@article {JRN55283,
	title = {Provoking a Cultural Shift in Data Quality},
	journal = {BioScience},
	year = {2021},
	month = {March 31, 2021},
	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 use 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. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies},
	keywords = {Cultural shift, data quality, ecological, ecosystem},
	doi = {10.1093/biosci/biab020},
	url = {files/bibliography/21-017.pdf},
	author = {Sarah McCord and Nicholas Webb and Justin Van Zee and Sarah Burnett and Erica M. Christensen and Ericha Courtright and C. Laney and Lunch, Claire and Connie J. Maxwell and Jason W. Karl and A. L. Slaughter and Nelson Stauffer and Craig Tweedie}
}
