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This paper introduces a framework for a navigable map of datasets in Engineering Design and Systems Engineering (EDSE) to address the fragmented nature of engineering data. The framework uses a multi-dimensional taxonomy to classify datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. A prototype interactive tool employing a knowledge graph data model is presented, revealing "data deserts" in early-stage design and "data oases" in predictive maintenance, highlighting areas for future data collection efforts.
Engineering design research lacks benchmark datasets, but this framework and prototype promise to change that by mapping the data landscape and revealing critical gaps.
The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this ``digital thread''has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a ``Map of Datasets in EDSE.''The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas (``data deserts'') in early-stage design and system architecture, as well as relatively well-represented areas (``data oases'') in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.
