A fundamental pillar in (open) science is to verify the scientific results of others to advance knowledge. The lack of reproducibility in scientific studies brings challenges in understanding and recreating the results of others, a situation that may be common in data-based and algorithm-based research like in geocomputation. In general, many authors define reproducibility as the ability to compute exactly the same results of a study based on original input data and analysis workflow. In other words, “to rerun the same computational steps on the same data the original authors used”. Replicability is often seen as obtaining similar conclusions about a research question derived from an independent study or experiment. In the field of GIScience and geocomputation, in particular, a reproduction is always an exact copy or duplicate, with exactly the same features and scale, while a replication resembles the original but allows for variations in scale, for example. Hence, reproducibility is exact whereas replicability means confirming the original conclusions, although not necessarily with the same input data, methods, or results.