Reproducibility: Best practise examples

In the blog article "Why it is important to share your code and make your paper accessible", we advocated for sharing source code together with papers that involve a simulation-based evaluation. One argument is that such papers will be of higher utility to the reader and therefore, will create higher impact.

To help in this effort, the paper

Wilfried Elmenreich, Philipp Moll, Sebastian Theuermann, and Mathias Lux. Making simulation results reproducible - Survey, guidelines, and examples based on Gradle and Docker. PeerJ Computer Science, 5(e240):1–27, Dezember 2019. (doi:10.7717/peerj-cs.240) 

gives a good overview of which tools are suitable to provide your code in a useful way. It is also a paper that shares its code.

If you want to prepare datasets well, have a look at this paper, which provides a useful set of checks to be considered for electricity consumption datasets.

Christoph Klemenjak, Andreas Reinhardt, Lucas Pereira, Mario Berges, Stephen Makonin, and Wilfried Elmenreich. Electricity consumption data sets: Pitfalls and opportunities. In BuildSys ’19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pages 1–4. ACM New York, November 2019. (doi:10.1145/3360322.3360867)

Last but not least I would like to draw your attention to an excellent list of "Papers with Code", compiled by researcher Christoph Klemenjak:

 

On this page a list of papers targeting the field of load disaggregation can be accessed. And the name of the page does not lie - all of them come with code that can be freely accessed.

Happy researching!