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How can we protect the integrity of science?

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The fabrication of scientific data has 
far-reaching negative consequences.

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In our work, we organize common patterns
found across 10 manipulated tabular datasets,

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and grouped them into common themes.

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We call these artifacts of manipulation.

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We also developed a visualization tool 
that makes these artifacts salient,

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while providing guidance to 
help prevent misinterpretation.