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15 results for “Acoustic survey”
The International Soundscape Database: An integrated multimedia database of urban soundscape surveys -- questionnaires with acoustical and contextual information
<h1>Introduction</h1> <p>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.</p> <p>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].</p> <p>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. </p> <h2>Analysis</h2> <p>Code for exploring and analysing this dataset is included as part of the <a href="https://soundscapy.readthedocs.io/en/latest/">Soundscapy package</a>.</p> <h2>Included Files</h2> <p>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.</p> <p>The basic directory structure and contents can be found below. </p> <h3>Survey Data (.csv)</h3> <p>'ISD v1.0 Data.csv' organises the data according to the labels given above.</p> <h3>Survey Metadata (.xlsx)</h3> <p>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.</p> <h3>Psychoacoustic Analysis (.csv)</h3> <p>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.</p> <p>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/'.</p> <h3>Sound Level Meter logs (.xlsx)</h3> <p>'SLM_<city>/' folders include session-long (i.e. ~3hrs) sound level meter log data in.xlsx files for each SessionID.</p> <h3>Binaural Recordings (32-bit floating point .wav)</h3> <p>'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. </p> <h3>Cleaning and Compilation Scripts (.py)</h3> <p>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.</p> <pre><code><br>├── 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> <p><strong>Citation</strong>: If you use the ISD or part of it, please cite our paper describing the data collection protocol [1] and this dataset itself.</p> <p><strong>License and reuse</strong>: 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.</p> <p> </p> <p>[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. <em>Applied Sciences</em>. 2020; 10(7):2397. <a href="https://www.mdpi.com/2076-3417/10/7/2397">https://doi.org/10.3390/app10072397 </a></p> <p>[2] ISO/TS 12913-2:2018 (2018). “Acoustics – Soundscape – Part 2: Data collection and reporting requirements” International Organization for Standardization, Geneva, Switzerland, 2018</p> <p>[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.<em> Journal of the Acoustical Society of America</em>. 2021.</p>
WiggleZ Dark Energy Survey Baryon Acoustic Oscillation Random Catalogues
<p>Data products associated with the WiggleZ Dark Energy survey measurement of the Baryon Acoustic Oscillations (BAO), as described in Blake et al. arXiv:1108.2635 (2011, MNRAS, 418, 1707)</p> <p>Data is given for 6 WiggleZ regions (01, 03, 09, 11, 15, 22 hrs) in 3 different overlapping redshift slices (z=0.2-0.6, 0.4-0.8, 0.6-1.0), matching the datasets analyzed in the final WiggleZ baryon acoustic peak paper Blake et al. (2011, MNRAS, 418, 1707). For each sub-region the following files are given:</p> <p><strong>Correlation function:</strong> xi_**hr*.dat - correlation function measurement and covariance matrix for each sub-region. Format of the file:</p> <ul> <li>1st line: nbin, ngalaxy</li> <li>nbin lines: mean separation [Mpc/h], xi(s), sqrt[Cov(i,i)]</li> <li>nbin x nbin lines: i, j, Cov(i,j)</li> </ul> <p>The covariance matrix is from lognormal realizations, not jack-knife regions.</p> <p><strong>Combined correlation function:</strong> xi_combined*.dat - correlation function measurement for each redshift range combining the measurements in each sub-region, in the same format as above.</p> <p><strong>Integral constant:</strong> wigglez_ic.dat - integral constraint correction which should be added to the measured correlation function, format of the file is:</p> <ul> <li>Region [hr],</li> <li>zmin,</li> <li>zmax,</li> <li>integral constraint delta-xi</li> </ul> <p>We are considering adding another 90 random catalogues in the near future. For now the random catalogues used in the previous analysis are available below.</p>
Figure 2 in The Distribution Of The Northern Bat Eptesicus Nilssonii (Keyserling & Blasius, 1839) In Latvia Assessed By Passive Acoustic Survey
Figure 2. Dot plot showing the activity of E. nilssonii in each observation site. The outer dot in each region represents the mean and the whiskers the standard error of the mean. Different letters indicate statistically significant differences (p<0.05) (A). Data visualization depicting the gradient of the activity of E. nilssonii in four parts of Latvia. The darker color represents the higher activity (B).
Figure 1 in The Distribution Of The Northern Bat Eptesicus Nilssonii (Keyserling & Blasius, 1839) In Latvia Assessed By Passive Acoustic Survey
Figure 1. The map of Latvia divided in four regions under LKS92 25x25 km square network. Bat activity was studied in randomly selected squares (visited squares marked grey). In total, 60 squares were surveyed with six survey sites chosen in each square (n=360).
