Tag: science

Jan 02 2010

SomeBeans’ feeling for snow

As you may have gathered from the header for my blog and my profile picture, I’m rather fond of snow. Although this love has been with me for many years, it was science which got me into skiing: science is very international, and a couple of my students grew up close to the Alps and naturally went skiing every winter. This was the spur that sent me and The Inelegant Gardener on our first skiing holiday, in the Austrian village of Westendorf. After a week of being too hot, too cold, too much in pain, too scared: in the car back from the airport we swore we wouldn’t book a second holiday for at least a week, we lasted three days before booking the next trip!

Why is it so addictive? Perhaps it’s the massive amount of light you get from a blue sky and a white ground at a time of winter’s deepest darkness, perhaps it’s the gorgeous scenery made magical by snow, perhaps it’s the feeling of moving at speed with little effort, or the feeling of powdery snow piling up to your knees as you glide, with your skis submerged, through fresh snow.

Between looking at the spectacular views, eating the goulash soup in the toasty mountain restaurants, gliding down the mountain with grace and elegance and the moments of panic when discovering you are on a piste somewhat beyond your ability, there is much of scientific interest to be found on the mountains.

To start with there are snowflakes, lots of snowflakes:

Image by Wilson Alwyn Bentley

Growing up in England, I’d never really believed that snowflakes had six-fold symmetry – English snow seems to come in big puffy flakes or rain. Actually to demonstrate the point, we have just been subjected to a fall of little icy pellets. Whilst skiing I was exposed to proper perfect snowflakes which I watched settling on my coat arm as I trundled up a slow chairlift. The difference is all down to how cold the air is and how much water vapour there is in it, this is shown in the snowflake shape diagram here. Actually, this simplifies things a little: the diagram shows what you get when you make snow in the laboratory under carefully controlled, fixed conditions. In real life a snowflake will experience a range of conditions as it falls to earth, which will all contribute to the shape it’s in when it lands. In England this means ‘an irregular blob’, in the Alps it means ‘pretty snowflake’. You can find out much more about snowflakes on Kenneth G. Libbrecht’s website.

There are also sundogs, I’ve seen these a couple of times on days when there is diamond dust in the air:

Sundogs are the short arcs of light either side of the sun. These form under certain atmospheric conditions, bloody cold ones in my experience, the air is filled with tiny thin, hexagonal plates of ice, which drift gently to earth. As they fall they align so that their flat faces are parallel to the ground. They act like little prisms, the little prisms mean that light coming towards you from the sun is thrown out to the side – leaving a gap close to the sun and a bright spot further out. Since they are aligned relative to the ground the sundogs are most obvious either side of the sun (as opposed to a ring all the way around). This is explained in more detail here, along with many other atmospheric optical effects.

Snow also impinges on my own field: the physics of appearance. Consider this: water in a glass is a colourless, transparent liquid; ice (made properly) is similarly transparent, yet clouds (made from tiny water droplets) and snow made from crystals of transparent ice are white. The difference being the microscopic structure of the material. Calculating the details of the reflectivity of snow and clouds is an active area of research for people interested in atmospheric physics, and climate change.

There are so many other things I left out of this post such as wind-sculpted snow, glaciers and the mechanics of skiing itself (although I’ve found that thinking too much about what I’m attempting to do on skis normally leads to a fall). There is a book dedicated to mechanical aspects of skiing: The Physics of Skiing by David Lind and Scott Sanders.

Ever the keen observer, I have discovered that the hairs in my nostrils freeze when the air temperature is around -10°C, take a deep breathe through your nose: if you get a prickling sensation then it is at, or below, -10°C. I did try snowboarding once, and from this learnt where my coccyx was and just how much it could hurt! And by the power of wikipedia, I discover there is a special name for this hurt: coccydynia.

