The widespread use of videos in modern indoor livestock facilities coupled with the availability of efficient and low-cost computer vision  algorithms provides strong incentives for continuously monitoring farm animal behaviour. Deciphering how pigs behave when experiencing prolonged heat stress (HS) is particularly important for animal welfare, as it helps us to better understand how animals use various thermoregulation and heat dissipation mechanisms. This dataset includes the monitoring of continuous behavioural traits for 24 growing pigs first housed at thermoneutrality and then exposed to HS. The data can be used to illustrate the frequencies of specific behavioural traits (time budget) and their deviations due to heat stress, either on average or in animal-centred view (recurrence of patterns, etc.). Outputs can be used to perform behavioural patterns mining, behaviour clustering and modelling. An important effort was made to ensure consistency of the behavioural dataset, with comparison with readings of automatic feeders to decipher feededin visits vs. non-feeding visits. Further video processing algorithms may benefit from the training (labelled images) dataset, but also from the multiple annotation approach (postures and events). This dataset can be used to train any machine learning methods for behaviour prediction from videos in conventional growing pigs.  Data were collected on 24 pigs that were video-monitored day and night under two contrasted conditions: thermoneutral (TN, 22°C) and HS (32°C). All pigs were housed individually and had free access to an automatic electronic feeder delivering pellets four times a day, and to water. Environmental conditions (temperature, humidity) in the room were recorded by sensors. After acquisition, videos were processed using YOLOv11, a real-time object detection algorithm  that uses a convolutional neural network (CNN), to extract the following behavioural traits: drinking, willingness to eat, lying down, standing up, moving around, curiosity towards the littermate housed in the neighbouring pen, and contact between the two animals (cuddling). A minute frequency sampling rate was applied (each minute correspond to 150 frames processed) for a continuous period of 16 days, spanning the two different thermal conditions (9 days on TN, 6 days on HS, 1 day back to TN). The algorithm was first trained thanks to manual video analysis labelling at the individual scale. Consistency with the automatic electronic feeder’s data (also provided) was thoroughly checked. The dataset allows quantitative criterion to be analysed to decipher inter-individual differences in animal behaviour and their dynamic adaptation to heat stress.

The widespread use of videos in modern indoor livestock facilities coupled with the availability of efficient and low-cost computer vision  algorithms provides strong incentives for continuously monitoring farm animal behaviour. Deciphering how pigs behave when experiencing prolonged heat stress (HS) is particularly important for animal welfare, as it helps us to better understand how animals use various thermoregulation and heat dissipation mechanisms. This dataset includes the monitoring of continuous behavioural traits for 24 growing pigs first housed at thermoneutrality and then exposed to HS. The data can be used to illustrate the frequencies of specific behavioural traits (time budget) and their deviations due to heat stress, either on average or in animal-centred view (recurrence of patterns, etc.). Outputs can be used to perform behavioural patterns mining, behaviour clustering and modelling. An important effort was made to ensure consistency of the behavioural dataset, with comparison with readings of automatic feeders to decipher feededin visits vs. non-feeding visits. Further video processing algorithms may benefit from the training (labelled images) dataset, but also from the multiple annotation approach (postures and events). This dataset can be used to train any machine learning methods for behaviour prediction from videos in conventional growing pigs.  Data were collected on 24 pigs that were video-monitored day and night under two contrasted conditions: thermoneutral (TN, 22°C) and HS (32°C). All pigs were housed individually and had free access to an automatic electronic feeder delivering pellets four times a day, and to water. Environmental conditions (temperature, humidity) in the room were recorded by sensors. After acquisition, videos were processed using YOLOv11, a real-time object detection algorithm  that uses a convolutional neural network (CNN), to extract the following behavioural traits: drinking, willingness to eat, lying down, standing up, moving around, curiosity towards the littermate housed in the neighbouring pen, and contact between the two animals (cuddling). A minute frequency sampling rate was applied (each minute correspond to 150 frames processed) for a continuous period of 16 days, spanning the two different thermal conditions (9 days on TN, 6 days on HS, 1 day back to TN). The algorithm was first trained thanks to manual video analysis labelling at the individual scale. Consistency with the automatic electronic feeder’s data (also provided) was thoroughly checked. The dataset allows quantitative criterion to be analysed to decipher inter-individual differences in animal behaviour and their dynamic adaptation to heat stress.

