High-Level Architecture for The Australian Biomedical Imaging Research Database

Farnoosh Sadeghian¹, Professor David Abbott^2,3,4, Professor Caroline Rae^4,6,7, Dr Michael Green^4,6,7, Dr Ryan Sullivan⁵, Dr Chao Suo⁸, Gagan Sharma¹⁰, Associate Professor Brad Moffat^4,9, Professor Wojtek Goscinski^1,4

¹Monash e-Research Centre, Monash University, Melbourne, Australia, ²Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, ³Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia, ⁴National Imaging Facility, University of Queensland,  St Lucia, Australia, ⁵The University of Sydney, Sydney, Australia, ⁶Neuroscience Research Australia, Sydney, Australia, ⁷School of Medical Sciences, The University of New South Wales, Sydney, Australia, ⁸BrainPark, Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging Facility, Monash University, Melbourne, Australia, ⁹The Melbourne Brain Centre Imaging Unit, Department of Radiology, The University of Melbourne, Melbourne, Australia, ¹⁰Department of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Melbourne, Australia

The Australian Biomedical Imaging Research Database (ABIRD) is an initiative of the National Imaging Facility (NIF). It aims to establish a database of shared imaging data for re-use and will provide a resource for further population based research. Data initially collected by ABIRD will consist of healthy controls’ MRI scans acquired with informed consent from multiple NIF nodes across Australia.

To make these data available to scientists in an effective way, the Australian Characterisation Commons at Scale (ACCS) aimed to develop a high level architecture and a prototype for ABIRD as a multi-node Australian imaging data collection.

Requirements included:

– Efficient utilisation of national integrated infrastructure

– Scalability

– Security

– Data privacy preservation

– Integration with High Performance Computing (HPC)

– Data to be Findable, Accessible, Interoperable and Reusable (FAIR)

To satisfy these requirements a “hybrid” architecture model is proposed. An open source XNAT instance of the Australian Imaging Service (AIS) is deployed in a federated model in each NIF site to manage imaging data and is integrated with a REDCap database for managing the survey data. Data can be transferred to Characterisation Virtual Laboratory (CVL) sites for accessing HPC to enable advanced data processing. Some data may become centrally managed if a site withdraws from participating in ABIRD. This hybrid model provides advantages of using federated data management, such as flexibility, scalability, cost reduction, better data governance, while ensuring data will be managed centrally in special cases so that valuable collected and shared data will be preserved.

Biography:

Farnoosh Sadeghian is Data Architect at Murdoch Children’s Research Institute. This work has been done when she was a senior data engineer at Monash e-Research Centre; Monash University. She is data expert in digital research infrastructure with special interest in health research area.