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This week there’s been a lot of activity in the group around public engagement. Firstly a deadline looms for contributions to the upcoming open day at the Centre for Medical Image Computing. Everyone at the centre is contributing to a large exhibit where interested non-specialists can find out what it is we do. I’ve been working on a poster with one of our PhD students on the simulation work I do and how we apply it to biomedical imaging. I’ve also been putting together demonstrations and software for our group’s display. That’s a bit more ongoing.
Also this week I’ve been working with a colleague at CABI on a proposal for an exhibit at a public engagement event being held by the Wellcome Trust. This one is for a stall at a scientific street fair at the Barbican next spring. We’ve come up with a few ideas to illustrated our ideas using edible brains (well, not actual brains, but edible things in the shape of brains) and presenting some of our ideas around non-invasive in vivo microscopy and disease progression. Fingers crossed on that one.
All of this is quite exciting and a nice break from the norm. Another memorable event this week was a seminar on using Bayesian inference and random graphs to estimate brain connectivity – so much of our work involves having highly technical conversations with other specialists.
“Are you sure that it’s appropriate to assume that because the adjacency matrix element is zero, the inverse covarience matrix element is also zero?”
Well, actually no now you come to mention it but… oh, one of the statisticians in the room is saying that the assumption’s safe, phew. That was a close one.
Instead, we’re trying to explain as clearly as possible why our research is interesting and worthwhile in a way that’s engaging and accessible without being patronising.
“What we’re trying to do is look at the microscopic structure of your brain without having to cut you open. This would eliminate the need to stick needles through your skull to check if there’s anything wrong.”
Come to think of it, that might be a bit too direct.
Public engagement is undoubtedly important. My research is publicly funded, and so telling the public what I’m up to is the least I can do, frankly. This is also linked to things like Open Science, whereby you should be able to download my code, repeat my experiments, and see that I’m not making up, and Open Access, whereby you should be able to read what I publish under peer-reviewed journals for free.
Science should be open and free and accessible and available. Getting knowledge out there enables people to use it in new ways. Making research interesting will (hopefully) excite the next generation of scientists and developing communication skills with interested non-specialists at the very least makes for interesting dinner conversations.
I’m all for it. Drop by if you’re in the area.
It’s been a week of transitions around these parts. Firstly, We had a student pass their PhD viva. For those outside the academic fold, to get your PhD you need to have accomplished two things: the first is to have undertaken enough original research and developed a sufficient understanding to produce a fairly lengthy thesis and the second is to successfully defend that thesis in a freeform exam called a viva.
In the UK, the viva involves you, the PhD student, in a room with two examiners both of whom are experts on the field of your research, one from your own university, the other from a different institution. There’s no time limit, and the examiners are free to ask you absolutely anything they whether it is covered in the thesis or not.
Make no mistake, this is a very difficult process and a definite right of passage. Most scientists will talk about their viva in a similar way to warriors in tribal cultures might talk about a spirit quest and it is certainly not something you forget. This week one of our students went through the crucible and emerged anew the other side. She has a few minor changes to make to her thesis (which is about the best result anyone can hope for) and will shortly be entitled to call herself Doctor.
The other transition this week was the inaugural lecture of a new Professor. This is a much more civilised affair. At about 5pm we all shuffled off to see an excellent presentation from a newly promoted Prof, who gave a thoroughly entertaining account of his work in image registration and shape categorisation. Excellent speaker, excellent research, and an audience of at east a couple of hundred.
I guess where I’m going with this is an observation about the career pattern of scientists – it’s very much like the progression of a traditional artisan. A PhD is very much like an apprenticeship: despite the funding structure in the UK they usually take about 4 or 5 years in practice, and they lay the groundwork for all the basics: lab or analytical techniques, critical thinking, familiarity with the literature, independent working and thinking, technical writing and presentation, and a nascent network of collaborators and allies.
After the apprenticeship we become journeymen. I am unabashedly a journeyman scientist – refining my skills, learning new ones, helping out with teaching the new lot and helping the group run smoothly. Just as with other artisans, many Journeymen remain so for the rest of their careers. (Right now I’m not sure I will ever be anything else, but I must admit I kind of like it that way).
