Location: Range Management ResearchTitle: Provoking a cultural shift in data quality
|WEBB, NICHOLAS - New Mexico State University|
|Van Zee, Justin|
|BURNETT, SARAH - Bureau Of Land Management|
|CHRISTENSEN, ERICA - New Mexico State University|
|ERICHA, COURTRIGHT - New Mexico State University|
|LANEY, CHRISTINE - Battelle Memorial Institute|
|LUNCH, CLAIRE - Battelle Memorial Institute|
|MAXWELL, CONNIE - New Mexico State University|
|KARL, JASON - University Of Idaho|
|Slaughter, Amalia - Amy|
Submitted to: Bioscience
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/17/2021
Publication Date: 3/31/2021
Citation: McCord, S.E., Webb, N.P., Van Zee, J.W., Burnett, S.H., Christensen, E.M., Ericha, C.M., Laney, C.M., Lunch, C., Maxwell, C., Karl, J.W., Slaughter, A.L., Stauffer, N.G. 2021. Provoking a cultural shift in data quality. Frontiers in Ecology and the Environment. 71(6):647-657. https://doi.org/10.1093/biosci/biab020.
Interpretive Summary: Attention to data quality throughout the data lifecycle is critical to ecological research and resource management. The success of the current data quality paradigm, led by DataOne, needs to be expanded with improved quality assurance and quality control at every part of the data lifecycle. A new framework acknowledging the variety of approaches for ensuring data quality between data types, among collaborators, and in sustained observational time series is presented. Ecologists should look for opportunities to shift cultural norms in addition to adopting tools and technologies that improve ecological data quality.
Technical Abstract: Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low utilization of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. While conceptual and technological advances have improved ecological data access and management, a paradigm shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this paradigm shift. The data quality framework flexibly supports different collaboration models, all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.