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1,300 results for “Sounds”
PIE LTER, Year 2018, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts
Year 2018, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA.
PIE LTER, Hach, OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2021.
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2021. OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
PIE LTER 15-minute OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2024.
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2024. An OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. The RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2024.
Wind speed and direction measurements for 2024 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
PIE LTER 15-minute OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2025.
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2025. An OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. The RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2025.
Wind speed and direction measurements for 2025 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
LISA Sensitivity to Gravitational Waves from Sound Waves
<p>Supplemental material for the paper of the same name, consisting of LISA's (1) strain noise power spectrum and (2) peak-integrated sensitivities for all the different spectral shapes of the signal and observing times presented in this paper.</p>
Duhumbi Grammar - Sound Files, Toolbox and Transcriber File, PDFs of files
<p>This data set contains the .wav sound files, .trs Transcriber files, .txt Toolbox-compatible Notepad files and .pdf files with the completely transcribed, glossed, parsed and translated examples of the recordings that belong to the following publication:</p> <p>Bodt, Timotheus Adrianus. 2020. Grammar of Duhumbi. Leiden: Brill. ISBN 978-90-04-40947-7. <a href="https://brill.com/view/title/55767">https://brill.com/view/title/55767</a></p> <p>The explanation of all the grammatical features that occur in these sound files can be found in the Grammar of Duhumbi.</p> <p>The main Toolbox files can be found in the zip file “Settings”, this includes the IPA keys for Duhumbi, the entire setup of the Toolbox database, and the Duhumbi dictionary and Parsing dictionary.</p> <p>The .wav, .txt and .trs files combined in the same folder will enable to open Toolbox and work with the recordings, e.g. play them sentence for sentence and see the transcriptions and translations.</p> <p>Transcriber version 1.5.1: <a href="http://trans.sourceforge.net/en/presentation.php">http://trans.sourceforge.net/en/presentation.php</a> or <a href="https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/">https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/</a></p> <p>Toolbox version 1.6.1: <a href="https://software.sil.org/toolbox/download/">https://software.sil.org/toolbox/download/</a></p> <p>This data set contains the files belonging to the sound files as mentioned in the pdf file “Duhumbi Grammar All Files Upload 1”. The S/N code corresponds to the code used in the Grammar to identify the text from which an example was taken. The name of the file refers to the name of the .wav, .trs, .txt and .pdf files in this upload. The subject is a short description of the topic of the text. The duration is the duration of the recording.</p> <p>For the metadata of the sound files in this data set, I refer to Chapter 13 Texts in the Grammar of Duhumbi. This Chapter has a complete listing of the texts, their topics, the speakers and their background etc.</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 collector of the material. By downloading this 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: monpasang (at) gmail (dot) com</p>
Duhumbi Grammar - Sound Files, Toolbox and Transcriber File, PDFs of files (Part 2)
<p>This data set contains the .wav sound files, .trs Transcriber files, .txt Toolbox-compatible Notepad files and .pdf files with the completely transcribed, glossed, parsed and translated examples of the recordings that belong to the following publication:</p> <p>Bodt, Timotheus Adrianus. 2020. Grammar of Duhumbi. Leiden: Brill. ISBN 978-90-04-40947-7. <a href="https://brill.com/view/title/55767">https://brill.com/view/title/55767</a></p> <p>The explanation of all the grammatical features that occur in these sound files can be found in the Grammar of Duhumbi.</p> <p>The main Toolbox files can be found in the zip file “Settings”, this includes the IPA keys for Duhumbi, the entire setup of the Toolbox database, and the Duhumbi dictionary and Parsing dictionary.</p> <p>The .wav, .txt and .trs files combined in the same folder will enable to open Toolbox and work with the recordings, e.g. play them sentence for sentence and see the transcriptions and translations.