As part of a research agenda in scholarly impact I wrote an R program to analyse Google Scholar data for sets of scholars, e.g., all the researchers in a Department or a University. The R package scholar does the work of scraping the Google data for individual scholars; scholarNET takes individual Google Scholar IDs, retrieves the data for each scholar, and produces a ranking based on h index:
The program also retrieves the publications for each scholar and then matches them on title to detect coauthorship relationships, which are then represented in a social network graph.
The social network shows that Shaw and Grant have coauthored two papers and Creazza and Colicchia have coauthored 14 papers. There is not a lot of evidence of coauthorship activity in this particular network. The network is also written out in GML (graph modelling language) format for visualisation in Gephi.
In setting up scholarNET the hard work is collating the Google scholar IDs for individual researchers, which is a manual task involving cutting the Google Scholar ID from the URL string for each academic’s profile. Of course, the approach also relies on academics having established a Google Scholar profile. However, more and more academics are setting themselves up on Google Scholar as they seek to demonstrate impact (e.g., at job interviews, for promotion cases) and to understand how their work is being used by others.
Download the R code for scholarNET and the list of scholar ids to try out the analysis. This was the first program I wrote in R to do something useful and the code is not pretty! If there is interest in it I will rewrite it.