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opencc-by-4.0Oct 2024View details →

The language of sound search: Examining User Queries in Audio Search Engines (supplementary materials)

<h2>Overview</h2> This dataset accompanies the <a href="https://dcase.community/documents/workshop2024/proceedings/DCASE2024Workshop_Weck_54.pdf" target="_blank" rel="noopener">paper</a> titled "The Language of Sound Search: Examining User Queries in Audio Search Engines." The study investigates user-generated textual queries within the context of sound search engines, which are commonly used for applications such as foley, sound effects, and general audio retrieval. The paper addresses the gap in current research regarding the real-world needs and behaviors of users when designing text-based audio retrieval systems. By analyzing search queries collected from two sources — a custom survey and Freesound query logs — the study provides insights into user behavior in sound search contexts. Our findings reveal that users tend to formulate longer and more detailed queries when not constrained by existing systems, and that both survey and <a href="https://freesound.org/">Freesound</a> queries are predominantly keyword-based. This dataset contains the raw data collected from the survey and annotations of Freesound query logs. <h2>Files in This Dataset</h2> The dataset includes the following files: <ol> <li> <code>participants.csv</code> Contains data from the survey participants. Columns: <ul> <li><code>id</code>: A unique identifier for each participant.</li> <li><code>fluency</code>: Self-reported English language proficiency.</li> <li><code>experience</code>: Whether the participant has used online sound libraries before.</li> <li><code>passed_instructions</code>: Boolean value indicating whether the participant advanced past the instructions page in the survey.</li> </ul> </li> <li> <code>annotations.csv</code> Contains annotations of the survey responses, detailing the participants' interaction with the sound search tasks. Columns: <ul> <li><code>id</code>: A unique identifier for each annotation.</li> <li><code>participant_id</code>: Links to the participant’s ID in <code>participants.csv</code>.</li> <li><code>stimulus_id</code>: Identifier for the stimulus presented to the participant (audio, image, or text description).</li> <li><code>stimulus_type</code>: The type of stimulus (audio, image, text).</li> <li><code>audio_result_id</code>: Identifier for the hypothetical audio result presented during the search task.</li> <li><code>query1</code>: Initial search query submitted based on the stimulus.</li> <li><code>query2</code>: Refined search query after seeing the hypothetical search result.</li> <li><code>aspects1</code>: Aspects considered important when formulating the initial query.</li> <li><code>aspects2</code>: Aspects considered important when refining the query.</li> <li><code>result_relevance</code>: Participant's rating of the hypothetical search result's relevance.</li> <li><code>time</code>: Time taken to complete the search task.</li> </ul> </li> <li> <code>freesound_queries_annotated.csv</code> Contains annotated Freesound search queries. Columns: <ul> <li><code>query</code>: Text of the search query submitted to Freesound.</li> <li><code>count</code>: The number of times the specific query was submitted.</li> <li><code>topic</code>: Annotated topic of the query, based on an ontology derived from AudioSet, with an additional category, <code>Other</code>, which includes non-English queries and NSFW-related content.</li> </ul> </li> <li> <code>survey_stimuli_data.zip</code> This ZIP file contains three CSV files corresponding to the three stimulus types used in the survey: <ul> <li>Audio stimuli: Categorized sound recordings presented to participants.</li> <li>Image stimuli: Annotated images that prompted sound-related queries.</li> <li>Text stimuli: Summarized descriptions of sounds provided to participants.</li> </ul> </li> </ol> More details on the stimuli and the survey methodology can be found in the accompanying paper. <h2>Citation</h2> If you use this dataset in your research, please cite the corresponding paper: <div> <pre>B. Weck and F. Font, ‘The Language of Sound Search: Examining User Queries in Audio Search Engines’, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024), Tokyo, Japan, Oct. 2024, pp. 181–185.</pre> <pre><code>@inproceedings{Weck2024, author = "Weck, Benno and Font, Frederic", title = "The Language of Sound Search: Examining User Queries in Audio Search Engines", booktitle = "Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024)", address = "Tokyo, Japan", month = "October", year = "2024", pages = "181--185" }</code></pre> </div>

opencc-by-4.0Jul 2021View details →

CLDF dataset derived from the Johansson et al.'s "The typology of sound symbolism" from 2020