</p> <p>Transcriber version 1.5.1: <a href="http://trans.sourceforge.net/en/presentation.php">http://trans.sourceforge.net/en/presentation.php</a> or <a href="https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/">https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/</a></p> <p>Toolbox version 1.6.1: <a href="https://software.sil.org/toolbox/download/">https://software.sil.org/toolbox/download/</a></p> <p>This data set contains the files belonging to the sound files as mentioned in the pdf file “Duhumbi Grammar All Files Upload 2”. The S/N code corresponds to the code used in the Grammar to identify the text from which an example was taken. The name of the file refers to the name of the .wav, .trs, .txt and .pdf files in this upload. The subject is a short description of the topic of the text. The duration is the duration of the recording.</p> <p>For the metadata of the sound files in this data set, I refer to Chapter 13 Texts in the Grammar of Duhumbi. This Chapter has a complete listing of the texts, their topics, the speakers and their background etc.</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 collector of the material. By downloading this 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: monpasang (at) gmail (dot) com</p>
TAU-NIGENS Spatial Sound Events 2020
<p><strong>DESCRIPTION:</strong></p> <p>The <strong>TAU-NIGENS Spatial Sound Events 2020</strong> dataset contains multiple spatial sound-scene recordings, consisting of sound events of distinct categories integrated into a variety of acoustical spaces, and from multiple source directions and distances as seen from the recording position. The spatialization of all sound events is based on filtering through real spatial room impulse responses (RIRs), captured in multiple rooms of various shapes, sizes, and acoustical absorption properties. Furthermore, each scene recording is delivered in two spatial recording formats, a microphone array one (<strong>MIC</strong>), and first-order Ambisonics one (<strong>FOA</strong>). The sound events are spatialized as either stationary sound sources in the room, or moving sound sources, in which case time-variant RIRs are used. Each sound event in the sound scene is associated with a trajectory of its direction-of-arrival (DoA) to the recording point, and a temporal onset and offset time. The isolated sound event recordings used for the synthesis of the sound scenes are obtained from the <a href="https://doi.org/10.5281/zenodo.2535878">NIGENS general sound events database</a>. These recordings serve as the development dataset for the <a href="http://dcase.community/challenge2020/task-sound-event-localization-and-detection">DCASE 2020 Sound Event Localization and Detection Task</a> of the <a href="http://dcase.community/challenge2020/">DCASE 2020 Challenge</a>.</p> <p><strong>REPORT & REFERENCE:</strong></p> <p>If you use this dataset please cite the report on its creation, and the corresponding DCASE2020 task setup:</p> <p>Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</em>, Tokyo, Japan.</p> <p>A longer version with more detailed information can be also found <a href="https://arxiv.org/pdf/2006.01919.pdf">here</a>.</p> <p><strong>AIM:</strong></p> <p>The dataset includes a large number of mixtures of sound events with realistic spatial properties under different acoustic conditions, and hence it is suitable for training and evaluation of machine-listening models for sound event detection (SED), general sound source localization with diverse sounds or signal-of-interest localization, and joint sound-event-localization-and-detection (SELD). Additionally, the dataset can be used for evaluation of signal processing methods that do not necessarily rely on training, such as acoustic source localization methods and multiple-source acoustic tracking. The dataset allows evaluation of the performance and robustness of the aforementioned applications for diverse types of sounds, and under diverse acoustic conditions.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li>600 one-minute long sound scene recordings (development dataset).</li> <li>200 one-minute long sound scene recordings (evaluation dataset).</li> <li>Sampling rate 24kHz.</li> <li>About 700 sound event samples spread over 14 classes (see <a href="http://doi.org/10.5281/zenodo.2535878">here</a> for more details).</li> <li>8 provided cross-validation splits of 100 recordings each, with unique sound event samples and rooms in each of them.</li> <li>Two 4-channel 3-dimensional recording formats: first-order Ambisonics (<strong>FOA</strong>) and tetrahedral microphone array.</li> <li>Realistic spatialization and reverberation through RIRs collected in 15 different enclosures.</li> <li>From about 1500 to 3500 possible RIR positions across the different rooms.</li> <li>Both static reverberant and moving reverberant sound events.</li> <li>Up to two overlapping sound events allowed, temporally and spatially.</li> <li>Realistic spatial ambient noise collected from each room is added to the spatialized sound events, at varying signal-to-noise ratios (SNR) ranging from noiseless (30dB) to noisy (6dB).