Find research datasets worth reusing

opencc-by-4.0Feb 2024View details →

The International Soundscape Database: An integrated multimedia database of urban soundscape surveys -- questionnaires with acoustical and contextual information

<h1>Introduction</h1> The International Soundscape Database contains the results of a series of soundscape assessment campaigns carried out across Europe and China. The data collection process was conducted according to the <a href="https://www.mdpi.com/2076-3417/10/7/2397">SSID Protocol [1]</a> which integrates in situ questionnaires about users' soundscape experience, with binaural recordings, sound level meter readings, and 360 degree video. The core of this database are individual soundscape questionnaires collected for 3,500+ participants completed in situ in cities across Europe and China, and the psychoacoustic analysis of 30s binaural recordings which can be matched up to each questionnaire. The SSID Protocol was based on the ISO 12913 standard for soundscape data collection [2]. For more information on the specifics of how this data is collected, please see [1]. It is the intention that this dataset be added to and augmented with new locations, cities, and contexts in the future. This will be done both by the SSID team at University College London, but we also strongly welcome contributions from other researchers and practicioners. If a soundscape assessment is collected according to the SSID Protocol, it can be integrated with the rest of the database to form a large, cohesive, and ever-growing database of soundscape assessments.  <h2>Analysis</h2> Code for exploring and analysing this dataset is included as part of the <a href="https://soundscapy.readthedocs.io/en/latest/">Soundscapy package</a>. <h2>Included Files</h2> This dataset incorporates surveys taken in multiple urban public spaces across several cities in Europe and China. These urban spaces include places like parks, urban squares, green spaces, and market streets. At each location, up to 100 questionnaires were collected over a series of multi-hour long sessions. Therefore the data is organised by LocationID, then SessionID, then GroupID. The basic directory structure and contents can be found below.  <h3>Survey Data (.csv)</h3> 'ISD v1.0 Data.csv' organises the data according to the labels given above. <h3>Survey Metadata (.xlsx)</h3> In addition a metadata file ('ISD v1.0 Metadata.xlsx') with photos and descriptions of each of the locations is provided. This metadata file also includes Data Dictionaries for each of the survey instrument versions included. These data dictionaries document precisely the questions asked and the available reponse labels and coding, along with the relevant translations. <h3>Psychoacoustic Analysis (.csv)</h3> The compiled csv file is formatted with a row for each individual participant's questionnaire response, then includes the psychoacoustic analysis of the 30s binaural recording taken while the participant was completing the questionnaire. Details about the psychoacoustic analyses is given in the 'Acoustic Settings' tab in the metadata file. The compiled survey and psychoacoustic analysis data is contained in 'ISD v1.0 Data.csv'. This is compiled from raw survey data files contained in 'Survey_Data', with individual cleaned survey and psychoacoustic data files included in 'Survey_Data/Interim_<date>'. The scripts for compiling this data are included in 'Scripts/'. <h3>Sound Level Meter logs (.xlsx)</h3> 'SLM_<city>/' folders include session-long (i.e. ~3hrs) sound level meter log data in.xlsx files for each SessionID. <h3>Binaural Recordings (32-bit floating point .wav)</h3> 'WAV_<city>/' folders include the ~30s binaural recordings in 32 bit floating point .wav format. Within each city folder are a set of LocationID folders containing their associated recordings. The wav files are titled with its GroupID, which is matched to the corresponding survey GroupIDs.  <h3>Cleaning and Compilation Scripts (.py)</h3> Python code for cleaning and compiling the data from the raw survey data (within Survey_Data/source_data) are provided. These can be run within the provided demo notebook, or from the terminal by calling 'python -m ISDv1_main' with the relevant arguments. See the README.md file in this directory for more information. <pre><code> ├── ISD v1.0 Data.csv ├── ISD v1.0 Metadata.xlsx ├── SLM_Granada │ ├── CampoPrincipe1_SLM.xlsx │ ├── ... ├── SLM_Groningen │ └── Noorderplantsoen1_SLM.xlsx ├── SLM_etc ├── Scripts │ ├── ISDcleanDemo.ipynb │ ├── ISDcleaning.py │ ├── ISDpsycho.py │ ├── ISDv1_main.py │ ├── README.md │ └── pyproject.toml ├── Survey_Data │ ├── Interim_2024-02-08_cleaned │ └── source_data ├── WAV_Granada_1 │ ├── CampoPrincipe │ ├── ... ├── WAV_etc</code></pre> Citation: If you use the ISD or part of it, please cite our paper describing the data collection protocol [1] and this dataset itself. License and reuse: All ISD recordings are provided under the Creative Commons Attribution 4.0 International (CC BY 4.0) License and are free to use. We encourage other researchers to replicate the SSID protocol and contribute new locations to the dataset. We also encourage the use of these recordings and the perceptual data for further soundscape research purposes. Please provide the proper attribution and get in touch with the authors if you would like to contribute new data or for any other collaborations.   [1] Mitchell A, Oberman T, Aletta F, Erfanian M, Kachlicka M, Lionello M, Kang J. The Soundscape Indices (SSID) Protocol: A Method for Urban Soundscape Surveys—Questionnaires with Acoustical and Contextual Information. Applied Sciences. 2020; 10(7):2397. <a href="https://www.mdpi.com/2076-3417/10/7/2397">https://doi.org/10.3390/app10072397 </a> [2] ISO/TS 12913-2:2018 (2018). “Acoustics – Soundscape – Part 2: Data collection and reporting requirements” International Organization for Standardization, Geneva, Switzerland, 2018 [3] Mitchell A, Oberman T, Aletta F, Kachlicka M, Lionello M, Erfanian M, Kang J. Investigating Urban Soundscapes of the COVID-19 Lockdown: A predictive soundscape modeling approach. Journal of the Acoustical Society of America. 2021.

