Reproducibility in Data Sciences Why, What, and How

One of the key principles of proper scientific procedure is the act of repeating an experiment or analysis and being able to reach similar conclusions. Published research based on computational analysis, e.g. bioinformatics or computational biology, have often suffered from incomplete method descriptions (e.g. list of used software versions); unavailable raw data; and incomplete, undocumented and/or unavailable code. This essentially prevents any possibility of attempting to reproduce the results of such studies. The term “reproducible research” has been used to describe the idea that a scientific publication based on computational analysis should be distributed along with all the raw data and metadata used in the study, all the code and/or computational notebooks needed to produce results from the raw data, and the computational environment or a complete description thereof.

Reproducible research not only leads to proper scientific conduct but also provides other researchers the access to build upon previous work. Most importantly, the person setting up a reproducible research project will quickly realize the immediate personal benefits: an organized and structured way of working. The person that most often has to reproduce your own analysis is your future self!

In this talk, I quickly present myself, current work, and go through a presentation about key motivation factors and issues in building reproducible code. I end-up show casing tools, and a strategy for building a reproducible research ecosystem.

You may access the slides here.