Quality Assurance Plans: Recommended Practices and Examples

By Data Management

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