The fact of the matter is that all the above malpractices in which readers are fully informed or are outright misled about the provenance of the data are frowned upon by most scientific journals (see Kassirer & Angell, 1995) and most of the major scientific writing guides caution against them (e.g., Iverson, et al., 2007).
The apparent glut of quality scientific journals notwithstanding, a paper that appears in two different journals unbeknownst to readers and editors may have robbed other authors of the opportunity to publish their worthwhile original work. In addition, while a paper can always benefit from additional critical peer review, journal referees often must volunteer their valuable time to review others’ work in the service of science and scholarship. Refereeing what turns out to be a duplicate or redundant publication places undue time and limited resource constraints on the editorial and peer review system. More importantly and particularly in the sciences, is the fact that covert dual/redundant publications likely result in readers being misled as to the true nature of a given phenomenon or process. For example, an author who wishes to study the significance of an experimental effect or phenomenon using sophisticated statistical techniques, such as meta-analysis, will likely overestimate or perhaps underestimate the magnitude or reliability of an effect if the same experiment were to be counted twice. Consider the following anecdote reported by Wheeler (1989):
In one such instance, a description of a serious adverse pulmonary effect associated with a new drug used to treat cardiovascular patients was published twice, five months apart in different journals. Although the authors were different, they wrote from the same medical school about patients that appear identical. Any researcher counting the incidence of complications associated with this drug from the published literature could easily be misled into concluding that the incidence is higher than it really is (p.1).”
Redundant publication practices can distort the conclusions of literature reviews if the various segments of a salami publication or the augmented data that represent data from the same subject sample, are included in a meta analysis under the assumption that all of the data are derived from independent samples (Tramer, Reynolds, Moore, and McQuay, 1997) and evidence indicates that some meta-analytic studies have been contaminated by duplicate data (Choi, Song, Ock, Kim, Lee, Chang, & Kim, 2014). For this reason, all forms of covert data reuse can have serious negative consequences for the integrity of the scientific database. In certain key areas of biomedical and social science research the consequences of duplicated data can result in wrong health policy recommendations that could place the public at risk.