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.

翻译：系统生命周期中数据的激增为工程设计与系统工程（EDSE）带来了重大机遇与挑战。虽然这种"数字主线"具有推动创新的潜力，但现有数据集碎片化、难以获取的特性阻碍了方法验证，限制了可重复性，并延缓了研究进展。与受益于成熟基准测试生态系统的计算机视觉和自然语言处理等领域不同，工程设计研究往往依赖小型、专有或临时性数据集。本文通过提出一种系统性的"EDSE数据集地图"框架来应对这一挑战。该框架基于一个多维分类体系构建，旨在按领域、生命周期阶段、数据类型和格式对工程数据集进行分类，从而实现分面发现。本文详细介绍了一种交互式发现工具架构，并通过工作原型加以验证，该原型采用知识图谱数据模型来捕获数据集、工具和出版物之间丰富的语义关系。对当前数据格局的分析揭示了早期设计和系统架构中代表性不足的领域（"数据荒漠"），以及预测性维护和自主系统中相对充分的领域（"数据绿洲"）。本文识别了数据策展与可持续性的关键挑战，并提出了缓解策略，为建立动态的、社区驱动的资源以加速数据驱动的工程研究奠定了基础。