Protect your science

–at any stage

Stop integrity threats before it is too late.

As mentioned in

“Publishers launch joint effort to tackle altered images in research papers”
“State and university partner to award over $1.5 million in funding to University of Colorado innovations”
“Researchers have finally created a tool to spot duplicated images across thousands of papers”
“Giant study finds untrustworthy trials pollute gold-standard medical reviews”

Features

Image duplication

Duplication detection in any image, within the same image or manuscript—or across science. Create your private library of images to compare against.

Statistical inconsistencies

Detect statistical inconsistencies in reporting. Up to 43 % more detectable cases than Statcheck

Missing citations

Identify claims that need a citation or citations that should not be there.

Coming soon

Figure accessibility

Advanced figure accessibility issues detection: low contrast, color-blind violations, and readability issues.

Coming soon

Manipulation and Beautification

Detect inappropriate image beautification and manipulation in western blots and microscopy.

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Research

Better science–
backed by science

Explore the research behind our technology

Figure Reuse

Bioscience-scale automated detection of figure element reuse

This study presents an algorithm that detects figure element reuse in a large dataset of 760 thousand open access articles and 2 million figures, robust to various transformations. A panel review estimates that 0.6% of all articles contain fraudulent reuses, with inappropriate reuses occurring 43% across articles, 28% within an article, and 29% within a figure.

Missing Citation

Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models

This study develops an automated system using advanced neural networks to pinpoint sentences needing citations. It enhances document quality by identifying citation errors using a comprehensive dataset.

Figure Accessibility

A computational analysis of accessibility, readability, and explainability of figures in open access publications

This study develops computational techniques to assess the accessibility, readability, and explainability of figures in scientific publications. Our analysis reveals that approximately 20.6% of open access publications have issues related to these aspects, with detailed statistics provided for specific problems.

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