Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
131
datasets available to search
ShareScore release 0.9.0
Dataset results
131 results for “Event detection”
Table S27: Target and identified unknown organic micropollutants detected in surface water samples taken during heavy rain events
<p>In the following table, peak intensities of detected organic micropollutants in water samples are displayed.</p> <p>This data table is part of the appendix of Chapter 4 of the PhD thesis “Novel approaches to identify drivers of chemical stress in small rivers” by Liza-Marie Beckers prepared at RWTH Aachen University and at the Helmholtz Centre for Environmental Research-UFZ. In Chapter 4, precipitation-related pollutant patterns and indicator compounds during heavy rain events were identified in the Holtemme River by nontarget screening and cluster analysis. The table contains peak heights of organic micropollutants detected in water samples taken during heavy rain events in the Holtemme River (Saxony – Anhalt, Germany). The table is structured into the following columns: Compound name, use class of compound (e.g., pharmaceutical or pesticide), distinction between target or identified unknown compounds, mass-to-charge ratio (m/z), retention time (RT), assignment to a pattern identified by cluster analysis (i.e., “Base” or “Quick”), the probability of belonging to the assigned pattern as number between 0 and 1 as well as the peak height of the compound in each sample. The samples are indicated by "B" for "bottle" and a number from 1-16. The use class “NA” indicates that now major use class for this compound could be identified.</p> <p>The sampling was triggered by combined sewer overflow at a wastewater treatment plant upstream of the sampling point. Samples were taken by an automated sampler in 30-min composite samples for 8 hours resulting in 16 samples per rain event. In total, 6 heavy rain events from May to September 2016 were sampled during this study. The table is divided into 6 subtables (i.e., Table S27 A-F). Each subtable displays compounds and their peak heights detected in samples from one heavy rain event. The different rain events are abbreviated by the sampling date:</p> <p>Table S27A displays results from the rain event samples May 29<sup>th</sup> 2016 : E2905</p> <p>Table S27B displays results from the rain event samples June 01<sup>st</sup> 2016 : E0106</p> <p>Table S27C displays results from the rain event samples June 24<sup>th</sup> 2016 : E1306</p> <p>Table S27D displays results from the rain event samples June 13<sup>th</sup> 2016 : E2406</p> <p>Table S27E displays results from the rain event samples July 13<sup>th</sup> 2016 : E1307</p> <p>Table S27F displays results from the rain event samples September 17<sup>th</sup> 2016 : E1709</p> <p>Chemical analysis of the water samples was performed by liquid chromatography (UltiMate 3000 LC system (Thermo Scientific)) coupled to high resolution mass spectrometry (Q Exactive Plus, Thermo Scientific) with a heated electrospray ionization (HESI) source. Nontarget screening was performed as it allows for a comprehensive characterization of the chemical exposure during heavy rain events. However, only annotated target compounds and unknown compounds identified by structure elucidation are presented in the table. Details on data evaluation methods are described in Chapter 4 of the PhD thesis.</p> <p>Beckers, L.M. (2019): Novel approaches to identify drivers of chemical stress in small rivers. RWTH Aachen University, Aachen.</p>
PEDRo: an Event-based Dataset for Person Detection in Robotics
<p>This event-based dataset is specifically designed for person detection in robotics applications. </p>
Joint Microseismic Event Detection and Location with a Detection Transformer
<p>Synthetic passive seismic data and their corresponding labels used for training and testing the network in the paper "Joint Microseismic Event Detection and Location with a Detection Transformer".</p>
MARSIS Transient Layer Detections during Comet Siding Spring Event 2014
<p><strong>This data is used in the publication, "Prolonged Lifetime of the Transient Ionized Layer in the Martian Atmosphere Caused by Comet Siding Spring" by Luppen et al. 2020.</strong></p> <p>MARSIS_transient_layer_detections.csv: The data contained in this file are measurements from the Mars Express spacecraft's MARSIS instrument, collected during the Comet Siding Spring flyby event in 2014. The comet is known to have deposited a large amount of material into Mars' ionosphere, producing a transient layer of plasma that was noticed by multiple spacecraft instruments immediately following the comet's closest approach to the planet. The layer was originally thought to have had a lifetime of 1-2 days. Follow-up analysis, represented in this data file, indicate that this layer could have lasted at least 7 days and as many as 32 days after closest approach, much longer than previously reported. Our results help to constrain models of metal ion chemistry in the Martian upper atmosphere.