# Guide for Reproducible Research

***This guide covers topics related to skills, tools and best practices for research reproducibility.***

_The Turing Way_ defines reproducibility in data research as data and code being available to fully rerun the analysis.

There are several definitions of reproducibility in use, and we discuss these in more detail in the {ref}`rr-overview-definitions` section of this chapter.
While it it absolutely fine for us each to use different words, it will be useful for you to know how _The Turing Way_ defines *reproducibility* to avoid misunderstandings when reading the rest of the handbook.

| ![A person showing another person what steps to take to make your data research reproducible](../figures/reproducibility.jpg) |
| ---------------|
| _The Turing Way_ project illustration by Scriberia. Zenodo. [http://doi.org/10.5281/zenodo.3332807](http://doi.org/10.5281/zenodo.3332807) |

_The Turing Way_ started by defining reproducibility in the context of this handbook, lay out its importance for science and scientists, and provide an overview of the common concepts, tools and resources.
The first few chapters were on {ref}`version control<rr-vcs>`, {ref}`testing<rr-testing>`, and {ref}`reproducible computational environments<rr-renv>`.
Since the start of this project in 2019, many additional chapters have been written, edited, reviewed, read and promoted by over 100 contributors.

We welcome your contributions to improve these chapters and to add other important concepts in reproducibility and how to empower researchers to work reproducibly from the start.
Check out our [contributing guidelines](https://github.com/alan-turing-institute/the-turing-way/blob/master/CONTRIBUTING.md) to get involved.