Ole Bialas
I studied Biology at the University of Tübingen, where I first learned how to code using Matlab. Then, I moved to Leipzig, where I did a master’s degree and later a PhD in neurobiology. In my research, I studied how the brain processes sound location using electroencephalography (EEG) and custom experimental setups for spatial audio. During that time, I started using Python and eventually co-authored “slab”, a Python toolbox for psychoacoustic experiments. After my PhD, I moved to the University of Rochester in New York, where I studied how the brain processes naturalistic speech by modeling EEG that was recorded while the participants listened to audiobooks. For this research I published another toolbox, originally written in Matlab, called “mTRFpy”. As my postdoc was coming to an end, I was looking for a position where I could combine my interest in neuroscience with my passion for programming. I found such a position at the University Clinic Bonn where I currently work as a research software consultant. In this position, I develop and teach workshops where neuroscience researchers can improve their software skills. I also do one-on-one consulting to help neuroscientists deal with the computational challenges they are faced in their research.
University Clinic Bonn
Position / Job –Research Software Consultant
Homepage – X handle –@NeuroObi
GitHub/GitLab profile URL – LinkedIn – Photo – euroscipy-2025/question_uploads/ole.JPG_tFd9JLT.jpgSession
The flourishing of open science has created an unprecedented opportunity for scientific discovery through the global exchange of data and collaboration between researchers. DataLad (datalad.org) supports this by providing the tools to develop flexible and decentralized collaborative workflows while upholding scientific rigor. It is free and open source data management software, built on top of the version control systems Git and git-annex. Among its major features are version control for files of any size or type, data transport logistics, and digital process provenance capture for reproducible digital transformations.
In this hands-on workshop, we will start by exploring DataLad’s basic functionality and learn how to run and re-run analyses while versioning and keeping track of your data. Following this, we will explore DataLad’s collaborative features and learn how to install and work with existing datasets and how to share and distribute your work online. After completing this tutorial, you will be equipped to start using DataLad to manage your own research projects and share them with the world.