Data Quality Assurance for Test and Evaluation of Complex Soldier–Machine Formations

Author(s):

Jason S. Metcalfe

Abstract:

This report details the Data Quality Assurance (DQA) processes developed and implemented within ARL’s Human–Autonomy Teaming program during FY23 and evolving through the end of FY25. Recognizing the critical importance of data integrity in Soldier–Machine Integration research, this work outlines a framework for continuous DQA interwoven throughout the entire experimental lifecycle. The report emphasizes a Directed Action Team structure, aligning specialized expertise with specific phases of experimentation from protocol development and measurement definition to data collection and analysis. Key best practices are identified, including proactive protocol management, robust communication strategies, and iterative tool improvement. This report provides practical guidance for Science and Technology Reinvention Laboratories seeking to establish and sustain high-quality data pipelines, ultimately enhancing the reliability and validity of research findings and accelerating the transition of innovative technologies to the Warfighter. The lessons learned underscore the necessity of balancing rigorous planning with adaptability in dynamic research environments.