The final stage, though, is to become a Master. In academia we call them Professors. One you get here you should be at the top of your game, world class and well known in your field. In fact, really, you should have made th sort of contribution to your subject that is at least deserving of a footnote in the history books. In a couple of hundred years, some dusty historian of science should know who you were. Like I say, not everyone gets to this stage.
I find this similarity a little pleasing. The fact that the pursuit of knowledge can be thought of as a craft, and that the pattern established so long ago to reflect skill, learning, and diligence is as applicable now as it was in the middle ages is all pleasing and reassuring and provides a connection with the past. Of course, you could also take this as a criticism that science is very old fashioned, but having just returned from a non-academic job in an insanely bureaucratic organisation, I’ve seen first hand that you can certainly do a lot worse.
I mentioned a couple of weeks back that I was going hiking in the Lake District, and this week (partly because I couldn’t think of anything else to post) I thought I’d share some photos.
We were in a place called Buttermere, which is on the western side of the Lake District, and one day in particular had spectacular weather. Here are a few shots I snapped with my phone.
This is Buttermere, the peaks are High Stile and Red Pike.
And this is the end of the lake (quite like the blasted-tree chic)
This is from a little higher up. We’d been walking for about an hour at this point.
A particularly arty cairn.
A couple of my cousins as we stopped for break.
I have a few more, but you know, less is more. To sign off, though, here’s a photo from a week or two later from Rosemary Gardens in London. It’s a tiny park near my house, which is currently in full autumnal glory.
Hope you like them!
This week, one of our PhD student, Matt Rowe, showed an unexpected talent for lol-catz. He also showed an unexpected talent for exceptionally specific nerdy humour. Matt’s work went minimally viral, in that everyone in the group thought they were ace and exchanged a flurry of emails about them.
Our PI, suitably impressed with Matt’s efforts, decreed that although they were indeed excellent they would not be featuring on the Microstructure Imaging Group’s website. It was an oddly parental moment. A sound judgement in my opinion, but this means that the circulation of said masterpieces falls to a less official vector: this blog.
So, what’s more nerdy than a lol-cat? a lol-cat based on insider medical image analysis humour.
There are plans afoot to do something similar every month or so. Let’s see how that works out…
It’s been an interesting week, scientifically speaking. I’ve been at this science game for a few years now and for most of that time I’ve worked with simulation, data analysis, maths and theory. Computation, that’s my thing. What can I calculate? What can I predict? What hidden gems of genuinely useful Information are buried in that torrent of data that’s just arrived, still warm from the oven?
Way back when I was getting interested in physics, it seemed that Grand Unification and Cosmology seemed to be where it was at, then an undergraduate dissertation on Order and Chaos switched me on to Complex Systems, which in turn lead to simulation and biological systems, with a fair dollop of optimisation thrown in for good measure. That’s a lot of time in front of various computers: decent typing speed, poor distance vision, gratuitous use of the word “functionality”, that sort of thing.
None of which was much preparation for where I found myself on Tuesday – alone in a departmental meeting room faced with 6 boxes of EEG equipment borrowed from the virtual reality lab downstairs. No instruct manual, needless to say. So, I spent an hour unpacking and cataloging the equipment, untangling the cables and laying everything out then another hour connecting various bits and pieces together and yet another hour installing software. I just about managed to acquire some completely useless data.
I have learned several things. Firstly, experimental work involves lugging a lot of stuff around. Secondly, it’s no less frustrating than coding, but it’s frustrating in a completely different way. Instead of “why the hell is that equal to an insane number?” it’s “how the hell does this particular box talk to that particular box, and where does the data go anyway?”. I’ve also learned that although three hours is about as long as I can reasonably concentrate, it’s not long-enough to get an experiment completely ready to go from scratch. So I’ll have another go on Monday.
One other thing, though, is that data, and experimentation, is at least half of the scientific process. Without data, it’s just speculation and theorems. Without data, it’s something else entirely. Which means that gathering my own data, instead getting a mate to do it, is a nice thing to try, even if I am pretty useless at it.