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Hence, to eliminate data redundancy from a data mining perspective, as a final step in the creation of the datasets, we detect and remove duplicate instances in the data.
It also reduces the risk of inconsistencies due to duplicate VM instances running simultaneously in both the source and destination DCs.
Samples were measured in duplicate.
Submit fiction entries in duplicate.
All injections were made in duplicate.
Each sample was run in duplicate.
All experiments were performed in duplicate.
All qPCR reactions were performed in duplicate.
All amplifications were performed in duplicate.
All samples were analyzed in duplicate.
Each sample was measured in duplicate.
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