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CC BY 4.0

This work is licensed by Robert Haase, ScaDS.AI Dresden/Leipzig under a Creative Commons Attribution 4.0 International License unless mentioned otherwise.

Bio-image Data Science

This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. Corresponding PPTx files can be found on zenodo.

Teaching Goal

Students learn the full workflow of common bio-image data science projects to a degree that they can execute a scientific data analysis project in this context on their own. They will be familiar with common bio-image analysis algorithms and workflows, how to choose them according to a scientific goal, and how to measure quality of derived results. Attending the lecture and executing the practicals qualifies the students to work as bio-image data scientist in the pharmaceutical industry or basic biological research.

Course contents

Pre-requesites

  • Basic Python programming skills are required

Literature

More literature might be added during the lecture.

Covered Python libraries

In this course we will use the following Python libraries to analyse mciroscopy image data

See also

Former lectures and related materials

A lecture covering similar contents was held in the past years at TU Dresden:

Image Analysis

Python

Contributing

Contributions to this repository are welcome! If you see typos, bugs or have general feedback, please create a github issue to let us know. If you would like to add additional lessons or want to suggest improvements to existing ones, pull-requests are very welcome!

Acknowledgements

Deepest thanks goes to people who shared their training materials, preprints, figures and example data openly which became part of the materials used in this lecture series: Marcelo Leomil Zoccoler, Till Korten, Johannes Soltwedel, Daniela Vorkel, Laura Žigutytė, Ryan Savill, Mara Lampert, Lena Maier-Hein, Annika Reinke, Martin Schätz, Douglas G. Altman, J. Martin Bland, Constantin Pape, Benedict Diederich, Jennifer Waters, Tony Collins, Mike Kayser, Mauricio Rocha Martins, Kota Miura, Anna Pascual-Reguant, Peter Bankhead, Sreenivas Bhattiprolu, Henning Falk, Carsen Stringer, Marius Pachitariu, Alexander Krull, Uwe Schmidt, Martin Weigert, Dominic Waithe, Alex Bird, Dan White, Nasreddin Abolmaali, Alba Villaronga Luque, Jesse Veenvliet, Greg Kamradt, Josh Moore, Matthias Täschner, Ricardo Henriques, Anwai Archit, Jay Alammar, Loic A. Royer, Pranab Sahoo, Timo Kaufmann, Patrick Lewis, Noah Shinn, Xuezhi Wang, Jianing Wang, Jason Wei, Cheng Li, Yihe Deng, Robin Rombach, Aditya Ramesh, Akash Ghosh, Aditya Ramesh, Alec Radford, Alexey Dosovitskiy, Haotian Liu, Alexandr Khrapichev, Zishan Guo, Stephanie Lin, Mark Chen, Carlos E. Jimenez, Yuhang Lai, et al.

Some of the materials in this repository originate from the BioImageAnalysis Notebooks, were written by Robert Haase et al and were licensed CC-BY 4.0. We acknowledge the financial support by the Federal Ministry of Education and Research of Germany and by Sächsische Staatsministerium für Wissenschaft, Kultur und Tourismus in the programme Center of Excellence for AI-research „Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig“, project identification number: ScaDS.AI

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