The Domino Effect of a Pandemic- Data Quality Impact to a Full Cycle QA Program
2024
- 43Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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- Abstract Views16
Lecture / Presentation Description
Data Quality Assessment of a Long Running QA Program – The Domino Effect of a Pandemic Authors Ms. Amanda Cover - United States - Environmental Standards Mr. Rock Vitale - United States - Environmental Standards Ms. Jennifer Gable - United States - Environmental Standards Ms. Veronica Barredo - United States - TVA Abstract Keywords: Quality Assurance, Data Quality It should be no secret that a full-cycle quality assurance (QA) program for Groundwater Monitoring Programs establishes a “well-oiled machine” that results in high quality, legally defensible data sets that can be reliably used to make critical decisions. Under normal conditions, a QA program will encounter and resolve laboratory analytical issues at a relatively low frequency -- but what happens when laboratories encounter significant obstacles beyond their control? The QA program goes into overdrive. This presentation will discuss the data quality trends observed over a five-year period and the challenges presented during and after a global pandemic. This presentation will provide a case study of changes in frequency of QA issues due to external challenges caused by the pandemic. This presentation will also highlight the benefits of a robust QA program which identified and corrected data quality issues through stringent technical requirements, laboratory audits, and critical data validation.
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