[{"id":307,"full_name":"Giorgia Mori","website":"","orcid":"https://orcid.org/0000-0003-3469-5632","description":"Giorgia is a Data Science Trainer at the Sydney Informatics Hub (SIH), University of Sydney. She has a background in microbiology and bioinformatics. She is a Software, Data and Library Carpentry instructor, and she has been supervising, teaching and training staff and students in the development of computational skills for conducting efficient and reproducible research. She volunteers in diversity initiatives (RLadies, PyLadies) to support the representation of gender minorities in the programming community.","location":"Sydney, Australia","experience":"competant"},{"id":128,"full_name":"Matt Plummer","website":"https://mattsresbazsite.netlify.app/","orcid":"https://orcid.org/0000-0002-2737-2707","description":"My background spans the arts and technology, and I enjoy working with researchers from different disciplines to help them utilise technology in innovative and transformative ways. I've assisted with the development of open-source projects and research tools, coordinated numerous community-building and training events, and enjoy the opportunity to introduce researchers to new approaches and collaborators.","location":"Wellington, New Zealand","experience":"competant"},{"id":46,"full_name":"Melissa Burke","website":"","orcid":"https://orcid.org/0000-0002-5571-8664","description":"Melissa is the Australian BioCommons Training and Communications Officer. She collaborates with bioinformatics experts and trainers from across Australia and internationally to develop and deliver bioinformatics webinars and workshops.","location":"Brisbane, Australia","experience":""},{"id":339,"full_name":"Murray Cadzow","website":"","orcid":"https://orcid.org/0000-0002-2299-4136","description":"Scientific Programmer at the University of Otago. A Carpentries Instructor and Instructor Trainer.","location":"University of Otago","experience":"competant"},{"id":440,"full_name":"Tim Langlois","website":"https://globalarchivemanual.github.io/CheckEM/index.html","orcid":"https://orcid.org/0000-0001-6404-4000","description":"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 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.","location":"GitHub","experience":"expert"}]