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openmit-licenseFeb 2018View details →

ICSE 2018 and ICPC 2019 Session Video Recordings

Session recordings analyzed in two following research works: <ul> <li>Title: The Road to Live Programming: Insights From the Practice <ul> <li>Authors: Juraj Kubelka, Romain Robbes, Alexandre Bergel</li> <li><a href="https://www.icse2018.org/event/icse-2018-technical-papers-the-road-to-live-programming-insights-from-the-practice">ICSE 2018</a></li> </ul> </li> <li>Title: Live Programming and Software Evolution: Questions during a Programming Change Task <ul> <li>Authors: Juraj Kubelka, Romain Robbes, Alexandre Bergel</li> <li><a href="https://conf.researchr.org/home/icpc-2019">ICPC 2019</a></li> </ul> </li> </ul> The dataset includes the following audio and video recordings and the file names are composed of <session-id>-<participant-id>. Session Video Recordings [session id, participant id, spoken language]: <ol> <li>S1-P1, Audio is excluded at the request of the participant</li> <li>S2-P2, English</li> <li>S3-P3, Spanish</li> <li>S4-P5, English</li> <li>S5-P8, Spanish</li> <li>S6-P9, Spanish</li> <li>S7-P11, English</li> <li>S8-P4, English</li> <li>S9-P5, Spanish</li> <li>S10-P6, English</li> <li>S11-P4, English</li> <li>S12-P5, Spanish</li> <li>S13-P6, English</li> <li>S14-P8, Spanish</li> <li>S15-P9, Spanish</li> <li>S16-P10, English</li> <li>S17-P11, English</li> </ol> Participants who spoke Spanish during the session spoke their mother tongue. English-speaking participants use English as a second language.

opencc-by-4.0Nov 2017View details →

14 channels Emotiv epoc recordings

We have recorded the data using a commercial 14 channel Emotiv EEG headset from 5 different subjects. Before placing the headset on the scalp, the electrodes are slightly wetted with a saline solution that improves skin contact (higher conductivity). Here, we have performed two types of measurements: brain activity in a resting state with closed eyes and sitting on a comfortable chair while observing a screen flickering at 15 Hz.  In the first task, the subjects close their eyes and brain activity is measured during 10 seconds. A similar process is repeated for the flickering task, in which the subject looks at a flashing screen with alternating colors (black and white) at a 15 Hz frequency. Each folder contains the recordings for a different subjects and each file (realization) contains a matrix, in which each column represents a channel and each row represents a sample (128 samples per second).

opencc-by-sa-4.0Mar 2018View details →

ASSOS recorded data

This 7zip file contains the in the <a href="https://www.researchgate.net/project/ASSOS-Animal-Sound-Sensor-Observation-Service">ASSOS project</a> recorded datasets. In addition to the raw data, the analyzed data and the used Python 3.7 code is stored as well. The played in vitro bird sounds are part of the <a href="http://www.tierstimmenarchiv.de/">Tierstimmenarchiv</a>. The data collectors are cited as contributors below.

Fig. 3 in Three new species of Anacharis Dalman, 1823 (Hymenoptera: Figitidae), with revised taxonomy and distribution records of Palaearctic and Indomalayan species

Fig. 3. Anacharis belizini sp. nov. A. Head in front view. B. Mesosoma in lateral view. C. Mesosoma in dorsal view. D. Propodeum.

Fig. 2. A–C in Three new species of Anacharis Dalman, 1823 (Hymenoptera: Figitidae), with revised taxonomy and distribution records of Palaearctic and Indomalayan species

Fig. 2. A–C. Mesosoma in dorsal view. A–B. Anacharis eucharoides (Dalman, 1818). C. A. immunis Walker, 1835. – D–F. Mesosoma in lateral view. D. A. eucharoides (Dalman, 1818). E–F. A. immunis Walker, 1835, F is detail of scutellum.

Fig. 4 in Three new species of Anacharis Dalman, 1823 (Hymenoptera: Figitidae), with revised taxonomy and distribution records of Palaearctic and Indomalayan species

Fig. 4. Anacharis fergussoni sp. nov. A. Mesosoma in dorsal view. B. Mesosoma in lateral view. C. Head in dorsal view. – Anacharis norvegica sp. nov. D. Mesosoma in dorsal view. E. Mesosoma in lateral view. F. Petiole.

