Covertype
Landscape covariates and forest-cover labels for studying spatially dependent classification.
Research, software, data and methods. Inspect the context, compare your options, and take a useful next step.
Landscape covariates and forest-cover labels for studying spatially dependent classification.
Fused optical and radar descriptors for examining agricultural land-cover prediction.
Printing-process measurements and defect labels for testing robust industrial classification.
Task graphs and parallel collections for array, table, and general Python computation.
Versioned research-data management and provenance workflows built on Git and git-annex.
Drive-system measurements for examining sensorless motor-state classification.
Dataset loading and transformation tools useful for preserving example identity, split boundaries and preprocessing provenance in AI experiments.
Phylogenetic tree manipulation, simulation, and analysis in Python.
Bean morphology descriptors for an agricultural multiclass classification and calibration study.
Protein descriptors and cellular-location labels from a microbial classification benchmark.
Ocean and atmospheric observations for investigating temporal forecasting and missing records.
Simulated electrical-network parameters and stability outcomes for examining surrogate reliability.
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