</li> </ul> <p>The IRs were collected in Finland by staff of Tampere University between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The older measurements from five rooms were also used for the earlier <a href="https://doi.org/10.5281/zenodo.2580091">development</a> and <a href="https://doi.org/10.5281/zenodo.3066124">evaluation</a> datasets <strong>TAU Spatial Sound Events 2019</strong>, while ten additional rooms were added for this dataset. The data collection received funding from the European Research Council, grant agreement <a href="https://cordis.europa.eu/project/id/637422">637422 EVERYSOUND</a>.</p> <p>More detailed information on the dataset can be found in the included README file.</p> <p><strong>EXAMPLE APPLICATION:</strong></p> <p>An implementation of a trainable model of a convolutional recurrent neural network, performing joint SELD, trained and evaluated with this dataset is provided <a href="https://github.com/sharathadavanne/seld-dcase2020">here</a>. This implementation serves as the baseline method in the <a href="http://dcase.community/challenge2020/task-sound-event-localization-and-detection">DCASE 2020 Sound Event Localization and Detection Task</a>.</p> <p><strong>DEVELOPMENT AND EVALUATION:</strong></p> <p>Version 1.0 of the dataset included only the 600 development audio recordings and labels, used by the participants of Task 3 of DCASE2020 Challenge to train and validate their submitted systems. Version 1.1 included additionally the 200 evaluation audio recordings without labels, for the evaluation phase of DCASE2020. The latest version 1.2, published after the completion of the challenge, includes also the labels for the evaluation files.</p> <p>If researchers wish to compare their system against the submissions of DCASE2020 Challenge, they will have directly comparable results if they use the evaluation data as their testing set.</p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>The three files, <strong><em>foa_dev.z01</em></strong>,<strong><em> foa_dev.z02</em></strong>, and <strong><em>foa_dev.zip</em></strong>, correspond to audio data of the <strong>FOA </strong>recording format.<br> The three files, <strong><em>mic_dev.z01</em></strong>,<strong><em> mic_dev.z02</em></strong>, and <strong><em>mic_dev.zip</em></strong>, correspond to audio data of the <strong>MIC</strong> recording format.<br> The <strong><em>metadata_dev.zip</em></strong> is the common metadata for both formats.</p> <p>The file, <em><strong>foa_eval.zip</strong></em>, corresponds to audio data of the <strong>FOA</strong> recording format for the evaluation dataset.<br> The file, <em><strong>mic_eval.zip</strong></em>, corresponds to audio data of the <strong>MIC</strong> recording format for the evaluation dataset.<br> The <em><strong>metadata_eval.zip</strong></em> is the common metadata for both formats. An info file is included (<em>metadata_eval_info.txt</em>) which specifies which of the two evaluation folds the mix file belongs to, and what is its number of overlapping events.</p> <p>Download the zip files corresponding to the format of interest and use your favorite compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <ol> <li>Combine the split archive to a single archive: <pre>zip -s 0 split.zip --out single.zip</pre> </li> <li>Extract the single archive using unzip: <pre>unzip single.zip</pre> </li> </ol>
Dataset-AOB: urban sounds events classification
<p>The dataset Dataset-AOB is an audio dataset collected and manually edited for urban sounds events classification using Convolutional Neural Networks for the Master Thesis: </p> <p>Ospina, A. "Audio Event Classification using Deep Learning. Use case: Urban Sounds Events classification with Convolutional Neural Networks," M.Eng. thesis, Beuth University of Applied Sciences, Berlin, 2020.</p> <p>- 10 audio events: alarm-siren, children playing, dog bark, engine, footsteps, glass breaking, gun shot, metro train, rain and screams.</p> <p>- duration: < 4 seconds</p> <p>- format: (.wav)</p> <p>- sampling rate: 22KHz - 44KHz</p> <p>- files: Dataset-AOB: development dataset (4831 samples), DatasetEVAL-AOB: evaluation (218 samples)</p> <p>- metadata: (.csv)</p> <p>- sources per class: (.png)</p> <p>Contact: aospinab@gmail.com</p>
Listening test results for sound field synthesis localization experiment -- head movement data
<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>
Audio clips of Orca (Orcinus orca) and non-orca sounds for the exploration of multiple acoustic representations
<p>Data and code associated with "Comparing acoustic representations for deep learning-based classification of underwater acoustic signals: a case study on orca (Orcinus orca) vocalizations."</p> <p>A collection of 9600 audio clips recorded by a hydrophone off San Juan Island, WA, USA. The clips are 3 seconds in duration with a sampling rate of 64KHz, and contain a variety of orca vocalizations (in the srkw folder), as well as non-orca sounds, both humpbacks (hb folder) and unspecified sounds typical of the location (neg folder). </p> <p>The code for each of the representations used in this study is also included.</p>
