Datensatz:

Datasets for "Uncovering developmental time and tempo using deep learning"

Vorschaubild

Datum der Erstveröffentlichung

2023

Autor:innen

Andere Beitragende

Repositorium der Erstveröffentlichung

KonDATA

Version des Datensatzes

DOI (Link zu den Daten)
https://doi.org/10.48606/50
Link zur Lizenz

CC BY 4.0

Angaben zur Forschungsförderung

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Sammlungen

Biologie: Datensätze
Exzellenzcluster "Centre for the Advanced Study of Collective Behaviour": Datensätze
Core Facility der Universität Konstanz
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Titel in einer weiteren Sprache

Publikationsstatus
Published

Zusammenfassung

This is the data repository for training and testing the Twin Network. The imaging data repositories are divided into several packages based on independent experiments. The data comprises bright-field time-lapse images of zebrafish embryos acquired in multiple batches within multi-well plates using an Acquifer Imaging Machine. Individual embryo segments were identified and extracted using a trained neural network for object detection. Within these experiment folders, data are organized by microscope position and embryo number.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
570 Biowissenschaften, Biologie

Schlagwörter

Biology, Twin Network, TwinNet, zebrafish, embryogenesis, deep learning, machine learning, high-throughput, developmental biology, computational biology

Zugehörige Publikationen in KOPS

Vorschaubild
Publikation
Zeitschriftenartikel
Uncovering developmental time and tempo using deep learning
(2023) Toulany, Nikan; Morales-Navarrete, Hernán; Capek, Daniel; Grathwohl, Jannis; Ünalan, Murat; Müller, Patrick
Erschienen in: Nature Methods. Springer. 2023, 20(12), S. 2000-2010. ISSN 1548-7091. eISSN 1548-7105. Verfügbar unter: doi: 10.1038/s41592-023-02083-8
Link zu zugehöriger Publikation
Link zu zugehörigem Datensatz
https://doi.org/10.48606/94

Zitieren

ISO 690TOULANY, Nikan, 2023. Datasets for "Uncovering developmental time and tempo using deep learning"
BibTex
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