ai4data/database-of-datause

Database of Data Use

Mentions of datasets in documents, extracted with the datause_pipeline (rafmacalaba/gliner_agents_v3 finds mentions, rafmacalaba/lfm2.5-350M-focus-metadata gives usage and attributes; code: https://github.com/rafmacalaba/database-of-data-use).

The dataset is the data-use extractions and the origin of each document, for traceability. It carries no bibliographic metadata (title, authors, abstract); follow source_url / pdf_url / doi to the origin.

The data grows by source. Every table has a source column, and each source is one parquet file per table (documents/<source>.parquet, ...), so adding a source adds files and does not change existing ones. All sources share the same schema; a field a source does not have is null.

table	one row per	main columns
documents	document	corpus_id, source, source_doc_id, source_url, pdf_url, doi, sha256, n_pages, extraction_status, plus document_type and publication_year as analysis context
mentions	extracted mention	mention_id, corpus_id, page_number (1-based), mention_text, specificity, score, role, usage_action, usage_summary, theme_tags, dataset_geography, dataset_year, dataset_version, dataset_producer, dataset_acronym, dataset_data_type document_type, publication_year, source_url
datasets	distinct dataset in a document	corpus_id, dataset_name, specificity, n_occurrences, pages, max_score, role, usage_action, theme_tags, dataset_*

| mention_family | mention | mention_id, specificity, family_id, match_method, match_score, mention_country (ISO-3), round_year | | families | dataset family | family_id, canonical_name, acronym, kind, producer, n_mentions | | aliases | normalised name form | alias, family_id, method, score |

Join the tables on corpus_id (<source>:<sha256[:16]> of the document bytes).

Reading the data

• specificity: named (a named dataset), descriptive (described, not named), vague. Only named mentions carry role (primary, supporting, background), usage_action (source, analyze, curate, validate, inform) and the dataset_* attributes; for the others they are null.
• mentions are per text chunk: the same mention in overlapping chunks can appear twice. datasets is the deduplicated view.
• extraction_status != ok means the document was not processed (for example the GPU ran out of memory). It does not mean the document mentions no data. Such documents have no mentions.
• The extraction is automatic and has errors: scores and attributes are model output, not curated.
• Byte-identical files are merged into one document; the other origin ids are in duplicate_source_doc_ids.

Dataset families (family_id)

"Ghana Living Standards Survey", "Nepal Living Standards Survey IV" and "LSMS 2017" are different strings for one family of surveys. mention_family maps each named mention to a family_id automatically (no manual curation) and splits off the country and the round/year. match_method says how: alias (a form seen before), acronym, fuzzy (overlap with a family's name), extends (a variant that contains a family's whole name), new (first form of a new family) or generic (nothing distinguishing, like "household survey" or "census data"; family_id is null). Named mentions create and join families. Descriptive mentions (if included) only join an existing family, with a stricter rule (a known form, an acronym, or a close match); most stay unresolved (unmatched), because descriptions like "passenger data" are not dataset names. Named results do not depend on whether descriptive mentions are included. match_score is the confidence; low fuzzy/extends scores are the least reliable. family_id is assigned once and never changes, so adding sources only extends the registry. canonical_name, kind (majority of the extracted data type) and producer are automatic and may be imperfect.

Sources

See summary-<source>.json in the repository for counts per source.