Transport maintenance depots are critical infrastructure for road network functionality and economic productivity. In developing nations, systematic evaluations of their operational efficiency are scarce, hindering evidence-based asset management and investment. This data descriptor presents a structured, quasi-experimental framework designed to methodologically evaluate the efficiency of transport maintenance depot systems. The primary objective is to provide a replicable dataset for diagnosing performance gaps and measuring causal efficiency gains from interventions. A longitudinal, panel dataset was constructed from administrative records, direct observations, and controlled engineering audits across multiple depot sites. The core analytical model is a stochastic frontier analysis (SFA) specified as $\ln(Output_{it}) = \beta_0 + \beta X_{it} + (v_{it} - u_{it})$, where $u_{it}$ represents technical inefficiency. Robust standard errors were clustered at the depot level to account for intra-group correlation. The dataset reveals significant heterogeneity in technical efficiency scores across depots, with a central tendency indicating that average operational efficiency is approximately 0.65. Diagnostic analysis identifies spare parts inventory management as a predominant theme linked to inefficiency, with a negative correlation coefficient of -0.42. The constructed dataset provides a robust empirical foundation for analysing depot system performance. It confirms the applicability of the quasi-experimental framework for isolating operational inefficiencies in this engineering context. Future research should apply this framework to longitudinal intervention studies. Depot managers should prioritise inventory control systems, as indicated by the diagnostic correlations within the dataset. infrastructure management, stochastic frontier analysis, technical efficiency, maintenance engineering, panel data, developing countries This work provides the first publicly available, structured dataset applying a quasi-experimental design and stochastic frontier modelling to evaluate transport maintenance depot efficiency in a sub-Saharan African context.