Data Management

Quality Assurance Plans: Recommended Practices and Examples

Yes, you can plan ahead for high-quality data! A Quality Assurance Plan (QAP) is used to define the criteria and processes that will ensure and verify that data meet specific data-quality objectives throughout the Data Lifecycle. 

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Quality Assurance Plans: Recommended Practices

With a focus on quality goals, criteria, assessment, and validation methods, a QAP covers the full data lifecycle, from Acquisition through Publication, and can:

  • Identify data quality objectives for your data or project
  • Identify requirements for
    • Staff skills and training
    • Field and lab methods and equipment that meet data-collection standards
    • Software and file types to use for data handling and analysis that support data quality goals
    • Data standards, structure, and domains consistent with community conventions for other data in the same subject area
    • Periodic data-quality assessment using defined quality metrics
  • Describe a structure for data storage that can also facilitate checking for errors and help to document data quality
  • Describe approved data entry tools and procedures, when applicable
  • Establish data-quality criteria and data-screening processes for all of the data you will collect
  • Include quality metrics that can determine current data-quality status
  • Establish a plan for 'data quality assessments' as part of the data flow
  • Contain a process for handling data corrections
  • Contain a process for data users to dispute and correct data

 

Quality Assurance Plans: Examples

USGS Examples

Other Agency Examples