The importance of being (R)eproducible

Reproducible Research (RR) or reproducible data analysis is the idea and practice to complement scholarly journal articles with all the information needed to reproduce the results they present.

Very often scientific studies rely on complex textual explanations of what has been done to analyze the data that can overwhelm the reader that has to accept them as an act of faith.

I think you should be more explicit here in step two

To avoid this, a good way to understand better what has been done is to provide the raw data and an univoque description of the procedures used to analyze it. This practice will allow the scientific community to reproduce the results, work along with the data and assert the validity of the results.

Many tools have appeared with the advent of Big Data and the need to analyze large datasets, specially around R, a language and environment for statistical computing and graphics that’s becoming a kind of standard de facto in open science.

Here are some tools of the R ecosystem that allows to publish the results along with the methods and the data.

Tools for Reproducibility

About this post

This post was first published on LibTechNotes, a blog from the Library team at the Universitat Oberta de Catalunya to share our everyday findings, solutions and inspirations.