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Transformation of Thematic Profiles of Serial Publications in an Information Center Documents Input System: Case Study of the VINITI RAS Database

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Scientific and Technical Information Processing Aims and scope

Abstract

The study investigates the dynamics of the Russian scientific journals input stream based on the results of article classification, according to the State Rubricator of Scientific and Technical Information in the VINITI RAS Database. The authors aim to trace the changes in the information provision of the codes of the subject field Chemistry, chemical technology, and the chemical industry. The research procedure presented includes the methods of calculating the indicators that allow to automate the assessment of journals thematic profiles—the similarity and scattering coefficients. The proposed methodology facilitates the identification of trends in the thematic profiles of serial publications, i.e., growth or degradation of their headings, thus contributing to the optimization of the information center documents input stream.

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Notes

  1. https://grnti.ru/.

  2. http://www.arc.gov.au/australian-and-new-zealand-standard-research-classification-anzsrc.

  3. http://clc.nlc.cn/ztfbb.jsp.

  4. https://journals.aps.org/PACS.

  5. https://www.cas.org/support/documentation/references/ca-sections.

  6. https://www.aeaweb.org/econlit/jelCodes.php.

REFERENCES

  1. De Bellis, N., Bibliometrics and Citation Analysis: From the Science Citation Index to Cybermetrics, Lanham, Md.: The Scarecrow Press, 2009.

    Google Scholar 

  2. Akoev, M.A., Markusova, V.A., Moskaleva, O.V., and Pislyakov, V.V., Rukovodstvo po naukometrii. Indikatory razvitiya nauki i tekhnologii (Guide Book to Scientometrics: Indicators of Development of Science and Technology), Yekaterinburg: Izd-vo Ural. Univ., 2021.

  3. Glänzel, W. and Schubert, A., A new classification scheme of science fields and subfields designed for scientometric evaluation purposes, Scientometrics, 2003, vol. 56, no. 3, pp. 357–367.  https://doi.org/10.1023/A:1022378804087

    Article  Google Scholar 

  4. Moed, H.F., Citation Analysis in Research Evaluation, Information Science and Knowledge Management, vol. 9, Dordrecht: Springer, 2006.  https://doi.org/10.1007/1-4020-3714-7

  5. Web of Science in Russian, Learning and Training, Subject area classifier Citation Topics. https://www.youtube.com/watch?v=58g6dQyJCW8. Cited March 16, 2022.

  6. Scopus: Content Coverage Guide, 2020. https://www.elsevier.com/_data/assets/pdf_file/0007/69451/Scopus_ContentCoverage_Guide_WEB.pdf. Cied March 16, 2022.

  7. Ioanidis, J.P.A., Baas, J., Klavans, R., and Boyack, K.W., A standardized citation metrics author database annotated for scientific field, PLoS Biol., 2019, vol. 17, no. 8, p. e3000384.  https://doi.org/10.1371/journal.pbio.3000384

    Article  Google Scholar 

  8. Javitz, H., et al., US academic scientific publishing, Working paper SRS 11-201, Arlington, Va.: National Science Foundation, Division of Science Resources Statistics, 2010.

  9. Archambault, É., Beauchesne, O.H., and Caruso, J., Towards a multilingual, comprehensive and open scientific journal ontology, Proc. 13th Int. Conf. of the Int. Soc. for Scientometrics and Informetrics (ISSI), Noyons, B., Ngulube, P., and Leta, J., Eds., Durban, South Africa: Leiden Univ., Univ. of Zululand, 2011, vol. 1, pp. 66–77. https://www.issi-society.org/publications/issi-conference-proceedings/proceedings-of-issi-2011/.

