Scripts for data analysis of a High-Content Live-Cell Multiplex Screen for Chemogenomic Compound Annotation based on Nuclear Morphology

A suite of Python scripts to facilitate research data management for High-Content Screens, specifically for supporting EUbOPEN's multiplex assay, depositing in BioImage Archive. As described in the publication (https://doi.org/10.1016/j.xpro.2022.101791), here you can find the Python scripts to download into the "root path" as well as the Excel template for documenting your data. Instructions for use: Download the file "Archive.zip" into your "root path", and unzip it there. To install the required Python packages, go back to the Anaconda Prompt, and type: pip install numpy pandas

The source code is here: https://github.com/rgiessmann/data-management-for-high-content-screening

Related Organizations

Institute for Globally Distributed Open Research and Education
Indonesia
Bayer (Germany)
Germany

Keywords

FAIR data, multiplex live-cell imaging, RDM, research data management, high-content screen

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average