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33 results for “acoustic sensor”
SINS database - Node 1 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://kuleuvenadvise.github.io/SINS_database/">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
Listening and watching: do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?
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SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network
<p><strong>SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network</strong></p> <p>Version 2.3, September 2020</p> <p> </p> <p><strong>Created by</strong></p> <p>Mark Cartwright (1,2,3), Jason Cramer (1), Ana Elisa Mendez Mendez (1), Yu Wang (1), Ho-Hsiang Wu (1), Vincent Lostanlen (1,2,4), Magdalena Fuentes (1), Graham Dove (2), Charlie Mydlarz (1,2), Justin Salamon (5), Oded Nov (6), Juan Pablo Bello (1,2,3)</p> <ol> <li>Music and Audio Research Lab, New York University</li> <li>Center for Urban Science and Progress, New York University</li> <li>Department of Computer Science and Engineering, New York University</li> <li>Cornell Lab of Ornithology</li> <li>Adobe Research</li> <li>Department of Technology Management and Innovation, New York University</li> </ol> <p> </p> <p><strong>Publication</strong></p> <p>If using this data in an academic work, please reference the DOI and version, as well as cite the following paper, which presented the data collection procedure and the first version of the dataset:</p> <p>Cartwright, M., Cramer, J., Mendez, A.E.M., Wang, Y., Wu, H., Lostanlen, V., Fuentes, M., Dove, G., Mydlarz, C., Salamon, J., Nov, O., Bello, J.P. SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context. In <em>Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE)</em>, 2020.<br> <a href="https://arxiv.org/abs/2009.05188">[pdf]</a></p> <p> </p> <p><strong>Description</strong></p> <p>SONYC Urban Sound Tagging (SONYC-UST) is a dataset for the development and evaluation of machine listening systems for realistic urban noise monitoring. The audio was recorded from the <a href="https://wp.nyu.edu/sonyc">SONYC</a> acoustic sensor network. Volunteers on the <a href="https://zooniverse.org">Zooniverse</a> citizen science platform tagged the presence of 23 classes that were chosen in consultation with the New York City Department of Environmental Protection. These 23 fine-grained classes can be grouped into 8 coarse-grained classes. The recordings are split into three sets: training, validation, and test. The training and validation sets are disjoint with respect to the sensor from which each recording came, and the test set is displaced in time. For increased reliability, three volunteers annotated each recording. In addition, members of the SONYC team subsequently created a subset of verified, ground-truth tags using a two-stage annotation procedure in which two annotators independently tagged and then collectively resolved any disagreements. This subset of recordings with verified annotations intersects with all three recording splits. All of the recordings in the test set have these verified annotations. In v2 version of this dataset, we have also included coarse spatiotemporal context information to aid in tag prediction when time and location is known. For more details on the motivation and creation of this dataset see the <a href="http://dcase.community/challenge2020/task-urban-sound-tagging-with-spatiotemporal-context">DCASE 2020 Urban Sound Tagging with Spatiotemporal Context Task website</a>.</p> <p> </p> <p><strong>Audio data</strong></p> <p>The provided audio has been acquired using the SONYC acoustic sensor network for urban noise pollution monitoring. Over 60 different sensors have been deployed in New York City, and these sensors have collectively gathered the equivalent of over 50 years of audio data, of which we provide a small subset. The data was sampled by selecting the nearest neighbors on VGGish features of recordings known to have classes of interest. All recordings are 10 seconds and were recorded with identical microphones at identical gain settings. To maintain privacy, we quantized the spatial information to the level of a city block, and we quantized the temporal information to the level of an hour. We also limited the occurrence of recordings with positive human voice annotations to one per hour per sensor.