ArticleData in brief2026
BanglaRegionalTextCorpus: A curated dataset for four regional bangla dialects with standard Bangla and English translation.
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The BanglaRegionalTextCorpus is introduced as a curated dataset documenting four regional Bangla dialects: Rangpur, Barisal, Narail, and Khulna along with their corresponding Standard Bangla and English translations. The corpus contains 4653 manually validated sentences, collected from community interactions, field recordings, and publicly available digital sources. Rigorous pre-processing steps, including duplicate removal, normalization, and linguistic validation by native speakers, were employed to ensure data accuracy and consistency. This dataset serves as a comprehensive resource for dialect identification, machine translation, and text classification, as well as for research in sociolinguistics and regional language variation. By capturing phonetic, lexical, and syntactic distinctions across four dialects, it enables the development of inclusive and context-aware NLP models for low-resourced languages. Furthermore, the dataset supports comparative linguistic studies between regional and standardized Bangla, contributing to the preservation and computational representation of dialectal diversity. The BanglaRegionalTextCorpus provides a benchmark resource for future research in Bangla NLP, promoting collaboration, cultural preservation, and equitable language technology development across diverse linguistic communities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.