Bibliometrics

Many of the most popular bibliometrics databases allow the user to create some summary graphs and reports (SciVal, in particular, is strong at this).

However, the most flexible, powerful, and reproducible way to interact with and analyze bibliometric data is to use popular, open-source programming languages like Python or R. Both languages offer a variety of libraries / packages that can assist you in acquiring, processing, and analyzing bibliometric data. Some examples include pybliometrics for Python and Bibliometrix for R.

For some low- or no-code options see:

Sci2
Modular toolset specifically designed for the study of science; temporal, geospatial, topical, and network analysis and visualization of scholarly datasets at the micro (individual), meso (local), and macro (global) levels

PoP (Publish or Perish)
Calculates citation metrics from data in Google Scholar

Citespace
Freely available Java application for visualizing and analyzing trends and patterns in scientific literature.

Gephi
Open-source network and visualization software; import data from almost anywhere under-supported file formats. Requires knowledge of Java and/or OpenGL