The clinical application of mass spectrometry imaging has developed into a sizable subdiscipline of proteomics and metabolomics because its seamless integration with pathology enables biomarkers and biomarker profiles to be determined that can aid patient and disease stratification (diagnosis, prognosis, and response to therapy). Confident identification of the discriminating peaks remains a challenge owing to the presence of nontryptic protein fragments, large mass-to-charge ratio ions that are not efficiently fragmented via tandem mass spectrometry or a high density of isobaric species. A public database of identifications has been initiated to aid the clinical development and implementation of mass spectrometry imaging. The MSiMass list database ( www.maldi-msi.org/mass ) enables users to assign identities to the peaks observed in their experiments and provides the methods by which the identifications were obtained. In contrast with existing protein databases, this list is designed as a community effort without a formal review panel. In this concept, authors can freely enter data and can comment on existing entries. In such, the database itself is an experiment on sharing knowledge, and its ability to rapidly provide quality data will be evaluated in the future.