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Study Type: observational Subject: Computer Science, Systems and Electrical Engineering

1 to 10 of 10 Results

A Tree in a Tree: Measuring Biases of Partial DNS Tree Exploration Feb 28, 2025 Steurer, Florian; Anja Feldmann; Tobias Fiebig, 2024, "A Tree in a Tree: Measuring Biases of Partial DNS Tree Exploration", https://doi.org/10.17617/3.UBPZXP, Edmond, V4 Data of comprehensive DNS resolutions for "A Tree in a Tree: Measuring Biases of Partial DNS Tree Exploration". For getting started, have a look at the readme.md. The full raw data is too large to host here. You can find it at: https://data.measurement.network/
Additional dataset for: Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation Dec 20, 2024 Chimento, Michael; Chan, Hoi Hang; Aplin, Lucy; Kano, Fumihiro, 2024, "Additional dataset for: Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation", https://doi.org/10.17617/3.ZQMOJ3, Edmond, V2 This dataset includes sample data to reproduce the 3D-SOCS pipeline, as well as an annotated dataset and trained model weights for object detection and 2D keypoint estimation for Great tits and Blue tits. For more details, code and implementation of the pipeline, we refer to: https://github.com/alexhang212/3D-SOCS
Pat-SPECTER embeddings for Patstat 2023 Fall (USPTO and EPO) Dec 17, 2024 Rose, Michael; Buunk, Erik; Erhardt, Sebastian; Ghosh, Mainak; Li, Cheng; Harhoff, Dietmar, 2024, "Pat-SPECTER embeddings for Patstat 2023 Fall (USPTO and EPO)", https://doi.org/10.17617/3.ES5ZSC, Edmond, V1 Pat-SPECTER embeddings for all patent families as of Patstat 2023 Fall release. File "Pat-SPECTER_EPO_patstat-2023-fall.parquet" contains embeddings for all patent families with an EPO member (3.9M). File "Pat-SPECTER_USPTO_patstat-2023-fall.parquet" contains embeddings for all patent families with a USPTO member (9.2M). We encoded one representati...
PaECTER embeddings for Patstat 2023 Fall (USPTO and EPO) Dec 17, 2024 Ghosh, Mainak; Erhardt, Sebastian; Rose, Michael; Buunk, Erik; Harhoff, Dietmar, 2024, "PaECTER embeddings for Patstat 2023 Fall (USPTO and EPO)", https://doi.org/10.17617/3.BGRPMI, Edmond, V1 PaECTER embeddings for all patent families as of Patstat 2023 Fall release. File "PaECTER_EPO_patstat-2023-fall.parquet" contains embeddings for all patent families with an EPO member (3.9M). File "PaECTER_USPTO_patstat-2023-fall.parquet" contains embeddings for all patent families with a USPTO member (9.2M). We encoded one representative member of...
BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes Oct 22, 2024 naik, Hemal; Yang, Junran; Das, Dipin; Crofoot, Margaret; Rathore, Akanksha; Sridhar, Vivek Hari, 2024, "BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes", https://doi.org/10.17617/3.JCZ9WK, Edmond, V1 The dataset contains UAV footage of wild antelopes (blackbucks) in grassland habitats. It can be mainly used for two tasks: Multi-object tracking (MOT) and Re-Identification (Re-ID). We provide annotations for the position of animals in each frame, allowing us to offer very long videos (up to 3 min) completely annotated while maintaining the identi...
Sample Dataset for YOLO-Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos Aug 22, 2024 Chan, Hoi Hang; Putra, Prasetia; Schupp, Harald; Köchling, Johanna; Straßheim, Jana; Renner, Britta; Schroeder, Julia; Pearse, William D; Nakagawa, Shinichi; Burke, Terry; Griesser, Michael; Meltzer, Andrea; Lubrano, Saverio; Kano, Fumihiro, 2024, "Sample Dataset for YOLO-Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos", https://doi.org/10.17617/3.EZNKYV, Edmond, V1 Sample dataset and weights for YOLO-Behaviour framework. Required to run demo code in the documentation. Also provided bounding box datasets for 4 case studies in YOLO format. The human dataset is not included due to privacy concerns. Code: https://github.com/alexhang212/YOLO_Behaviour_Repo Documentation: https://alexhang212.github.io/YOLO_Behaviou...
MeerKAT: Meerkat Kalahari Audio Transcripts Jul 2, 2024 Schaefer-Zimmermann, Julian; Demartsev, Vlad; Averly, Baptiste; Dhanjal-Adams, Kiran; Duteil, Mathieu; Gall, Gabriella; Faiß, Marius; Johnson-Ulrich, Lily; Stowell, Dan; Manser, Marta; Roch, Marie; Strandburg-Peshkin, Ariana, 2024, "MeerKAT: Meerkat Kalahari Audio Transcripts", https://doi.org/10.17617/3.0J0DYB, Edmond, V1 A large-scale reference dataset for bioacoustics Please find the accompanying code at our official repository: github.com/livingingroups/animal2vec [Optional ]You can find the animal2vec model weights using the MeerKAT dataset here. MeerKAT is a 1068h large-scale dataset containing data from boom-mics and audio-recording collars worn by free-rangin...
Wild-MuPPET - Multi-view posture dataset of foraging pigeons in the wild Jan 3, 2024 Waldmann, Urs; Chan, Hoi Hang Alex; Naik, Hemal; Nagy, Mate; Couzin, Iain D.; Deussen, Oliver; Goldluecke, Bastian; Kano, Fumihiro, 2024, "Wild-MuPPET - Multi-view posture dataset of foraging pigeons in the wild", https://doi.org/10.17617/3.ENDMTI, Edmond, V1 Wild-MuPPET is a multi-view posture dataset of foraging pigeons outdoors, with 500 frames of 3D-ground truth and 2000 frames of 2D-ground truth of 9 keypoints. Paper: https://doi.org/10.48550/arXiv.2308.15316 Git repository: https://github.com/alexhang212/3D-MuPPET
covid_twitter_data Jun 5, 2023 Jin, Zhijing, 2023, "covid_twitter_data", https://doi.org/10.17617/3.TYNBEQ, Edmond, V1
Data for: Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision Feb 8, 2023 Koger, Benjamin; Adwait Deshpande; Jeffrey T. Kerby; Jacob M. Graving; Blair R. Costelloe; Iain D. Couzin, 2023, "Data for: Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision", https://doi.org/10.17617/3.EMRZGH, Edmond, V1 This is data used for the worked examples in the paper "Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision", published in the Journal of Animal Ecology.

