Data-Intensive Science and Research Integrity

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

Acknowledgments

We are grateful to Kendra Cheruvelil and Georgina Montgomery for helpful discussions.

Funding

This research is funded, in part, by the Intramural Program of the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH); and the National Science Foundation grants EF-1065786 to PAS and SES-1449466 to KCE and PAS. EMS is funded by a joint scholarship from the Fonds de Recherché du Quebec en Sante and the NIH. It does not represent the view of the NIEHS, NIH, NSF, or the U.S. government.

Notes

¹ Wilhelm Gottfried Leibniz independently developed calculus (Kline 1982).

Additional information

Funding

This research is funded, in part, by the Intramural Program of the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH); and the National Science Foundation grants EF-1065786 to PAS and SES-1449466 to KCE and PAS. EMS is funded by a joint scholarship from the Fonds de Recherché du Quebec en Sante and the NIH. It does not represent the view of the NIEHS, NIH, NSF, or the U.S. government.