Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

I-BiDaaS - TID - Synthetic Call Centre Data

This is a simulated dataset based on both real phone interactions and conversations usually performed by real call centres. It is comprised of several simulated customer interactions with an agent representative, both roles performed by actors. Phone call recordings are performed using different mobile and landline devices. The scripting, both from customer and agent, aims to develop typical scenarios by telco-oriented call centre operations. Both raw waveform recordings and speech transcription are provided. The latter as obtained by an automatic speech recognition (ASR) prototype developed by TID. The word segmentation timestamps are also provided for those recognized. Additionally, a confidence score is also provided per token basis.

opencc-by-4.0Jan 2021View details →

Underlying data - Results from the Open Call: How Citizens can participate in solar energy research?

Underlying data to the "Results from the Open Call: How Citizens can participate in solar energy research?" @ <pre>https://zenodo.org/record/3554901#.YAgimxaCE2w</pre> Answers to the online survey in "Call for ideas_answers online_survey.xlsx" Notes from the World Cafe and other meetings from the secretaries: "notes_MMLs_GRECO_2019.pdf

opencc-zeroFeb 2024View details →

Data from: Not afraid of the Big Bad Wolf: calls from large predators do not silence mesopredators

Large predators are known to shape the behavior and ecology of sympatric predators via conflict and competition, with mesopredators thought to avoid large predators, while dogs suppress predator activity and act as guardians of human property. However, interspecific communication between predators has not been well-explored and this assumption of avoidance may oversimplify the responses of the species involved. We explored the acoustic activity of three closely related sympatric canids: wolves Canis lupus, coyotes Canis latrans, and dogs Canis familiaris. These species have an unbalanced triangle of risk: coyotes, as mesopredators, are at risk from both apex-predator wolves and human-associated dogs, while wolves fear dogs, and dogs may fear wolves as apex predators or challenge them as intruders into human-allied spaces. We predicted that risk perception would dictate vocal response with wolves and dogs silencing coyotes as well as dogs silencing wolves. Dogs, in their protective role of guarding human property, would respond to both. Eleven passive acoustic monitoring devices were deployed across 13 nights in Central Wisconsin, and we measured the responses of each species to naturally occurring heterospecific vocalizations. Against our expectation, silencing did not occur. Instead, coyotes were not silenced by either species: when hearing wolves, coyotes responded at greater than chance rates and when hearing dogs, coyotes did not produce fewer calls than chance rates. Similarly, wolves responded at above chance rates to coyotes and at chance rates when hearing dogs. Only the dogs followed our prediction and responded at above chance rates in response to both coyotes and wolves. Thus, instead of silencing their competitors, canid vocalizations elicit responses from them suggesting the existence of a complex heterospecific communication network.

opencc-by-4.0Apr 2024View details →

Call data records

This is a pre-processed subset of the call data records dataset from the <a href="https://arxiv.org/abs/1210.0137">Data for Development: the D4D Challenge on Mobile Phone Data</a>. The dataset consists of 8 time series of 3336 time steps sampled at hourly resolution. The 8 time series represent: <ol> <li>The number of incoming calls in the area covered by the antenna.</li> <li>The volume in minutes of the incoming calls in the area covered by the antenna.</li> <li>The number of outgoing calls in the area covered by the antenna.</li> <li>The volume in minutes of the outgoing calls in the area covered by the antenna.</li> <li>The sine of the hour of the day when the telephone activity has been registered.</li> <li>The cosine of the hour of the day when the telephone activity has been registered.</li> <li>The sine of the day of the week when the telephone activity has been registered.</li> <li>The cosine of the day of the week when the telephone activity has been registered.</li> </ol> This <a href="https://imgur.com/a/e3INnkG">image</a> shows the first 500 hours in each time series (time series 5-6 and 7-8 are plot together).

opencc-zeroAug 2022View details →

Acoustic data of calls of Manx shearwater on Lundy Island

Vocalizations are widely used to signal behavioural intention in animal communication, but may also carry additional information encoded in the signal, in particular, vocalisations may carry acoustic signatures unique to the calling individual. Manx shearwater (Puffinus puffinus) are nocturnal seabirds that breed in dense colonies, where they must recognize and locate mates among thousands of conspecifics calling in the dark. There is evidence for individual vocal signatures in two shearwater species, but quantitative data on the vocalisations of Manx shearwater are lacking. We recorded calls of 13 Manx shearwaters on Lundy Island, UK, by eliciting vocal responses to playback of conspecific calls. We measured several spectral and temporal parameters of the calls, applied linear discriminate analysis with leave-one-out cross-validation, and have confirmed the individual vocal signatures. We then calculated among-individual repeatability of 34 features describing the vocalization to determine the extent to which these features may contribute to individual signature coding. We found that calls cluster by individual in both temporal and spectral characteristics, suggesting these are contributing to Manx shearwaters' unique call signatures.

