Twinnetworkdataset004

doi:doi:10.48606/61

Twinnetworkdataset004

Other: The data comprises bright-field time-lapse images of zebrafish embryos acquired in multiple batches within multi-well plates using an Acquifer Imaging Machine. Embryos from incrosses of sqt+/-, crosses of Tg(sebox:EGFP) and sqt+/-, and crosses of Tg(sebox:EGFP) and Tg(gsc:TurboRFP) zebrafish strains were used for imaging. Imaging started at 4 hpf and ended at 22.5 hpf. Images of embryos were acquired at 134 acquisition time points with a time interval of 498 s between each acquisition time point. Individual embryo segments were identified and extracted using a trained neural network for object detection. Within this experiment folder, data are organized by microscope position and embryo number.

BibTex:

@dataset{Toulany_Nikan_2023,
    abstract = {Other: The data comprises bright-field time-lapse images of zebrafish embryos acquired in multiple batches within multi-well plates using an Acquifer Imaging Machine. Embryos from incrosses of sqt+/-, crosses of Tg(sebox:EGFP) and sqt+/-, and crosses of Tg(sebox:EGFP) and Tg(gsc:TurboRFP) zebrafish strains were used for imaging. Imaging started at 4 hpf and ended at 22.5 hpf. Images of embryos were acquired at 134 acquisition time points with a time interval of 498 s between each acquisition time point. Individual embryo segments were identified and extracted using a trained neural network for object detection. Within this experiment folder, data are organized by microscope position and embryo number.},
    author = {Toulany, Nikan},
    doi = {10.48606/61},
    institution = {RADAR},
    keyword = {'Twin Network', 'TwinNet', 'computational biology', 'deep learning', 'developmental biology', 'embryogenesis', 'zebrafish'},
    title = {Twinnetworkdataset004},
    url = {https://service.tib.eu/ldmservice/vdataset/rdr-doi-10-48606-61},
    year = {2023}
}