Replication data for: Examining the OpenAlex Concepts: A Detailed Case Study of Machine-Derived Classification
收藏DataCite Commons2025-11-20 更新2026-02-08 收录
下载链接：
https://borealisdata.ca/citation?persistentId=doi:10.5683/SP3/29QGMP
下载链接
链接失效反馈官方服务：
资源简介：
Machine-learning techniques are becoming increasingly popular in metadata and clas-
sification work due to their ability to operate at scale, but insufficient consideration
has been given to how effective such techniques truly are against traditional prac-
tice. This dataset was generated as part of a thesis project to analyze the machine-generated OpenAlex concept hierarchy and its associated machine-learning model in comparison to established classification standards and practices. It consists of several files in CSV and JSON-L format that elucidate structural relationships within the OpenAlex concepts hierarchy. It was generated using, and is limited to, the October 2023 OpenAlex snaphot.
提供机构：
Borealis
创建时间：
2024-12-13