opencc-by-4.0May 2024View details →

Data and Code: Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway

<h3>Code</h3> MATLABWhaleDetectionCodeNWA.zip The code used in the manuscript Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway (Goblot et al., in review) to detect whale calls in seismic waveforms was written by Alexandre Plourde and uploaded here with permission. This .zip file contains 3 folders, ‘Numerical Methods’, ‘Signal Processing’ and ‘Whale Subroutines’, as well as 3 main scripts (x2 for fin and blue whales).  <ul> <li>Pre-processing: <ul> <li>Convert .mseed to SAC files and rename with the format: YYYY.MM.DD.NETWORK.STATION..CHANNEL.SAC. Each SAC file must then be placed in a folder named with the format YYYYMMDD, referred to as events (evs) in the code. Each of these folders must then be placed into a single folder known as the events directory (drE) in the code.</li> </ul> </li> </ul> <ul> <li>Processing: Fin whales <ul> <li>Run ‘RecordWhaleNoiseLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘FinWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createFinWhaleDectectionList.m' to check which 120s time windows have W > threshold (3.0 in our case). These time windows are stored in the matrix FWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 120s time segments in the FWD matrix. The labelled call times are stored in the matrix FWC.</li> </ul> </li> </ul> <ul> <li>Processing: Blue whales <ul> <li>Run ‘RecordBlueWhalesLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘BlueWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createBlueWhaleDectectionList.m' to check which 720s time windows have W > threshold (1.5 in our case). These time windows are stored in the matrix BWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 720s time segments in the BWD matrix. The labelled call times are stored in the matrix BWC.</li> </ul> </li> </ul> <h3>Data</h3> The whale detection code was applied to seismic waveform data downloaded through the EarthScope Consortium Web Services (<a href="https://service.iris.edu/" target="_blank" rel="noopener">https://service.iris.edu/</a>), including the following seismic network: CN (Natural Resources Canada, 1975). All whale calls were detected using the characteristic reccurence interval method (MATLABWhaleDetectionCodeNWA.zip). Table S2 WhaleDetectionsFeb2020Jan2022LSZ.mat <ul> <li>This dataset contains the center time of fin whale and blue whale detections and calls, from land 6 seismometers (CNQ, ICQ, SMQ, SNFQ, PMAQ, RISQ) in the Lower St-Lawrence Seaway between February 2020 and January 2022. <ul> <li>FWD_ <ul> <li>Column 1: list of fin whale detections (2 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWD_ <ul> <li>Column 1: list of blue whale detections (12 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>FWC_ <ul> <li>Column 1: list of individual ~1s fin whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWC_ <ul> <li>Column 1: list of individual ~8s blue whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>stadir: list of stations and labels (1-6)</li> </ul> </li> </ul> Table S3 a) MonthlyBlueWhaleDetectionsOct2015Jan2022.csv and b) MonthlyFinWhaleDetectionsOct2015Jan2022.csv <ul> <li> <div> <div> <div> Number of monthly a) fin whale detections and b) blue whale detections from October 2015 to January 2022. Empty cells indicate periods when stations were not operating. Quiet day detections are included. Note the Oct 2015-Feb 2020 catalogue is from Plourde and Nedimović (2022) and includes up to 14 stations throughout this period from the following seismic networks: CN (Natural Resources Canada, 1975) and C8 (Natural Resources Canada, 2002). The Feb 2020-Jan 2022 catalogue is from Goblot et al. (in review) and includes the same stations from Table S1. </div> </div> </div> </li> </ul> <h3>Additional</h3> Movie S1 LSLSWhales.mp4 <ul> <li>This movie contains an audiovisual representation of a series of fin whale calls and blue whale calls detected by 2 land seismometers in the Lower St-Lawrence Seaway. The fin whale detection was recorded by land seismometer CNQ (Côte-Nord) on Dec 12 2021 from 18:19:40 to 18:21:40. The blue whale detection was recorded by land seismometer SNFQ (Sainte-Félicité) on Aug 22 2021 from 05:36:00 to 05:48:00.</li> <li>The MATLAB soundsc(x) function was applied to seismic waveform data with fin whale and a blue whale calls. The signal with the fin whale detection is bandpassed from 18-21 Hz and the blue whale signal is bandpassed from 16-18 Hz. These signals were sped up 1000x in order to make them audible.</li> <li>The audio file was then uploaded to veed.io to produce a frequency response visualization of the whale calls.</li> <li>More audiovisuals can be viewed <a href="https://seismicsoundscapes.myportfolio.com/" target="_blank" rel="noopener">here</a>.</li> </ul>

opencc-by-4.0Mar 2019View details →

Training data for 'Somatic variant calling' tutorial (Galaxy Training Material)

