Dr Darya Vanichkina is a Data Science Consultant and Analytics Trainer at the University of Sydney’s Informatics Hub (SIH). She delivers impact through solving research questions using big data, programming, machine learning, high performance & cloud computing. Darya develops and delivers...
Location: Sydney, NSW, Australia
VanichkinaDaryadarya.vanichkina@sydney.edu.auSydney, NSW, AustraliaDr Darya Vanichkina is a Data Science Consultant and Analytics Trainer at the University of Sydney’s Informatics Hub (SIH). She delivers impact through solving research questions using big data, programming, machine learning, high performance & cloud computing. Darya develops and delivers materials, and trains others to do the same by leading the SIH data science training program, coaching and mentoring other data scientists in the team via workshops and guidance around evidence-based training practices. Darya holds a PhD in Genomics and Bioinformatics and is a Fellow of the UK HEA.["English", "Russian"]https://daryavanichkina.comhttps://orcid.org/0000-0002-0406-164X
Stereo imagery is widely used by research institutions and management bodies around the world as a cost-effective and non-destructive method to research and monitor fish and habitats (Whitmarsh, Fairweather and Huveneers, 2017). Stereo-video can provide accurate and precise size and range...
Location: GitHub
LangloisTimtim.langlois@uwa.edu.auGitHubStereo imagery is widely used by research institutions and management bodies around the world as a cost-effective and non-destructive method to research and monitor fish and habitats (Whitmarsh, Fairweather and Huveneers, 2017). Stereo-video can provide accurate and precise size and range measurements and can be used to study spatial and temporal patterns in fish assemblages (McLean et al., 2016), habitat composition and complexity (Collins et al., 2017), behaviour (Goetze et al., 2017), responses to anthropogenic pressures (Bosch et al., 2022) and the recovery and growth of benthic fauna (Langlois et al. 2020). It is important that users of stereo-video collect, annotate, quality control and store their data in a consistent manner, to ensure data produced is of the highest quality possible and to enable large scale collaborations. Here we collate existing best practices and propose new tools to equip ecologists to ensure that all aspects of the stereo-video workflow are performed in a consistent way.["English", "French"]https://globalarchivemanual.github.io/CheckEM/index.htmlhttps://orcid.org/0000-0001-6404-4000
