When even your own publication list makes no sense

A few years ago, the head of my research group asked if I’d like to help write a chapter for a book. I weighed up the pros: it was an updated version of a previous book (so not too much work), it was invited (so not too many battles with reviewers) and it’s another item to go on the CV. The cons: typically, this kind of article appears in an obscure, closed publication that no-one ever reads or cites. So I said sure, why not and we wrote it.

It’s listed on my publications page at this blog as:

Saunders, N.F.W., Brinkworth, R.I., Kemp, B.E. and Kobe, B. (2010). Substrates of Cyclic Nucleotide-Dependent Protein Kinases. In: Handbook of Cell Signalling (Bradshaw, R.A., Dennis, E., eds.). Academic Press San Diego, 182:1489-1495. [DOI]

and sure enough, if you visit that DOI (and have a Science Direct subscription), you’ll find chapter 182 in the Handbook of Cell Signalling.

I thought no more about it, until I updated my Google Scholar citations page, where I found this:

Substrates of Cyclic Nucleotide-Dependent Protein Kinases
Neil FW Saunders, Ross I Brinkworth, Bruce E Kemp, Bostjan Kobe
Transduction Mechanisms in Cellular Signaling: Cell Signaling Collection 399
Academic Press

And here’s the link at Google Books. Same article, same editors – but in chapter 41 of a different book: Transduction Mechanisms in Cellular Signaling: Cell Signaling Collection, on pages 399-405.

So apparently, my chapter has been “repurposed” for a completely different publication. Perhaps this transpired in consultation with the research group after I left. Perhaps there’s a long-forgotten email trail in which I agreed to this. Or perhaps we have so little control over our own work that strange things like this can just happen.

10 years on, same old same old

September 2, 2002

So what new skills will postdocs need to ensure that they don’t become science relics? The answer is math, statistics, and knowledge of a scripting language for computers.

— ­The Scientist, “Bioinformatics Knowledge Vital to Careers.” 16(17): 53.

February 8 2012

But two other skills are increasingly necessary: expertise in computer-programming languages designed to aid manipulation of large data sets, such as R, Perl or Python, and the ability to use these languages to analyse large amounts of data quickly.

— Nature, “Biostatistics: Revealing analysis.” 482: 263–265.