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Bibliometrics

We may be more familiar with software such as VOSviewer, Bibliometrix, and analytical features in databases such as Scopus and PubMed. However, the use of these tools can be summarized in a science called Bibliometrics. Bibliometrics is a branch of library science that has been frequently discussed recently. Students and senior researchers use bibliometrics to look for novelty, research gaps, trends in a field of science, and search for collaboration prospects. Research institutions and universities also use bibliometrics to evaluate and plan research carried out by their academic community.

Bibliometrics is application of mathematics and statistical methods to books and other media of communication (Alan Pritchard, 1969)

“Although recognizably bibliometric techniques have been applied for at least a century, the emergence of bibliometrics as a scientific field was triggered (in the 1960s) by the development of  the Institute for Scientific Information (ISI) Science Citation Index (SCI) by Eugene Garfield, as a logical continuation of his drive to support scientific literature searching.” (Mike Thelwall, 2008)

(Source: Roemer and Borchardt, 2015)

(Source: Donthu, et al., 2021)

(Source: https://en.wikipedia.org/wiki/Informetrics)

Source: Cobo, et al. (2012)

(Source: Donthu, et al., 2021)

 

DATABASE

FEATURES

PERFORMANCE ANALYZER

ArXiv Indexing database for:

  • physics
  • mathematics
  • computer science
  • quantitative biology
  • quantitative finance
  • statistics
  • electrical engineering and systems science
  • economics database indexing
arXiv Usage Statistics

Submissions by Category since 2009+ – arXiv info

Submissions by Institution (2022) – arXiv info

Reports – arXiv info

PubMed
  • Biomedical indexing database
  • Metadata export tools (.csv)
iCite
PrePrints Indexing database Statistics | Preprints.org
Scopus Indexing database SciVal
Web of Science Indexing database InCites
ERIC Not available
PubChem Not available
IEEE
  • Electrical and electronics engineering indexing database
  • Metadata export tools (.csv)
Not available
AGRIS Agricultural indexing database Not available
MathSciNet Mathematics indexing database Not available
MedRxiv Health science indexing database Not available
BioRxiv Biology indexing database Not available

SCIENCE MAPPING = BIBLIOMETRICS + SOCIAL NETWORK ANALYSIS

SOCIAL NETWORK ANALYSIS is the process of investigating social structures through the use of networks and graph theory (Otte and Rousseau, 2002)

SCIENCE MAPPING is a spatial representation of how disciplines, fields, specialities, and individual papers or authors are related to one another (Small, 1999)

 

(Source: Donthu, et al., 2021)

 

 

(Source: Blondel, et al., 2008)

Data presented here are synthesized using GPT-4o, rechecked and rearranged by Tri Hardian S.

 

ADVANTAGES IN

RESEARCHERS

INSTITUTIONS

Identifying research trends
  • Trend analysis
  • Identifying research gaps
Identifying research gaps
Career advancement, recruitment and talent management Academic promotions and tenure Attracting top researchers
Measuring research impact Citation analysis
Discovering influential works and authors Literature review
Promoting interdisciplinary research Identifying research gaps
=============== =============== ===============
  • Assessing research performance
  • Benchmarking and performance evaluation
  • Improving research visibility and reputation
Institutional comparisons
  • Institutional benchmarking
  • Global rankings
Facilitating collaboration and networking Network analysis Partnerships
  • Informing funding and policy decisions
  • Enhancing funding and resource allocation
Grant applications Grant and funding proposals
  • Resource and collection management
  • Enhancing research quality
  • Library and collection management
  • Journal selection
  • Library and database management
  • Journal selection
Public accountability, engagement and transparency Public engagement
  • Demonstrating impact
  • Transparency
Informing strategic planning Research strategy development
  • Evaluating faculty and departments
  • Enhancing academic programs
  • Performance reviews
  • Curriculum development
Facilitating knowledge transfer and innovation Industry collaboration

Data presented here are synthesized using GPT-4o, rechecked and rearranged by Tri Hardian S.

