Find research datasets worth reusing

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 2015

Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2015. 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 2014, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2014, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, Ipswich, MA.

Year 2015, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2015, 15 minute measurements of water column temperature, salinity, oxygen and depth in Plum Island Sound at the Ipswich Bay Yacht Club, 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, year 2016

Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2016. 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 2016, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2016, 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)Feb 2018View details →

PIE LTER, Year 2017, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth near the mouth of Plum Island Sound, Massachusetts

Year 2017, 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)Feb 2019View details →

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 2018

Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2018. 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.

Biomass of Spartina alterniflora collected in July 2018 from various marsh edges around Plum Island Sound, MA, PIE LTER.

Biomass of Spartina alterniflora was collected at various locations within the Plum Island Sound estuary. Samples were collected during July 2018. Only Spartina alterniflora was collected. To collect biomass, 25 X 25 cm quadrats were placed over the plants. Aboveground biomass was clipped to soil surface. Biomass was dried in the lab in a drying oven until a constant weight was reached, and then weighed. Biomass values were then convertedto grams of dry weight per square meter (g/m2).

PIE LTER, Year 2013-2018, remote sensing derived sediment concentration maps, movies, transect averaged sediment concentation, water level, dh/dt, wind direction and speed, river discharges at Plum Island Sound, Massachusetts.

PIE LTER, Year 2013-2018, remote sensing (Landsat8 OLI sensors and Sentinel-2A/2B) derived sediment concentration maps, transect averaged sediment concentation, water level, dh/dt, wind direction and speed, river discharges for Plum Island Sound estuary, Massachusetts.

PIE LTER trapping capacity of suspended cohesive sediments of subdomains at Plum Island Sound, Massachusetts (numerical simulations).

Trapping capacity of suspended cohesive sediments of subdomains at Plum Island Sound, Massachusetts (from numerical simulations) are predicted using 2D Delft3D FLOW/MOR model and vegetation module during different stages of tidal height and direction.

PIE LTER, Year 2017-2018, locations, date, sediment concentration and spectral reflectance measurement methods of 40 water samples at Plum Island Sound and deep ocean, Massachusetts.

PIE LTER, Year 2017-2018, locations, date, sediment concentration and spectral reflectance measurement methods of 40 water samples at Plum Island Sound and deep ocean, Massachusetts

opencc-by-4.0Feb 2020View details →

Knocking Sound Effects With Emotional Intentions

The dataset was recorded by the professional foley artist Ulf Olausson at the FoleyWorks (http://foleyworks.se/) studios in Stockholm on the 15th October, 2019. Inspired by previous work on knocking sounds [1]. we chose five type of emotions to be portrayed in the dataset: anger, fear, happiness, neutral and sadness. In order to imagine a situation where the knocking action is performed with a particular emotional intention, we provided the foley artist with common situations where these actions can happen. The emotions, alongside the context are the following: <ul> <li>Anger: telling a flatmate for the 4th time to turn down the very loud music.</li> <li>Fear: alerting a neighbor of a possible risk.</li> <li>Happiness: telling a flatmate you won a prize.</li> <li>Neutral: parcel delivery.</li> <li>Sadness: telling a friend someone passed away.</li> </ul> We also encouraged the foley artist to perform diverse interpretations of the context provided in order to have a wider variety of sounds. The dataset was recorded with a Rode NT1 microphone, performing the knocks to a closed door. We recorded a total of 600 knocking actions (120 actions per category). An action is a sequence of individual knocks. We discarded 20 actions per category to filter out unwanted noise. The final 500 audio files were edited only to trim the audio so each file starts on the first knock onset and finish on the last knock decay.      [1] R. Vitale and R. Bresin, “Emotional Cues in Knocking Sounds,” in 10th International Conference on Music Perception and Cognition, Sapporo, Japan, August 25-29, 2008, 2008, p. 276.

opencc-by-4.0Mar 2020View details →

Database of Spherical Harmonic Representations of Sound Source Directivities

This is a database of complete spherical harmonic representations of the directivities of sound sources. The data are provided as impulse responses that represent the directivity of the given source in a given discrete direction. The Matlab script <code>compute_spherical_harmonics_model.m</code> demonstrates how a spherical harmonic representation can be computed from the data. We do not provide spherical harmonic coefficients directly because of the multitude of definitions of spherical harmonics and also of the Discrete Fourier transform. We rather ask you to select the combination of definitions you would like to use and compute the spherical harmonic coefficients on demand. You may want to add re-sampling or zero padding and the like to make the data compatible with your intended application. As of now, all spherical harmonic representations are based on previously published data. Please do not forget to site this repository as well as the original repositories when using the data. References to the original sources are provided with each dataset. All data are bandlimited to the spherical harmonic order <code>N</code> that is specified in the corresponding file name. The conversion between raw data and spherical harmonic coefficients is therefore essentially lossless.

