@@ -133,10 +133,12 @@ The following procedures are implemented to ascertain data quality in the databa
- Primary key: Ensures that uniqueness as well as a not-null condition of a column entry or a combination of column entries are satisfied. As user are not supposed to import the id column as it is automatically generated, its only relevance to the users is, that the id column needs to get selected as custom key when deleting or updating data (compare issue #10).
- Not Null: Ensures that a not-null condition of a column entry or a combination of column entries are satisfied. In the \__column_definition\_ view those columns are marked in the \_mandatory\_ column.
- Check: Ensures the satisfaction of certain conditions based on logical queries or the existence of other column entries in this row.
-<details>
<summary>Example1</summary>
Check constraints for numeric values: Wherever possible numeric values are checked on insertion for their plausibility (e.g.areas, discharges, inhabitants) to be within a meaningful range, usually \> 0 and smaller a maximum value. The catchment area of a river must be larger than 0 km² and smaller than 7000000 km² (size of the Amazon river basin). CONSTRAINT check_catchment_area_range CHECK ( catchment_area_km2 \> 0 AND catchment_area_km2 \< 7000000 )
</details>
-<details><summary>Example1</summary>
Check constraints for numeric values: Wherever possible numeric
values are checked on insertion for their plausibility (e.g.areas, discharges, inhabitants) to be
within a meaningful range, usually \> 0 and smaller a maximum value. The catchment area of a river must
be larger than 0 km² and smaller than 7000000 km² (size of the Amazon river basin). CONSTRAINT
