MARK-AGE data management: Cleaning, exploration and visualization of data. (November 2015)

• Record Type: Journal Article
• Authors: Baur, Jennifer; Moreno-Villanueva, Maria; Kötter, Tobias; Sindlinger, Thilo; Bürkle, Alexander; Berthold, Michael R.; Junk, Michael
• Is Part Of: Mechanisms of ageing and development. Volume 151(2015)
• Page Start: 38
• Page End: 44
• Publication Date: 2015-11
• Subjects: Data cleaning -- Missing data -- Batch effects -- Outliers -- Data visualization; Aging -- Periodicals; Developmental biology -- Periodicals; Aging -- Periodicals; Developmental Biology -- Periodicals; Vieillissement -- Périodiques; Biologie du développement -- Périodiques; Aging; Developmental biology; Periodicals; 612.67
• DOI: 10.1016/j.mad.2015.05.007
• Languages: English
• ISSNs: 0047-6374

Abstract

Highlights: MARK-AGE was European study to identify a powerful set of biomarkers of human aging. A large body data has been collected in the MARK-AGE database. We present the methods applied for dealing with errors in the database. We describe the detection of missing values, outliers and batch effects. We describe our tools for data exploration and data sharing within the consortium. Abstract: 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.