Published June 30, 2021 | Version v1
Journal article Open

Development of the set models and a method to form information spaces of scientific activity subjects for the steady development of higher education establishments

Creators

  • 1. Astana IT University; Systems and Technologies Taras Shevchenko National University of Kyiv
  • 2. Taras Shevchenko National University of Kyiv
  • 3. Uzhhorod National University
  • 4. Astana IT University
  • 5. D. Serikbayev East Kazakhstan Technical University; Astana IT University

Description

This paper describes the basic conceptual apparatus required to form information spaces for scientific activity subjects. Multiple models have been built to identify collective and individual scientific activity subjects, including information on the subjects' publication citations, their abstracts, as well as their indicators in scientometric databases, etc. A conceptual scheme of interaction between collective and individual scientific activity subjects has been described, taking into consideration the dynamics of their productivity.

A method has been proposed to form the information spaces for the collective and individual scientific activity subjects such as higher education establishments and scientists. The method involves a series of stages to identify and construct citation and scientific cooperation networks, to form subject scientific spaces, and, based on them, to devise methods in order to quantify productivity. The results of methods application form the components of the relevant information spaces of scientific activity subjects. The spaces to be built could be used to solve the task of selecting subjects for the implementation of joint scientific and educational projects. In addition, these spaces could be applied to form the organizational and functional framework of the collective scientific activity subjects, including their structural units, which would contribute to ensuring their stable development.

Creating the information spaces of scientific activity subjects underlies resolving those issues that would stimulate investment in research and innovation, strengthen cooperation between universities, improve the efficiency and productivity of the scientific enterprise. It has been confirmed experimentally that the potential of a collective subject of scientific activity, including individual subjects, the rate of change of identifiers of whom is positive, would have a non-negative potential. A rate of change in the normalized indicators of identifiers of individual and collective scientific activity subjects has been calculated for the period from January 2019 to December 2020 for three higher education establishments