Dec 29 2009

What kind of scientist am I? (audio version)

My earlier “What kind of scientist am I?” post is now available as a podcast: http://bit.ly/6EA17H – Posterous allows the easy posting of audio. I’m not sure I’ll do it again but it was fun to try. I used a basic Logitech headset microphone, Audacity to do the capture and editing with the Lame plugin for MP3 export.

Dec 27 2009

Grant Applications II

This post is probably not for you, unless you’re interested in grant applications!

I touched on grant applications a few posts ago with reference to the THES debate on blue-skies research, I mentioned my abysmal grant application record, the generally low success rate and the pain involved for all concerned. Here I intend to add a few additional comments arising, in part, from my experience in industry.

It’s worth stating what I believe the grant application process is for: on the face of it is a method by which discretionary funding is provided to researchers to provide resources for research; that is to say equipment, consumables and personnel. However, in addition to this it has a hidden purpose in that it is felt by many to be part of an rating process for researchers. Researchers believe that the more grant applications they win, the higher their ranking. Therefore top-down attempts to limit the number of applications a researcher can make cause consternation because they impact on the perceived worth of that researcher. This additional function is not explicit, and in a way it arises for a lack of any better measure of apparent researcher worth.

I believe this perception arises because university departments don’t do a very good job of career management for academics. As an employee of a very large company, I have regular discussions about where my career within the company is going – indeed in my first year I spent about an hour and half talking about just this subject, whilst in academia I *never* in 8 years post-doctoral employment, had a formal discussion about my career development. This applies both to those who have successfully made it to permanent lecturing positions, and the many post-doctoral research assistants who aspire to a limited number of permanent posts.

The grant application process takes no account of an attempt to create a wider research program. Grant applications are made to acquire a specific piece of equipment and/or someone to carry out the research proposed. Typically the equipment will be used long after the end of the grant, and there will be no formal mechanism of replacement.

I am still involved in writing internal research proposals, these differ in two ways from grant applications. Firstly, they are much shorter than grant applications – a couple of sides of A4; secondly, they are much more concerned with all the things ‘around’ the core of the proposal rather than an explicit description of the research to be done. Funding and allocation of resources is made at the level of projects comprising of order 10 or more people, rather than at the 1 or 2 researcher level at which the typical grant application aims. Furthermore there is a longer cascade in the resource allocation process, rather than each ‘end user’ approaching the holder of a central pot, resources are allocated at a higher level. This reduces the number of people in the grant application business and means that rounds of allocation are smaller affairs.

The winning of grants appears to contain a large element of lottery, that is to say the outcome depends to a moderate degree on chance. To improve your chances of winning a lottery, you buy more tickets. This has caused the EPSRC, at least, problems since although the amount available for grants has increased, the amount applied for has increased more rapidly.

There are two solutions to the problem of researcher disillusionment through the low success rate of grant applications, one is to increase the amount of cash available (which is unlikely to happen in the current economic climate), the other is to reduce the number of grant applications made – here the problem is how to do this in an equitable fashion. Part of the problem here is that the number of potential researchers is governed by the number of people required to teach the undergraduates population, rather than a judgement on the number of people required to consume the research allocation pot.

So what does this suggest for the grant application process:
1. Better career management for academics, in order that the grant application process is not used as a rating tool for academics;
2. Devolution of spending to a lower level;
3. More thought paid to providing continuity.

I guess in my ideal world an academic will develop a coherent, over-arching research plan which is executed in pieces by application to research funds at something like the university scale. The success of such applications depends largely on past performance, and on the coherence or otherwise of the over-arching research plan rather than an attempt to evaluate the quality of a particular piece of research, or idea, in advance.

It’s worth noting that academic research is seriously difficult, in that your ideas should be globally competitive – you should be developing thoughts about how nature operates at that are unique. Your competition is thousands of other, very clever researchers spread across the world. Compared to this, my job as an industrial researcher is easier – I need to communicate the answer to the question at hand to the appropriate person, if the answer already exists then that’s fine. Also I get to do more research with my own hands than I would in an equivalent position as an academic.

