Sentence examples for data quality objectives from inspiring English sources

Data quality objectives (DQOs) are qualitative and quantitative statements that clarify study objectives, define appropriate types of data, and specify tolerable levels of potential decision errors that are used as the basis for establishing the quality and quantity of data needed to support decisions.

This chapter addresses all aspects of project planning, from the development of data quality objectives to the selection of analytical laboratory and preparation for field sampling.

The quality and quantity of samples are determined by data quality objectives (DQOs), which are defined by the objectives of the overall contaminant assessment plan.

The method validation data indicates that the system's data quality objectives are adequate for radiological or nuclear emergency response or targeted surveillance programs where gamma-ray analysis is needed.

An overall uncertainty of circa 9% for the measurement of benzene is calculated for the validation tests, in compliance with the data quality objectives of the EU air quality directive 2008/50/EC.

The EPA acknowledged the value of a stepwise approach to the planning phase when it proposed the data quality objectives (DQO) process as a tool for determining the type, quality, and quantity of data that would be sufficient for valid decision-making.

While Part I described the theory and the framework, this part addresses two steps in detail – characterization of problems, and development of data-quality objectives (DQOs).

Data extraction and amalgamation from multiple sources requires a systematic approach, considering data quality assurance and research objectives.

Given that source integration usually improves data quality, one of the objectives is keeping the computational complexity sufficiently low to allow an optimal assimilation and mining of all the information.

However, application of these metrics in these areas of study is possible if initial preconditions such as spatio-temporal data quality, scale of application, and objectives for their adoption are satisfied.

The data quality control and processing use objective, statistically robust techniques.