BCCVL Project Manager Hamish Holewa says using a cloud-based system allows the lab to be available to a wider audience, to those that “wouldn’t normally have the computer programming expertise or skills to do modelling.”

“For example, for a typical ecologist to pick up the skills and expertise to access the data sets and get the code working it would take about three months for them to get up to speed. Using BCCVL, it takes five seconds.”

For more advanced lab users BCCVL uses QRIScloud to provide the compute, data and algorithms needed to configure multi-species models and scripts in complex ways.

Mr Holewa says the cloud makes it easier to integrate data, share outputs and grant access to those with a BCCVL login, which is free for anyone to set up.

“The cloud allows the lab to access other data sets around the country easily, quickly and cost effectively. BCCVL chose to work with QRIScloud because of its fast response and being situated on a research network, it’s quick and easy to access large data sets.”

Established in 2013, BCCVL is the result of a collaborative effort between Griffith University, James Cook University, Macquarie University, University of NSW, Australian National University, Intersect, QCIF and NeCTAR. The lead project partners are Griffith University and James Cook University.

BCCVL is open to all researchers in Australian universities, CSIRO and government agencies.

Prof Mackey said in a NeCTAR case study: “We wanted to lower the bar in terms of accessibility, but at the same time raise the bar in terms of the quality of the modelling—ultimately providing a more rigorous approach.”

Prof Mackey says the virtual lab saves time, allows collaboration with researchers without programming skills, and provides a standardised set of tools for analysis and computational limitations.

“Essentially the BCCVL has enabled us to ask questions that we couldn’t ask before.”

BCCVL is continuing to innovate with new functions and features being added. The lab is looking to provide software and analysis services for a wide variety of modelling applications in ecology and environmental management, including terrestrial, aquatic and marine environments.

The lab will continue to expand its distribution modelling and analysis, including:

● multi-species modelling
● regional constraint modelling
● increased temporal analysis frequency
● integration of international datasets.

It will also have a comprehensive set of API's to provide services to other modelling software.

BCCVL is on the brink of introducing an online open course in Species Distribution Modelling. Those interested can register their email and/or sign-up to BCCVL’s mailing list to keep in the loop.

Funding for BCCVL came from NeCTAR and the Australian National Data Service (ANDS), with contributions from Research Data Services (RDS) through the Terrestrial Data Project.

What researchers say about BCCVL

Associate Professor Fabiana Santana, Faculty of Education, Science, Technology & Mathematics, University of Canberra

“The BCCVL is a sound project from the scientific perspective. It is also one of the projects for modelling purposes that offer more features and functionalities among the solutions and packages available nowadays.

“The project is moving forward in a very good direction and the results achieved so far are outstanding, especially when compared with other similar projects that have been developed in the past. It is time to start asking: the old problems have been solved, and now what?”

Dr Willow Hallgren, Research Fellow, Griffith Climate Change Response Program, and BCCVL Project Scientist

“The BCCVL has allowed me to design and implement much more complex species distribution modelling [SDM] and climate impact experiments than I otherwise might have done. This is due to the simplification of the preparation of input data, the ease of implementing SDM algorithms and the facilitation of analysis of modelling results afforded by the BCCVL, and the subsequent time-savings that this generates.

“As a researcher the BCCVL has enabled me to be more productive and to be able to focus on answering scientific questions rather than the more mundane tasks of data preparation, the setting up of multiple modelling algorithms and the visualisation of results.”