Updated project metadata. We generated two comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent and Data-Indepentdent Acquisition modes. This dataset contain a balanced two-class design (cell lines: A549 vs K562), allowing for investigating mixed effects from class, batch and acquisition method. Investigators can also compare and integrate DDA and DIA platforms, delve into the various patterns and mechanisms of missing values, benchmark batch effects correction algorithms and assess confounding between different technical issues.