  Research publication requires public datasets. In recommender systems, some
datasets are largely used to compare algorithms against a --supposedly-- common
benchmark. Problem: for various reasons, these datasets are heavily
preprocessed, making the comparison of results across papers difficult. This
paper makes explicit the variety of preprocessing and evaluation protocols to
test the robustness of a dataset (or lack of flexibility). While robustness is
good to compare results across papers, for flexible datasets we propose a
method to select a preprocessing protocol and share results more transparently.
