Retracted Paper Records
Database of retracted papers with reasons — training data for scientific integrity detection AI.
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What Is Retracted Paper Records?
Retracted Paper Records is a specialized database cataloging scientific papers that have been withdrawn from publication due to integrity violations, methodological flaws, or ethical concerns. This dataset documents the reasons behind retractions, creating a comprehensive resource for training artificial intelligence systems designed to detect scientific integrity issues before publication. The database captures trends in retraction causes, affected research domains, and author patterns, serving as critical training material for developing AI models that can identify problematic research characteristics and prevent future publication of flawed work. With retraction numbers reaching record levels in recent years, this data has become increasingly valuable for institutions, publishers, and research integrity organizations seeking to understand and combat systemic issues in academic publishing.
Market Data
Over 14,000
Retraction Notices Issued (2023)
Source: Medium
More than 9,000
Additional Retractions (2024)
Source: Medium
Over 5,000
Retractions by August 2025
Source: Medium
236 papers (Joachim Boldt)
Highest Individual Retraction Count
Source: Retraction Watch
ChatGPT and other AI chatbots documented citing retracted papers
AI Models Using Retracted Material
Source: MIT Technology Review
Who Uses This Data
What AI models do with it.do with it.
AI Research Integrity Detection Systems
Training datasets for machine learning models that identify hallmarks of retracted or compromised research, enabling automated pre-publication screening and early detection of methodological or ethical red flags.
Academic Publishers and Journals
Editorial teams and peer review platforms leverage retraction databases to recognize patterns associated with problematic submissions, improving manuscript evaluation processes and reducing publication of flawed research.
AI Developers and Large Language Models
Critical for improving the quality of training data used in chatbots and search tools, helping prevent AI systems from propagating misinformation derived from withdrawn scientific papers.
Research Institutions and Compliance Officers
Universities and research centers use retraction data to monitor researcher conduct, establish integrity benchmarks, and implement preventive measures across their scientific communities.
Pricing depends on the proposed terms
We do not have a verified, comparable price for your dataset. Consider the permitted uses, license term, exclusivity, provenance, coverage and quality. A seller asking price is a proposal, not an appraisal or guaranteed sale. Compare prices only when the source, date, currency, unit and license scope are known.
What Buyers Expect
What makes it valuable.valuable.
Complete Retraction Metadata
Comprehensive documentation including original publication details, retraction date, official reason for withdrawal, and link to retraction notice for verification and traceability.
Standardized Categorization
Consistent classification of retraction causes (fraud, data fabrication, methodological flaws, duplicate publication, ethical violations) to enable effective machine learning feature engineering.
Author and Institutional Context
Detailed information on affiliated authors, institutions, and funding sources to identify patterns and systemic issues in research integrity across organizations and geographies.
Historical Trend Data
Temporal analysis showing retraction patterns by year, discipline, and journal to help AI systems understand evolving integrity challenges and emerging risk factors in academic publishing.
Topic and Field Segmentation
Classification by research domain, methodology type, and subject area to ensure training datasets are relevant to specific AI applications in targeted scientific fields.
Potential applications and organizations
Who's buying.buying.
Training data for improving AI chatbot accuracy and detecting when models cite retracted research; critical for reducing misinformation in AI-generated scientific responses.
Editorial screening systems and peer review support tools that use retraction patterns to flag suspicious submissions and improve manuscript quality gates.
Institutional compliance monitoring and researcher conduct evaluation, using retraction databases to assess integrity risks and implement preventive safeguards.
Enhancing search relevance algorithms to deprioritize or flag retracted papers, ensuring users access reliable research in query results.
FAQ
Common questions.questions.
Why is retracted paper data valuable for AI training?
Retracted papers represent concrete examples of research integrity failures with documented reasons. This data teaches AI systems to recognize patterns—methodological red flags, statistical anomalies, ethical violations—that characterize flawed research, enabling automated detection of similar issues in new manuscripts before publication.
What are the main reasons papers get retracted?
Common retraction causes include data fabrication, methodological errors, duplicate publication, plagiarism, and ethical violations. The database documents these categories systematically, allowing buyers to train models on specific failure modes relevant to their domain.
How current is this data?
Retraction rates are accelerating—2024 saw over 9,000 new retractions, and 2025 exceeded 5,000 by August alone. High-quality datasets should offer real-time or frequently updated feeds to capture emerging integrity trends, particularly in fast-moving AI research.
Can this data prevent AI systems from citing retracted papers?
Yes. Retracted paper datasets allow developers to identify withdrawn articles in their training corpora and implement filtering mechanisms. This prevents AI chatbots from generating responses based on compromised research, directly addressing documented problems where ChatGPT and similar tools propagated misinformation from retracted sources.
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