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As such, the original fields were formatted to be consistent with the valid value range and data format.
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The quality of data was ensured by setting up logic constraining conditions and valid value ranges of relevant variables.
The data quality was also controlled by setting up logic constraining conditions and valid value ranges of relevant variables.
Referential data rules, valid values, range checks, and consistency checks against data already stored in the database (ie, longitudinal checks) will be supported.
Data will be entered into a user-friendly Filemaker platform and integrity will be enforced through a variety of mechanisms, including referential data rules, valid values, range checks and consistency checks.
Data integrity will be enforced by cross-checking with the recorded interview after the intervention is complete and by valid value and range checks at the time of data entry.
Table 3 gives examples of the value range restrictions and valid data formats that were used to ensure that the value entries for the spatial attributes in the projects database were consistent and accurate.
For numeric data types, valid values in the validation range are used.
Web-based EDC can only affect the latter, through structured data collection, valid value lists, and on-screen checks for values that are missing, out of range, or inconsistent.
Every valid value was equally likely.
Valid values are in the range of 0-9, inclusive.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com