Bibliometrix

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Bibliometrix package is a R tool providing a set of tools for quantitative research in scientometrics and bibliometrics.[1]

Bibliometrics is the application of quantitative analysis and statistics to publications such as journal articles and their accompanying citation counts. Quantitative evaluation of publication and citation data is now used in almost all science fields to evaluate growth, maturity, leading authors, conceptual and intellectual maps, trend of a scientific community. Bibliometrics is also used in research performance evaluation,[2] especially in university and government labs, and also by policymakers,[3] research directors and administrators, information specialists and librarians, and scholars themselves.[2][4][5][6][7]

The package is written in R-language. R is an open-source environment and ecosystem. The existence of substantial of good statistical algorithms, access to high-quality numerical routines, and integrated data visualization tools are perhaps the strongest qualities to prefer R to other languages for scientific computation.

Bibliometrix supports scholars in key phases of analysis:

  1. Data importing and conversion to R data-frame;
  2. Descriptive analysis of a publication dataset;
  3. Network extraction for co-citation, coupling, and collaboration analyses. Matrices are the input data for performing network analysis, factorial analysis or multidimensional scaling analysis;
  4. Text mining of manuscripts (title, abstract, authors' keywords, etc.);
  5. Co-word analysis.

Main functions of Bibliometrix package

The following table lists the main functions of bibliometrix package:

Software assisted

workflow steps[8]

Bibliometrix function[9] Description
Data loading and converting Convert2df() It creates a bibliographic data frame
Data Analysis

Descriptive bibliometric analysis

biblioAnalysis()

Summary() and plot()

citations()

localCitations()

dominance()

Hindex()

lotka()

It returns an object of class bibliometrix

They summarize the main results of the bibliometric analysis

It identifies the most cited references or authors

It identifies the most cited local authors

It calculates the authors’ dominance ranking

It measures productivity and citation impact of a scholar

It estimates Lotka’s law coefficients for scientific productivity

Data Analysis

Term Extraction

termExtraction() • it extracts terms from textual fields (abstracts, titles, author's keywords, etc.) of a bibliographic collection
Data Analysis

Bi-partite networks

cocMatrix() It computes a bipartite network
Data Analysis

Normalization

couplingSimilarity() It calculates Jaccard or Salton similarity coefficient among manuscripts of a coupling network
Data Analysis

Data Reduction

External functions from other R packages Other R packages suggested for bibliometric analysis

factominer: for PCA and MCA

cmdscale: for MDS

cluster: for clustering

Data Analysis

Similarity matrix

(square network matrix)

biblioNetwork() It calculates the most frequently used coupling networks
Data visualization

Mapping

External functions from other R packages Other R packages suggested for mapping

igraph for social network

ggplot2 for bi-dimensional maps

cluster for dendrogram

References

  1. Pritchard, A (1969). "Statistical bibliography or bibliometrics". Journal of Documentation 25, 348. 
  2. 2.0 2.1 Cuccurullo, Corrado; Aria, Massimo; Sarto, Fabrizia (2016-05-21). "Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains" (in en). Scientometrics 108 (2): 595–611. doi:10.1007/s11192-016-1948-8. ISSN 0138-9130. 
  3. Sarto, Fabrizia; Cuccurullo, Corrado; Aria, Massimo (2015). "Exploring healthcare governance literature: systematic review and paths for future research". Mecosan (91): 61–80. doi:10.3280/mesa2014-091004. 
  4. Cuccurullo, C., Aria, M., & Sarto, F. (2015). Twenty years of research on performance management in business and public administration domains. Presentation at the Correspondence Analysis and Related Methods conference (CARME 2015) in September 2015.
  5. Cuccurullo, C., Aria, M., & Sarto, F. (2013). Twenty years of research on performance management in business and public administration domains. In Academy of Management Proceedings (Vol. 2013, No. 1, p. 14270). Academy of Management.
  6. Ramos-Rodríguez, Antonio-Rafael; Ruíz-Navarro, José (2004-10-01). "Changes in the intellectual structure of strategic management research: a bibliometric study of the Strategic Management Journal, 1980–2000" (in en). Strategic Management Journal 25 (10): 981–1004. doi:10.1002/smj.397. ISSN 1097-0266. 
  7. Rousseau, D. M. (2012). The Oxford handbook of evidence-based management. .. Oxford University Press. 
  8. Cobo, M.j.; López-Herrera, A.g.; Herrera-Viedma, E.; Herrera, F. (2011-07-01). "Science mapping software tools: Review, analysis, and cooperative study among tools" (in en). Journal of the American Society for Information Science and Technology 62 (7): 1382–1402. doi:10.1002/asi.21525. ISSN 1532-2890. 
  9. [1]

External links