Data & Integrity (Introduction)

One of the principles of modern scholarship is the ability to build on past research results. This is true for the humanities as well as for the social sciences and especially the natural sciences. Fake or unreliable data undermines any new work that tries to build on it. The cases in this section all involve some form of false data that reached public notice, but not all were intentional fraud. Contamination of samples and disagreements about the interpretation of data played a role in some. More problematic are other cases where process-falsification undermined the credibility of the data, such as fake peer reviews or falsified consent forms. There are also cases where notable researchers just grew weary of the work involved in creating real data and knew enough about how people in the research world thought to reverse engineer the process in order to create data that reflected the desired results.


It is hard to say how much false data is part of the current scholarly record. Replication studies are not popular with journal editors or scholars, and many experiments go untested. Scholarly data are too infrequently made available for re-analysis, and recreating the data themselves is expensive and time-consuming. Some attempts have been made to create fraud detection tools, but the tools tend to be discipline-specific and often rely on statistical expectations whose fit to a particular case must always be examined.


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