5. Academic Life

Gender bias at SfN 2014 (a micro-study)

According to some very hasty analysis of 749 posters I attended, males and females are presented at SfN equally well, and their posters get very similar patterns of attention from spectators. However a disproportionately large share of "hot" posters (those that gather a crowd of spectators) were presented by male scientists (p=0.01, exact Fisher test). More details: here.
On what is known as the "oversupply of PhDs", 2014

My musings about how competition for TT jobs comes with an inevitable conflict between fairness (which implies that everybody should have a chance to prove themselves) and "cruelness" (current system allows people trying and trying again, for years, if not decades). Some people say that we just should not let people into grad schools, so that every PhD would get a guaranteed TT job. I try to argue that it would be both ridiculously inefficient, and astronomically unfair. Read more here and here.

Cumulative impact-factor of successful postdocs, 2013

My original claim (as described here) was that one needs about 100 points of cumulative impact factor to successfully land a TT position in research university. I'm no longer sure that it is true, but it was definitely true back in 2012-2013, at least for those several recently tenured scientists I knew back then.

Careers in academia, 2012

A potentially useful slide about career paths available for a life scientist these days (last updated in 2012). The numbers come from various reports, analytics, NIH statistics and other sources of this kind. Some of the numbers may be Neuroscience-specific.
(Click to see a bigger version)


Scientific research per capita in different countries, 2013

Research and Development Spending per Capita

Total spending was taken from this Wikipedia page, population came from here, and for countries that are not shown in the R&D list (those with smaller spending values) I linearly extrapolated R&D spending based on their health expenditure per capita. It's an arbitrary choice, but it probably does not matter too much, as these countries all ended up as pale green on the map anyway. The map was generated in Google Fusion.

Known mistakes: Svalbard actually belongs to Norway; Taiwan should not be colored like mainland China, but should be about the same shade as Canada, while Netherlands should be about the same shade as Australia.

One possible interpretation of this map is "How much of their pocket money every citizen of this country spends on R&D". The drawback is that a) people in different countries may have very different amount of pocket money; b) R&D is obviously funded not only from income taxes, but also from corporate taxes.

Another interpretation is the "potential share of engineers and doctors in the population". Number of R&D jobs = R&D spending / average salary, but average salary is (probably) proportional to the cost of living, so "Adjusted (real terms) R&D spending" is proportional to the number of R&D positions in the country. If we now divide this value onto country population, we should get a value proportional to the share of R&D jobs in the country. A potential issue here is that the average R&D salary, as compared to the average salary across all occupations in the country, would probably be very different in different parts of the world. For example in Russia all R&D people live below poverty level, while in the US most of them live rather decently. Still, at least as a rough estimate, it may work. In a way, one may argue that this map assumes a "global world" situation, in which an R&D specialist can move from one country to another, depending on the funding situation.