Presenting the idea behind SciDir – A scientific software distribution repository for bringing reproducible software containers securely to HPCs in Australia
Steffen Bollmann1, Aswin Narayanan2, Sarah Beecroft3, Greg D’Arcy5, Nigel Ward6, Peter Marendy4 1The University Of Queensland, St Lucia, QLD, Australia2National Imaging Facility, St Lucia, QLD, Australia3Pawsey Supercomputing Research Centre, Kensington, WA, Australia4The Queensland Cyber Infrastructure Foundation (QCIF) , St Lucia, QLD, Australia5AARNet, Chatswood, NSW, Australia6Australian BioCommons, North Melbourne, Victoria, Australia
Abstract
Situation: The analysis of scientific data requires specialised scientific software and processing pipelines. However, researchers often spend inordinate amounts of time compiling the requisite software and troubleshooting dependency conflicts. Furthermore, research results are often difficult to reproduce due to system dependency differences, even when given the original data and analysis code.
Task: In this project, we aim to develop an open-source, community-oriented project that addresses the issues of accessibility and reproducibility of scientific software. In this session, we would like to present the idea, receive feedback from the community and plan how we can work together toward an implementation.
Action: We are proposing to build on previous work around containers on CVMFS in the Neurodesk project and BioCommons to develop a secure scientific software distribution system. The proposed platform consists of a software container build system, where the scientific community proposes software applications and reference datasets. These artefacts are built, packaged in software containers, and scanned for vulnerabilities before being uploaded to a container registry. The software container metadata is stored in a database for fast and transparent tool discovery. A flexible distribution mechanism will enable this software to be used on various computing endpoints.
Result: Our approach would accelerate progress in all scientific disciplines dealing with the processing of data on high-performance computers. It would enable the flexible processing of scientific data across different computing platforms and the portability of analyses between them.
Biography
Dr. Steffen Bollmann is a senior researcher fellow in the field of Biomedical Engineering, renowned for his expertise in multimodal imaging and computational methods for magnetic resonance imaging (MRI) data processing. He obtained his Master’s degree in Biomedical Engineering from the Ilmenau University of Technology. Dr. Bollmann furthered his academic pursuits by completing a PhD on multimodal imaging at the University Children’s Hospital and the prestigious ETH Zurich in Switzerland.
Dr. Bollmann’s career has been marked by significant contributions and collaborations across esteemed institutions. As a National Imaging Facility Fellow, he joined the Centre for Advanced Imaging at the University of Queensland, where he worked alongside the eminent Professor Markus Barth. During this tenure, Dr. Bollmann made pioneering advancements by applying deep learning methods to quantitative susceptibility mapping. His innovative research provided valuable insights into the field and led to 2 patents with Siemens Healthineers.
In 2019, Dr. Bollmann embarked on a one-year industry exchange with the Siemens Healthineers collaborations team at the MGH Martinos Center in Boston. There, he focused on the translation of deep learning reconstruction techniques into clinical applications, contributing to the intersection of cutting-edge technology and patient care.
Since 2020, Dr. Bollmann has held a position at the School of Information Technology and Electrical Engineering, University of Queensland. Within this role, he has been devoted to the development of computational methods for processing magnetic resonance imaging data, furthering the understanding and utilization of this critical medical imaging modality.
Dr. Bollmann recently joined the Queensland Digital Health Centre as a key figure leading the translation of Artificial Intelligence (AI) advances into clinical applications.
Dr. Bollmann’s visionary leadership has led to the establishment of the computational imaging research group consisting of three post-doctoral fellows and two PhD students. Together, they work on pioneering research endeavors focused on reproducible neuroimaging, medical image processing, and federated machine learning. This research group is the driving force behind the development of the NeuroDesk platform – an open-source initiative to develop a flexible, scalable, and browser-based data analysis environment for reproducible neuroimaging.
With an unwavering commitment to industrial and clinical translation, Dr. Bollmann’s research group aims to bridge the gap between academia and healthcare practice. Their collective efforts strive to empower clinicians by providing them with the latest image-processing algorithms, enabling improved patient diagnosis and treatment outcomes.