KazakhLawCorpus: A Large-Scale Metadata Corpus for Kazakh Legislation

The availability of large, structured legal datasets is essential for advancing legal natural language processing, information retrieval, and the development of reliable language technologies. However, such resources remain scarce for Kazakh, a morphologically rich and comparatively low-resource language, limiting the development and evaluation of domain-specific NLP methods. This data descriptor presents KazakhLawCorpus, a curated corpus of 215,889 document-level legislative metadata records from the Republic of Kazakhstan. The dataset was derived from a PostgreSQL export containing 223,245 source records and constructed through Kazakh-language field selection, essential-field filtering, source-preserving normalization, schema reconstruction, and systematic quality validation, resulting in a retention rate of 96.705%. The final release comprises 25 fields covering document identifiers, temporal attributes, legal status, registry information, Kazakh-language titles and descriptions, legal and institutional categories, geographical information, and source URLs. The records cover legislative acts adopted between 30 October 1947 and 20 July 2026. Quality-control procedures confirmed unique law_id values, complete title and description fields, and parseable adoption dates, while 4635 records with potential source-level or historical anomalies were retained and transparently documented through diagnostic flags. KazakhLawCorpus provides a large-scale, traceable resource for metadata-based legal information retrieval, document classification, temporal and institutional analysis, provenance tracking, and the development of downstream Kazakh legal NLP resources.