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952 results for “Noise”
Marlim R3D (MR3D) - The full azimuth noise-free CSEM dataset
<p>Marlim R3D (MR3D) is an open-source realistic geoelectric model for CSEM simulations of the post-salt turbiditic reservoirs at the Brazilian offshore margin.<br> Here we make available the complete noise-free CSEM dataset, with inline and broadside geometries, of two simulated surveys on the MR3D model. We computed the six electromagnetic field components (Ex, Ey, Ez, Hx, Hy, and Hz) at six frequencies in the 0.125–1.25 Hz range.<br> The output dataset was generated in the current industry netCDF-based data format.<br> You can follow the updates of the marlim project at:<br> https://www.researchgate.net/project/Marlim-R3D-A-realistic-model-for-mCSEM-simulations</p>
Global occurrence of higher-bands ECH waves in radiation belts based on a novel noise reduction algorithm
<p>The database of ECH waves based on observations from Van Allen Probes between September 17, 2012 and October 13, 2019. The probe and the year of observations are marked in the data file names. The columns from left to right are: the mean value of the SuperMAG electrojet index in the previous hour, L-Shell, the magnetic local time, the magnetic latitude, the wave amplitude of band 1to band 4.</p>
Data for "Noise-correlation spectrum for a pair of spin qubits in silicon"
<p>This dataset contains the data files for "Noise-correlation spectrum for a pair of spin qubits in silicon".</p>
Dataset of CCFs, dispersion data, synthetic ambient noise data, and rupture simulation data in the Anninghe study
<p>This is the main dataset supporting the Anninghe paper.</p> <p>sta.loc represents the station list file.<br> 3DVelocityModel folder contains the final velocity model file Anninghe_Vs3D.dat.<br> CCFs_Original folder contains the original CCFs obtained from the Anninghe array.<br> CCFs_Separated folder contains the CCFs after model separation. In CCFs_Separated folder, the merge folder is the final average CCFs used for later tomography.<br> DisperData folder contains the seimiautopicked dispersion data from the separated CCFs.<br> RuptureSimulation folder contains the rupture simulation data in the paper.<br> SynNoiseData folder contains the CCFs calculated from synthetic ambient noise data.<br> </p>
Teleseismic P records and ambient noise cross-correlations from a dense ocean-bottom seismometer array at Hyuga-nada
<p><strong>Telesesimic P and ambient noise cross-correlation dataset from a dense OBS array at Hyuganada, SW Japan. </strong> The dataset are used in a manuscript titled 'Shallow low-velocity layer in the Hyuga-nada accretionary prism and its hydrological implications: insights from a passive seismic array', which is submitted to the Journal of Geophysical Research: Solid Earth. It also contains necessary data for reproducing inversion analysis performed in the manuscript. </p> <ol> <li><em>Teleseismic_P.tar.gz</em>: contains teleseismic P wave forms in SAC format </li> <li><em>Noise_cross_corr.tar.gz</em>: contains ambient noise cross-correlation functions in SAC format</li> <li><em>Reproduce.tar.gz</em>: dataset for reproducing inversion analysis. <pre><code class="language-bash"># Example shell script for reproduce inversion results HDA01 cd Reproduce/INV/HDA01 mpirun -np 20 joint_inv joint_inv.in</code></pre> <p>The SEIS_FILO software (https://doi.org/10.5281/zenodo.6330840) is required for this reproduction.</p> </li> </ol>
Corresponding Dataset for Precision of Spacecraft Doppler Tracking at Low Signal-to-Noise Ratios
<p><br> Corresponding Dataset for Precision of Spacecraft <br> Doppler Tracking at Low Signal-to-Noise Ratios<br> README FILE<br> Dustin Buccino<br> January 1, 2023<br> Jet Propulsion Laboratory<br> California Institute of Technology</p> <p>=============================================================================<br> INTRODUCTION<br> =============================================================================</p> <p> This dataset contains open-loop data collected by the Planetary Radar<br> and Radio Science Group at the Jet Propulsion Laboratory from the Low-SNR<br> Ground System Demonstration with DSS-25 in May 2020. Futhermore, it<br> also provides the necessary data behind the figures in the corresponding<br> publication. This dataset is provided in order to supplement the submitted<br> article to the "Radio Science" journal</p> <p> Buccino, D.R., et al (2023), Precision of Spacecraft Doppler Tracking <br> at Low Signal-to-Noise Ratios, Radio Science, submitted<br> January 2023.