Wednesday, 19 March 2025

MDPI Self-citations study highlights industry alignment and integrity

By Dr Giulia Stefenelli, Scientific Communications Lead, MDPI and Dr Enric Sayas, Business Analyst, MDPI– Silver Sponsor at University Press REDUX Conference 2025

 

The context of self-citation scrutiny

Self-citations are a key topic in academic publishing. While self-citations can naturally reflect a journal’s specialization and research depth, concerns arise when they artificially inflate metrics like the Impact Factor (IF) and CiteScore, potentially distorting a journal’s perceived value.

This topic garnered significant attention following the release of Clarivate’s 2024 Journal Citation Report (JCR), which included the suppression of 17 journals from a range of publishers for excessive self-citation patterns (see, Clarivate Suppression List 2023). This prompted MDPI to analyze the citation patterns of its 237 JCR-indexed journals, ensuring alignment with industry norms and reinforcing our commitment to transparency, improvement, and integrity.

Methodology

As part of this assessment, we compared MDPI journals to others found in the same JCR category. Using data from Web of Science (WoS), which aligns with the data impacting JCR scores and is independently accessible, enabled an accurate and open comparison with other publishers’ datasets.

The original data and python scripts used can be found here: 10.5281/zenodo.14844342

Key Findings: Alignment with industry norms

Findings reveal MDPI’s citation practices are sound, consistent, and comparable to prevailing industry trends and established norms.

The evaluation includes 237 MDPI journals indexed in the JCR and analyzed their Impact Factor (IF) both with and without self-citations for the latest available year, 2024. Self-citations rates (SCRs) were also examined relative to category averages and assess how self-citations influence their overall ranking within their respective categories.

Findings show some variability in self-citation. Overall, however, MDPI journals align to industry norms and the limits established by WoS. MDPI has very few journals with high SCRs in their category compared to other leading publishers.

In 2024, MDPI’s average SCR was 14.85%, positioning the publisher 6th among the top ten publishers by SCR and 4th by publication volume (Figure 1).

 

SCRs across MDPI’s journals were calculated individually and compared to the average rates within their respective JCR categories. For journals indexed in multiple categories, the first category was selected.

Beyond Citations: Emphasizing broader research impact

We also examined the overall distribution of SCRs across all JCR journals and compared them to MDPI journals. Figure 2 shows SCRs are not normally distributed but exhibit a right-skewed distribution. This indicates that while most journals have relatively low SCRs, some exhibit significantly higher rates.

 

Based on this distribution, we analysed SCRs using percentile rankings, defining outlier journals as those exceeding the 95th percentile (top 5%). The median self-citation rate of all JCR journals was 0.12 while the median for MDPI-only journals was 0.14. This difference was shown to be not significative using a Wilcoxon rank-sum test (P-value 0.33374); there is no statistically significant difference in SCRs between MDPI journals and all journals in the dataset. Any observed difference in medians is due to random variation rather than a true difference in behaviour.

A similar approach can be applied within each JCR category, identifying outliers based solely on SCRs within their respective categories.

This was performed by grouping journals by their primary JCR category and generating SCR distributions within each. This allows for identifying outliers at the category level based on SCRs within the same field.

Per Figure 3, outlier SCRs vary widely, from 20% to nearly 100%. For MDPI journals, however, the range is more contained, between 20% and 40%. Notably, some MDPI journals with relatively high SCRs are not classified as outliers, suggesting they belong to JCR categories with inherently high SCRs. This highlights the need for category-specific assessments rather than a universal threshold.

Overall, we found that among the top 10 publishers (excluding MDPI), 4.7% of journals were classified as outliers based on their SCRs, whereas for MDPI, this figure was 2.3%.

Minimal impact on Rankings

We also examined whether excluding self-citations from the IF calculation significantly affects journal rankings within their categories. To do this, we ranked all the journals per category based on their IF both with and without self-citations, then observed the changes in ranking and quartile distribution. The analysis revealed that any category ranking changes were modest, with shifts averaging 7% based on 2024 data.

Key findings from the 237 MDPI journals analyzed reveal:

  • 105 journals (44%) saw a ranking change between 10% and -10%, when self-citations were excluded.
  • 74 journals saw a ranking decrease, and 49 journals saw a ranking increase when not counting self-citations.
  • Six journals had a ranking decrease higher than 20%, while eight journals had a ranking increase higher than 20%.

The analysis also looked at whether any MDPI journals experienced changes in quartile rankings due to self-citations. As Table 2 shows, 14 journals decreased quartile when self-citations were removed: five journals from Q1 to Q2 and nine journals from Q2 to Q3. However, we also observed seven journals that increased their quartile when self-citations were removed: one journal from Q4 to Q3, three journals from Q3 to Q2, and three journals from Q2 to Q1.  Removing self-citations from MDPI journal IFs left 91% of journals in their original quartile. While some moved out of Q1, others moved in, with 97% of MDPI’s indexed portfolio unaffected by these shifts.

The results reinforce that self-citations have minimal influence on MDPI journal rankings, emphasizing the integrity of editorial practices and the absence of any deliberate efforts to manipulate impact.

Reinforcing transparency and integrity

As part of our continued commitment to integrity, transparency and protecting the scientific record, we welcome further scrutiny of the data used. We believe research impact goes beyond metrics to advancing knowledge, addressing societal challenges, and creating meaningful academic conversations. Broader indicators, such as research quality, societal relevance, and overall scholarly influence are equally important.

We invite our authors, editors, and readers to explore this analysis further and engage with us in conversations about responsible publishing. Together, we can continue to uphold the high standards of scholarly communication.


About MDPI

Headquartered in Switzerland, MDPI is an Open Access publisher with a portfolio of more than 440 journals across all scientific disciplines. To date, MDPI has published the works of over 3.7 million researchers, collaborating with an extensive network of academic institutions and scientific societies worldwide. Above all, MDPI is committed to ensuring that high-quality research is freely accessible to readers across the globe.

About the Authors

Dr. Giulia Stefenelli, Scientific Communications Lead, leverages her expertise in science communication, editorial processes, and Open Access (OA) promotion to address both institutional and country-specific challenges. She collaborates with teams across PR, Editorial Procedures, Institutional Partnerships, Research Integrity, AI and more, developing strategies to engage stakeholders, including policymakers, academics, and industry leaders. Her responsibilities include enhancing MDPI’s scientific network and promoting OA initiatives at events worldwide supporting different departments efforts, and ensuring effective communication to both specialist and broader audiences.



Dr. Enric Sayas is a Business Analyst specializing in the integration of AI and Machine Learning in scientific publishing. Within MDPI’s AI team, he combines his editorial expertise with his passion for AI to support the development of AI-driven solutions tailored to editorial needs, enhancing efficiency and decision-making. His interests extend to data science methodologies applied to scientific publishing, as well as the broader impact of AI on open science, peer review, and the future of scientific publishing.



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