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References

  • Communication from the commission to the council, the european parliament, the economic and social committee and the committee of the regions. Towards a European research area (2000). Commission of the European Communities. Available at: https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2000:0006:FIN:EN:PDF
  • European research area (ERA). Available at: https://ec.europa.eu/info/research-and-innovation/strategy/era_en
  • Glänzel, W. (2012). Bibliometric methods for detecting and analysing emerging research topics. El Profesional de La Informacion, 21 (2), 194–201. doi: https://doi.org/10.3145/epi.2012.mar.11
  • Egghe, L., Rousseau, R. (1993). Evolution of information production processes and its relation to the Lorenz dominance order. Information Processing & Management, 29 (4), 499–513. doi: https://doi.org/10.1016/0306-4573(93)90045-f
  • Lizunov, P., Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Biloshchytska, S. (2020). The use of probabilistic latent semantic analysis to identify scientific subject spaces and to evaluate the completeness of covering the results of dissertation studies. Eastern-European Journal of Enterprise Technologies, 4 (4 (106)), 21–28. doi: https://doi.org/10.15587/1729-4061.2020.209886
  • Kremen, V., Bykov, V. (2013). Category "space" and "environment": features model submissions and educational application. Teoriya i praktyka upravlinnia sotsialnymy systemamy, 2, 3–16.
  • Otte, E., Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28 (6), 441–453. doi: https://doi.org/10.1177/016555150202800601
  • Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and Its Applications, 311 (3-4), 590–614. doi: https://doi.org/10.1016/s0378-4371(02)00736-7
  • Hou, H., Kretschmer, H., Liu, Z. (2008). The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75 (2), 189–202. doi: https://doi.org/10.1007/s11192-007-1771-3
  • Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98 (2), 404–409. doi: https://doi.org/10.1073/pnas.98.2.404
  • Biloshchytskyi, A., Kuchansky, A., Andrashko, Yu., Biloshchytska, S., (2020). Use of the link ranking method to evaluate scientific activities of scientific space subjects. Scientific Journal of Astana IT University, 1, 12–20. doi: https://doi.org/10.37943/aitu.2020.1.63600
  • Biloshchytskyi, A., Kuchansky, A., Andrashko, Yu., Biloshchytska, S., Kuzka, O., Shabala, Ye., Lyashchenko, T. (2017). A method for the identification of scientists' research areas based on a cluster analysis of scientific publications. Eastern-European Journal of Enterprise Technologies, 5 (2 (87)), 4–11. doi: https://doi.org/10.15587/1729-4061.2017.112323
  • Lizunov, P., Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Biloshchytska, S. (2019). Improvement of the method for scientific publications clustering based on n-gram analysis and fuzzy method for selecting research partners. Eastern-European Journal of Enterprise Technologies, 4 (4 (100)), 6–14. doi: https://doi.org/10.15587/1729-4061.2019.175139
  • Kuchansky, A., Andrashko, Yu., Biloshchytskyi, A., Danchenko, E., Ilarionov, O., Vatskel, I., Honcharenko, T. (2018). The method for evaluation of educational environment subjects' performance based on the calculation of volumes of m-simplexes. Eastern-European Journal of Enterprise Technologies, 2 (4 (92)), 15–25. doi: https://doi.org/10.15587/1729-4061.2018.126287
  • Hnatiienko, H., Snytyuk, V., Tmienova, N., Voloshyn, O. (2020). Determining the effectiveness of scientific research of universities staff. CEUR Workshop Proceedings, 2833, 164–176. Available at: http://ceur-ws.org/Vol-2833/Paper_15.pdf
  • Bykov, V. Yu., Spirin, O. M., Soroko, N. V. (2015). Elektronni bibliometrychni systemy yak zasib informatsiyno-analitychnoi pidtrymky naukovo-pedahohichnykh doslidzhen. Informatsiyno-komunikatsiyni tekhnolohiyi v suchasniy osviti: dosvid, problemy, perspektyvy, 1, 91–100.
  • Garcez, M. P., Sbragia, R., Kruglianskas, I. (2014). Factors for selecting partners in innovation projects – qualitative evidences from non-equity bilateral alliances in the Brazilian petrochemical leader. Review of Administration and Innovation - RAI, 11 (2), 241. doi: https://doi.org/10.5773/rai.v11i2.1292
  • Feng, W. D., Chen, J., Zhao, C. J. (2000). Partners selection process and optimization model for virtual corporations based on genetic algorithms. Journal of Tsinghua University (Science and Technology), 40, 120–124.
  • Lukianov, D., Bespanskaya-Paulenka, K., Gogunskii, V., Kolesnikov, O., Moskaliuk, A., Dmitrenko, K. (2017). Development of the markov model of a project as a system of role communications in a team. Eastern-European Journal of Enterprise Technologies, 3 (3 (87)), 21–28. doi: https://doi.org/10.15587/1729-4061.2017.103231
  • Korzh, R., Peleshchyshyn, A., Syerov, Y., Fedushko, S. (2016). University's Information Image as a Result of University Web Communities' Activities. Advances in Intelligent Systems and Computing, 115–127. doi: https://doi.org/10.1007/978-3-319-45991-2_8
  • Kolomiiets, A., Morozov, V. (2021). Investigation of Optimization Models in Decisions Making on Integration of Innovative Projects. Lecture Notes in Computational Intelligence and Decision Making, 51–64. doi: https://doi.org/10.1007/978-3-030-54215-3_4
  • Morozov V., Kalnichenko O., Mezentseva, O. (2020). The method of interaction modeling on basis of deep learning the neural networks in complex IT-projects. International Journal of Computing, 19 (1), 88–96. doi: https://doi.org/10.47839/ijc.19.1.1697
  • Fruchterman, T. M. J., Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21 (11), 1129–1164. doi: https://doi.org/10.1002/spe.4380211102
  • Hu, Y. (2005). Efficient, high-quality force-directed graph drawing. The Mathematica Journal, 10 (1), 37–71. Available at: http://asus.myds.me:6543/paper/ktall/37%20-%201984%20-%20Efficient,%20High-Quality%20Force-Directed%20Graph%20Drawing.pdf
  • Huang, Q., Feng, J., Zhang, Y., Fang, Q., Ng, W. (2015). Query-aware locality-sensitive hashing for approximate nearest neighbor search. Proceedings of the VLDB Endowment, 9 (1), 1–12. doi: https://doi.org/10.14778/2850469.2850470
  • Altszyler, E., Ribeiro, S., Sigman, M., Fernández Slezak, D. (2017). The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text. Consciousness and Cognition, 56, 178–187. doi: https://doi.org/10.1016/j.concog.2017.09.004
  • Tymchenko, D., Korogod, N., Novorodovska, T. (2020). Technology transfer office model. Scientific Journal of Astana IT University, 3, 83–90. doi: https://doi.org/10.37943/aitu.2020.73.19.008
  • Kropachev, P., Imanov, M., Borisevich, Y., Dhomane, I. (2020). Information technologies and the future of education in the Republic of Kazakhstan. Scientific Journal of Astana IT University, 1, 30–38. doi: https://doi.org/10.37943/aitu.2020.1.63639