</p> <p> </p> <p><strong>Label taxonomy</strong></p> <p>The label taxonomy is as follows:</p> <ol> <li>engine<br> 1: small-sounding-engine<br> 2: medium-sounding-engine<br> 3: large-sounding-engine<br> X: engine-of-uncertain-size</li> <li>machinery-impact<br> 1: rock-drill<br> 2: jackhammer<br> 3: hoe-ram<br> 4: pile-driver<br> X: other-unknown-impact-machinery</li> <li>non-machinery-impact<br> 1: non-machinery-impact</li> <li>powered-saw<br> 1: chainsaw<br> 2: small-medium-rotating-saw<br> 3: large-rotating-saw<br> X: other-unknown-powered-saw</li> <li>alert-signal<br> 1: car-horn<br> 2: car-alarm<br> 3: siren<br> 4: reverse-beeper<br> X: other-unknown-alert-signal</li> <li>music<br> 1: stationary-music<br> 2: mobile-music<br> 3: ice-cream-truck<br> X: music-from-uncertain-source</li> <li>human-voice<br> 1: person-or-small-group-talking<br> 2: person-or-small-group-shouting<br> 3: large-crowd<br> 4: amplified-speech<br> X: other-unknown-human-voice</li> <li>dog<br> 1: dog-barking-whining</li> </ol> <p>The classes preceded by an <code>X</code> code indicate when an annotator was able to identify the coarse class, but couldn’t identify the fine class because either they were uncertain which fine class it was or the fine class was not included in the taxonomy. <code>dcase-ust-taxonomy.yaml</code> contains this taxonomy in an easily machine-readable form.</p> <p> </p> <p><strong>Data splits</strong></p> <p>This release contains a training subset (13538 recordings from 35 sensors), and validation subset (4308 recordings from 9 sensors), and a test subset (669 recordings from 48 sensors). The training and validation subsets are disjoint with respect to the sensor from which each recording came. The sensors in the test set will not disjoint from the training and validation subsets, but the test recordings are displaced in time, occurring after any of the recordings in the training and validation subset. The subset of recordings with verified annotations (1380 recordings) intersects with all three recording splits. All of the recordings in the test set have these verified annotations.</p> <p> </p> <p><strong>Annotation data</strong></p> <p>The annotation data are contained in <code>annotations.csv</code>, and encompass the training, validation, and test subsets. Each row in the file represents one multi-label annotation of a recording—it could be the annotation of a single citizen science volunteer, a single SONYC team member, or the agreed-upon ground truth by the SONYC team (see the <em>annotator_id</em> column description for more information). Note that since the SONYC team members annotated each class group separately, there may be multiple annotation rows by a single SONYC team annotator for a particular audio recording.</p> <p> </p> <p> </p> <p><strong>Columns</strong></p> <p><em>split</em></p> <p>The data split. (<em>train</em>, <em>validate, test</em>)</p> <p><em>sensor_id</em></p> <p>The ID of the sensor the recording is from.</p> <p><em>audio_filename</em></p> <p>The filename of the audio recording</p> <p><em>annotator_id</em></p> <p>The anonymous ID of the annotator. If this value is positive, it is a citizen science volunteer from the Zooniverse platform. If it is negative, it is a SONYC team member. If it is <code>0</code>, then it is the ground truth agreed-upon by the SONYC team.</p> <p><em>year</em></p> <p>The year the recording is from.</p> <p><em>week</em></p> <p>The week of the year the recording is from.</p> <p><em>day</em></p> <p>The day of the week the recording is from, with Monday as the start (i.e. <code>0</code>=Monday).</p> <p><em>hour</em></p> <p>The hour of the day the recording is from</p> <p><em>borough</em><br> The NYC borough in which the sensor is located (<code>1</code>=Manhattan, <code>3</code>=Brooklyn, <code>4</code>=Queens). This corresponds to the first digit in the 10-digit NYC parcel number system known as Borough, Block, Lot (BBL).</p> <p><em>block</em></p> <p>The NYC block in which the sensor is located. This corresponds to digits 2—6 digit in the 10-digit NYC parcel number system known as Borough, Block, Lot (BBL).</p> <p><em>latitude</em></p> <p>The latitude coordinate of the <strong>block</strong> in which the sensor is located.</p> <p><em>longitude</em></p> <p>The longitude coordinate of the <strong>block</strong> in which the sensor is located.</p> <p><em><coarse_id>-<fine_id>_<fine_name>_presence</em></p> <p>Columns of this form indicate the presence of fine-level class. <code>1</code> if present, <code>0</code> if not present. If <code>-1</code>, then the class was not labeled in this annotation because the annotation was performed by a SONYC team member who only annotated one coarse group of classes at a time when annotating the verified subset.</p> <p><em><coarse_id>_<coarse_name>_presence</em></p> <p>Columns of this form indicate the presence of a coarse-level class. <code>1</code> if present, <code>0</code> if not present. If <code>-1</code>, then the class was not labeled in this annotation because the annotation was performed by a SONYC team member who only annotated one coarse group of classes at a time when annotating the verified subset. These columns are computed from the fine-level class presence columns and are presented here for convenience when training on only coarse-level classes.