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A plot of my citations in Google Scholar vs. Web of Science

There has been some discussion about whether Google Scholar or one of the proprietary software companies numbers are better for citation counts. I personally think Google Scholar is better for a number of reasons:

1. Higher numbers, but consistently/adjustably higher
2. It’s free and the data are openly available. 
3. It covers more ground (patents, theses, etc.) to give a better idea of global impact
4. It’s easier to use

I haven’t seen a plot yet relating Web of Science citations to Google Scholar citations, so I made one for my papers.

GS has about 41% more citations per paper than Web of Science. That is consistent with what other people have found. It also looks reasonably linearish. I wonder what other people’s plots would look like? 

Here is the R code I used to generate the plot (the names are Pubmed IDs for the papers):

library(ggplot2)

names = c(16141318,16357033,16928955,17597765,17907809,19033188,19276151,19924215,20560929,20701754,20838408, 21186247,21685414,21747377,21931541,22031444,22087737,22096506,22257669) 

y = c(287,122,84,39,120,53,4,52,6,33,57,0,0,4,1,5,0,2,0)

x = c(200,92,48,31,79,29,4,51,2,18,44,0,0,1,0,2,0,1,0)

Year = c(2005,2006,2007,2007,2007,2008,2009,2009,2011,2010,2010,2011,2012,2011,2011,2011,2011,2011,2012)

q <- qplot(x,y,xlim=c(-20,300),ylim=c(-20,300),xlab=”Web of Knowledge”,ylab=”Google Scholar”) + geom_point(aes(colour=Year),size=5) + geom_line(aes(x = y, y = y),size=2)

R.A. Fisher is the most influential scientist ever

You can now see profiles of famous scientists on Google Scholar citations. Here are links to a few of them (via Ben L.). Von Neumann, Einstein, Newton, Feynman

But their impact on science pales in comparison (with the possible exception of Newton) to the impact of one statistician: R.A. Fisher. Many of the concepts he developed are so common and are considered so standard, that he is never cited/credited. Here are some examples of things he invented along with a conservative number of citations they would have received calculated via Google Scholar*. 

1. P-values - 3 million citations
2. Analysis of variance (ANOVA) - 1.57 million citations
3. Maximum likelihood estimation - 1.54 million citations
4. Fisher’s linear discriminant 62,400 citations
5. Randomization/permutation tests 37,940 citations
6. Genetic linkage analysis 298,000 citations
7. Fisher information 57,000 citations
8. Fisher’s exact test 237,000 citations

A couple of notes:

1. These are seriously conservative estimates, since I only searched for a few variants on some key words
2. These numbers are BIG, there isn’t another scientist in the ballpark. The guy who wrote the “most highly cited paper” got 228,441 citations on GS. His next most cited paper? 3,000 citations. Fisher has at least 5 papers with more citations than his best one. 
3. This page says Bert Vogelstein has the most citations of any person over the last 30 years. If you add up the number of citations to his top 8 papers on GS, you get 57,418. About as many as to the Fisher information matrix. 

I think this really speaks to a couple of things. One is that Fisher invented some of the most critical concepts in statistics. The other is the breadth of impact of statistical ideas across a range of disciplines. In any case, I would be hard pressed to think of another scientist who has influenced a greater range or depth of scientists with their work. 

• Calculations of citations #####################

  1. As described in a previous post
  2. # of GS results for “Analysis of Variance” + # for “ANOVA” - “Analysis of Variance”
  3. # of GS results for “maximum likelihood”
  4. # of GS results for “linear discriminant”
  5. # of GS results for “permutation test” + # for ”permutation tests” - “permutation test”
  6. # of GS results for “linkage analysis”
  7. # of GS results for “fisher information” + # for “information matrix” - “fisher information”
  8. # of GS results for “fisher’s exact test” + # for “fisher exact test” - “fisher’s exact test”