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952 results for “Noise”
Data from: Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes
<p>Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering -- that is, excluding returns from LiDAR point clouds based on simple height thresholds -- is a common practice meant to improve the 'signal' content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types.<br> <br> In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman's $\rho$) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha^-1^ statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful 'signal' than extraneous 'noise' in these cases.</p>
Code for noise-based seismic velocity changes estimation with the Bezymianny volcano data set. Journal of Volcanology and Geothermal Research.
<p>This file contains all the data and the python scripts used to estimate seismic velocity changes for the Bezymianny volcano (Klyuchevskoy volcano group). It also includes a guideline README.pdf with the description how to reproduce all the results presented in the paper <strong>Berezhnev Y., Belovezhets N., Shapiro N., Koulakov I. (2022), Temporal changes of seismic velocities below Bezymianny volcano prior to its explosive eruption on 20.12.2017, Journal of Volcanology and Geothermal Research</strong></p>
Seismic Noise from Gornergletscher, Switzerland (Summer 2007)
<p>This is a subset of open access data from the following reference; please cite accordingly: Walter, F. (2009). Seismic activity on gornergletscher during gornersee outburst floods. Dissertation, 31(1-2), 14–27. https://doi.org/10 .1007/BF03322148</p> <p>Curated by T. Sawi for the manuscript (in submission), "An Unsupervised Machine-Learning Approach to Understanding Seismicity at an Alpine Glacier" (2022) by T. Sawi; B. Holtzman; M. Nettles; F. Walter; J. Paisley; submitted to JGR: Earth Surface in August 2022.</p> <p>This data set contains sixty-second samples of glacial seismic noise (N=1,478) from the vertical channel of a Geospace GS-11D geophone operating in shallow borehole at Gornergletscher between June 14 and July 22, 2007. Sampling rate is 1000 samples per second, and the natural frequency of the instrument is around 8 Hz. An STA/LTA filter has been applied to remove records with prominent icequakes in them, although many smaller icequakes remain in the records.</p> <p>Waveform files are named after their event origin time in format 7{MDhms}. For example, waveform "70614004000.sac" occurred on June 14 00:40:00, 2007.<br> </p>
Audio Examples for "Directional Frequency Filtering of Recordings in Spherical Harmonics Domain for Innovative Noise Reduction Strategies"
<p>This is a collection of audio examples of the processing corresponding to the master thesis "Directional Frequency Filtering of Recordings in Spherical Harmonics Domain for Innovative Noise Reduction Strategies"<br> The examples (ambisonics and binaural) contain recordings of moving sources in an anechoic chamber, which were made within different scenes partly containing noise barriers. Furthermore the simulation of noise barriers was implemented using directional filtering within a plane wave decomposition of the signals in sh domain.</p>
Data set for "1/f Noise Characterization of Bilayer MoS2 Field-Effect Transistors on Paper with Inkjet-Printed Contacts and hBN Dielectrics"
<p>Data set of the experimental results presented in the article "1/f Noise Characterization of Bilayer MoS2 Field-Effect Transistors on Paper with Inkjet-Printed Contacts and hBN Dielectrics" published in Advanced Electronics Materials.</p>
Geothermal and structural features of La Palma island (Canary Islands) imaged by ambient noise tomography
<p>These folders contain all the results obtained in the ambient noise tomography of La Palma for geothermal exploration. </p>
Data from: Ultra-low-noise Microwave to Optics Conversion in Gallium Phosphide
<p>Source data for Figures.</p>
Túngara frog call-timing decisions arise as internal rhythms interact with fluctuating chorus noise