Fig. 1. A–B. Anacharis antennata Belizin, 1951, mesosoma. A. Dorsal view. B. Lateral view. – C–D. A. parapsidalis Belizin, 1951, mesosoma. C. Dorsal view. D in Three new species of Anacharis Dalman, 1823 (Hymenoptera: Figitidae), with revised taxonomy and distribution records of Palaearctic and Indomalayan species

Fig. 1. A–B. Anacharis antennata Belizin, 1951, mesosoma. A. Dorsal view. B. Lateral view. – C–D. A. parapsidalis Belizin, 1951, mesosoma. C. Dorsal view. D. Lateral view.

opencc-by-4.0Jun 2013View details →

DEMAND: a collection of multi-channel recordings of acoustic noise in diverse environments

DEMAND: Diverse Environments Multichannel Acoustic Noise Database A database of 16-channel environmental noise recordings Introduction Microphone arrays, a (typically regular) arrangement of several microphones, allow for a number of interesting signal processing techniques. The correlation of audio signals from microphones that are located in close proximity with each other can, for example, be used to determine the spatial location of sound source relative to the array, or to isolate or enhance a signal based on the direction from which the sound reaches the array. Typically, experiments with microphone arrays that consider acoustic background noise use controlled environments or simulated environments. Such artificial setups will in general be sparse in terms of noise sources. Other pre-existing real-world noise databases (e.g. the <a href="http://catalog.elra.info/product_info.php?products_id=693">AURORA-2</a> corpus, the <a href="http://spandh.dcs.shef.ac.uk/projects/chime/PCC/datasets.html">CHiME</a> background noise data, or the <a href="http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html">NOISEX-92</a> database) tend to provide only a very limited variety of environments and are limited to at most 2 channels. The DEMAND (Diverse Environments Multichannel Acoustic Noise Database) presented here provides a set of recordings that allow testing of algorithms using real-world noise in a variety of settings. This version provides 15 recordings. All recordings are made with a 16-channel array, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm. License This work, the audio data and the document describing it, is licensed under a <a href="http://creativecommons.org/licenses/by-sa/3.0/deed.en_CA">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>. The data A description of the data and the recording equipment is provided in the file DEMAND.pdf. All recordings are available as 16 single-channel WAV files in one directory at both 48 kHz and 16 kHz sampling rates. All files are compressed into "zip" files. Other information The MATLAB scripts listed in the documentation can be found in the file scripts.zip. The Authors This work was created by Joachim Thiemann (IRISA-CNRS), Nobutaka Ito (University of Tokyo), and Emmanuel Vincent (Inria Rennes - Bretagne Atlantique). It was supported by Inria under the Associate Team Program <a href="http://versamus.inria.fr">VERSAMUS</a>.

Pyramic Dataset : 48-Channel Anechoic Audio Recordings of 3D Sources

The Pyramic Dataset contains recordings done using the <a href="https://github.com/LCAV/Pyramic">Pyramic</a> 48 channel microphone array in an anechoic chamber. The recordings consist of 8 different samples (2x sweeps, 1x noise, 5x speech) repeated at 180 angles (every 2 degrees) and from 3 different heights. The audio samples recorded are <ul> <li>Linear and exponential sweeps</li> <li>Noise sequence</li> <li>2x male and 3x female speech</li> </ul> This dataset allows to evaluate the performance of array processing algorithms on real-life recordings done using MEMS microphones similar to those used in mobile phones with all the non-idealities involved. The dataset is suitable for both 2D and 3D scenarios. By subsampling the 48 microphones, a large number of array configurations can be tested.  Example of algorithms are: <ul> <li>Direction of arrival (DOA) estimation</li> <li>Beamforming</li> <li>Source separation</li> <li>Array calibration</li> </ul> Another application is the generation of realistic room impulse by combining the impulse responses of microphones from sources at multiple angles with a variant of the image source model. In addition to the raw (compressed or not) and segmented recordings, the impulse responses of all the microphones for every source locations were recovered from the exponential sweep measurements and are distributed together with the dataset. The initial manual measurement of loudspeakers and microphones locations was improved upon using a blind calibration method. This record contains <ul> <li>The compressed recordings (TTA format)</li> <li>Segmented recorded samples</li> <li>Impulse responses</li> <li>Documentation and code (also available on <a href="https://github.com/fakufaku/pyramic-dataset">github</a>)</li> </ul> The raw measurements in wav format are available as a separate <a href="https://zenodo.org/deposit/1209005">record</a> (10.5281/zenodo.1209005). The best way to get started is to only get the documentation and code from <a href="https://github.com/fakufaku/pyramic-dataset">github</a> (a copy is available in pyramic-dataset-doc-d2a456b4.zip) and follow the instructions in the README. The version on github is most up-to-date. If possible, please use that one.

