| There are tools and data available at the Science of Science (Sci^2) site [1], part of the Cyberinfrastructure for Network Science Center, funded by NSF. I haven't looked closely in a couple years,
but my impression is NSF hopes such tools can help show the effects (and "ROI") of research grant money, connection with PI's and institutions, and their impact both on publication citation and economic impact (development of technology etc.). Ideally this could also be used to measure growth in technical fields to determine whether more (or less) funding is required to answer bigger questions in basic science (which may not have economic incentives yet), methodologies, public policy, and education (will there be enough Ph.D's in the pipeline to meet demands for fields that will exist in ten years?). Scientometrics [2] (the journal) has been around for nearly 40 years, and I assume people were thinking about such issues then. Sci^2 looks to me like a more "big data" approach to not only understanding this, but seeing if it is possible to "push" the frontiers
(but I admit I don't know anything that goes on at NSF or how their decision-making process works). Another tool, Publish Or Perish [3], is aimed at individual academics to understand their (or another's) impact in terms of citation metrics that are used in the games for academic (and other) hiring purposes. I stumbled on Sci^2 when trying to learn some new fields
(computer vision, hpc / parallel computing, network science, sensemaking)
and wanted to quickly find seminal papers (ie highly cited, or literature reviews)
to quickly get a broad overview. Not having the patience or time to read lots,
playing with interesting tools and trying to extract data from Google Scholar and the like was more attractive. Being impatient, I wanted a way to process knowledge like data.
To measure something like growth of a field, it seems something like scientometrics with some natural language processing and ontology engineering is needed. The Google paper seems to be more about an analysis of Google Scholar data and what can be gleaned there. Maybe an update of Google Scholar Metrics is coming? I am surprised no reference to scientometrics in the arXiv paper (maybe they aren't familiar with the literature?). 1. https://sci2.cns.iu.edu/user/index.php
2. http://en.wikipedia.org/wiki/Scientometrics_%28journal%29
3. http://www.harzing.com/pop.htm |