponding TESS light curve. The middle column shows the dot product of the radius and period posteriors generated by DYNAMITE using TESS light curves up to and including Sector 76. The right-most column shows the detectability (SNR) modulated by the DYNAMITE posteriors, showing the location in (P, Rp) space where an additional planet in this system is most likely to be detected under each model.</p>
Pure Electronic Noise of an Orbitrap Mass Spectrometer (36 replicates)
<p>The provided data was produced by performing measurements in an HPLC-ESI-Orbitrap instrument setup without the ESI being connected to an eluent flow, and consequently not producing a spray cone. We provide this data to allow researchers developing algorithms for data analysis in mass spectrometry to estimate the behaviour of their tools when confronted with real, non-chemical noise. </p> <p>As a consequence of the experimental setup, chromatographic information is included in the provided files. This does not reflect any condition of the system, as the HPLC was not connected to the MS. When using a (chromatographc) peak finding algorithm, consequently no peaks should be found. This property of the data was confirmed using the qAlgorithms program, with no peaks and at most single-digit numbers of EICs being detected.</p> <p>All data is provided in profile mode, both as the .raw file and converted to .mzML using msconvert. </p>
Magmatic System of the Hainan Hotspot Revealed by Ambient Noise Tomography
<p>The datasets consists of three parts:</p> <p>1.Cross-correlation Functions Data(all_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> <p><br>2. Rayleigh Wave Phase Dispersion Data (CDisp.zip):<br>Format:<br> Lon (station A) Lat (station A)<br> Lon (station B) Lat (station B)<br> Period (s) Vs (km/s)</p> <p>3.S-Velocity Model:<br>Format:<br> Longitude Latitude Depth Absolute_velocity Average_velocity Relative_velocity</p>
Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network
<p>In this repository we provide raw and processed datasets for the article: “Genome-wide gene expression noise in <em>Escherichia coli </em>is condition-dependent and determined by propagation of noise through the regulatory network<strong>” </strong>by Arantxa Urchueguía, Luca Galbusera, Dany Chauvin, Gwendoline Bellement, Thomas Julou and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: <a href="https://doi.org/10.1101/795369">https://doi.org/10.1101/795369</a>. </p> <p>The repository consists of the following datasets: </p> <p><strong>1. preprocessed_datasets.zip(~22GB)</strong></p> <ul> <li>This dataset contains raw data from the flow cytometry experiments (FACS Canto II, BD Bioscience) in all measured conditions in RData format. Raw fcs files were processed with the tools described in the publication ''Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria" published here: <a href="https://doi.org/10.1371/journal.pone.0240233">https://doi.org/10.1371/journal.pone.0240233</a>. The tools themselves are available here: <a href="https://github.com/vanNimwegenLab/E-Flow">https://github.com/vanNimwegenLab/E-Flow</a>. Included in the files are the outputs of these processing tools together with all raw values that came directly from the flow cytometer. The file <em>directory_structure_in_preprocessed </em>contains information about how the files are organized.</li> </ul> <p><strong>2. info_files: </strong>This is a set of csv files containing detailed information about the experiments done to acquire the preprocessed_datasets as well as annotation files that we used to retrieve promoter information. </p> <p><strong>3. processed_datasets:</strong> These files correspond to the processed datasets from the raw Rdata files under 1 above. The processed data provide mean and variance estimates in fluorescence of E.coli promoters across the different growth conditions. Note that we discarded flow cytometry measurements from promoter/growth-condition combinations that contained abnormal fluorescence distributions (due to contamination) as well as measurements from reporters with annotation mismatches. The folder contains the following clean dataset files that were used in the paper:</p> <ul> <li><strong>FULL_dataset_mean_var_wreplicates:</strong> In this dataset we include the processed means and variances (in both logarithmic and linear scale) of all promoters in each condition. Included as well are replicate measurements for some conditions.. We also include the name and Blattner number of the gene immediately downstream of each promoter, the DNA sequence of each promoter, and regulatory information (number of unique inputs for transcription factors sites and their names) which we obtained from RegulonDB v 10.5 (<a href="https://doi.org/10.1093/nar/gky1077">https://doi.org/10.1093/nar/gky1077</a>). </li> <li><strong>dataset_with_noise_estimates: </strong>In this dataset we provide noise estimates for all promoters expressed above an expression threshold (mean GFP fluorescence at least as large as autofluorescence). Note that the noise estimate correspond to the difference between the promoter’s variance in