Using this comprehensive package of data quality procedures, reliable and high quality information was obtained.
The monitoring of data quality procedures, however, was stressed throughout the measurement period.
In order to ensure a high level of data quality, complex data validation procedures are installed.
Hence, data cleaning, checking of data quality, and handling of outliers are important, though not all problems inherent in the data can be solved by those procedures.
Then, data are stored in the database, and a sequence of validation procedures (data quality control procedures) is applied to the information to qualify the data values [10].
We also used the measurements to study the influence (on the accuracy of retrieved source strength) of discretionary elements of inverse dispersion procedure: data quality criteria; optimal placement of detectors relative to the source(s); and the impact of alternative spatial representations of the source, supposing one had but partial information in that regard.
Quality assurance consists of procedures for prevention of insufficient data quality, detection of inaccurate or incomplete data and action to improve data quality, e.g. user training sessions, automatic plausibility and integrity checks within the remote data entry system and data error reports for the local centres.
One advantage of CLImAT is the correction and normalization procedure for improving data quality of unpaired tumor samples.
Procedures for prevention of insufficient data quality, detection of inaccurate or incomplete data and actions to improve data quality will be performed, e.g. user reliability trainings, automatic plausibility and integrity checks and data error reports to the collaborating centers.
Procedures for prevention of insufficient data quality, detection of inaccurate or incomplete data, and actions to improve quality will be performed, for example, user reliability trainings, automatic plausibility, and integrity checks.
