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@article{Blackhurst:2023:bioadv/vbad132,
author = {Blackhurst, L and Gilestro, G},
doi = {bioadv/vbad132},
journal = {Bioinformatics Advances},
title = {Ethoscopy and ethoscope-lab: a framework for behavioural analysis to lower entrance barrier and aid reproducibility behavioural analysis to lower entrance barrier and aidreproducibility},
url = {http://dx.doi.org/10.1093/bioadv/vbad132},
volume = {3},
year = {2023}
}
TY - JOUR
AB - High-throughput analysis of behaviour is a pivotal instrument in modern neuroscience, allowing researchers to combine modern genetics breakthrough to unbiased, objective, reproducible experimental approaches. To this extent, we recently created an open-source hardware platform (ethoscope (Geissmann et al., 2017)) that allows for inexpensive, accessible, high-throughput analysis of behaviour in Drosophila or other animal models. Here we equip ethoscopes with a Python framework for data analysis, ethoscopy, designed to be a user-friendly yet powerful platform, meeting the requirements of researchers with limited coding expertise as well as experienced data scientists. Ethoscopy is best consumed in a prebakedJupyter-based docker container, ethoscope-lab, to improve accessibility and to encourage the use of notebooks as anatural platform to share post-publication data analysis. Ethoscopy is a Python package available on GitHub and PyPi. Ethoscope-lab is a docker container available on DockerHub. A landing page aggregating all the code and documentation is available at https://lab.gilest.ro/ethoscopy.
AU - Blackhurst,L
AU - Gilestro,G
DO - bioadv/vbad132
PY - 2023///
SN - 2635-0041
TI - Ethoscopy and ethoscope-lab: a framework for behavioural analysis to lower entrance barrier and aid reproducibility behavioural analysis to lower entrance barrier and aidreproducibility
T2 - Bioinformatics Advances
UR - http://dx.doi.org/10.1093/bioadv/vbad132
UR - https://doi.org/10.1093/bioadv/vbad132
UR - http://hdl.handle.net/10044/1/106725
VL - 3
ER -
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