Dec 23 2009

What kind of scientist am I?

Following on from my earlier blog post on the tree of life, this post is about the taxonomy of my area of science: physics. I should point out now that I’m not too keen on the division science in this way. These divisions are relatively recent, as an example: the Cavendish Laboratory, the department of physics at Cambridge University, was only founded in 1874.

I am an experimental soft-matter physicist.

So taking the first word: experimental. This is one of the three great kingdoms of physics, the others being  computer simulation and the theory. “Experimental” means I spend a large part of my time trying to do actually experiments on objects in the real world, this may involve substantial computational work to process the output data and should generally involve some comparison to theory when published, although serious development of theory tends to end up in the hands of specialists. Computer simulation is distinct from from theory: simulation is like doing an experiment in a computer – give a set of entities some rules to live by and set them at it, measure results after some time. Theory on the other hand attempts to model the measurements without the fuss of explicitly modelling each entity in the collection.

Next to the physicist bit: In a sense theory is the essence of what physics is about: building an accurate model of the world. The important thing with physics is abstraction, to take an example I’m interested in granular materials; from a physics point of view this means I’m looking for a model that covers piles of ball bearings, avalanches, sand dunes, grain in silos, cereals in a box and possibly even mayonnaise all in a single framework.

And so to the final division: soft-matter. Physical Review Letters, which is the global house journal for physics, has the following subdivisions (in italics):

  • General Physics: Statistical and Quantum Mechanics, Quantum Information, etc; Domain of Schrödingers cat, Alice and Bob exchanging secure messages, and Bose-Einstein condensates.
  • Gravitation and Astrophysics; Physicists go large. Stephen Hawking lives here – black holes, the big bang.
  • Elementary Particles and Fields; down to the bottom, with things very small studied by things very large (like the Large Hadron Collider at CERN). Here be Prof Brian Cox.
  • Nuclear Physics; The properties of the atomic nucleus, including radioactivity, fission and fusion. This is Jim Al-Khalili‘s field. 
  • Atomic, Molecular, and Optical Physics; Stuff where single atoms and molecules are important, things like spectroscopy, fluorescence and luminescence go here.
  • Nonlinear Dynamics, Fluid Dynamics, Classical Optics, etc; Pendulums attached to pendulums, splashes and invisibility cloaks!
  • Plasma and Beam Physics; Matter in extreme conditions of temperature: fusion power goes here.
  • Condensed Matter: Structure, etc; Condensed matter is stuff which isn’t a gas – i.e. liquids and solids, and is acting in a reasonable size lump. 
  • Condensed Matter: Electronic Properties, etc; This is where your semiconductors, from which computer chips are made, live. 
  • Soft Matter, Biological, and Interdisciplinary Physics; Soft-matter refers to various squishy things, plastics, big stringy molecules in solution (polymers), little particles (colloids, like emulsion paint or mayonnaise), liquid crystals, and also granular materials (gravel, grain, sand and so forth).

So there I am in the last division, studying squishy things.

Since I’ve provided a means to wind up most sorts of scientist in previous blog posts, I thought I could provide a few here for me. Theoreticians can wind me up by assuming that experiments, and the analysis of the resulting data, are trivially easy to do and if they don’t fit their theory then I need to try again. Simulators I have a bit more sympathy with, simulations are experiments on a computer, however when you’re writing a paper perhaps you should say in the title you ran a simulation, rather than did a  proper experiment like a real man ;-)

Update: I made this post into a podcast: http://bit.ly/6EA17H – it’s on Posterous because uploading of audio is easier. I used a basic Logitech headset microphone, Audacity to do the capture and editing with the Lame plugin for MP3 export.  I’m not sure I’ll do it again but it was fun to try!

Dec 17 2009

The Professionals

Lecturing is a tough business, and half the job is largely ignored.

