MARK-AGE data management: Cleaning, exploration and visualization of data

Here we present an overview of the methods we applied for dealing with errors in the MARK-AGE database. We especially describe our strategies for the detection of missing values, outliers and batch effects and explain how they can be handled to improve data quality. Finally we report about the tools used for data exploration and data sharing between MARK-AGE collaborators.