Mastering the Literature

Distill a Scientific Domain with Bibliometric Analysis in R

Colton Baumler
ccbaumler@ucdavis.edu

University of California, Davis

Wednesday, the 12th of February, 2025

Overview

  • Background

  • Databases

  • A Shiny Approach

  • A Scripted Approach

Background

  • Why do I feel so bad all the time?

  • What is bibliometrics?

  • Why bibliometrics can help?

A foundation is key to pushing boundaries

Background

Graduate school

  • Understand what is known across a domain
  • Identify and explain something that is unknown (Preferably, coherently)
  • Ultimately, unstructured with many possible options

Improve personal professional care with systematic approaches

Background

Feelings of:

  • guilt
  • all-consuming prioritization
  • wealth disparity
  • depression
  • anxiety
  • stupidity
  • uncertainty and disconnection (I.e. Lost)
  • urgency and pressure (I.e. time slipping away
  • regret
  • stress
  • isolation

Solutions?

Navigating mental health challenges in graduate school

Navigating mental health challenges in graduate school

The valley of despair is the graduate school experience

Background

Dunning-Krueger Effect

Gartner Hype Cycle

The vallies are peak imposter syndrome

Background

Dunning-Kruger Effect

Gartner Hype Cycle

Bibliometrics can rapidly build your compentence in a domain

Bibliometrics can expose you to the techniques of a domain

Bibliometrics provides an accesible framework for equitable scholarship

Background

“[Bibliometrics is] The measurement of all aspects related to the publication and reading of books and documents.”

  • Bibliometrics uses

    • Archival database
    • Article metadata
    • Statistical analysis

  • Bibliometrics answers

    • where the field has grown from
    • its most relevant articles
    • its emerging topics

Bibliometrics yields networks based on references

Background

Minimize your time in the Valley with bibliometrics

Background

  • General bibliometric science yields:
    • A solid foundation of the necessary concepts
    • The questions answered by the field
    • Some open areas of discovery within the field
  • My proposed use of a bibliometric framework
    • Use reproducible methods
    • for systematic evaluation
    • in a quantifiable way!
    • Ultimately, ending with justifiable literature for your needs

Databases

  • Basics of literature search
    • Which databases to use
    • Searching techniques
    • Sample size
    • Exporting search metadata
    • Filtration techniques

Your database choice will affect your results

Databases

Databases focus on different levels of content selection, curation, and comprehensiveness.

  • Web of Science (WoS)
  • Scopus
  • PubMed
  • Dimensions
  • CrossRef
  • Semantic Scholar
  • Microsoft Academic
  • Lens.org
  • Cochran Library
  • Google Scholar

“Scopus and WoS compliment each others as neither resource is all inclusive”

Your database choice will affect your results

Databases

Databases focus on different levels of content selection, curation, and comprehensiveness.

  • Web of Science (WoS)
  • Scopus
  • PubMed
  • Dimensions
  • CrossRef
  • Semantic Scholar
  • Microsoft Academic
  • Lens.org
  • Cochran Library
  • Google Scholar

“Scopus and WOS compliment each others as neither resource is all inclusive”

Signing up, search and exporting can be nuianced

Databases

  • Register with university email for Scopus and WoS access when off university wifi/library VPN
    • PubMed does not require registration at all
    • Scopus (Multidisciplinary)
      • 82.4 million records
      • 1788 – present
      • bibtex
      • full record
      • 20,000 export limit
    • WoS (Multidisciplinary)
      • Core, 79 million
      • 1900 - present
      • plaintext or bibtex
      • custom selection
      • 1000 export limit
    • PubMed (Only life science and biomedical)
      • 35 million
      • 1966 - present (*1809)
      • plaintext
      • pubmed format
      • 10,000 export limit

Wildcards allow the capture of many individual words

Databases

Table 1: Wildcards
Wildcard Charater Definition Example Result
* Any amount of character/s to include zero *man man, woman, human, superman, superwoman, {etc}
? Any single character wom?n woman, women
$ (WoS only) Any single or no character $$man woman, man, human

Boolean operators are vital to focus a corpus

Databases

Table 2: Boolean Operators
Operator Affect on Search Definition Example
AND Narrows Intersects all terms separated by operator migration AND butterfl*
OR Broadens Unites any and all terms separated by operator migration OR butterfl*
NOT Narrows Excludes term following operator migration AND bird* NOT butterfl*
NEAR/x(WoS) Narrows Find terms joined by operator near each other by \(x\) words America NEAR/10 butterfly
SAME(WoS) Narrows Find terms joined by operator if in the same sentence America SAME butterfly
W/n(Scopus) Narrows Find terms joined by operator within each other by \(n\) words American W/10 butterfly
Pre/n(Scopus) Narrows Find terms where preceeding term to operator is within \(n\) words of following term American Pre/3 butterfly

Combine all search techniques for the best corpus results

Databases

WoS = 184

ALL=((“colorectal cancer*” OR “colorectal neoplas*” OR “adenomatous polyposis coli” OR “colon* neoplas*” OR “rectal neoplas*” OR “hereditary nonpolypo*”) AND (“metagenom*” AND “metabol*”))

Scopus = 367 & PubMed = 248

TITLE-ABS-KEY ( “colorectal cancer*” OR “colorectal neoplas*” OR “adenomatous polyposis coli” OR “colon* neoplas*” OR “rectal neoplas*” OR “hereditary nonpolypo*” AND “metagenom*” AND “metabol*” )

