RCC will be involved in the development of three new research-oriented platforms this year, funded by the Australian Research Data Commons (ARDC).
ARDC announced last December it would channel $12 million of Federal Government funds, with $20.47 million in co-investments from collaborating organisations, for 10 new platform projects after an open call for submissions.
The successful platforms and services projects will provide researchers and industry access to a range of resources. They cover all of the national science and research priorities and National Research Infrastructure Roadmap focus areas, such as digital data and eResearch platforms.
RCC has begun working in collaboration with the lead agents and other project members on three projects, namely:
- Australian Imaging Service (XNAT) — lead agent: University of Sydney
- Australian Characterisation Commons at Scale (ACCS) — lead agent: Monash University
- Environments to Accelerate Machine Learning-Based Discovery — lead agent: Monash University.
Australian Imaging Service
Universities and clinical sites across Australia are struggling to manage large volumes of imaging data, while balancing patient privacy and the need for data sharing and accessibility in the research community.
The Australian Imaging Service will transform the imaging and radiology sector by leveraging institutional investments and providing enhanced data management and analysis.
The distributed federation will consist of multiple institutional deployments linked with a federated search layer, common community practice, support for expanded data types and a Trusted Tool Repository ensuring ongoing ownership and accountability of data.
The project team will use XNAT as a basis to create a user-friendly imaging service platform for researchers.
XNAT is an extensible open-source imaging informatics software platform dedicated to imaging-based research. Its functionality is well-suited to the Australian Imaging Service project's goals, and the platform is already in use on a smaller scale within the Australian imaging community.
Australian Characterisation Commons at Scale (ACCS)
To succeed in science and engineering it is essential to be able to characterise the properties of materials.
The ACCS will develop a coherent and accessible compute and data environment that promotes collaboration, increases return on investment for characterisation instruments, and delivers value for thousands of researchers in domains including Health, Advanced Manufacturing, Soil and Water, Food, Energy and Transport, and Resources.
The proposed infrastructure, building on the Characterisation Virtual Laboratory (CVL), will be a rich ecosystem of computing systems, data repositories, workflows, and services, connected with instruments.
Environments to Accelerate Machine Learning-Based Discovery
The confluence of Big Data and Machine Learning (ML) techniques is permeating all aspects of our lives, but access for researchers to necessary tools, training and resources is still patchy and uncoordinated.
This ML platform will accelerate adoption of these techniques by Australian researchers, building on an international survey of research groups.
The platform will support core ML tools for preprocessing, annotating, training, and validation, and integrate with software development environments to provide a consolidated platform for ML-based research.
This article includes information directly from the ARDC website.
