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Best Practice: Mark data with quality control flags


Best Practices by Data Life Cycle

Mark data with quality control flags

Data Life Cycle stage(s): Assure

As part of any review or quality assurance of data, potential problems can be categorized systematically. For example data can be labeled as 0 for unexamined, -1 for potential problems and 1 for “good data.” Some research communities have developed standard protocols; check with others in your discipline to determine if standards for data flagging already exist.

The marine community has many examples of quality control flags that can be found on the web. There does not yet seem to be standards across the marine or terrestrial communities.

Description Rationale

Data quality should be able to be assessed by potential data users so that any problems are identified early in a project.


Some marine examples - Several standards and cross walk between standards at solar radiation data:



Cite this best practice:

Eric Lind, Robert Stevenson, DataONE  (May 11, 2011) "Best Practice: Mark data with quality control flags". Accessed through the Data Management Skillbuilding Hub at on Aug 22, 2019


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