Skip to main content
SpringerLink
Log in

Big Data Technology in the Set of Methods and Means of Scientific Research in Modern Scientometrics

  • Published:
Scientific and Technical Information Processing Aims and scope

Abstract

The problems of applying big data technology in modern scientometric analysis are considered. The importance and relevance of these problems are determined by the ability of big data to significantly increase the efficiency of scientometric research through in-depth analysis of mega-volumes of heterogeneous data and to identify on this basis new semantic relationships and patterns. The main content of big data technology is disclosed. The article presents a list of data requirements that allow data to be classified as big data; the importance of this technology as the advanced means of scientific research in scientometrics is noted; detailed characteristics of methods of scientific research are given. The features of bibliometric, altmetric, webometric, and probabilistic-statistical methods are analyzed. The important place of big data technology in the modern set of methods and means of scientific research in scientometrics is emphasized.

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

Similar content being viewed by others

Notes

  1. One zettabyte (ZB) equals 1021 byte.

  2. Metric in Greek means measurement.

  3. A scientist’s h-index is h if the scientist has published h articles, each of which has been cited at least h times.

  4. The bibliographic coupling method was introduced by the American scientist Kessler in 1963 in his work “Bibliographic coupling between scientific documents” (Kessler, M.M., Bibliographic coupling between scientific papers, American Documentation, Wiley-Blackwell, 1963, vol. 14, no. 1, pp. 10–25). J. Garfield developed and more deeply worked out this concept in relation to scientific publications.

  5. If all the words of a sufficiently long text are sorted by the frequency of their use, then the frequency nth word in such a list will be approximately inversely proportional to its ordinal number n.

  6. Cognitive from lat. cognitive; means possessing the ability of knowledge, comprehension.

REFERENCES

  1. Reports DataAge 2020 and DataAge 2025 of the analytical company International Data Corporation (United States). www.idc.com. Cited February 23, 2022.

  2. Nalimov, V.V. and Mul’chenko, Z.M., Naukometriya: izuchenie razvitiya nauki kak informatsionnogo protsessa (Scientometrics: Studying the Development of Science as Information Process), Moscow: Nauka, 1969.

  3. Gilyarevski, R.S. and Melnikova, E.V., Rejection of the priority of international science citation indexes in the evaluation of results of scientific activity in China, Sci. Tech. Inf. Process., 2020, vol. 47, no. 3, pp. 194–199. https://doi.org/10.3103/S0147688220030107

    Article  Google Scholar 

  4. Hicks, D., Wouters, P., Waltman, L., Rijcke, S., and Rafols, I., Bibliometrics: The Leiden Manifesto for research metrics, Nature, 2015, vol. 520, pp. 429–431. https://doi.org/10.1038/520429a

    Article  Google Scholar 

  5. Mel’nikova, E.V., Comparative analysis of modern approaches of Russia and China to assessment of results of scientific activity, Probl. Nats. Strategii, 2022, no. 1, pp. 153–162.

  6. Moskaleva, O.V., Development of scientometrics: Main stages, Rukovodstvo po naukometrii. Indikatory razvitiya nauki i tekhnologii (Guide to Scientometrics: Indicators of Advance of Science and Technology), Akoev, M.A., Ed., Yekaterinburg: Ural Univ., 2021.

    Google Scholar 

  7. Gilyarevskii, R.S. and Mel’nikova, E.V., US Institute of Scientific Information: Ideology, transformations, products, Nauchn.-Tekhn. Inform., Ser. 1. Organ. Metod. Inf. Rab., 2017, no. 10, pp. 26–31.

  8. Hirsch, J.E., An index to quantify an individual’s scientific research output, Proc. Natl. Acad. Sci. U. S. A., 2005, vol. 102, no. 46, pp. 16569–16572.  https://doi.org/10.1073/pnas.0507655102

    Article  MATH  Google Scholar 

  9. Egghe, L., Expansion of the field of informetrics: Origins and consequences, Inf. Process. Manage., 2005, vol. 41, no. 6, pp. 1311–1316.

    Article  Google Scholar 

  10. Prathap, G., Hirsch-type indices for ranking institutions’ scientific research output, Current Sci., 2006, vol. 91, no. 11, p. 1439.