Fig. 2 in An acoustic trap to survey and capture two Neoscapteriscus species
Fig. 2. (A) The Borelli Vicinus Acoustic Pool trap used to attract and capture N. borellii and N. vicinus mole crickets throughout the study. (B) Total Neoscapteriscus mole crickets captured in Borelli Vicinus Acoustic Pool traps from late spring (23 Apr to 6 Jul) 2017 and early spring (7 Feb to 5 Apr) 2018 in northern Florida, USA. Dark circles represent N. borellii and light circles represent N. vicinus. The size of each circle indicates the total individuals captured per site from 0 to 250, as illustrated in the figure legend. The same traps with the exact same settings were used in both yr.
Fig. 1 in An acoustic trap to survey and capture two Neoscapteriscus species
Fig. 1. (A) Oscillogram and (B) spectrogram of Neoscapteriscus borellii and N. vicinus species-specific mating calls broadcast from speakers in a continuous loop. Darker shading in spectrogram indicates greater energy at specified frequency and time.
Acoustic optical survey data for snapper survey in shark bay July 2020
<p>Dataset to accompany Scoulding et al. 2023. Estimating abundance of fish associated with structured habitats by combining<strong> </strong>acoustics and optics. Journal of Applied Ecology.</p> <p>The dataset includes:</p> <p>1. Acoustic integration outputs from Echoview</p> <p>2. Snapper lengths from RUV deployments</p> <p>3. Snapper lengths from commercial catch</p> <p>4. Fish species length-weight relationships</p> <p>5. Habitat validation determined from camera deployments</p> <p>6. Proportions of fish species determined per RUV deployment</p> <p>Raw data files (acoustic and optics) are too large for inclusion in this repository but can be provided on request. The Python code used to analysis the data is being packaged and will be added to the repository once complete.</p>
Acoustic Current Profiler data from Multi-Year (2020-2022) Autonomous Underwater Glider Surveys in the Anegada Passage
<p>This dataset contains glider acoustic doppler profiler observations from four glider deployments in the Anegada Passage region from 2020-2022. The 2020-2021 data are from a Nortek AD2CP and the 2022 data are from a Teledyne RDI Pathfinder. The RDI Pathfinder .PD0 files can be read directly in the code that is used for this analysis. The Nortek AD2CP .ad2cp files are processed using Nortek's MIDAS software to generate NetCDFs. <br> </p>
Data and code from: Addressing widespread detection heterogeneity in avian occupancy modeling using passive acoustic surveys
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Figure 1 in Eumops perotis (Schinz, 1821) (Chiroptera, Molossidae): a new genus and species for Chile revealed by acoustic surveys
Figure 1: Distribution map of Eumops perotis: (A) geographic range according to Velikov (2019), (B) southernmost record known in the Pacific coast of South America (black star) until the present study, and new records in the Arica and Parinacota region, Chile (red stars).
Figure 3 in Eumops perotis (Schinz, 1821) (Chiroptera, Molossidae): a new genus and species for Chile revealed by acoustic surveys
Figure 3: Discriminant function analysis (DFA) used in the classification of the echolocation calls of Eumops perotis.
Figure 2 in Eumops perotis (Schinz, 1821) (Chiroptera, Molossidae): a new genus and species for Chile revealed by acoustic surveys
Figure 2: Sonogram of echolocation call of Eumops perotis recorded in the Arica and Parinacota region, Chile.
Data from: Acoustic and camera surveys inform models of current and future vertebrate distributions in a changing desert ecosystem
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Delta-X: Acoustic Doppler Current Profiler Channel Surveys, MRD, Louisiana, 2021, V2
This dataset provides river discharge measurements collected at selected locations in the Atchafalaya and Terrebonne Basins within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Delta-X 2021 field efforts from 2021-03-25 to 2021-04-11 (spring) and 2021-08-16 to 2021-09-25 (fall). Channel surveys were conducted with a Teledyne RiverPro acoustic doppler current profiler (ADCP) or a Sontek M9 RiverSurveyor ADCP on selected wide channels (>100 m wide) and a few selected narrow channels (approximately 10 m wide) near the Delta-X intensive study sites. River discharge was measured on cross-channel transects. Reported data include bathymetry, discharge (m3 s-1), and flow velocity.
Field Data: Acoustic survey design for species richness
<p>BirdNET results when applied to acoustic survey data collected with Swift recorders in Central New York, USA and the northern Sierra Nevada, USA. Detections with a confidence score <0.5 have been excluded.</p>
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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.