Consistency - This quality dimension is assessed by examining the quality of the collection over time. It also relates to the standardisation of methods and collection standards throughout the collection. Internal consistency of the collection relates to the release of products as well as the interoperability of the data with other data collections.
Integrity - This quality dimension is assessed by how the data has changed over time from collection, supply, through processing and publishing processes. It also relates to how the data is transferred and stored for analysis and consumption. The degree to which the data self-overlaps or contains errors that cannot be explained is a good indicator for integrity. This is sometimes known as coherence.
Uniqueness - This quality dimension indicates the level of overlap or duplication with similar datasets. It also indicates if the same or similar datasets already exist.
Questions to answer for Consistency, Integrity, and Uniqueness:
Format and structure across the entirety of the dataset is consistent?
Does the data contain no duplicates – internal integrity?
Does the data contain no orphan records, or broken relationships – referential integrity?
Survey data for this location does not already exist - uniqueness?
Where data already exists, does this dataset add value to original survey - uniqueness?
Is it possible to compile datasets and observe no changes overtime - consistency?