QA refers to utilizing written criteria, methods and processes that will ensure the production of data that meet a specified quality standard. 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. Quality Assurance (QA) - Preventing Data Issues Quality Assurance Plans: Recommended Practices and Examples.Like the DMP, the QAP (if a separate document) would be revised as needed during a project timeline to reflect the reality of the data workflow and activities. Some agencies and organizations require a QAP as part of a research proposal, before funding a project (for example, USEPA). 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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