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Analysis of the Russian scientific environment in terms of hematology oncology using machine learning methods and social graphs. / Komotskiy, E.; Fadichev, B.; Medvedeva, M.
In: AIP Conference Proceedings, Vol. 2849, No. 1, 090021, 2023.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Komotskiy, E, Fadichev, B & Medvedeva, M 2023, 'Analysis of the Russian scientific environment in terms of hematology oncology using machine learning methods and social graphs', AIP Conference Proceedings, vol. 2849, no. 1, 090021. https://doi.org/10.1063/5.0163218

APA

Komotskiy, E., Fadichev, B., & Medvedeva, M. (2023). Analysis of the Russian scientific environment in terms of hematology oncology using machine learning methods and social graphs. AIP Conference Proceedings, 2849(1), [090021]. https://doi.org/10.1063/5.0163218

Vancouver

Komotskiy E, Fadichev B, Medvedeva M. Analysis of the Russian scientific environment in terms of hematology oncology using machine learning methods and social graphs. AIP Conference Proceedings. 2023;2849(1):090021. doi: 10.1063/5.0163218

Author

Komotskiy, E. ; Fadichev, B. ; Medvedeva, M. / Analysis of the Russian scientific environment in terms of hematology oncology using machine learning methods and social graphs. In: AIP Conference Proceedings. 2023 ; Vol. 2849, No. 1.

BibTeX

@article{fa2866e0c6aa4df38e9dd538d44b3d72,
title = "Analysis of the Russian scientific environment in terms of hematology oncology using machine learning methods and social graphs",
abstract = "Recent studies have shown that scientists can benefit from participation in scientific networks as they can receive more and better information in a timely manner and have communication between employees. This paper shows an approach based on the use of methods of analysis of social graphs and thematic modeling to study the community of scientists working on the topic of hematology oncology in Russia. The proposed approach makes it possible to effectively identify research teams, as well as understand what issues they are working on at the moment. The use of this approach allows bringing the strategic management of scientific research within the framework of a scientific or educational organization to a new level, since it allows making data-based decisions in terms of scientific collaboration and assessing the long-term consequences of such decisions. In the work under consideration, the data of 2140 scientific articles from the Scopus/WoS databases on the topic of hematology oncology for 2016-2020 were used. {\textcopyright} 2023 American Institute of Physics Inc.. All rights reserved.",
author = "E. Komotskiy and B. Fadichev and M. Medvedeva",
year = "2023",
doi = "10.1063/5.0163218",
language = "English",
volume = "2849",
journal = "AIP Conference Proceedings",
issn = "0094-243X",
publisher = "American Institute of Physics Publising LLC",
number = "1",

}

RIS

TY - JOUR

T1 - Analysis of the Russian scientific environment in terms of hematology oncology using machine learning methods and social graphs

AU - Komotskiy, E.

AU - Fadichev, B.

AU - Medvedeva, M.

PY - 2023

Y1 - 2023

N2 - Recent studies have shown that scientists can benefit from participation in scientific networks as they can receive more and better information in a timely manner and have communication between employees. This paper shows an approach based on the use of methods of analysis of social graphs and thematic modeling to study the community of scientists working on the topic of hematology oncology in Russia. The proposed approach makes it possible to effectively identify research teams, as well as understand what issues they are working on at the moment. The use of this approach allows bringing the strategic management of scientific research within the framework of a scientific or educational organization to a new level, since it allows making data-based decisions in terms of scientific collaboration and assessing the long-term consequences of such decisions. In the work under consideration, the data of 2140 scientific articles from the Scopus/WoS databases on the topic of hematology oncology for 2016-2020 were used. © 2023 American Institute of Physics Inc.. All rights reserved.

AB - Recent studies have shown that scientists can benefit from participation in scientific networks as they can receive more and better information in a timely manner and have communication between employees. This paper shows an approach based on the use of methods of analysis of social graphs and thematic modeling to study the community of scientists working on the topic of hematology oncology in Russia. The proposed approach makes it possible to effectively identify research teams, as well as understand what issues they are working on at the moment. The use of this approach allows bringing the strategic management of scientific research within the framework of a scientific or educational organization to a new level, since it allows making data-based decisions in terms of scientific collaboration and assessing the long-term consequences of such decisions. In the work under consideration, the data of 2140 scientific articles from the Scopus/WoS databases on the topic of hematology oncology for 2016-2020 were used. © 2023 American Institute of Physics Inc.. All rights reserved.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85176724378

U2 - 10.1063/5.0163218

DO - 10.1063/5.0163218

M3 - Conference article

VL - 2849

JO - AIP Conference Proceedings

JF - AIP Conference Proceedings

SN - 0094-243X

IS - 1

M1 - 090021

ER -

ID: 48547239