ChikungunyaQA: A Clinical Dataset of Questions and Answers on Chikungunya

Chikungunya is a mosquito-borne viral disease and an important public health challenge, particularly in regions where competent Aedes vectors are established. High-quality question-answering resources grounded in authoritative clinical guidance are needed to support the development and evaluation of Artificial Intelligence (AI) applications for this disease. ChikungunyaQA is a Brazilian Portuguese (PT-BR) question-answering dataset designed to support the development and evaluation of language-model applications grounded in authoritative Chikungunya guidance. The dataset was constructed from six Brazilian clinical and technical documents, which were semantically segmented and enriched with provenance and clinical metadata. GPT-4o mini generated multi-persona question–answer pairs for physicians, patients, and caregivers using an inductive, source-grounded pipeline. All instances were independently evaluated with Claude Sonnet 4.5 using G-Eval across faithfulness, completeness, persona appropriateness, and clinical relevance, then pairs below a threshold underwent feedback-guided repair and re-evaluation. The final dataset contains 1262 retained QA pairs generated from 112 productive source fragments, among 141 fragments extracted from six documents. Mean G-Eval score was 97.12/100, with 99.7% of generated pairs retained. Human evaluation of 200 pairs by a physician and a physical therapist showed high ratings for clinical accuracy, completeness, and persona adherence, with Gwet’s AC2 ranging from 0.944 to 0.961 in the shared subset. ChikungunyaQA provides a reusable resource intended to support research on fine-tuning and evaluating language models, developing RAG-based systems, and investigating factual consistency and hallucinations in healthcare AI.