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

Databases are an organized collection of data and necessary to investigate a wide spectrum of research questions. For data evaluation analyzers should be aware of possible data quality problems that can compromise results validity. Therefore data cleaning is an essential part of the data management process, which deals with the identification and correction of errors in order to improve data quality. In our cross-sectional study, biomarkers of ageing, analytical, anthropometric and demographic data from about 3000 volunteers have been collected in the MARK-AGE database. Although several preventive strategies were applied before data entry, errors like miscoding, missing values, batch problems etc., could not be avoided completely. Such errors can result in misleading information and affect the validity of the performed data analysis. 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.

The MARK-AGE phenotypic database: Structure and strategy.

In the context of the MARK-AGE study, anthropometric, clinical and social data as well as samples of venous blood, buccal mucosal cells and urine were systematically collected from 3337 volunteers. Information from about 500 standardised questions and about 500 analysed biomarkers needed to be documented per individual. On the one hand handling with such a vast amount of data necessitates the use of appropriate informatics tools and the establishment of a database. On the other hand personal information on subjects obtained as a result of such studies has, of course, to be kept confidential, and therefore the investigators must ensure that the subjects' anonymity will be maintained. Such secrecy obligation implies a well-designed and secure system for data storage. In order to fulfil the demands of the MARK-AGE study we established a phenotypic database for storing information on the study subjects by using a doubly coded system.