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Collections of labeled and unlabeled data underpin AI systems by offering the examples needed for training and validation.

Different tasks require tailored dataset structures and labeling schemes. Text datasets for NLP require careful handling of tokenization, context markers, and annotation standards.

Ethical and legal considerations shape dataset creation and sharing policies. Publicly available datasets spur research but should include protections for individuals.

Evaluation datasets and benchmarks enable objective comparison of models. Reproducing results requires stable dataset versions and detailed split protocols.

Time-series and sensor datasets demand synchronized timestamps and noise characterization.