Dataset of linguistic and network structures linking clinical terminology with patient discourse in interstitial cystitis/bladder pain syndrome

This dataset provides the full linguistic and network data supporting the analysis of communication patterns in interstitial cystitis/bladder pain syndrome (IC/BPS). It contains validated clinical vocabulary terms derived from standardized questionnaires, their occurrence frequencies in patient-generated text, and the weighted co-occurrence matrices used for network construction. The dataset also includes derived network metrics such as node centrality, clustering coefficients, and high-strength associations between symptom and anatomical terms. All data were obtained from anonymized public Reddit posts (August 2024–July 2025) and processed through a reproducible Python-based workflow for text normalization, tokenization, and graph generation. The files enable replication of the network statistics presented in the associated Scientific Reports article and can be reused for comparative studies on clinical–patient language convergence in digital health communication. This resource is released to promote open, transparent, and reproducible computational research in urology, pain medicine, and natural language processing.