Is OpenAlex Suitable for Research Quality Evaluation and Which Citation Indicator is Best?

2025-02-25

AI Summary

Assessing the suitability of OpenAlex for research quality evaluation and identifying optimal citation-based metrics. Method: Leveraging a citation network of 28.6 million publications, we comparatively evaluated OpenAlex against Scopus in data quality and rigorously tested raw citation counts, normalized citation scores (NCS), and log-normalized citation scores (NLCS) using two gold-standard benchmarks—REF2021 expert assessments and ChatGPT-based evaluations. Contribution/Results: OpenAlex outperforms Scopus in citation coverage and accuracy; its broad yet fine-grained disciplinary classification substantially enhances metric validity. Raw citation counts achieve superior predictive performance in single-year evaluations, challenging the assumed necessity of field normalization. NCS significantly outperforms NLCS, while inter-field normalization efficacy varies markedly across disciplines. These findings establish an empirical benchmark and methodological guidance for leveraging open citation databases—particularly OpenAlex—in research assessment.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Search and Optimization: Evaluation and Analysis
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

This article compares (1) citation analysis with OpenAlex and Scopus, testing their citation counts, document type/coverage and subject classifications and (2) three citation-based indicators: raw counts, (field and year) Normalised Citation Scores (NCS) and Normalised Log-transformed Citation Scores (NLCS). Methods (1&2): The indicators calculated from 28.6 million articles were compared through 8,704 correlations on two gold standards for 97,816 UK Research Excellence Framework (REF) 2021 articles. The primary gold standard is ChatGPT scores, and the secondary is the average REF2021 expert review score for the department submitting the article. Results: (1) OpenAlex provides better citation counts than Scopus and its inclusive document classification/scope does not seem to cause substantial field normalisation problems. The broadest OpenAlex classification scheme provides the best indicators. (2) Counterintuitively, raw citation counts are at least as good as nearly all field normalised indicators, and better for single years, and NCS is better than NLCS. (1&2) There are substantial field differences. Thus, (1) OpenAlex is suitable for citation analysis in most fields and (2) the major citation-based indicators seem to work counterintuitively compared to quality judgements. Field normalisation seems ineffective because more cited fields tend to produce higher quality work, affecting interdisciplinary research or within-field topic differences.

Problem

• Evaluate OpenAlex for research quality
• Compare citation indicators effectiveness
• Assess field normalization impact

Innovation

• OpenAlex citation analysis
• Normalised Citation Scores
• raw citation counts evaluation