opencc-by-4.0Jun 2020View details →

Data & example videos: Exploring effects of sound on the time budget of fishes: an experimental approach with captive cod

Data abstract: The daily proportions of time each individual fish spent foraging, swimming and being stationary, this data was used for the analysis in the reference below.   Video abstract: Some example video clips from which the behavioural state of both individuals was scored.   Conference proceeding abstract: To estimate population level effects of anthropogenic sound on fish, data on energy intake and expenditure is needed. We present an experimental design of a controlled behavioural experiment that allows to collect relatively long-term data on foraging and swimming behaviour of Atlantic cod during sound exposures. Data on such behavioural states can be used as proxies for energy intake and expenditure. The Atlantic cod exhibited natural foraging behaviour in the experimental basins and the design allowed for efficient scoring of the behaviour throughout the 6-day trials. We conducted a pilot of three trials and share the experimental design to encourage other researchers to collect data on (proxies for) energy intake and expenditure to aid estimation of population level effects of sound exposures.   Reference: Hubert, J., Wille, D.A., Slabbekoorn, H. (2020) Exploring effects of sound on the time budget of fishes: an experimental approach with captive cod. Proceedings of Meetings on Acoustics. 37 (1). 010012. DOI:10.1121/2.0001253.

opencc-by-4.0Dec 2019View details →

Relationship between Epilarynx Tube Shape and the radiated Sound Pressure Level during Phonation is gender specific

There are two types of files: .wav files contain the audio recordings .tif files the mri-data necessary for the study. Please do not use the data without referencing its origin.

opencc-by-4.0Jun 2020View details →

Distorted sound for evaluation of CNN

Distorted sound for evaluation of CNN, original files are from urbansound8k.

opencc-by-4.0Jul 2020View details →

"Deepening Presence - Probing the hidden artefacts of everyday soundscapes" sound examples

Sound examples for the paper "Deepening Presence - Probing the hidden artefacts of everyday soundscapes"