    Google Scholar 

  10. Börner, K., Klavans, R., Patek, M., Zoss, A.M., Biberstine, J.R., Light, R.P., Larivière, V., and Boyack, K.W., Design and update of a classification system: The UCSD map of science, PLoS One, 2012, vol. 7, no. 7, p. 239464. https://doi.org/10.1371/journal.pone.0039464

    Article  Google Scholar 

  11. Klavans, R. and Boyack, K.W., Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge?, J. Am. Soc. Inf. Sci. Technol., 2017, vol. 68, no. 4, pp. 984–998.  https://doi.org/10.1002/asi.23734

    Article  Google Scholar 

  12. Rafols, I. and Leydesdorff, L., Content-based and algorithmic classifications of journals: perspectives on the dynamics of scientific communication and indexer effects, J. Am. Soc. Inf. Sci. Technol., 2009, vol. 60, no. 9, pp. 1823–1835.  https://doi.org/10.1002/asi.21086

    Article  Google Scholar 

  13. Garfield, E., Malin, M.V., and Small, H., A system for automatic classification of scientific literature, J. Indian Inst. Sci., 1975, vol. 57, no. 2, pp. 61–74.

    Google Scholar 

  14. Shu, F., Dinneen, J.D., Asadi, B., and Julien, Ch.-A., Mapping science using library of congress subject headings, J. Informetrics, 2017, vol. 11, no. 4, pp. 1080–1094.  https://doi.org/10.1016/j.joi.2017.08.008

    Article  Google Scholar 

  15. Griffith, B.C., Small, H.G., Stonehill, J.A., and Dey, S., The structure of scientific literatures II: Toward a macro-and microstructure for science, Sci. Stud., 1974, vol. 4, no. 4, pp. 339–365.  https://doi.org/10.1177/030631277400400402

    Article  Google Scholar 

  16. Waltman, L. and van Eck, N.J., A new methodology for constructing a publication-level classification system of science, J. Am. Soc. Inf. Sci. Technol., 2012, vol. 63, no. 12, pp. 2378–2392.  https://doi.org/10.1002/asi.22748

    Article  Google Scholar 

  17. Rivest, M., Vignola-Gagné, E., and Archambault, É., Article-level classification of scientific publications: A comparison of deep learning, direct citation and bibliographic coupling, PLoS One, 2021, vol. 16, no. 5, p. e0251493.  https://doi.org/10.1371/journal.pone.0251493

    Article  Google Scholar 

  18. Egorov, V.S., Kozlova, E.S., Lomotin, K.E., Fedorets, O.V., Filimonov, A.V., and Shapkin, A.V., The VINITI RAS automatic text classification system for processing the flow of scientific publications, Autom. Doc. Math. Linguist., 2020, vol. 54, no. 3, pp. 113–123.  https://doi.org/10.3103/S0005105520030048

    Article  Google Scholar 

  19. Shu, F., Julien, Ch.-A., Zhang, L., Qui, J., Zhang, J., and Larivière, V., Comparing journal and paper level classifications of science, J. Informetrics, 2019, vol. 13, no. 1, pp. 202–225.  https://doi.org/10.1016/j.joi.2018.12.005

    Article  Google Scholar 

  20. Soloshenko, N.S., Advantages of document deep thematic indexing for the information center acquisition services, Nauchn. Tekh. Bibl., 2019, no. 3, pp. 75–84.  https://doi.org/10.33186/1027-3689-2019-3-75-84

  21. VINITI RAS Knowledge Area Classifier, 2022. http://www.viniti.ru/products/classification-systems/ rubricator-viniti. Cited July 1, 2022.

  22. Yurtsev, B.A. and Semkin, B.I., Studies of concrete and partial floras with the aid of mathematical methods, Bot. Zh., 1980, vol. 65, no. 12, pp. 1706–1718.

    Google Scholar 

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Funding

The study was conducted within the state assignment of the All-Russian Institute for Scientific and Technical Information, Russian Academy of Sciences, project no. 0003-2022-0003.

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Authors and Affiliations

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

    N. S. Soloshenko, O. V. Fedorets & T. N. Domnina

Authors
  1. N. S. Soloshenko
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  2. O. V. Fedorets
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  3. T. N. Domnina
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Corresponding authors

Correspondence to N. S. Soloshenko, O. V. Fedorets or T. N. Domnina.

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Translated by L. Solovyova

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Soloshenko, N.S., Fedorets, O.V. & Domnina, T.N. Transformation of Thematic Profiles of Serial Publications in an Information Center Documents Input System: Case Study of the VINITI RAS Database. Sci. Tech. Inf. Proc. 49, 220–230 (2022). https://doi.org/10.3103/S0147688222040050

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