Spatial Data Quality Control for the Coral Triangle Atlas

2014 ◽  
Vol 42 (2) ◽  
pp. 128-142 ◽  
Author(s):  
Annick Cros ◽  
Ruben Venegas-Li ◽  
Shwu Jiau Teoh ◽  
Nate Peterson ◽  
Wen Wen ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Spatial Data ◽  
Coral Triangle ◽  
Data Quality Control ◽  
Spatial Data Quality

scholarly journals Data quality control and homogenization of air temperature and precipitation series in the area of the Czech Republic in the period 1961–2007

2009 ◽  
Vol 3 (1) ◽  
pp. 23-26 ◽  
Author(s):  
P. Štěpánek ◽  
P. Zahradníček ◽  
P. Skalák
Keyword(s):  
Quality Control ◽  
Czech Republic ◽  
Data Quality ◽  
Air Temperature ◽  
Spatial Data ◽  
Statistical Tests ◽  
Homogeneity Test ◽  
Data Quality Control ◽  
The Czech Republic ◽  
Temperature And Precipitation

Abstract. Quality control and homogenization has to be undertaken prior to any data analysis in order to eliminate any erroneous values and non climatic biases in time series. In this work we describe and then apply our own approach to data quality control, combining several methods: (i) by applying limits derived from interquartile ranges (ii) by analyzing difference series between candidate and neighbouring stations and (iii) by comparing the series values tested with "expected'' values – technical series created by means of statistical methods for spatial data (e.g. IDW, kriging). Because of the presence of noise in series, statistical homogeneity tests render results with some degree of uncertainty. In this work, the use of various statistical tests and reference series made it possible to increase considerably the number of homogeneity test results for each series and thus to assess homogeneity more reliably. Inhomogeneities were corrected on a daily scale. These methodological approaches are demonstrated by use of the daily data of air temperature and precipitation measured in the area of the Czech Republic. Series were processed by means of developed ProClimDB and AnClim software (http://www.climahom.eu).

scholarly journals Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

2021 ◽  
pp. 106239
Author(s):  
Antonella D. Pontoriero ◽  
Giovanna Nordio ◽  
Rubaida Easmin ◽  
Alessio Giacomel ◽  
Barbara Santangelo ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Data Quality ◽  
Pet Imaging ◽  
Data Quality Control ◽  
Brain Pet

scholarly journals An interactive graphical system for XBT data quality control and visualization

2001 ◽  
Vol 27 (7) ◽  
pp. 867-876 ◽  
Author(s):  
Pankajakshan Thadathil ◽  
Aravind K Ghosh ◽  
J.S Sarupria ◽  
V.V Gopalakrishna
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Quality Control ◽  
Graphical System

Implementation of Marine Environment Monitoring Data Quality Control System

2014 ◽  
Vol 926-930 ◽  
pp. 4254-4257 ◽  
Author(s):  
Jin Xu ◽  
Da Tao Yu ◽  
Zhong Jie Yuan ◽  
Bo Li ◽  
Zi Zhou Xu
Keyword(s):  
Quality Control ◽  
Control System ◽  
Data Quality ◽  
Marine Environment ◽  
Quality Control System ◽  
Monitoring Data ◽  
Environment Monitoring ◽  
Data Quality Control ◽  
Labor Costs ◽  
Marine Environment Monitoring

Traditional artificial perception quality control methods of marine environment monitoring data have many disadvantages, including high labor costs and mistakes of data review. Based on GIS spatial analysis technology, Marine Environment Monitoring Data Quality Control System is established according to the Bohai Sea monitoring regulation. In the practical application process, it plays the role of improving efficiency of quality control, saving the manpower and financial resources. It also provides an important guarantee for the comprehensive analysis and management of marine environment data.

Data quality control in a multi-insitutional cancer trial

1980 ◽  
Vol 1 (2) ◽  
pp. 171-172
Author(s):  
M.M. Koretz ◽  
M. Kohler ◽  
E. McGuigan ◽  
J.F. Hannigan ◽  
B.W. Brown
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Quality Control ◽  
Cancer Trial

scholarly journals Monitoring the impacts of weather radar data quality control for quantitative application at the continental scale

2020 ◽  
Vol 27 (4) ◽  
Author(s):  
Daniel Michelson ◽  
Bjarne Hansen ◽  
Dominik Jacques ◽  
François Lemay ◽  
Peter Rodriguez
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Weather Radar ◽  
Radar Data ◽  
Data Quality Control ◽  
Continental Scale
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