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ShareScore release 0.9.0
Dataset results
16 results for “wav”
FFT and WAV files of 10MHz WWV Doppler shift during 8-21-2017 Solar eclipse
<p>HL Serra N6NC</p> <p>San Diego CA 32-50-36N 117-16-16W</p> <p>FFT and WAV files started 1415UT-2300UT 8-21-2017 [FFT file starts 24 hrs earlier for comparison FFTs)</p> <p>Omnidirectional 8" "active antenna"</p> <p>Rcvr Racal RA 6790 in USB mode</p> <p>Rcvr and HP-3325A GPSDO-controlled 10 MHz.</p> <p>Tuned rcvr to 9,999,000 Hz in USB mode to record in SpecLab the offset from 1000Hz beat note of 10 MHz signal.</p> <p>Very little if any Doppler shift of signal during eclipse, probably because I am located on the same side of the eclipse totality as WWV in Boulder CO. Besides rcvr, I tracked WWV frequency with GPSDO-controlled HP-3325 and scope comparing the tuned HP frequency with the rcvr's 455kHz IF to find recorded FFT offset. HP stable for two days showing WWV freq as 10,000,000.013 Hz by watching unmoving Lissajous figure on scope, so no visually perceptible Doppler excursions in frequency detected, but the FFT data will show whatever frequency movement there might have been.</p> <p> </p> <p>73, Larry N6NC</p>
Dataset: Western Acquisition Ventures Corp. (WAVS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
BarkMeowDB - WAV Files of Dogs and Cats
<p>A small dataset that contains dogs' barking sounds and cats' meowing sounds. Used for 'hello-world' binary audio classification using machine learning and deep learning.</p>
Soundscape records (.wav files) used for: Species Assembly of Highland Anuran Communities in Equatorial Africa (Virunga Massif): Soundscape, Acoustic Niches, and Partitioning
<p>The data set comprises sample recordings used for a paper published in "Animals" .</p> <p>Title: "Species assembly of highland anuran communities in equatorial Africa (Virunga Massif): soundscape, acoustic niches and partitioning", authors: Ulrich Sinsch<sup>1</sup>*, Deogratias Tuyisingize<sup>2</sup>, J. Maximilian Dehling<sup>1</sup> and Yntze van der Hoek<sup>2; </sup><sup>1</sup> Institute of Integrated Sciences, Department of Biology, University of Koblenz, D-56070 Koblenz, Germany; <a href="mailto:sinsch@uni-koblenz.de">sinsch@uni-koblenz.de</a>, <a href="mailto:dehling@uni-koblenz.de">dehling@uni-koblenz.de; </a><sup>2 </sup>Dian Fossey Gorilla Fund, Ellen DeGeneres Campus, Kinigi, Rwanda; <a href="mailto:dtuyisingize@gorillafund.org">dtuyisingize@gorillafund.org</a>, <a href="mailto:yvanderhoek@gorillafund.org">yvanderhoek@gorillafund.org</a> .</p> <p>Citation: Animals 2024, 14, 2360. https://doi.org/10.3390/ani14162360 </p> <p>https://www.mdpi.com/journal/animals</p> <p> </p> <p>Descriptor of each file is the heading. Example:</p> <p>Ngezi 20191217_190000 castaneus glandicolor karissimbensis kivuensis</p> <p>Ngezi = Locality in VNP;</p> <p>20191217_190000 = record date December 17, 2019, at 19.00 h = 7 pm</p> <p>castaneus glandicolor karissimbensis kivuensis = Anuran species recorded <em>Hyperolius castaneus, Hyperolius</em> <em>glandicolor, Leptopelis karissimbensis</em> and <em>Leptopelis kivuensis</em>.</p> <p>Further details are given in the text of the paper</p>
Griots Interviews, Bambara Language WAV, 30 hours, Recorded 2022, Cultural and ASR Training Resource
<p><strong>Source material to this project:</strong></p> <ul> <li> <p>Addition to 200,000 lines Bambara-French clean synchronized corpus</p> </li> <li> <p>Co-project with Google, recorded 30 hours video interviews with Griots</p> </li> <li> <p>30 hours manually transcribed and translated to French</p> </li> <li> <p>10 hours used in training ASR system and MT transformer</p> </li> <li> <p>100% Open Sourced</p> </li> <li> <p>Cultural/Technical Exhibition to be hosted online and in the National Museum of Mali</p> </li> <li> <p>Record, preserve, and share Malian culture with the world</p> </li> <li> <p>Contribute to the science of low-resource language NLP</p> </li> <li> <p>Reinforce the development of written Bambara</p> </li> <li> <p>Enable Bambara to reach status as a “first-class internet language”</p> </li> </ul> <p>Corresponding transcribed data can be found at the following <a href="https://github.com/robotsmali-ai/jeli-asr">Github repository</a></p>