The data provided here are part of a Galaxy Training Network tutorial that demonstrates identification of somatic and germline variants from tumor and normal sample pairs.

opencc-by-4.0Jun 2019View details →

SNP call data for: The current epidemic of the barley pathogen Ramularia collo-cygni derives from a recent population expansion and shows global admixture

Ramularia Leaf Spot is becoming an ever increasing problem in main barley growing regions since the 1980s, causing up to 70% yield loss in extreme cases. Yet, the causal agent Ramularia collo-cygni, remains poorly studied. The diversity of the pathogen in the field thus far remains unknown. Furthermore, it is unknown to which extend the pathogen has a sexual reproductive cycle. To date, the teleomorph of R. collo-cygni has not been observed. To study the genetic diversity of R. collo-cygni and to get more insights into its biology, we sequenced the genomes of 19 R. collo-cygni isolates from multiple geographic locations and diverse hosts. Here we share the SNP call data as well as the reference genome. The reference genome files and assembly can be found on ENI: GCA_900074925.1 https://www.ebi.ac.uk/ena/data/view/GCA_900074925.1 The raw sequence data is also available through ENI: ERX2296228 https://www.ebi.ac.uk/ena/data/view/ERX2296228

Figure 8 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species

Figure 8. SEM image of the stridulatory file in Poecilimon affinis dinaricus, Ogoreljača–Mali Jadovnik (meadow).

Figure 10 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species

Figure 10. Poecilimon pseudornatus, oscillograms of male calling song at 28 °C from: A) Sopotnica Ra; B) Ogoreljača–Mali Jadovnik (roadside).

Figure 7 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species

Figure 7. Calling song of Poecilimon affinis dinaricus males from Ogoreljača–Mali Jadovnik (meadow). Syllables at different temperatures: A) syllable at 20 °C; B) syllable at 28 °C.

Figure 3 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species

Figure 3. Isophya clara, oscillograms of male calling song from Ovčar-Kablar Gorge. A) 6 syllables; B) syllable with after-click.

Figure 4 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species

Figure 4. SEM photographs of the stridulatory files: (A) Isophya clara, Gvozd; (B) Isophya clara, Ovčar-Kablar Gorge.

Figure 2 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species

Figure 2. Isophya clara, oscillograms of male calling song from Gvozd. A) 6 syllables; B) syllable with after-click.

Figure 11 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species

Figure 11. SEM photographs of the stridulatory files: (A) Poecilimon pseudornatus, Sopotnica Ra; (B) Poecilimon pseudornatus, Ogoreljača–Mali Jadovnik (roadside).

opencc-by-4.0Sep 2024View details →

Data, Code & Supplementary Material for "Notes' order as a mechanism to recognize unknown calls: an experiment with artificial mobbing calls"

Data set and R code used to analyse the output of a field experiment. This experiment tested the response of great tits and blue tits to either conspecific mobbing calls, artificial calls with their own call order, or artificial calls with a different call order.

opencc-zeroJan 2023View details →

Data for: PerchPicker classifier model v7: A catalog of American silver perch (Bairdiella chrysoura) calls for machine learning

This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep - 2023), Automated cataloging of American silver perch (Bairdiella chrysoura) calls using machine learning. The software accompanying this paper is known as PerchPicker (<a href="https://github.com/drbohnen/PerchPicker" rel="noopener">GitHub - drbohnen/PerchPicker)</a>, and the classifier model presented in the paper is v7. It consists of more than 6000 labeled perch and 6000 labeled other signals. Labeled scalogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 90 ms long. The center 30 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'perch' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the PerchPicker model or develop alternative classifiers.

opencc-zeroJul 2023View details →

Data from: Both learning and syntax recognition are used by great tits when answering to mobbing calls

Mobbing behavior, in addition to its complex cooperative aspects, is particularly suitable to study the mechanisms implicated in heterospecific communication. Indeed, various mechanisms ranging from pure learning to innate recognition have been proposed. One promising, yet understudied mechanism could be syntax recognition, especially given the latest works published on syntax comprehension in birds. In this experiment, we test whether great tits use both learning and syntax recognition when responding to heterospecifics. In the first part of the experiment, we demonstrate that great tits show different responses to the same heterospecific calls depending on their sympatric status. In a second part, we explore the impact of reorganizing the notes of the heterospecific mobbing calls to fit the syntax of great tits. Great tits showed an increased mobbing response toward the heterospecific calls when they shared their own call organization. Our results corroborate the recent finding that syntactic rules in bird calls may have a strong impact on their communication systems and enlighten how various mechanisms can be used by the same species to respond to heterospecific calls.