</p> <p>MARSIS_regular_ionospheric_measurements.csv: The data contained in this file are measurements of the regular ionosphere during the comet Siding Spring event. These data are plotted in Figures 3 and 4 of the manuscript.</p> <p>MAVEN_normalized_euv_irradiance.csv: The data contained in this file are measurements collected from the Extreme UltraViolet (EUV) monitor that is part of the Langmuir Probe and Waves (LPW) instrument on the MAVEN spacecraft collected during the comet Siding Spring event. These data are plotted in Figure 5 of the manuscript.</p> <p>FISM_normalized_euv_irradiance.csv: The data contained in this file are measurements collected from the Flare Irradiance Spectral Model (FISM) collected during the comet Siding Spring event. These data are plotted in Figure 5 of the manuscript.</p> <p>MARSIS_averaged_data_figure4.csv: The data contained in this file are derived products from the MARSIS_regular_ionospheric_measurements.csv data. A running mean was calculated with 2 degree SZA bins, the SZA centers of those bins were identified, and a standard deviation was calculated for each bin. These data are plotted in Figure 4 of the manuscript. Note that we make use of a shaded region signifying a 3 sigma standard deviation.</p>
Phlorest phylogeny derived from Hruschka et al. 2015 'Detecting regular sound changes in linguistics as events of concerted evolution'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hruschka, D. J., Branford, S., Smith, E. D., Wilkins, J., Meade, A., Pagel, M., & Bhattacharya, T. (2015). Detecting regular sound changes in linguistics as events of concerted evolution. Current Biology, 25(1), 1-9.</p> </blockquote>
DCASE 2024 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description:</strong></p> <p>The development set for task 5 of DCASE 2024 "Few-shot Bioacoustic Event Detection" consists of 217 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p><strong>Folder Structure:</strong></p> <p><em>Development_set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___WMW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p> |__Validation_Set/</p> <p> |___HB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB24/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___RD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_set_annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br>TRAINING SET<br>-----------------------------------------------------<br>Number of audio recordings | 174<br>Total duration | 21 hours<br>Total classes | 47<br>Total events | 14229<br>-----------------------------------------------------<br>TRAINING SET/BV<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 10 hours<br>Total classes | 11<br>Total events | 9026<br>Sampling rate | 24000 Hz<br>-----------------------------------------------------<br>TRAINING SET/HT<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 5 hours<br>Total classes | 5<br>Total events | 611<br>Sampling rate | 6000 Hz<br>-----------------------------------------------------<br>TRAINING SET/JD<br>-----------------------------------------------------<br>Number of audio recordings | 1<br>Total duration | 10 mins<br>Total classes | 1<br>Total events | 357<br>Sampling rate | 22050 Hz<br>-----------------------------------------------------<br>TRAINING SET/MT<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 1 hour and 10 mins<br>Total classes | 4<br>Total events | 1294<br>Sampling rate | 8000 Hz<br>-----------------------------------------------------<br>TRAINING SET/WMW<br>-----------------------------------------------------<br>Number of audio recordings | 161<br>Total duration | 4 hours and 40 mins<br>Total classes | 26<br>Total events | 2941<br>Sampling rate | various sampling rates<br>-----------------------------------------------------</p> <p>-----------------------------------------------------<br>VALIDATION SET<br>-----------------------------------------------------<br>Number of audio recordings | 43<br>Total duration | 49 hours and 57 minutes<br>Total classes | 7<br>Total events | 3504<br>-----------------------------------------------------<br>VALIDATION SET/HB<br>-----------------------------------------------------<br>Number of audio recordings | 10<br>Total duration | 2 hours and 38 minutes<br>Total classes | 1<br>Total events | 712<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 3 hours<br>Total classes | 2<br>Total events | 292<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/ME<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 20 minutes<br>Total classes | 2<br>Total events | 73<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB24<br>-----------------------------------------------------<br>Number of audio recordings | 4<br>Total duration | 2 hours<br>Total classes | 2<br>Total events | 350<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/RD<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 18 hours<br>Total classes | 1<br>Total events | 1372<br>Sampling rate | 48000 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PW<br>-----------------------------------------------------<br>Number of audio recordings | 15<br>Total duration | 24 hours<br>Total classes | 1<br>Total events | 705<br>Sampling rate | 96000 Hz<br>-----------------------------------------------------</p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br>---------------------<br>Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br>---------------------<br>Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2024_task5_training_set_classes.csv and DCASE2024_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set. Additionally, DCASE2024_task5_validation_set_classes.csv also provides a recording names column.