I’ve been reflecting on the importance of maintaining data integrity in our long-term experiments. Recently, we faced issues with sample contamination over extended periods, which highlighted gaps in our tracking protocols. I’m curious if others have implemented specific strategies or tools to enhance data reliability in similar research settings.