bibliometrix (version 1.4)

bibliometrix-package: Tool for quantitative research in scientometrics and bibliometrics.

Description

It provides various routines for importing bibliographic data from SCOPUS and Thomson Reuters' ISI Web of Knowledge databases, perfoming bibliometric analysis and building data matrices for co-citation, coupling and scientific collaboration analysis.

Details

Package:

bibliometrix

Type:

Package

Version:

0.1

Date:

2016-05-05

License:

GPL-3

References

Cuccurullo C., Aria M., Sarto F. (2016) Foundations and Trends in Performance Management. A Twenty-five Years Bibliometric Analysis in Business and Public Administration Domains, Scientometrics, DOI: 10.1007/s11192-016-1948-8.

Koseoglu, M. A. (2016). Growth and structure of authorship and co-authorship network in the strategic management realm: Evidence from the Strategic Management Journal. BRQ Business Research Quarterly.

Batagelj, V., Cerinsek, M. (2013). On bibliographic networks. Scientometrics, 96(3), 845-864.

Yan, E., Ding, Y. (2012). Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other. Journal of the American Society for Information Science and Technology, 63(7), 1313-1326.

Rousseau, R. (2010). Bibliographic coupling and co-citation as dual notions. The Janus faced scholar. A Festschrift in honour of Peter Ingwersen, 173-183.

Leydesdorff, L., Vaughan, L. (2006). Co-occurrence matrices and their applications in information science: Extending ACA to the Web environment. Journal of the American Society for Information Science and technology, 57(12), 1616-1628.

Examples

## load scientometrics data set
# data(scientometrics_text)

## Convert text data into a bibliographic data frame
# scient_df <- convert2df(scientometrics_text, dbsource="isi", format="plaintext")

## Perform a bibliometric analysis of the bibliographic data frame  
# results <- biblioAnalysis(scient_df)

## summarize results
# summary(results, k=10, pause=FALSE)

## plot results
# plot(results, k=10, pause=FALSE)

## Estimate Lotka's law coefficients
# L=lotka(results)
# L

## Perform authors' dominance analysis
#DF=dominance(results)
#DF