</p> <p>DCASE2024_task5_training_set_classes.csv<br>---------------------<br>dataset, class_code, class_name</p> <p>DCASE2024_task5_validation_set_classes.csv<br>---------------------<br>dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for this task will be released on the 1 June 2024</p> <p><strong>Open Access:</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info:</strong></p> <p>Please send any feedback or questions to:</p> <p>Burooj Ghani - burooj.ghani@naturalis.nl | Ines Nolasco - i.dealmeidanolasco@qmul.ac.uk</p> <p>Alternately, join us on Slack: <a href="https://join.slack.com/t/dcase/shared_invite/zt-12zfa5kw0-dD41gVaPU3EZTCAw1mHTCA">task-fewshot-bio-sed</a></p> <p> </p>
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
Variation in Detected Adverse Events using Trigger Tools: A Systematic Review and Meta-Analysis
<p>Raw data sets for the meta-analysis.</p> <p>Data collection file with all the information extracted from the included studies.</p> <p>QAT file with the information from the quality assessment tool (QAT) for all included studies.</p> <p>ReadMe with information on data sets and updates.</p> <p>Codebooks for both data sets.</p>
Wikipedia. Events and collective memory detection dataset
<p>This is the data accompanying the code (https://github.com/mizvol/WikiBrain), required to reproduce results of "Wikipedia graph mining: dynamic structure of collective memory" paper (https://arxiv.org/abs/1710.00398).</p>
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica
<p>This repository contains the waveform data used by Ho et al. (2024), along with all generated fine-tuned models and event catalogs. See the README file for a summary. The corresponding software packages are available on GitHub at <a href="https://github.com/jakewalter/easyQuake.git">https://github.com/jakewalter/easyQuake.git</a>, <a href="https://github.com/seisbench/seisbench">https://github.com/seisbench/seisbench</a>, and <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a>. See the README.md file at <a href="https://github.com/longmho/Transfer_Learning_and_Seisbench">https://github.com/longmho/Transfer_Learning_and_Seisbench</a> for additional details.</p>
DCASE 2021 Task 5: Few-shot Bioacoustic Event Detection Evaluation Set
<p><strong>General Description</strong></p> <p>The evaluation set for task 5 of DCASE 2021 "Few-shot Bioacoustic Event Detection" consists of 31 audio files acquired from different bioacoustic sources. </p> <p>In Evaluation_Set_Annotations: the first 5 annotations are provided for each file, with events marked as positive (POS) for the class of interest. This is the same setup used during the DCASE 2021 challenge.</p> <p>In Evaluation_Set_Full_Annotations: the full annotations are provided for each file, with events marked as positive (POS) or unknown (UNK) for the class of interest.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p><em>Evaluation_Set.zip contains audio files and annotation files with 5 first POS events (as used during DCASE 2021 challenge)</em></p> <p>|__Evaluation_Set/</p> <p> |___DC/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ML/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p><em>Evaluation_Set_Audio.zip</em> has the same structure but contains only the *.wav files.</p> <p><em>Evaluation_Set_Annotations.zip</em> has the same structure but contains only the *.csv files with first 5 POS annotations.</p> <p><em>Evaluation_Set_Full_Annotations.zip</em> has the same structure but contains only the *.csv files with all POS annotations.</p> <p>The subfolders denote different recording sources and there may or may not be overlap between classes of interest from different wav files.</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:<br> [ Audiofilename, Starttime, Endtime, Q ]</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2021_task5_evaluation_set.csv provides a table with class code correspondance to class name for all the recordings of the Evaluation set.</p> <p>DCASE2021_task5_evaluation_set.csv<br> -------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Development Set</strong></p> <p>The development set for the same task can be found at: <a href="https://doi.org/10.5281/zenodo.5412896">https://doi.org/10.5281/zenodo.5412896</a></p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info</strong></p> <p>Please send any feedback or questions to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk</p>
