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10 results for “Noise Maps”
Reducing cardiac-induced noise in brain maps of R2* and magnetic susceptibility
<p>This high resolution dataset contain MR images of one participant acquired with a standard linear sampling and a cartesian pseudo-spiral sampling, with 3 repetitions each. It also contain the corresponding R2* and QSM maps shown in the paper. The data are presented both as .nii and .mat files.</p>
Repository: Rayleigh-wave attenuation and phase velocity maps of the greater Alpine region from ambient noise
<p><br>Repository organized by Henrique Berger Roisenberg for the paper Roisenberg et al. (2024). The files are organized as follows:</p> <p><strong>Folders:</strong></p> <p><strong>-dispersion_curves:</strong><br>inside this folder there is a .zip file that contains all the dispersion curves calculated;</p> <p><strong>-attenuation:</strong><br>comprising three files with the results of attenuation calculations, i.e., the attenuation values, the grid, and the periods;</p> <p><strong>-c:</strong><br>comprising three files with the results of phase velocity calculations, i.e., the phase velocity values, the grid, and the periods;</p> <p><strong>-scripts: </strong><br>contains two python scripts, one called 'figures' to plot the figure 1, 4, and 6 of the paper, and another called 'alparray_computations' to perform the computations with the original alparray data, using seislib, resulting on the figures 2, 3, and 5 of the paper.</p> <p>Inside the folder '<strong>inputs</strong>' there are three folders that serve as input for the figures of the paper, to be used in the scripts. These are:</p> <p><strong>-raster: </strong><br>contains the topography raster used to plot the map of the study area;</p> <p><strong>-shapefiles:</strong><br>contains the shapefiles used in the regionalization analysis;</p> <p><strong>-station locations: </strong><br>contains the latitudes and longitudes of the stations used in this study.</p> <p> </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>
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>
A characterization of cardiac-induced noise in R2* maps of the brain
<p>A subset of data used in the original publication for computations of the results. These data contain 5D k-space data (kx/ky/kz/cardiac phase/TE) from one of the participants and can be used to perform the cardiac-induced noise characterization and retrieve the results from the paper.</p>
Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston
Open the record for dataset details and reuse information.
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>
Noise Maps ACTION pilot data 2020
<p>The Noise Maps project focused on deploying a citizen science process in the Barcelona neighborhoods of Sagrada Familia and the Raval to address the challenge of noise pollution. The sound data was generated between May and September 2020.</p>
Raw and post-processed data from the study "Mapping the spectrotemporal regions influencing perception of French stop consonants in noise"
<p>The current dataset contains all data needed to replicate the study from Carranante et al. "Mapping the spectrotemporal regions influencing perception of French stop consonants in noise"<strong><br></strong></p> <p><strong>Use these data:</strong></p> <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text:<br> <pre><code>publ_carranante2024_figs('fig1','zenodo'); </code></pre> <p>To generate the figures of the supplementary materials:</p> <pre><code>publ_carranante2024_figs('fig1_suppl','zenodo');</code></pre> </li> </ol> <p><strong>References:</strong></p> <ul> <li>Paper not published yet</li> <li><strong>fastACI toolbox</strong>: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>
Effect of genomic and cellular environments on gene expression noise [bulk-mapping]
GEO Series GSE223369. Homo sapiens. 3 samples. Type: Other.
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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.