39 research outputs found
With or without h-index? Comparing aggregates of rankings based on seven popular bibliometric indicators
We apply five majority-rule-based ordinal ranking methods to data on economic, management and political science journals in order to produce aggregate journal rankings. First, we calculate aggregates for the set of rankings based on seven popular bibliometric indicators (impact factor, 5-year impact factor, immediacy index, article influence score, h-index, SNIP and SJR). Then, we exclude the Hirsch index and repeat the calculations. We perform the comparative correlation analysis of the aggregates and the initial rankings. We use two rank measures of correlation, Kendallβs tau and the share of coinciding pairs. The analysis demonstrates that aggregate rankings represent the set of single-indicator-based rankings better than any of the seven rankings themselves. Among the single-indicator-based rankings themselves, the best representations of their set are produced by the 5-year impact factor. The least representative are rankings based on the immediacy index. The exclusion of the Hirsch index from the set of indicators does not change these results
Bibliometric Indicators in the Thomson Reuters Information Resources
Π Π³Π»Π°Π²Π΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΡ, ΠΎΡΠ΅Π½ΠΈΠ²Π°ΡΡΠΈΠ΅ ΡΠΈΡΠΈΡΡΠ΅ΠΌΠΎΡΡΡ ΠΆΡΡΠ½Π°Π»ΠΎΠ², Π°Π²ΡΠΎΡΠΎΠ², Π½Π°ΡΡΠ½ΡΡ
ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠ²ΠΎΠ², ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ ΠΈ ΡΠ΅Π»ΡΡ
ΡΡΡΠ°Π½. ΠΠ°Π΅ΡΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈΠΌΠΏΠ°ΠΊΡ-ΡΠ°ΠΊΡΠΎΡΠ° ΠΈ Π΅Π³ΠΎ Π²Π°ΡΠΈΠ°ΡΠΈΠΉ, ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠΈΡΠΈΡΡΠ΅ΠΌΠΎΡΡΠΈ, ΡΠ°Π½Π³ΠΎΠ²ΡΡ
ΠΌΠ΅ΡΡΠΈΠΊ, ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° Π½Π΅ΡΠΈΡΠΈΡΡΠ΅ΠΌΠΎΡΡΠΈ, Β«Π²Π·Π²Π΅ΡΠ΅Π½Π½ΡΡ
Β» ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ² (ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΠΉ ΡΠ°ΠΊΡΠΎΡ, ΠΈΠ½Π΄Π΅ΠΊΡ Π²Π»ΠΈΡΠ½ΠΈΡ ΡΡΠ°ΡΡΠΈ), ΠΈΠ½Π΄Π΅ΠΊΡΠ° Π₯ΠΈΡΡΠ° ΠΈ ΡΡΠ΄Π° Β«Π₯ΠΈΡΡ-ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΡ
Β» ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΠΈ Π΄Ρ. Π‘ΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΠΉ ΡΠ°Π·Π΄Π΅Π» ΠΏΠΎΡΠ²ΡΡΠ΅Π½ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌ, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΠΌ Ρ
ΡΠΎΠ½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π±ΠΈΠ±Π»ΠΈΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΡΠ»ΠΎΠΊ. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»Π΅Π½ΠΎ ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠ°ΠΌ Π½ΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΈΡΠΈΡΡΠ΅ΠΌΠΎΡΡΠΈ, Ρ Π½ΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ ΠΏΠΎ ΠΎΠ±Π»Π°ΡΡΡΠΌ Π½Π°ΡΠΊΠΈ ΠΈ ΠΏΠΎ ΠΆΡΡΠ½Π°Π»Π°ΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΡ
ΡΠΎΠ²ΠΎΠΊΡΠΏΠ½ΠΎΠΌΡ Π°Π½Π°Π»ΠΈΠ·Ρ. ΠΠ»Π°Π²Π° ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ Π²Π²ΠΎΠ΄Π½ΡΡ ΡΠ°ΡΡΡ, Π² ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΈΠ·Π»Π°Π³Π°ΡΡΡΡ ΠΎΡΠ½ΠΎΠ²Ρ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ Π±Π°Π· Π΄Π°Π½Π½ΡΡ
Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π€ΠΈΠ½Π°Π»ΡΠ½ΡΠΉ ΡΠ°Π·Π΄Π΅Π» ΠΏΠΎΠ΄ΡΠ΅ΡΠΊΠΈΠ²Π°Π΅Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ Π³ΡΠ°ΠΌΠΎΡΠ½ΠΎΠΉ ΠΈ Π°ΠΊΠΊΡΡΠ°ΡΠ½ΠΎΠΉ ΡΡΠ°ΠΊΡΠΎΠ²ΠΊΠΈ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ² ΠΏΡΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΠΈ Π°Π΄ΠΌΠΈΠ½ΠΈΡΡΡΠ°ΡΠΈΠ²Π½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ, ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π³ΡΠ°Π½ΡΠΎΠ², ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½ΠΈΠΈ ΠΊΠ°Π΄ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ.This chapter examines bibliometric indicators related to citedness of journals, authors, research groups, institutions and whole countries. The introductory section deals with the basics of bibliometric analysis and features of citation databases. The author discusses the usage of various bibliometric indicators: the impact factor, average citedness, share of uncited papers, Eigenfactor and Article Influence Scores, Hirsch and Hirsch-type indices, and others. A special section investigates indicators of chronological distribution of references. Particular attention is paid to normalized indicators, including indicators normalized by research disciplines, as well as by publication sources. The final section emphasizes the importance of informed and reasonable use of bibliometric indicators in research policy-making, funding allocation, and faculty and research personnel recruitment
Confidence and RISC: How Russian papers indexed in the national citation database Russian Index of Science Citation (RISC) characterize universities and research institutes
The paper analyses Russian Index of Science Citation (RISC), a national citation database. We continue our previous study (Moskaleva et al., 2018) and focus on difference between bibliometric indicators calculated on, so to say, ""the best"" journals, so called RISC Core, and those which take into account all Russian journals available. Such a difference may show focuses of insitutional actors on different document types, publication strategies etc
Π‘Π°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ Π΅Π³ΠΎ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° ΠΎΡΠ΅Π½ΠΊΡ Π½Π°ΡΡΠ½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ: ΠΎΠ±Π·ΠΎΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ. Π§Π°ΡΡΡ I
The author reviews publications on the problem of self-citation and resulting mispresentations at the stage bibliometric analysis. He introduces the definition of self-citation and its special cases, i. e. authorβs, institutional, publisherβs and disciplinary. The formulas for general self-citation metrics, i. e. self-citation and self-citedness coefficients, are provided. The global publications on authorβs, institutional, national and journal self-citation are reviewed thoroughly. The current concepts of the role and impact of self-citation on scientific activity assessment are generalized. On the basis of his analytical review, the author argues that the researchers obtain consensus on several aspects, e. g.: Π°) excessive and absent self-citation are both seen as pathologies; Π²) self-citation has insignificant impact on large research entities though this influence can be critical when analyzing contributions by individual authors; Ρ) self-citation impact is well-expressed for scientific entities with weak bibliometric indicators, while the top scientists, organizations, journals, etc., get most of external links. The author examines the response of bibliometric indicators and databases to self-citation manipulations to adjust the indicators.Part I of the review is intended to define the basic concepts and terms and to examine the most popular authorβs self-citation.ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΠΎΠ±Π·ΠΎΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ, ΠΏΠΎΡΠ²ΡΡΡΠ½Π½ΠΎΠΉ Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΠΌ ΠΎΡ ΡΡΠΎΠ³ΠΎ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠΌ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΡΠΌ ΠΏΡΠΈ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·Π΅. ΠΠ²ΠΎΠ΄ΠΈΡΡΡ ΠΎΠ±ΠΎΠ±ΡΡΠ½Π½ΠΎΠ΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ Π΅Π³ΠΎ ΡΠ°ΡΡΠ½ΡΡ
Π²Π°ΡΠΈΠ°Π½ΡΠΎΠ²: Π°Π²ΡΠΎΡΡΠΊΠΎΠ³ΠΎ, ΠΈΠ½ΡΡΠΈΡΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ, ΡΡΡΠ°Π½ΠΎΠ²ΠΎΠ³ΠΎ, ΠΆΡΡΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ, Π΄ΠΈΡΡΠΈΠΏΠ»ΠΈΠ½Π°ΡΠ½ΠΎΠ³ΠΎ, ΠΈΠ·Π΄Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠΎΡΠΌΡΠ»Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΌΠ΅ΡΡΠΈΠΊ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ β ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠ² ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΡΠ΅ΠΌΠΎΡΡΠΈ. ΠΠΎΠ΄ΡΠΎΠ±Π½ΠΎ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Π° ΠΌΠΈΡΠΎΠ²Π°Ρ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ° ΠΏΠΎ Π°Π²ΡΠΎΡΡΠΊΠΎΠΌΡ, ΠΈΠ½ΡΡΠΈΡΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΌΡ, ΡΡΡΠ°Π½ΠΎΠ²ΠΎΠΌΡ ΠΈ ΠΆΡΡΠ½Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΠ±ΠΎΠ±ΡΠ΅Π½Ρ ΡΠ΅ΠΊΡΡΠΈΠ΅ Π²Π·Π³Π»ΡΠ΄Ρ Π½Π° ΡΠΎΠ»Ρ ΠΈ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΠΎΡΠ΅Π½ΠΊΠ΅ Π½Π°ΡΡΠ½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ. ΠΡΠΈ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΠΈ ΡΡΠ°ΡΠ΅ΠΉ, ΠΏΠΎΡΠ²ΡΡΡΠ½Π½ΡΡ
ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π²ΡΡΡΠ½ΡΠ΅ΡΡΡ, ΡΡΠΎ Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ ΡΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΡ ΠΏΠΎ ΡΡΠ΄Ρ ΠΏΠΎΠ·ΠΈΡΠΈΠΉ, Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ: Π°) ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠ΅ΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΊΠ°ΠΊ Π³ΠΈΠΏΠ΅ΡΡΡΠΎΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ΅ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅, ΡΠ°ΠΊ ΠΈ Π΅Π³ΠΎ ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅; Π±) ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ°Π»ΠΎ Π²Π»ΠΈΡΠ΅Ρ Π½Π° ΠΎΡΠ΅Π½ΠΊΡ ΠΊΡΡΠΏΠ½ΡΡ
Π½Π°ΡΡΠ½ΡΡ
Π΅Π΄ΠΈΠ½ΠΈΡ, Π½ΠΎ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΊΡΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΡΡΡΠ½ΡΡ
; Π²) Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²ΡΡΠ°ΠΆΠ΅Π½ΠΎ Ρ Π½Π°ΡΡΠ½ΡΡ
Π΅Π΄ΠΈΠ½ΠΈΡ ΡΠΎ ΡΠ»Π°Π±ΡΠΌΠΈ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌΠΈ, Π² ΡΠΎ Π²ΡΠ΅ΠΌΡ ΠΊΠ°ΠΊ ΡΠΎΠΏΠΎΠ²ΡΠ΅ ΡΡΡΠ½ΡΠ΅, ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ, ΠΆΡΡΠ½Π°Π»Ρ ΠΈ Π΄Ρ. ΠΏΠΎΠ»ΡΡΠ°ΡΡ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠ΅Π΅ ΡΠΈΡΠ»ΠΎ ΡΡΡΠ»ΠΎΠΊ ΠΈΠ·Π²Π½Π΅. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΎ ΡΠ΅Π°Π³ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ°ΠΌΠΈΡ
Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠ² ΠΈ Π±Π°Π· Π΄Π°Π½Π½ΡΡ
Ρ ΡΠ΅Π»ΡΡ ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΡΠΎΠ²ΠΊΠΈ ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ² Π² ΡΠ»ΡΡΠ°Π΅ ΠΌΠ°Π½ΠΈΠΏΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ.ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½Π°Ρ ΠΏΠ΅ΡΠ²Π°Ρ ΡΠ°ΡΡΡ ΠΎΠ±Π·ΠΎΡΠ° ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΏΠΎΠ½ΡΡΠΈΠΉ ΠΈ ΡΠ΅ΡΠΌΠΈΠ½ΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΠΎΠ³ΠΎ ΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½ΡΠ½Π½ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ β Π°Π²ΡΠΎΡΡΠΊΠΎΠ³ΠΎ
Π‘Π°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ Π΅Π³ΠΎ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° ΠΎΡΠ΅Π½ΠΊΡ Π½Π°ΡΡΠ½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ: ΠΎΠ±Π·ΠΎΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ. Π§Π°ΡΡΡ II
This review summarizes papers which analyze the impact of self-citation on research evaluation. We introduce a generalized definition of self-citation and its variants: author, institutional, country, journal, discipline, and publisher selfcitation. Formulae of the basic self-citation measures are given, namely self-citing and self-cited rates. World literature on author, institutional, country, and journal self-citation is studied in more detail. Current views on the role and impact of self-citation are compiled and analyzed. It is found that there is a general consensus on some points: a) excessive self-citation and its total absence are both seen as pathological; b) self-citation has low impact on large research entities but may be critical for the analysis of individual researchers; c) share of self-citations is generally higher for entities with poor bibliometric performance, while top scientists, institutions, journals receive the majority of their citations from outside. This review also considers how bibliometric tools and databases respond to the challenge of possible manipulation by self-citations and how some bibliometric indicators are adjusted by them. The first part of the review presented here deals with the fundamental terms and definitions, and the most discussed and studied type of the self-citation, author self-citation.This second and final part of the review considers institutional, country and journal self-citation. It also examines new bibliometric indicators which adjust for self-citation.ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΠΎΠ±Π·ΠΎΡ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ, ΠΏΠΎΡΠ²ΡΡΡΠ½Π½ΠΎΠΉ Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΠΌ ΠΎΡ ΡΡΠΎΠ³ΠΎ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠΌ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΡΠΌ ΠΏΡΠΈ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·Π΅. ΠΠ²ΠΎΠ΄ΠΈΡΡΡ ΠΎΠ±ΠΎΠ±ΡΡΠ½Π½ΠΎΠ΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ Π΅Π³ΠΎ ΡΠ°ΡΡΠ½ΡΡ
