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Quality Assurance of Data

The aim of Quality Assurance (QA) is to document and comment on evaluation methods together with suspicious results. The QA does not perform any corrections.

Quality Assurance (QA) of primary data plays an important role in the data publication process as well as for data re-use.

 

QA tasks and responsibilities are divided between:

  • Data Publishing Agency performing automated checks on data and metadata consistency (Technical Quality Assurance - TQA), and
  • Data Creator controlling the scientific data quality (Scientific Quality Assurance - SQA).

Technical Quality Assurance at WDCC

During the cross- and double-checks of WDCC's publication agent at least the following criteria are checked:

1. Number of data sets is correct and > 0
2. Size of every data set is > 0
3. The data sets and corresponding metadata are accessible
4. The data sizes are controlled and correct
5. The spatial-temporal coverage description (metadata) is consistent to
the data, time steps are correct and the time coordinate is continuous
6. The format is correct
7. Variable description and data are consistent

 

Contents of quality checks (especially in SQA) as well as definitions of quality levels and the overall quality procedures vary significantly between data types and scientific disciplines.

The World Data Center Climate (WDCC) at DKRZ is currently involved in two projects, in which quality assessment procedures are developed and applied:

CMIP5 (Coupled Model Intercomparison Project Phase 5):

with a Quality Control (QC) tool suitable for model data TQA checks developed at WDCC: More information on the CMIP5 quality procedure at http://cmip5qc.wdc-climate.de.

Publication of Environmental Data:

with a QC package for observational data checks developed by the University of Bonn (http://meteo.uni-bonn.de/) to support the data creator's SQA:
More information on the quality package at http://cran.r-project.org/web/packages/qat/.


Development of a Quality Assessment System

Quality Assurance of data and metadata plays an important role in the data publication process at WDCC, which is crucial for data re-use. However, quality control procedures and quality documentation vary greatly among scientific data.  In general every project defines their quality procedures, e.g. CMIP5 (Stockhause et al., 2012).

What is WDCC’s Quality Assessment System?

In order to make the different Quality Assessments of the projects comparable, WDCC develops a generic Quality Assessment System. Based on the self-assessment approach of a maturity matrix, an objective and uniform quality level system for all data at WDCC is derived. It consists of 5 maturity quality levels, starting with the initial level=1.

Goals of the Quality Assessment System

  • Encourage data creators to improve their quality assessment procedures to reach the next quality level.
  • Enable data consumers to decide, whether a dataset has a quality that is sufficient for usage in the aimed application.

Quality Maturity Matrix

Different criteria are defined, which are subdivided into aspects. For every aspect the 5 maturity quality levels are defined. The criteria are:

-          Consistency

-          Completeness

-          Accessibility

-          Accuracy

-          Provenance/Lineage (download PDF)

-          (Re-)Usability (download PDF)

State of the Quality Assessment System

The Quality Assessment System is currently developed. The information on this page will be updated as progress is made. Comments and suggestions are welcome.

contact: hoeck AT dkrz.de, stockhause AT dkrz.de

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