A Framework and Prototype for a Navigable Map of Datasets in Engineering Design and Systems Engineering

TL;DR

This paper introduces a systematic framework and prototype for a navigable, multi-dimensional map of engineering datasets to improve discoverability, facilitate research, and address data fragmentation in engineering design and systems engineering.

Contribution

It presents a novel taxonomy and architecture for an interactive dataset discovery tool using a knowledge graph model, addressing data fragmentation and underrepresentation in EDSE.

Findings

Identified underrepresented data areas in early-stage design and architecture.

Developed a prototype knowledge graph-based discovery tool.

Highlighted challenges in dataset curation and sustainability.

Abstract

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.