Crowd-generated data from human activities are increasingly used as an input to scientific research. This session explores lessons learned from research on data collected, analyzed, or shared through methods that engage or harness inputs from the public. We hope participants share experiences in managing challenges given the characteristics of contributed and opportunistic data. Topics might include ethical considerations, risks to participants - especially from environmental hazards, validity, motivation, unevenly sampled spatial data, documentation and metadata, access and privacy, and communication to the public. This section will highlight empirical methods and theory used to address technical, social, and ethical crowdsourced data challenges in research and practice. Abstracts will be considered from all crowdsourcing or citizen science related works from virtual crowdsourcing platforms, citizen science collection as sensors or observers of physical environments, connected networks of devices or the Internet of Things (IoT), and social media content applications.

• NH - Natural Hazards