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1,300 results for “Sounds”
[Dataset] In situ laser-ultrasonic monitoring of Poisson's ratio and bulk sound velocities of steel plates during thermal processes
<p>Data generated and analyzed in the work titled "In situ laser-ultrasonic monitoring of Poisson’s ratio and bulk sound velocities of steel plates during thermal processes". See the associated publication for more context.</p> <p>All files are stored in Matlab's binary MAT-file format.</p> <ul> <li>cutOffs_ZGVs_nu_S1S2_A2A3_S3S6_A4A7.mat <ul> <li>Dispersion relation data of plates obtained from numerical calculation with a range of Poisson's ratios and otherwise arbitrary but fixed material properties.</li> <li>S1S2-, A2A3-, S3S6- and A4A7-ZGV resonance frequencies and k-values</li> <li>L1 and T1 thickness resonance frequencies</li> </ul> </li> <li>lusResults_jmat_dilatometry_data.mat <ul> <li>LUS measurement data and resulting material properties (raw displacement data recorded on the oscilloscope is stored separately to keep the file size reasonable.)</li> <li>Dilatometer measurements</li> <li>JMatPro simulation</li> </ul> </li> <li>lusOscilloscope_data.mat <ul> <li>Normal surface displacement measurement data obtained in situ with LUS and recorded with an oscilloscope</li> </ul> </li> </ul>
USM Dataset - A Dataset for Polyphonic Sound Event Tagging in Urban Sound Monitoring Scenarios
<p>This dataset includes 24,000 5-seconds-long polyphonic stereo soundscapes composed of sounds taken from the FSD50k dataset:</p> <p>- Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, Xavier Serra. FSD50K: an Open Dataset of Human-Labeled Sound Events (<a href="https://arxiv.org/abs/2010.00475">https://arxiv.org/abs/2010.00475</a>)</p> <p>FSD50k samples used in the USM dataset were selected to allow for commercial usage.</p> <p>Find more details about the USM dataset at <a href="https://github.com/jakobabesser/USM">https://github.com/jakobabesser/USM</a></p>
Urban Sound & Sight (Urbansas) - Labeled set
<p><strong>Urban Sound & Sight (Urbansas): </strong></p> <p>Version 1.0, May 2022</p> <p><strong>Created by</strong><br> Magdalena Fuentes (1, 2), Bea Steers (1, 2), Pablo Zinemanas (3), Martín Rocamora (4), Luca Bondi (5), Julia Wilkins (1, 2), Qianyi Shi (2), Yao Hou (2), Samarjit Das (5), Xavier Serra (3), Juan Pablo Bello (1, 2)<br> 1. Music and Audio Research Lab, New York University<br> 2. Center for Urban Science and Progress, New York University<br> 3. Universitat Pompeu Fabra, Barcelona, Spain<br> 4. Universidad de la República, Montevideo, Uruguay<br> 5. Bosch Research, Pittsburgh, PA, USA</p> <p><strong>Publication</strong></p> <p>If using this data in academic work, please cite the following paper, which presented this dataset:<br> M. Fuentes, B. Steers, P. Zinemanas, M. Rocamora, L. Bondi, J. Wilkins, Q. Shi, Y. Hou, S. Das, X. Serra, J. Bello. “Urban Sound & Sight: Dataset and Benchmark for Audio-Visual Urban Scene Understanding”. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.</p> <p><strong>Description</strong></p> <p>Urbansas is a dataset for the development and evaluation of machine listening systems for audiovisual spatial urban understanding. One of the main challenges to this field of study is a lack of realistic, labeled data to train and evaluate models on their ability to localize using a combination of audio and video.<br> We set four main goals for creating this dataset: <br> 1. To compile a set of real-field audio-visual recordings;<br> 2. The recordings should be stereo to allow exploring sound localization in the wild;<br> 3. The compilation should be varied in terms of scenes and recording conditions to be meaningful for training and evaluation of machine learning models;<br> 4. The labeled collection should be accompanied by a bigger unlabeled collection with similar characteristics to allow exploring self-supervised learning in urban contexts.<br> Audiovisual data<br> We have compiled and manually annotated Urbansas from two publicly available datasets, plus the addition of unreleased material. The public datasets are the TAU Urban Audio-Visual Scenes 2021 Development dataset (street-traffic subset) and the Montevideo Audio-Visual Dataset (MAVD):</p> <p><br> Wang, Shanshan, et al. "A curated dataset of urban scenes for audio-visual scene analysis." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.</p> <p>Zinemanas, Pablo, Pablo Cancela, and Martín Rocamora. "MAVD: A dataset for sound event detection in urban environments." Detection and Classification of Acoustic Scenes and Events, DCASE 2019, New York, NY, USA, 25–26 oct, page 263--267 (2019).</p> <p><br> The TAU dataset consists of 10-second segments of audio and video from different scenes across European cities, traffic being one of the scenes. Only the scenes labeled as traffic were included in Urbansas. MAVD is an audio-visual traffic dataset curated in different locations of Montevideo, Uruguay, with annotations of vehicles and vehicle components sounds (e.g. engine, brakes) for sound event detection. Besides the published datasets, we include a total of 9.5 hours of unpublished material recorded in Montevideo, with the same recording devices of MAVD but including new locations and scenes.</p> <p>Recordings for TAU were acquired using a GoPro Hero 5 (30fps, 1280x720) and a Soundman OKM II Klassik/studio A3 electret binaural in-ear microphone with a Zoom F8 audio recorder (48kHz, 24 bits, stereo). Recordings for MAVD were collected using a GoPro Hero 3 (24fps, 1920x1080) and a SONY PCM-D50 recorder (48kHz, 24 bits, stereo). </p> <p>When compiled in Urbansas, it includes 15 hours of stereo audio and video, stored in separate 10 second MPEG4 (1280x720, 24fps) and WAV (48kHz, 24 bit, 2 channel) files. Both released video datasets are already anonymized to obscure people and license plates, the unpublished MAVD data was anonymized similarly using this anonymizer. We also distribute the 2fps video used for producing the annotations.</p> <p>The audio and video files both share the same filename stem, meaning that they can be associated after removing the parent directory and extension.</p> <p>MAVD:<br> video/<location_id>_<mavd_clip_id>_<clip_split_id>.mp4<br> audio/<location_id>_<mavd_clip_id>_<clip_split_id>.wav</p> <p>TAU:<br> video/<location_id>_<tau_clip_id>.mp4<br> audio/<location_id>_<tau_clip_id>.wav</p> <p><br> where location_id in both cases includes the city and an ID number.</p> <p><br> city & places & clips & mins & frames & labeled mins \\<br> Montevideo & 8 & 4085 & 681 & 980400 & 92 \\<br> Stockholm & 3 & 91 & 15 & 21840 & 2 \\<br> Barcelona & 4 & 144 & 24 & 34560 & 24 \\<br> Helsinki & 4 & 144 & 24 & 34560 & 16 \\<br> Lisbon & 4 & 144 & 24 & 34560 & 19 \\<br> Lyon & 4 & 144 & 24 & 34560 & 6 \\<br> Paris & 4 & 144 & 24 & 34560 & 2 \\<br> Prague & 4 & 144 & 24 & 34560 & 2 \\<br> Vienna & 4 & 144 & 24 & 34560 & 6 \\<br> London & 5 & 144 & 24 & 34560 & 4 \\<br> Milan & 6 & 144 & 24 & 34560 & 6 \\<br> \midrule<br> Total & 50 & 5472 & 912 & 1.3M & 180 \\</p> <p><br> <strong>Annotations</strong></p> <p><br> Of the 15 hours of audio and video, 3 hours of data (1.5 hours TAU, 1.5 hours MAVD) are manually annotated by our team both in audio and image, along with 12 hours of unlabeled data (2.5 hours TAU, 9.5 hours of unpublished material) for the benefit of unsupervised models. The distribution of clips across locations was selected to maximize variance across different scenes. The annotations were collected at 2 frames per second (FPS) as it provided a balance between temporal granularity and clip coverage.</p> <p>The annotation data is contained in video_annotations.csv and audio_annotations.csv. </p> <p><strong>Video Annotations</strong></p> <p>Each row in the video annotations represents a single object in a single frame of the video. The annotation schema is as follows:</p> <ul> <li>frame_id: The index of the frame within the clip the annotation is associated with. This index is 0-based and goes up to 19 (assuming 10-second clips with annotations at 2 FPS)</li> <li>track_id: The ID of the detected instance that identifies the same object across different frames. These IDs are guaranteed to be unique within a clip.