Data generated during ecological research or monitoring projects form the foundation for understanding ecosystem processes, and often become the basis for critical decisions affecting the management of those ecosystems. In addition, nearly all ecological projects rely on existing data to facilitate project planning or to supplement data collected by the project team. This is particularly true for projects employing big data applications to address complex ecological questions. For new or existing data, it is important to demonstrate that these data are of sufficient quality for their intended use. Our objective for this presentation is to identify quality assurance activities for the review of ecological data.
Results/Conclusions
Eleven best practices for data quality review will be presented to help ecologists identify and resolve data issues and thereby improve the quality and reliability of the data they have collected or are using from other sources. In particular, steps to ensure that data were collected properly (data verification) and that they make sense from a scientific point of view (data validation) will be identified. These best practices are based on a recently completed inter-agency document entitled “Application of quality assurance and quality control principles to ecological restoration project monitoring” (EPA 905-K-19-001). Quality assurance and quality control principles have broad application across ecological research, and are a critical topic for inclusion in university curriculum.