Data from: Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics

Time-lapse cameras facilitate remote and high-resolution monitoring of
wild animal and plant communities, but the image data produced require
further processing to be useful. Here we publish pipelines to process raw
time-lapse imagery, resulting in count data (number of penguins per image)
and ‘nearest neighbour distance’ measurements. The latter provide useful
summaries of colony spatial structure (which can indicate phenological
stage) and can be used to detect movement – metrics which could be
valuable for a number of different monitoring scenarios, including image
capture during aerial surveys. We present two alternative pathways for
producing counts: 1) via the Zooniverse citizen science project Penguin
Watch and 2) via a computer vision algorithm (Pengbot), and share a
comparison of citizen science-, machine learning-, and expert- derived
counts. We provide example files for 14 Penguin Watch cameras, generated
from 63,070 raw images annotated by 50,445 volunteers. We encourage the
use of this large open-source dataset, and the associated processing
methodologies, for both ecological studies and continued machine learning
and computer vision development.