The widespread use of videos in modern indoor livestock facilities coupled with the availability of efficient and low-cost computer vision  algorithms provides strong incentives for continuously monitoring farm animal behaviour. Deciphering how pigs behave when experiencing prolonged heat stress (HS) is particularly important for animal welfare, as it helps us to better understand how animals use various thermoregulation and heat dissipation mechanisms. This dataset includes the monitoring of continuous behavioural traits for 24 growing pigs first housed at thermoneutrality and then exposed to HS. The data can be used to illustrate the frequencies of specific behavioural traits (time budget) and their deviations due to heat stress, either on average or in animal-centred view (recurrence of patterns, etc.). Outputs can be used to perform behavioural patterns mining, behaviour clustering and modelling. An important effort was made to ensure consistency of the behavioural dataset, with comparison with readings of automatic feeders to decipher feededin visits vs. non-feeding visits. Further video processing algorithms may benefit from the training (labelled images) dataset, but also from the multiple annotation approach (postures and events). This dataset can be used to train any machine learning methods for behaviour prediction from videos in conventional growing pigs.  Data were collected on 24 pigs that were video-monitored day and night under two contrasted conditions: thermoneutral (TN, 22°C) and HS (32°C). All pigs were housed individually and had free access to an automatic electronic feeder delivering pellets four times a day, and to water. Environmental conditions (temperature, humidity) in the room were recorded by sensors. After acquisition, videos were processed using YOLOv11, a real-time object detection algorithm  that uses a convolutional neural network (CNN), to extract the following behavioural traits: drinking, willingness to eat, lying down, standing up, moving around, curiosity towards the littermate housed in the neighbouring pen, and contact between the two animals (cuddling). A minute frequency sampling rate was applied (each minute correspond to 150 frames processed) for a continuous period of 16 days, spanning the two different thermal conditions (9 days on TN, 6 days on HS, 1 day back to TN). The algorithm was first trained thanks to manual video analysis labelling at the individual scale. Consistency with the automatic electronic feeder’s data (also provided) was thoroughly checked. The dataset allows quantitative criterion to be analysed to decipher inter-individual differences in animal behaviour and their dynamic adaptation to heat stress.

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BreizhSR, a super-resolution Sentinel-2 to SPOT-6/7 dataset 1. Dataset motivationBreizhSR is a dataset targetting super-resolution of (RGB bands of) Sentinel-2 images by providing time series colocated in space and time with SPOT-6/7 acquisitions. This dataset is composed of cloud free Sentinel-2 time series (visible bands at 10m resolution) and SPOT-6/7 pansharpened color images resampled 2.5m resolution. The study area is the region of Brittany (Breizh in the local language), located on the northwestern coast of France with an oceanic climate. The dataset covers about 35 000 km² with mostly agricultural areas (about 80 %). All acquisitions are from 2018 in the Brittany region of France.2. Dataset organizationThe dataset folder follows the structure detailed below :BreizhSR├── dataset_test.pkl├── dataset_train.pkl├── README.md├── x├── x_test├── y└── y_testThe README.md file contains the same information as this description.Actual image patches are stored in the x and x_test folders for Sentinel-2 patches, and in the y and y_test folders for ground truth SPOT patches. Subfolders are organized using a integer identifier (e.g. 8355) that denote the series identifier. Therefore, for the S2 series x/8355, the corresponding SPOT patch is in subfolder y/8355.This organization and additional metadata are described in two Pandas Dataframes : dataset_train.pkl and dataset_test.pkl. These files are Dataframes serialized using the pickle Python serialization protocol. The columns available in these Dataframes are described in the table below.xywktspot6_namesen2_acquisitionsdates_sen2dates_spot6splitLatitude of the center point (expressed in