Until recently, the quantitative study of science has focused on studying patterns in publications, such as citation counts to discern impact, and in coauthorship networks to discern collaboration. However, two major trends are converging that offer the field of scientometrics a novel opportunity to understand scientific discovery and also to influence how science is done. The first is the advent of vast computational resources and storage capacity available to scientists, and the second is automated science. These innovations offer the potential for a new type of scientometrics: quantitatively examining scientific discoveries themselves. This study of discoveries, rather than simply of scientific publications, offers the opportunity to understand science at a deeper level. We term this discovery-based approach to scientometrics as eurekometrics.
Eurekometrics aims to supplement the traditional bibliometric approach of scientometrics by examining the properties of scientific discoveries themselves rather than examining the properties of scientific publications. This is not simply a methodological development but a conceptual one. By using new types of data, we may be able to ask entirely different sorts of questions than we could before. For example, we are now able to examine both the material properties of phenomena that are discovered, such as their physical size, intrinsic entropy, or informational complexity, as well as the human properties of the phenomena, such as how much money, time, or effort it takes to discover them.
Arbesman, S., & Christakis, N. (2011). Eurekometrics: Analyzing the Nature of Discovery PLoS Computational Biology, 7 (6) DOI: 10.1371/journal.pcbi.1002072