Guidelines for data analysis scripts
This addresses the issue of programmer errors in neuroscience research, but it is incremental as it builds on existing best practices without introducing new computational methods.
The paper tackles the problem of unorganized and error-prone data analysis code in neuroscience by proposing guidelines for organizing analysis scripts, resulting in a structured approach to improve reliability and reduce retractions.
Unorganized heaps of analysis code are a growing liability as data analysis pipelines are getting longer and more complicated. This is worrying, as neuroscience papers are getting retracted due to programmer error. In this paper, some guidelines are presented that help keep analysis code well organized, easy to understand and convenient to work with: 1. Each analysis step is one script 2. A script either processes a single recording, or aggregates across recordings, never both 3. One master script to run the entire analysis 4. Save all intermediate results 5. Visualize all intermediate results 6. Each parameter and filename is defined only once 7. Distinguish files that are part of the official pipeline from other scripts In addition to discussing the reasoning behind each guideline, an example analysis pipeline is presented as a case study to see how each guideline translates into code.