Cite the source of the dataset as: <blockquote> Erben Johansson, N., Anikin, A., Carling, G., & Holmer, A. (2020). The typology of sound symbolism: Defining macro-concepts via their semantic and phonetic features, Linguistic Typology , 24(2), 253-310. doi: https://doi.org/10.1515/lingty-2020-2034 </blockquote>

opencc-by-nc-4.0Feb 2021View details →

TAU-NIGENS Spatial Sound Events 2021

DESCRIPTION: The TAU-NIGENS Spatial Sound Events 2021 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 (MIC), and first-order Ambisonics one (FOA). 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 single direction-of-arrival (DoA) if static, a trajectory DoAs if moving, 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/challenge2021/task-sound-event-localization-and-detection">DCASE 2021 Sound Event Localization and Detection Task</a> of the <a href="http://dcase.community/challenge2021/">DCASE 2021 Challenge</a>. This dataset is the third iteration of spatialized sound event datasets based on real room responses and ambient noise from multiple spaces, with each iteration introducing more challenging conditions closer to real-life. Those iterations, including the present one, are: <ul> <li>TAU Spatial Sound Events 2019, <a href="https://doi.org/10.5281/zenodo.2580091">development</a> and <a href="https://doi.org/10.5281/zenodo.3066124">evaluation</a> datasets. 5 rooms, high direct-to-reverberant ratios (DRR), static sources only, minimum DoA separation 10°, discrete grid of DoAs, high SNR for ambient noise, maximum polyphony of 2 simultaneous events</li> <li><a href="https://doi.org/10.5281/zenodo.4064792">TAU-NIGENS Spatial Sound Events 2020</a>, development and evaluations datasets. 13 rooms, low-to-high DRRs, static and moving sources, continuous DoAs, low-to-high SNR for ambient noise, maximum polyphony of 2 simultaneous events</li> <li>TAU-NIGENS Spatial Sound Events 2021. Same as 2020, with the following exceptions: a more natural temporal distribution of sound events, maximum polyphony of 3 target events, inclusion of additional out-of-target-classes directional interference events</li> </ul> The inclusion of directional interferences is the main new challenging property of the new dataset. They are spatialized in the scene in the same way as the target events, and can be either static or moving. The interfering events are sourced from the "engine", "fire", and "general" classes of the NIGENS sound event database. The interferers are considered unknown and no activity or directional labels of them are provided with the training datasets. REPORT & REFERENCE: If you use this dataset please cite the report on its creation, and the corresponding DCASE2020 task setup: Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021). A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection.  In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021), Barcelona, Spain. available <a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Politis_43.pdf">here</a>. AIM: 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. SPECIFICATIONS: <ul> <li>600 one-minute long sound scene recordings with metadata (development dataset).</li> <li>200 one-minute long sound scene recordings without metadata (evaluation dataset).</li> <li>Sampling rate 24kHz.</li> <li>About 500 sound event samples distributed over the 12 target classes (see [here](http://doi.org/10.5281/zenodo.2535878) for more details).</li> <li>About 400 sound event samples used as interference events (see [here](http://doi.org/10.5281/zenodo.2535878) for more details).</li> <li>Two 4-channel 3-dimensional recording formats: first-order Ambisonics (FOA) and tetrahedral microphone array.</li> <li>Realistic spatialization and reverberation through multichannel RIRs collected in 13 different enclosures.</li> <li>From 1184 to 6480 possible RIR positions across the different rooms.</li> <li>Both static reverberant and moving reverberant sound events.</li> <li>Three possible angular speeds for moving sources of approximately 10, 20, or 40deg/sec.</li> <li>Up to three overlapping sound events possible, temporally and spatially.</li> <li>Simultaneous directional interfering sound events with their own temporal activities, static or moving.</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) conditions.</li> </ul> The IRs were collected in Finland by staff of Tampere University between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement <a href="https://cordis.europa.eu/project/id/637422">637422 EVERYSOUND</a>. More detailed information on the dataset can be found in the included README file. EXAMPLE APPLICATION: An implementation of a trainable model of a convolutional recurrent neural network, performing joint SELD, trained and evaluated with this dataset will be provided soon. That implementation will serve as the baseline method in the <a href="http://dcase.community/challenge2021/task-sound-event-localization-and-detection">DCASE 2021 Sound Event Localization and Detection Task</a>. DEVELOPMENT AND EVALUATION: The current and final version (Version 1.2) of the dataset includes the 600 development audio recordings and labels, used by the participants of Task 3 of DCASE2021 Challenge to train and validate their submitted systems, and the 200 evaluation audio recordings including their labels, used in the evaluation phase of DCASE2021. If researchers wish to compare their system against the submissions of DCASE2021 Challenge, they will have directly comparable results if they use the evaluation data as their testing set. DOWNLOAD INSTRUCTIONS: The three files, foa_dev.z01, and foa_dev.zip, correspond to audio data of the FOA recording format. The three files, mic_dev.z01, and mic_dev.zip, correspond to audio data of the MIC recording format. The metadata_dev.zip is the common metadata for both formats. The file, foa_eval.zip, corresponds to audio data of the FOA recording format for the evaluation dataset. The file, mic_eval.zip, corresponds to audio data of the MIC recording format for the evaluation dataset. The metadata_eval.zip is the common metadata for both formats. 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: <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>