</p> <p><em><coarse_id>-<fine_id>_<fine_name>_proximity</em></p> <p>Columns of this form indicate the proximity of a fine-level class. After indicating the presence of a fine-level class, citizen science annotators were asked to indicate the proximity of the sound event to the sensor. Only the citizen science volunteers performed this task, and therefore this data is not included in the verified annotations. This column may take on one of the following four values: (<code>near</code>, <code>far</code>, <code>notsure</code>, <code>-1</code>). If <code>-1</code>, then the proximity was not annotated because either the annotation was not performed by a citizen science volunteer, or the citizen science volunteer did not indicate the presence of the class.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>Dataset created by Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez, Yu Wang, Ho-Hsiang Wu, Vincent Lostanlen, Magdalena Fuentes, Graham Dove, Charlie Mydlarz, Justin Salamon, Oded Nov, and Juan Pablo Bello</p> <p>The SONYC-UST dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license:<br> <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p> <p>The dataset and its contents are made available on an “as is” basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, New York University is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the SONYC-UST dataset or any part of it.</p> <p> </p> <p><strong>Feedback</strong></p> <p>Please help us improve SONYC-UST by sending your feedback to:</p> <ul> <li>Mark Cartwright: <a href="mailto:mcartwright@gmail.com">mcartwright@gmail.com</a></li> </ul> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We would like to thank all the Zooniverse volunteers who continue to contribute to our project. This work is supported by <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544753">National Science Foundation award 1544753</a>.</p> <p> </p> <p><strong>Change log</strong></p> <ul> <li>2.3 Added the ground truth annotations for the test set, and regrouped the audio files for upload to Zenodo.</li> <li>2.2 Added the audio for the test set (audio-eval.tar.gz).</li> <li>2.1 The DCASE 2020 development dataset. 14778 new recordings added along with coarse spatiotemporal context information.</li> <li>1.0 Data is the same as v0.4. Publication added to README.</li> <li>0.4 Fixed error in annotations. Previously, the coarse class "machinery-impact" was accidentally indicated as present whenever "non-machinery-impact" was present regardless of the presence of "machinery-impact". This error has been fixed.</li> <li>0.3 Test set annotations added</li> <li>0.2 Test set audio files added</li> </ul>
Data for "Quantifying nocturnal thrush migration using sensor data fusion between acoustics and vertical-looking radar"
<p>This repository contains the data used for the analyses conducted and described in the article "Quantifying nocturnal thrush migration using sensor data fusion between acoustics and vertical-looking radar", as well as the Random Forest classifier built from such data. </p> <p>Files:</p> <p><em>2021_AcousticDataset.xlsx:</em> excel dataset containing the daily number of calls for each of the three thrush species considered recorded in Helsinki (Finland) during the study period (year 2021). The dates refer to UTC time.</p> <p><em>2022_ AcousticDataset.xlsx: </em>excel dataset containing the daily number of calls for each of the three thrush species considered recorded in Helsinki (Finland) during the study period (year 2022) . The dates refer to UTC time.</p> <p><em>RandomForestClassifier.Rdata: </em>R object containing the Random Forest classifier built using the eight most important echo features. The purpose of the classifier is to categorize echoes into 'thrush' and 'non-thrush' classes. </p> <p><em>2021_RadarDataset.rds: </em>R object containing the radar data collected with AVLR BirdScan MR1 in Helsinki (Finland) during the study period (year 2021).</p> <p><em>2022_RadarDataset.rds:</em> R object containing the radar data collected with AVLR BirdScan MR1 in Helsinki (Finland) during the study period (year 2022).</p> <p><em>license.txt: </em>the license applying to the data.</p>
SONYC-Backgrounds: a collection of urban background recordings from an acoustic sensor network
<p><strong>Created by</strong></p> <p>Aurora Cramer <sup>(1, 2)</sup>, Mark Cartwright <sup>(3)</sup>, Fatemeh Pishdadian <sup>(4)</sup>, Juan Pablo Bello <sup>(1,2,5,6)</sup></p> <p> 1. Music and Audio Research Lab, New York University<br> 2. Department of Electrical and Computer Engineering, New York University<br> 3. Department of Informatics, New Jersey Institute of Technology<br> 4. Interactive Audio Lab, Northwestern University<br> 5. Center for Urban Science and Progress, New York University<br> 6. Department of Computer Science and Engineering, New York University</p> <p><br> <strong>Publication</strong></p> <p>If you use this data in your work, please cite the following paper, which introduced this dataset:</p> <p>[1] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J.P. Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021. [<a href="https://arxiv.org/pdf/2105.02911">pdf</a>]</p> <p><br> <strong>Description</strong></p> <p>SONYC-Backgrounds is an open dataset of recordings of urban background noise obtained from the <a href="https://wp.nyu.edu/sonyc/">SONYC</a> acoustic sensor network [2]. This dataset was developed with the goal of synthesizing soundscapes with a diverse set of realistic sounding background activity, for use in developing and evaluating machine listening systems in urban settings.</p> <p><br> <strong>Data acquisition</strong></p> <p>The provided audio has been acquired using the <a href="https://wp.nyu.edu/sonyc/">SONYC</a> acoustic sensor network for urban noise pollution monitoring [2]. Over 50 different sensors have been deployed in New York City. All recordings are 10 seconds and were recorded with identical microphones at identical gain settings.</p> <p><br> <strong>Recording selection</strong></p> <p>From the large collection of audio recordings acquired in 2017, we obtain a much smaller subset of likely background recordings. We first process the dataset using a sensor fault detector to filter out recordings with artifacts caused by hardware failures in the sensors. The sensor fault detector is a random forest, trained with a small collection of audio examples using active learning [3].</p> <p>We then determine if a recording is background or not using an urban sound classifier trained to detect the presence of sources of interest to urban noise pollution monitoring [4, 5]. We use the classifier to find recordings that *do not* contain the sound classes of interest. The classifier model is a multi-layer perception with two hidden layers, which takes as input an OpenL3 embedding [6] for a 1 s clip of audio and produces multi-label prediction probabilities for each class. This model is nearly identical to the one used for the <a href="http://dcase.community/challenge2019/task-urban-sound-tagging">DCASE 2019 Challenge Urban Sound Tagging Task</a> baseline model, aside from the addition of an extra hidden layer.</p> <p>Predictions for entire recordings are obtained by max-pooling the predictions for each class across time. A recording is considered background if the probabilities of the target classes fall below their respective detection thresholds, i.e. no target classes are detected. The classifier was trained on the SONYC-UST v1 dataset [4], and the detection thresholds for each class were tuned to correspond to 70% <em>negative</em> recall (true negative rate) on the test set to increase the likelihood that recordings are background.</p> <p>After this selection process, we obtain 441 background clips.</p> <p> </p> <p><strong>Metadata</strong></p> <p>To maintain privacy, the recordings in this release have been distributed in time and location, and recording times have been quantized to the hour. Sensor IDs are consistent with those SONYC-UST dataset [4]. The corresponding location of the sensors can be found in the SONYC-UST v2 dataset [5], though these locations have been mapped to the "block" level to maintain privacy. See the <a href="http://dcase.community/challenge2020/task-urban-sound-tagging-with-spatiotemporal-context">DCASE 2020 Challenge Urban Sound Tagging with Spatiotemporal Context Task page</a> for more information on the metadata.</p> <p><br> <strong>Data splits</strong></p> <p>The dataset is partitioned into a train/valid/test split of roughly 60/20/20, using a simple greedy method to assign sensors to subsets.</p> <p><br> <strong>Files</strong></p> <p>The dataset directory contains the directories `train`, `valid`, and `test` for each of the respective data subsets. Each directory contains recordings, with the file format: `<sensor-id>_<year>-<month>-<day>_<hour>_<instance-num>.wav`, where `<instance-num>` is used to distinguish recordings from the same sensor occurring during the same hour. Aside from `<year>`, each of these fields in the format are lead zero padded to two places (i.e. `printf` format `"%02d"`).</p> <p><br> <strong>Conditions of use</strong></p> <p>Dataset created by Aurora Cramer, Mark Cartwright, Fatemeh Pishdadian, and Juan Pablo Bello.</p> <p>The SONYC-Backgrounds dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license: <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p> <p>The dataset and its contents are made available on an “as is” basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, New York University is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the SONYC-Backgrounds dataset or any part of it.</p> <p> </p> <p><strong>Contact</strong></p> <p>If you have any questions, comments, or concerns, please direct correspondence to Aurora Cramer (aurora (dot) linh (dot) cramer (at) gmail (dot) com).</p> <p><br> <strong>References and Links</strong></p> <p>[1] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J.P. Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021.</p> <p>[2] Bello, J. P., Silva, C., Nov, O., Dubois, R. L., Arora, A., Salamon, J., C. Mydlarz, and Doraiswamy, H. (2019). Sonyc: A system for monitoring, analyzing, and mitigating urban noise pollution. Communications of the ACM, 62(2), 68-77.