A Tree in a Tree: Measuring Biases of Partial DNS Tree Exploration

Feb 28, 2025

Steurer, Florian; Anja Feldmann; Tobias Fiebig, 2024, "A Tree in a Tree: Measuring Biases of Partial DNS Tree Exploration", https://doi.org/10.17617/3.UBPZXP, Edmond, V4

Data of comprehensive DNS resolutions for "A Tree in a Tree: Measuring Biases of Partial DNS Tree Exploration". For getting started, have a look at the readme.md. The full raw data is too large to host here. You can find it at: https://data.measurement.network/

Additional dataset for: Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation

Dec 20, 2024

Chimento, Michael; Chan, Hoi Hang; Aplin, Lucy; Kano, Fumihiro, 2024, "Additional dataset for: Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation", https://doi.org/10.17617/3.ZQMOJ3, Edmond, V2

This dataset includes sample data to reproduce the 3D-SOCS pipeline, as well as an annotated dataset and trained model weights for object detection and 2D keypoint estimation for Great tits and Blue tits. For more details, code and implementation of the pipeline, we refer to: https://github.com/alexhang212/3D-SOCS

Pat-SPECTER embeddings for Patstat 2023 Fall (USPTO and EPO)

Dec 17, 2024

Rose, Michael; Buunk, Erik; Erhardt, Sebastian; Ghosh, Mainak; Li, Cheng; Harhoff, Dietmar, 2024, "Pat-SPECTER embeddings for Patstat 2023 Fall (USPTO and EPO)", https://doi.org/10.17617/3.ES5ZSC, Edmond, V1

Pat-SPECTER embeddings for all patent families as of Patstat 2023 Fall release. File "Pat-SPECTER_EPO_patstat-2023-fall.parquet" contains embeddings for all patent families with an EPO member (3.9M). File "Pat-SPECTER_USPTO_patstat-2023-fall.parquet" contains embeddings for all patent families with a USPTO member (9.2M). We encoded one representati...

