Coating Trial Data Imports: Detect Duplicate Records Without Deleting Repeat Measurements

The key distinction

Removing every repeated value can erase valid observations, while retaining copied records can give one sample too much influence. The data structure should identify the event that each row represents. Preserve the original import and record any correction so the final analysis remains traceable.

Practical checks

1. Define a unique record key appropriate to the trial.
2. Compare suspected duplicates against the source record and measurement history.
3. Document confirmed corrections while preserving legitimate repeats.

Example

Illustrative example: two measurements on the same panel happen to have the same value. They are not duplicates merely because the number matches. By contrast, importing the same instrument file twice can create duplicate events even if the rows look like a larger sample set.

Scope and limits

Do not delete inconvenient observations through an automated deduplication rule that ignores their meaning. This guide does not specify a database design. It establishes the need to identify the measurement event before deciding whether a row is redundant or part of the evidence.

Related guidance

• Coating Trial Exclusions: Publish the Rule Before Removing Data
• Coating Trial Data: Keep Raw Observations Separate from Scores
• Coating Data Imports: Check Decimal Separators Before Calculating
• Coating Trial Dates: Use an Unambiguous Format Before Combining International Records

Ask NANOLAB about the exact product and intended use. Use the current product documentation for formulation-specific instructions.