P.I. Charter: A Shiny Application for Collecting, Cleaning, Compiling, and Communicating Point-Intercept Aquatic Plant Data

Even when data needed to address complex, high-priority ecological questions exist, when they are collected by many different entities for diverse purposes, they can be difficult to discover and unify. Web applications can address these challenges by serving as submission portals that clean and combine data programmatically. Furthermore, web apps can incentivize contributions—e.g., by providing public recognition and novel visualizations for reporting—while insulating users from needing to apply programming skills or interoperability practices. Here, we introduce P.I. Charter, an R Shiny web application that solicits, tidies, compiles, and shares point-intercept (PI) aquatic plant survey data collected across Minnesota, USA by dozens of entities. We summarize the app's features and how these align with our goals regarding ease of use, accessibility, education, open access, and user experience. Since P.I. Charter's launch, it has received thousands of visitors and hundreds of submissions. Familiar contributors can submit surveys in minutes, and satisfaction with the app is high. We present four case studies that show how the app and its database could be used to: (1) better plan and conduct future surveys, (2) strategize surveillance for new cryptic invasions, (3) investigate relationships between species occurrence and environmental factors, and (4) elucidate long-term trajectories of plant community composition in lakes responding to global change processes and management. Apps like P.I. Charter could revolutionize data aggregation in contexts wherein large data volumes are gathered and stored disparately. For us, developing P.I. Charter was relatively straightforward, but we caution that using R Shiny to build a complex, science-focused web application is not without challenges, especially with respect to achieving digital accessibility. Nonetheless, P.I. Charter's capacity to make fuller use of hard-earned data to address ecological questions has proven so valuable we wish to share our experiences so they may serve as a model for others.