一个经过精心编纂的文献数据库，用于监控超过30年的Ansys Granta产品使用情况
A Curated Literature Database for Monitoring More Than 30 Years of Ansys Granta Product Usage
AI总结：
该研究提出一个结构化数据库，用于监控Ansys Granta产品在学术和技术文献中的使用情况，通过半自动化和专家编纂方法整合使用证据，支持定量分析和证据评估。
摘要：
工程和材料软件在学术和技术文献中越来越难以跟踪，因为出版物数量迅速增长，而软件引用实践仍然不一致。这在Ansys Granta产品系列上尤为明显，该系列产品用于材料教育、材料和工艺选择、以可持续性为导向的设计以及企业材料信息管理。我们提出了一种结构化且可重复的框架，以整合操作Granta使用的证据，并支持对采用模式、应用领域和技术影响的定量监控。该框架实现为一个经过精心编纂的参考数据库，在Ansys Granta MI Enterprise中：文献元数据通过半自动化方式（例如通过DOI和引用文件解析）获取，并通过专家对使用描述符（产品、情境、应用领域和技术深度）的编纂进行补充，与作者和机构建立关系链接。下游分析使用Python、仪表板和文献计量/网络可视化工具进行，以实现可重复的查询和报告。截至2025年9月，该数据库包含超过1100条经过编纂的记录，涵盖期刊、会议、论文、书籍、专利、标准和报告，支持快速检索经过验证的案例研究、可重复的文献综述和技术 scouting。示例分析突显了主导领域、关键机构和反复与CAD/CAE/FEM环境的集成。总体而言，该方法将异构的软件使用证据转化为结构化的、可分析的知识，以提高工程软件影响的可见性，并支持基于证据的评估和战略决策。
英文摘要：
Engineering and materials software is increasingly difficult to track in the scholarly and technical literature because publication volume is growing rapidly and software citation practices remain inconsistent. This is particularly true for the Ansys Granta product family, which is used for materials education, materials and process selection, sustainability-driven design, and enterprise materials information management. We present a structured and reproducible framework to consolidate evidence of \emph{operational} Granta usage and to support quantitative monitoring of adoption patterns, application domains, and technical impact. The framework is implemented as a curated reference database in \textit{Ansys Granta MI Enterprise}: bibliographic metadata are ingested semi-automatically (e.g., via DOI and citation-file parsing) and complemented by expert curation of usage descriptors (product, context, application domain, and technical depth), with relational links to authors and institutions. Downstream analytics are performed with Python, dashboards, and bibliometric/network visualization tools to enable reproducible querying and reporting. As of September~2025, the database contains more than 1{,}100 curated records spanning journals, conferences, theses, books, patents, standards, and reports, and supports rapid retrieval of validated case studies, reproducible literature reviews, and technology scouting. Example analyses highlight dominant domains, key institutions, and recurring integrations with CAD/CAE/FEM environments. Overall, the approach converts heterogeneous software-usage evidence into structured, analyzable knowledge to improve visibility of engineering software impact and to support evidence-based assessment and strategic decision-making.
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