The clustering of mass spectra is a critical component of many proteomics applications. The clustering validation science is just as important, having evolved side by side with the clustering algorithms themselves. In this work, we build on Rieder et al. 's cluster validation framework, and we discuss the problem of selection bias in cluster validation measures; we introduce an assessment measure that is biased toward the number of peptide ion species; we introduce a cluster assessment framework for proteomics and demonstrate its importance by evaluating the performance of 8 clustering algorithms in 7 proteomics datasets, and we discuss the tradeoff between assessment measures. Finally, the validation methods presented here can be of broad applicability be-yond the clustering of mass spectra. This PRIDE entry describes in detail sample preparation, LC-MS/MS analysis, and protein identification of one of the proteomics datasets used in this work (> 10 kDa Bothrops jararaca snake venom proteome).