Abstract
Data lost or misconstrued due to malfunctioning research equipment affects accuracy, results and analyses that driving researchers report. Carefully studying the data collection system with an Abstraction Hierarchy gathers necessary information about the equipment location and capabilities which can highlight issues in the collected data where completeness and quality are lacking. Use of this structured method to describe the instrumented research vehicle information system is a new application; however, Abstraction Hierarchy has been used in complex systems such as power, chemical and nuclear industries in order to improve the reliability of the human-machine interfaces. In this application, the Abstraction Hierarchy identified potential operating temperature issues affecting the quality of the data, provided avenues to clearly define how behavioral variables were measured, equated input and output sampling rates to show potential problems in the system and used similar variables to cross-check the consistency of the data. This is the first step in developing a structured approach to verifying data quality.
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