WSPR raw .wav baseband data received at Boston University
<p>WSPR stations received at W1BUR Boston University ham radio station using 20m end-fed dipole or broadband HF dipole.</p>
Carsington Water bioacoustics project - wav files
<p>wav files recorded on Wildlife Acoustics SM2 recorders.</p> <p>Data from four recorders placed on land at northeast side of Carsington Water in April/May 2018.</p> <p>Locations of recorders: </p> <p><strong>latitude longitude site_name unit microphones</strong></p> <p>53.0748524 -1.6096798 Reedbed 8621 Two</p> <p>53.0711544- 1.6118927 Plantation mixed 8535 Two</p> <p>53.0668604 -1.6170512 Scrub 8607 Two</p> <p>53.0703651 -1.6140515 Woodland 8552 Two</p>
WSPR wav files from, EM75dx. 2017-08-21 eclipse
<p>WSPR wav audio data during 2017-08-21 solar eclipse from EM75dx grid square: 35° 58' 38.8" N, 85° 41' 28.8" W. (35.977441, -85.691343). </p> <p> </p>
Wav files for Audio Moment Retrieval (Part 3)
<p>This page includes wav files of three datasets used in <a href="https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/">Language-based audio moment retrieval</a> [1].</p> <ul> <li>Clotho-Moment</li> <li>UnAV100-subset</li> <li>TUT Sound Events 2017</li> </ul> <p>Please download all parts (from Part 1 to Part 4).</p> <ol> <li><a href="../records/13836117">https://zenodo.org/records/13836117</a></li> <li><a href="../records/13836248">https://zenodo.org/records/13836248</a></li> <li><a href="../records/13836250">https://zenodo.org/records/13836250</a></li> <li><a href="../records/13836252">https://zenodo.org/records/13836252</a></li> <li><a href="../records/13836931">https://zenodo.org/records/13836931</a></li> </ol> <p>[1] H. Munakata, T. Nishimura, S. Nakada, T. Komatsu, "Language-based Audio Moment Retrieval", 2024, under review.</p> <h2>How to Use</h2> <p>Unzip the file with the following commands<br>Clotho-moment: </p> <pre><code>for file in clotho-moment_wav.tar.part-*.gz; do gunzip "$file"; done clotho-moment_wav.tar.part-* > clotho-moment_wav.tar tar -xvf clotho-moment_wav.tar </code></pre> <p><br>UnAV100-subset, TUT Sound Events 2017: </p> <pre><code>tar -xvf tut2017_wav.tar.gz tar -xvf unav100-subset_wav.tar.gz</code></pre> <p> </p>
Wav files for Audio Moment Retrieval (Part 2)
<p>This page includes wav files of three datasets used in <a href="https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/">Language-based audio moment retrieval</a> [1].</p> <ul> <li>Clotho-Moment</li> <li>UnAV100-subset</li> <li>TUT Sound Events 2017</li> </ul> <p>Please download all parts (from Part 1 to Part 4).</p> <ol> <li><a href="../records/13836117">https://zenodo.org/records/13836117</a></li> <li><a href="../records/13836248">https://zenodo.org/records/13836248</a></li> <li><a href="../records/13836250">https://zenodo.org/records/13836250</a></li> <li><a href="../records/13836252">https://zenodo.org/records/13836252</a></li> <li><a href="../records/13836931">https://zenodo.org/records/13836931</a></li> </ol> <p>[1] H. Munakata, T. Nishimura, S. Nakada, T. Komatsu, "Language-based Audio Moment Retrieval", 2024, under review.</p> <h2>How to Use</h2> <p>Unzip the file with the following commands<br>Clotho-moment: </p> <pre><code>for file in clotho-moment_wav.tar.part-*.gz; do gunzip "$file"; done clotho-moment_wav.tar.part-* > clotho-moment_wav.tar tar -xvf clotho-moment_wav.tar </code></pre> <p><br>UnAV100-subset, TUT Sound Events 2017: </p> <pre><code>tar -xvf tut2017_wav.tar.gz tar -xvf unav100-subset_wav.tar.gz</code></pre> <p> </p>
Wav files for Audio Moment Retrieval (Part 5)
