Gold Datasets of Verified Affiliation Strings with ROR Matches from zbMATH Open Publications

These datasets contain affiliation strings from zbMATH Open publications together with their corresponding Research Organization Registry (ROR) matches, intended to support research on affiliation disambiguation and institutional analysis in the mathematical research domain. The data were selected from zbMATH Open publications. The affiliation strings match institutions from different countries. Dataset g1 consists of the following columns: an: zbMATH publication identifier aff_str: the original affiliation string as it appears in the publication ror_ids: matched ROR identifier(s) for the affiliated organization ror_names: organization name(s) corresponding to the matched ROR identifier(s) Dataset g2 extends g1 with an additional column: an: zbMATH publication identifier aff_str: the original affiliation string as it appears in the publication ror_ids: matched ROR identifier(s) for the affiliated organization ror_names: organization name(s) corresponding to the matched ROR identifier(s) match_status: classification of the match result (see below) Each affiliation string in g2 was independently verified and classified using the following categories: TP (True Positive): the affiliation string is correctly matched with the corresponding ROR identifier and organization name FP_partial (False Positive – partial): the system identified a related but not exact organization FP_wrong (False Positive – wrong): the match is completely incorrect FN (False Negative): a valid affiliation that was missed by the matcher TN (True Negative): affiliations for which no ROR ID exists in the registry (as of the version used in this study) The match is considered correct if the aff_str matches the name of the exact organization or its parent organization. Note that aff_str may contain department names; however, the matcher currently identifies institute-level names only. It is important to note that repetition across subsequent rows may occur; this happens when multiple authors of a publication share the same affiliation. The zbMATH identifier (an) can be resolved via https://zbmath.org/ to obtain additional metadata, including DOIs. For example, an = 7199248 is associated with https://zbmath.org/7199248. The matching was carried out using a particular version of the Research Organization Registry (ROR). Consequently, some identifiers in the dataset may differ from those in the latest ROR release; however, earlier identifiers are automatically redirected to the corresponding current records in ROR. Possible applications include: Analyzing institutional contributions to mathematics research Studying author-affiliation patterns in mathematical publications Evaluating and benchmarking affiliation parsing and disambiguation methods Users are invited to contact the first author with suggestions or corrections. Future releases will extend the dataset with additional publication metadata, author-institution signatures, negative samples, and descriptive statistics.本数据集包含来自zbMATH Open出版物的单位隶属字符串及其匹配的研究机构注册表（Research Organization Registry，ROR）条目，旨在支撑数学研究领域内的单位隶属消歧与机构分析相关研究。 本数据集选自zbMATH Open收录的出版物，其中的单位隶属字符串对应不同国家的科研机构。 数据集g1包含如下字段： an：zbMATH出版物标识符 aff_str：出版物中原始呈现的单位隶属字符串 ror_ids：匹配到的隶属机构的ROR标识符 ror_names：匹配到的ROR标识符对应的机构名称 数据集g2在g1的基础上新增了一列字段： an：zbMATH出版物标识符 aff_str：出版物中原始呈现的单位隶属字符串 ror_ids：匹配到的隶属机构的ROR标识符 ror_names：匹配到的ROR标识符对应的机构名称 match_status：匹配结果的分类标签（详见下文） g2中的每条单位隶属字符串均经过独立核验，并按照以下类别进行分类： TP（真阳性，True Positive）：单位隶属字符串与对应的ROR标识符及机构名称匹配正确 FP_partial（假阳性-部分匹配，False Positive – partial）：系统识别出了相关但不完全匹配的机构 FP_wrong（假阳性-完全错误，False Positive – wrong）：匹配结果完全错误 FN（假阴性，False Negative）：匹配器遗漏的有效隶属信息 TN（真阴性，True Negative）：在本次研究使用的ROR版本中无对应标识符的隶属信息 当单位隶属字符串与目标机构或其上级机构的名称匹配时，即视为匹配正确。需注意，隶属字符串中可能包含院系名称，但当前匹配器仅能识别研究所级别的机构名称。 需特别说明：同一出版物的多位作者共享同一隶属信息时，后续行可能出现重复条目。可通过https://zbmath.org/查询zbMATH标识符（an）以获取包括DOI在内的额外元数据，例如标识符an=7199248对应的链接为https://zbmath.org/7199248。 本次匹配使用了特定版本的ROR数据库，因此数据集中的部分标识符可能与最新版ROR中的条目存在差异，但旧版标识符会自动重定向至ROR中对应的当前记录。 本数据集的潜在应用场景包括： 1. 分析科研机构对数学研究的贡献 2. 研究数学出版物中的作者-隶属关系模式 3. 评估并基准测试单位隶属信息解析与消歧方法 欢迎用户联系第一作者提出建议或修正意见。未来版本的数据集将新增出版物元数据、作者-机构关联信息、负样本及描述性统计数据等内容。