A process approach to quality management doubles NEON sensor data quality. Issue 9 (31st July 2022)

• Record Type: Journal Article
• Authors: Sturtevant, Cove; DeRego, Elizabeth; Metzger, Stefan; Ayres, Edward; Allen, Dan; Burlingame, Teresa; Catolico, Nora; Cawley, Kaelin; Csavina, Janae; Durden, David; Florian, Christopher; Frost, Shalane; Gaddie, Ross; Knapp, Elizabeth; Laney, Christine; Lee, Robert; Lenz, Dawn; Litt, Guy; Luo, Hongyan; Roberti, Joshua; Slemmons, Caleb; Styers, Kevin; Tran, Chau; Vance, Tanya; SanClements, Michael
• Is Part Of: Methods in ecology and evolution. Volume 13:Issue 9(2022)
• Page Start: 1849
• Page End: 1865
• Publication Date: 2022-07-31
• Subjects: data quality -- National Ecological Observatory Network -- quality assurance -- quality control -- quality management -- sensor network; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13943
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: A quality management system is critical for ensuring that the data and services provided by an organization meet the needs of its mission. With a mission to collect long‐term open‐access ecological data to better understand how US ecosystems are changing, the National Ecological Observatory Network (NEON) is a highly standardized measurement network distributed across the United States and Puerto Rico collecting data on the biosphere and its interfaces with the pedosphere, hydrosphere and atmosphere. In order to achieve high‐quality, comparable data across the network, a quality management system was developed by applying the seven ISO 9001:2015 principles of quality management: customer focus, leadership, engagement of people, process approach, improvement, evidence‐based decision making and relationship management . The resultant system is integrated throughout NEON's organizational structure with an approach that connects people and operational processes throughout the data life cycle ( process approach ). We describe the system with respect to sensor data (automated measurements), demonstrating its effectiveness through examples, lessons learned and a continuous history of improvement towards quality goals, including a doubling of data quality in NEON's meteorological and soil datasets since 2015 and substantial gains in other sensor datasets. Owing to a focus on quality management principles and particularly the interconnectedness of human and information systems, NEON's quality management system can serve as a model for networks with a variety of organizational structures and sizes.