Lambert 93 CRS)Longitude of the center point (expressed in Lambert 93 CRS)Area of interest geometry in well-known text formatPath to the SPOT ground truthPaths to the Sentinel-2 input seriesAcquisition dates for the Sentinel-2 imagesAcquisition date for the SPOT ground truthtrain or test3. Data collection and preprocessingSentinel-2Sentinel-2 constellation has twin satellites launched by the European Space Agency (ESA) in 2015 and 2017 that cover all Earth’s surfaces every five days at the equator. Level-2A images of the BreizhSR dataset are gathered via the THEIA platform, which employs the MAJA pre-processing algorithm to obtain atmospherically corrected ground reflectance. To match the SPOT-6 spectral characteristics, only RGB bands at a 10-meter spatial resolution (B4, B3,and B2) are used in the analysis. The images were collected for the nine tiles covering the Brittany region from the 1st of April 2018 to the 31st of August 2018, filtering images with a cloud cover under 5 %. Since the SPOT-6 data was acquired in the summer of 2018, the Sentinel-2 time period was chosen to include images from before and after the SPOT-6 acquisitions while staying in a range of similar seasonal and climate conditions.Sentinel-2 tiles are cropped into 3x74x74 patches. The dataset is preprocessed with a min-max normalization, using the 2% and 98% percentile as an estimation of minimum and maximum values of Sentinel-2 data to take into account the presence of outliers due to artifacts such as clouds and their shadows.SPOT-6/7Orthorectified SPOT data under the Licence Ouverte is collected from the DINAMIS platform. Multispectral images at 6m resolution are pansharpened using the panchromatic 1.5m reference using the RCS algorithm Orfeo ToolBox, similar to the Brovey pansharpening algorithm. The pansharpened tiles are preprocessed with a min-max normalization, downsampled at 2.5m resolution and patches are finally cropped with dimensions 3x296x296.4. LicenseSPOT images and the Sentinel-2 Theia L2A products are released under the Licence Ouverte 2.0 from the French government. This dataset contains modified Coprnicus Sentinel data from 2018, made available under free access by EU law. Other files in the dataset are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).AcknowledgementsWe thank the support of GDR IASIS for funding this work under the SESURE project, the DINAMIS consortium, CNES/Airbus and IGN for access to the SPOT-6 data, and ESA for access to Sentinel-2 data. During the conduct of this research, Simon Donike received a European scholarship to engage in Master Copernicus in Digital Earth, Erasmus Mundus Joint Master Degree (EMJMD). We thank Dirk Tiede (Uni. Salzburg) for his help and feedback on BreizhSR. This work was performed using HPC resources from GENCI–IDRIS (grant 2022-AD011013003).

BreizhSR, a super-resolution Sentinel-2 to SPOT-6/7 dataset 1. Dataset motivationBreizhSR is a dataset targetting super-resolution of (RGB bands of) Sentinel-2 images by providing time series colocated in space and time with SPOT-6/7 acquisitions. This dataset is composed of cloud free Sentinel-2 time series (visible bands at 10m resolution) and SPOT-6/7 pansharpened color images resampled 2.5m resolution. The study area is the region of Brittany (Breizh in the local language), located on the northwestern coast of France with an oceanic climate. The dataset covers about 35 000 km² with mostly agricultural areas (about 80 %). All acquisitions are from 2018 in the Brittany region of France.2. Dataset organizationThe dataset folder follows the structure detailed below :BreizhSR├── dataset_test.pkl├── dataset_train.pkl├── README.md├── x├── x_test├── y└── y_testThe README.md file contains the same information as this description.Actual image patches are stored in the x and x_test folders for Sentinel-2 patches, and in the y and y_test folders for ground truth SPOT patches. Subfolders are organized using a integer identifier (e.g. 8355) that denote the series identifier. Therefore, for the S2 series x/8355, the corresponding SPOT patch is in subfolder y/8355.This organization and additional metadata are described in two Pandas Dataframes : dataset_train.pkl and dataset_test.pkl. These files are Dataframes serialized using the pickle Python serialization protocol. The columns available in these Dataframes are described in the table below.xywktspot6_namesen2_acquisitionsdates_sen2dates_spot6splitLatitude