We analyzed 68,658 Retraction Watch records to find the biggest patterns, surprising signals, and practical actions. For a large publisher like Elsevier, this is an early-warning dashboard for where trust, reputation, and legal risk can spike. Source data: retraction_watch.csv (Crossref + Retraction Watch).
Each row is one retraction with reasons, journal, country, and dates. That lets us study patterns. But we do not have total papers published, so we cannot calculate true retraction rates. For a publisher, this means you should use this to spot risk hotspots, then validate with your own submission and acceptance volumes.
Conservative interpretation: use this dataset to identify process failures, detection shifts, and concentrations of risk, not to claim misconduct “rates” by country, field, or publisher without publication-volume denominators.
Try the toggle: removing Hindawi and IEEE shrinks the biggest spikes a lot. So this is not just a steady collapse everywhere; it is big cleanup waves plus a rising background trend. For Elsevier-like portfolios, the implication is clear: one or two portfolio events can dominate annual risk reporting.
When retractions are concentrated in a small group, the system becomes fragile: one publisher shock can affect the whole research ecosystem. For a large publisher, this means concentration should be tracked like a risk KPI, not treated as random noise.
Recent notices use more reason tags and more flags like paper mills, fake peer review, and AI-generated content. This likely reflects both real behavior changes and better detection. For publishers, this means screening tools and editor training need regular upgrades, not one-time fixes.
Network shows top reason co-occurrence pairs; heavy links reveal repeated investigative templates.
The map shows total counts, not rates. The bubble chart compares 2018–2020 vs 2023–2025. Big jumps point to places where targeted action may help most. For publishers, this supports region-specific integrity support instead of the same policy everywhere.
Each bubble is a publisher: x = time to retract, y = share of industrial misconduct signals, size = number of retractions. Far right means slower detection. Higher up means stronger manipulation signals. For a publisher like Elsevier, this shows which journal groups need faster escalation and tighter pre-publication checks.
Opaque notice share % = the percent of a publisher's retraction records that use low-detail labels (for example: "Notice - Limited or No Information," "Date Unknown," or "Removed"). Higher values mean less transparent explanations.
Some years have very large journal-specific events. Use this view to pick focused audits instead of broad one-size-fits-all rules. For big publishers, this helps allocate audit budget to the few journals that drive most downside risk.
These are simple moves a publisher can start now. They are practical, high-impact, and designed to reduce retractions before they happen.
Before accepting a paper, run automatic checks for fake reviewers, copy-paste submissions, and strange citation patterns.
Why this matters: many problem papers look similar, so one shared check can block many bad submissions.
If one journal suddenly gets a wave of suspicious papers, pause new intake briefly and run an outside audit.
Simple trigger: sharp jump in peer-review red flags and complex misconduct reasons.
Use a standard reason list and always state who investigated. Clear notices reduce confusion and stop bad papers from being cited again.
Result: better accountability, better learning, and less repeat damage.
Robustness checks run: major-event exclusion (Hindawi+IEEE), alternative reason-family buckets, pre/post cohort deltas, concentration metrics (Top-1, Top-3, HHI), and lag quantiles instead of means.
Fallacy controls: no causal claims from correlation, avoid base-rate fallacy (missing denominator), watch Simpson effects when countries are multi-label, and separate behavior changes from detection changes.
Recommendation: pair this dataset with Crossref/Dimensions volume data to compute true retraction rates and confidence intervals by field/journal.
Context links are used to corroborate macro patterns; this deck’s quantitative claims come from the local `retraction_watch.csv` analysis.