An earlier post showed the data (blue and green symbols) above but not the fits (cyan and re curves). The data are for the number of papers published in two scientific journals, Nature and PLOS One, as a function of the number of citations (+1 so it fits on a log-log scale plot), that the paper received. So for example, the blue circle at the top left edge is at x and y coordinates of 100 = 1 and 13,000, meaning that PLOS One published 13,000 articles that were cited 1-1 = 0 times. The papers were published in 2013 and 2014, and the citations were in 2015. The mean number of citations is the Journal Impact Factor (JIF), so the JIFs of the two journals are the means of the distributions above.
The modern way to measure the performance of universities, departments and academics, is via metrics, i.e., numbers, as opposed to qualitative opinions of peers in the field. All sorts of numbers are available nowadays, and these numbers are much much faster to work with than getting an expert opinion. They can be used by managers who don’t have the expertise to assess the quality of research, and by academics who don’t have the time. And you need numbers to compile league tables, which are very popular.
A couple of weeks ago I was in on an University open day, talking to (approximately 17-years old) prospective students and their parents, today I was at our graduation reception, talking to the (21 to 22-year old) graduating students and their parents. The parents don’t change much between these events, the kids change a lot. It is just great to chat to our happy graduates and their proud parents. The parents are so proud of their kids. These graduates have worked hard and it is pleasing to see them being rewarded. It is a great feeling to know that you helped them.
I am writing this after a day at a statistical physics conference in London, and before kick-off of the Portugal-Wales Euro 2016 semi-final. My team (Wales) are one match from their first ever final of a major championship, so I am partly writing this as a distraction from getting stressed. The pre-match build up heavily features Wales’ star Gareth Bale.
Dragon-King sounds like the title of a kids’ movie, but in fact it is the impressive sounding name a statistical physicist called Didier Sornette has given to a type of extreme (large) event, such as a stock market crash, a big earthquake, a massive meteorite strike, a once-in-a-century heatwave, etc. The idea is that if you want to model say stock market movements, then you may need one model for the run-of-the-mill market fluctuations but a different model for the once-in-fifty-years crash. This crash is your dragon-king, king because it is so big, dragon because it has a different, more mysterious origin, than the usual events. Continue reading
Famously, each snow flake is unique, no two are the same. This is really true. As the crystal lattice of ice has six-fold symmetry, so do snow flakes, which are ice crystals. But this symmetry is never perfect. If you rotate the crystal above by 60 degrees then it would look different, just not as different as if you rotated it by say 45 degrees. If you rotate it by 60 degrees, then you rotate each arm into the position of the next arm and as the arms are roughly similar the crystal will not look very different, bit it will look different.
A PhD student working with a colleague and I has some great data on crystallisation of a molecule that can crystallise into not one but two different types of crystals (usually called crystal polymorphs). The molecule is a small amino acid called glycine, and the two different crystals we see are the alpha and gamma polymorphs. The motivation is kind of to see how you would get one polymorph or the other, and to understand what is going on. Following earlier workers we have found that if we add salt to the solution we get more gamma and less alpha. So that is relatively straightforward.
Although I am physicist, I don’t have much to do with quantum mechanics. I teach elementary bits of it here and there, which is fun, but I do research on classical systems, so I don’t have a particularly good grasp of quantum stuff. So although I occasionally see some of the hype, and controversy, over quantum computers, I have not really understood what they actually are. But recently, I came across the blog of Scott Aaronson, a scientist in the field who, I think, explains it really well.
I started this post on the Boeing 777 whose left wing is in the foreground of the picture above. The flight was from San Francisco to London. It took off in the evening, so we got to see a stunning sunset with a beautiful blood red sky. In the plane, as we were so high up, the view of the sun and backlight clouds was amazing. Continue reading
I am writing this during the second of the three visits to San Francisco airport I will be making this week. A Singapore airlines plane has just taken off, and a Jumbojet is taxing past the window in front of me. I am in San Francisco to visit a colleague at a research lab called Laurence Berkley National Laboratory (LBNL), and I squeezed in a quick visit to a university, Stanford, just south of here. California is a beautiful place, above is a view from LBNL which is on hills above San Francisco bay, and on the other side of the bay from San Francisco. The view is looking down and across the bay and you can see San Francisco itself on the other side of the blue water of the bay. Berkley is in the foreground this side of the bay. It is a beautiful view.
The standard way of studying the structures of crystals on atomic lengthscales, is X-ray diffraction. We fire X-rays at a sample and then detect the X-rays that bounce off the sample, as a function of the angle X-rays come out. This gives us what is called a structure factor, S(k), where k is called the wavector, which has dimensions of inverse length, i.e., m-1.