FASCIFFT

The FASCIFFT project aims to address the growing problem of fake scientific publications (FSPs), which undermine the integrity of research and erode public trust in science. By quantifying the prevalence of FSPs, developing automated detection methods, cleaning the scientific record, building a global network, and educating the scientific community, FASCIFFT will contribute to preserving scientific integrity and promoting responsible research practices.

Approach

The project will employ a multidisciplinary approach, combining expertise in computer science, linguistics, and medical research. Key steps include literature review, data collection, feature engineering, machine learning, network analysis, and outreach and collaboration.

Project Goals

The expected outcomes of the project include a comprehensive understanding of the FSP problem, automated detection tools, a global network, recommendations for improving the peer review process, and educational resources. By addressing the FSP problem, FASCIFFT will enhance the credibility and reliability of scientific research and foster a more trustworthy and ethical scientific ecosystem.

Team and Contact

Prof. Dr.-Ing. Andreas Nürnberger                      andreas.nuernberger@ovgu.de

Prof. Dr. Bernhard Sabel                                           bernhard.sabel@ovgu.de

Ahmar Kamal Hussain, M.Sc.                                  ahmar.hussain@ovgu.de

Marcus Thiel, M.Sc.                                                      marcus.thiel@ovgu.de

Funding

The project is funded by the Ministry of Science, Energy, Climate Protection and Environment of the State of Saxony-Anhalt.

Publications and Talks

1. Sabel, BA, Seifert, R. (2021) How criminal science publishing gangs damage the genesis of knowledge and technology – a call to action to restore trust. Naunyn-Schmiedeberg's Archives of Pharmacology, 394(11):2147-2151. link
2. Sabel, B.A.; Seifert, R. Global attack on the integrity of science research & teaching, German Association of University Professors and Lecturers / Bonn, pp. 918-920, 2021/28/11. (Global attack on the integrity of science research & teaching) link
3. Sabel Bernhard A, et al. “Fake Publications in Biomedical Science: Red-Flagging Method Indicates Mass Production.” Naunyn-Schmiedeberg S Archives of Pharmacology, 24 Sept. 2025. link
4. Sabel, B. (2024). Fake Mafia in Science. German Monograph. Kohlhammer Publishing House, 250 pages. link
5. Hussain AK, Thiel M, Sabel BA, Nürnberger A (2024). Fake Papers in Science: Paper Mills, Characteristics and Detection Strategies.  20th European Computer Science Summit  , Malta. link [ Poster ]
6. Sabel B. A. , Hussain A. K. , Larhammar D. (2025). Permanent Scientific Record at Risk by a Flood of Fake Publications Using AI. 11Th International Conference on Ethics and Integrity in Academia 2025.
7. Hussain A. K., Sabel B. A. , Marcus Thiel, Andreas Nürnberger (2025). Automated Detection of Fake Biomedical Papers: A Machine Learning Perspective. In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 1, pages 662-670. link
8. Sabel, B., & Larhammar, D. (2025). Reformation of science publishing: the Stockholm Declaration. Royal Society Open Science, 12(11). link
9. Thiel, M. & Hussain, A.K. & Sabel, B. & Nürnberger, A. (2026). Using Large Language Models for the Automated Detection of Fake Biomedical Papers. Lecture Notes in Business Information Processing, No. 16, Springer, in press.