A deduplicated dataset is a collection of records that has been cleaned to remove repeated entries. In job-posting data, this can mean identifying listings that refer to the same opening and counting them once, reducing the risk that reposted or duplicated ads inflate totals.
Deduplication matters because repeated records can distort analysis and lead to misleading conclusions about demand or trends. Researchers and analysts use matching rules—such as comparing titles, employers, locations, and posting details—to identify likely duplicates. The result is more useful for comparing counts, but it is not a count of jobs filled: it still measures advertised openings, and the accuracy depends on how duplicates are detected.