During October 2025, Alex Combest enhanced the adap/flower repository by refactoring and documenting the seed parameter across dataset partitioner classes. Using Python and a focus on documentation and refactoring, Alex clarified how the seed initializes the random number generator, directly impacting dataset shuffling and other random processes within each partitioner. This work unified seed handling, reducing ambiguity and enabling deterministic experiments for more reliable benchmarking across teams. The changes improved maintainability and reproducibility, supporting repeatable experiments and clearer collaboration. Alex’s contribution demonstrated depth in understanding reproducibility challenges and addressed them through targeted code and documentation improvements within the Flower datasets library.