title: [A new assessment method for the quality of ecological monitoring data: taking CERN's tree growth dataset as a case].
abstract: This paper presented a new and simple assessment method for the quality of ecological monitoring data. This method theorized the associations between the data reliability as an ordinal variable with different number of classes and the data sources such as natural main ecological processes, secondary ecological processes, and extraneous and exotic processes, and offered a new data quality index to estimate the quality of the whole dataset by using the reasonableness ratio of observations. The assessment results provided the reliability class of each dataset, good explanations for outlier (or error data) flagging decisions, and quality value of the whole dataset. The method was applied to assess two tree growth datasets from Chinese Ecosystem Research Network (CERN), and the results demonstrated that the new data quality index could quantitatively evaluate the quality of the tree growth datasets. The new method would facilitate the development of corresponding software.
authors.affiliation: Huitong Experimental Station of Forest Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China. yan@soilbio.com
authors.forename: Shao-kui
authors.initials: SK
authors.lastname: Yan
authors.forename: Dong-xiu
authors.initials: DX
authors.lastname: Wu
authors.forename: An
authors.initials: A
authors.lastname: Singh
authors.forename: Yuan-liang
authors.initials: YL
authors.lastname: Li
authors.forename: Wen-shan
authors.initials: WS
authors.lastname: Wei
authors.forename: Yang
authors.initials: Y
authors.lastname: Cui
authors.forename: Si-long
authors.initials: SL
authors.lastname: Wang
authors.forename: Guang-biao
authors.initials: GB
authors.lastname: Xu
mesh_terms: D003625:Data Collection; D003657:Decision Making; D004463:Ecology / Q000379:methods; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D011786:Quality Control; D018570:Risk Assessment / Q000379:methods*; D014197:Trees / Q000254:growth & development*