Introductory Bibliometrics (WS 22/23)

In the course of this exercise series students will be introduced to practical knowledge and skills to perform bibliometrics. Sooner or later students of bibliometrics will encounter challenges that are far beyond the functionalities of trustworthy spreadsheet applications. The analysis may require gathering and extracting data from web-based APIs or databases, mangle data to very specific needs, merge data sources, do simple statistical analyses or even perform advanced analyses, such as text-mining, network analysis, multivariate analyses or statistical modelling. At other times when doing bibliometrics, reproducibility of analyses may be important, such as automation of mundane, annoying, repetitive – in short brain-dead – tasks, such as running the same analysis over and over again using different data. In this exercise series students will be introduced to working with R to perform bibliometric analyses. As with all programming languages the only way of “learning” is “learning by doing”. Students will be introduced to the basics of the R syntax, how to search for packages and use them, load and process data, do some basic statistical analyses, visualize results, and, maybe most importantly, how to make use of the help function and process the supplementary material available for most R packages. No prior experience in programming is required. The course is aligned with which attendance is highly recommended for contextual purposes.