Journal Submission Numbers Correlated with Impact Factor and Retractions

Ten years of submission data from over a thousand journals was analyzed by Thomas Gaston of John Wiley & Sons. The data includes journal Impact Factor and retraction information. Statistical analysis identified correlations between changes in these factors and subsequent submission numbers.

Use Cases

• Modeling journal submission trends based on changes in Impact Factor.
• Analyzing the impact of published retractions on a journal's submission volume.
• Investigating the role of peer review reputation on researcher submission behavior.

Strengths

• Data spans ten years, providing longitudinal insight.
• Analysis covers over a thousand journals, suggesting a broad sample.
• Statistical significance was reported for key correlations.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

John Wiley & Sons (United States)

Collection Method

Retrieved from journal submission records and public metrics.

Time Range

Ten years (specific dates unknown)