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Key performance indicators of Russian universities for 2015–2018: Dataset and Benchmarking Data

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dc.contributor.authorGuseva, A. I.
dc.contributor.authorKalashnik, V. M.
dc.contributor.authorKaminskii, V. I.
dc.contributor.authorKireev, S. V.
dc.contributor.authorГусева, Анна Ивановна
dc.contributor.authorКалашник, Вячеслав Михайлович
dc.contributor.authorКаминский, Владимир Ильич
dc.contributor.authorКиреев, Сергей Васильевич
dc.contributor.otherФакультет бизнес-информатики и управления комплексными системами
dc.date.accessioned2024-12-26T07:16:37Z
dc.date.available2024-12-26T07:16:37Z
dc.date.issued2022
dc.description.abstract© 2021This article presents a performance dataset of 93 Russian universities, collected from 2015 to 2018 and evaluated according to 24 indicators. These data were gathered from materials, published in the process of monitoring the effectiveness of higher education institutions by the Ministry of Science and Higher Education of the Russian Federation, Web of Science (citation-based research analytics tool InCites) and Scopus (citation-based research analytics tool SciVal) databases, and information from international ranking agencies QS, THE, ARWU. The dataset comprises the assessments of university performances according to the most important indicators used in socio-economic studies of comparative analysis of higher education system development levels in different countries: educational, scientific and research, international, financial and economic performance and international public recognition (university positions in leading international rankings). Evaluated universities are grouped pursuant to their missions: Federal Universities (FU), National Research Universities (NRU), Flagship Universities (FlU) and university-participants of the Russian Academic Excellence Project (Project 5-100). The indicators for the comparative analysis are aggregated by the type of activities and analyzed based on the calculation of median values and Displaced Ideal Method. The dataset can be helpful to researchers, university administration, specialists of higher education system, etc. Data processing can be executed using data mining methods, machine learning, and pattern analysis for the development of intellectual structures, applicable for university performance assessment in different educational systems. Presented data allows us to assert that the implementation of targeted support for leading Russian universities has a positive impact on the development of Russian higher education increasing its role on the international academic arena. Leading national research university-participants of the Project 5-100 had the greatest influence on increasing the competitiveness of Russian education in the world.
dc.identifier.citationKey performance indicators of Russian universities for 2015–2018: Dataset and Benchmarking Data / Guseva, A.I. [et al.] // Data in Brief. - 2022. - 40. - 10.1016/j.dib.2021.107695
dc.identifier.doi10.1016/j.dib.2021.107695
dc.identifier.urihttps://www.doi.org/10.1016/j.dib.2021.107695
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85121469245&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000778979600001
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/28614
dc.relation.ispartofData in Brief
dc.titleKey performance indicators of Russian universities for 2015–2018: Dataset and Benchmarking Data
dc.typeData Paper
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oaire.citation.volume40
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