Really reproducible behavioural paper
2019-09-04, 13:15–14:45, Posters at 16:00

A heavily XKCD themed poster about writing a really reproducible behavioural paper in Python environment.
The poster is also available online.


In recent years replication crisis in life sciences has received significant attention. Reproducibility of behavioural experiments may be affected by many factors, such as lack of standardisation of experimental conditions or human errors. While use of standardized systems for automated phenotyping (such as IntelliCage) leads to interlaboratory replicability of experiments (1), manual analysis of the obtained data still remains a potential source of irreproducibility due to human errors. Luckily, a countermeasurement for that issue is known for more than least twenty years: automation of data analysis with a non-interactive computer program (2).

To facilitate development of Python programs for automated analysis of mice behavioural data obtained from IntelliCage system PyMICE library (RRID:nlx_158570) has been developed. The title paper is the publication presenting the library to the scientific community (3). As it has been written according to literate programming paradigm (4), all programs used for analysing the experimental data are embedded in the source code of the paper itself which makes the presented results highly reproducible and the methodology of analysis transparent.

Authors

  • Jakub M. Dzik,
  • Alicja Puścian,
  • Zofia Mijakowska,
  • Kasia Radwanska,
  • Szymon Łęski

Bibliography

  1. A. Codita, A. H. Mohammed, A. Willuweit, A. Reichelt, E. Alleva, I. Branchi, F. Cirulli, G. Colacicco, V. Voikar, D. P. Wolfer, F. J. U. Buschmann, H.-P. Lipp, E. Vannoni, S. Krackow (2012)
    Effects of Spatial and Cognitive Enrichment on Activity Pattern and Learning Performance in Three Strains of Mice in the IntelliMaze.
    Behavior Genetics doi:10.1007/s10519-011-9512-z
  2. J. B. Buckheit, D. L. Donoho (1995)
    WaveLab and Reproducible Research. Lecture Notes in Statistics.
    doi:10.1007/978-1-4612-2544-7_5
  3. J. M. Dzik, A. Puścian, Z. Mijakowska, K. Radwanska, S. Łęski (2017)
    PyMICE: A Python library for analysis of IntelliCage data.
    Behavior Research Methods. doi:10.3758/s13428-017-0907-5
  4. D. E. Knuth (1984) Literate Programming.
    The Computer Journal. doi:10.1093/comjnl/27.2.97

Acknowledgement

Project funded from the Polish National Science Centre's SYMFONIA (2013/08/W/NZ4/00691) grant.


Abstract as a tweet – If you like the https://xkcd.com webcomics or care about research reproducibility you may enjoy this poster. Project Homepage / Git – https://github.com/Neuroinflab/PyMICE_SM Project Homepage / Git – https://github.com/Neuroinflab/PyMICE_SM Python Skill Level – basic Domain Expertise – none Domains – Data Visualisation, Medicine/Health, Open Source, Scientific data flow and persistence, none of the above,