Mar Quiroga is a Senior Research Data Specialist at the Melbourne Data Analytics Platform (MDAP) in the University of Melbourne, where she collaborates with researchers across all domains to bring the benefits of data-intensive methods to their fields. She is currently leading the HASS Taskforce,...
Location: Melbourne, Australia
QuirogaMarmar.quiroga@unimelb.edu.auMelbourne, AustraliaMar Quiroga is a Senior Research Data Specialist at the Melbourne Data Analytics Platform (MDAP) in the University of Melbourne, where she collaborates with researchers across all domains to bring the benefits of data-intensive methods to their fields. She is currently leading the HASS Taskforce, an exciting initiative that aims to empower and inspire researchers in the Humanities, Arts, and Social Sciences to embrace the potential of digital methodologies and create communities of practice and peer support.
Mar is a certified Carpentries instructor and holds a BSc/MSc in Mathematics from the University of Cordoba, Argentina, and a PhD in computational and experimental neuroscience from Rutgers University, USA.
["English", "Portuguese", "Spanish; Castilian"]https://marstudio.netlify.app/https://orcid.org/0000-0002-8943-2808
Based within the Monash Bioinformatics Platform and a certified Carpentries instructor, Nick is also involved with Monash Data Fluency in training and teaching on genomics, bioinformatics and R.
Location: Melbourne, Australia
WongNicknick.wong@monash.eduMelbourne, AustraliaBased within the Monash Bioinformatics Platform and a certified Carpentries instructor, Nick is also involved with Monash Data Fluency in training and teaching on genomics, bioinformatics and R. ["English", "Chinese"]https://research.monash.edu/en/persons/nick-wonghttps://orcid.org/0000-0003-4393-7541
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
