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Maintaining records of data sources, collection procedures, and inherent biases is an essential practice.

Different tasks require tailored dataset structures and labeling schemes. Temporal data needs precise timestamps and an understanding of measurement noise.

Ethical and legal considerations shape dataset creation and sharing policies. Open datasets accelerate progress but must balance accessibility with participant protection.

Evaluation datasets and benchmarks enable objective comparison of models. To ensure reproducibility, fixed dataset releases and documented train-test splits are necessary.

Consent, transparency, and governance frameworks are essential for responsible dataset use.