Pyramic Dataset : 48-Channel Anechoic Audio Recordings of 3D Sources (Raw)

The Pyramic Dataset contains recordings done using the <a href="https://github.com/LCAV/Pyramic">Pyramic</a> 48 channel microphone array in an anechoic chamber. The recordings consist of 8 different samples (2x sweeps, 1x noise, 5x speech) repeated at 180 angles (every 2 degrees) and from 3 different heights. The audio samples recorded are <ul> <li>Linear and exponential sweeps</li> <li>Noise sequence</li> <li>2x male and 3x female speech</li> </ul> This dataset allows to evaluate the performance of array processing algorithms on real-life recordings done using MEMS microphones similar to those used in mobile phones with all the non-idealities involved. The dataset is suitable for both 2D and 3D scenarios. By subsampling the 48 microphones, a large number of array configurations can be tested.  Example of algorithms are: <ul> <li>Direction of arrival (DOA) estimation</li> <li>Beamforming</li> <li>Source separation</li> <li>Array calibration</li> </ul> Another application is the generation of realistic room impulse by combining the impulse responses of microphones from sources at multiple angles with a variant of the image source model. In addition to the raw (compressed or not) and segmented recordings, the impulse responses of all the microphones for every source locations were recovered from the exponential sweep measurements and are distributed together with the dataset. The initial manual measurement of loudspeakers and microphones locations was improved upon using a blind calibration method. <ul> </ul> This record contains only the raw measurements in wav format and archive of the documentation and code. The post-processed data is available in a separate <a href="https://zenodo.org/record/1209563">record</a> that contains: <ul> <li>The compressed recordings (TTA format)</li> <li>Segmented recorded samples</li> <li>Impulse responses</li> <li>Documentation and code (also available on <a href="https://github.com/fakufaku/pyramic-dataset">github</a>)</li> </ul> The best way to get started is to only get the documentation and code from <a href="https://github.com/fakufaku/pyramic-dataset">github</a> and download the data as needed into the unzipped archive. Then follow the instructions in README.md.

opencc-by-4.0Dec 2017View details →

Seismic and meteorological records from Trakarding-Trambau Glacier system, Nepal Himalaya (October 21 - November 9, 2017)

The following geophysical field data (collected between October 21 and November 9, 2017) at Trakarding-Trambau Glacier system in Nepal Himalaya is provided in this dataset:    (1) Hourly air-temperature measured at four sites (AWS, T1, T2 and T3). Units are degrees Celcius. Local time. [file name] Hourly_Air_temperature_AWS_T1_T2_T3_degC.csv   (2) Hourly wind speed measured at the AWS site. Units are meters per second. Local time. [file name] Hourly_Wind_speed_AWS_MperS.csv   The aforementioned stations had the following dGPS-derived coordinates: {Station, lat [deg], lon [deg], elevation [m] } AWS, 27.84356265, 86.4867231, 4805.582206 T1, 27.84575824, 86.49189978, 4591.4429 T2, 27.82976456, 86.51989785, 4768.8465 T3, 27.855872, 86.531197, 5390.48   (3) Raw seismic data (vertical component) recorded at five locations (DAM, C1, C2, C3, and C4) by four data-loggers (AKX, AKS, AKT, AKU) with a sampling frequency of 400 Hz. The file format (*.pri0) corresponds to a standard [.mseed]-format. Units are counts representing velocity. UTC time.   The aforementioned stations had the following dGPS-derived coordinates: {StationName-Logger, lat [deg], lon [deg], elevation [m] }   DAM-AKX, 27.870954, 86.463243, 4375.030762 C1-AKS, 27.84546885, 86.49247316, 4594.0462 C2-AKT, 27.82939476, 86.52001441, 4777.5211 C3-AKU, 27.83593425, 86.53248327, 5288.3311 C4-AKX, 27.87962877, 86.542076, 5555.2085

