This is a seminal paper on reproducibility in cancer biology. It should be a gold standard for reproducible research work. Therefore, it should be attempted to reproduce it. Supposedly, this will be pretty easy to reproduce and can be used as a *positive control* in repro hacks!
We spend a lot of time to make our analyses reproducible. A review would allow us to collect some information on whether we are successful with it.
Most of the material is available through Jupyter notebooks in GitHub, and it should be easy to reproduce with the help of Binder. With the notebooks, you could experiment with different parameters to the ones analyzed in the paper. It also contains a large dataset of physical parameters of galaxies analysed in this work. We expect this work to be easily reproducible in the steps described in the repository.
If all went right, the analysis should be fully reproducible without the need to make any adjustments. The paper aims to find optimal locations for new parkruns, but we were not 100% sure how 'optimal' should be defined. We provide a few examples, but the code was meant to be flexible enough to allow potential decision makers to specify their own, alternative objectives. The spatial data set is also quite interesting and fun to play around with. Cave: The full analysis takes a while to run (~30+ min) and might require >= 8gb ram.