opencc-by-4.0Nov 2022View details →

Sounding data of SO284

Radiosonde data from the RV Sonne cruise SO284: Tropical Atlantic Circulation and Climate: Mooring Rescue (June 27, 2021 - August 16, 2021) The dataset includes both raw data as well as post-processed products: <table> <thead> <tr> <th>Processing level</th> <th>Description</th> <th>Usage examples</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>mwx sounding files as delivered by Vaisalas sounding software</td> <td>Checking specific setup of sounding station, Archival of data</td> </tr> <tr> <td>1</td> <td>Level 0 data converted to netCDF4</td> <td>Analysis of single soundings for the most accurate measurements possible</td> </tr> <tr> <td>2</td> <td>Level 1 data interpolated to a vertical grid</td> <td>Analysis of entire campaign or comparison with other observations or simulations</td> </tr> </tbody> </table> A full description of the dataset and the processing scripts involved can be found in the following repository: <a href="https://github.com/observingClouds/soundings_circbrazil">https://github.com/observingClouds/soundings_circbrazil</a> Soundings were conducted by H. Franke, I. Quaglia, K. Stolla and J. Windmiller. Technical support has generously been given on-board by R. Engelmann, J. Lehmke, T. Ruhtz and A. Skupin. Post-processing of the data has been done by H. Schulz. Planning of the cruise and the atmospheric measurement strategy are the work of J. Windmiller. Acknowledgment The crew of R/V Sonne greatly contributed to the success of the cruise. Financial support was provided by the German Science Foundation (DFG), by the EU H2020 under grant agreement 817578 TRIATLAS project and by the German Federal Ministry for Economic Affairs and Energy (BMWi) under grant no. 50EE1721C. We also acknowledge the public support of the conducted basic research through the Max Planck Society.

opencc-by-4.0Mar 2023View details →

Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data

Abstract: The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations. Content: <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a>  <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a>  <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a>  <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a>  <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a>  <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> Publications refering to this dataset: Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes." Funding: This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6.  License: CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a> Version history: Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file. <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>

opencc-by-4.0Dec 2022View details →

SPASS dataset: A synthetic polyphonic dataset with spatiotemporal labels of sound sources

SPASS is a synthetic dataset that consists of 10-seconds audio segments from 5 acoustic scenes: <ul> <li>Park</li> <li>Square</li> <li>Street</li> <li>Waterfront</li> <li>Market</li> </ul> Each acoustic scene has 5,000 audio recordings and its corresponding metadata. The audio recordings were created using a 3D acoustic simulation environment (RAVEN, <a href="https://www.virtualacoustics.org/RAVEN/">https://www.virtualacoustics.org/RAVEN/</a>). SPASS was made as a training dataset for the FuSA system (<a href="https://www.acusticauach.cl/fusa/">https://www.acusticauach.cl/fusa/</a>).  This is a polyphonic dataset for Sound Event Detection (SED) tasks. The metadata files includes the class of each sound event, their onset and offset in time, the position in the space (cartesian) and their final position if the class was moving. This research was funded by ANID FONDEF grant number ID20I10333.

openCC0Jan 2024View details →

Sound Recording and Ethological Data on Vitelline Warblers (S. vitellina) on Little Cayman Island, 2023

The Vitelline Warbler (Setophaga vitellina) is an understudied species endemic to a few islands in the western Caribbean. Little is known beyond its phylogenetic relationship to other New World warblers. We used island-wide surveys and bioacoustic recordings to investigate the distribution, vocalizations, and ecology of S. vitellina across a significant portion of the species’ range on Little Cayman Island. We recorded 417 songs from 91 individuals and analyzed the length, frequency, and shape of various song components. We observed and characterized high variation in the composition and character of songs.

Stable isotope (carbon, nitrogen and sulfur) data for primary producers and consumer organisms in the Plum Island Sound Estuary.

Flora and fauna stable isotope study to help characterize organic matter/primary production sources important to the food web of the Plum Island Sound estuary. Sampling occured during 1993 and 1994.

Year 2007, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2007, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA.

Year 2009, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2009, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA.

Year 2008, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2008, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA.

Year 2010, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2010, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA.

openCC (other)Jul 2021View details →

Descriptive data file for information regarding microbial genetic research in the environs of Plum Island Sound watersheds, PIE LTER, Massachusetts.

This is a descriptive, tabular dataset of publications related to microbial or genomic research conducted within PIE. Assession numbers for genetic sequences generated from PIE samples are provided where available, followed by a very brief description of analysis type and study objectives. Sampling locations within PIE, sampling dates, and habitat type (sea water, fresh water, sediment, marsh) are also given. Environmental data are included in some publications and are listed here (if brief) or availability is described. Links to sequence archives are given in Methods.

Year 2011, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2011, water quality sonde data. 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club pier in Ipswich, MA.

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, partial year 2011 and all of 2012.

Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for partial year 2011 and all of 2012. 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.

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, Ipsiwch, MA, year 2013

Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2013. 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.

Year 2012, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2012, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club.

Year 2013, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2013, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club.

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 2014

Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2014. 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.