</p> <p>=============================================================================<br> ARCHIVE INFORMATION<br> =============================================================================</p> <p> This archive contains several data types, located within subdirectories.<br> <br> ROOT<br> `- 159TEST2022133_1845X25X25RO.A02_XATnn_nndBHz<br> <br> These files are the open-loop data collected at the DSN <br> during the test. There are six files, one for each SNR level. The<br> files are in the DSN-standard 0159-Science format (binary data).<br> To decode these files, one should utilize the software <br> interface specification documents from the Planetary Data System<br> radio science documentation bundle:</p> <p> https://pds-geosciences.wustl.edu/<br> radiosciencedocs/urn-nasa-pds-radiosci_documentation/<br> <br> `- figures.zip<br> <br> This directory contains all data necessary to reproduce the figures<br> in the publication. Data include Doppler residuals, SNR measurement,<br> Allan deviation and associated parameters. Each file in this <br> directory is plain-text.</p> <p>=============================================================================<br> ACKNOWLEDGMENTS<br> =============================================================================</p> <p>This work was carried out at the Jet Propulsion Laboratory, <br> California Institute of Technology, under contract with the National <br> Aeronautics and Space Administration. Government sponsorship acknowledged.</p> <p>=============================================================================<br> PRIMARY POINT OF CONTACT<br> =============================================================================</p> <p>Dustin Buccino<br> Jet Propulsion Laboratory<br> Planetary Radar and Radio Sciences<br> (818) 393 - 1072<br> Dustin.R.Buccino@jpl.nasa.gov</p> <p>=============================================================================<br> ACRONYMS AND ABBREVIATIONS<br> =============================================================================</p> <p> ASCII American Standard Code for Information Interchange<br> DOY Day of year<br> DSN Deep Space Network<br> JPL Jet Propulsion Laboratory<br> NAIF Navigation Ancillary Information Facility<br> NASA National Aeronautics and Space Administration<br> PDS Planetary Data System<br> RS Radio Science<br> RSS Radio Science Subsystem<br> SIS Software Interface Specification<br> TXT Text file<br> UTC Universal Time, Coordinated<br> </p>
Simulation Data for Testing Method of Ambient Noise in Temperature Data
<p>Simulation data to accompany manuscript submitted to Geophysical Journal International. This file can be used in conjungtion with the matlab code on the github repository to produce Figures 3 and 4 of the paper. Paper abstract below. </p> <p> </p> <p><strong>Determining Hydraulic Diffusivity from Ambient Noise in Subsurface Flow Rate and Temperature Data</strong></p> <p><strong>Patrick M. Fulton<sup>1</sup> and Emily E. Brodsky<sup>2</sup></strong></p> <p><strong><sup>1</sup>Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA. </strong></p> <p><strong><sup>2</sup>Department of Earth and Planetary Sciences, University of California Santa Cruz, Santa Cruz, CA, USA.</strong></p> <p><strong>Submitted January 22, 2023</strong></p> <p><strong>Corresponding Author: Emily E. Brodsky <a href="mailto:brodsky@ucsc.edu">brodsky@ucsc.edu</a></strong></p> <p> </p> <p><strong>Summary</strong></p> <p><strong>Determining the properties that control fluid flow and pressure migration through rocks is essential for understanding groundwater, energy reservoirs, and fault zones. However, direct measurements of these properties underground generally require expensive and invasive processes such as pumping large volumes of water in or out of the ground. These conventional methods may change the structure that they are trying to measure and do not resolve variations in space without complex, multiple experiments. Here we capitalize on the existing, natural fluctuations of pore pressure, flow and temperature that are always present underground, likely driven by tides, seismic waves and other noise in the Earth. We develop the theoretical framework to utilize these noises to determine hydrogeologic structure. We