STInt: An integrated dataset covering science, technology and industry information in the pharmaceutical field

STInt-DatasetSTInt (<b>S</b>cience-<b>T</b>echnology-<b>I</b>ndustry i<b>nt</b>eractions) Dataset1. IntroductionIn an era of rapid economic development, science, technology, and industry influence each other and develop together. Scientific research and technological development promote the rapid progress of industries. In turn, industrial progress is also an important driving force for scientific research and technological development1. As early as the early 1990s, some scholars noticed the close connections among science, technology and industry, and conducted relevant research from the perspective of innovation. In the literature, scientific publications, patents, and products are usually viewed as respective proxies of scientific research, technological development, and industrial progress.Last two decades witnessed significant progress in the science-technology or science-technology-industry interactions. Several perspectives have been investigated as follows: (1) thematic structure based linkages among these three resources (Xu et al., 2019; 2021); (2) academic inventors bridging science and technology (Xu et al., 2023); (3) concordance table among science, technology, and industry classification systems (徐硕等，2024; 2025); (4) mutual citations among scholarly articles, patents, and products (Xu et al., 2024; 2025b).A multi-source integrated dataset covering science, technology, and industry information, named as STInt (<b>S</b>cience-<b>T</b>echnology-<b>I</b>ndustry i<b>nt</b>eractions) dataset (Xu et al., 2025a) is developed. This dataset will further promote systematic understanding of the linkages among science, technology, and industry.2. Tables and ViewsThe STInt dataset is stored in the format of MySQL database, which comprises the 48 tables and 5 views as follows:2.1 tablesaffiliation, article, article_annotation, article_author, article_author_affiliation, article_category, article_cited_article, article_descriptor, article_descriptor_qualifier, atc_code, author, category, cited_article, cited_patent, country_region, cpc, descriptor, descriptor_qualifier, descriptor_tree_number, drug, drug_article, drug_atc_code, drug_descriptor, drug_drugbank_id, drug_interaction, drug_manufacturer, drug_mixture, drug_patent_original, drug_synonym, entity_category, ingredient, inventor_applicant, mixture_ingredient, ipc, manufacturer, mixture, non_patent, organization_linkage, patent, patent_annotation, patent_applicant, patent_cited_patent, patent_cpc, patent_inventor, patent_ipc, patent_non_patent, patent_original, patent_priority, qualifier, qualifier_tree_number, researcher_linkage, and synonym2.2 viewsview_citation_article_article, view_citation_article_patent, view_citation_drug_patent, view_citation_patent_article, and view_citation_patent_patent3. References[1] Shuo Xu, Zhen Liu, and Xin An, 2025a. STInt Dataset: A Multi-Source Integrated Dataset Covering Science, Technology, and Industry Information in the Pharmaceutical Field. <i>Scientific Data</i>, Vol. 12, pp. 1056.[2] 徐硕，张跃富，安欣，2025. 全领域多层级科学−技术分类体系映射研究. <i>情报学报</i>，Vol. 44，No. 8，pp. 933-949.[3] Shuo Xu, Zhen Liu, Xin An, Hong Wang, and Hongshen Pang, 2025b. Linkages among Science, Technology, and Industry on the basis of Main Path Analysis. <i>Journal of Informetrics</i>, Vol. 19, No. 1, pp. 101617.[4] Shuo Xu, Xinyi Ma, Hong Wang, Xin An, and Ling Li, 2024. A Recommendation Approach of Scientific Non-Patent Literature on the basis of Heterogeneous Information Network. <i>Journal of Informetrics</i>, Vol. 18, No. 4, pp. 101557.[5] 徐硕，孙童菲，罗贵缘，苑洲桐，连佳欣，刘畅，2024. 分类体系双向映射视角下的科学-技术互动分析. <i>中国发明与专利</i>，Vol. 21，No. 4，pp. 4-15.[6] Shuo Xu, Ling Li, and Xin An, 2023. Do Academic Inventors have Diverse Interests? <i>Scientometrics</i>, Vol. 128, No. 2, pp. 1023-1053.[7] Shuo Xu, Ling Li, Xin An, Liyuan Hao, and Guancan Yang, 2021. An Approach for Detecting the Commonality and Specialty between Scientific Publications and Patents. <i>Scientometrics</i>, Vol. 126, No. 9, pp. 7445-7475.[8] Shuo Xu, Dongsheng Zhai, Feifei Wang, Xin An, Hongshen Pang, and Yirong Sun, 2019. A Novel Method for Topic Linkages between Scientific Publications and Patents. <i>Journal of the Association for Information Science and Technology</i>, Vol. 70, No. 9, pp. 1026-1042.