There is a numerical benchmark reported in Fig. 4 with absolute runtimes and memory usages that can directly be reproduced with the provided source code. The benchmark was performed on the author's computer, and since numerical performance and parallel scaling can be somewhat hardware-dependent, it would be of interest to see whether a performance that is comparable to the one reported in the paper can be reproduced by others on their own computers in practice. The benchmark simulates a growing tissue from one to 10,000 cells in just ten minutes, so this offers an easy entry point into tissue modeling and simulation. No input data is needed to reproduce the output. The program has no dependencies.
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.
