Data Brief. 2026 Jun 19;67:112983. doi: 10.1016/j.dib.2026.112983. eCollection 2026 Aug.

ABSTRACT

This article presents KannadaLit4NLP, a large-scale, machine-readable corpus of Kannada literary texts designed to support natural language processing (NLP) research for a low-resource language. The dataset comprises 24,746 literary verses from three major Kannada literary traditions-Vachanas (11th-19th century), Tripadis (16th century), and Kagga (20th century)-along with 22,369 corresponding interpretations curated from scholarly sources. The corpus captures linguistic, stylistic, and semantic variations across historical periods and literary forms. The dataset was developed through a systematic pipeline that included source identification, digitisation via optical character recognition (OCR), manual verification, and structured annotation. Each entry is organised in a structured format that includes the original verse, metadata (literary form, author, and source), and associated interpretation(s), enabling its use in tasks such as semantic textual similarity, textual entailment, information retrieval, and generative modelling. KannadaLit4NLP addresses the limited availability of culturally grounded Kannada datasets by providing a resource that integrates classical and modern literary content with interpretative annotations. The dataset can facilitate the development and evaluation of NLP models in areas such as semantic understanding, translation, and knowledge representation, while also supporting computational studies of literary and cultural texts. The dataset is made publicly available to encourage further research and reproducibility in Kannada NLP.

PMID:42389175 | PMC:PMC13320459 | DOI:10.1016/j.dib.2026.112983

Ambix. 2026 Jun 25:1-30. doi: 10.1080/00026980.2026.2668735. Online ahead of print.

ABSTRACT

This article documents the creation of EMLAP (Early Modern Latin Alchemical Prints), a machine-readable corpus of one hundred Latin alchemical printed works produced within the TOME (The Origins of Modern Encyclopaedism, 2023-2025) project. It first situates the corpus within both the digital humanities landscape and the historiography of alchemy, where the availability of reliable machine-readable texts remains limited. It then addresses the challenges of converting early modern Latin printed text into machine-readable format with a high standard of quality. The article argues that producing a high-quality transcribed corpus at scale still requires human scholarly intervention, and that a transcription project must balance the ideal of digital edition standards against the practical constraints of time and resources. The article describes the practical experience of building EMLAP: the selection of the Transkribus platform for AI-powered automatic text recognition, the choice of transcription models, the development of human quality standards to complement automated metrics, and the construction of a computational pipeline to process and enrich the transcriptions. The EMLAP corpus has been made publicly available in open access (Zenodo repository) as well as in the form of a website that offers different search opportunities for researchers.

PMID:42345208 | DOI:10.1080/00026980.2026.2668735

Data Brief. 2026 May 10;66:112835. doi: 10.1016/j.dib.2026.112835. eCollection 2026 Jun.

ABSTRACT

This dataset was collected during on-site fieldwork conducted in the district of Chinchero, located in the province of Urubamba, Cusco, Peru, a region internationally recognized for its rich Andean textile tradition rooted in Inca Culture heritage. The dataset comprises high-quality Photographic images of traditional handwoven Andean textile iconographies produced by local artisan communities. These images were captured directly at textile centers where the fabrics are woven, dyed and finished using ancestral techniques measuring authentic representation of colors, textures, and symbolic patterns under natural and controlled conditions. The dataset consists of 1358 images organized into 28 distinct classes, each corresponding to a specific textile iconography characteristic of the Chinchero tradition. The images are provided in a processed and curated format, facilitating organization enables systematic analysis of visual motifs that are often challenging to distinguish due to their intricate geometric patterns and cultural symbolism. The primary reuse potential of this dataset lies in its application to Artificial Intelligence (AI) and Machine Learning (ML) research focused on image classification, pattern recognition, and cultural heritage preservation. Researchers can leverage the dataset to develop and evaluate models capable of identifying and differentiating traditional Andean textile iconographies, addressing the growing difficulty faced by younger generations, local communities, and visitors in recognizing the cultural expressions. Additionally, the dataset supports interdisciplinary research in digital humanities, ethnography, textile studies, and cultural informatics contributing to the documentation and preservation of intangible cultural heritage. By making this dataset publicly available, this work aims to support the development of AI-driven tools for cultural preservation, educational applications, and heritage awareness, while fostering collaboration between researchers, technologists, and local artisan communities to safeguard ancestral knowledge for future generations.

PMID:42220648 | PMC:PMC13217882 | DOI:10.1016/j.dib.2026.112835