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1 to 10 of 12 Results
Additional dataset for: Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation
Jun 13, 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, V1
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: htt...
NeuroQuantify Dataset
May 28, 2024
Dang, Truong Ka My, 2023, "NeuroQuantify Dataset", https://doi.org/10.17617/3.UIBMJX, Edmond, V3
The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essen...
3D-POP - An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture
May 7, 2024
Naik, Hemal; Chan, Alex Hoi Hang; Yang, Junran; Delacoux, Mathilde; Couzin, D. Iain; Kano, Fumihiro; Nagy, Mate, 2023, "3D-POP - An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture", https://doi.org/10.17617/3.HPBBC7, Edmond, V6
The 3DPOP dataset is a large scale 2D to 3D posture, identity and trajectory dataset for freely moving pigeons. We use marker-based motion tracking to first track precise head and body position and orientation for multiple individuals, then propagated custom keypoints based on th...
A data-driven estimation of the ribosome drop-off rate in S. cerevisiae reveals a correlation with the genes length.
Mar 26, 2024
Awad, Sherine; Valleriani, Angelo; Chiarugi, Davide, 2024, "A data-driven estimation of the ribosome drop-off rate in S. cerevisiae reveals a correlation with the genes length.", https://doi.org/10.17617/3.H7OPIB, Edmond, V1
Ribofilio is a software package that estimates the drop-off rate of ribosomes. It is a python package that takes as a required input the reference genome and the footprint. If the mRNA reads are available, this can be used as an optional parameter for normalization. The tool uses...
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
Supplementary Material for "Performance Review of Retraining and Transfer Learning of DeLTA 2.0 for Image Segmentation for Pseudomonas fluorescens SBW25"
Supplementary Material for "Performance Review of Retraining and Transfer Learning of DeLTA 2.0 for Image Segmentation for Pseudomonas fluorescens SBW25"
Dec 18, 2023
Fortmann-Grote, Carsten; Gericke, Beate Constanze; Werth, Sören; Degner, Finn; Hüttmann, Tom, 2023, "Supplementary Material for "Performance Review of Retraining and Transfer Learning of DeLTA 2.0 for Image Segmentation for Pseudomonas fluorescens SBW25"", https://doi.org/10.17617/3.MCK0KK, Edmond, V1
This upload contains all raw data, trained tensorflow models, and python code used for training and validation of deep learning image segmentation as described in the forthcoming paper "Performance Review of Retraining and Transfer Learning of DeLTA 2.0 for Image Segmentation for...
Drosophila Brain Model
Sep 21, 2023
Shiu, Philip; Spiller, Nico, 2023, "Drosophila Brain Model", https://doi.org/10.17617/3.CZODIW, Edmond, V3
A computational model of the Drosophila brain (bioRxiv, Github)
Photomanipulation of minimal synthetic cells: area increase, softening and interleaflet coupling of membrane models doped with azobenzene-lipid photoswitches
Photomanipulation of minimal synthetic cells: area increase, softening and interleaflet coupling of membrane models doped with azobenzene-lipid photoswitches
Aug 25, 2023
Dimova, Rumiana, 2023, "Photomanipulation of minimal synthetic cells: area increase, softening and interleaflet coupling of membrane models doped with azobenzene-lipid photoswitches", https://doi.org/10.17617/3.CWOZQO, Edmond, V1
Supporting data (simulation files, movies) for Aleksanyan et al. (2023) Advanced Science
Experimental dynamic optogenetic control of the adenosine triphosphatase enzyme using knowledge-based and Gaussian process-supported models
Aug 1, 2023
Behrendt, Gerrich, 2023, "Experimental dynamic optogenetic control of the adenosine triphosphatase enzyme using knowledge-based and Gaussian process-supported models", https://doi.org/10.17617/3.H5GT8I, Edmond, V1
Genetic elements created for "Experimental dynamic optogenetic control of the adenosine triphosphatase enzyme using knowledge-based and Gaussian process-supported models"
Data for reproducing the training of eDICE model ("Getting Personal with Epigenetics: Towards Individual-specific Epigenomic Imputation with Machine Learning")
Jun 8, 2023
Visona, Giovanni, 2023, "Data for reproducing the training of eDICE model ("Getting Personal with Epigenetics: Towards Individual-specific Epigenomic Imputation with Machine Learning")", https://doi.org/10.17617/3.VKEFB6, Edmond, V3
Data repository for the files needed to reproduce the results in the paper "Getting Personal with Epigenetics: Towards Individual-specific Epigenomic Imputation with Machine Learning", Hawkins-Hooker et al.
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