manual-dataset-cleaning

To deduplicate interview datasets while keeping provenance, merge records only when meaning matches and preserve all source URLs and raw text snapshots. This ensures auditability while preventing data loss during synchronized multi-file cleaning.

The best way to synchronize dataset cleaning across CSV, JSON, and Markdown is to choose a source of truth and apply narrow, anchored edits. Parser-based verification then checks that row counts and selected fields remain consistent across all mirrored artifacts.

To normalize Nowcoder interview records without hallucinating missing data, keep the raw question text and create a cleaned version separately. This conservative extraction approach prevents fabricating missing fields during the normalization process.

Parser-based verification prevents CSV JSON Markdown drift by automatically checking row counts, enforcing field allowlists, and reporting mismatches. This replaces visual inspection to guarantee mirrored artifacts remain strictly consistent after edits.

Yes, you can collect interview datasets from platforms other than Nowcoder. The provenance-first schema requires capturing platform, source URL, post ID, title, author, and raw snapshots before any normalization, making it adaptable to various sources.