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

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Data cleaning is crucial for research validity. This study details methods to identify and correct errors in the MARK-AGE database, ensuring high-quality data for ageing research.

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Batch effectsData cleaningData visualizationMissing dataOutliers

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Area of Science:

  • Biomedical Informatics
  • Gerontology
  • Data Science

Background:

  • Databases are essential for research, but data quality issues can compromise results.
  • Data cleaning, including error identification and correction, is vital for data management.
  • The MARK-AGE database contains diverse data (biomarkers, analytical, anthropometric, demographic) from ~3000 volunteers.

Purpose of the Study:

  • To present methods for handling errors in the MARK-AGE database.
  • To describe strategies for detecting and managing missing values, outliers, and batch effects.
  • To improve the overall data quality for subsequent analysis.

Main Methods:

  • Cross-sectional study design.
  • Application of data cleaning techniques to identify and correct errors.
  • Specific strategies for detecting and handling missing values, outliers, and batch effects.
  • Utilized tools for data exploration and sharing among collaborators.

Main Results:

  • Identified common data errors including miscoding, missing values, and batch problems despite preventive measures.
  • Developed and applied methods to effectively detect and manage these errors.
  • Improved data quality within the MARK-AGE database.

Conclusions:

  • Data cleaning is indispensable for ensuring the validity of research findings.
  • The applied methods successfully addressed data quality issues in the MARK-AGE database.
  • Effective data management and sharing tools facilitate collaborative ageing research.