Ten practical questions to improve data quality

Abstract

High-quality rangeland data are critical to supporting adaptive management. However, concrete, cost-saving steps to ensure data quality are often poorly defined and understood. Data quality is more than data management. Ensuring data quality requires 1) clear communication among team members; 2) appropriate sample design; 3) training of data collectors, data managers, and data users; 4) observer and sensor calibration; and 5) active data management. Quality assurance and quality control are ongoing processes to help rangeland managers and scientists identify, prevent, and correct errors in past, current, and future monitoring data. We present 10 guiding data quality questions to help managers and scientists identify appropriate workflows to improve data quality by 1) describing the data ecosystem, 2) creating a data quality plan, 3) identifying roles and responsibilities, 4) building data collection and data management workflows, 5) training and calibrating data collectors, 6) detecting and correcting errors, and 7) describing sources of variability. Iteratively improving rangeland data quality is a key part of adaptive monitoring and rangeland data collection. All members of the rangeland community are invited to participate in ensuring rangeland data quality.