Uploaded March 2025 | Updated September 2026, 2 weeks ago
#ShinyConf #ShinyConf2024
Abstract: We developed an interactive web application to facilitate multi-taxon field sampling and data collection using the Shiny for Python platform. This app allows researchers, citizen scientists, students, and others to easily design and conduct standardized surveys for organisms such as arthropods, birds, and plants. Users can input their own data in real-time in the field via a user interface optimized for both desktop and mobile devices. Location data capture utilizes native GPS on devices to map survey points. Images can also be captured in-app and linked to observations. Built-in modules access local weather data to record conditions at time of sampling. To maximize data quality, validity checks on species identifications are implemented by querying established taxonomic databases. All data inputs are uploaded to collaborative spreadsheets (Google sheets) which update in real-time, allowing researchers to instantly access the latest results. This facilitates rapid analytics and minimizes post-processing needs. Overall, this customizable open-source app provides an adaptable solution to generate systematic, standardized data across replicable studies. It helps meet critical needs for biodiversity monitoring programs, ecological field studies, and environmental impact assessments globally.
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Join us this year! Sign up for ShinyConf 2025 here: go.appsilon.com/register-shinyconf2025
#ShinyConf #ShinyConf2024
Abstract: We developed an interactive web application to facilitate multi-taxon field sampling and data collection using the Shiny for Python platform. This app allows researchers, citizen scientists, students, and others to easily design and conduct standardized surveys for organisms such as arthropods, birds, and plants. Users can input their own data in real-time in the field via a user interface optimized for both desktop and mobile devices. Location data capture utilizes native GPS on devices to map survey points. Images can also be captured in-app and linked to observations. Built-in modules access local weather data to record conditions at time of sampling. To maximize data quality, validity checks on species identifications are implemented by querying established taxonomic databases. All data inputs are uploaded to collaborative spreadsheets (Google sheets) which update in real-time, allowing researchers to instantly access the latest results. This facilitates rapid analytics and minimizes post-processing needs. Overall, this customizable open-source app provides an adaptable solution to generate systematic, standardized data across replicable studies. It helps meet critical needs for biodiversity monitoring programs, ecological field studies, and environmental impact assessments globally.
____________________________
Join us this year! Sign up for ShinyConf 2025 here: go.appsilon.com/register-shinyconf2025
