This article draws on research traditions and insights from Criminology to elaborate on the problems associated with current practices of measuring scientific misconduct. Analyses of the number of retracted articles are shown to suffer from the fact that the distinct processes of misconduct, detection, punishment, and publication of a retraction notice, all contribute to the number of retractions and, hence, will result in biased estimates. Self-report measures, as well as analyses of retractions, are additionally affected by the absence of a consistent definition of misconduct. This problem of definition is addressed further as stemming from a lack of generally valid definitions both on the level of measuring misconduct and on the level of scientific practice itself. Because science is an innovative and ever-changing endeavor, the meaning of misbehavior is permanently shifting and frequently readdressed and renegotiated within the scientific community. Quantitative approaches (i.e., statistics) alone, thus, are hardly able to accurately portray this dynamic phenomenon. It is argued that more research on the different processes and definitions associated with misconduct and its detection and sanctions is needed. The existing quantitative approaches need to be supported by qualitative research better suited to address and uncover processes of negotiation and definition. Full article

Repeated failures to replicate reported experimental results could indicate scientific misconduct or simply result from unintended error. Experiments performed by one individual involving tritiated thymidine, published in two papers in Radiation Research, showed exponential killing of V79 Chinese hamster cells. Two other members of the same laboratory were unable to replicate the published results in 15 subsequent attempts to do so, finding, instead, at least 100-fold less killing and biphasic survival curves. These replication failures (which could have been anticipated based on earlier radiobiological literature) raise questions regarding the reliability of the two reports. Two unusual numerical patterns appear in the questioned individual’s data, but do not appear in control data sets from the two other laboratory members, even though the two key protocols followed by all three were identical or nearly so. This report emphasizes the importance of: (1) access to raw data that form the background of reports and grant applications; (2) knowledge of the literature in the field; and (3) the application of statistical methods to detect anomalous numerical behaviors in raw data. Furthermore, journals and granting agencies should require that authors report failures to reproduce their published results. Full article