Informed environmental-economic policy decisions require a solid understanding of the economy's biophysical basis. Global physical input–output tables (gPIOTs) collate a vast array of information on the world economy's physical structure and its interdependence with the environment, which can help to monitor progress toward a sustainable circular economy. However, building gPIOTs requires dealing with mismatched and incomplete primary data with high uncertainties, which makes it a time-consuming and labor-intensive endeavor. We address this challenge by introducing the PIOLab: A virtual laboratory for building gPIOTs. This represents the newest branch of the industrial ecology virtual laboratory (IELab) concept, a cloud-computing platform and collaborative research environment through which participants can pool resources to assemble individual input–output tables that target specific research questions. To overcome the lack of primary data, the PIOLab builds extensively upon secondary data derived from a variety of models commonly used in industrial ecology. We use the case of global iron-steel supply chains to describe the architecture of the PIOLab and highlight its analytical capabilities. A major strength of the gPIOT is its ability to provide mass-balanced indicators on both apparent/direct and embodied/indirect flows, for regions and disaggregated economic sectors. We present the first gPIOTs for 10 years (2008–2017), covering 32 regions, 30 processes, and 39 types of iron/steel flows. Diagnostic tests of the data reconciliation show a good level of adherence between raw data and the values realized in the gPIOT. We conclude with elaborating on how the PIOLab will be extended to cover other materials and energy flows.