</li> <li>x, y, w, h: The top-left corner and width and height of the object’s bounding box in the video. The values are given in absolute coordinates with respect to the image size (1280x720). </li> <li>class_id: The index of the class corresponding to: [0, 1, 2, 3, -1] — see label for the index mapping. The -1 value corresponds to the case where there are no events, but still clip-level annotations, like night and city. When operating on bounding boxes, class_id of -1 should be filtered.</li> <li>label: The label text. This is equivalent to LABELS[class_id], where LABELS=[car, bus, motorbike, truck, -1]. The label -1 has the same role as above.</li> <li>visibility: The visibility of the object. This is 1 unless the object becomes obstructed, where it changes to 0.</li> <li>filename: The file ID of the associated file. This is the file’s path minus the parent directory and extension.</li> <li>city: The city where the clip was collected in.</li> <li>location_id: The specific name of the location. This may include an integer ID following the city name for cases where there are multiple collection points.</li> <li>time: The time (in seconds) of the annotation, relative to the start of the file. Equivalent to frame_id / fps .</li> <li>night: Whether the clip takes place during the day or at night. This value is singular per clip.</li> <li>subset: Which data source the data originally belongs to (TAU or MAVD).</li> </ul> <p><strong>Audio Annotations</strong></p> <p>Each row represents a single object instance, along with the time range that it exists within the clip. The annotation schema is as follows:</p> <ul> <li>filename: The file ID odd the associated audio file. See filename above. </li> <li>class_id, label: See above. Audio has an additional class_id of 4 (label=offscreen) which indicates an off-screen vehicle - meaning a vehicle that is heard but not seen. A class_id of -1 indicates a clip-level annotation for a clip that has no object annotations (an empty scene).</li> <li>non_identifiable_vehicle_sound: True if the region contains the sound of vehicles where individual instances cannot be uniquely identified. </li> <li>start, end: The start and end times (in seconds) of the annotation relative to the file. </li> </ul> <p><strong>Conditions of use</strong></p> <p>Dataset created by Magdalena Fuentes, Bea Steers, Pablo Zinemanas, Martín Rocamora, Luca Bondi, Julia Wilkins, Qianyi Shi, Yao Hou, Samarjit Das, Xavier Serra, and Juan Pablo Bello.</p> <p>The Urbansas dataset is offered free of charge under the following terms:</p> <ul> <li>Urbansas annotations are release under the CC BY 4.0 license</li> <li>Urbansas video and audio replicates the original sources licenses: <ul> <li> MAVD subset is released under CC BY 4.0 </li> <li> TAU subset is released under a Non-Commercial license</li> </ul> </li> </ul> <p><strong>Feedback</strong></p> <p>Please help us improve Urbansas by sending your feedback to:</p> <ul> <li>Magdalena Fuentes: mfuentes@nyu.edu</li> <li>Bea Steers: bsteers@nyu.edu </li> </ul> <p>In case of a problem, please include as many details as possible.</p> <p><strong>Acknowledgments</strong></p> <p>This work was partially supported by the National Science Foundation award 1955357 and Bosch RTC.</p>
Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection
<p>Coswara is a dataset containing diverse set of respiratory sounds and rich meta-data from COVID-19 positive and Non-COVID subjects.</p>
Seeing Sound Dataset v1.0.1
<p>This is dataset contains the synthesized soundscapes and crowdsourced audio annotations that accompany the paper,</p> <blockquote> <p>M. Cartwright, A. Seals, J. Salamon, A. Williams, S. Mikloska, D. MacConnell, E. Law, J. Bello, and O. Nov. "Seeing sound: Investigating the effects of visualizations and complexity on crowdsourced audio annotations." In <em>Proceedings of the ACM on Human-Computer Interaction</em>, 1(2), 2017. https://doi.org/10.1145/3134664</p> </blockquote> <p>which investigates the effects of soundscape complexity and sound visualizations on the quality and speed of annotations of sound events (i.e. start time, end time, sound class, and proximity).</p> <p>In this dataset, we varied the soundscape complexity along two dimensions: maximum polyphony (3 levels) and Gini polyphony (2 levels). Maximum polyphony is the maximum number of sound events that occurred simultaneously in the soundscape. Gini polyphony is a measure of the concentration of sound events. For each of the 6 (3 x 2) combinations of complexity levels, we synthesized 10 soundscapes, each of which was 10 seconds long, for a total of 60 soundscapes. Each soundscape was annotated by 90 participants from Amazon's Mechanical Turk. Of these 90 participants, 30 were aided by waveform visualization, 30 were aided by a spectrogram visualization, and 30 did not have any visualization aid. For more details on how this data was collected, please refer to the paper.</p>
