Evidence-Unit Mapping (EUM) Database of MASS Safety and Economic Performance Claims (2017–2026)

This dataset contains the granular analytical data supporting the Systematic Literature Review (SLR) titled "Mapping the Safety Dip: A Systematic Evidence-Unit Mapping of Performance Trajectories in Autonomous Shipping." While traditional systematic reviews analyze papers as single units, this dataset employs an Evidence-Unit Mapping (EUM) approach, deconstructing 67 high-impact studies into 158 discrete performance claims regarding safety and economic efficiency in Maritime Autonomous Surface Ships (MASS). The dataset includes (1) Source Coding: Identification of original studies (P1–P67); (2) Thematic Metadata: Categorization by IMO Degrees of Autonomy (DoA 1–4), primary area of focus, and methodological approach (Quantitative, Qualitative, Review); (3) Performance Impact Scores: Binary and weighted consensus coding (-1, 0, +1) for safety and cost-efficiency impacts; (4) Evidence Justification: A dedicated column of "Exact Quotes" from the source literature, providing the qualitative evidence used for each quantitative score.