Australia’s Monash University has secured AU$ 4.3 million from the Australian Research Data Commons (ARDC) to lead four major data and cloud infrastructure science projects.
These projects will reportedly advance the artificial intelligence (AI), data science and research technology capabilities at the university.
About the initiative
ARDC, which is run through the Federal Government initiative, National Collaborative Research Infrastructure Strategy (NCRIS), awarded four projects led by the Monash eResearch Centre
All of the projects focus on building scalable data environments for data-centric research, sensitive data and strengthening the use of AI techniques, such as machine learning (ML).
The University will work in partnership with other leading research organisations and universities to deliver these projects.
Doing so will harness the combined resources and knowledge to achieve improved high-performance data environments for researchers.
In one of these projects, the University was one of four organisations that received funding to upgrade its node of ARDC’s Nectar Research Cloud.
This national research cloud infrastructure provides core services to more than 16,000 researchers in approximately 1,600 currently active projects, enabling Australia’s research community to store, access, and analyse data.
The funding is critically important as the research community is now generating more data than ever and needs new solutions.
Researchers are producing incredible amounts of complex and in some cases, unstructured data.
As stated by the ARDC, the successful platforms projects cover all of the National Science and Research Priorities and National Research Infrastructure Roadmap focus areas.
- Establishing Australia’s Scalable Drone Cloud (ASDC)
Unmanned Aerial Vehicles (UAVs), commonly known as drones, provide sensing capabilities that address the critical scale-gap between ground and satellite-based observations.
It offers a competitive advantage for researchers through the ability to deliver near real-time societally-relevant information.
ASDC will fuse and establish a national best practice approach for experimental and scalable drone data analytics, driven by exemplar data-processing pipelines.
The platform will integrate sensing capabilities with easy-to-use storage, processing, visualisation and data analysis tools, which include computer vision / deep learning techniques, to establish a national ecosystem for drone data management.
- Environments to Accelerate ML Based Discovery
The convergence of big data and ML techniques is spreading through all aspects of people’s lives.
However, the access of researchers to necessary tools, training and resources is still patchy and uncoordinated.
This platform will accelerate the adoption of these techniques by Australian researchers, building on an international survey of research groups.
It will support core ML tools for pre-processing, annotating, training, and validation. It will also integrate with software development environments to provide a consolidated platform for ML-based research.
- Australian Characterisation Commons at Scale (ACCS)
The ACCS will develop a coherent and accessible compute and date environment that promotes collaboration, increases ROI for the characterisation instruments, and delivers value for thousands of researchers in domains.
These domains include health, advanced manufacturing, soil and water, food, energy and transport, and resources.
Building on the Characterisation Virtual Laboratory, the proposed infrastructure will be a rich ecosystem of computing systems, data repositories, workflows, and services, connected with instruments.
- Infrastructure Refresh – ARDC Nectar Research Cloud at Monash (Generation 2)
The University is committed to significant continued involvement with ARDC’s Nectar Research Cloud. ARDC’s support will refresh cloud compute and storage infrastructure for Nectar funded equipment that has reached the end of its useful life.
It will also maintain the capacity required to meet the demand for cloud resources from nationally prioritised research activities.