Lubrang Brokpa Lexicon - sound files
<p>These sound files constitute the elicitation of the lexical entries of the Basic Word List in the Lubrang variety of Brokpa. Lubrang village is a recent (early 20<sup>th</sup> century) settlement of Brokpa speaker originating in Sakteng village of Bhutan. They left Bhutan due to its heavy taxation of the semi-nomadic Brokpa households and its anti-Gelukpa policies and settled in the then Tibetan-administered area on land belonging to the Khispi people of Lish village, to which they continue to pay an annual tax. Lubrang Brokpa should hence be close to Merak and Sakteng (Bhutan) Brokpa, and not so close to Nyukmadung and Senge Brokpa spoken closer by. Because of the speaker’s paternal background there may be some admixture with Dirang Tshangla.</p>
Dikhyang Bugun language data: sound files
<p>These sound files constitute the elicitation and the triple-repetition of the lexical entries of the Basic Word List in the Dikhyang variety of Bugun. Dikhyang village is a recent (1977) satellite settlement of Wanggo (‘Wangho’) village, via Rambung and Hemrai settlements. Wanggo is located on the other side of the ridge. The Bugun variety of Dikhyang should hence closely match the Bugun variety of Wanggo (e.g. ‘Wangho’ in Abraham et al. 2005).</p>
Bangru Language Data: sound files (uncut)
<p>These files form the empirical basis for the following article:</p> <p>Bodt, Timotheus Adrianus and Ismael Lieberherr. 2015. First notes on the phonology and classification of the Bangru language of India. <em>Linguistics of the Tibeto-Burman Area 38:1</em> (2015), 66–123.</p> <p>doi 10.1075/ltba.38.1.03bod</p> <p>issn 0731–3500 / e-issn 2214–5907 © John Benjamins Publishing Company</p> <p>These data were collected in Sarli circle, Kurung Kumey district, Arunachal Pradesh, India.</p> <p>The data collectors were the following faculty, students and associated researchers of the Department of English and Foreign Languages, Tezpur University, Assam, India:</p> <p>Nupur Sinha (Faculty), Ismael Lieberherr (Affiliated PhD scholar), Timotheus A. Bodt (Affiliated PhD scholar), Diksha Konwar, Eshani Baishya, Nawaf Helmi, Pinaz Mirza, Ratul Mahela, Sansuma Brahma (students).</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p>
DATASET Invertebrate sounds from photic to mesophotic coral reefs reveal vertical stratification and diel diversity
<p>This dataset contains 17 wave folders. The original files were used for the study published by Raick et al. (2024) in Oecologia (10.1007/s00442-024-05572-5), while subsampled versions of these files were used for the studies published by Raick et al. (2023) in Coral Reefs (10.1007/s00338-022-02343-7) and Raick et al. (2023) in Scientia Marina (10.3989/scimar.05395.078).</p>
[Dataset] Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy
<p>Research data for the purpose of reproducing the results presented in the journal publication titled "Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy"</p>
Duhumbi Stories - Sound files
<p>This data set contains all the original sound files with the stories of the 'Duhumbi Storybook' (Monpasang Publications, ISBN 978-90-818610-1-4) published in autumn 2018. A separate Zenodo DOI contains all the Toolbox-compatible .txt files and Transcriber .trs files with the transcribed, parsed, glossed, translated examples (10.5281/zenodo.1400505). The sound files largely correspond with the texts in the 'Duhumbi Storybook'. The following list contains the sound file names, the shortcut code for the sentence names and the title of the story in Duhumbi, English and Hindi.