Dataset for: A revised and expanded deep radiostratigraphy of the Greenland Ice Sheet from airborne radar sounding surveys between 1993–2019
<p>Version 5 changes the variable names in the NetCDF file so that are more readable by xarray.</p> <p>The NetCDF v4 file (Greenland_radiostratigraphy_v2.nc) contains both the gridded depths of synthetic isochrones (i.e., at ages of interest that were not necessarily directly observed) and the age at regular normalized depths (10–80%) in the ice sheet.</p> <p>For each campaign that was traced, there is an HDF5-compliant .mat MATLAB file (Greenland_radiostratigraphy_v2_X_Y, where X is the year the campaign was flown and Y is the aircraft that was used) that contains the original traced and dated reflections for each traced segment in that campaign. If using Python instead of MATLAB, the mat73 package and its loadmat function can be used to load these files. An example Jupyter notebook is included that illustrates how to access the full contents of .mat file using Python and this package. There is also a zipped (.zip) archive for each campaign that contains GeoPackage (.gpkg) files for each traced segment of that campaign. Because of format restrictions, these GeoPackages only include the traced reflections' depths, with the ages in the metadata. </p> <p> </p>
Dataset of publication "Speed of Sound Measurements in Helium at Pressures from 15 to 100 MPa and Temperatures from 273 to 373 K"
<p>This is a dataset of the speed of sound in helium, which was measured along five isotherms in a temperature range from 273 to 373 K at pressures from 15 to 100 MPa with a relative expanded uncertainty (k = 2) from 0.02 to 0.04%. A dual-path pulse-echo device was utilized to conduct these measurements.</p>
Phlorest phylogeny derived from Chacon & List 2015 'Improved computational models of sound change shed light on the history of the Tukanoan languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chacon TC, List J-M (2015) Improved computational models of sound change shed light on the history of the Tukanoan languages. Journal of Language Relationship, 3:177–203.</p> </blockquote>
Phlorest phylogeny derived from Hruschka et al. 2015 'Detecting regular sound changes in linguistics as events of concerted evolution'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hruschka, D. J., Branford, S., Smith, E. D., Wilkins, J., Meade, A., Pagel, M., & Bhattacharya, T. (2015). Detecting regular sound changes in linguistics as events of concerted evolution. Current Biology, 25(1), 1-9.</p> </blockquote>
sounding_out_chorus
<p>This repository contains the data for the paper <strong>Towards interpretable learned representations for Ecoacoustics using variational auto-encoding</strong>.<strong> </strong>This dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each sample has 26 acoustic indices calculated and a full list of avian species and abundances. This dataset is an updated version of a <a href="https://zenodo.org/record/1255218">previous release (10.5281/zenodo.1255218)</a> including KML maps containing GPS data for each sample site and updated label metadata. A data module for use in a PyTorch machine learning pipeline is <a href="https://gitlab.com/ecolistening/sounding_out_torch">available here</a>.</p> <p><strong>Abstract</strong><br> Ecoacoustics is an emerging science that seeks to understand the role of sound in ecological processes.<br> Passive acoustic monitoring is increasingly being used to collect vast quantities of whole-soundscape<br> audio recordings in order to study variations in acoustic community activity across spatial and<br> temporal scales. However, extracting relevant information from audio recordings for ecological<br> inference is non-trivial. Recent approaches to machine-learned acoustic features appear promising<br> but are limited by inductive biases, crude temporal integration methods and few means to interpret<br> downstream inference. To address these limitations we developed and trained a self-supervised<br> representation learning algorithm - a convolutional Variational Auto-Encoder (VAE) - to embed<br> latent features from acoustic survey data collected from sites representing a gradient of habitat<br> degradation in temperate and tropical ecozones and use prediction of survey site as a test case for<br> interpreting inference. We investigate approaches to interpretability by mapping discriminative<br> descriptors back to the spectro-temporal domain to observe how soundscape components change<br> as we interpolate across a linear classification boundary traversing latent feature space; we advance<br> temporal integration methods by encoding a probabilistic soundscape descriptor capable of capturing<br> multi-modal distributions of latent features over time. Our results suggest that varying combinations<br> of soundscape components (biophony, geophony and anthrophony) are used to infer sites along a<br> degradation gradient and increased sensitivity to periodic signals improves on previous research using<br> time-averaged representations for site classification. We also find the VAE is highly sensitive to<br> differences in recorder hardware’s frequency response and demonstrate a simple linear transformation<br> to mitigate the effect of hardware variance on the learned representation. Our work paves the way for<br> development of a new class of deep neural networks that afford more interpretable machine-learned<br> ecoacoustic representations to advance the fundamental and applied science and support global<br> conservation efforts.