How to be a good reviewer when suspecting dubious results or being sent contributions outside my field of expertise? Recently I have been appointed as a reviewer for an IEEE journal in CS. This journal is indexed on Scimago and its ranking is a Q3 journal.

I know that one cannot know all the topics covered in the diverse areas of CS, but as a reviewer, I have been assigned some papers that do not specifically belong to my field of expertise. Even though the journal appears to check the familiarity of the reviewer with the topic before reviewing it, I just feel it is not close enough.

Apart of that sometimes I get some papers in which the practical part or the methodological part is jumped quickly, and for me, it lacks consistency and is rather descriptive; I know the limitations of space, but I believe some authors should put more effort on that section.

Also, how can I know that the results that they present are for real? I have read many cases in which some authors fake their own results or push them to show nice results when in reality is not like that.

What to do in the aforementioned situations?
In any field, once you start reviewing, you're going to see a lot of problematic submissions, because there are a lot of people out there who submit really bad manuscripts. In some cases, the authors are unaware of their problems, while in others they're trying to slip a minimal paper past peer review because they aren't really interested in the science---they just need the publication to graduate or get promoted.

Your job as a reviewer is to make sure that what gets published is credible.  That means that if you don't understand something or don't feel that the evidence presented supports their conclusions, you have a duty to report that as a problem with the manuscript. Depending on the severity of the problem, you might recommend either revision or rejection---and the handling editor will make the actual decision.

You shouldn't, however, demand that authors to do new work just because you think it would be interesting. You also shouldn't expect to know every tiny detail of their methodology.  So, where to draw the line?  Here is how I think about it:


The authors need to provide enough methodological detail that no significant implementation decisions are missing.  
The conclusions need to be well-supported by the evidence presented.
The overall thrust of the paper should be interesting to a typical reader of the journal (this one varies highly by journal).


None of this will allow you to detect clever fraud, but not-so-clever faking often leaves a clear trace (data that looks too nice), and cherrypicking of data is usually detectable because the authors present data from very few samples or trials.