</p> <p>[3] Wang, Y., Mendez, A.E.M., Cartwright, M., and Bello, J.P. Active Learning for Efficient Audio Annotation and Classification with a Large Amount of Unlabeled Data. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.</p> <p>[4] Cartwright, M., Mendez, A.E.M., Cramer, A., Lostanlen, V., Dove, G., Wu, H., Salamon, J., Nov, O., and Bello, J.P. SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network. In Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) , 2019.</p> <p>[5] Cartwright, M., Cramer, A., Mendez, A.E.M., Wang, Y., Wu, H., Lostanlen, V., Fuentes, M., Dove, G., Mydlarz, C., Salamon, J., Nov, O., and Bello, J.P. SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context. In Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020.</p> <p>[6] Look, Listen and Learn More: Design Choices for Deep Audio Embeddings<br> Cramer, A., Wu, H.-H., Salamon J., and Bello. J.P. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.</p> <p><br> <strong>Acknowledgements</strong></p> <p>We would like to thank <a href="https://wp.nyu.edu/sonyc/people/">all those involved in the SONYC project</a>. This work is partially supported by National Science Foundation <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633259">award 1633259</a> and <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544753">award 1544753</a>.</p> <p> </p>
Mini-acoustic sensors reveal occupancy and threats to koalas (Phascolarctos cinereus) in private native forests
<p>1. Forests on private land have a wide range of uses that span activities such as recreation, primary production and nature conservation. Traditionally, it has been difficult for researchers to access private land to undertake systematic surveys. We used mini-acoustic sensors (Audiomoth) mailed via the postal service to overcome landholder concerns about researchers accessing private property, with a focus on properties used for private native forestry.</p> <p>2. We surveyed koalas, an iconic threatened marsupial, in north-east New South Wales, Australia using passive acoustics, with repeat surveys over consecutive nights to account for imperfect detection in an occupancy modelling framework.</p> <p>3. Over three years, we surveyed 128 sites and recorded 2,560 male bellows. Detection probability over seven nights was high (>0.79), but varied substantially between years, due to use of different sensors, housings and weather conditions. After accounting for detection probability, modelling revealed that koalas commonly occupied private native forests of the study region (probability of occupancy = 0.58±0.08).</p> <p>4. Occupancy was modelled against several covariates and it varied with the landscape extent of sealed roads (-ve), NDVI (-ve) and a habitat suitability model (+ve, but minor). There was no support for occupancy in private forests to be related to a range of other factors including extent of surrounding cleared land, timber harvesting history, fire and other measured habitat features.</p> <p>5. Synthesis and applications. We conclude that mini-acoustic recorders mailed to landholders were a highly effective method for assessing koala occupancy on private land and the approach could be deployed more widely for a range of species. Private native forests in partly cleared landscapes are commonly occupied by koalas, highlighting that practices seeking to balance conservation and production should be encouraged.</p>
Source code and data files for the acoustic sensor-based wearable fetal movement monitor
<p>This repository contains data files and source code for the acoustic sensor-based wearable fetal movement monitor.</p>
Mini-acoustic sensors reveal occupancy and threats to koalas (Phascolarctos cinereus) in private native forests
Open the record for dataset details and reuse information.
Data from: Using mobile phones as acoustic sensors for high-throughput mosquito surveillance
Open the record for dataset details and reuse information.
Feasibility of Remote Evaluation and Monitoring of Acoustic Pathophysiological Signals With External Sensor Technology in Covid-19
ClinicalTrials.gov study NCT04695821. IPD Sharing: NO. Countries: 1. Publications: 0.
Feasibility of Remote Evaluation and Monitoring of Acoustic Pathophysiological Signals With External Sensor Technology
ClinicalTrials.gov study NCT04693091. IPD Sharing: NO. Countries: 1. Publications: 0.
Performance Equivalence of Rainbow Acoustic Monitoring (RAM) Small Sensor and RAM Revision D Sensor
ClinicalTrials.gov study NCT03122405. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Accuracy of Acoustic Rainbow Monitoring (ARM) Sensor
ClinicalTrials.gov study NCT03124862. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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)
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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.