Alternativer Identifier:
-
Verwandter 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
Ersteller/in:
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]
Beitragende:
(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]
Titel:
Datasets for "EmbryoNet: Using deep learning to link embryonic phenotypes to signaling pathways"
Weitere Titel:
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Beschreibung:
(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.
Schlagworte:
machine learning, phenotypes, cell signaling, development, phenomic screen, high-throughput, zebrafish, medaka, stickleback
Zugehörige Informationen:
-
Sprache:
-
Herausgeber/in:
Universität Konstanz
Erstellungsjahr:
Fachgebiet:
Biology
Objekttyp:
(Dataset) Overview of the EmbryoNet datapackages
Datenquelle:
-
Verwendete Software:
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Datenverarbeitung:
-
Erscheinungsjahr:
Lizenz:
Rechteinhaber/in:
Müller, Patrick
Förderung:
-
Name Speichervolumen Metadaten Upload Aktion

Zugriffe der letzten sechs Monate

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Gesamtstatistik

Zeitraum Aufrufe der Datenpaket-Seite Datenpaket heruntergeladen
Apr. 2024 60 1
März 2024 159 4
Feb. 2024 270 3
Jan. 2024 281 6
Dez. 2023 201 8
Nov. 2023 187 3
Vorher 1141 59
Gesamt 2299 84
Status:
Publiziert
Eingestellt von:
39cb8a70dcf9f41e7cacc31b8f092a7f
Erstellt am:
Archivierungsdatum:
2022-09-26
Archivgröße:
25,1 kB
Archiversteller:
a73b8c277d10e12cb86f91edde66677a
Archiv-Prüfsumme:
fff0c700c8d24248b739e0620393f34f (MD5)
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