Data Quality Assessment and Review: Recommended Practices

<< Back to Manage Quality

• Develop a data assessment strategy (test plan, specific goals)
• Schedule data-quality reviews at important points in your workflow
• Maintain data-quality metadata and documentation
• Track data changes and implement a versioning scheme for your data
• Periodically run test data through all processing scripts to verify expected functionality
• Compare new data to historical values
• Plot spatial data on a map to verify locations
• Calculate summary statistics for data or display data using common graphs such as box plots to evaluate for possible anomalies.
• Review field notes for unusual occurrences or events that may help explain data anomalies
• Use comment fields and data quality indicators to qualify data anomalies

Data Quality Assessment and Review: References

USGS References

• A critical review of published coal quality cata From the Southwestern part of the Powder River Basin, Wyoming
• Sources of contamination in an urban basin in Marquette, Michigan and an analysis of concentrations, loads, and data quality

Non-USGS References

• The Challenges of Data Quality and Data Quality Assessment in the Big Data Era
• European Commision Handbook on Data Quality Assessment Methods and Tools (PDF)
• Guidelines for data acquisition and data quality evaluation in environmental chemistry (PDF)
• Integrated Ocean Observing System (IOOS): processes for quality control of real-time Ocean data.
• Sensor Data Management Middleware: ESIP, using modules for processing and analysis.
• National Ecological Observatory Network (NEON):automated processing of field observatory data into derived data products, with provenance.