Visualizations of Data Created to Aid in the Detection of Laboratory Data

Abstract

Research and service activities in fundamental and clinical pharmacology rely heavily on the assessment of pharmacological and endogenous chemical concentrations. Using data science visualizations of laboratory data, it is shown on a real-life example 
that recommended standard visual techniques of data inspection or basic statistical investigation of laboratory test findings may fail to discover systemic laboratory mistakes. For instance, conventional data quality control approaches could miss data 
pathologies like an experiment run where all probes provide the same result. We find that sys- thematic laboratory mistakes may of individual data organized by assay is suggested.be better detected when using data visualizations that highlight diverse perspectives on the data. To better understand the data range, outliers, and a specific kind of systematic error—where identical values are incorrectly obtained in all probes—a dot plot