Twinnetworkdataset011

doi:doi:10.48606/69

Twinnetworkdataset011

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 Tg(sebox:EGFP), incrosses of Tg(gsc:GFP), crosses of Tg(sebox:EGFP) and Tg(lhx1a:EGFP), and crosses of Tg(sebox:EGFP) and Tg(gsc:GFP) zebrafish strains were used for imaging. Imaging started at 2.25 hpf and ended at 2.5 hpf. Images of embryos were acquired at 3 acquisition time points with a time interval of 6:18 min 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 Tg(sebox:EGFP), incrosses of Tg(gsc:GFP), crosses of Tg(sebox:EGFP) and Tg(lhx1a:EGFP), and crosses of Tg(sebox:EGFP) and Tg(gsc:GFP) zebrafish strains were used for imaging. Imaging started at 2.25 hpf and ended at 2.5 hpf. Images of embryos were acquired at 3 acquisition time points with a time interval of 6:18 min 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 = {10.48606/69},
    institution = {RADAR},
    keyword = {'Twin Network', 'TwinNet', 'computational biology', 'deep learning', 'developmental biology', 'embryogenesis', 'zebrafish'},
    title = {Twinnetworkdataset011},
    url = {https://service.tib.eu/ldmservice/vdataset/rdr-doi-10-48606-69},
    year = {2023}
}