Speaker
Description
Citizen science can turn complex heliophysics mission data into high quality, reusable datasets at scale. We present three citizen science projects built on heliophysics observations, spanning different stages of maturity: (1) a Magnetospheric Multiscale (MMS) project on differentiating magnetosheath types with a data paper ready for submission; (2) a second MMS project on boundary layers identification launched and collecting data; and (3) a THEMIS and TREx all sky imager project for auroral identification nearing launch.
Across these projects, we use visual classification tasks to have volunteers identify boundaries and morphologies that are challenging to capture with automated methods alone. Methodological commonalities include: task decomposition tailored to non experts, intuitive data displays, detailed tutorials, and quality controls such as redundant classifications, expert benchmarks, and consistency checks. These design choices enable the construction of well documented labeled datasets suitable for traditional analysis and machine learning applications.
We will compare lessons learned across the three development stages, showing how careful citizen science methodology can produce robust, community ready MMS and THEMIS data products and inform future heliophysics citizen science efforts.