BigData? Analysis taking too long? Frustrated by command-line tools? Want menus? CVL provides the infrastructure you need.
CVL (Characterisation Virtual Laboratory) (www.cvl.org.au) is a managed eResearch desktop space available to all Australian researchers working in imaging and characterisation sciences. Using their AAF login, researchers can access the virtual laboratory using their choice of desktop client or web-based interface, on multiple deployments across Australia. CVL allows researchers to easily access HPC setups from their own PCs over the internet for processing large volumes of imaging data on remote systems. With 3 preset virtual desktop configurations, researchers can opt for basic, intermediate or heavy compute setups, enabling rapid transfer and analysis of terabyte-sized datasets. CVL comes preinstalled with over 100 imaging tools and a team ready to install new packages in response to the changing needs of the characterisation community.
Here, we provide an overview of CVL, including an explanation of the infrastructure itself and deployed nodes, as well as software available. We will provide explicit instructions for how to register to use CVL. After registering, researchers are invited to join us online on 1 March, 2021, when we will be conducting a live workshop using FIJI/ImageJ within CVL (particularly with reference to light microscopy) led by Dr Nick Condon, UQ, and on 2 March, 2021, when we will explore routes for data transfer in and out of CVL, focussing on sFTP, OMERO and CloudStor+. We will provide information on how to register for these events.
National Image Facility, University of Queensland
Kathryn Hall is the Training and Community Engagement Manager for the Australian Characterisation Commons at Scale (ACCS) project, and works at the National Imaging Facility. Based at UQ, her role is to assist researchers and facility managers to gain access to the Characterisation Virtual Laboratory (CVL) and provide training around FAIR data management principles for the characterisation community. She has a background in invertebrate animal taxonomy, with extensive experience working with the evolution of morphological traits of animals using DNA and microscopy. After completing her studies at UQ, Kathryn has worked for over 15 years in museums on groups such as sponges, polychaete worms and digenean parasites. Her experience gives her the perspective of an end-user of analysis tools and she tries hard to bring an accessible and jargon-free approach to working with Big Data.