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Data · dataset · 2025

Replication data for: Examining the OpenAlex Concepts: A Detailed Case Study of Machine-Derived Classification

Listed in Borealis

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.

Description

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.

Links

Where it is published

Catalogue records · 1

Topics

Inferred from text
Data management and data science 71%
Provenance · 1 source records, 8 field assertions
SourceKeyLast seenRaw
Borealisdoi:10.5683/SP3/29QGMP8 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[field].anzsrc:group:4605enrichment · borealisdata cataxonomy-embedding@1.1.0title+keywords+description (71%)
concepts[field].dataverse_subject:computer-and-information-sciencesource · borealisdata caconnector:borealisdata_ca@1.0.0/subjects
created_datesource · borealisdata caconnector:borealisdata_ca@1.0.0
descriptionsource · borealisdata caconnector:borealisdata_ca@1.0.0/description
publication_datesource · borealisdata caconnector:borealisdata_ca@1.0.0
titlesource · borealisdata caconnector:borealisdata_ca@1.0.0/name
updated_datesource · borealisdata caconnector:borealisdata_ca@1.0.0
version_labelsource · borealisdata caconnector:borealisdata_ca@1.0.0