Easy ORCID

The first-party ORCID data dump uses a data structure that is overly complex for most use cases. This Zenodo record contains a derived version that is much more straightforwards, accessible, and smaller. So far, this includes employers, education, external identifiers, and publications linked to PubMed. It adds additional processing to ground employers and educational instutitions using the Research Organization Registry (ROR). It also does some minor string processing, such as standardization of education types (e.g., Bachelor of Science, Master of Science) and standardization of PubMed references. Records The records.jsonl.gz file is a JSON Lines file where each row represents a single ORCID record in a simple, well-defined schema (see schema.json). The records_hq.jsonl.gz file is a subset of the full records file that only contains records that have at least one ROR-grounded employer, at least one ROR-grounded education, or at least one publication indexed in PubMed. The point of this subset is to remove ORCID records that are generally not possible to match up to any external information. Updates since last version: country field is now countries and contains a list locale field added date field is now a dictionary instead of a straight integer for year This record also contains a SQLite database orcid.db that contains tables for researchers and for organizations. This is useful for quick lookup of data based on an ORCID local unique identifier. Nomenclature Authority Cross-References Websites, social links, and other identifiers are parsed and standardized to comply with the Bioregistry then shared using the Simple Standard for Sharing Ontological Mappings (SSSOM) in the sssom.tsv.gz file. This allows for getting Scopus, Web of Science, GitHub, Google Scholar, and other profiles for records that include them. This information is also available through the main records file. Authorship Links Authorships are extracted and standardized in the pubmeds.tsv.gz file, which contains an ORCID column and PubMed column that has been pre-sanitized to only contain local unique identifiers. This information is also available through the main records file. Lexical Indexes It includes two pre-built Gilda indexes for named entity recognition (NER) and named entity normalization (NEN). One contains all records, and the second is filtered to high-quality records. The following Python code snipped can be used for grounding: from gilda import Grounder url = "https://zenodo.org/records/11474470/files/gilda_hq.tsv.gz?download=1" grounder = Grounder(url) results = grounder.ground("Charles Tapley Hoyt") Ontology Artifacts The file orcid.ttl.gz is an OWL-ready RDF file that can be opened in Protégé or used with the Ontology Development Kit. It can also be converted into OWL XML, OWL Functional Notation, or other OWL formats using ROBOT. This artifact can serve as a replacement for the ones generated by https://github.com/cthoyt/orcidio, which was a smaller-scale way of turning ORCID records for contributors to OBO Foundry Ontologies into a small OWL file. Now, the export here contains all ORCID records with names. Reproduction It is automatically generated with code in https://github.com/cthoyt/orcid_downloader.官方开放研究者与贡献者身份识别码（ORCID）数据转储所采用的数据结构对于绝大多数应用场景而言过于复杂。本Zenodo记录提供了经过衍生处理的版本，其结构更为简洁、易于访问且体积更小。截至目前，该衍生版本涵盖雇主信息、教育经历、外部标识符以及与PubMed关联的出版物。 该版本额外借助研究组织注册表（Research Organization Registry, ROR）对雇主与教育机构进行实体接地处理，同时还进行了少量字符串标准化处理，例如统一教育类型的表述（如理学学士、理学硕士）以及规范化PubMed参考文献格式。 ## 记录文件 records.jsonl.gz 为JSON Lines格式文件，每一行对应一条采用简洁明了的预定义模式的ORCID记录（详见schema.json）。records_hq.jsonl.gz 则是完整记录文件的子集，仅包含至少拥有一个经ROR接地的雇主信息、至少一个经ROR接地的教育经历，或至少一篇被PubMed收录的出版物的记录。该子集的设计目的是剔除那些无法与任何外部信息进行关联的ORCID记录。 ## 版本更新内容 自上一版本以来的更新如下： 1. country字段更改为countries，且为列表类型； 2. 新增locale字段； 3. date字段改为字典格式，不再仅以单一整数表示年份。 本记录还附带了SQLite数据库文件orcid.db，其中包含研究者与机构相关的数据表，可用于基于ORCID本地唯一标识符快速检索数据。 ## 命名权威交叉引用 网站、社交链接及其他标识符均经过解析与标准化处理，以符合生物注册表（Bioregistry）规范，并通过sssom.tsv.gz文件按照共享本体映射简单标准（Simple Standard for Sharing Ontological Mappings, SSSOM）进行共享。此举可支持检索包含上述标识符的记录对应的Scopus、Web of Science、GitHub、Google Scholar等学术档案信息，该信息也可通过主记录文件获取。 ## 作者关联 作者身份信息已从数据中提取并标准化，存储于pubmeds.tsv.gz文件中，该文件包含ORCID与PubMed两列，且已预先清理为仅保留本地唯一标识符。该信息同样可通过主记录文件获取。 ## 词汇索引 该数据集包含两个预构建的Gilda词汇索引，分别用于命名实体识别（Named Entity Recognition, NER）与命名实体归一化（Named Entity Normalization, NEN）。其中一个索引覆盖全部记录，另一个则仅包含经过筛选的高质量记录。可使用以下Python代码片段进行实体接地： python from gilda import Grounder url = "https://zenodo.org/records/11474470/files/gilda_hq.tsv.gz?download=1" grounder = Grounder(url) results = grounder.ground("Charles Tapley Hoyt") ## 本体制品 orcid.ttl.gz 为兼容Web本体语言（Web Ontology Language, OWL）的资源描述框架（Resource Description Framework, RDF）文件，可在Protégé中打开，或与本体开发套件（Ontology Development Kit）配合使用。该文件可通过ROBOT工具转换为OWL XML、OWL Functional Notation或其他OWL格式。 该本体制品可替代此前由https://github.com/cthoyt/orcidio生成的制品，后者仅能将OBO Foundry（开放生物本体论坛）贡献者的ORCID记录转换为小型OWL文件。而本数据集的导出文件则包含所有带有姓名信息的ORCID记录。 ## 复现方法 本数据集可通过https://github.com/cthoyt/orcid_downloader中的代码自动生成。