Quality Assurance Plans: Recommended Practices and Examples

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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

• USGS Quality Assurance Plan for Digital Aerial Imagery (PDF)
• U.S. Geological Survey quality-assurance plan for surface-water activities in Kansas, 2015
• Quality-assurance plan for the analysis of fluvial sediment by the U.S. Geological Survey Kentucky Water Science Center Sediment Laboratory
• USGS National Water Quality Laboratory: NWQL Quality Links, including Quality Assurance Plan

Other Agency Examples

• National Atmospheric Deposition Program (NADP): Quality Assurance Plans
• U.S. EPA Guidance on preparing a QA Project Plan