This is a fairly digestible paper with statistical analyses and data visualization that rely heavily on open data from citizen science projects.
This will probably be a non-trivial example to reproduce, owing to: (1) long-running code, (2) dependency on external data sources, (3) possibly challenging software dependencies -- both trivial ones (e.g. setting up custom fonts and plot themes) and critical ones (requires an external R package wrapping a C++ algorithm, not available on CRAN and can sometimes have interesting compiler issues, like when Apple decided to break the clang compiler in 10.0). Ideally one could just run the R code given in the appendix on your local R session, but that may take a bit of effort. We've tried to take steps to address those issues by providing caches of slow-running parts, providing a docker container, and providing sufficient annotations, but who knows!
This is one of the very few papers in biomolecular simulation for which code and data are available and which should be reproducible. But it is also three years old, so it is an interesting test case for the longevity of reproducible research. The infrastructure software is available at http://www.activepapers.org/python-edition/ (with instructions for installation and use)
I believe this represents the only example of a reproducible paper from scattering data collected at Diamond Light Source (UK) and the Institute Laue-Langevin (France)
There are data and code written in RMarkdown which allows to reproduce the entire analysis and plots shown of the paper. It also allows to generate HTML document, which is a nice interface that facilitates the reader to understand better why some procedures were adopted and how to run them.
This is a two-for one. The repository contains code for companion papers, the model development and the model implementation and analysis. As the repository notes, some data are not freely available so I've made an effort to allow the paper to be replicated as best possible with what's available.
This was my third attempt at making a paper fully reproducible. To date I it's the most reproducible that I have published. I'm interested to know what stumbling blocks exist that I'm not aware of (aside from needing software like ArcGIS to fully rerun the complete analysis).