Crossref Metadata Quality Sample for Cultural Analysis, March 2022

A purposefully constructed sample of 68 DOI prefixes from the Crossref API, drawn on March 8, 2022. Records were selected based on a hypothesis that those with known quality issues would be more likely to reveal problems related to cultural meanings and identity. This dataset was created by Julie Shi of the University of Toronto and Crossref staff.

Use Cases

• Identify patterns of metadata errors based on the purposeful sampling of records with known issues.
• Analyze potential cultural bias in scholarly metadata across 51 general and 17 Open Journal Systems-specific DOI prefixes.
• Benchmark metadata quality for journals using the Open Journal Systems platform, noted for its international and multilingual reach.

Strengths

• Sample is purposefully constructed to test a specific hypothesis about metadata quality and cultural identity.
• Includes records from 68 distinct DOI prefixes, covering both general publishers and the Open Journal Systems platform.
• Created through collaboration between a university researcher (University of Toronto) and Crossref staff.

Limitations

• Row count, column names, and sample data are unknown, which limits suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Crossref API

Collection Method

Purposeful and random sampling of records with at least one known quality issue, plus random selection from the same prefixes.

Time Range

Sample drawn on March 8, 2022. Temporal coverage of the underlying records is unspecified.