A.D.I.C.T.: Update after revision 1

Welcome to Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.)

Be kind and cite the following publications if you use A.D.I.C.T.:

Mayer B, Schwan M, Thormann KM and Graumann PL. Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.): a robust imaging workflow to monitor antibiotic stress response in bacterial cells in vivo [version 1; peer review: 1 approved with reservations]. F1000Research 2021, 10:277 (https://doi.org/10.12688/f1000research.51868.1)

Test data is available under:

Mayer, B., Schwan, M., Thormann, K. M., & Graumann, P. L. (2021, March 15). Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.): test data for workflow deployment and reproduction. https://doi.org/10.17605/OSF.IO/FNJ5G

This introduction gives advice, how to use the scripts published in F1000 to reproduce the workflow.

-Please make sure that the folders containing your data (may they be part of your personal research or test data from the paper) is properly stored in respective folders.

-Keep in mind that addressing the correct folders within this workflow is case sensitive, so in case it won't work as it should it could be explained by misspelling.

-This is a combined workflow which can be used differently depending on the task you might want to address as part of your project. This is why the scripts are split into subscripts to highlight individual steps of the workflow. However, you might find one script more useful for your purposes as the other, so just keep on trying and modifying for your analytical needs. Many crucial code snippets are labelled to explain their usage (in ImageJ macro it starts with "//").

-However, feel free to ask questions around this workflow if you are facing any trouble.

-THIS TOOLBOX COMES WITHOUT ANY WARRANTY!; please check LICENSE

Major changes for version 1.0.1.:

• ImageJ2/FIJI integration into Jupyter-notebook for scripts used as part of the revision