DCASE 2021 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description</strong></p> <p>The development set for task 5 of DCASE 2021 "Few-shot Bioacoustic Event Detection" consists of 19 audio files acquired from different bioacoustic sources. The dataset is split into training and validation Sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p> </p> <p><strong>Folder Structure</strong></p> <p><em>Development_Set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |__Validation_Set/</p> <p> |___HV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_Set_Audio.zip</em> has the same structure but contains only the *.wav files.</p> <p><em>Development_Set_Annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br> TRAINING SET<br> -----------------------------------------------------<br> Number of audio recordings | 11<br> Total duration | 14 hours and 20 mins<br> Total classes (excl. UNK) | 19<br> Total events (excl. UNK) | 4,686<br> -----------------------------------------------------<br> TRAINING SET/BV<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 10 hours<br> Total classes (excl. UNK) | 11<br> Total events (excl. UNK) | 2,662<br> Sampling rate | 24,000 Hz<br> -----------------------------------------------------<br> TRAINING SET/HT<br> -----------------------------------------------------<br> Number of audio recordings | 3<br> Total duration | 3 hours<br> Total classes (excl. UNK) | 3<br> Total events (excl. UNK) | 435<br> Sampling rate | 6,000 Hz<br> -----------------------------------------------------<br> TRAINING SET/JD<br> -----------------------------------------------------<br> Number of audio recordings | 1<br> Total duration | 10 mins<br> Total classes (excl. UNK) | 1<br> Total events (excl. UNK) | 355<br> Sampling rate | 22,050 Hz<br> -----------------------------------------------------<br> TRAINING SET/MT<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 1 hour and 10 mins<br> Total classes (excl. UNK) | 4<br> Total events (excl. UNK) | 1,234<br> Sampling rate | 8,000 Hz<br> -----------------------------------------------------</p> <p><br> -----------------------------------------------------<br> VALIDATION SET<br> -----------------------------------------------------<br> Number of audio recordings | 8<br> Total duration | 5 hours<br> Total classes (excl. UNK) | 4<br> Total events (excl. UNK) | 310<br> -----------------------------------------------------<br> VALIDATION SET/HV<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 2 hours<br> Total classes (excl. UNK) | 2<br> Total events (excl. UNK) | 50<br> Sampling rate | 6,000 Hz<br> -----------------------------------------------------<br> VALIDATION SET/PB<br> -----------------------------------------------------<br> Number of audio recordings | 6<br> Total duration | 3 hours<br> Total classes (excl. UNK) | 2<br> Total events (excl. UNK) | 260<br> Sampling rate | 44,100 Hz<br> -----------------------------------------------------</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2021_task5_training_set_classes.csv and DCASE2021_task5_validation_set_classes.csv provide a table with class code correspondace to class name for all classes in the Development set.</p> <p>DCASE2021_task5_training_set_classes.csv<br> ---------------------<br> dataset, class_code, class_name</p> <p>DCASE2021_task5_validation_set_classes.csv<br> ---------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for the same task can be found at: <a href="https://doi.org/10.5281/zenodo.5413149">https://doi.org/10.5281/zenodo.5413149</a></p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><br> <strong>Contact info</strong></p> <p>Please send any feedback or questions to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk<br> </p>
Dataset: Swarms in Central Utah – event detection lists, arrival picks, relocations & moment tensor solutions
<p>Dataset containing results of moment tensor inversions, relocations and event detections for our study "Petersen & Pankow (2023): Small-magnitude seismic swarms in Central Utah: Interactions of regional tectonics, local structures and hydrothermal systems" (<a href="https://doi.org/10.1029/2023GC010867">https://doi.org/10.1029/2023GC010867</a>).<br>Please read the pdf-README file for more information on the dataset.</p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Global dry and hot extreme events detection
<p>Workflow for the global detection of dry and hot extreme weather events. ERA5 is the ECMWF Reanalysis of the climate. PET is potential reference evapotranspiration. PEI is the daily difference between precipitation and evapotranspiration averaged over the preceding days (here 30, 90 and 180). Data cubes are stored in zarr format. Statistics are saved in a csv table.</p>
Dataset for training Dev-ResNet for detection of developmental events in Lymnaea stagnalis
