ABSTRACT
There has been increasing evidence in recent years that research in life sciences is lacking in reproducibility and data quality. This raises the need for effective systems to improve data integrity in the evolving non-GxP research environment. This chapter describes the critical elements that need to be considered to ensure a successful implementation of research quality standards in both industry and academia. The quality standard proposed is founded on data integrity principles and good research practices and contains basic quality system elements, which are common to most laboratories. Here, we propose a pragmatic and risk-based quality system and associated assessment process to ensure reproducibility and data quality of experimental results while making best use of the resources.
