How can we protect the integrity of science?
The fabrication of scientific data has far-reaching negative consequences.
In our work, we organize common patterns found across 10 manipulated tabular datasets,
and grouped them into common themes.
We call these artifacts of manipulation.
We also developed a visualization tool that makes these artifacts salient,
while providing guidance to help prevent misinterpretation.