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。尽管这种"数字主线"具有推动创新的潜力，但现有数据集的碎片化与难以访问性阻碍了方法验证、限制了可复现性并减缓了研究进展。与计算机视觉和自然语言处理等受益于成熟基准生态系统的领域不同，工程设计研究往往依赖于小型、专有或临时构建的数据集。本文通过提出一个"EDSE数据集地图"的系统性框架来应对这一挑战。该框架建立在多维分类法之上，旨在通过领域、生命周期阶段、数据类型和格式对工程数据集进行分类，从而实现分面检索。本文详细阐述了一种交互式检索工具的架构，并通过一个可运行的原型进行演示，该原型采用知识图谱数据模型来捕获数据集、工具与文献之间丰富的语义关系。对当前数据版图的分析揭示了早期设计与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覆盖的领域（"数据绿洲"）。本文指出了数据策管与可持续性方面的关键挑战，并提出了缓解策略，为构建动态的、社区驱动的资源以加速以数据为中心的工程研究奠定了基础。