opencc-zeroDec 2023View details →

Acoustic noise radiation measurements of three disel-electric ferries

This dataset contains measured noise radiation for three diesel-electric hybrid ferries, both in air and in water. The ferries have been measured in fully electric battery powered propulsion as well as in hybrid propulsion with the on-board diesel generator running.

opencc-by-4.0Apr 2024View details →

Labelled acoustic dataset of roding Eurasian Woodcock (Scolopax rusticola)

This dataset contains manually labelled audio data of roding Eurasian Woodcock  (Scolopax rusticola).  <h2>Description</h2> Bioacoustic surveys of roding Eurasian Woodcock were conducted in Baden-Württemberg, Germany in May and June in 2020 and 2021. The audio data of this collection was used for the evaluation of BirdNET as a means for the automated analysis of large quantities of audio data. The original dataset consisted of 12.236 minutes of recording, which were reviewed manually. Each call element of a male roding Woodcock (i.e. croak, whistle, chasing male) was annotated. Individual call elements were subsequently clustered into so called roding events, which are ecologically more meaningful. BirdNET was then tested against this manually labelled dataset. The dataset uploaded to zenodo contains: <ul> <li>audio data of 2545 woodcock call element selections with a duration of 145 minutes</li> <li>audio data of 782 aggregated woodcock roding events with a duration of 115 minutes </li> <li>selection tables for call elements and roding events</li> <li>associated metadata</li> </ul> Audio information in between roding events (i.e. non woodcock audio) ist not included due to data privacy reasons (see below).  <h3>Selections</h3> Woodcock call elements were manually selected/annotated in Raven Pro with bounding boxes. For this dataset, all selections with a duration of less than 3 seconds were extended symmetrically until 3 seconds were reached. This may result in overlapping selections in the case of croaks that are directly followed by a whistle. Signals at the beginning or end of these selections may thus be included twice. <h3>Roding events</h3> A roding event was defined as a continuous series of Woodcock call elements with a maximum gap of six seconds between consecutive elements. Each event can be interpreted as a roding bird that passes by the recording location, similar to a typical woodcock roding survey conducted by a human observer. Roding events were not created with the extended 3 seconds clips described above, but with the original bounding box selections drawn in Raven Pro. <h3>Audio files</h3> <ul> <li>selections.zip: each wav-file contains a single selections. Filenames correspond to the column selec in the table selections.csv</li> <li>events.zip: each wav-file contains a single roding event, typically consisting of multiple call elements (croaks and/or whistles). In the case of faint signals of distant birds, roding events may consist of a single call element only. Filenames correspond to the column event.id in the table events.csv.</li> </ul> <h2>Data collection</h2> All wav-files in this dataset originate from audio files that were recorded with autonomous recording units of the type AudioMoth. ARUs were housed in  custom made waterproof casings (See details and files for 3D-printing: https://www.thingiverse.com/thing:6428228). ARUs were programmed to record continuously for 2 hours during dusk and were placed at edges of forest clearings. The devices were mounted to tree trunks at a height of approximately 1.5m above ground.  <h2>Metadata files</h2> <table> <tbody> <tr> <td>filename</td> <td>content</td> </tr> <tr> <td>sites.csv</td> <td> contains locations of the recording sites. Since exact recording locations can not be made public, only recording sites (= cells of the 1km² UTM-grid) are provided. CRS: EPSG - 25832, ETRS89 / UTM 32N  Data source of the underlying ETRS89 UTM 32N grid: https://gdz.bkg.bund.de/index.php/default/digitale-geodaten/nicht-administrative-gebietseinheiten/geographische-gitter-fur-deutschland-in-utm-projektion-geogitter-national.html columns site.id = unique id of recording sites, cellcode = official cellcode of the 1km²-UTM-grid elevation = mean elevation a.s.l. x.centroid = x-coordinate of centroid (EPSG: 25832) y.centroid = y-coordinate of centroid (EPSG: 25832) wkt.geometry = polygon geometry of the grid cell </td> </tr> <tr> <td>arus.csv</td> <td> metadata of the recording hardware   columns aru.id = unique id of recording