How much error correction, labeling, and cleanup has been done on a dataset before delivery. Raw data is cheap but messy; highly processed data is convenient but may hide assumptions or biases introduced during cleanup.
The processing status was high—but they couldn't tell which fields had been inferred versus measured directly.
