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An attribute is a data field or column in the import file that is assigned to a target field in the Labordatenbank during import mapping.

Overview of attribute editing

Configuration options

Attribute name

• Attribute: Name of the data field or column in the import file.
• For identifying the associated values.
• Can be changed if necessary.
• Example: Sampler

Default value

• Default: Allows fields in the Labordatenbank to be automatically filled with the default value stored here if nothing is stored for this field in the imported data.
• Example: Sampling by client as default value

Combine attributes by concatenation

• Stringing together: Creates a new (virtual) attribute by combining several attributes that are present in the import file and merges the corresponding values. This new attribute can only be used if it does not appear in the CSV file itself.
• Example: The first and last names of the samplers are in separate columns in the import file. These columns/attributes are called First name and Last name. The corresponding values should be imported together into one field in the Labordatenbank.
  The attribute Sampler does not exist in this file; it should be created as a new, virtual attribute by combining the attributes First and Last.
  This combination of first name (attribute1) and last name (attribute2) with a space (free text) is achieved by concatenating the corresponding attributes.

Stringing together: First name, ‘ ’, Last name

CSV file:

Values resulting from the concatenation for the virtual attribute Sampler:
Max Petridish
Pia Sample

Pattern (RegEx validation)

• Pattern: The RegEx pattern stored in this field validates the data of this attribute before import. It enables data validation using regular expressions. If the data does not match the format specified by the RegEx pattern, the import is canceled.
• The validation rules are available via the Regex validation rules link.

Regex examples:

• [A-Z]{3} ... exactly 3 uppercase letters
• [0-9]{1,6} ... between 1 and 6 digits
• [0-9]+(\.[0-9]+)? ... any number with . as the decimal point
• [0-9]+(,[0-9]+)? ... any number with , as the decimal point
• NOT_EMPTY ... the field must not be empty

AI Data Type Configuration

• AI Data Type: When using the AI extension for import interfaces, the selected AI data type forms the basis for structured AI outputs. This ensures that the data is processed by the AI exactly in the required form.

Available AI data types:

• automatic (default): Automatically uses the appropriate data type based on the selected destination where the data is stored.
• String: Intelligent text processing and normalization
• Date and time: Automatic recognition and standardization of date/time information
• Integer: Automatic recognition and cleaning of integers
• Number: Intelligent recognition and conversion of numerical values (decimal numbers and measured values)
• Boolean: Intelligent interpretation of yes/no values
• List: Independently definable lists of possibilities
• Example: AI Extension for Import Interfaces, AI Import: Automatically assign external calibration certificates