device type = recorder type manufacturer = manufacturer of recording hardware hardware.version = hardware version of the recording device acquisition.date = date the device was purchased (for reasons of microphone degradation) </td> </tr> <tr> <td>deploys.csv</td> <td> information on recorder deployment, includes aru settings, location, recording times    columns deploy.id = unique id of recorder deployment aru.id = unique id of deployed aru start.date = date the aru was deployed in the field (YYYY-MM-DD) end.date = date the aru was collected (YYYY-MM-DD) firmware = firmware version used in this deployment rec.periods = number of daily recording periods (corresponds to start.rec1, start.rec2 ...) sample.rate = sample rate in kHz gain = gain setting sleep.duration = duration off stand-by phases in seconds, when set on a sleep/record-cycle rec.duration = duration of each recording in seconds, when set on a sleep/record-cycle start.rec1 = start of first recording period (UTC, hh:mm:ss) end.rec1 = end of first recording period (UTC, hh:mm:ss) start.rec2 = start of secondrecording period (UTC, hh:mm:ss) end.rec2 = end of second recording period (UTC, hh:mm:ss) site.id = unique id of recording site </td> </tr> <tr> <td>recordings.csv</td> <td> metadata of the audio files from which the roding events originate    columns recording.id = unique id of the recording deploy.id = unique id of aru deployment, during which the recording was made date = date on which the recording was made (YYYY-MM-DD) time = time of day at which the recording started (UTC, hh:mm:ss) duration = duration in seconds sampler.rate = sample rate in kHz channels = number of channels bits = bit depth samples = number of audio samples gain = gain setting of the aru voltage = battery voltage of the aru during recording temperature = ambient temperature during recording  reviewer = anonymous id of staff who reviewed the file and annotated calls   </td> </tr> <tr> <td>selections.csv</td> <td> manually labelled woodcock call elements (i.e. croaks, whistles, chases). Short selections were extended to 3 seconds by symmetrically adding time before and after the original selection. In the format of raven pro selection tables.    columns selec = unique id of the selection. Corresponds to the filename of the wav-files in the archive selections.zip deploy.id = unique id of the aru deployment during which the roding event was recorded channel = audio channel start = start of the event in seconds from the start of the recording end = end of the event in seconds from the start of the recording bottom.freq = bottom frequency of the annotation bounding box top.frequency = top frequency of the annotation bounding box species.code = species code as used by BirdNET common.name = English common name as used by BirdNET annotation = contains annotations of call elements that are pooled in the roding event. Thus typcally equal to the number of annotated call element  recording.id = id of the recording this roding eventoriginates from </td> </tr> <tr> <td>events.csv</td> <td> aggregated roding events consisting of contiuous sequences of manually labelled call elements. In the format of raven pro selection tables    columns event.id = unique id of roding event. Corresponds to the filename of the wav-files in the archive events.zip  channel = audio channel start = start of the event in seconds from the start of the recording end = end of the event in seconds from the start of the recording bottom.freq = bottom frequency of the annotation bounding box top.frequency = top frequency of the annotation bounding box species.code = species code as used by BirdNET common.name = English common name as used by BirdNET annotation = contains annotations of call elements that are pooled in the roding event. Thus typcally equal to the number of annotated call element  recording.id = id of the recording this roding eventoriginates from deploy.id = unique id of the aru deployment during which the roding event was recorded </td> </tr> <tr> <td>removed_audio_files.txt</td> <td>selection ids and event ids of audio files that were deleted because they included voices. Their metadata is still included in the files described above</td> </tr> </tbody> </table>   <h2>Data privacy</h2> Selections and roding events were checked for human voices and audio information was removed, in case it contained any. Audio segments that did not contain woodcock calls were not completely checked for human voices  and can thus not be made available.

