AI 中文总结
该研究推出OpenSubAffil大规模数据集，整合NER与嵌入聚类流程及多信号评分函数，实现亚机构名称消歧与层级构建，弥合个体研究者与顶级机构的粒度差距。
AI 中文摘要
将学术成果准确归属到特定亚机构单元（如大学的学院或系所）对于精细化研究评估和政策制定至关重要。尽管顶级机构已有可靠标识符，但隶属字符串的语言和结构变异性导致亚层级单元的标准化数据仍十分匮乏。本研究推出OpenSubAffil，这是一个将OpenAlex的原始隶属字符串映射到已消歧亚机构实体及其层级结构的大规模数据集。我们开发了一个整合命名实体识别（NER）与基于嵌入的聚类的流程，还提出了一种多信号评分函数，该函数综合词汇与共现证据以重建亚机构层级。OpenSubAffil包含4000万条隶属字符串到18635个顶级机构下638843个已消歧亚单元的映射，以及它们的层级关系。针对Wikidata基准的验证和人工调查表明，我们的方法取得了良好性能。总体而言，该数据集弥合了个体研究者与顶级机构之间的粒度差距，支持在亚机构层面开展高分辨率的学术产出与交流分析。OpenSubAffil数据集可通过此httpsURL公开获取。
英文摘要
Accurate attribution of scholarly work to specific sub-institutional units, such as schools or departments of a university, is crucial for granular research assessment and policymaking. While robust identifiers exist for top-level institutions, standardized data for sub-level units remains scarce due to the linguistic and structural variability of affiliation strings. In this study, we introduce OpenSubAffil, a large-scale dataset mapping raw affiliation strings from OpenAlex to disambiguated sub-institutional entities and their hierarchical structures. We developed a pipeline integrating named entity recognition (NER) with embedding-based clustering. Furthermore, we proposed a multi-signal scoring function that synthesizes lexical and co-occurrence evidence to reconstruct the sub-institutional hierarchy. OpenSubAffil comprises mappings for 40 million affiliation strings to 638,843 disambiguated sub-units across 18,635 top-level institutions, together with their hierarchical relationships. Validation against Wikidata benchmarks and manual investigation show that our method achieves promising performance. Overall, this dataset bridges the granularity gap between individual researchers and top-level institutions, enabling high-resolution analyses of scholarly output and communication at the sub-institutional level. The OpenSubAffil dataset is publicly available at https://doi.org/10.5281/zenodo.19602782.
Journal refLiang, Z., Mao, J. & Li, G. A large-scale dataset of sub-institution name disambiguation and hierarchical structures from OpenAlex. Sci Data (2026)
DOI：10.1038/s41597-026-08136-9
