Species extinction rates are at an all-time high in modern times and natural scientists are faced with the challenge of needing to rapidly increase their efforts to gather reliable ecosystem information at broader scales in order to mitigate threats.
Traditional methods of collecting ecological data can often be time consuming, invasive and can alter the natural habitat of the study site.
With this in mind, a James Cook University coastal ecology PhD student has developed an alternative scientific workflow to collect biological and ecological data using computer vision and machine learning to scale up data collection to required levels and improve its efficiency and utility. To do so, he used a node on QRIScloud, QCIF’s cloud, specially designed for machine learning work.
A significant University of Queensland evolutionary discovery published in Nature has had the helping hand of QFAB for the last four years.
QFAB Database and Systems Administrator Nick Rhodes spends about one day a week working with the Degnan Marine Genomics Labs, with half of that day embedded within the research group.
“I do all the IT wrangling—creating accounts, advising students, installing and updating software, handling data storage and just generally ensuring that everyone has the resources they need to analyse their data,” said Nick.
“When we get new members in the lab, I usually tell them that it's my job to make sure that they can do their job.”