log-expression and the minimal variance as a function of its mean expression (i.e. the so called noise floor was subtracted). Apart from the mean, variance, noise and promoter features (sequence, name of gene downstream, number of unique regulatory inputs and name of the TFs binding), we also include the parameters used for fitting the minimal noise, i.e. noise floor, in each of the conditions. </li> <li><strong>time_course_data_SI</strong>: This dataset contains mean and variance measurements of one of the plates of the library measured at different time points during growth in Minimal media 0.4M NaCl: 0h (just after dilution), 1h, 2h, 3h, 5h, 6.5h, 8.5h, 10h and 11h. </li> <li><strong>growth_curves_SI</strong>: Growth data (OD<sub>600</sub> as a function of time) for a subset of the promoters from the library across different growth conditions.</li> <li><strong>singlecell_areas_SI: </strong>Single-cell areas estimated using agar patches of cells growing in each condition. Each row of the table contains data for a single-cell. </li> <li><strong>synthetic_promoters_dataset: </strong>This dataset contains mean, variance and noise measurements of a set of constitutive promoters from <a href="https://doi.org/10.7554/eLife.05856.001">https://doi.org/10.7554/eLife.05856.001</a> across different conditions.</li> <li><strong>MARA_results:</strong> All transcription factor activities results explaining measured noise levels in each condition. This data has been obtained after performing Motif Activity Response Analysis on the noise levels of all measured promoters in each condition.</li> </ul>
Echolocation call parameters of Daubenton's bats during exposure to masking noise
<p>Echolocating bats hunt prey on the wing under conditions of poor lighting by emission of loud calls and subsequent auditory processing of weak returning echoes. To do so, they need adequate echo-to-noise ratios (ENRs) to detect and distinguish target echoes from masking noise. Early obstacle avoidance experiments report high resilience to masking in free-flying bats, but whether this is due to spectral or spatiotemporal release from masking, advanced auditory signal detection or an increase in call amplitude (Lombard effect) remains unresolved. We hypothesized that bats with no spectral, spatial or temporal release from masking noise, defend a certain ENR via a Lombard effect. We trained four bats (<em>Myotis daubentonii</em>) to approach and land on a target that broadcasted broadband noise at four different levels. An array of seven microphones enabled acoustic localization of the bats and source level estimation of their approach calls. Call duration and peak frequency did not change, but average call source levels (SL<sub>RMS</sub>, at 0.1 m as dB re. 20 μPa, root-mean-square) increased, from 112 dB in the no-noise treatment, to 118 dB (maximum 129 dB) at the maximum noise level of 94 dB. The magnitude of the Lombard effect was small (0.13 dB SL<sub>RMS</sub>/dB of noise), resulting in mean broadband and narrowband ENRs of -11 and 8 dB respectively at the highest noise level. Despite these poor ENRs, the bats still performed echo-guided landings, making us conclude that they are very resilient to masking even when they cannot avoid it spectrally, spatially or temporally.</p>
Electrical Low-Frequency 1/fγ Noise Due to Surface Diffusion of Scatterers on an Ultra-low-Noise Graphene Platform
<p>Experimental dataset for article “Electrical Low-Frequency 1/<em>f<sup>γ</sup></em> Noise Due to Surface Diffusion of Scatterers on an Ultra-low-Noise Graphene Platform”, <em>Nano letters</em>, <strong>21</strong>(18), 7637-7643 (2021), <a href="https://doi.org/10.1021/acs.nanolett.1c02325">doi: 10.1021/acs.nanolett.1c02325</a></p>
VMM Noise Measurement Data
<p>This release contains measured data to study the noise of VMM3a readout chips,<br> which are intended to be used in the front-end readout system of a Triple-GEM detector.<br> Their noise is characterised both in term of RMS output voltage and the equivalent noise<br> charge (ENC). The dependence of the noise on a variety of operation parameters, including<br> peaking time and gain factor, are also studied.<br> The measurements have been performed by Emorfili Terzimpasoglou as part of her<br> Master Thesis ”Investigation on the ASIC for Triple-GEM Detectors” at the University<br> of Bonn.</p>
UAV ESD Noise Recording with HEIST
<p>Data recordings using HEIST while UAV is exposed to ESD.</p> <p>https://github.com/MaSkr09/heist_datalog.git</p>
UAV exposed to Ultra Bandwidth Noise Recorded using HEIST
<p>Noise was recorded on an SBUS communication link on a UAV while exposed to UWB noise. HEIST is recording the data using an FPGA.</p> <p> </p> <p>https://github.com/MaSkr09/heist_datalog.git</p>
Burst Noise Injection through Antenna Recorded on a UAV at Logic Level using HEIST
<p>A UAV exposed to burst noise injected through an antenna. The noise is recorded at logic level using HEIST.</p> <p> </p> <p>https://github.com/MaSkr09/heist_datalog.git</p>
Hippocampal CA1 pyramidal cell membrane voltage recorded in response to noise stimuli at two temperatures.
