Data Standardization for Digital Twins in Autonomous Vehicles: Systematic Review Replication Dataset (PRISMA 2020)

This dataset provides the complete replication package for the systematic literature review titled "Data Standardization for Digital Twins in Autonomous Vehicles: A Systematic Review of the Vertical Integration Gap" (submitted to IEEE Transactions on Intelligent Transportation Systems, 2026). The review was conducted following the PRISMA 2020 protocol and analyzed 94 primary studies published between 2021 and 2026, retrieved from Scopus (24 March 2026), IEEE Xplore (31 March 2026), and Web of Science (1 April 2026). Files included:- 01_Protocol.txt: Complete review protocol with PICOC, research questions, inclusion/exclusion criteria, and methodology- 02_Search_Strings.txt: Database-specific search strings and execution dates- 03_RIS_Deduplicated_155.ris: Deduplicated record set (155 unique references)- 04_RIS_Final_94.ris: Final included corpus (94 references)- 05_Extraction_Master_v3.xlsx: Complete data extraction spreadsheet (94 studies, 13 fields, plus six-layer mapping and quality assessment)- README.md: Description of files and usage instructions Key findings: The review identifies and characterizes the "vertical integration gap" in the DT-AV standards stack: while horizontal layer coverage is broadly comprehensive (mean 5.03 of 6 layers per study), standards adoption is uneven across layers. ROS 2 and DDS dominate the operational layer in 88% of the corpus, whereas semantic and asset-management standards (AAS/IDTA, ISO 23247, ASAM family) remain marginal. Within the analyzed corpus, no study was found to document a clear end-to-end integration path connecting the operational layer to the semantic layer. The dataset enables i