I had this idea that it’d be fun to look at all PubMed’s articles from 2011 and extract country affiliation for each individual country. So I set out to do just that, but in addition to just look at 2011 I also looked at proportional change in publication 1980–2010 for the top 20 countries. The data for 2011 is visualized on a world map both as a bubble plot and as a heat map.
It turned that this project weren’t as straightforward as I first had anticipated. Mainly because PubMed’s affiliation field is a veritable mess with no apparent reporting standard. I imagine there are databases who are much more suited to this task than PubMed.
There were 986 427 articles published in PubMed in 2011; so I, naturally, used R to extract national publication counts. I did this by downloading all citations into one 8.37 Gb XML-file, imported the affiliation strings into MySQL and then used R to extract country affiliation using grep and regular expressions.
To avoid unnecessary manual work I used lists of country names, U.S state & university names, India states and Japan universities. I also looked at word frequencies for the affiliations strings that couldn’t be matched, and used this to make additional pattern lists. Lastly, I also used mail-suffixes to extract affiliation.
To find out how many mismatches my script perfomed, I drew a random sample (n = 2000) and manually screened for errors. 22 errors were found, and all of them entailed the string being matched to the correct country plus one incorrect country, i.e. this string were matched to both UK and US (because “Bristol” is matched to UK):
Department of Biotransformation, Bristol-Myers Squibb, Route 206 and Province Line Road, Princeton, NJ 08543, USA. email@example.com
It’s not really a big problem since it only occurs in 1.1 % of the sample. The following countries had erroneous extra matches in my random screening sample:
x freq 2 Australia 1 3 Austria 1 4 China 3 5 France 1 6 India 2 7 Japan 1 8 Oman 1 9 Saint Lucia 2 10 UK 8 11 USA 2
Moreover 1.8% of the affiliation strings couldn’t be matched to any country, by analyzing the word frequencies for the unmatched strings, I concluded there didn’t appear to be any words that could be used to identify an significant amount of countries.
Additionally, I compared the number of hits for my top 20 countries to the corresponding hits when searching PubMed using rudimentary country queries. These were the results:
search R PubMed dif error 1 United States of America[ad] OR United States[ad] OR US[ad] OR USA[ad] 252796 242050 10746 0.04 2 China[ad] 77614 76359 1255 0.02 3 UK[ad] OR United Kingdom[ad] OR England[ad] 56069 54661 1408 0.03 4 Japan[ad] 51740 48518 3222 0.06 5 Germany[ad] OR Deutschland[ad] 48183 44405 3778 0.08 6 Canada[ad] 31926 29386 2540 0.08 7 Italy[ad] 31883 30971 912 0.03 8 France[ad] 31233 28832 2401 0.08 9 Spain[ad] 24901 21268 3633 0.15 10 Australia[ad] 23807 22891 916 0.04 11 Korea[ad] 23796 23778 18 0.00 12 India[ad] 23371 23093 278 0.01 13 Netherlands[ad] 20002 19602 400 0.02 14 Brazil[ad] OR Brasil[ad] 18868 18223 645 0.03 15 Taiwan[ad] 12324 12321 3 0.00 16 Switzerland[ad] 11685 10320 1365 0.12 17 Sweden[ad] 11018 10506 512 0.05 18 Belgium[ad] 8551 8146 405 0.05 19 Poland[ad] 7914 6526 1388 0.18
The measurement error is a bit high in countries like Poland, Switzerland and Spain. Nonetheless, I decided to use these PubMed quires to look at annual publications for these countries 1980–2010, using my PubMed trend script
I really don’t know why USA had such a boost in the 1990s, perhaps it got something to do with PubMed’s indexing or maybe it’s a consequence of the “1990s United States boom”? The reason for the sudden increase in US citations in the 90s is that prior to 1995 MEDLINE did only record institution, city, and state including zip code for authors affiliated with the US. So naturally, my queries will miss most US publications prior to 1995. However, the apparent question is: when will china surpass US in scientific output?
PS 1. Thanks to Allan Just for telling me how to extract centroid values from the country polygons.
PS 2. My plan is to do some more in-depth analyzes if this data, e.g. to look at publications per capita (in a vain attempt to increase Sweden’s rankings) and some traditional statistical analysis. Update: Publications per capita added.