<p>Electrophysiological recording of the membrane voltage (whole-cell patch-clamp) of three hippocampal CA1 pyramidal cells. Cells are stimulated with a current step chosen to ensure a firing rate around 5-10Hz (amplitude of the current step is given in the filenames) and a noise stimulus with zero mean (Ornstein-Uhlenbeck process with 4ms timescale). Each CSV file contains three columns, the timepoints (saved at 10000Hz), the noise stimulus, and the voltage trace recorded in response to the given noise stimulus. Voltages are recorded at low temperatures (around 32 degrees Celsius) and at high temperatures (around 37 degrees Celsius for cell 1, around 40 degrees Celsius for cells 2 and 3), exact temperatures are given in the filenames. The file metadata.csv contains additional information.</p>
Main Sequence + Compact Object binary candidates from Gaia DR3 astrometric and spectroscopic excess noise
<p>MS+CO systems selected from Gaia DR3 via inferred periods and mass ratios derived from astrometric and spectroscopic errors.</p> <p>The sample is split into a bronze list (significant astrometric and spectroscopic RUWE, mass ratio > 1 and companion mass > 3 Msun). </p> <p>A subset of these is chosen as a silver list (propagating errors on mass ratio and companion mass to deselect systems which are not significantly above the previous criteria)</p> <p>Finally, a gold list is constructed from the subset of the silver list which shows no evidence of being significantly brighter than a single MS star and with no significant excess photometric noise.</p> <p>We include the most relevant Gaia data for the system, as well as our inferred spectroscopic and photometric errors and RUWEs, and the inferred periods and mass ratios. Gaia's DR3 source id, and the ra, dec position are included and thus other Gaia data, or data from other astronomical catalogs, can be found for these systems.</p> <p>The catalog and the underlying methods are explained in more detail in <a href="https://arxiv.org/abs/2206.04392">Andrew et al. 2022</a>.</p>
Anthropogenic noise, song, and territorial aggression in southern house wrens
<p>Anthropogenic noise constrains the transmission of birdsong and alters the behavior of receivers. Many birds adjust their acoustic signals to minimize the interference of anthropogenic noise on signal transmission. Birds may also change their acoustic signals to exchange information during aggressive interactions. However, it is unclear how birds deal with a potential trade-off between adjusting their acoustic signals to better transmit in noisy environments versus mediating aggressive interactions. Additionally, we do not know how urbanization and anthropogenic noise alters the territorial behavior of receivers. We investigated the interplay among song, territorial aggression, urbanization, and anthropogenic noise, in males of the southern house wren (<em>Troglodytes aedon musculus</em>), using recordings of spontaneous songs (non-aggressive context) and a playback experiment simulating a male territorial intrusion (aggressive context). We found that urban wrens behaved more aggressively in response to the intruder by singing more and spent more time closer to the intruder than rural wrens regardless of noise. Males produced songs with lower minimum frequency and trills with wider frequency bandwidth and higher vocal performance under acute (playback) than relaxed (post-playback) aggressive encounters. These results suggest that males use songs to communicate aggressive intent or fighting ability. Urban wrens produced higher-pitched songs and trills than rural wrens irrespective of aggressive context. Urban wrens in the noisiest territories also produced the highest-pitched trills but only in the non-aggressive context. Rural wrens in the noisiest territories tended to produce the longest songs (non-aggressive context) or produced the shortest songs (aggressive context). Results suggest that urbanization affects territorial and vocal behaviors in southern house wrens. Males in this species seem to primarily adjust acoustic signals in response to the territorial intruder rather than noise.</p>
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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.