Data integrity: how to know when you are doing something wrong

Dr. Free-Ride gives some pointers:

Honesty is central to scientific credibility, but I think we need to recognize that what counts as honest communications of scientific results is not necessarily obvious to the scientific trainee. The grown-up scientists training new scientists need to take responsibility for teaching trainees the right way to do things — and for modeling the right ways themselves in their own communications with other scientists. After all, if everyone in a field agrees that a particular way of treating outlying data points is reasonable, there can be no harm in stating explicitly, “These were the outliers we left out, and here’s why we left them out.”

On the other hand, if your treatment of the data is a secret you’re trying to protect — even as you’re discussing those results in a paper that will be read by other scientists — that’s a warning sign that you’re doing something wrong.

Just the other day, while discussing grain size measurements obtained using EBSD, Prof. Suwas was telling how crucial it is to let others know what criterion was used in obtaining the grain sizes since you can end up with pertty much any number you want. Hence, I think, it is the responsibility of the individual (as noted by Dr. Free-Ride) to give out such information (so that other members of the community are also forced to do so) as well as that of the community (reviewers should insist that information of this sort is not glossed over but is provided either in the paper or in the supplementary).

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