PaECTER embeddings for Patstat 2023 Fall (USPTO and EPO)

Dec 17, 2024

Ghosh, Mainak; Erhardt, Sebastian; Rose, Michael; Buunk, Erik; Harhoff, Dietmar, 2024, "PaECTER embeddings for Patstat 2023 Fall (USPTO and EPO)", https://doi.org/10.17617/3.BGRPMI, Edmond, V1

PaECTER embeddings for all patent families as of Patstat 2023 Fall release. File "PaECTER_EPO_patstat-2023-fall.parquet" contains embeddings for all patent families with an EPO member (3.9M). File "PaECTER_USPTO_patstat-2023-fall.parquet" contains embeddings for all patent families with a USPTO member (9.2M). We encoded one representative member of...

BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes

Oct 22, 2024

naik, Hemal; Yang, Junran; Das, Dipin; Crofoot, Margaret; Rathore, Akanksha; Sridhar, Vivek Hari, 2024, "BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes", https://doi.org/10.17617/3.JCZ9WK, Edmond, V1

The dataset contains UAV footage of wild antelopes (blackbucks) in grassland habitats. It can be mainly used for two tasks: Multi-object tracking (MOT) and Re-Identification (Re-ID). We provide annotations for the position of animals in each frame, allowing us to offer very long videos (up to 3 min) completely annotated while maintaining the identi...

Sample Dataset for YOLO-Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos

Aug 22, 2024

Chan, Hoi Hang; Putra, Prasetia; Schupp, Harald; Köchling, Johanna; Straßheim, Jana; Renner, Britta; Schroeder, Julia; Pearse, William D; Nakagawa, Shinichi; Burke, Terry; Griesser, Michael; Meltzer, Andrea; Lubrano, Saverio; Kano, Fumihiro, 2024, "Sample Dataset for YOLO-Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos", https://doi.org/10.17617/3.EZNKYV, Edmond, V1

Sample dataset and weights for YOLO-Behaviour framework. Required to run demo code in the documentation. Also provided bounding box datasets for 4 case studies in YOLO format. The human dataset is not included due to privacy concerns. Code: https://github.com/alexhang212/YOLO_Behaviour_Repo Documentation: https://alexhang212.github.io/YOLO_Behaviou...

MeerKAT: Meerkat Kalahari Audio Transcripts

Jul 2, 2024

Schaefer-Zimmermann, Julian; Demartsev, Vlad; Averly, Baptiste; Dhanjal-Adams, Kiran; Duteil, Mathieu; Gall, Gabriella; Faiß, Marius; Johnson-Ulrich, Lily; Stowell, Dan; Manser, Marta; Roch, Marie; Strandburg-Peshkin, Ariana, 2024, "MeerKAT: Meerkat Kalahari Audio Transcripts", https://doi.org/10.17617/3.0J0DYB, Edmond, V1

A large-scale reference dataset for bioacoustics Please find the accompanying code at our official repository: github.com/livingingroups/animal2vec [Optional ]You can find the animal2vec model weights using the MeerKAT dataset here. MeerKAT is a 1068h large-scale dataset containing data from boom-mics and audio-recording collars worn by free-rangin...

Wild-MuPPET - Multi-view posture dataset of foraging pigeons in the wild

Jan 3, 2024

Waldmann, Urs; Chan, Hoi Hang Alex; Naik, Hemal; Nagy, Mate; Couzin, Iain D.; Deussen, Oliver; Goldluecke, Bastian; Kano, Fumihiro, 2024, "Wild-MuPPET - Multi-view posture dataset of foraging pigeons in the wild", https://doi.org/10.17617/3.ENDMTI, Edmond, V1

Wild-MuPPET is a multi-view posture dataset of foraging pigeons outdoors, with 500 frames of 3D-ground truth and 2000 frames of 2D-ground truth of 9 keypoints. Paper: https://doi.org/10.48550/arXiv.2308.15316 Git repository: https://github.com/alexhang212/3D-MuPPET

covid_twitter_data

Jun 5, 2023

Jin, Zhijing, 2023, "covid_twitter_data", https://doi.org/10.17617/3.TYNBEQ, Edmond, V1

Data for: Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision

Feb 8, 2023

Koger, Benjamin; Adwait Deshpande; Jeffrey T. Kerby; Jacob M. Graving; Blair R. Costelloe; Iain D. Couzin, 2023, "Data for: Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision", https://doi.org/10.17617/3.EMRZGH, Edmond, V1

This is data used for the worked examples in the paper "Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision", published in the Journal of Animal Ecology.

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