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Oh wow, looking at their graph and the portion they cut out for "aesthetic reasons" this looks almost like fraud to me. It's at least hard to imagine how Kouwen

Oh wow, looking at their graph and the portion they cut out for "aesthetic reasons" this looks almost like fraud to me. It's at least hard to imagine how Kouwenhoven or anyone in his group could not have found this highly problematic. I did my PhD in a similar field (superconducting quantum computing) and if I had cut data from a graph like that I would've gotten a really strong reprimand from my supervisors. If you do a series of IV curves at equidistant points you cannot simply cut out the data you don't like, and if you do remove irrelevant data points (again, you probably shouldn't) you need to replace them with a placeholder value such as a black background to make it immediately clear that you left out some data. Just cropping your graph (which also makes most labels on the X axis invalid) is just insane, they hammer that into your head during the first undergrad lab classes already. I'm sure if they had marked the missing curves with a placeholder the reviewers would've noticed immediately and this article would probably not have been published. So from an outside perspective this doesn't look good at all for the authors. Well, I guess this shows why it's so important to publish the raw data as supplementary material along with the article.

c1ccccc1 6y ago
I remember that when I was working as an undergrad research assistant in a physics lab, a PhD student and I had plotted some data, to see if the relationship between 2 variables was linear. We had cut off many of the data points to the left and right, keeping only a small region in the middle. When we showed it to our supervisor, he came back with "of course it looks linear if you only look at a small interval, that's just Taylor expansion." He then explained to us that it's incredibly scientifically sketchy to leave out perfectly good data from one's plots. Not fooling yourself is surprisingly hard when you're wishing for a specific outcome. On a related note to the supplementary data thing, I've recently seen a lot of people complaining online about not being able to reproduce the results of machine learning papers that don't have the code for their models included in the supplement. "Code, or it didn't happen." I guess each field has its own problems with replication of results.

c1ccccc1 6y ago

I remember that when I was working as an undergrad research assistant in a physics lab, a PhD student and I had plotted some data, to see if the relationship between 2 variables was linear. We had cut off many of the data points to the left and right, keeping only a small region in the middle. When we showed it to our supervisor, he came back with "of course it looks linear if you only look at a small interval, that's just Taylor expansion." He then explained to us that it's incredibly scientifically sketchy to leave out perfectly good data from one's plots. Not fooling yourself is surprisingly hard when you're wishing for a specific outcome. On a related note to the supplementary data thing, I've recently seen a lot of people complaining online about not being able to reproduce the results of machine learning papers that don't have the code for their models included in the supplement. "Code, or it didn't happen." I guess each field has its own problems with replication of results.