Skip to main content

US Publishing Dominance?

I ran across this data on science publications by country from the World Bank.


Some quick thoughts:

  • It seems that the EU, contrary to popular wisdom, has maintained a slight lead over the US as the academic science publishing centre for a bit more than a decade.
  • Of course the US (pop. ~ 307 million) is still publishing above its population adjusted weight relative to the EU (pop. ~ 501 million).
  • However, assuming that universities are places where resources are transfered from teaching (i.e. students) to research and given the incredible rise in US student debt (see my previous post) I would have expected to see a larger increase in US publications because presumably US universities would have more resources. Of course there are many different reasons that student debt can increase without an increase in university resources, but an essentially flat absolute number of publications over the entire period is kind of strange. 
  • Finally, what countries are producing the big gains in total global publications? It doesn't seem to be any of the those in the graph.

Comments

PinskVinsk said…
Regarding the countries (other than India, China, and the US) that are producing the gains in research...South Korea? Singapore? Japan? Given that it's science and tech, rather than arts and social science, I would guess that's where all the publishing is coming from.
Unknown said…
Oddly, none of those countries individually seem to have had very big increases. Maybe just a lot of emerging market economies are adding a bit to the cumulative number of publications.

Popular posts from this blog

Dropbox & R Data

I'm always looking for ways to download data from the internet into R. Though I prefer to host and access plain-text data sets (CSV is my personal favourite) from GitHub (see my short paper on the topic) sometimes it's convenient to get data stored on Dropbox . There has been a change in the way Dropbox URLs work and I just added some functionality to the repmis R package. So I though that I'ld write a quick post on how to directly download data from Dropbox into R. The download method is different depending on whether or not your plain-text data is in a Dropbox Public folder or not. Dropbox Public Folder Dropbox is trying to do away with its public folders. New users need to actively create a Public folder. Regardless, sometimes you may want to download data from one. It used to be that files in Public folders were accessible through non-secure (http) URLs. It's easy to download these into R, just use the read.table command, where the URL is the file name

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame. I've found the various R methods for doing this hard to remember and usually need to look at old blog posts . Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command– slide –to the DataCombine R package (v0.1.5). Building on the shift function TszKin Julian posted on his blog , slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data. Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996

A Link Between topicmodels LDA and LDAvis

Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background). LDAvis doesn't fit topic models, it just visualises the output. As such it is agnostic about what package you use to fit your LDA topic model. They have a useful example of how to use output from the lda package. I wanted to use LDAvis with output from the topicmodels package. It works really nicely with texts preprocessed using the tm package. The trick is extracting the information LDAvis requires from the model and placing it into a specifically structured JSON formatted object. To make the conversion from topicmodels output to LDAvis JSON input easier, I created a linking function called topicmodels_json_ldavis . The full function is below. To