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Curating high-quality datasets demands careful attention to diversity, representativeness, and annotation accuracy.
Different tasks require tailored dataset structures and labeling schemes. Temporal data needs precise timestamps and an understanding of measurement noise.
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Evaluation datasets and benchmarks enable objective comparison of models. Reproducing results requires stable dataset versions and detailed split protocols.
Privacy preservation techniques such as anonymization and differential privacy can mitigate risks.