There are several existing project datasets, which involve separate data sources for simulated and real projects, individual and multiprojects, and single- and multimodal attributes. In addition, their file structures are heterogeneous; therefore, scholars can usually use only one dataset to test a proposed scheduling or resource allocation algorithm. Since the internal structures of these projects are also very different, it is difficult to ensure that an algorithm optimized for a given type of project will also perform well on projects with other structures. The parser we propose can read several types of projects: simulated, real, individual, and multiprojects, as well as single- and multimodal attributes. In addition, it can consider the priorities of activities and the flexibility of their dependencies, which is essential for modeling the structural flexibility employed by agile, hybrid, and extreme project management approaches. Thus, researchers can build a large project database for testing and comparing different scheduling and resource allocation algorithms.