In this post: a brief summary of what I got up to, work-wise, in 2016 and my plans for a rather different 2017. The short version: it’s goodbye bioinformatics and hello educational data science!
May. No blog posts yet in 2016. “What’s going on Neil?” asked no-one at all. For anyone who may be wondering…
Last November, I resigned from my position with my previous employer after almost 7 years. Just before Christmas, I was offered a position as a data scientist with a Sydney-based healthcare technology start-up. I started working there in early January and so far, it has been a terrific experience. Had I known how enjoyable it could be, I would have made a move like this 10 years ago. Career advice: there are many more jobs that can engage scientists and utilise their skills than academic research.
So what does that mean for this blog? It means that I’m no longer a researcher, at least in the narrow sense that science would use that word. It means that the things I learn during a working day are unlikely to translate into blog posts of broader interest (confidentiality issues not withstanding). And quite frankly, given where I’m at in my life (balancing working for a startup with raising my family), it means that I no longer have time to write regular blog posts.
Like a band that never officially breaks up, I’m not ready to declare the end just yet. So I’m placing the blog “on hiatus”, indefinitely. I’ll still be active online, which right now mostly means Twitter.
Career advice: switching to computational research
Laboratory work, of the “wet” kind, not working out for you? Or perhaps you just need new challenges. Think you have some aptitude with data analysis, computers, mathematics, statistics? Maybe a switch to computational biology is what you need.
That’s the topic of the Nature Careers feature “Computing: Out of the hood“. With thoughts and advice from (on Twitter) @caseybergman, @sarahmhird, @kcranstn, @PavelTomancak, @ctitusbrown and myself.
I enjoyed talking with Roberta and she did a good job of capturing our thoughts for the article. One of these days, I might even write here about my own journey in more detail.
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
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.
Conservative (with a small “c”) research
This is really interesting. I’m reading it at work so I can’t tell you if it’s behind the paywall, but I sincerely hope not; it deserves to be read widely:
Edwards, A.M. et al. (2011)
Too many roads not taken.
Nature 470: 163–165
Most protein research focuses on those known before the human genome was mapped. Work on the slew discovered since, urge Aled M. Edwards and his colleagues.
The article includes some nicely-done bibliometric analysis. I’ve lifted a few quotes that illustrate some of the key points.
- More than 75% of protein research still focuses on the 10% of proteins that were known before the genome was mapped
- Around 65% of the 20,000 kinase papers published in 2009 focused on the 50 proteins that were the ‘hottest’ in the early 1990s
- Similarly, 75% of the research activity on nuclear hormone receptors in 2009 focused on the 6 (of 48) receptors that were most studied in the mid 1990s
- A common assumption is that previous research efforts have preferentially identified the most important proteins – the evidence doesn’t support this
- Why the reluctance to work on the unknown? […] scientists are wont to “fondle their problems”
- Funding and peer-review systems are risk-averse
- The availability of chemical probes for a given receptor dictates the level of research interest in it; the development of these tools is not driven by the importance of the protein
I love the phrase “fondle their problems.”
I’ve long felt that academic research has increasingly little to do with “advancing knowledge” and is more concerned with churning out “more of the same” to consolidate individual careers. However, that’s just me being opinionated and anecdotal. What do you think?
Does your LinkedIn Map say anything useful?
LinkedIn, the “professional” career-oriented social network, is one of those places on the Web where I maintain a profile for visibility. I’m yet to gain any practical value whatsoever from it. That said, I know plenty of people who do find it useful – mostly, it seems, those living near the north-east or west coast of the USA.
LinkedIn have something of a reputation for innovation – see LinkedIn Labs, their small demonstration products, for example. The latest of these is named InMaps. It’s been popping up on blogs and Twitter for several days. Essentially, it creates a graph of your LinkedIn network, applies some community detection algorithm to cluster the members and displays the results as a pretty, interactive graphic that you can share.
What seems to have captured the imagination is that the graphs indicate communities that are instantly recognisable to the user. There’s mine on the right (click for full-size version). It’s not a large, complex or especially interesting network but when I “eyeballed” it, I was immediately able to classify the three sub-graphs:
- Orange – mostly people with whom I have worked or currently work, plus a few “random” contacts: note that this group is hardly interconnected at all
- Green – people who work in bioinformatics or computational biology, particularly genomics: two major hubs connect me with this group
- Blue – the largest, densest network is composed largely of what I’d call the “BioGang”: people that I interact with on Twitter and FriendFeed, many of whom I haven’t met in person
This confirms what I’ve long suspected: I prefer to network with smart strangers than my immediate peers and colleagues. Or as Bill Joy said, “no matter who you are, most of the smartest people work for someone else.” I’ve seen this misquoted as “where you are”, which makes more sense to me.
Who wants my old job?
Are you looking for a postdoctoral position in structural bioinformatics? Preferably at a highly-regarded university, on an attractive campus in a lovely city with a great climate? Did I mention great colleagues?
I highly recommend my old job – details here.
Temporary blogging hiatus
I’m now immersed in the process of moving from Brisbane to Sydney (with no new accommodation as yet), in preparation for my new role at CSIRO starting March 2.
Consequently, it will be quiet here for at least a couple of weeks. I’ll be sending the occasional tweet and the odd item to FriendFeed, but nothing substantial until home internet is up and running again.
New year, new job: farewell postdoc treadmill, hello (again) Sydney!
I’ve signed the contract and told the boss. So, for those of you who expressed interest, here are the details of my latest career move.
Read the rest…