opencc-by-4.0May 2019View details →

SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"

This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309).  A Github repository containing code for using the SQLite database also accompanies this paper at: <a href="https://github.com/cbalantic/false-positive-mitigation">http://github.com/cbalantic/false-positive-mitigation</a>

edi52/100

Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler

The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics

openCC (other)Mar 2023View details →

opencc-by-4.0Jan 2022View details →

DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring

This dataset aims to support the work presented in <blockquote> Bouffaut, L., Taweesintananon, K., Kriesell, H. J., Rørstadbotnen, R. A., Potter, J. R., Landrø, M., Johansen, S. E., Brenne, J. K., Haukanes, A., Schjelderup, O., & Storvik, F. (2022). Eavesdropping at the Speed of Light: Distributed Acoustic Sensing of Baleen Whales in the Arctic. Frontiers in Marine Science, 9, 901348. <a href="https://doi.org/10.3389/fmars.2022.901348">https://doi.org/10.3389/fmars.2022.901348</a>. </blockquote> It contains recordings from a dark fiber optic (FO) cable converted into a distributed acoustic sensing (DAS) array of 120km long spreading from Longyearbyen, Svalbard, Norway, out to the open ocean, through Isfjorden. <a href="https://www.frontiersin.org/files/Articles/901348/fmars-09-901348-HTML/image_m/fmars-09-901348-g002.jpg">This DAS array</a>, measuring nano strain, was spatially sampled every ~4m and had a sampling frequency of 645.16 Hz, generating data stored into spatio-temporal matrices.  The exact position of the FO cable is proprietary information belonging to Uninett. The space component is therefore given as a vector in “channel number” (sensing node number along the FO cable) and distance from the shore station (m). The data necessary to produce each manuscript example is saved into multiple files corresponding to subsequent groups of channels along the FO cable, to facilitate storage and sharing. The file naming system satisfies the following: Date in the format YYYYMMDD, UTC time at the beginning of the file, channels, whale_raw, duration of the file Lxxs, all separated by underscores “_”. Data is shared as *.mat file saved in HDF format and readable in different programming languages. For example  <ul> <li>in <a href="https://www.mathworks.com/help/matlab/ref/load.html">Matlab</a>  <pre><code>load('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre>   </li> </ul> <ul> <li>in <a href="http://https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat">Python</a> <pre><code>scipy.io.loadmat('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre>   </li> </ul> Each file contains the following variables <ul> <li>data: The DAS-recorded nano strain data</li> <li>info_GL_m: Used gauge length (m)</li> <li>info_nsamples: Number of temporal samples in the file</li> <li>info_ntraces: Number of spatial samples (channels) in the file</li> <li>info_sample_interval_s: Sampling period (s)</li> <li>info_sampling_frequency_Hz: Sampling frequency (Hz)</li> <li>info_SSI_m: Spatial sampling interval (m)</li> <li>info_timestamp: Date and time (UTC) of the first