The economy of science has been traditionally shaped around the design of metrics that attempt to capture several different facets of the impact of scientific works. Analytics and mining around (co-)citation and co-authorship graphs, taking into account also parameters such as time, scientific output per field, and active years, are often the fundamental pieces of information that are considered in most of the well adopted metrics. There are, however, many other aspects that can contribute further to the assessment of scientific impact, as well as to the evaluation of the performance of individuals, and organisations, e.g., university departments and research centers. Such facets may cover for example the measurement of research funding raised, the impact of scientific works in patented ideas, or even the extent to which a scientific work constituted the basis for the birth of a new discipline or a new scientific (sub)area. In this work we are going to present an overview of the most recent trends in novel metrics for assessing scientific impact and performance, as well as the technical challenges faced by integrating a plethora of heterogeneous data sources in order to be able to shape the necessary views for these metrics, and the novel information extraction techniques employed to facilitate the process.
|Original language||American English|
|Publication status||Published - 2020|