The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Analysis of scientific paper retractions due to data problems: Revealing challenges and countermeasures in data management
Open accessAbstract
Background Scientific data, the cornerstone of scientific endeavors, face management challenges amid technological advances. While retractions are analyzed, a rigorous focus on data problems leading to them is missing.Methods This study collected 49,979 retraction records up to 17 December 2023. After screening 16,842 records were related to data problems and 19,656 were due to other reasons. Methods such as descriptive statistics, hypothesis testing, and the BERTopic (Bidirectional Encoder Representations from Transformers Topic Modelling) were applied to conduct a topic analysis of article titles.Result The results show that since 2000, retractions due to data problems have increased significantly (p < 0.001), with the percentage in 2023 exceeding 75%. Among 16,842 data-related retractions, 59.0% were in Basic Life Sciences and 40.2% in Health Sciences. Data problems involve accuracy, reliability, validity, and integrity. There are significant differences (p < 0.001) in subjects, journal quartiles, retraction intervals, and other characteristics between data-related and other retractions. Data-related retractions are more concentrated in high-impact journals (Q1 37.6% and Q2 43.0%).Conclusions Institutions, publishers, and journals should adopt image-screening tools, enforce data deposition, standardize retraction notices, provide ethics training, and strengthen peer review to address these data problems, guiding better data management and healthier scientific development.
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“In a study that collected almost 50,000 retractions in 2023, nearly 19–32% of those retracted articles were related to data handling and analysis.34,35”
From Artificial Intelligence in Detecting Statistical Errors: Implications for Authors, Reviewers, and Editors (2025) · cites this paper as [35]
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1 retracted
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New tools show promise for tackling paper mills
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Cited by
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Alnaimat, F., AlSamhori, A. R. F., El Sharu, H., Othman, L., Oralbek, A., & Zimba, O. (2025) · Journal of Korean Medical Science · doi:10.3346/jkms.2025.40.e342 · Cited by 4
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Metadata
- Authors
- Hu, W., Yan, G., Zhang, J., Chen, Z., Qian, Q., & Wu, S.
- Affiliations
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Corresponding author
- Qian, Qing
- Wu, Sizhu
- Venue
- Accountability in Research
- Publisher
- Informa UK Limited
- Published
- 2025-07-20
- Volume
- 33
- Issue
- 4
- Pages
- 2531987-2531987
- ISSN
- 0898-9621 (ISSN-L)
- 1545-5815
- OpenAlex
- W4412507053
- Cited by
- 7
- References
- 34
- Citations by year
- 2026: 6 · 2025: 1
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Accountability in Research (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science and Technology Major Project for the Prevention and Treatment of Cancer, Cardiovascular, Respiratory, and Metabolic Diseases (2023ZD0509702)
- Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences Program (2021-I2M-1-057)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- data challenges, research data management, data integrity, data accuracy, data reliability, data validity, peer review, research ethics training, data deposition
- MeSH terms
- Data Management, Humans, Retraction of Publication as Topic, Reproducibility of Results, Scientific Misconduct
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