sample</li> <li>info_units: Global unit information</li> <li>x1_absolute_channel: Vector containing the absolute channel number</li> <li>x1_distance_from_shore_m: Vector containing the distance along the FO cable from shore (m)</li> <li>x1_position_m: Vector containing the distance along the FO cable from the interrogator (m)</li> <li>x1_recwdepthz_m: Vector containing the water column depth used as a proxy for the fiber optic cable depth at each sensing location (m)</li> <li>x1_relative_channel: Vector containing the channel number</li> <li>x2_time_s: Time vector (s)</li> </ul>   List of the files and related manuscript examples Example of at least 3 vocalizing baleen whales recorded simultaneously at three different locations along the Svalbard fiber optic DAS array  - Figure 4 in Bouffaut et al. (2022) - between 35-95 km and on 2020-06-26 between 052440-052720 UTC <ul> <li>20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat          </li> <li>20200627_052441_ch10001_to_ch15000_whale_raw_L160s.mat          </li> <li>20200627_052441_ch15001_to_ch20000_whale_raw_L160s.mat           </li> <li>20200627_052441_ch20001_to_ch25000_whale_raw_L160s.mat  </li> </ul> Example of series of blue whale calls recorded with a move out on the Svalbard DAS array -  Figure 5 & &B in Bouffaut et al. (2022) - between 85-90 km and on 2020-07-16 between 154300-155500 UTC <ul> <li>20200716_154302_ch20001_to_ch21000_whale_raw_L720s.mat                                                      </li> <li>20200716_154302_ch21001_to_ch22000_whale_raw_L720s.mat  </li> <li>20200716_154302_ch22001_to_ch23000_whale_raw_L720s.mat</li> <li>20200716_154302_ch23001_to_ch24000_whale_raw_L720s.mat</li> <li>20200716_154302_ch24001_to_ch25000_whale_raw_L720s.mat</li> </ul> Example of a blue whale non-stereotyped call recorded inside Isfjorden and further used to provide correlated seismic profiles - Figure 6A n Bouffaut et al. (2022) - between 23-28 km on 2020-06-27 between 192255-192805 UTC <ul> <li>20200627_192255_ch05001_to_ch07000_whale_raw_L310s.mat        </li> <li>20200627_192255_ch07001_to_ch08500_whale_raw_L310s.mat </li> </ul> -------------- Analysis tools  To reproduce the paper's result, we suggest using the following Python package available on <a href="https://github.com/leabouffaut/DAS4Whales">GitHub</a>: <blockquote> Léa Bouffaut (2023). DAS4Whales: A Python package to analyze Distributed Acoustic Sensing (DAS) data for marine bioacoustics (v0.1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/10.5281/zenodo.7760187</a> </blockquote> Here is an example of the use of the DAS4Whales package with this dataset's data format: <a href="https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa">https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa</a> -------------- Please cite as  <blockquote> Léa Bouffaut and Kittinat Taweesintananon, “DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring”. Zenodo, Jan. 10, 2022. doi: <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.5823343">10.5281/zenodo.5823343</a>. </blockquote> -------------- Contact <a href="mailto:lb736@cornell.edu">Contact</a> | <a href="https://www.birds.cornell.edu/ccb/lea-bouffaut/">Webpage</a> | <a href="https://twitter.com/LeaBouffaut">Twitter</a>

