Robert Haase

Robert Haase is computer scientist by training and follows his curiosity deeper and deeper into data science and life science. He received a PhD from the Faculty of Medicine Carl Gustav Carus of the TU Dresden for his work on swarm intelligence based algorithms for medical image segmentation in the cancer research context. He served as lecturer for bio-image analysis, bio-statistics and programming at the Biotechnology Center of the TU Dresden. His postdoctoral research in Gene Myers lab at the Center for Systems Biology and the Max Planck Institute for Molecular Cell Biology and Genetics concentrates focused on bridging the disciplines computer science and biology to forward understanding of how tissues and organisms form. He headed the Bio-image Analysis Technology Development Group at the Cluster of Excellence "Physics of Life" at TU Dresden.Today, he works as lecturer and training coordinator at the Data Science Center of Leipzig University and the Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Dresden / Leipzig.

About Robert Haase

Microscopy background: Image Analysis

Posts by Robert Haase

Creating a Research Data Management Plan using chatGPT

Posted by , on 6 November 2023

TL;DR: Data Management Plans (DMPs) are documents which describe what happens to data in a [research] project. More and more funding agencies require these documents when scientists apply for funding. However, different funding agencies may require different information in DMPs. In this blog post I will demonstrate how chatGPT can be used to combine a

A schedule for organizing international symposia

Posted by , on 5 September 2023

TL;DR: Coordinating an international conference can be a complex endeavor, especially for scientists whose primary focus often lies outside the realm of event management. A major challenge in this context is timing. In this blog post I outline a potential schedule of tasks for organizing an international conference such as the PoLBIAS23. As a senior

If you license it, it'll be harder to steal it. Why we should license our work

Posted by , on 6 May 2023

TL;DR: When we publish work such as manuscripts, code and data, the platforms we publish on typically ask to select license. If we find a nice figure on the internet, are we allowed to reuse it? If there is no copyright statement, am I stealing? If code is available open-source, can we incorporate it into

Scientific Data Analysis: User Documentation 101

Posted by , on 30 April 2023

TL;DR: When publishing open-source tools for bio-image analysis special emphasis should be put on user-documentation. Users and developers have a different background and a language barrier limits knowledge exchange on how to use tools correctly. Writing a good user guide is a huge opportunity and worth the effort: Users get the most out of their

Sharing research data with Zenodo

Posted by , on 15 February 2023

TL;DR: Sharing data open access is good scientific practice. If data is shared via online portals such as, we can implement best practices for sharing, licensing, reusing and citing research data. In this blog post I guide through the minimal procedures that are necessary to share a dataset publicly following the FAIR principles; to

Comments by Robert Haase

Yes, I think so. Unfortunately, making environment.yml files that work on all operating systems can be tricky, especially when working with deep-learning, GPUs and other advanced stuff. That's why installation instructions are sometimes a bit cumbersome. I hope this will change one day.
by Robert Haase in Managing Scientific Python environments using Conda, Mamba and friends on 27 February 2023
Correct! Mamba is faster than conda in determining which packages should be installed. The installation itself should be similarly fast and after installation there is no difference, too.
by Robert Haase in Managing Scientific Python environments using Conda, Mamba and friends on 9 December 2022