Low biomedical Data Quality (DQ) leads into poor decisions which may affect the care process or the result of evidence-based studies. Most of the current approaches for DQ leave unattended the shifting behaviour of data underlying concepts and its relation to DQ. There is also no agreement on a common set of DQ dimensions and how they interact and relate to these shifts. In this paper we propose an organization of biomedical DQ assessment based on these concepts, identifying characteristics and requirements which will facilitate future research. As a result, we define the Data Quality Vector compiling a unified set of DQ dimensions (completeness, consistency, duplicity, correctness, timeliness, spatial stability, contextualization, predictive value and reliability), as the foundations to the further development of DQ assessment algorithms and platforms.