Quality Assurance (QA) and Quality Control (QC) are both critical to data quality in ecological research and both are often misunderstood or underutilized. QA is a set of proactive processes and procedures which prevent errors from entering a data set, e.g., training, written data collection protocols, standardized data entry formats, can calibration. Meanwhile QC is a reactive process which deals with errors that have already been introduced, e.g. identifying missing or incorrect data. Collecting and using any data sets involve some form of both QA and QC, although there are ways to go about them that make them more or less effective at producing and maintaining high quality data.
McCord et al. propose a cultural shift in how ecologists approach QA and QC in order to improve the quality, applicability, and accessibility of data. In their paper, the authors explore a broader QA and QC framework that covers the entire lifecycle of data, including planning, data management, and analysis. Within this framework, there are five stages where QA and QC are applied: planning, collecting, reviewing, maintaining, and analyzing. Every person who touches the data has some kind of QA and/or QC responsibilities that help to ensure that the data remain intact and usable. At each stage, there is an emphasis on the specific roles and responsibilities of each person handling data and the authors illustrate these using the Bureau of Land Management’s Assessment, Inventory, and Monitoring program as an example.
McCord, S. E., Webb, N. P., Van Zee, J. W., Burnett, S. H., Christensen, E. M., Courtright, E. M., Laney, C. M., Lunch, C., Maxwell, C., Karl, J. W., Slaughter, A., Stauffer, N. G., & Tweedie, C. (2021). Provoking a Cultural Shift in Data Quality. BioScience, biab020. https://doi.org/10.1093/biosci/biab020