Overview of Reproducible Research

Overview of Reproducible Research


No previous knowledge needed.

The research process is represented as a perpetual cycle of generating research ideas, performing data planning and design, data collection, and data processing and analysis, publishing, preserving and hence, allowing re-use of data.

Fig. 4 The Turing Way project illustration by Scriberia. Used under a CC-BY 4.0 licence. DOI: 10.5281/zenodo.3332807.


Scientific results and evidence are strengthened if those results can be replicated and confirmed by several independent researchers (see definitions).

When researchers employ transparency in their research - in other words, when they properly document and share the data and processes associated with their analyses - the broader research community is able to save valuable time when reproducing or building upon published results. Often, data or code from prior projects will be re-used by new researchers to verify old findings or develop new analyses.

Learn about some of the other benefits of reproducible research in the Added Advantages subchapter.

Major media outlets have reported on investigations showing that a significant percentage of scientific studies cannot be reproduced. This leads to other academics and society losing trust in scientific results [Bak16].

In addition, “negative results” can be published easily, helping avoid other researchers wasting time repeating analyses that will not return the expected results [DL10].

For further reading resources on reproducibility, please check out the Resources subchapter.

Chapter Tags: This chapter is curated for the Turing Data Study Group (turing-dsg).