Skip to main content
SpringerLink
Log in

Prospective Scientific Research Trend Identification Methods (Based on the Analysis of Gas Fuel-Related Publications)

  • INFORMATION ANALYSIS
  • Published:
Automatic Documentation and Mathematical Linguistics Aims and scope

Abstract

This article reviews prospective scientific research-trend identification methods based on the analysis of scientific publications focused on compressed natural gas. The methods for identifying publication directions and patterns by popularity of separate topics within one subject area are presented. The materials used in the article consist of scientific research on natural gas available in citation databases of Russian Science Citation Index (RSCI), Scopus, and Web of Science and the data from the EGISU R&D system of Russian researches. The results were processed in the VOSviewer software.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.
Fig. 14.
Fig. 15.
Fig. 16.
Fig. 17.

Similar content being viewed by others

REFERENCES

  1. Makri, A., Pakistan and Egypt had highest rises in research output in 2018, Nature, 2018.  https://doi.org/10.1038/d41586-018-07841-9

  2. Sushentsova, N.V. and Chekalina, T.A., Open access scientific electronic libraries, Obraz. Kar’era. Obshchestvo, 2013, no. 4-1, pp. 31–34.

  3. Birkle, C., Pendlebury, Schnell, J., and Adams, J. Web of Science as a data source for research on scientific and scholarly activity, Quant. Sci. Stud., 2020, vol. 1, no. 1, pp. 363–376.  https://doi.org/10.1162/qss_a_00018

    Article  Google Scholar 

  4. Mongeon, P. and Paul-Hus, A., The journal coverage of web of science and scopus: a comparative analysis, Scientometrics, 2016, vol. 106, no. 1, pp. 213–228.  https://doi.org/10.1007/s11192-015-1765-5

    Article  Google Scholar 

  5. Savel’eva, Yu.V. and Khoperskov, A.V., Scientific journals and efficiency of scientific work: Search systems and databases, Upr. Bol’shimi Sist.: Sb. Trudov, 2013, no. 44, pp. 381–407.

  6. Van Eck, N.J. and Waltman, L., VOSviewer Manual, Leiden: Univ. Leiden, 2013, vol. 1, no. 1, pp. 1–53.

    Google Scholar 

  7. Butenko, Yu.I., Nikolaeva, N.S., and Margaryan, T.D., Structural models of terminological word combinations for marking up a corpus of scientific and technical texts, Vestn. Novosib. Gos. Univ. Ser.: Lingvist. Mezhkul’turnaya Kommun., 2021, vol. 19, no. 3, pp. 46–56.  https://doi.org/10.25205/1818-7935-2021-19-3-45-56

    Article  Google Scholar 

  8. Sidnyaev, N.I., Butenko, Yu.I., and Bolotova, E.E., Formal grammar theory in recognition methods of unknown objects, Autom. Doc. Math. Linguist., 2020, vol. 54, pp. 215–225.  https://doi.org/10.3103/S000510552004007X

    Article  Google Scholar 

  9. Sidnyaev, N.I., Butenko, Ju.I., and Garazha, V.V., Mathematical apparatus for engineering-linguistic models, AIP Conf. Proc., 2019, vol. 2195, p. 020033.  https://doi.org/10.1063/1.5140133

    Article  Google Scholar 

  10. Melnikov, A.K. and Ronzhin, A.F., Generalized statistical method of text analysis based on calculation of probability distributions of statistical values, Inf. Ee Prim., 2016, vol. 10, no. 4, pp. 89–95.  https://doi.org/10.14357/19922264160409

    Article  Google Scholar 

  11. Volkov, A.V., Peculiarities of computer processing of scientific text, Upr. Innovatsiyami: Teoriya, Metodol., Prakt., 2013, no. 5, pp. 144–151.

  12. Yatsko, V.A., Algorithms and programs for automatic text processing, Vestn. Irkut. Gos. Lingvist. Univ., 2012, vol. 1, no. 17, pp. 150–160.

    Google Scholar 

  13. Pronin, E.N., Africa: Gas-engine fuel market development, Transp. Al’ternativnom Toplive, 2014, no. 3, pp. 68–71.

  14. Tollefson, J., World’s carbon emissions set to spike by 2% in 2017, Nature, 2017, vol. 551, p. 283.  https://doi.org/10.1038/nature.2017.22995

    Article  Google Scholar 

  15. Randles, B.M., Pasquetto, I.V., Golshan, M.S., and Borgman, C.L., Using the Jupyter notebook as a tool for open science: An empirical study, ACM/IEEE Joint Conf. on Digital Libraries (JCDL), Toronto, 2017, IEEE, 2017, pp. 1–2.  https://doi.org/10.1109/JCDL.2017.7991618

Download references

Author information

Authors and Affiliations

  1. Bauman Moscow State Technical University, Moscow, Russia

    Iu. I. Butenko & V. V. Garazha

  2. National Research University Higher School of Economics, Moscow, Russia

    I. N. Telnova

Authors
  1. Iu. I. Butenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. N. Telnova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. V. Garazha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Iu. I. Butenko, I. N. Telnova or V. V. Garazha.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by S. Avodkova

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Butenko, I.I., Telnova, I.N. & Garazha, V.V. Prospective Scientific Research Trend Identification Methods (Based on the Analysis of Gas Fuel-Related Publications). Autom. Doc. Math. Linguist. 56, 11–25 (2022). https://doi.org/10.3103/S0005105522010034

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0005105522010034

Keywords:

Search

Navigation