<p>Dataset for training <a href="https://github.com/EmbryoPhenomics/dev-resnet">Dev-ResNet</a> for detection of developmental events in<em> Lymnaea stagnalis. </em></p> <p> </p> <p>The following naming scheme is used for the individual GIF files:</p> <p>Example: "./video/20C_A_B1_315hr.gif"</p> <ul> <li><strong>"./video/"</strong> --> Source folder.</li> <li><strong>"20C" </strong> --> Treatment, here incubation temperature.</li> <li><strong>"A"</strong> --> Egg mass label.</li> <li><strong>"B1" </strong> --> Individual embryo label.</li> <li><strong>"315hr" </strong>--> Hours since first cell division.</li> </ul> <p> </p>
HGT breakpoints and events detected by LocalHGT in 2098 real data
<p> </p> <table> <tbody> <tr> <td>filter_hgt_results.zip</td> <td>the HGT breakpoints of all samples, in this folder, each CSV file represents a sample. </td> </tr> <tr> <td>identified_event.csv</td> <td>the HGT events of all samples, the results of all samples are in this file.</td> </tr> <tr> <td>TableS1</td> <td>phenotypes of all samples</td> </tr> <tr> <td>use.zip</td> <td>Microbial abundances of all samples, in this folder, all samples from the same cohort are in a single file.</td> </tr> </tbody> </table> <p> </p>
A Twitter dataset (with labels) for Life-Event detection
<p>The file contains an anonymized version of the dataset collected in the framework of the “Tsundoku” project financed by the Autonomous Province of Trento according to the province law 13 of December 1999, n. 6 (and subsequent modifications), art. 5. - “Aids for promoting research and development”, financing approved with APIAE manager’s provision n. 691. The purpose of this project is training a Deep Learning (DL) model capable of detecting the occurrence of a so-called Life Event - a wedding and/or the birth of a child - in a person’s life on the basis of the contents she shared on social media (Twitter, in this case). </p> <p>More precisely, the dataset consists of the most recent tweets - up to approximately 3200 for each account - of 8 Italian and 27 English-speaking users (all of them randomly picked), totalling 74722 tweets, 20302 written in Italian and 54420 in English.</p> <p>For each user (labelled as ‘user_<em>x</em>’ with <em>x</em> an integer number between 1 and 35), the file includes the language her tweets are written in as well as the list of said tweets. For every one of the latter, the information made available includes the text of the tweet (appropriately modified as explained below), its length, the number of hashtags and of user mentions it featured, the number of retweets and of likes it received, two Boolean flags (“True” or “False”) assessing whether it was a quote or a reply and a label (‘birth’, ‘wedding’ and ‘not Life-Event-related’, depending on whether the tweet refers to a birth/wedding experienced by the user or not). With respect to the label, it is worth stressing that a tweet was labelled as ‘birth’/‘wedding’ only if the event “actively” involved the user (thus, a tweet reading “Today I get married” is labelled as ‘wedding’, while the label ‘not Life-Event-related’ is associated with ‘Today my sister gets married’) and if the tweet is <strong>by itself</strong> unambiguous (in other words, a tweet reading ‘My wife is pregnant’ is labelled as ‘birth’, while ‘Josephine is pregnant’ does not, since the tweet alone does not allow to determine who Josephine exactly is - this could perhaps be inferred from other tweets but this kind of contextualization is very hard to be carried out and was out of the scope of this project.).</p> <p>In order to comply with GDPR, each one of the texts included in the file was obtained from the original text after taking the following anonymization steps:</p> <p> </p> <ul> <li> <p>every web link got replaced by the ‘WEBLINK’ string;</p> </li> <li> <p>every mention to another Twitter user (for instance, “@joedoe”) got replaced by the “OTHERUSER” string;</p> </li> <li> <p>every hashtag got replaced by the “HASHTAG” string;</p> </li> <li> <p>every name and surname detected by the spaCy library (see <a href="https://spacy.io/">https://spacy.io</a> for more info) got replaced by the “NAME/SURNAME” string;</p> </li> <li> <p>10% of the available words got randomly picked and erased. </p> </li> </ul> <p> </p> <p> </p>
DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description:</strong></p> <p>The development set for task 5 of DCASE 2022 "Few-shot Bioacoustic Event Detection" consists of 192 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p><strong>this version (3):</strong><br> * fixes issues with annotations from HB set</p> <p> </p> <p><strong>Folder Structure:</strong></p> <p><em>Development_Set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___WMW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p> |__Validation_Set/</p> <p> |___HB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_Set_Annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>## Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br> TRAINING SET<br> -----------------------------------------------------<br> Number of audio recordings | 174<br> Total duration | 21 hours<br> Total classes | 47<br> Total events | 14229<br> -----------------------------------------------------<br> TRAINING SET/BV<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 10 hours<br> Total classes | 11<br> Total events | 9026<br> Ratio event/duration | 0.04<br> Sampling rate | 24000 Hz<br> -----------------------------------------------------<br> TRAINING SET/HT<br> -----------------------------------------------------<br> Number of audio recordings | 5<br> Total duration | 5 hours<br> Total classes | 5<br> Total events | 611<br> Ratio event/duration | 0.05<br> Sampling rate | 6000 Hz<br> -----------------------------------------------------<br> TRAINING SET/JD<br> -----------------------------------------------------<br> Number of audio recordings | 1<br> Total duration | 10 mins<br> Total classes | 1<br> Total events | 357<br> Ratio event/duration | 0.06<br> Sampling rate | 22050 Hz<br> -----------------------------------------------------<br> TRAINING SET/MT<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 1 hour and 10 mins<br> Total classes | 4<br> Total events | 1294<br> Ratio event/duration | 0.04<br> Sampling rate | 8000 Hz<br> -----------------------------------------------------<br> TRAINING SET/WMW<br> -----------------------------------------------------<br> Number of audio recordings | 161<br> Total duration | 4 hours and 40 mins<br> Total classes | 26<br> Total events | 2941<br> Ratio event/duration | 0.24<br> Sampling rate | various sampling rates<br> -----------------------------------------------------</p> <p>-----------------------------------------------------<br> VALIDATION SET<br> -----------------------------------------------------<br> Number of audio recordings | 18<br> Total duration | 5 hours and 57 minutes<br> Total classes | 5<br> Total events | 1077<br> -----------------------------------------------------<br> VALIDATION SET/HB<br> -----------------------------------------------------<br> Number of audio recordings | 10<br> Total duration | 2 hours and 38 minutes<br> Total classes | 1<br> Total events | 712<br> Ratio event/duration | 0.7<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------<br> VALIDATION SET/PB<br> -----------------------------------------------------<br> Number of audio recordings | 6<br> Total duration | 3 hours<br> Total classes | 2<br> Total events | 292<br> Ratio event/duration | 0.003<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------<br> VALIDATION SET/ME<br> -----------------------------------------------------<br> Number of audio recordings | 2<br> Total duration | 20 minutes<br> Total classes | 2<br> Total events | 73<br> Ratio event/duration | 0.01<br> Sampling rate | 44100 Hz<br> -----------------------------------------------------</p> <p> </p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br> ---------------------<br> Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2022_task5_training_set_classes.csv and DCASE2022_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set.</p> <p>DCASE2022_task5_training_set_classes.csv<br> ---------------------<br> dataset, class_code, class_name</p> <p>DCASE2022_task5_validation_set_classes.csv<br> ---------------------<br> dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for this task will be released on the 1st of June 2022</p> <p><strong>Open Access:</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info:</strong></p> <p>Please send any feedback or questions to:</p> <p>Ines Nolasco - i.dealmeidanolasco@qmul.ac.uk</p>
Fireball events detected in Romania by the FRIPON-MOROI network
<p>In this table we present the fireball events detected by the Fireball Recovery and Inter Planetary Observation Network (FRIPON) (Colas et al., 2020) with the use of the Meteorite Orbits Reconstruction by Optical Imaging (MOROI) network (Nedelcu et al. 2018). The data include events recorded by at least two different fireball cameras installed over the Romanian territory. For each event we provide: the name, the stations that detected it, whether it has noticeable deceleration (V<sub>f</sub>/V<sub>0</sub> < 0.8, where V<sub>f</sub> is the velocity of the meteoroid at the end of the luminous trajectory and V<sub>0</sub> is the initial velocity of the meteoroid).</p> <p>The events are named by the time when they were captured as follows: year, month, day followed by hour, minute, second in UT.</p> <p><strong>The events with noticeable deceleration are analysed in (Boaca et al. 2022), https://iopscience.iop.org/article/10.3847/1538-4357/ac8542.<br> If using this data, please reference the above publication. </strong></p> <p><em>The online presentation of the paper can be found on the AAS youtube channel:</em></p> <p>https://www.youtube.com/watch?v=Wx186QB5ZVE</p> <p> </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
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.