Notes on Disordered Matter

Great comical contribution of Jorge Cham’s PhD Comics: Nature vs Science

• Part 1
• Part 2
• Part 3
• Part 4

The Nature Journal liked it, as apparent from their blog post According to Jorge Cham, their general comment was:

Use this comic for procrastination or decompression, as you see fit.

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Less sad than the recent Note on publishing a scientific comment… As I am in a constant process of trying to understand the requirements for publishing high-impact scientific papers better (slow process… , I am always eager to see what others write about it.

Recently, I linked to some PLOS editorials about Ten simple rules for nearly everything, including writing papers.

Along this line, the presentation given by the Phys. Rev. Lett. Editor Manolis Antonoyiannakis in Japan end of last year, is very interesting. In addition to hints for using the right phrasing when writing about scientific results, he also gives some insight – from the viewpoint of the Editorial Office of a high impact phyics journal – into the inner workings of paper predecision (by Editor) and general acceptance rate. Read the rest of this entry »

Via the blog Dynamics of Cats: How to publish a scientific comment in 123 Easy Steps by Prof. Rick Trebino. I do not have first hand experience myself, but the described exchange between commentator and editor is very interesting, and indeed very disturbing for an open-minded scientist! See also a comment on Trebino’s essay in the blog Adventures in Ethics and Science.

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Via Academic Productivity: Interesting comment, What can Science learn from Google? I especially like that it starts with a quote by George Box (you know I like them

All models are wrong, but some are useful.

The article takes the provocative stance that we do not need models any more to describe the world, as petabyte data clouds combined with massive computing power are able to correlate data.

Data without a model is just noise. But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete. [...] Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

I humbly disagree. Understanding needs models, predictions need models. Of course, in order to find models, correlations – probably found by using computers – can show the way.

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rhetoric also counts in publishing papers: see this blog entry by Jose Quesada from Academic Productivity, “Writing style” vs. “content”: Watson & Crick’s example.

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