begin with prior work demonstrating a connection between cross-correlation and Green’s functions for diffusive processes and illustrate how the existing theory could be used specifically for the case of inferring hydraulic diffusivity from closely spaced observations of pore pressure. We extend the current theory to include a capability for inferring Green’s functions from closely spaced flow rate measurements, rather than pore pressure data. We then note that closely spaced temperature measurements are likely much more practical as an observational system, however, diffusive transport is likely not the dominant cause of high-frequency fluctuations of temperature in the Earth. Advective transport is a more likely controlling factor and thus we need to incorporate advection into the theory and model the corresponding response functions. We therefore develop a semi-analytical method that includes advection and numerically compute the corresponding response function. We use these synthetics to illustrate how cross-correlation of ambient noise in subsurface temperature measurements carries information about hydraulic structure and show that it is possible to constrain hydraulic diffusivity values from cross-correlation of closely spaced continuous temperature measurements. This new method opens up the possibility of passive determination of reservoir properties with high spatial resolution from closely-spaced, continuous temperature data, which is a realistic deployment strategy that could be used in a wide variety of settings.</strong></p>
Forecasting ocean wave-induced seismic noise; supporting dataset
<p>Dataset supporting the manuscript "Forecasting ocean wave-induced seismic noise".</p>
Power spectral density estimates of underwater wind and rain noise from the northeast Pacific continental margin
<p>Power spectral density (PSD) estimates of of underwater wind and rain noise used in the papers</p> <ul> <li> <p>Schwock, F., & Abadi, S. (2021a). Characterizing underwater noise during rain at the northeast Pacific continental margin. <em>The Journal of the Acoustical Society of America</em>, <em>149</em>(6), 4579–4595. https://doi.org/10.1121/10.0005440</p> </li> <li> <p>Schwock, F., & Abadi, S. (2021b). Statistical analysis and modeling of underwater wind noise at the northeast pacific continental margin. <em>The Journal of the Acoustical Society of America</em>, <em>150</em>(6), 4166–4177. https://doi.org/10.1121/10.0007463</p> </li> </ul> <p>The PSD estimates were computed using raw data from the Ocean Observatories Initiative (https://ooinet.oceanobservatories.org/). The data and documentation can be downloaded here:</p> <p>https://drive.google.com/drive/u/1/folders/19jvgp_86Ou2zW_GugETP29ZwVK2_GmUb</p>
Open data for "3D integration enables ultra-low-noise isolator-free lasers in Si photonics"
<p>Open data for "3D integration enables ultra-low-noise isolator-free lasers in Si photonics"</p>
Real and Psuedosynthetic timeseries used in "Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog"
<p><strong>5Hz GNSS Velocity Data for the submitted work: </strong>"Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog" Dittmann et al (202?)</p> <p><strong>Datasets included:</strong></p> <ol> <li>Pseudosynthetic timeseries, ambient timeseries and training featuresets generated for Dittmann, et al (202?) </li> <li>Real GNSS 5Hz validation featuresets from <a href="https://doi.org/10.1029/2022JB024854">Dittmann, et al. (2022) </a></li> </ol> <p>Timeseries and Featuresets are stored in <a href="https://parquet.apache.org/">Apache Parquet</a> format.</p> <p><strong>Getting Started:</strong><br> Notebook demos for reading using <a href="https://docs.conda.io/en/latest/">conda</a>+ jupyterlab (easiest).<br> In a terminal:</p> <ol> <li>Unzip untar (mac/linux tar -xvf psuedo_synth_gnssvel.tar.gz)</li> <li> <pre><code>conda env create -f environment.yml conda activate pgv23_zenodo jupyter lab</code></pre> <p> </p> </li> <li>Open the notebook “reading_data.ipynb”</li> </ol> <p> </p> <p><strong>Data References:</strong></p> <p><a href="https://ngawest2.berkeley.edu/">NGA-West 2 (NGAW2) Ground Motion Database </a></p> <p><a href="https://github.com/crowellbw/SNIVEL">SNIVEL</a> GNSS Velocity Processing</p> <p> </p>
"Trust Your Neighbours: Handling Noise in Multi-Objective Optimisation Using kNN-Averaging" - Supporting Data