Combine all search techniques for the best corpus results

Databases

WoS = 184

ALL=((“colorectal cancer*” OR “colorectal neoplas*” OR “adenomatous polyposis coli” OR “colon* neoplas*” OR “rectal neoplas*” OR “hereditary nonpolypo*”) AND (metagenom*” AND “metabol*”))

Scopus = 367 & PubMed = 248

TITLE-ABS-KEY ( “colorectal cancer*” OR “colorectal neoplas*” OR “adenomatous polyposis coli” OR “colon* neoplas*” OR “rectal neoplas*” OR “hereditary nonpolypo*” AND “metagenom*” AND “metabol*” )

Sample Size goals of 500 - 1500 articles

Databases

Avatar
Results from power simulation, showing power as a function of sample size, with effect sizes shown as different colors, and alpha shown as line type. The standard criterion of 80 percent power is shown by the dotted black line.

Build a corpus for your interests

Databases

Focus on WoS and/or Scopus

10:00
Table 4: Wildcards, Boolean Operators, Phrase Searching
Wildcards
Wildcard Charater Definition Example Result
* Any amount of character/s to include zero *man man, woman, human, superman, superwoman, {etc}
? Any single character wom?n woman, women
$ (WoS only) Any single or no character $$man woman, man, human
Boolean Operators
Operator Affect on Search Definition Example
AND Narrows Find all terms separated by operator migration AND butterfl\*
OR Broadens Find any and all terms separated by operator migration OR butterfl\*
NOT Narrows Excludes term following operator migration AND bird\* NOT butterfl\*
NEAR/x(WoS) Narrows Find terms joined by operator near each other by \(x\) words America NEAR/10 butterfly
SAME(WoS) Narrows Find terms joined by operator if in the same sentence America SAME butterfly
W/n(Scopus) Narrows Find terms joined by operator within each other by \(n\) words American W/10 butterfly
Pre/n(Scopus) Narrows Find terms where preceeding term to operator is within \(n\) words of following term American Pre/3 butterfly
Phrase Searching
Phrase type Example Search
Loose "phrase searching" phrase search, phrase searches, phrase searching
Exact(Scopus) {phrase searching} phrase searching

Filtration techniques are whatever work for you

Databases

Literature filtration

  • Prisma

Filtration techniques are whatever work for you

Databases

Literature filtration

  • Prisma
  • 80:20

Filtration techniques are whatever work for you

Databases

Literature filtration

  • Prisma
  • 80:20
  • metagear?

A Shiny Approach

  • The Bibliometrix package
  • Biblioshiny interface
  • Some standard bibliometric plots

Install, load, and run biblioshiny() in almost no code

A Shiny Approach

# Install the bibliometrix package

install.packages('bibliometrix')
# Load the bibliometrix library

library(bibliometrix) # Load and analyze bibliograpic data
# Run the biblioshiny shiny application

biblioshiny()
# Install the bibliometrix package

install.packages('bibliometrix')

# Lazy load and Run the biblioshiny shiny application

bibliometrix::biblioshiny()

Your console should look like…

A Shiny Approach

Image of biblioshiny script in console

This is the landing page every time

A Shiny Approach

Image of biblioshiny landing page

Loading allows only a single file…

A Shiny Approach

Image of biblioshiny ready to load data

Metadata report of my corpus

A Shiny Approach

Image of the biblioshiny loaded data report

Loaded data in tabular form

A Shiny Approach

Image of biblioshiny with data loaded

Relavant sources to include in my RSS feeds

A Shiny Approach

Image of biblioshiny most relevant sources lolli pop plot

The most cited documents across the database

A Shiny Approach

Image of biblioshiny most globally cited lolli pop plot

A normalized citation count allows for more current articles

A Shiny Approach

Image of biblioshiny most globally cited table organized by normalized total citations (NTC)

A longitudinal network focuses in on local seminal papers

A Shiny Approach

Image of biblioshiny historiograph plot

Peaks within the 5 year median may be cornerstone papers

A Shiny Approach

Image of biblioshiny reference publication year spectroscopy plot (1970-2022)

At a small peak no papers stand out

A Shiny Approach

Image of biblioshiny reference publication year spectroscopy table (2000)

At greater peaks, some articles outlie the norm

A Shiny Approach

Image of biblioshiny reference publication year spectroscopy table (2009)

At greater peaks, some articles outlie the norm

A Shiny Approach

Image of biblioshiny reference publication year spectroscopy table (2012)

Explore the corpus you have created

A Shiny Approach

10:00

A Scripted Approach

  • Replicate biblioshiny() plots

  • Examine, plot, and summarize data

  • Deeply understand some functions

  • Integrate R script output with Zotero

Summary

You have learned:

  • Why the struggle exists
    • One method to alleviate some pressure
  • Databases
    • Bibliographic databases
    • Search engine techniques
    • Sampling size
    • Exportation and Filtration
  • Bibliometric Analysis
    • One tool
    • Two approaches
    • Standard data analysis in R
    • Interoperation with RSS and Reference management

Acknowledgments

“Alone we can do so little; together we can do so much.”
Helen Keller

  • C. Titus Brown
  • Pamela Reynolds
  • Bryshal Moore
  • Megan Van Noord
  • DIB Lab members
  • DataLab members