Research communities takeaways from the virtual C3DIS conference

Following the cancellation of the 2020 Collaborative Conference on Computational and Data Intensive Science (C3DIS) due to COVID-19 concerns, the conference Organising Committee decided to pivot and deliver an online version of the event, hosting C3DIS VIRTUAL from 06 April - 05 May 2020.

As one of NeSI’s Research Communities Advisors, I’d planned on attending C3DIS in person because I saw value in not only connecting with colleagues in Australia, but also learning about other regions’ projects, ideas, and approaches to computational and data science. 

Over the course of the month, I was able to tune into a number of the virtual presentations, focusing on ones related to data analytics, partnerships, and community engagement strategies.

Below are a few of the key takeaways and personal highlights from the sessions I attended.



Melbourne Data Analytics Platform
Speaker: Dr Edoardo Tescari, University of Melbourne

  • This presentation gave an overview of the first six months of operations of the new Melbourne Data Analytics Platform (MDAP), which was formed as part of Melbourne University’s Petascale Campus Initiative (PCI), a five year plan to accelerate the University’s capacity for data-intensive research over 2018 to 2022.
  • MDAP provides researchers with collaborative support to enhance data-driven research in all faculties. As experts in digital methods, they help connect researchers and computational scientists, and connect academics to best practices.
  • MDAP’s involvement in research projects spans areas from high performance computing to natural language processing, machine learning, and augmented reality. 

  • In terms of operations and engagement, there’s no hierarchy. Team members are flexible and often jump in and out of projects. MDAP values not only tech skills but also people-focused, ethical, and legal skills.

  • Their focus areas include:

    • Advocacy (ie. storage and access to cloud computing)

    • Consultancy (ie. help with technical aspects of grants)

    • Community (ie. collaboration within and across faculties)

  • They have seen a great need to uplift and support data intensive research. To respond to this, MDAP has two streams of engagement:

    • Collaborations - formal; high strategic value

    • Projects - informal; open; consultancy and advice

  • Other activities they’re involved with include:

    • Projects

    • Internship programs

    • Community events - seminars; tech talks; community lunches

For more information on the MDAP, click here to visit their website


Developing Capability in Bioimaging Research Software Engineering with National Programs and Partnerships

Speaker: Dr Paula Andrea Martinez, National Imaging Facility

  • This presentation shared some of the goals and lessons learned from last year’s operations (2019) of the Characterisation Community, a project that underpins national capability by connecting national infrastructure, expertise and best practice to turn data into new discoveries.

  • The project is co-funded by the Australian Research Data Commons (ARDC) and its lead partners are three Australian national institutions (NIF, Microscopy Australia and ANSTO), plus five Australian Universities (UQ, UoM, Monash, UWA, SydUni and UoW).

  • Last year they developed and improved effective and networked relationships across Australia, managing a series of events targeted at researchers at the beginner, intermediate, and advanced levels of computational research. 

  • Her presentation focused on the outcomes of an intermediate-advanced tutorial they hosted, titled: “Automating Neuroimaging Workflows”, where the target audience was research software engineers and neuroscientists.

  • Through that tutorial, they learned that:

    • Automating workflows are still a key requirement from the community to advance science. Their tutorial provided examples of existing workflows and also encouraged attendees to develop their own workflows with the community.

    • There is value in giving participants accessibility to computational capacity. In this case, they used containers and access to the Characterisation Virtual Laboratory (CVL).

    • You can build community and collaborations amongst practitioners and end-users by providing opportunities for professional development. 

  • Overall, their collaboration and partnerships strategy was developed using an iterative process, facilitating collaboration across parties, and building a roadmap that identifies key outcomes that are centred around people.

For more information on Paula and others’ work in this area, you can read more here


Collaboration in computation at Curtin University: Investigating human activity recognition and joint kinematics in ballet

Speaker: Kathryn Napier, Curtin Institute For Computation, Curtin University

  • This presentation looked at a collaboration between the Curtin Institute for Computation (CIC) and the School of Physiotherapy and Exercise Sciences to develop a novel machine learning based approach to accurately measure ballet dancer training load and joint kinematics. 

  • Currently training load is estimated from dancer’s written diary entries, while the investigation of joint kinematics requires sophisticated and expensive laboratory based optical motion capture systems. 

  • By combining expertise in both physiotherapy and computation, they developed a novel methodology using wearable sensors that can accurately measure dancer training load and joint kinematics in real-world ballet classes.

To read an earlier case study on this work, visit the CIC website.