<p>Extracted data used to create the figures supporting the publication "Trust Your Neighbours: Handling Noise in Multi-Objective Optimisation Using kNN-Averaging", submitted to Applied Soft Computing.</p> <p> </p> <p> </p>
Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
<p>The benchmark datasets used to evaluate Gaussian noise augmentation-based scRNA-seq contrastive learning (GsRCL) against scRNA-seq cell-type identification tasks.</p>
Broadband White Noise BCI
<p>Hello everyone,</p> <p>As I prepare for my upcoming PhD defense and finalize my thesis, I wanted to inform you that I will be providing the necessary code for replication in <strong>June</strong>. I apologize for any inconvenience this may cause, but I hope you find the dataset I'm sharing useful for your own research endeavors. Should you have any questions or require further assistance, please don't hesitate to reach out to me via email at shinl.thu@gmail.com.</p> <p>Thank you for your understanding and support.</p> <p>Best regards,<br>Nanlin</p> <p>---</p> <p>This repository serves as an initial collection of data and code associated with the manuscript titled "<em>Estimating and Approaching the Maximum Information Rate of Noninvasive Visual Brain-Computer Interface.</em>" Further updates and additions will be provided in the near future.</p>
Source data for Ordouie, E. et al. Differential phase-diversity electrooptic modulator for cancellation of fiber dispersion and laser noise.
<p>The experimental data and primary simulation results.</p>
Impact of noise on inverse design: The case of NMR spectra matching
<p>Code and data for the corresponding preprint:</p> <p>Impact of noise on inverse design: The case of NMR spectra matching<br> Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld</p> <p>https://doi.org/10.48550/arXiv.2307.03969</p> <p><br> SI_DFT_geo.xyz and SI_DFT_NMR.txt belong to the QM9NMR paper<br> Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules<br> Amit Gupta, Sabyasachi Chakraborty and Raghunathan Ramakrishnan<br> Mach. Learn.: Sci. Technol. 2 (2021) 035010</p> <p>originally hosted here:<br> https://moldis-group.github.io/qm9nmr/<br> https://doi.org/10.17172/NOMAD/2021.10.16-1</p>
Experiment-based data published in "Entropy, pitch, and noise: organization and disorganization in the perception of closure for different types of spectra", JNMR
<p>This folder contains both the sonic excerpts used for an experiment measuring the level of perceived closure for sound stimuli presenting pitch, bell, and noise spectral structures, and the web application created to run the experiment. The web application can be uploaded in any server for trying it our.</p>
Figure 3. Centroids with 95 in Insect noise avoidance in the dawn chorus of Neotropical birds
Figure 3. Centroids with 95% confidence intervals plotted for the first two discriminant functions from a quadratic discriminant function analysis using peak frequency, song complexity and song rate for 477 songs from 27 different bird species (see Appendix, Table A1 for species names).
Figure 5 in Insect noise avoidance in the dawn chorus of Neotropical birds
Figure 5. First call time measured as minutes from sunrise plotted against (a) peak frequency of each species and (b) the difference between background noise at first call time and over all average background noise for the 1 h period. Background noise values were calculated at each species' peak frequency band. Each point represents the mean over all days and sites (±SE) per species. Negative noise values indicate that birds started singing at times when ambient noise level at their songs' peak frequency was lower than the rest of the recording period. Singing location is displayed as either in the canopy (circles, solid line) or below (triangles, dashed line) the canopy. See Table 2 for the parameter estimates associated with these variables.
Figure 2 in Insect noise avoidance in the dawn chorus of Neotropical birds
Figure 2. (a) Avian dawn chorus as defined by the number of singing events by any species averaged (±SE) over sites and days for each 5 min time bin. Note that calling activity peaked before the end of the recording period. (b) The first call time averaged (±SE) over sites and days for each species (see Appendix, Table A1 for species names) displayed as time from sunrise.
ScienceDex guides
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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.