Discovering data spaces: A classification of design options
针对数据空间概念模糊、缺乏设计工具的问题，通过文献综述、实际案例分析和专家访谈，提出了一套数据空间设计选项的分类体系，帮助从业者更高效地设计和理解数据空间。
Technical coordination between organizations and security concerns are among the major barriers to data sharing. Data spaces are an emerging digital infrastructure that helps address these challenges by sovereignly sharing data across institutional boundaries. The data space concept is at the core of many high-profile research initiatives in the European Union and receives great adoption in practice. Despite the great interest, there is, however, a demand for more conceptual clarity and approaches to describe and design them purposefully. We propose a taxonomy of data space design options grounded in a literature review, an analysis of real-world objects, and over nine hours of expert interviews with data space initiatives. The taxonomy advances our understanding of data space designs and gives a framework to practice making informed design decisions. Our work provides a comprehensive solution space for data space designers to (a) (re-)design data spaces more efficiently and (b) acquire a ‘big picture’ of what needs to be considered. • Taxonomy of key characteristics to describe, classify, and redesign data spaces. • Grounded in literature review, analysis of real-world objects, and expert interviews. • Advances understanding of data space designs by providing a framework for informed design decisions. • Addresses the need for enhanced conceptual clarity and effective design tools for data spaces. • Illustrates the wide application of data spaces in various industries.