Data from: Give the machine a hand: a Boolean time-based decision-tree template for rapidly finding animal behaviours in multi-sensor data

1. The development of multi-sensor animal-attached tags, recording data at
high frequencies, has enormous potential in allowing us to define animal
behaviour. 2. The high volumes of data, are pushing us towards
machine-learning as a powerful option for distilling out behaviours.
However, with increasing parallel lines of data, systems become more
likely to become processor limited and thereby take appreciable amounts of
time to resolve behaviours. 3. We suggest a Boolean approach whereby
critical changes in recorded parameters are used as sequential templates
with defined flexibility (in both time and degree) to determine individual
behavioural elements within a behavioural sequence that, together, makes
up a single, defined behaviour. 4. We tested this approach, and compared
it to a suite of other behavioural identification methods, on a number of
behaviours from tag-equipped animals; sheep grazing, penguins walking,
cheetah stalking prey and condors thermalling. 5. Overall behaviour
recognition using our new approach was better than most other methods due
to; (i) its ability to deal with behavioural variation and (ii) the speed
with which the task was completed because extraneous data are avoided in
the process. 6. We suggest that this approach is a promising way forward
in an increasingly data-rich environment and that workers sharing
algorithms can provide a powerful library for the benefit of all involved
in such work.