What makes a paper bad instead of just wrong

The editor of the journal Remote Sensing just resigned over the fact that his journal published a paper that should never have been published.  Real Climate explains what that means – being controversial or eventually shown wrong is *not* an indication that a paper shouldn’t have been published. This is what makes a paper bad:

But what makes a paper ‘bad’ though? It is certainly not a paper that simply comes to a conclusion that is controversial or that goes against the mainstream, and it isn’t that the paper’s conclusions are unethical or immoral. Instead, a ‘bad’ paper is one that fails to acknowledge or deal with prior work, or that makes substantive errors in the analysis, or that draws conclusions that do not logically follow from the results, or that fails to deal fairly with alternative explanations (or all of the above). Of course, papers can be mistaken or come to invalid conclusions for many innocent reasons and that doesn’t necessarily make them ‘bad’ in this sense.

Winning science by attrition is boring

Nature has an interesting piece on the 24/7 lab:

It’s just about midnight on a hot Friday night in July, Enrique Iglesias’ ‘ Dirty Dancer ‘ is on the radio, and 26-year-old graduate student Sagar Shah is starting to look winded. The problem, he says, is not how late it is, or even that he has spent the past three hours working in a cramped sterile cell-culture hood. The problem is that the routine cell-culture maintenance he is doing, bathing his collection of rare human tumour cells with fresh medium, produces no data. And a lack of data, says Sagar, makes him “hungry” for it.

The piece goes on to talk about one of those high-intensity, work all weekends and holidays labs. That lab, and many like it, certainly crank out papers, but they are basically factories. Continue reading “Winning science by attrition is boring”

Science musings

What biologists need to do more of:

A major goal in all sciences is to be able to explain large-scale phenomena as consequences of the interactions of small-scale components. This is what drives me to study what I’m studying – in my case, the large scale-phenomena are patterns of gene expression, and the small-scale components are transcription factors and DNA binding sites.

Biologists do a lot of measuring of large-scale phenomena, via genomics or classical genetic phenotying. Biologists also spend a lot of time discovering what the small-scale components and interactions are. But they don’t really spend enough effort trying to understand how it is that large-scale phenomena are consequences of the interactions of small-scale components.

Just to be clear: your typical blob-and-arrow pathway diagram featured in Figure 7 of nearly every Cell paper (Fred Cross calls these ‘Figure 7 models’) is not the answer to this question, because it is essentially impossible, in nearly all cases, to predict the large-scale behavior just by looking over one of those diagrams.

Wetware

Just added to the stack: Wetware, by Dennis Bray. Bray has been a systems biologists at the University of Cambridge, since way back before they were calling people like him systems biologists. His papers have long inspired me, and I recently had the pleasure of conversing with him over lobster dinner at the Cold Spring Harbor Computational Cell Biology meeting earlier this year. (Yes, I lead a glamorous life.)

The blurb on the back of the book is exactly the question in biology that fascinates me more than any other:

How is a single-cell creature able to hunt living prey, respond to external stimuli, and display complex sequences of movements without the benefit of a nervous system?

Of course, this question does not just pertain to single-celled organisms. Think of the prey-hunting macrophages in your own body, or really any cell, whether it hunts or not – all cells sense and respond to their environments without nervous systems.

Stay tuned for updates on the book.

John Baez does network theory

I love John Baez’s blog that was a blog before we called things blogs, This Week’s Finds. (He also writes the Azimuth blog, linked in our blogroll below. And I tip my hat to my father-in-law, who first introduced me to Baez’s blog.)

Baez is now writing on network theory. Baez typically focuses on math and physics, but this series is great for biologists:

I wish there were a branch of mathematics—in my dreams I call it green mathematics—that would interact with biology and ecology just as fruitfully as traditional mathematics interacts with physics. If the 20th century was the century of physics, while the 21st is the century of biology, shouldn’t mathematics change too? As we struggle to understand and improve humanity’s interaction with the biosphere, shouldn’t mathematicians have some role to play?

And while you’re over there, check out his section on how to learn math and physics, and his advice to young scientists.