How good are medical students and researchers in detecting duplications in digital images from research articles: a cross-sectional survey
Abstract Background Inappropriate manipulations of digital images pose significant risks to research integrity. Here we assessed the capability of students and researchers to detect image duplications in biomedical images. Methods We conducted a pen-and-paper survey involving medical students who ha...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-08-01
|
| Series: | Research Integrity and Peer Review |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41073-025-00172-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849389510099468288 |
|---|---|
| author | Antonija Mijatović Marija Franka Žuljević Luka Ursić Nensi Bralić Miro Vuković Marija Roguljić Ana Marušić |
| author_facet | Antonija Mijatović Marija Franka Žuljević Luka Ursić Nensi Bralić Miro Vuković Marija Roguljić Ana Marušić |
| author_sort | Antonija Mijatović |
| collection | DOAJ |
| description | Abstract Background Inappropriate manipulations of digital images pose significant risks to research integrity. Here we assessed the capability of students and researchers to detect image duplications in biomedical images. Methods We conducted a pen-and-paper survey involving medical students who had been exposed to research paper images during their studies, as well as active researchers. We asked them to identify duplications in images of Western blots, cell cultures, and histological sections and evaluated their performance based on the number of correctly and incorrectly detected duplications. Results A total of 831 students and 26 researchers completed the survey during 2023/2024 academic year. Out of 34 duplications of 21 unique image parts, the students correctly identified a median of 10 duplications (interquartile range [IQR] = 8–13), and made 2 mistakes (IQR = 1–4), whereas the researchers identified a median of 11 duplications (IQR = 8–14) and made 1 mistake (IQR = 1–3). There were no significant differences between the two groups in either the number of correctly detected duplications (p = .271, Cliff’s δ = 0.126) or the number of mistakes (p = .731, Cliff’s δ = 0.039). Both students and researchers identified higer percentage of duplications in the Western blot images than cell or tissue images (p < .005 and Cohen’s d = 0.72; p < .005 and Cohen’s d = 1.01, respectively). For students, gender was a weak predictor of performance, with female participants finding slightly more duplications (p < .005, Cliff's δ = 0.158), but making more mistakes (p < .005, Cliff's δ = 0.239). The study year had no significant impact on student performance (p = .209; Cliff's δ = 0.085). Conclusions Despite differences in expertise, both students and researchers demonstrated limited proficiency in detecting duplications in digital images. Digital image manipulation may be better detected by automated screening tools, and researchers should have clear guidance on how to prepare digital images in scientific publications. |
| format | Article |
| id | doaj-art-32546cb251ba4e14a2988ad64323930a |
| institution | Kabale University |
| issn | 2058-8615 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Research Integrity and Peer Review |
| spelling | doaj-art-32546cb251ba4e14a2988ad64323930a2025-08-20T03:41:57ZengBMCResearch Integrity and Peer Review2058-86152025-08-011011710.1186/s41073-025-00172-0How good are medical students and researchers in detecting duplications in digital images from research articles: a cross-sectional surveyAntonija Mijatović0Marija Franka Žuljević1Luka Ursić2Nensi Bralić3Miro Vuković4Marija Roguljić5Ana Marušić6Department of Research in Biomedicine and Health, Center for Evidence-Based Medicine, School of Medicine, University of SplitDepartment of Medical Humanities, Center for Evidence-Based Medicine, School of Medicine, University of SplitDepartment of Research in Biomedicine and Health, Center for Evidence-Based Medicine, School of Medicine, University of SplitDepartment of Research in Biomedicine and Health, Center for Evidence-Based Medicine, School of Medicine, University of SplitDepartment of Research in Biomedicine and Health, Center for Evidence-Based Medicine, School of Medicine, University of SplitDepartment of Periodontology, Study of Dental Medicine, School of Medicine, University of SplitDepartment of Research in Biomedicine and Health, Center for Evidence-Based Medicine, School of Medicine, University of SplitAbstract Background Inappropriate