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

DOI

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)

Identifier
DOI https://doi.org/10.11588/data/L3PITA
Metadata Access https://heidata.uni-heidelberg.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.11588/data/L3PITA
Provenance
Creator Johannes Knabbe (ORCID: 0000-0003-3170-111X)
Publisher heiDATA
Contributor Johannes Knabbe
Publication Year 2023
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Johannes Knabbe (Heidelberg University)
Representation
Resource Type Dataset
Format application/gzip; text/tab-separated-values
Size 31839817058; 79538832568; 173; 33678000933; 14110930; 143905275
Version 1.0
Discipline Life Sciences; Medicine