opencc-by-4.0Mar 2020View details →

Free Universal Sound Separation Dataset

The Free Universal Sound Separation (FUSS) Dataset is a database of arbitrary sound mixtures and source-level references, for use in experiments on arbitrary sound separation.  This is the official sound separation data for the DCASE2020 Challenge Task 4: Sound Event Detection and Separation in Domestic Environments. Citation: If you use the FUSS dataset or part of it, please cite our paper describing the dataset and baseline [1].  FUSS is based on <a href="https://annotator.freesound.org/fsd/">FSD data</a> so please also cite [2]: Overview: FUSS audio data is sourced from a pre-release of <a href="https://annotator.freesound.org/fsd/">Freesound dataset</a> known as (FSD50k), a sound event dataset composed of Freesound content annotated with labels from the AudioSet Ontology. Using the FSD50K labels, these source files have been screened such that they likely only contain a single type of sound. Labels are not provided for these source files, and are not considered part of the challenge. For the purpose of the DCASE Task4 Sound Separation and Event Detection challenge,  systems should not use FSD50K labels, even though they may become available upon FSD50K release. To create mixtures, 10 second clips of sources are convolved with simulated room impulse responses and added together. Each 10 second mixture contains between 1 and 4 sources. Source files longer than 10 seconds are considered "background" sources. Every mixture contains one background source, which is active for the entire duration. We provide: a software recipe to create the dataset, the room impulse responses, and the original source audio. Motivation for use in DCASE2020 Challenge Task 4:  This dataset provides a platform to investigate how source separation may help with event detection and vice versa.  Previous work has shown that universal sound separation (separation of arbitrary sounds) is possible [3], and that event detection can help with universal sound separation [4].  It remains to be seen whether sound separation can help with event detection. Event detection is more difficult in noisy environments, and so separation could be a useful pre-processing step. Data with strong labels for event detection are relatively scarce, especially when restricted to specific classes within a domain. In contrast, source separation data needs no event labels for training, and may be more plentiful. In this setting, the idea  is to utilize larger unlabeled separation data to train separation systems, which can serve as a front-end to event-detection systems trained on more limited data. Room simulation: Room impulse responses are simulated using the image method with frequency-dependent walls. Each impulse corresponds to a rectangular room of random size with random wall materials, where a single microphone and up to 4 sources are placed at random spatial locations. Recipe for data creation: The data creation recipe starts with scripts, based on<a href="https://github.com/justinsalamon/scaper"> scaper</a> [5], to generate mixtures of events with random timing of source events, along with a background source that spans the duration of the mixture clip.  The scipts for this are at<a href="https://github.com/google-research/sound-separation/tree/master/datasets/fuss"> this GitHub repo</a>. The data are reverberated using a different room simulation for each mixture. In this simulation each source has its own reverberation corresponding to a different spatial location. The reverberated mixtures are created by summing over the reverberated sources. The dataset recipe scripts support modification, so that participants may remix and augment the training data as desired. The constituent source files for each mixture are also generated for use as references for training and evaluation.   The dataset recipe scripts support modification, so that participants may remix and augment the training data as desired. Note: no attempt was made to remove digital silence from the freesound source data, so some reference sources may include digital silence, and there are a few mixtures where the background reference is all digital silence.   Digital silence can also be observed in the event recognition public evaluation data, so it is important to be able to handle this in practice.   Our evaluation scripts handle it by ignoring any reference sources that are silent.   Format:  All audio clips are provided as uncompressed PCM 16 bit, 16 kHz, mono audio files. Data split:  The FUSS dataset is partitioned into "train", "validation", and "eval" sets, following the same splits used in FSD data. Specifically, the train and validation sets are sourced from the FSD50K dev set, and we have ensured that clips in train come from different uploaders than the clips in validation. The eval set is sourced from the FSD50K eval split. Baseline System:  A baseline system for the FUSS dataset is available at  <a href="https://github.com/google-research/sound-separation/tree/master/datasets/fuss">dcase2020_fuss_baseline</a>. License:  All audio clips (i.e., in  FUSS_fsd_data.tar.gz) used in the preparation of Free Universal Source Separation (FUSS) dataset are designated Creative Commons (CC0) and were obtained from<a href="http://freesound.org"> freesound.org</a>.  The source data in FUSS_fsd_data.tar.gz were selected using labels from the<a href="https://annotator.freesound.org/fsd/"> FSD50K corpus</a>, which is licensed as Creative Commons Attribution 4.0 International (CC BY 4.0) License. The FUSS dataset as a whole, is a curated, reverberated, mixed, and partitioned preparation, and is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. This license is specified in the `LICENSE-DATASET` file downloaded with the `FUSS_license_doc.tar.gz` file. Notes: Added in v1.2:  <ul> <li>FUSS_baseline_dry_model.tar.gz: baseline separation model trained on non-reverberated (dry) data. </li> <li>FUSS_DESED_baseline_dry_2_model.tar.gz:: baseline separation model for the DESED task, trained on a mixture of DESED in-domain data and FUSS data</li> </ul> Added in v1.3: <ul> <li>FUSS_DESED_baseline_dry_1_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED mixtures from dry FUSS mixtures (DmFm)</li> <li>FUSS_DESED_baseline_dry_4_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED background, dry FUSS mixture, and 5 DESED foreground sources with PIT (PIT)</li> <li>FUSS_DESED_baseline_dry_4np_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED background, 10 DESED classes, and dry FUSS mixture without PIT (Classwise)</li> <li>FUSS_DESED_baseline_dry_6_model.tar.gz: baseline separation model for the DESED task, trained to separate DESED background, 5 DESED foreground sources, 4 dry FUSS sources, with groupwise PIT (GroupPIT)</li> </ul> The names in parentheses are the task names from Table 3 of the following paper:  <a href="https://arxiv.org/pdf/2007.03932.pdf">Nicolas Turpault,  Scott Wisdom, Hakan Erdogan, John R. Hershey, Romain Serizel, Eduardo Fonseca, Prem Seetharaman, and Justin Salamon, "Improving Sound Event Detection in Domestic Environments using Sound Separation",   DCASE 2020.</a>

opencc-by-4.0Jan 2021View details →

Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts Data and Scripts

The repository contains the data corresponding to the Paper "Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts". Random30, Random50, Random100, Similiar10, Similar30 and Similar50.zip contain the data sets (obj Files). R30_physical_images.zip and sim50_physical_images.zip contain the photos made from the physical components which are used for the evaluation.