</p> <ul> <li>[CHUK260413A4A] / OMAK / Dangpu budunbakaq tsawa / The origin of mankind / मानव जाती की उत्पत्ति</li> <li>[CHUK290412A8A] / CHLN / Duhum chakpaqkho tam – 1 / Settlement history of Duhum – 1/ दुहुम के निवास का इतिहास – 1</li> <li>[CHUK230512E1B] / CHT / Duhum chakpaqkho tam – 2 / Settlement history of Duhum – 2 / दुहुम मे निवास का इतिहास – 2</li> <li>[CHUK240314A1] / SPZP / Shawa Pema Zomba / शावा पेमा जोम्बा</li> <li>[CHUK230512F1A; CHUK230512F2A; CHUK230512F3A; CHUK230512F4A; CHUK230512F5A; CHUK230512F6A; CHUK230512F7A] and [CHUK230512G1A; CHUK230512G2A; CHUK230512G3A] / KDZ1 – KDZ10 / Khandro Drowa Zangmu / खांडरों द्रोवा जांग्मो</li> <li>[CHUK110614A1; CHUK110614B1; CHUK110614C1; CHUK110614D1; CHUK110614E1; CHUK110614F1; CHUK110614G1; CHUK110614H1; CHUK110614I1] / LGG1 – LGG9 / Ling Gesar Gepuwaq namthar / The life of King Ling Gesar / लिंग गेसर गेपु की जीवनी</li> <li>[CHUK110413A1A] / TNZY / Tshongpon Norbu Zangpo dangngaq waq uda / Tshongpon Norbu Zangpo and his son / त्शोंग्पोन नोरबू जांगपो और उसका बेटा</li> <li>[CHUK070115A1] / BMDC / Shadong dangngaq gomchen phawang / The macaque and the bat / बंदर और चमगादर</li> <li>[CHUK070115B] / BUDC / Pempelingngaq tam / The butterfly effect / तितली का असर</li> <li>[CHUK211015E1] / RSTT / Samtu dangngaq grongthang / The squirrel and the rat / गिलहरी और चूहा</li> <li>[CHUK130115E] / FBDC / Shalaqbaknyi men shikhennaq ama / The mother that fed the children with ‘men’ / माँ जिसने बच्चों को ‘मेन’ खिलाया</li> <li>[CHUK300115C1] / MTDC / Mani tam / मनी ताम</li> <li>[CHUK260413A4A] / MOD / Bengkhannaq dontha / The meaning of dreams / सपनों का मतलब</li> </ul> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Duhumbi Personal Narratives - Sound files
<p>This data set contains all the original sound files of the personal narratives in the 'Grammar of Duhumbi' (Brill) published in 2019. A separate Zenodo DOI contains all the Toolbox-compatible .txt files and Transcriber .trs files with the transcribed, parsed, glossed, translated examples (DOI 10.5281/zenodo.1406176). The following list contains the sound file names, the shortcut code for the sentence names and the title of the text.</p> <ul> <li>[CHUK230512D1A] / CMT / The story of the former CM’s death</li> <li>[CHUK230512C1A] / LHT / The history of Laphek village</li> <li>[CHUK230512B1] / THT / Hunting takin</li> <li>[CHUK260413A3A]/ ACK / Alcohol consumption</li> <li>[CHUK131014] / DTPK / Chasing the demons</li> </ul> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Duhumbi Procedural Texts - Sound files
<p>This data set contains all the original sound files of the procedural texts of the 'Grammar of Duhumbi' (Brill) published in 2019. A separate Zenodo DOI contains all the Toolbox-compatible .txt files and Transcriber .trs files with the transcribed, parsed, glossed, translated examples (DOI 10.5281/zenodo.1406154). The following list contains the sound file names, the shortcut code for the sentence names and the title of the text.</p> <ul> <li>[CHUK210512I1] / PHPT / Hunting for porcupine </li> <li>[CHUK230512A1A] / SBDC / Making fermented soybean</li> <li>[CHUK220413A1] / CTTT / Catching frogs </li> <li>[CHUK220413B1] / CWTT / Collecting beeswax </li> <li>[CHUK220413C1] / CHTT / Collecting hornets</li> <li>[CHUK240413A1] / SNAP / Collecting stinging nettle </li> <li>[CHUK240413B1] / LCYT / Leather craft </li> </ul> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Duhumbi Elicitation - Sound files
<p>This upload contains all the .wav sound files of the written elicitation notes belonging to the 'Grammar of Duhumbi (Chugpa)' (Brill, 2019). These notes are accompanied by the original transcripts of the notes in pdf format (DOI 10.5281/zenodo.1406850). Please note that the elicitation sessions are mainly in Tshangla and Duhumbi. Also, the analysis from the notes, including the labels and glosses, the description, and the phonetic representation, may differ from the final analysis in the grammatical description. For any questions please contact the author directly.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Duhumbi Discussions - Sound files
