Measuring Research Productivity at Scale

The increasing availability of digital data on scholarly inputs and outputs – from research funding, productivity, and collaboration to paper citations and scientist mobility – offers unprecedented opportunities to explore the structure and evolution of science. It has driven the development of a new field - the science of science (SciSci) - which in essence is a transdisciplinary approach that uses large data sets to study the mechanisms underlying the doing of science-from the choice of a research problem to career trajectories and progress within a field. Like any other discipline, the science of science field is dependent to a great degree on their instruments of measurement. The more accurate the instrument, the better researchers will be able to observe specific phenomena. In this paper, we present a framework for data collection and analysis of the large publicly available data in Google Scholar (GS) which has gained ground as a free scholarly literature retrieval source. We will discuss the design and implementation of an automatic web-scraper for GS and its ability to produce reliable scholarly productivity metrics to serve as measures in a large-scale study addressing research questions surrounding the academic promotion & tenure process.