Qualitative Dataset for Industrial Process Optimization Using AI and IoT in Industry 5.0

This dataset supports the study “A Qualitative Framework for Industrial Process Optimization Using AI and IoT: Advancing Human-Centric Goals in Industry 5.0” and provides the complete qualitative and bibliometric evidence used to develop the proposed framework. The dataset integrates multiple qualitative research components, including a systematic literature review, bibliometric mapping, expert interviews, and triangulated thematic analysis. It comprises structured records from 180 initially identified studies, 120 full-text screened publications, and a final corpus of 48 peer-reviewed articles selected according to qualitative rigor and thematic relevance criteria. In addition, the dataset documents eight conceptual categories, sixteen theoretical links, and twenty-eight emergent themes that characterize the role of artificial intelligence and the Internet of Things in human-centric industrial process optimization. Expert-level qualitative data are included through anonymized profiles of fifteen professionals from academia, industry, and consulting, alongside interview guides, thematic matrices, and a validated codebook aligned with NVivo-based analysis. Methodological robustness is ensured through documented triangulation procedures, intercoder reliability assessment (Cohen’s Kappa κ = 0.79), and compliance with the Standards for Reporting Qualitative Research (SRQR). This dataset is intended to enhance transparency, reproducibility, and methodological traceability in qualitative research on Industry 5.0. It enables independent verification of the study findings, supports secondary qualitative analysis, and serves as a reference resource for researchers investigating AI–IoT integration, human-centric digital transformation, and sustainable industrial systems.