    Google Scholar 

  11. Garfild, E., “Science citation index”—A new dimension in indexing: This unique approach underlies versatile bibliographic systems for communicating and evaluating information, Science, 1964, vol. 144, pp. 649–654. https://doi.org/10.1126/science.144.3619.649

    Article  Google Scholar 

  12. Tsvetkova, V.A. and Kalashnikova, G.V., Altmetric indicators in assessing the regional publication activity, Inf. Resursy Rossii, 2021, no. 4, pp. 20–23.

  13. Simonenko, T.V., Scientometrics: The object, the subject, the methodology, Naukometriya: metodologiya, instrumenty, prakticheskoe primenenie (Scientometrics: Methodology, Tools, and Practical Application), Grush, A.I., Ed., Minsk: Belorusskaya Nauka, 2018, pp. 35–45.

    Google Scholar 

  14. Dadenko, V.A. and Dadenko, S.V., Metric studies as a form of analysis of scientific productivity, Znanie. Ponimanie. Umenie, 2019, no. 2, pp. 125–136.  https://doi.org/10.17805/zpu.2019.2.11

  15. Mitskevich, A.K., Toward the essence and origin of political mediametry, Filosofsko-gumanitarnye nauki. Sbornik Nauchnykh Statei (Philosophy and Humanities: Collection of Papers), Gaisenok, V.A. et al., Ed., Minsk: RIVSh, 2017.

  16. Bredford, S.C., Sources of information on specific subjects, Engineering, 1934, vol. 137, pp. 85–86.

    Google Scholar 

  17. Lotka, A.J., The frequency distribution of scientific productivity, J. Washington Acad. Sci., 1926, vol. 16, no. 12, pp. 317–323.

    Google Scholar 

  18. Zipf, G.K., Selected Studies of the Principle of Relative Frequency in Language, Cambridge, Mass.: Harvard Univ. Press, 1932.

    Book  Google Scholar 

  19. Rumyantsev, D.M., Social engineering and big data technology, Shestaya mezhdunarodnaya nauchno-prakticheskaya konferentsiya BIG DATA and Advanced Analytics. BIG DATA i analiz vysokogo urovnya (Sixth Int. Sci.-Pract. Conf. BIG DATA and Advanced Analytics. BIG DATA and Higher-Level Analysis), Minsk, 2020, Minsk: Bestprint, 2020, vol. 3.

  20. Wook, M., Hasbullah, N.A., Zainudin, N.M., Jabar, Z.Z.A., Ramli, S., Razali, N.A.M., and Yusop, N.M.M., Expolring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling, J. Big Data, 2021, vol. 8, p. 49.  https://doi.org/10.1186/s40537-021-00439-5

    Article  Google Scholar 

  21. Elshawi, R., Sakr, S., Talia, D., and Trunfio, P., Big data systems meet machine learning challenges: towards big data science as a service, Big Data Res., 2018, vol. 14, pp. 1–11.  https://doi.org/10.1016/j.bdr.2018.04.004

    Article  Google Scholar 

  22. Melnikova, E.V., Features of planishing scientific data bases for efficient application of big data technology, Inf. Resursy Rossii, 2021, no. 4, pp. 6–11.  https://doi.org/10.52815/0204-3653_2021_04182_6

  23. Guba, K.S., Big data in studies of science: New research field, Sotsiol. Issled., 2021, no. 6, pp. 24–33.  https://doi.org/10.31857/S013216250013878-8

  24. Rawat, K.S. and Sood, S.K., Emerging trends and global scope of big data analytics: A scientometric analysis, Qual. Quant., 2021, vol. 51, no. 2, pp. 1–26.  https://doi.org/10.1007/s11135-020-01061-y

    Article  Google Scholar 

Download references

Funding

The article was prepared as part of the study on the topic FFFU-2021-0002 of the State Order of the All-Russian Institute of Scientific and Technical Information, Russian Academy of Sciences, and with the support of the Russian Foundation for Basic Research, project no. 20-07-00014.

Author information

Authors and Affiliations

  1. All-Russian Institute of Scientific and Technical Information, Russian Academy of Sciences, Moscow, Russia

    E. V. Melnikova

Authors
  1. E. V. Melnikova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. V. Melnikova.

Ethics declarations

The author declares that she has no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Melnikova, E.V. Big Data Technology in the Set of Methods and Means of Scientific Research in Modern Scientometrics. Sci. Tech. Inf. Proc. 49, 102–107 (2022). https://doi.org/10.3103/S0147688222020083

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0147688222020083

Keywords:

Search

Navigation