<p>For chorusing males, optimally timing their calls relative to nearby rivals' calls and fluctuations in background chorus noise is crucial for reproductive success. A caller's acoustic environment will vary by chorus density and the properties of his chorus-mates' calls and will fluctuate unpredictably due to chorusing dynamics emerging among his chorus-mates. Thus, callers must continuously monitor moment-to-moment fluctuations in the acoustic scene they perceive at the chorus for advantageous times to call. In live experimental choruses, we investigated the factors influencing túngara frog call-timing responses to chorus-mates' calls on an interaction-by-interaction basis, revealing that intrinsic and extrinsic factors influenced call-timing decisions. Callers were more likely to overlap calls from smaller chorus-mates and chorus-mates at intermediate distances, as well as calls containing lower frequencies and exhibiting lower final amplitude minima. Consequently, variation among males in call properties led to variation in levels of call-interference received when calling in the same social environment. Additionally, callers were more likely to overlap chorus-mates' calls after experiencing extended periods of inhibition and were less likely to overlap synchronized chorus-mates' calls relative to single calls. In chorusing species, female choice is influenced by inter-caller dynamics, selecting for male call-timing strategies which, in turn, constitute the selective environment further refining these same strategies. Thus, understanding the specific factors driving call-timing decisions is essential for understanding how sexual selection operates in chorusing taxa.</p>
Investigating food production-associated DNA methylation changes in paleogenomes: lack of consistent signals beyond technical noise
<p>The Neolithic transition introduced major diet and lifestyle changes to human populations across continents. Beyond well-documented bioarchaeological and genetic effects, whether these changes also had molecular-level epigenetic repercussions in past human populations has been an open question. In fact, methylation signatures can be inferred from UDG-treated ancient DNA through postmortem damage patterns, but with low signal-to-noise ratios; it is thus unclear whether published paleogenomes would provide the necessary resolution to discover systematic effects of lifestyle and diet shifts. To address this we compiled UDG-treated shotgun genomes of 13 pre-Neolithic hunter-gatherer (HGs) and 21 Neolithic farmer (NFs) individuals from West and North Eurasia, published by six different laboratories and with coverage c.1x-58x (median=9x). We used epiPALEOMIX and a Monte Carlo normalization scheme to estimate methylation levels per genome. Our paleomethylome dataset showed expected genome-wide methylation patterns such as CpG island hypomethylation. However, analysing the data using various approaches did not yield any systematic signals for subsistence type, genetic sex, or tissue effects. Comparing the HG-NF methylation differences in our dataset with methylation differences between hunter-gatherers vs. farmers in modern-day Central Africa also did not yield consistent results. Meanwhile, paleomethylome profiles did cluster strongly by their laboratories of origin. Using larger data volumes, minimizing technical noise and/or using alternative protocols may be necessary for capturing subtle environment-related biological signals from paleomethylomes.</p>
Continental Reworking in the Eastern South China Block and Its Adjacent Areas Revealed by F-J Multimodal Ambient Noise Tomography
<p><span>It contains ZZ-component cross-correlation functions and the 3-D Vsv model in the eastern South China Block.</span></p>
A Mid-crustal Channel of Positive Radial Anisotropy Beneath the Eastern South China Block From F-J Multimodal Ambient Noise Tomography
<p><span>It contains the CCAB-FJpy, the Python code package for calculating the multi-component cross-correlation functions (CCFs) and extracting multimodal Love dispersions with multi-component frequency-Bessel (F-J) transform method. . Also, it contains multi-component CCFs (i.e., RR and TT), Vsh model and 3-D crustal radial anisotropy model of the eastern South China Block (ESCB).</span></p>
Data for "Overcoming Laser Phase Noise for Low-cost Coherent Optical Communication"
<p>The files contain the data for the paper "Overcoming laser phase noise for low-cost coherent optical communication".</p>
Model for random atmospheric inhomogeneities in engine noise auralization: Audio files for validation
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: A. Prescher, A. Moreau, S. Schade, "<a href="https://doi.org/10.1007/s13272-024-00764-4" target="_blank" rel="noopener"><em>Model for random atmospheric inhomogeneities in engine noise auralization</em></a>", CEAS Aeronautical Journal, 2024.</p> <p>Selected binaural audio files to illustrate the impact of random atmospheric inhomogenities on the noise characteristics of a turbofan engine.</p> <p>The corresponding time signals and spectrograms are available in the associated paper in Figure 7.</p>
Data for LIGO operates with quantum noise below the Standard Quantum Limit
<p>This repository hosts data sets used to create all the figures in the paper "LIGO operates with quantum noise below the Standard Quantum Limit". </p> <p> </p> <p>Version 1: initial upload</p> <p>Version 2: Add README.txt and MCMC set-up code</p>