<p>This page includes wav files of three datasets used in <a href="https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/">Language-based audio moment retrieval</a> [1].</p> <ul> <li>Clotho-Moment</li> <li>UnAV100-subset</li> <li>TUT Sound Events 2017</li> </ul> <p>Please download all parts (from Part 1 to Part 4).</p> <ol> <li><a href="../records/13836117">https://zenodo.org/records/13836117</a></li> <li><a href="../records/13836248">https://zenodo.org/records/13836248</a></li> <li><a href="../records/13836250">https://zenodo.org/records/13836250</a></li> <li><a href="../records/13836252">https://zenodo.org/records/13836252</a></li> <li><a href="../records/13836931">https://zenodo.org/records/13836931</a></li> </ol> <p>[1] H. Munakata, T. Nishimura, S. Nakada, T. Komatsu, "Language-based Audio Moment Retrieval", 2024, under review.</p> <h2>How to Use</h2> <p>Unzip the file with the following commands<br>Clotho-moment: </p> <pre><code>for file in clotho-moment_wav.tar.part-*.gz; do gunzip "$file"; done clotho-moment_wav.tar.part-* > clotho-moment_wav.tar tar -xvf clotho-moment_wav.tar </code></pre> <p><br>UnAV100-subset, TUT Sound Events 2017: </p> <pre><code>tar -xvf tut2017_wav.tar.gz tar -xvf unav100-subset_wav.tar.gz</code></pre> <p> </p>
Wav files for Audio Moment Retrieval (Part 4)
<p>This page includes wav files of three datasets used in <a href="https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/">Language-based audio moment retrieval</a> [1].</p> <ul> <li>Clotho-Moment</li> <li>UnAV100-subset</li> <li>TUT Sound Events 2017</li> </ul> <p>Please download all parts (from Part 1 to Part 4).</p> <ol> <li><a href="../records/13836117">https://zenodo.org/records/13836117</a></li> <li><a href="../records/13836248">https://zenodo.org/records/13836248</a></li> <li><a href="../records/13836250">https://zenodo.org/records/13836250</a></li> <li><a href="../records/13836252">https://zenodo.org/records/13836252</a></li> <li><a href="../records/13836931">https://zenodo.org/records/13836931</a></li> </ol> <p>[1] H. Munakata, T. Nishimura, S. Nakada, T. Komatsu, "Language-based Audio Moment Retrieval", 2024, under review.</p> <h2>How to Use</h2> <p>Unzip the file with the following commands<br>Clotho-moment: </p> <pre><code>for file in clotho-moment_wav.tar.part-*.gz; do gunzip "$file"; done clotho-moment_wav.tar.part-* > clotho-moment_wav.tar tar -xvf clotho-moment_wav.tar </code></pre> <p><br>UnAV100-subset, TUT Sound Events 2017: </p> <pre><code>tar -xvf tut2017_wav.tar.gz tar -xvf unav100-subset_wav.tar.gz</code></pre> <p> </p>
NJ0U/4 EM66fu 20M WSJTX FT8 *.wav
<p>WSJTX FT8 wave files captured during 8/21/17 Eclipse. EM66fu, 36.86487N, 87.50565W</p>
NJ0U_4 EM66FU Eclipse WSPR *.wav
<p>NJ0U/4 WSPR 20M *.wav files captured during 8/21/17 Eclipse. Grid EM66FU, 36.86487N, 87.50565W</p>
Wav files for Audio Moment Retrieval (Part 1)
<p>This page includes wav files of three datasets used in <a href="https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval/">Language-based audio moment retrieval</a> [1].</p> <ul> <li>Clotho-Moment</li> <li>UnAV100-subset</li> <li>TUT Sound Events 2017</li> </ul> <p>Please download all parts (from Part 1 to Part 4).</p> <ol> <li><a href="../records/13836117">https://zenodo.org/records/13836117</a></li> <li><a href="../records/13836248">https://zenodo.org/records/13836248</a></li> <li><a href="../records/13836250">https://zenodo.org/records/13836250</a></li> <li><a href="../records/13836252">https://zenodo.org/records/13836252</a></li> <li><a href="../records/13836931">https://zenodo.org/records/13836931</a></li> </ol> <p>[1] H. Munakata, T. Nishimura, S. Nakada, T. Komatsu, "Language-based Audio Moment Retrieval", 2024, under review.</p> <h2>How to Use</h2> <p>Unzip the file with the following commands<br>Clotho-moment: </p> <pre><code>for file in clotho-moment_wav.tar.part-*.gz; do gunzip "$file"; done clotho-moment_wav.tar.part-* > clotho-moment_wav.tar tar -xvf clotho-moment_wav.tar </code></pre> <p><br>UnAV100-subset, TUT Sound Events 2017: </p> <pre><code>tar -xvf tut2017_wav.tar.gz tar -xvf unav100-subset_wav.tar.gz</code></pre> <p> </p>
dkdf_ishizaka_wav
<p>dkdf_ishizaka_wav</p>
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.