Crowdsourced dataset of firefly trajectories obtained by automated stereo calibration of 360-degree cameras

Advancements in animal tracking techniques, spanning from migrating mammals to swarming insects, have resulted in remarkable progress in the fields of behavioral ecology and conservation science. Recently, we have devised a method for tracking luminous fireflies in their natural habitat using stereoscopic pairs of 360-degree cameras. This method offers affordability, versatility, and ease of setup; however, the process of camera calibration has remained tedious and time-consuming. Now, we have introduced an enhanced algorithm that achieves spatial and temporal stereo calibration directly from the data, eliminating the need for manual procedures both in the field and during video processing. The algorithm relies on cross-correlation of flashing patterns and numerical estimation of camera pose. Utilizing this improved protocol and processing software, we have compiled an extensive dataset comprising over 100 reconstructed firefly swarms of various species. This data was gathered throughout the United States by numerous contributors following a straightforward protocol. The dataset holds significant potential for advancing our comprehension of firefly collective behavior, facilitating population monitoring, and expanding citizen science initiatives.