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Harvard cancer centre retracts 6 studies, correct 31 others over fraud claims

A cancer centre and research institute affiliated to Harvard University had to retract six studies and correct 31 others as part of ongoing investigations into fraud allegations.

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The Dana Farber Cancer Institute.The Dana Farber Cancer Institute. (Wikimedia Commons)

The Dana-Farber Cancer Institute in Boston, which is a clinical affiliate and research institute of the Harvard Medical Center, is retracting six studies and correcting another 31 after allegations of data manipulation.

Molecular biologist Sholto David published a blog post in For Better Science, alleging that researchers falsified data by photoshopping (manipulating images). More than 50 papers are under review by Dana-Farber as part of its investigation into four researchers, according to CBS News. All four researchers have faculty appointments at Harvard Medical School. Four out of the 50 papers were authored by the institute’s CEO Laurie Glimcher.

“We are committed to a culture of accountability and integrity. Therefore, every inquiry is examined fully to ensure the soundness of the scientific literature Dana-Farber has been swift and decisive in this regard.” said Barrett Rollins, Dana-Farber’s research integrity officer and chief science officer emeritus, to CNN.

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The new allegations come as the university is already facing flak for the way it handled plagiarism allegations against former director Claudine Gay, who resigned over backlash regarding the same.

David’s blog post where he made the allegations had images like protein bands, data pilots and PCR results from papers that he claims were manipulated using photoshop or even simple copy and paste. In one paper, image of mice on the first day of a study seemed to make an appearance again on the 16th day, according to Smithsonian Magazine. Some images seemed to be flipped, stretched and resized.

In conclusion: A swathe of research coming out of DFCI authored by the most senior researchers and managers appears to be hopelessly corrupt with errors that are obvious from just a cursory reading the papers. Imagine what mistakes might be found in the raw data if anyone was allowed to look!,” wrote David in the blog.

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