<p>This data set contains all the original sound files of the discussions in the 'Grammar of Duhumbi' (Brill) published in 2019. A separate Zenodo DOI contains all the Toolbox-compatible .txt files and Transcriber .trs files with the transcribed, parsed, glossed, translated examples (DOI 10.5281/zenodo.1406191). The following list contains the sound file names, the shortcut code for the sentence names and the title of the text.</p> <ul> <li>[CHUKxxxx13A6] / LEL / Local elections (not included)</li> <li>[CHUK300412J2] / LGT / Planning a trip to Lagam</li> <li>[CHUK260413A2A] / NNK / Nicknames</li> </ul> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Duhumbi Religious Texts and Song - Sound files
<p>This data set contains all the original sound files of the religious texts and the song in the 'Grammar of Duhumbi' (Brill) published in 2019. A separate Zenodo DOI contains all the Toolbox-compatible .txt files and Transcriber .trs files with the transcribed, parsed, glossed, translated examples (DOI 10.5281/zenodo.1406181). The following list contains the sound file names, the shortcut code for the sentence names and the title of the text.</p> <ul> <li>[CHUK110413A2A] / RELJ / Buddhist admonition</li> <li> [CHUK221212D2A] / JIK / Bonpo prediction text</li> <li> [CHUK260413A1] / MSK / Impromptu song</li> </ul> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Kusunda - Raw sound files
<p>This data set contains the raw sound files of the interviews held with the last two Kusunda speakers from Nepal, Gyani Maiya Sen Kusunda and Kamala Khatri (Sen Kusunda), late July and early August 2019, in Kathmandu Nepal.</p> <p>There are a total of 79 files recorded with a Tascam handheld audio recorder, with a total length of 20 hours 40 minutes. There is elicitation of word lists and verbal paradigms; conversation between the two speakers; personal narratives; an origin story; retold stories from picture books; and comments on earlier video and audio recordings.</p> <p>The processed sound files and the associated video files and other information will be uploaded here in Zenodo in due course of time.</p> <p>This research was funded by a 2,000 USD grant from the Endangered Language Fund (<a href="http://www.endangeredlanguagefund.org/">http://www.endangeredlanguagefund.org/</a>), a 700 euro contribution by the European Research Council Starting Grant 715618 “Computer-Assisted Language Comparison” ( <a href="http://calc.digling.org/">http://calc.digling.org</a>), and a total of 2,320 euros raised through crowdfunding at GoFundMe (<a href="https://www.gofundme.com/f/saving-the-kusunda-language-in-nepal">https://www.gofundme.com/f/saving-the-kusunda-language-in-nepal</a>). Many thanks to all generous contributors.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>We greatly value feedback, suggestions, advice, analysis etc. based on this material which will help in the description of the Kusunda language, especially any comments and suggestions that will enable the revitalisation of the language, including a standardisation of the phonology and a phonologically consistent but also practical orthography in both देवनागरी Devanāgarī and Roman script.</p> <p>Uday Raj Aaley: aarambhkhabar (at) gmail (dot) com</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection
<p>This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset). It contains the sounds generated from four types of industrial machines, i.e. valves, pumps, fans, and slide rails. Each type of machine includes seven individual product models*1, and the data for each model contains normal sounds (from 5000 seconds to 10000 seconds) and anomalous sounds (about 1000 seconds). To resemble a real-life scenario, various anomalous sounds were recorded (e.g., contamination, leakage, rotating unbalance, and rail damage). Also, the background noise recorded in multiple real factories was mixed with the machine sounds. The sounds were recorded by eight-channel microphone array with 16 kHz sampling rate and 16 bit per sample. The MIMII dataset assists benchmark for sound-based machine fault diagnosis. Users can test the performance for specific functions e.g., unsupervised anomaly detection, transfer learning, noise robustness, etc. The detail of the dataset is described in [1][2].</p> <p>This dataset is made available by Hitachi, Ltd. under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.</p> <p>A baseline sample code for anomaly detection is available on GitHub: <a href="https://github.com/MIMII-hitachi/mimii_baseline/">https://github.com/MIMII-hitachi/mimii_baseline/</a></p> <p>*1: This version "public 1.0" contains four models (model ID 00, 02, 04, and 06). The rest three models will be released in a future edition.</p> <p>[1] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” arXiv preprint arXiv:1909.09347, 2019.</p> <p>[2] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019.</p>
