Advancements such as automated image-based tracking, bio-logging, genetic barcoding and remote sensing enable us to study plants, animals and ecosystems in more detail and in faster, less intrusive ways than ever before (Anderson & Gaston, 2013 Dell et al., 2014 Hebert et al., 2003 Hughey et al., 2018 Kays et al., 2015). The last few decades have seen tremendous technological progress that has transformed biological research (Chave, 2013 Snaddon et al., 2013).
I hope this paper will help generate more awareness about the Raspberry Pi among scientists and thereby both fuel the democratisation of science and ultimately help advance our understanding of biology, from the micro- to the macro-scale. To help accelerate the uptake of the Raspberry Pi by the scientific community, I provide detailed guidelines, recommendations and considerations, and 30+ step-by-step guides on a dedicated accompanying website ( ).
A hugely diverse range of applications exists that ranges from simple solutions to dedicated custom-build devices, including nest-box monitoring, wildlife camera trapping, high-throughput behavioural recording, large-scale plant phenotyping, underwater video surveillance, closed-loop operant learning experiments and autonomous ecosystem monitoring.