Research Papers in Computer Science
Since we recently announced our $10001 Binary Battle to promote applications built on the Mendeley API (now including PLoS as well), I decided to take a look at the data to see what people have to work with. My analysis focused on our second largest discipline, Computer Science. Biological Sciences (my discipline) is the largest, but I started with this one so that I could look at the data with fresh eyes, and also because it’s got some really cool papers to talk about. Here’s what I found:
What I found was a fascinating list of topics, with many of the expected fundamental papers like Shannon’s Theory of Information and the Google paper, a strong showing from Mapreduce and machine learning, but also some interesting hints that augmented reality may be becoming more of an actual reality soon.
The top graph summarizes the overall results of the analysis. This graph shows the Top 10 papers among those who have listed computer science as their discipline and chosen a subdiscipline. The bars are colored according to subdiscipline and the number of readers is shown on the x-axis. The bar graphs for each paper show the distribution of readership levels among subdisciplines. 17 of the 21 CS subdisciplines are represented and the axis scales and color schemes remain constant throughout. Click on any graph to explore it in more detail or to grab the raw data.(NB: A minority of Computer Scientists have listed a subdiscipline. I would encourage everyone to do so.)
1. Latent Dirichlet Allocation (available full-text)
LDA is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one instead of Shannon’s information theory paper (#7) or the paper describing the concept that became Google (#3). It turns out that interest in this paper is very strong among those who list artificial intelligence as their subdiscipline. In fact, AI researchers contributed the majority of readership to 6 out
What I found was a fascinating list of topics, with many of the expected fundamental papers like Shannon’s Theory of Information and the Google paper, a strong showing from Mapreduce and machine learning, but also some interesting hints that augmented reality may be becoming more of an actual reality soon.
The top graph summarizes the overall results of the analysis. This graph shows the Top 10 papers among those who have listed computer science as their discipline and chosen a subdiscipline. The bars are colored according to subdiscipline and the number of readers is shown on the x-axis. The bar graphs for each paper show the distribution of readership levels among subdisciplines. 17 of the 21 CS subdisciplines are represented and the axis scales and color schemes remain constant throughout. Click on any graph to explore it in more detail or to grab the raw data.(NB: A minority of Computer Scientists have listed a subdiscipline. I would encourage everyone to do so.)
1. Latent Dirichlet Allocation (available full-text)
LDA is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one instead of Shannon’s information theory paper (#7) or the paper describing the concept that became Google (#3). It turns out that interest in this paper is very strong among those who list artificial intelligence as their subdiscipline. In fact, AI researchers contributed the majority of readership to 6 out