Establishment of organotypic epithelial raft cultures as a model of high-grade cervical intraepithelial neoplasia and quantification of treatment effects through machine learning-based cell counting [data]

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Richard M. Köhler; Hans-Jürgen Stark; Iris Martin; Joel Altmann; Martin Simon Kalteis; Magnus von Knebel Doeberitz; Elena-Sophie Prigge, 2024, "Establishment of organotypic epithelial raft cultures as a model of high-grade cervical intraepithelial neoplasia and quantification of treatment effects through machine learning-based cell counting [data]", https://doi.org/10.11588/data/AVIYB0, heiDATA, V1

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Description	This dataset contains the raw data and code used for analyses in the doctoral dissertation "Establishment of organotypic epithelial raft cultures as a model of high-grade cervical intraepithelial neoplasia and quantification of treatment effects through machine learning-based cell counting". The data is comprised of annotations and cell detections in HE-stained sections from 3D organotypic raft cultures, as well as generated statistics and plots that are depicted in the results section of the corresponding doctoral dissertation. For instructions on how to navigate the dataset, run code and reproduce the results, please refer to the README file. (2024-08-20)
Subject	Medicine, Health and Life Sciences
Keyword	Human Papillomavirus Viruses, Papillomavirus Infections, Precancerous Conditions, Uterine Cervical Neoplasms, Cell Culture Techniques, Fluorouracil, Cisplatin, Epigallocatechin, Keratinocytes
Related Publication	Köhler, R. M. (2024). Establishment of organotypic epithelial raft cultures as a model of high-grade cervical intraepithelial neoplasia and quantification of treatment effects through machine learning-based cell counting [Doctoral dissertation, Ruprecht Karl University of Heidelberg]. Unpublished. Date: 2024-08-20.
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Citation Metadata

Persistent Identifier	doi:10.11588/data/AVIYB0
Publication Date	2024-08-21
Title	Establishment of organotypic epithelial raft cultures as a model of high-grade cervical intraepithelial neoplasia and quantification of treatment effects through machine learning-based cell counting [data]
Author	Richard M. Köhler (Department of Applied Tumor Biology, University Hospital Heidelberg, Germany and Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany) - ORCID: 0000-0002-5219-1289 Hans-Jürgen Stark (Department of Applied Tumor Biology, University Hospital Heidelberg, Germany and Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany) Iris Martin (Department of Applied Tumor Biology, University Hospital Heidelberg, Germany and Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany) Joel Altmann (Department of Applied Tumor Biology, University Hospital Heidelberg, Germany and Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany) Martin Simon Kalteis (Department of Applied Tumor Biology, University Hospital Heidelberg, Germany and Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany) - ORCID: 0000-0003-2549-9736 Magnus von Knebel Doeberitz (Department of Applied Tumor Biology, University Hospital Heidelberg, Germany and Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany) Elena-Sophie Prigge (Department of Applied Tumor Biology, University Hospital Heidelberg, Germany and Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany)
Point of Contact	Use email button above to contact. Richard M. Köhler (Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany)
Description	This dataset contains the raw data and code used for analyses in the doctoral dissertation "Establishment of organotypic epithelial raft cultures as a model of high-grade cervical intraepithelial neoplasia and quantification of treatment effects through machine learning-based cell counting". The data is comprised of annotations and cell detections in HE-stained sections from 3D organotypic raft cultures, as well as generated statistics and plots that are depicted in the results section of the corresponding doctoral dissertation. For instructions on how to navigate the dataset, run code and reproduce the results, please refer to the README file. (2024-08-20)
Subject	Medicine, Health and Life Sciences
Keyword	Human Papillomavirus Viruses (MeSH) Papillomavirus Infections (MeSH) Precancerous Conditions (MeSH) Uterine Cervical Neoplasms (MeSH) Cell Culture Techniques (MeSH) Fluorouracil (MeSH) Cisplatin (MeSH) Epigallocatechin (MeSH) Keratinocytes (MeSH)
Topic Classification	Human Papillomavirus Viruses (MeSH) Precancerous Conditions (MeSH) Uterine Cervical Neoplasms (MeSH)
Related Publication	Köhler, R. M. (2024). Establishment of organotypic epithelial raft cultures as a model of high-grade cervical intraepithelial neoplasia and quantification of treatment effects through machine learning-based cell counting [Doctoral dissertation, Ruprecht Karl University of Heidelberg]. Unpublished. Date: 2024-08-20.
Language	English
Producer	Magnus von Knebel Doeberitz (Department of Applied Tumor Biology, University Hospital Heidelberg, Germany and Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany)
Production Location	Department of Applied Tumor Biology, University Hospital Heidelberg, Germany
Funding Information	Heinrich F.C. Behr foundation, German Cancer Research Center (DKFZ), Heidelberg, Germany: MD thesis grant for Richard M. Köhler
Depositor	Otte, Christoph
Deposit Date	2024-08-16
Time Period	Start Date: 2019-10-01 ; End Date: 2020-05-31
Date of Collection	Start Date: 2021-01-29 ; End Date: 2021-02-12
Data Type	Tables of cell detections and cell counts derived from H&E stained sections; Analysis code
Software	QuPath, Version: 0.5.0 Python, Version: 3.10.9 Python, Version: 3.12.0 Groovy NDP.view, Version: 2.9.29

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