This post is stimulated, in part by an article in Physics World on the training of physicists for lecturing, and how they really don’t like it. It turns out it is rather timely since Times Higher Education has also published on the subject, in this case highlighting how universities place little emphasis on the importance of good teaching in promotion.

I taught physics at Cambridge University: small group tutorials and lab classes – I was a little short of a lecturer. I also taught physics as a lecturer at UMIST. I should point out that the following comments are general, I think they would apply equally to any of the older universities.

Mrs SomeBeans is a lecturer in further and higher education, the difference between the two of us is that she had to do a PGCE qualification whereas I was let loose on students with close to zero training.

I did spend an interesting day in lecturer training at Cambridge, a small group of new lecturers, and similar, spent a fairly pleasant day chatting and being video’d presenting short chunks of lectures. I learnt several things on that day:
1. Philosophy lecturers use hardly any overheads.
2. Most of us found lecturing pretty nerve-wracking, one of our number wrote out her lectures in full in longhand to cope.
3. Drinking as a cure for pre-lecture nerves doesn’t work well
4. I spoke like a yokel and was slightly tubbier than I thought!

Round two at my next employers was a bit more involved. I can’t remember much from the two day event, but many of the points from the Physics World post came out. Scientists are typically taught how to lecture together as group, and their point of view is somewhat in collision with those of educationalists who seem to be able to throw out three mutually incompatible theories before breakfast and not be interested in testing any of them.

I have an insight which may help scientists in these situations: outside science the idea of a “theory” has quite a different meaning from that inside science. This paradox is also found in management training. Non-scientists use a “theory” as a device to structure thought and discussion, not as a testable hypothesis. Therefore multiple contradictory, or apparently incompatible theories, can be presented together without the speaker’s head exploding. They’re not generally tested in any sense a scientist would understand, very few people attempt to quantify teaching Method A against teaching Method B. The thing is not to get hung up on the details of the theory, the important bit is being brought together to talk about teaching.

I enjoyed parts of teaching: physics tutorials for second years at Cambridge was something of a steeplechase with the not particularly experienced me, hotly pursued by rather cleverer undergraduate students over problems for which the lecturers did not deign to supply model answers. Exceedingly educational for all concerned. Practical classes were also fun: the first time a student presents you with a bird’s nest of wires on a circuit board it takes about 15 minutes to work out what the problem is, the second time you immediately spot the power isn’t connected to the chip – and students think Dr Hopkinson is a genius.

Lecturing I found pretty grim, except on the odd good day when I got an interesting demonstration working. I was faced with 80 or so students, many of an unresponsive kind. I ploughed through lecture notes on PowerPoint which I found interesting when I was writing but in the lecture theatre I found painfully long winded. Lecturing is the most nerve-wracking sort of public speaking I’ve done, and I suspect many lecturers find it the same. I remember one of my undergraduate lecturers was clearly a bag of nerves even in front of the small and friendly course to which I belonged (and I’m not good at picking up such things).

In a sense lecturing is a throwback, there are so many other ways to learn – and I fear we only teach via lecturing because that’s what we’ve always done. Nowadays it’s easy, although time consuming, to produce a beautiful set of printed lecture notes and distribute the overheads you use: but is it really a good use of time to go through those overheads (which I am sure is what nearly everyone does)? Nowadays I learn by reading, processing and writing (a blog post) or a program.

There’s another thing in Physics World article:

At universities the task is often performed by academics who are much more interested in research and therefore regard teaching as a chore.

This is absolutely true, in my experience. I’ve worked in three universities post-undergraduate, I’ve been interviewed for lectureships in a further six or so. And in everyone the priority has been research not teaching, which is odd because if you look at funding from the Department for Innovation, Universities and Skills something like £12billion is directed at teaching and something like £5bn at research.

So why did I write this post: perhaps it’s a reflection of opportunities missed and a time spent chasing the wrong goals. If I did it all again there seem to be so many more ways to talk to other lecturers about teaching. On twitter, in blogs.