Stress-Testing Citizen Science at Scale: Performance Insights from the GREENCROWD Platform

Citizen science has become a pivotal instrument for large-scale data collection across diverse scientific domains. This study introduces GREENCROWD, an open-source, modular, and gamified platform that facilitates geospatial data collection in collaborative research. Unlike conventional systems, GREENCROWD integrates a gamification engine (GAME) to enhance participant engagement through adaptive reward mechanisms while maintaining a flexible architecture that supports cross-domain deployment. We present a comprehensive technical evaluation of GREENCROWD, employing load and stress testing methodologies using tools such as Locust. Performance benchmarks were conducted across diverse cloud infrastructure configurations to assess system behavior under high concurrency and limited-resource scenarios. While technical scalability is a necessary foundation, the more critical scientific challenge is ensuring the platform’s acceptance, sustained adoption, and continuous usage by participants over time. These findings highlight essential scalability thresholds and underscore the need for optimized concurrency management and caching strategies. Beyond its case study, this work contributes a reproducible benchmarking framework for evaluating the technical viability of citizen science platforms, advancing the discourse on sustainable digital infrastructures for participatory science at scale.