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Accurate classification of major brain cell types using in vivo imaging and neural network processing

This dataset accompanies the article of the same title in the journal Plos Biology. It includes a) Ground truth datasets for the training of the StarDist neuronal network for nucleus segmentation (StardistTraining.tar.gz) b) The trained Stardist nucleus segmentation model (StardistModel.tar.gz c) ra...

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Bibliographic Details
Main Author: Knabbe, Johannes (Author)
Format: Database Research Data
Language:English
Published: Heidelberg Universität 2023-09-18
DOI:10.11588/data/L3PITA
Subjects:
Forschungsdaten
Datenbank
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.11588/data/L3PITA
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/L3PITA
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Author Notes:Johannes Knabbe
Description
Summary:This dataset accompanies the article of the same title in the journal Plos Biology. It includes a) Ground truth datasets for the training of the StarDist neuronal network for nucleus segmentation (StardistTraining.tar.gz) b) The trained Stardist nucleus segmentation model (StardistModel.tar.gz c) raw and segmented data for the training of the cell type classification (CelltypeClassification.tar.gz, CelltypeClassificationExcInhNeurons.tar.gz) d) the raw and segmented data for the results of the paper (RawdataResults.tar.gz) e) Ground truth data for the training of all classifiers (ClassificationTrainingDataSet.tab)
Item Description:Gesehen am 20.09.2023
Physical Description:Online Resource
DOI:10.11588/data/L3PITA

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