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Account Res. 2017;24(6):344-358. doi: 10.1080/08989621.2017.1327813. Epub 2017 May 8.

Data-Intensive Science and Research Integrity.

Author information

1
a National Institute for Environmental Health Sciences , National Institutes of Health , Research Triangle Park , North Carolina , USA.
2
b Lyman Briggs College , Michigan State University , East Lansing , Michigan , USA.
3
c Department of Fisheries and Wildlife , Michigan State University , East Lansing , Michigan , USA.
4
d Department of Philosophy , Michigan State University , East Lansing , Michigan , USA.

Abstract

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.

KEYWORDS:

Data-intensive science; deception; education; ethics; misconduct; research integrity; transparency

PMID:
28481648
PMCID:
PMC6060414
DOI:
10.1080/08989621.2017.1327813
[Indexed for MEDLINE]
Free PMC Article

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