Data-Intensive Science and Research Integrity #MMPMID28481648
Resnik DB; Elliott KC; Soranno PA; Smith EM
Account Res 2017[]; 24 (6): 344-58 PMID28481648show ga
In this commentary, we consider questions related to research integrity in data-intensive science and argue that there is no need to create a distinct category of misconduct that applies to deception related to processing, analyzing, or interpreting data. The best way to promote integrity in data-intensive science is to maintain a firm commitment to epistemological and ethical values, such as honesty, openness, transparency, and objectivity, which apply to all types of research, and to promote education, policy development, and scholarly debate concerning appropriate uses of statistics.