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Quality Assurance (QA) - Preventing Data Issues: Recommended Practices

Preventing the creation of defective data is the most effective means of ensuring the ultimate quality of your data products and the research that depends upon that data.

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  • Use and cite well-documented methods for data collection or acquisition
  • For new methods, write documentation that establishes quality criteria for the data
  • Utilize staff who are trained to correctly collect field data, or who demonstrate adherence to established data acquisition procedures
  • Set Quality Criteria for all of the data used in each project
  • Use discipline-specific standards for quality whenever possible (proven data structures, domains)

Quality Assurance (QA) - Preventing Data Issues: Examples

Generic: 7 Examples of Quality Assurance

Published Quality Assurance Standards

Use of Standard Methods

Page last updated 6/21/21.