KEY TAKEAWAYS

• Scientific image editing serves a vital role in clear communication, but seeking presentation clarity must not compromise data integrity.
• Combatting image manipulation requires systematic collaboration across the research ecosystem, including standardised guidelines and new verification technologies.

As concerns mount over image manipulation in scientific publishing, the research community has begun developing new strategies to balance visual clarity with data integrity. Writing in Nature, Sara Reardon explores the “fine line between clarifying and manipulating”, highlighting the challenge of making figures both accessible and faithful to original data.

The art and science of visual presentation

Scientific images often require editing for clarity, like adjusting brightness, adding scale bars, or enhancing contrast. While such modifications are essential for effective scientific communication, a 2021 study by Helena Jambor and colleagues revealed that poorly presented figures remain surprisingly common, suggesting researchers need better training in visual data presentation.

When enhancement becomes manipulation

The boundary between legitimate clarification and misconduct can be perilously thin. Science integrity consultant Elisabeth Bik warns that even minor edits – such as cloning image sections to cover dust particles – can undermine data credibility. Echoing a seminal 2004 article, Bik emphasises that “the images are the data”, meaning they should present the results actually observed rather than those the researchers expected. Any undisclosed alteration that changes the scientific message could constitute misconduct. As Reardon notes, the cardinal rule remains to “show your work” – enhancing clarity without obscuring underlying data.

“The boundary between legitimate clarification and misconduct can be perilously thin… the cardinal rule remains to ‘show your work’ – enhancing clarity without obscuring underlying data.”

Detection and prevention strategies

Phill Jones examines potential systemic solutions to what Bik calls science’s “nasty Photoshop problem” in The Scholarly Kitchen. Journals increasingly conduct pre-publication screening using image-integrity specialists or AI tools that have demonstrated substantial promise in identifying manipulated images. Guidelines such as those from the International Association of Scientific, Technical & Medical Publishers aim to standardise best practice, while individual journals are also establishing specific image integrity requirements. Beyond journals:

• Institutions are urged to provide training and embed image integrity expectations into research culture.
• Post-publication peer-review platforms also play a role in identifying problematic images after publication.

Looking ahead, technical innovations offer promise. Jones highlights developments such as encrypted hashes and digital ‘signatures’ embedded in images, akin to secure web certificates, that could enable reliable verification of image authenticity. Ongoing collaboration and systematic change across the research ecosystem will be required to ensure scientific images are both clear and credible.

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