QIceRadar Antarctic Index of Radar Depth Sounding Data
<p>Database of known Antarctic radar depth sounding data. </p> <ul> <li>qiceradar_antarctic_index.gpkg - Database with ground tracks, citation info, and URLs of available data</li> <li>qiceradar_antarctic_index.qlr - QGIS style file that organizes the transects into campaign/institution and styles them based on radargram availability.</li> </ul> <p>Download both files to the same directory, then drag qiceradar_antarctic_index.qlr into QGIS.</p> <p>(Do not rename them!)</p> <p>----------</p> <p>v0.2.0: Removed outlier points from CReSIS & UTIG surveys; imported tracks of SOAR Vostok survey</p> <p>v0.1.0: Support for BAS, CReSIS and most released UTIG data. Still missing some citations/references, waiting to hear back from data providers.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs
<p>Additional code and data for the paper by Groos et al. entitled "Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs"</p> <p>Correspondence: Alexander R. Groos (alexander.groos@fau.de)</p> <p><br>The repository contains:<br>(1) The raw data (log files) for each UAV-based atmospheric sounding<br>(2) The postprocessed and reformatted data for each sounding and vertical profile<br>(3) The commented R-Scripts for data processing, analysis and visualisation<br>(4) A subset of the meteorological data from the nearby weather stations</p> <p><br>Description of sub-folders:</p> <p>-aws_data<br>-- aws_fisistock.txt # meteorological data from AWS Fisistock for the period of the campaign<br>-- aws_gandegg.txt # meteorological data from AWS Gandegg for the period of the campaign<br>-- aws_sackhorn.txt # meteorological data from AWS Sackhorn for the period of the campaign</p> <p>- processed_data<br>-- kanderfirn_2021-06-16_10:45_p1_pprz.tab # meteorological data for first profile/descent at about <br>-- kanderfirn_2021-06-16_10:45_p2_fr.tab # flight recorder data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_p2_pprz.tab # meteorological data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 10:45 CEST<br>-- .<br>-- .<br>-- .<br>-- kanderfirn_2021-06-16_16:50_p1_pprz.tab # meteorological data for first profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_fr.tab # flight recorder data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_pprz.tab # meteorological data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 16:50 CEST<br>-- kanderfirn_soundings_2021-06-16.csv # summary table of vertical profiles (1 m height intervals): one column for each profile/descent and variable<br>-- kanderfirn_turbulence_2021-06-16.csv # summary table of vertical turbulence profiles (1 m height intervals): one column for each profile/descent</p> <p>- raw_data<br>-- fr_kanderfirn_2021-06-16_10:45.LOG # flight recorder data from the sounding at about 10:45 CEST (binary file)<br>-- .<br>-- .<br>-- .<br>-- fr_kanderfirn_2021-06-16_16:50.LOG # flight recorder data from the sounding at about 16:50 CEST (binary file)<br>-- pprz_kanderfirn_2021-06-16_10:45.LOG # meteorological data from the sounding at about 10:45 CEST (human readable text file)<br>-- .<br>-- .<br>-- .<br>-- pprz_kanderfirn_2021-06-16_16:50.LOG # meteorological data data from the sounding at about 16:50 CEST (human readable text file)</p> <p>- R_scripts<br>-- figures.R # Script to create Figures 5, 6, 8, 9, 10, 11, 12<br>-- lapse_rate.R # Script to calculate lapse rates and surface-based inversions (includes code for Figures 7 and B1)<br>-- postprocessing.R # Script to reformat preprocessed and preselected pprz-files<br>-- turbulence.R # Script for the calculation of the turbulence proxy from the recorded roll rate</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.