Generated cultural heritage question–answer dataset: Durga in multi-dimensional perspectives

TL;DR

This paper introduces a dataset of 21,395 question-answer pairs about Durga mythology to support AI applications in cultural heritage and education.

Contribution

The novelty lies in the creation of a manually curated, structured QA dataset focused on Durga from diverse cultural sources.

Findings

The dataset includes 21,395 QA pairs from scriptures, temple inscriptions, and storytelling records.

It supports generative QA models, chatbots, and digital preservation of cultural knowledge.

The dataset is ethically sourced and suitable for AI-driven cultural and educational applications.

Abstract

This dataset presents a valuable compilation of question–answer (QA) pairs derived from cultural texts and sources related to Durga mythology. A total of 21,395 QA pairs, encompassing textual materials such as scriptures, ritual narratives, temple inscriptions, and traditional storytelling records. Each entry includes the source reference, question, and corresponding answer, provided in a structured format compatible with Excel for seamless integration into downstream natural language processing (NLP) tasks. Data collection involved manual curation and annotation by domain experts, followed by preprocessing steps including text normalization, duplication removal, and verification of factual and contextual accuracy. The dataset is designed to support generative QA models, culturally aware chatbots, and digital preservation of heritage knowledge. It is particularly valuable for research in AI-driven cultural applications, educational tools, and digital humanities initiatives aiming to bridge traditional knowledge with computational methods. Researchers and practitioners may utilize the dataset for training generative models, creating interactive educational platforms, developing culturally sensitive AI agents, and supporting comparative studies in cross-cultural heritage. This openly accessible resource adheres to ethical standards, with proper attribution to source materials, and provides a foundational asset for both academic research and applied development in culturally informed artificial intelligence.

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