 

DISADVANTAGES IN

RESEARCHERS

INSTITUTIONS

Quality vs quantity Overemphasis on quantitative metrics Overemphasis on quantitative metrics
Disciplinary bias Field variation (different academic fields have varying publication and citation practices) Inherent differences (different academic fields have varying publication and citation practices)
Gaming the system Manipulative practices Manipulative practices
  • Neglecting uncited work
  • Neglecting early-career researchers
Bias against new and niche research Impact on new researchers
Limited scope Neglect of non-traditional outputs (books, policy reports, patents, etc.) Neglect of non-traditional outputs (books, policy reports, patents, etc.)
Data quality and availability Incomplete data (especially for older publications or those in non-English languages) Incomplete data (especially for older publications or those in non-English languages)
Lag time Delayed recognition Delayed recognition
Misuse and misinterpretation Simplistic evaluations Simplistic evaluations
Impact on collaboration and interdisciplinarity Competitive environment Competitive environment
  • Ethical and mental health concerns
  • Reputation and ranking pressures
  • Pressure to publish
  • Pressure and mental health
  • Publish or perish culture
  • Pressure and stress
  • Short-term focus
  • Impact on research culture
Impact of negative citations Negative attention
Resource allocation Skewed priorities

Before visualizing, we must collect data from various commonly known data source. One of the simplest science mapping tool is VOSviewer. VOSviewer is able to handle data source from:

  • Scopus
  • Pubmed
  • Dimensions
  • Lens
  • Web of Science

There are some other website capable of extracting data source ready for visualization, i.e. Europe PMS, OpenAlex, CrossRef, Semantic Scholar, Wikidata, IEEE, etc.

UNS institutional repository (https://digilib.uns.ac.id/) is also capable of extracting data for visualization, but it must be done manually as there are no specific tools to extract the data yet. Here, other data sources are extracted as a sample, you may use any keywords other than presented here. Use these data sources to generate visualization on VOSviewer or any other visualization tools.

        1. Digilib keywords from Faculty of Medicine
            1. Digilib: CSV undergraduate theses (extracted on July 27, 2023)
            2. PubMed: pubmed [qualityoflife – hypertension – depression]
            3. Scopus: scopus [qualityoflife – hypertension – depression] Full Metadata
            4. Lens: lens [qualityoflife – hypertension – depression]
            5. Dimensions: –
            6. Thesaurus template
            7. How to search articles in Pubmed, Scopus, and Web of Science: https://www.hitihe.org/e-modules-for-health-librarians/
        2. Digilib keywords from Faculty of Education and Teacher Training: CSV

Data cleaning is a must before conducting science mapping analysis. A tool usually used to prepare data before analysis is OpenRefine. Creating a thesaurus should also be done. One simple tool to create a thesaurus can be downloaded here.

Please visit this link or the video below on how to visualize research metadata with VOSviewer:

Profile of UNS research topics published in Scopus-indexed journals from 2018-2023

 

Profile of UNS research topics published in Scopus-indexed journals from 2018-2023

Read how to make visualization with CiteSpace here

Profile of UNS research topics published in Scopus-indexed journals from 2018-2023

Please open images in new browser tab for larger resolution.
Read how to make visualization with CiteSpace here

Variable 2018-2020 2021-2023
Tree maps
Trend topics
Three-field plot (source, author, keyword)
Co-occurrence network
Collaboration network by country
Collaboration network by author
Thematic map
Thematic evolution by map (2 time-slices)

Thematic evolution by network (2 time-slices)

VOSviewer Digilib PubMed
Novelty and research gap
Research hotspot
Research trend
Bibliometrix PubMed
Co-occurrence
Evolution
Thematic map
Bibliometric research, both performance analysis and science mapping, cannot necessarily be considered as a final conclusion. It still opens up opportunities for reinterpretation through different perspectives. The results of this interpretation can even be considered as the beginning of determining a hypothesis.
For further information regarding this topic, please kindly contact our librarian Tri Hardian S. (trihardians@staff.uns.ac.id)