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    Dataset: Datasets for "EmbryoNet: Using deep learning to link embryonic phenotypes to signaling pathways"

    Alternate identifier:
    -
    Related identifier:
    (Has Part) 10.48606/16 - DOI
    (Has Part) 10.48606/18 - DOI
    (Has Part) 10.48606/20 - DOI
    (Has Part) 10.48606/21 - DOI
    (Has Part) 10.48606/22 - DOI
    (Has Part) 10.48606/23 - DOI
    (Has Part) 10.48606/25 - DOI
    (Has Part) 10.48606/26 - DOI
    (Has Part) 10.48606/27 - DOI
    (Has Part) 10.48606/28 - DOI
    (Has Part) 10.48606/29 - DOI
    (Has Part) 10.48606/30 - DOI
    (Has Part) 10.48606/31 - DOI
    (Has Part) 10.48606/32 - DOI
    (Has Part) 10.48606/33 - DOI
    (Has Part) 10.48606/34 - DOI
    (Has Part) 10.48606/35 - DOI
    (Has Part) 10.48606/37 - DOI
    (Has Part) 10.48606/38 - DOI
    (Has Part) 10.48606/41 - DOI
    (Has Part) 10.48606/53 - DOI
    (Has Part) 10.48606/55 - DOI
    Creator/Author:
    Capek, Daniel https://orcid.org/0000-0001-5199-9940 [University of Konstanz]

    Kurzbach, Anica https://orcid.org/0000-0003-1531-3088 [Universität Konstanz]

    Safroshkin, Matvey https://orcid.org/0000-0003-3955-5081 [Computer Vision Studio Tübingen]

    Morales-Navarrete, Hernan https://orcid.org/0000-0002-9578-2556 [Universität Konstanz]

    Arutyunov, Grigory https://orcid.org/0000-0002-4372-9155 [Computer Vision Studio Tübingen]

    Toulany, Nikan https://orcid.org/0000-0003-3505-7325 [Universität Konstanz]
    Contributors:
    (Other)
    Bihler, Johanna [Friedrich-Miescher-Labor der Max-Planck-Gesellschaft]

    (Other)
    Jordan, Ben [Sense AI, Minneapolis]

    (Other)
    Hagauer, Julia [Friedrich-Miescher-Labor der Max-Planck-Gesellschaft]

    (Other)
    Kick, Sebastian [Friedrich-Miescher-Labor der Max-Planck-Gesellschaft]

    (Other)
    Jones, Felicity https://orcid.org/0000-0002-5027-1031 [Friedrich-Miescher-Labor der Max-Planck-Gesellschaft]

    (Other)
    Müller, Patrick https://orcid.org/0000-0002-0702-6209 [Universität Konstanz]
    Title:
    Datasets for "EmbryoNet: Using deep learning to link embryonic phenotypes to signaling pathways"
    Additional titles:
    -
    Description:
    (Abstract) This is the data repository of the training and test data sets for EmbryoNet. The data is structured in multiple packages. EmbryoNet_Models (DOI 10.48606/31) contains the trained neural networks, the other packages are imaging data. All data are brightfield timelapse images of one or multiple embryos recorded in multiwell plates in either the Acquifer Imaging Machine or the Keyence BZ-X810 microscope. The microscope type is included in the name of the archive, e.g. BMP_Acquifer.zip. Training data images are accompanied by json-files with the classification from human annotators, while test data sets also have the jsons of EmbryoNet's classification. The dataset EmbryoNet_Image-data: Stickleback 1 (DOI 10.48606/32) contains training data for the Stickleback version of EmbryoNet, and EmbryoNet_Test-data: Stickleback (DOI 10.48606/33) contains the evaluation data. EmbryoNet_Training-data: Medaka (DOI 10.48606/35) and EmbryoNet_Test-data: Medaka (DOI 10.48606/34) contain the respective data for Medaka. The other packages are zebrafish images. The two archives named EmbryoNet_Test-data 1&2 (DOI: 10.48606/29 & 10.48606/30) are the zebrafish test data sets. The zebrafish training data sets are named after the signaling molecule: EmbryoNet_training-data: BMP (DOI 10.48606/18), EmbryoNet_training-data: Retinoic acid (DOI 10.48606/20), EmbryoNet_training-data: Wnt (DOI 10.48606/21), EmbryoNet_training-data: FGF (DOI 10.48606/22), EmbryoNet_training-data: Nodal (DOI 10.48606/23), EmbryoNet_training-data: Shh (DOI 10.48606/25) and EmbryoNet_training-data: PCP (DOI 10.48606/26). EmbryoNet_training-data: WT (DOI 10.48606/16) contains the training data of untreated embryos. The datasets EmbryoNet_Training-data: Severities - Keyence (DOI 10.48606/28) and EmbryoNet_Training-data: Severities - Acquifer (DOI 10.48606/27) contain the training and evaluation data of the Severities experiments with different inhibitor concentrations. Inside a zip file the data is arranged in experiment folders, named in the format DATE_Molecule_concentration, e.g. 201222_FGF_10uM. Inside these experiment folders the data is organized after multiwell plate or microscope positions, A001-D006 for the Acquifer data and XY01-XY24 for the Keyence data.
    Keywords:
    machine learning, phenotypes, cell signaling, development, phenomic screen, high-throughput, zebrafish, medaka, stickleback
    Related information:
    -
    Language:
    -
    Publishers:
    Universität Konstanz
    Production year:
    Subject areas:
    Biology
    Resource type:
    (Dataset) Overview of the EmbryoNet datapackages
    Data source:
    -
    Software used:
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    Data processing:
    -
    Publication year:
    Rights holders:
    Müller, Patrick
    Funding:
    -
    Name Storage Metadata Upload Action
    Status:
    Published
    Uploaded by:
    39cb8a70dcf9f41e7cacc31b8f092a7f
    Created on:
    Archiving date:
    2022-09-26
    Archive size:
    25.1 kB
    Archive creator:
    a73b8c277d10e12cb86f91edde66677a
    Archive checksum:
    fff0c700c8d24248b739e0620393f34f (MD5)
    Embargo period:
    -