Digital Twin-Enabled Predictive Maintenance in IoT Environments: Systematic Review Corpus and Process Data (n = 128)

Supplementary dataset for the systematic literature review 'Digital Twin-Enabled Predictive Maintenance in IoT Environments: A Systematic Literature Review of Architectures, Methodologies, and Task Coverage (2020-2026)', submitted to the Journal of Industrial Information Integration (Elsevier). Contains: (1) SLR_ESM_Complete.xlsx - the 128-study included corpus with full 11-field structured extraction and classification of application domain, ML/AI method, and PdM task; (2) SLR_Process_Full_Data.xlsx - complete record-level data for every PRISMA-trAIce screening stage (identification, abstract screening, full-text screening, data extraction, quality assessment, inter-rater reliability); (3) Gap_Analysis_128.xlsx - classification of all 128 studies against six identified research gaps; (4) vosviewer_files.zip - bibliometric co-occurrence network files for VOSviewer. All data consist of bibliographic metadata and author-applied classification of publicly indexed journal articles; no primary human/experimental data are included.