Reproducibility Dataset for Resilient Freight Allocation across Existing and Planned Iraqi Road–Rail Networks: A Digital-Twin Transition Experiment

This reproducibility dataset supports the study “Resilient Freight Allocation across Existing and Planned Iraqi Road–Rail Networks: A Digital-Twin Transition Experiment.” The package contains the semi-empirical Iraqi freight-network inputs, OpenStreetMap-derived current-road corridor data, network node and edge tables, the asset–path capacity model, simulation and statistical-analysis code, model-verification outputs, matched-seed raw results for 7,200 factorial runs, summaries of 240 infrastructure–controller–demand–disruption cells, 450 capacity-sensitivity runs, statistical tests, and publication figures. The factorial experiment combines four infrastructure states (Existing, IREM-modernised, Phased-integration, and Planned-IDR), three demand levels, five disruption conditions, four allocation controllers, and 30 matched stochastic replications. The sensitivity experiment varies rail, road, and transfer-capacity multipliers. Official documents provide the existing/IREM railway topology and planned Development Road lengths and design capacities. OpenStreetMap and Geofabrik data provide the present-road geography. Current operational capacities, disruption severities, observation errors, estimator weights, and protection factors are transparent simulation assumptions rather than field-calibrated Iraqi measurements. The dataset establishes computational reproducibility and internal validity. It does not contain live railway, truck, terminal, customs, or border data; it does not represent an operational digital shadow or digital twin; and its numerical outputs should not be interpreted as field-validated forecasts or investment-appraisal estimates. The complete Geofabrik Iraq extract is not redistributed. Users may obtain the corresponding Iraq extract separately under the Open Database License. OpenStreetMap-derived files retain their applicable attribution and licensing requirements.