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引用次数: 0
摘要
随着经济的快速发展,科学、技术和工业之间的联系越来越紧密,越来越多的研究集中在三者之间的二元或三元相互作用上。然而,由于以往研究使用的数据集和领域不同,很难对研究结果进行直接比较和综合。这进一步影响了对科学、技术和工业之间二元和三元相互作用的全面理解。因此,本文在DrugBank数据库的基础上构建了涵盖科技、行业信息的多源集成数据集。我们的数据集命名为“科学-技术-工业交互”(sci - technology - industry interaction),它涵盖了文档实体、研究人员实体、组织实体和分类实体,并且包含了它们之间丰富的语义关系。此外,在Figshare上可以免费获得,并存储为MySQL数据库,并在GitHub上公开提供。我们的数据集将有助于开展科学、技术和工业之间二元或三元联系的研究,并有助于深入了解制药领域的发展模式。
STInt: An integrated dataset covering science, technology and industry information in the pharmaceutical field.
With the rapid development of the economy, the linkages among science, technology, and industry have become increasingly close, and more and more study focus on the binary or ternary interactions among the three. However, the different datasets and fields used in previous studies make it difficult to directly compare and synthesize research results. This further affects the comprehensive understanding of the binary and ternary interactions among science, technology, and industry. Therefore, this paper constructs a multi-source integrated dataset covering science, technology, and industry information based on the DrugBank database. Our dataset is named as STInt (Science-Technology-Industry interactions), which covers document entities, researcher entities, organization entities, and classification entities, and contains rich semantic relationships among them. In addition, STInt is freely available at Figshare, and stored as a MySQL database and make it openly available on GitHub. Our STInt dataset will be useful for conducting research on the binary or ternary linkages among science, technology, and industry, and for gaining a deeper understanding of the developmental patterns in the pharmaceutical field.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.