opencc-by-4.0Jun 2024View details →

Dataset accompanying the publication: Acoustic cues of keyboard mechanics enable auditory localization of upright piano tones

Dataset accompanying the publication: Acoustic cues of keyboard mechanics enable auditory localization of upright piano tones (in J. Acoust. Soc. Am., 2024)

opencc-by-4.0Jun 2019View details →

An acoustically isolated European starling song library

A dataset of song collected from 14 European starlings individually recorded in acoustically isolated chambers. Each folder contains vocalizations for one bird. These data were used for the publication, "Parallels in the sequential organization of birdsong and human speech". Nature Communications (2019). If you use this dataset, please cite this publication and this repository. Work supported by NSF Graduate Research Fellowship 2017216247 to TS and an NIH R56DC016408 to TQG.

openbsd-3-clauseSep 2024View details →

An Acoustic and Optical Dataset for the Perception of Underwater Unexploded Ordnance (UXO)

We present a dataset for acoustic and optical sensing of unexploded ordnance (UXO) underwater. UXO in the sea pose an environmental problem and a challenge for the growing offshore economy. It is best practice to perform the recovery of ammunition without explosions to protect anthropogenic structures and marine mammals. During explosive ordnance disposal (EOD), experts often rely on optical images. However, visibility underwater may be limited in harbor areas, after storm events or in waters with very mobile sediments. Thus, visual inspection is not always possible. EOD experts therefore use high-frequency sonars with large vertical apertures like the ARIS Explorer 3000 for acoustic imaging. While efforts have been made to use the available information for 3D reconstruction, existing solutions can be limited to predefined motion patterns.  The topic is inherently sensitive, and most of the data is acquired by and for private companies and not made available to the public, which impedes research in this area. Additionally, in-situ data often lacks sufficient pose information. To facilitate further research, we created a validation dataset that was recorded in a controlled experimental environment. It has the following properties: <ul> <li>Close to 100 recordings of 3 different UXO.</li> <li>More than 74000 matched and annotated imaging sonar and camera frames.</li> <li>UXO ground truths in the form of photogrammetric 3D models.</li> <li>Precise position and attitude sensor data with respect to the targets.</li> <li>Realistic motion trajectories achievable in non-experimental environments.</li> </ul> This dataset allows quantitative analysis with different algorithms. 3D models and trajectories can be compared against each other to evaluate different solutions.   The accompanying paper is: <blockquote> @INPROCEEDINGS{dahn2024uxo,   author={Dahn, Nikolas and Firvida, Miguel Bande and Sharma, Proneet and Christensen, Leif and Geisle, Oliver and Mohrmann, Jochen and Frey, Torsten and Kumar Sanghamreddy, Prithvi and Kirchner, Frank},   booktitle={OCEANS 2024 - Halifax},    title={An Acoustic and Optical Dataset for the Perception of Underwater Unexploded Ordnance (UXO)},    year={2024},   doi={10.1109/OCEANS55160.2024.10754316}} </blockquote> The paper is available on <a href="https://www.researchgate.net/publication/386124306_An_Acoustic_and_Optical_Dataset_for_the_Perception_of_Underwater_Unexploded_Ordnance_UXO">researchgate</a>.   Notes: <ul> <li>Labels have been (unfortunately) generated for the SD camera frames. To get the correct coordinates on the included FHD images, multiply all coordinates by 3.</li> </ul>   Files: <ul> <li>data_export_recordings.7z: main dataset</li> <li>data_export_polar.7z: contains only the polar-transformed sonar frames</li> <li>data_export_3dmodels.7z: 3d models of the UXO</li> <li>data_processed.7z: extracted and cut unmatched raw data</li> </ul>

opencc-by-4.0Jan 2023View details →

Data and code related to the paper: "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation"

This archive contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper: Kyriacos Yiannacou, Vipul Sharma and Veikko Sariola, "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation", Langmuir 2022, 38, 38, 11557–11564. <a href="https://doi.org/10.1021/acs.langmuir.2c01061">Link to the paper</a>. The scripts were tested on Matlab R2021a on Windows. The acoustofluidic controller software is the same as in our previous paper and is archived <a href="https://doi.org/10.5281/zenodo.4593021">here</a>. Generally speaking, there is a folder containing the plotting scripts for each figure(s) and/or movie(s). Within each folder, the raw data files are under the folder `data/`. Once ran, the scripts produce another folder called `output/`, to which they place the created plots and movies. Most folder contain a script name `plot_*.m` that makes the figure(s) and `video_*.m` that generates the video(s). You will need `ffmpeg` installed to convert the serial images into a video.

opencc-by-4.0Jan 2023View details →

Rainfall data monitored by acoustic sensors in Zurich and Milan during spring and summer 2022

The database contains rainfall information obtained from acoustic sensors and rain gauges (meteoblue AG) in the cities of Zurich (Switzerland) and Milan (Italy) during field work conducted in spring and summer 2022. Zurich: Continuous rainfall data is provided at 15 min intervals for April 2022; data_acoustic_Zurich.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm). Milan: Data is provided for 5 rain events in June 2022 at 1 min intervals; data_acoustic_Milan.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm). The locations of the acoustic sensors and rain gauges can be find in the metadata files: Metadata_acoustic.xlsx and Metadata_meteoblue.xlsx The presented-data passed only a primilinary quality control. Further infromation about the senor networks in Milan and Zurich can be found here: https://doi.org/10.5194/nhess-2022-257

opencc-by-4.0Jun 2023View details →

Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sounds.

Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior.

opencc-by-4.0Jul 2023View details →

Paired field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size compiled from various estuaries in the United States and Australia

Field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size are compiled from various estuaries in the United States and Australia to investigate the utility of combining optical and acoustic backscatter measurements for the estimation of suspended-sediment concentration under changes in floc particle size and density.  Theory, analysis, and interpretation of the data is available in Livsey et al (2023). Data collected from the Chesapeake Bay, US were compiled from Fall et al (2022). Data collected on the Brisbane River were collected by Livsey et al (2022).  Data collected for all other locations were compiled from Livsey et al (2022).   Data collected by Fall et al (2022) utilized a LISST 100x. Data collected by Livsey et al (2022, 2023) utilized a LISST 200x. Data files for each instrument are provided.  Funding for this research was provided by an Advance Queensland Industry Research Fellowship, Queensland University of Technology, and Queensland Department of Environment and Science. References Fall, Kelsey A., Massey, Grace M., and Friedrichs, Carl T., (2020). The importance of organic content to fractal floc properties in estuarine surface waters, insights from video, LISST, and pump sampling: Supporting data. Data. William & Mary. https://doi.org/10.25773/7gbc-794 6739 Livsey, D., Turner, R., Grace, P., and Crosswell, & Andy Steven. (2022). Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef (1.0). Data. Zenodo. https://doi.org/10.5281/zenodo.6788303 Livsey, D., Turner, R., and Grace, P. (2023). Combining optical and acoustic backscatter measurements for monitoring of fine suspended-sediment concentration under changes in particle size and density. Water Resources Research. <a href="https://doi.org/10.1029/2022WR033982">https://doi.org/10.1029/2022WR033982</a>

edi48/100

Flume Erosion Testing of Unamended and Organic Matter Amended Soil Samples Using an Acoustic Doppler Profiler, 2021

This data accompanies a publication titled "Soil Amended with Organic Matter Increases Fluvial Erosion Resistance of Cohesive Streambank Soil". Briefly, fluvial erosion testing was conducted on soil samples using an indoor flume channel. Soil samples were previously collected from the riparian zone of a river near Virginia Tech's campus in Blacksburg, VA, USA. The soil was subsequently air-dried and stored until use. Prior to erosion testing, soil samples were amended with varying amounts of organic matter (0%, 1%, and 4% OM by mass), compacted to a bulk density of 0.95 KilogramsPerCubicCentiMeters in growth containers, and allowed to mature in a greenhouse setting for 50 days prior to flume erosion testing. An Acoustic Doppler Profiler (ADP) was used to measure soil erosion and collect three-dimensional velocity data during erosion tests; raw velocity and soil depth data for each sample tested were stored in MATLAB files. Follow testing, the soil remaining from each sample was collected, stored, and analyzed for aggregate stability, soil organic matter (SOM), and extracellular polymeric substances (EPS). Additionally, soil temperature, water temperature, and volumetric water content were also measured prior to or during erosion testing. Data collected from this study, and the accompanying ADP MATLAB files, are presented here.

openCC0Feb 2022View details →

edi48/100

Palmer Deep Underwater Acoustic Mooring Deployments 2021-2024

Marine mammals play a crucial role in the ocean ecosystem, yet monitoring their occurrence, distribution, and abundance poses a challenge in remote areas, such as the Antarctic. Traditionally, human observers have conducted visual surveys to detect marine mammals, relying on the animals' need to surface periodically for air. This method is often costly, requiring a large team of observers and the use of ships or aircraft. Additionally, visual surveys are limited by weather and sighting conditions, such as fog, rain, heavy seas, and darkness. Despite their expense, visual surveys are often inefficient for continuous real-time monitoring of marine mammal presence. However, they remain essential for tasks like photo identification, health assessment, and abundance estimation for many species. In recent decades, passive acoustic recorders have become extremely popular for detecting vocally active marine mammals, as they can operate continuously for periods of months to years. This enables a passive method of observation of marine mammals during periods where visual surveys are untenable. We developed a long term mooring system equipped with an underwater acoustic recorder that is capable of recording at 60% for up to 200 days at a time. Since 2022, we have been maintaining these moorings to provide for annual acoustic coverage, and will continue to under the Palmer LTER. This data can be used to compose the underwater soundscape of the region, and a general assessment of year-round acoustic presence of sound producing animals. Further analysis could examine the impacts of inter- and intra- seasonal environmental conditions (e.g., sea ice advance and retreat, sea ice extent, storminess) on the phenology of acoustic presence in the region.

openCC (other)Feb 2025View details →

opencc-by-4.0Jan 2020View details →

Free-field sensitivity of four electro-acoustic measuring chains at 0° incidence angle in the frequency range 0.25 kHz to 100 kHz

