SCOPE

Data fraud is extensionally characterized by three fields: Data plagiarism, manipulation and fabrication.
While data plagiarism suppresses referring to the source or provenance of data used by the deceiver, data manipulation takes existing data and manipulates the content encapsulated in tables, diagrams or (historical) pictures. Mostly numbers of all data types are manipulated.
Finally, data fabrication generates more or less artificial data in a brute force way avoiding expensive data recording, time-consuming observations or running statistically well planned experiments.
There is no omnibus test available to detect data fraud of all kind. However, a bundle of techniques like substring and metadata matching, probability distribution dependent methods, Benford’s Law, inliers and outlier as well as tests of conformity between data and models exist to give hints to data fraud betrayers.
Famous historical cases and a bundle of useful tests are presented. Furthermore, there is some hope and a perspective that independent authorities like Research Gate and others will help to increase transparency of observational or experimental data at least in science. No doubt, in this domain we urgently need a “Data Deposit” for empirical studies to check repeatability and reproducibility.

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