opencc-by-4.0Aug 2018View details →

Earliest records of Oxalis corniculata by country

Oxalis corniculata L. is a weed with a global distribution. Here I have gathered first observations of this species in as many countries as possible. These data come from human observations, historic literature, herbarium specimens and paleobotanical studies. Many of these records come from the Global Biodiversity Information Facility at the references below, but also the monograph of Lourteig (1979). Custom data export (3rd Sept 2015). <a href="https://doi.org/10.15468/cdl.xxxx">https://doi.org/10.15468/dl.nuaaek</a> Available from <a href="https://www.gbif.org/occurrence/download/0001780-150831095807960">https://www.gbif.org/occurrence/download/0001780-150831095807960</a> [4th August 2018] Custom data export (1st March 2016). <a href="https://doi.org/10.15468/dl.bdlcpo">https://doi.org/10.15468/dl.bdlcpo</a> Available from <a href="https://www.gbif.org/occurrence/download/0003355-180730143533302">https://www.gbif.org/occurrence/download/0003355-180730143533302</a> [4th August 2018] Lourteig, A. (1979). Oxalidaceae extra-austroamericanae: 2. Oxalis L. Sectio Corniculatae DC. Phytologia, 42, 57−198. <a href="https://www.biodiversitylibrary.org/part/38832">https://www.biodiversitylibrary.org/part/38832</a>

opencc-by-4.0Sep 2016View details →

Fig. 9 in Three new generic records and descriptions of four new species of jumping spiders (Araneae, Salticidae) from Sri Lanka

Fig. 9. Habrocestum ohiyaensis sp. nov. A. Ƌ, habitus, dorsal view. B. Palp, ventral view. C. Palp, retrolateral view. D. Palp, ventral view. E. Palp, retrolateral view. F. Palp, prolateral view. Abbreviations: E = embolus; EB = embolic base; SD = sperm duct; T = tegulum; TL = tegular ledge; PLT = proximal lobe of tegulum; RTA = retrolateral tibial apophysis. Scale bars: A = 1 mm; B–F = 0.2 mm.

opencc-by-4.0Sep 2016View details →

Fig. 3 in Three new generic records and descriptions of four new species of jumping spiders (Araneae, Salticidae) from Sri Lanka

Fig. 3. Bristowia gandhii sp. nov. A, C–D. Ƌ (IFS_SAL 228). B, E. ♀ (IFS_SAL 297). A–B. Habitus, dorsal view. C. Palp, ventral view. D. Palp, retrolateral view. E. Epigynum, ventral view. Abbreviations: CD = copulatory duct; CO = copulatory opening; E = embolus; MI = median indentation; RTA = retrolateral tibial apophysis; S = spermathecae; SD = sperm duct; T = tegulum. Scale bars: A = 1 mm; B= 0.5 mm; C–E = 0.2 mm.

opencc-by-4.0Sep 2018View details →

EEG Recordings During a 3D Visuomotor Learning Task

This is a data set of EEG recordings and corresponding kinematic data during a 3D visuomotor learning task performed by thirty-four subjects. Alongside a readme, the folder consists of thirty-four .mat files for each participants' data and a channels.csv file for EEG recording channel locations. When using this data please cite: Ozdenizci, Ozan, et al. "Neural Signatures of Motor Skill in the Resting Brain." 2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2019.

Mechanisms for a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China: observation analysis and nested very-large-eddy simulation with the WRF Model

A video shows the processes of a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China simulated by WRF nested very-large-eddy simulation.

SINS database - Node 4 - Daily activities in a home environment recorded using a Acoustic Sensor Network

The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment. To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper. Please read to license file (available in this repository) before using the database. When using this database you should cite the following paper: Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.

SINS database - Node 7 - Daily activities in a home environment recorded using a Acoustic Sensor Network

SINS database - Node 6 - Daily activities in a home environment recorded using a Acoustic Sensor Network

SINS database - Node 12 - Daily activities in a home environment recorded using a Acoustic Sensor Network