of the center point (expressed in Lambert 93 CRS)Longitude of the center point (expressed in Lambert 93 CRS)Area of interest geometry in well-known text formatPath to the SPOT ground truthPaths to the Sentinel-2 input seriesAcquisition dates for the Sentinel-2 imagesAcquisition date for the SPOT ground truthtrain or test3. Data collection and preprocessingSentinel-2Sentinel-2 constellation has twin satellites launched by the European Space Agency (ESA) in 2015 and 2017 that cover all Earth’s surfaces every five days at the equator. Level-2A images of the BreizhSR dataset are gathered via the THEIA platform, which employs the MAJA pre-processing algorithm to obtain atmospherically corrected ground reflectance. To match the SPOT-6 spectral characteristics, only RGB bands at a 10-meter spatial resolution (B4, B3,and B2) are used in the analysis. The images were collected for the nine tiles covering the Brittany region from the 1st of April 2018 to the 31st of August 2018, filtering images with a cloud cover under 5 %. Since the SPOT-6 data was acquired in the summer of 2018, the Sentinel-2 time period was chosen to include images from before and after the SPOT-6 acquisitions while staying in a range of similar seasonal and climate conditions.Sentinel-2 tiles are cropped into 3x74x74 patches. The dataset is preprocessed with a min-max normalization, using the 2% and 98% percentile as an estimation of minimum and maximum values of Sentinel-2 data to take into account the presence of outliers due to artifacts such as clouds and their shadows.SPOT-6/7Orthorectified SPOT data under the Licence Ouverte is collected from the DINAMIS platform. Multispectral images at 6m resolution are pansharpened using the panchromatic 1.5m reference using the RCS algorithm Orfeo ToolBox, similar to the Brovey pansharpening algorithm. The pansharpened tiles are preprocessed with a min-max normalization, downsampled at 2.5m resolution and patches are finally cropped with dimensions 3x296x296.4. LicenseSPOT images and the Sentinel-2 Theia L2A products are released under the Licence Ouverte 2.0 from the French government. This dataset contains modified Coprnicus Sentinel data from 2018, made available under free access by EU law. Other files in the dataset are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).AcknowledgementsWe thank the support of GDR IASIS for funding this work under the SESURE project, the DINAMIS consortium, CNES/Airbus and IGN for access to the SPOT-6 data, and ESA for access to Sentinel-2 data. During the conduct of this research, Simon Donike received a European scholarship to engage in Master Copernicus in Digital Earth, Erasmus Mundus Joint Master Degree (EMJMD). We thank Dirk Tiede (Uni. Salzburg) for his help and feedback on BreizhSR. This work was performed using HPC resources from GENCI–IDRIS (grant 2022-AD011013003).

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This repository contains the datasets used for training and testing during the work discussed in the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data:A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0.In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing.Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components:GPS start of the file from which this segment comesstarting GPS time of this segmentduration of the segment [s]mass1 [solar masses]mass2 [solar masses]spin1zspin2zinclination [radians]coalescence phase [radians]distance [Mpc]right_ascension [radians]declination [radians]polarization [radians]SNR (signal to noise ratio)shift of the signal w.r.t. the timeseries [s]length of the signal [s]fraction of the signal contained in the time windowNumber of samples:80000 for the file glitch_test.npz69998 for the file glitch_train.npz500000 for the file noise_test.npz250000 for the file noise_train.npz500000 for the file signal_test.npz250000 for the file signal_train.npzAn example of few lines of python code to read each file is:import numpy as npf = np.load("filename.npz")X = f['X']Y = f['Y']m = f['metadata']For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3]. This research has made use of data or software obtained from the Gravitational Wave Open Science Center (gwosc.org), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan.[1] https://gwpy.github.io[2] https://pycbc.org[3] https://lscsoft.docs.ligo.org/lalsuite