Granular Aluminum Parametric Amplifier for Low-Noise Measurements in Tesla Fields
<p>Raw and processed data as well as a jupyter notebooks for the evaluation associated with the paper "Granular Aluminum Parametric Amplifier for Low-Noise Measurements in Tesla Fields" (publicly available on arXiv: <a href="https://arxiv.org/abs/2403.10669">arXiv:2403.10669</a>) by N. Zapata et al. acquired and prepared at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.</p> <p>The notebooks show how to analyze the main results of the manuscript.</p> <p>For additional information please contact: nicolas.gonzalez2@kit.edu or ioan.pop@kit.edu.</p>
Making Sense Barcelona Noise Pilot Datasets
<p><strong>Raw Datasets From the Making Sense Barcelona Noise Pilot captured by the community using the Smart Citizen Kit 1.5</strong></p> <p> </p> <p>Data archived from the Smart Citizen Plaform:</p> <p><a href="https://smartcitizen.me/kits/4294">https://smartcitizen.me/kits/4294</a> <a href="https://smartcitizen.me/kits/4293">https://smartcitizen.me/kits/4293</a> <a href="https://smartcitizen.me/kits/4282">https://smartcitizen.me/kits/4282</a> <a href="https://smartcitizen.me/kits/4283">https://smartcitizen.me/kits/4283</a> <a href="https://smartcitizen.me/kits/4271">https://smartcitizen.me/kits/4271</a> <a href="https://smartcitizen.me/kits/4307">https://smartcitizen.me/kits/4307</a> <a href="https://smartcitizen.me/kits/4309">https://smartcitizen.me/kits/4309</a> <a href="https://smartcitizen.me/kits/4308">https://smartcitizen.me/kits/4308</a> <a href="https://smartcitizen.me/kits/4301">https://smartcitizen.me/kits/4301</a> <a href="https://smartcitizen.me/kits/4302">https://smartcitizen.me/kits/4302</a> <a href="https://smartcitizen.me/kits/4262">https://smartcitizen.me/kits/4262</a> <a href="https://smartcitizen.me/kits/4265">https://smartcitizen.me/kits/4265</a> <a href="https://smartcitizen.me/kits/4281">https://smartcitizen.me/kits/4281</a> <a href="https://smartcitizen.me/kits/4280">https://smartcitizen.me/kits/4280</a> <a href="https://smartcitizen.me/kits/4267">https://smartcitizen.me/kits/4267</a> <a href="https://smartcitizen.me/kits/4300">https://smartcitizen.me/kits/4300</a> <a href="https://smartcitizen.me/kits/4299">https://smartcitizen.me/kits/4299</a> <a href="https://smartcitizen.me/kits/4296">https://smartcitizen.me/kits/4296</a> <a href="https://smartcitizen.me/kits/4295">https://smartcitizen.me/kits/4295</a> <a href="https://smartcitizen.me/kits/4297">https://smartcitizen.me/kits/4297</a> <a href="https://smartcitizen.me/kits/4298">https://smartcitizen.me/kits/4298</a> <a href="https://smartcitizen.me/kits/4468">https://smartcitizen.me/kits/4468</a> <a href="https://smartcitizen.me/kits/4470">https://smartcitizen.me/kits/4470</a></p> <p>You can also explore the data using the Smart Citizen API:</p> <p><a href="https://developer.smartcitizen.me/">https://developer.smartcitizen.me/</a></p>
Imaging subsurface structure of an urban area based on Diffuse-Field Theory concept using seismic ambient noise
<p>Ambient noise data for a small urban area of NER India. The data set constitute all the raw files that were used for figures in the paper by Bora et al.</p>
Time-lapse images of yeast cells with different levels of TDH3 median expression and expression noise
<p>Time-lapse images of strains YPW3064, YPW3047, YPW2868 and YPW2879. Images were acquired with a Zyla sCMOS camera (Andor) on an Olympus IX83 inverted microscope equipped with a CoolLED pE-300 illumination system in Lab513 at University Paris Diderot. These images were used to produce Figure 5 - figure supplement 1 as well as the corresponding Source Data tables in the paper: Fitness effects of altering gene expression noise in Saccharomyces cerevisiae. Fabien Duveau, Andrea Hodgins-Davis, Brian P.H. Metzger, Bing Yang, Stephen Tryban, Elizabeth A. Walker, Patricia Lybrook, Patricia J Wittkopp (doi: https://doi.org/10.1101/294603).</p>
Data from: Change in singing behavior of humpback whales caused by shipping noise (Tsujii et al., 2018)
<p>Audio (.wav) and comma-delimited text format (.csv) files are contained. <br> This dataset was used in the study, which was submitted in 2018 to PLOS ONE for publication:</p> <p>Koki Tsujii, Tomonari Akamatsu, Ryosuke Okamoto, Kyoichi Mori, Yoko Mitani, Naoya Umeda.<br> Change in singing behavior of humpback whales caused by shipping noise.</p> <p>This dataset was obtained in the research activity of underwater noise project of the Japan Ship Technology Research Association in the fiscal years of 2016 funded by the Nippon Foundation.</p>
Multilayer test masses to enhance the collapse noise
<p>multilayer.nb - Main program</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.