<br> <br> <strong>Sampling Methods (extract from paper)</strong><br> Surveys were designed to monitor the acoustic characteristics of sites across a gradient of degradation, ranging from primary forest, through secondary forest (or areas in the process of ecological restoration), to agricultural monocultures, providing a space-for-time substitution to investigate changes in soundscapes across a gradient of ecological status. Samples were taken for 1 minute in every 15 for 10 sequential days at each site. Full dawn and dusk recordings were also collected. In each site, 15 recorders were placed in a grid-like system spaced a minimum of 200m away from their neighbours in the UK - 300m in Ecuador - to mitigate acoustic overlap and avoid spatial pseudo-replication. Wildlife Acoustics Song Meters equipped with two channel omni-directional microphone were used. Seven SM2+ and eight SM3 devices were deployed. Gains were matched between recorders (analogue gains at +36dB on SM2+ and +12dB on SM3 which has inbuilt +12dB gain) and recordings made at resolution of 16 bits with a sampling rate of 48 kHz. To provide a cleaner validation data set, local weather recordings were used to select 3 days with lowest wind and rain from each site giving 4725 1 min recording in total. Sites are labelled by their quality in descending order i.e. UK1 (primary), UK2 (regenerating), UK3 (degraded).</p>
Source Data for Manuscript "Sodium salicylate improves detection of amplitude-modulated sound in mice"
<p>This repository contains the source data for our papers <strong>Sodium salicylate improves detection of amplitude-modulated sound in mice </strong>(van den Berg*, Wong*, Houtak, Williamson, Borst). The code to generate figure panels can be found in our github repository at https://github.com/aaronbwong/salicylateonam</p>
Multi-Needle Langmuir probe (mNLP) data on the Investigation of Cusp Irregularities (ICI) 4 sounding rocket
<p>Documentation for file: ICI4_mNLP_01112021.mat</p> <p> </p> <ol> <li> <p><strong>The mNLP system on ICI-4</strong></p> </li> </ol> <p>The mNLP system on ICI-4 consisted of four cylindrical Langmuir probes with a diameter of 0.51 mm and a length of 25 mm [1,2]. The instruments allowed for current measurements at a sampling rate of 8680.5 Hz. The mNLP data included in the file are 1) the raw currents (‘I_mnlp’) in which spikes have been removed using a median filter over ten data points, and 2) the “filtered currents” (‘I_mnlp_filt’). For the latter, the spin of the payload and the three first harmonics were removed using a band-pass filter [1,2]. Additionally, components with frequencies larger than 1kHz were also removed.</p> <p> </p> <p> </p> <ol> <li> <p><strong>Variables:</strong></p> </li> </ol> <table> <tbody> <tr> <td> <p>Variable name</p> </td> <td> <p>Units</p> </td> <td> <p>Description/Comment</p> </td> </tr> <tr> <td> <p>time_noNans</p> </td> <td> <p>seconds</p> </td> <td> <p>Time of flight since launch.</p> </td> </tr> <tr> <td> <p>Alt</p> </td> <td> <p>Km</p> </td> <td> <p>Altitude of the payload.</p> </td> </tr> <tr> <td> <p>I_mnlp</p> </td> <td> <p>Ampere</p> </td> <td> <p>Currents obtained by the four cylindrical Langmuir probes. The 1<sup>st</sup>-4<sup>th</sup> columns contain the currents obtained by the probes with bias voltages of 3V, 4.5V, 6V, and 7.5 V, respectively. The data was filtered using a median filter over ten data points.</p> </td> </tr> <tr> <td> <p>I_mnlp_filt</p> </td> <td> <p>Ampere</p> </td> <td> <p>Same as I_mnlp with additional filtering of currents using band-pass filters [1,2].</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgement</strong></p> <p>The mNLP experiment and the ICI-4 campaign were funded through the Research Council of Norway. Thanks to Lasse Clausen, Espen Trondsen, Jøran I. Moen, David Michael Bang-Hauge, Bjørn Lybekk, and the Mechanical Workshop at the University of Oslo, Norway. </p> <p><br> </p> <p>1. Bekkeng, T. A., K. S. Jacobsen, J. K. Bekkeng, A. Pedersen, T. Lindem, J.‐P. Lebreton, and J. I. Moen (2010), Design of a multi‐needle Langmuir probe system, Meas. Sci. Technol., 21, 085,903, doi:10.1088/0957‐0233/21/8/085903</p> <p>2. Jacobsen, K. S., Pedersen, A., Moen, J. I., & Bekkeng, T. A. (2010), A new Langmuir probe concept for rapid sampling of space plasma electron density. Measurement Science and Technology, 21(8), <a href="https://doi.org/10.1088/0957%E2%80%900233/21/8/085902">https://doi.org/10.1088/0957‐0233/21/8/085902</a></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.