Complex simulations and machine-learning models increase in application in research, industry, and governance. However, applying these systems with reasonable accuracy and efficiency requires large-scale efforts of data collection, data transformation, data analysis, and data visualization. At the same time, maintaining the required infrastructure, software, and personnel skyrockets making these tools unavailable to many potential users. The paradigm of the digital twin offers a novel perspective on how to manage the data efficiently and make these systems available more steadily at a lower cost. We introduce the first prototype of the Open Digital Twin Platform (ODTP) that is designed to be openly available to all interested parties to enable a common framework and baseline for digital twin based research. ODTP uses containerization, loose coupling, and micro-services to provide dynamically composable digital twins. ODTP also provides tools for licensing resolution, privacy and access control, and reproducibility. In its first iteration presented here, ODTP implements a common mobility research pipeline of the eqasim pipeline for MATSim. These kind of programs are usually difficult to assemble and use, thus leading to dangerous versions of 'never change a running system'. ODTP converts them into an easy-to-use version making it possible to initiate mobility simulations with one click. ODTP enables the quick adding of relevant data sources and analytical pipelines related to any topic and make them easily usable, accessible and shareable to research, industry, and governance. Thus, ODTP expands the FAIR principle from data to the complete data life cycle.