Tree of Science with Scopus: A Shiny Application
DOI:
https://doi.org/10.29173/istl2698Abstract
Tree of Science (ToS) is a scientific literature search tool that produces a small, selected list of citations from a larger pool of citations. Initially developed for searches in the Web of Science, this paper shows how to use it with bibliographic data from Scopus. This new Shiny web application analyzes data from Scopus. It processes a dataset from a Scopus search and creates three reports. The first one shows a descriptive analysis, the second one presents the Tree of Science of the search, and the third one presents a clustering analysis of the three main subtopics. The application is accessible from this link: https://coreofscience.shinyapps.io/scientometrics/.
Downloads
References
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Arumugaperumal, A., Velayudhan Krishna, D., Alaguponniah, S., Nallaperumal, K., & Sivasubramaniam, S. (2022). PeptCreatR: A web app for unique peptides in human. International Journal of Peptide Research and Therapeutics, 28(2), 64. https://doi.org/10.1007/s10989-022-10375-4
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Chang, W., Cheng, J., Allaire, J., Xie, Y., & McPherson, J. (2017). Shiny: Web application framework for R (R Package Version 1.5) [Computer software]. R Studio. https://rdrr.io/cran/shiny/
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609–1630. https://doi.org/10.1002/asi.22688
Duque, P., Meza, O. E., Giraldo, D., & Barreto, K. (2021). Economía social y economía solidaria: Un análisis bibliométrico y revisión de literatura. REVESCO. Revista de Estudios Cooperativos, 138, e75566–e75566. https://doi.org/10.5209/reve.75566
Durán-Aranguren, D. D., Robledo, S., Gomez-Restrepo, E., Arboleda Valencia, J. W., & Tarazona, N. A. (2021). Scientometric overview of coffee by-products and their applications. Molecules, 26(24), 7605. https://doi.org/10.3390/molecules26247605
Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152. https://doi.org/10.1007/s11192-006-0144-7
Evans, J. A., & Foster, J. G. (2011). Metaknowledge. Science, 331(6018), 721–725. https://doi.org/10.1126/science.1201765
Garfield, E. (1955). Citation indexes for science. Science, 122(3159), 108–111. https://www.jstor.org/stable/1749965
Gonzalez-Correa, C.-A., Tapasco-Tapasco, L.-O., & Gomez-Buitrago, P.-A. (2002). A method for a literature search on microbiota and obesity for PhD biomedical research using the Web of Science (WoS) and the Tree of Science (ToS). Issues in Science and Technology Librarianship, 99. https://doi.org/10.29173/istl2679
Grames, E. M., Stillman, A. N., Tingley, M. W., & Elphick, C. S. (2019). An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks. Methods in Ecology and Evolution, 10, 1645–1654. https://doi.org/10.1111/2041-210x.13268
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102
Nicolosi Gelis, M. M., Sathicq, M. B., Jupke, J., & Cochero, J. (2022). DiaThor: R package for computing diatom metrics and biotic indices. Ecological Modelling, 465, 109859. https://doi.org/10.1016/j.ecolmodel.2021.109859
Pornprasit, C., Liu, X., Kiattipadungkul, P., Kertkeidkachorn, N., Kim, K.-S., Noraset, T., Hassan, S.-U., & Tuarob, S. (2022). Enhancing citation recommendation using citation network embedding. Scientometrics, 127(1), 233–264. https://doi.org/10.1007/s11192-021-04196-3
Robledo, S., Grisales Aguirre, A. M., Hughes, M., & Eggers, F. (2021). “Hasta la vista, baby” – will machine learning terminate human literature reviews in entrepreneurship? Journal of Small Business Management, 1–30. https://doi.org/10.1080/00472778.2021.1955125
Ruiz-Rosero, J., Ramirez-Gonzalez, G., & Viveros-Delgado, J. (2019). Software survey: ScientoPy, a scientometric tool for topics trend analysis in scientific publications. Scientometrics, 121(2), 1165–1188. https://doi.org/10.1007/s11192-019-03213-w
Valencia-Hernandez, D. S., Robledo, S., Pinilla, R., Duque-Méndez, N. D., & Olivar-Tost, G. (2020). SAP algorithm for citation analysis: An improvement to Tree of Science. Ingeniería E Investigación, 40(1), 45–49. https://doi.org/10.15446/ing.investig.v40n1.77718
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Wickham, H. (2021). Mastering shiny: Build interactive apps, reports, and dashboards powered by R. O’Reilly.
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Sebastian Robledo, Martha Zuluaga, Luis Valencia, Oscar Arbelaez-Echeverri, Pedro Duque, Juan-David Alzate-Cardona
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