This dataset contains calibration data of the free-field sensitivity of four electro-acoustic measuring chains at 0° incidence angle in the frequency range 0.25 kHz to 100 kHz. Each of the four channels consisted of a ¼'' externally polarized free-field measurement microphone of the condenser type GRAS 40 BF, a ¼'' preamplifier GRAS 26AC, a power module GRAS 12AQ and an FFT analyzer Ono Sokki CF-9400. The calibration data was acquired in the laboratory of the Physikalisch-Technische Bundesanstalt (PTB).

opencc-by-4.0Sep 2024View details →

Audio clips of Orca (Orcinus orca) and non-orca sounds for the exploration of multiple acoustic representations

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." 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).  The code for each of the representations used in this study is also included.

opencc-by-4.0Nov 2023View details →

Acoustical measurements of a rabab reconstructed after a pictorial source from the 13th century (Cantigas de Santa María)

Instrument: rabab  Pictorial source: Cantigas de Santa Maria, E-Codex (Códice de los músicos), ca. 1284, fol. 118r, Cantiga 110, Madrid, San Lorenzo de El Escorial, Real Biblioteca del Monasterio del Escorial, Ms. b-I-2  Instrument maker: Thilo Hirsch  Year of manufacture: 2021  Location: Basel, property of the ensemble arcimboldoDimensions:  Total length: 468 mm  Max. Body width: 104 mm  Body depth: approx. 80 mmVibrating string lengths:  a-string: 403 mm  d-string: 401 mmMaterials:  Body: cherry  Pegbox: cherry  Fingerboard: maple  Bars: spruce  Nut/String attachment button: bone  Bridge: maple  Top: goatskinThe main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the two open rosettes on the fingerboard and then the upper one was sealed with a piece of wood (see photos of the setup).Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023Force: Impact hammer exciting at the bass side of the bridge  ACC: Acceleration measured on the same side, close to the impact point.  Average is the average of all measurements (to be used in the analysis).Photos of the setup: Thilo HirschFolder cantigas_rosette_o_offen:  Files: cantigas_roo_1 to 6  (Description: upper rosette open)  File: cantigas_roo_do (Description: upper rosette open / damper moved between 2 rosettes)    Folder cantigas_rosette_o_zu:  Files: cantigas_roz_1 to 6 (Description: upper rosette closed with wooden sheet)________________________How to read VIA-Files:  Line 1 to 9: Header, Line 8 holds the number of valuesData is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad]    4th col:  Real part [as Factor not dB!] 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are neededTo get dB Value: Amplitude[dB] = 20 log [Magnitude]Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075

opencc-by-4.0Nov 2023View details →

Acoustical measurements of a rabab reconstructed after a pictorial source from the 14th century

Instrument: rabab  Pictorial source: Francesc Comes, Madonna and Child with angel musicians, around 1394, Gold and tempera on wood, Pollença (Mallorca), Museu de Pollença  Instrument maker: Thilo Hirsch  Year of manufacture: 2022  Location: Basel, property of the ensemble arcimboldoDimensions:  Total length: 579 mm  Max. Body width: 112 mm  Body depth: approx. 95 mm Vibrating string lengths:  d-string: 500 mm  G-string: 497 mmMaterials:  Body: cherry  Pegbox: cherry  Fingerboard: serviceberry  Bars: spruce  Nut/String attachment button: bone  Bridge: boxwood  Top: goatskinThe main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the upper rosette and the two holes in the body closed, then with both rosettes and the body holes open, and finally only with the body holes closed.Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023Force: Impact hammer exciting at the bass side of the bridge  ACC: Acceleration measured on the same side, close to the impact point.  Average is the average of all measurements (to be used in the analysis).Photos of the setup: Thilo HirschFolder comes_rosette_o_loecher_zu  Files: comes_rolz_1 to 6 (Description: upper rosette closed with wooden sheet, body-holes closed)Folder comes_rosette_oO_loecher_offen  Files: comes_roo_lo_1 to 6 (Description: rosette open , body-holes open)Folder comes_rosette_oO_loecher_zu  Files: comes_roo_lz_1 to 6 (Description: rosette open , body-holes closed)          ________________________How to read VIA-Files:Line 1 to 9: Header, Line 8 holds the number of valuesData is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad], 4th col:  Real part [as Factor not dB!], 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed!To get dB Value: Amplitude[dB] = 20 log [Magnitude]Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075