NeuroDesk – A cross-platform, flexible, lightweight, scalable, out-of-the-box data analysis environment

Dr Steffen Bollmann1,2, Dr Oren  Civier2,3, Aswin  Narayanan1,2, Prof Markus Barth1,2, Prof Tom Johnstone2,3

1The University Of Queensland
2Australian National Imaging Facility
3Swinburne Neuroimaging, Swinburne University of Technology


Researchers require a diverse collection of tools to analyse data and answer their research questions. Scientific software relies on specific libraries leading to dependency conflicts between different tools and difficulties in installing these. Researchers struggle tremendously to run their analyses on adequate hardware and it is very challenging to move analyses between computing platforms due to the setup work required – ultimately limiting interoperability and reproducibility.


We developed a modular analysis ecosystem, and applied it first in the field of neuroimaging. The first module consists of a continuous integration system using github actions to automatically build various software containers. Our second module provides wrapper scripts that transparently integrate these containers into any existing workflow. The third layer downloads the containers on demand and manages them to keep the system lightweight, and the final layer provides a lightweight Linux desktop accessible via a browser that runs on any operating system.


Our solution ( enables researchers to use any software with any dependency on any operating system. Moreover, as it does not require any installation or specific hardware, it lowers the barriers for entry for less-technical users, while still permitting to scale the analysis at later stages.


We developed a scalable and interoperable way of running various scientific tools on any compute platform accessible to researchers, such as Nectar, the Characterisation Virtual Laboratory (CVL) or the Australian Imaging Service (AIS) XNAT. We are planning to extend our framework to other domains, such as electrophysiology.


Dr Steffen Bollmann is a national imaging facility fellow for 7T human MRI at the Centre for Advanced Imaging at the University of Queensland. Utilizing high performance computing and deep learning, he develops new methods for quantitative susceptibility mapping and population anatomy modeling with the goal to understand iron and myelin contributions of the MRI signal. He obtained a bachelor’s degree in biomedical engineering at the Ilmenau University of Technology, followed by a Masters degree in biomedical engineering & bioelectromagnetism. Following this, he completed a PhD utilizing fMRI and spectroscopy in ADHD at the Neuroscience Centre Zurich and ETH.

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