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A new approach for the IEQ (Indoor Environment Quality) assessment
Published in Paul Fazio, Hua Ge, Jiwu Rao, Guylaine Desmarais, Research in Building Physics and Building Engineering, 2020
Stefano P. Corgnati, Marco Filippi, Marco Perino
There is a number of examples where investigations have been compromised by inaccurate or missing data. An effective system for collecting data should have ongoing data verification and automated process for checking data reliability and data accuracy. Only in this way it is possible to keep the procedure on track and prevent sensor failure and data lost (ASHRAE 1995b). An item that has a paramount importance is, in the authors opinion, the automation of the procedure, otherwise the large amounts of data that are, invariably, collected risk not to be used. For all these this reasons the base elements on which the approach rely upon are: continuity, flexibility and reliability.
Data Verification and Validation
Published in Mark Edward Byrnes, Field Sampling Methods for Remedial Investigations, 2023
The EPA QA/G-8 guidance manual provides alternative approaches to assist users in verifying and validating environmental data. The term data verification refers to the process of evaluating the completeness, correctness, and conformance/compliance of a specific data set against methodological, procedural, or contractual requirements. On the other hand, the term data validation refers to an analyte- and sample-specific process that extends the evaluation of the data beyond data verification to determine the analytical quality of a specific data set (EPA 2002).
Quality assessment of Major Trauma Registry of Navarra: completeness and correctness
Published in International Journal of Injury Control and Safety Promotion, 2019
Bismil Ali Ali, Rolf Lefering, Tomas Belzunegui Otano
The literature shows different approaches to measuring the correctness of data, ranging from relatively simple approaches involving consistency and domain checks, to the monitoring of coding reliability and agreement with other databases and source data verification (O’Reilly et al., 2016). In source data verification, patients’ medical records remain the best method, or ‘gold standard’, for evaluating the correctness of data. However, hospital records might not be 100% valid (Datta, Findlay, Kortbeek, & Hameed, 2007). The overall correctness rate of 98% observed in the current study is significantly higher than or within the range of those reported in similar studies. While some researchers have shown error rates as high as 28% (Curtis et al., 2002), others have reported the correctness rate observed in the current study (Datta et al., 2007), even though the study designs used to determine data correctness differed significantly between studies.