manipulations of digital images pose significant risks to research integrity. Here we assessed the capability of students and researchers to detect image duplications in biomedical images. Methods We conducted a pen-and-paper survey involving medical students who had been exposed to research paper images during their studies, as well as active researchers. We asked them to identify duplications in images of Western blots, cell cultures, and histological sections and evaluated their performance based on the number of correctly and incorrectly detected duplications. Results A total of 831 students and 26 researchers completed the survey during 2023/2024 academic year. Out of 34 duplications of 21 unique image parts, the students correctly identified a median of 10 duplications (interquartile range [IQR] = 8–13), and made 2 mistakes (IQR = 1–4), whereas the researchers identified a median of 11 duplications (IQR = 8–14) and made 1 mistake (IQR = 1–3). There were no significant differences between the two groups in either the number of correctly detected duplications (p = .271, Cliff’s δ = 0.126) or the number of mistakes (p = .731, Cliff’s δ = 0.039). Both students and researchers identified higer percentage of duplications in the Western blot images than cell or tissue images (p < .005 and Cohen’s d = 0.72; p < .005 and Cohen’s d = 1.01, respectively). For students, gender was a weak predictor of performance, with female participants finding slightly more duplications (p < .005, Cliff's δ = 0.158), but making more mistakes (p < .005, Cliff's δ = 0.239). The study year had no significant impact on student performance (p = .209; Cliff's δ = 0.085). Conclusions Despite differences in expertise, both students and researchers demonstrated limited proficiency in detecting duplications in digital images. Digital image manipulation may be better detected by automated screening tools, and researchers should have clear guidance on how to prepare digital images in scientific publications.https://doi.org/10.1186/s41073-025-00172-0Image manipulationImage duplicationsMedical educationCross-sectional survey |
| spellingShingle | Antonija Mijatović Marija Franka Žuljević Luka Ursić Nensi Bralić Miro Vuković Marija Roguljić Ana Marušić How good are medical students and researchers in detecting duplications in digital images from research articles: a cross-sectional survey Research Integrity and Peer Review Image manipulation Image duplications Medical education Cross-sectional survey |
| title | How good are medical students and researchers in detecting duplications in digital images from research articles: a cross-sectional survey |
| title_full | How good are medical students and researchers in detecting duplications in digital images from research articles: a cross-sectional survey |
| title_fullStr | How good are medical students and researchers in detecting duplications in digital images from research articles: a cross-sectional survey |
| title_full_unstemmed | How good are medical students and researchers in detecting duplications in digital images from research articles: a cross-sectional survey |
| title_short | How good are medical students and researchers in detecting duplications in digital images from research articles: a cross-sectional survey |
| title_sort | how good are medical students and researchers in detecting duplications in digital images from research articles a cross sectional survey |
| topic | Image manipulation Image duplications Medical education Cross-sectional survey |
| url | https://doi.org/10.1186/s41073-025-00172-0 |
| work_keys_str_mv | AT antonijamijatovic howgoodaremedicalstudentsandresearchersindetectingduplicationsindigitalimagesfromresearcharticlesacrosssectionalsurvey AT marijafrankazuljevic howgoodaremedicalstudentsandresearchersindetectingduplicationsindigitalimagesfromresearcharticlesacrosssectionalsurvey AT lukaursic howgoodaremedicalstudentsandresearchersindetectingduplicationsindigitalimagesfromresearcharticlesacrosssectionalsurvey AT nensibralic howgoodaremedicalstudentsandresearchersindetectingduplicationsindigitalimagesfromresearcharticlesacrosssectionalsurvey AT mirovukovic howgoodaremedicalstudentsandresearchersindetectingduplicationsindigitalimagesfromresearcharticlesacrosssectionalsurvey AT marijaroguljic howgoodaremedicalstudentsandresearchersindetectingduplicationsindigitalimagesfromresearcharticlesacrosssectionalsurvey AT anamarusic howgoodaremedicalstudentsandresearchersindetectingduplicationsindigitalimagesfromresearcharticlesacrosssectionalsurvey |