h-index visualization
About
My project (GitHub Link) uses the Scopus API in Python through Pybliometrics to get citation data for any researcher in the world. The data is then processed to determine both historical and document-based (see repository README for clarification) h-indices and citation counts. The data is then visualized in an easily interpretable manner using the Matplotlib library.
Demo
Note: this demo does not have the same functionality as the repository; no API calls are made, results are pre-computed and searches will only work for Cornell CS professors as of Fall 2023, with data up to Fall 2024. Analysis is document-based.
Motivation
Currently, it's easy to find a researcher's current h-index through Google Scholar, however there are no (to my knowledge) tools to automatically compute h-index from some point in the past, say 5 years ago. Conversely, if there was a tool to compute past h-indices it could be easily extended to compute it for all years and to produce a nice visualization. Another hidden use of the tool is that currently google searching the names of many less well-known researchers (perhaps with common names) does not result in a hit for their Google Scholar page. This tool allows the contributions of almost any researcher in the world to be easily exposed. If you would like to use my visualizer for the purposes described above but do not have the neccesary requirements, (i.e. Elsevier Developer API Key, network access at an Elsevier-subscribing institution), please contact me.