  Publishing original scientific research is inherent to the work of a
researcher. However, the pressure to maintain productivity and scientific
impact can lead to research group publishing excessively, negatively affecting
the mental health of a researcher. Ph.D. students and early career researchers
are particularly susceptible to this pressure due to the inherent vulnerability
of their positions. At present, there are no resources that concisely summarise
the publication culture of a research group to help the researcher make an
informed decision before joining. In this article, we present the 'Super
Researcher' app, an R Shiny application(app) with a user-friendly interface.
Using text-mining methodology to extract publicly available author data from
Scopus, this pilot app has four fundamental functions to provide snapshot
information that will help researchers grasp the publication culture of a
research group within minutes. The 'Super Researcher' app provides information
on: 1) institution data, 2) author's publication, 3) co-author network plots
and 4) publication journals. The 'Super Researcher' app is built on R shiny
which provides an interactive interface to users. This app utilizes the Big
Data framework Apache Spark to mine relevant information from a huge author
information database. The author's information is stored and manipulated using
both SQL(SQLite) and NoSQL(HBase) databases. Hbase is used for local data
storage and manipulation while SQLite feeds data to the R Shiny interface. In
this paper, we introduce these functionalities and illustrate how this
information can help guide a researcher to select a new Principle Investigator
(PI) with better compatibility in terms of publication attitude using a case
study.
  Available: https://researchmind.co.uk/super-researcher/