Π²Π°ΡΠΈΠ°Π½ΡΠΎΠ²: Π°Π²ΡΠΎΡΡΠΊΠΎΠ³ΠΎ, ΠΈΠ½ΡΡΠΈΡΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ, ΡΡΡΠ°Π½ΠΎΠ²ΠΎΠ³ΠΎ, ΠΆΡΡΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ, Π΄ΠΈΡΡΠΈΠΏΠ»ΠΈΠ½Π°ΡΠ½ΠΎΠ³ΠΎ, ΠΈΠ·Π΄Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠΎΡΠΌΡΠ»Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΌΠ΅ΡΡΠΈΠΊ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ β ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠΎΠ² ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΡΠ΅ΠΌΠΎΡΡΠΈ. ΠΠΎΠ΄ΡΠΎΠ±Π½ΠΎ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Π° ΠΌΠΈΡΠΎΠ²Π°Ρ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ° ΠΏΠΎ Π°Π²ΡΠΎΡΡΠΊΠΎΠΌΡ, ΠΈΠ½ΡΡΠΈΡΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΌΡ, ΡΡΡΠ°Π½ΠΎΠ²ΠΎΠΌΡ ΠΈ ΠΆΡΡΠ½Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΠ±ΠΎΠ±ΡΠ΅Π½Ρ ΡΠ΅ΠΊΡΡΠΈΠ΅ Π²Π·Π³Π»ΡΠ΄Ρ Π½Π° ΡΠΎΠ»Ρ ΠΈ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΠΎΡΠ΅Π½ΠΊΠ΅ Π½Π°ΡΡΠ½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ. ΠΡΠΈ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΠΈ ΡΡΠ°ΡΠ΅ΠΉ, ΠΏΠΎΡΠ²ΡΡΡΠ½Π½ΡΡ
ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π²ΡΡΡΠ½ΡΠ΅ΡΡΡ, ΡΡΠΎ Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ ΡΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΡ ΠΏΠΎ ΡΡΠ΄Ρ ΠΏΠΎΠ·ΠΈΡΠΈΠΉ, Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ: Π°) ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠ΅ΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΊΠ°ΠΊ Π³ΠΈΠΏΠ΅ΡΡΡΠΎΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ΅ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅, ΡΠ°ΠΊ ΠΈ Π΅Π³ΠΎ ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅; Π±) ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ°Π»ΠΎ Π²Π»ΠΈΡΠ΅Ρ Π½Π° ΠΎΡΠ΅Π½ΠΊΡ ΠΊΡΡΠΏΠ½ΡΡ
Π½Π°ΡΡΠ½ΡΡ
Π΅Π΄ΠΈΠ½ΠΈΡ, Π½ΠΎ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΊΡΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΡΡΡΠ½ΡΡ
; Π²) Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²ΡΡΠ°ΠΆΠ΅Π½ΠΎ Ρ Π½Π°ΡΡΠ½ΡΡ
Π΅Π΄ΠΈΠ½ΠΈΡ ΡΠΎ ΡΠ»Π°Π±ΡΠΌΠΈ Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌΠΈ, Π² ΡΠΎ Π²ΡΠ΅ΠΌΡ ΠΊΠ°ΠΊ ΡΠΎΠΏΠΎΠ²ΡΠ΅ ΡΡΡΠ½ΡΠ΅, ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ, ΠΆΡΡΠ½Π°Π»Ρ ΠΈ Π΄Ρ. ΠΏΠΎΠ»ΡΡΠ°ΡΡ Π½Π°ΠΈΠ±ΠΎΠ»ΡΡΠ΅Π΅ ΡΠΈΡΠ»ΠΎ ΡΡΡΠ»ΠΎΠΊ ΠΈΠ·Π²Π½Π΅. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΎ ΡΠ΅Π°Π³ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ°ΠΌΠΈΡ
Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠ² ΠΈ Π±Π°Π· Π΄Π°Π½Π½ΡΡ
Ρ ΡΠ΅Π»ΡΡ ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΡΠΎΠ²ΠΊΠΈ ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ² Π² ΡΠ»ΡΡΠ°Π΅ ΠΌΠ°Π½ΠΈΠΏΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ.ΠΡΠΎΡΠ°Ρ, Π·Π°ΠΊΠ»ΡΡΠΈΡΠ΅Π»ΡΠ½Π°Ρ ΡΠ°ΡΡΡ ΠΎΠ±Π·ΠΎΡΠ° ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π°Β ΠΈΠ½ΡΡΠΈΡΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΌΡ, ΡΡΡΠ°Π½ΠΎΠ²ΠΎΠΌΡ ΠΈ ΠΆΡΡΠ½Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ Π²Π²Π΅Π΄Π΅Π½ΠΈΡ Π½ΠΎΠ²ΡΡ
Π±ΠΈΠ±Π»ΠΈΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ², ΡΠ°ΠΊ ΠΈΠ»ΠΈ ΠΈΠ½Π°ΡΠ΅ ΡΡΠΈΡΡΠ²Π°ΡΡΠΈΡ
Π½Π°Π»ΠΈΡΠΈΠ΅ ΡΠ°ΠΌΠΎΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ
Spark discharge synthesis of semiconductor nanoparticles for thick-film metal oxide gas sensors
Traditional methods of synthesis of metal-oxide gas sensing materials for semiconductor sensors are based on wet sol-gel processes. However, these processes lead to the formation of hydroxyl groups on the surface of oxide particles being responsible for the strong response of a sensing material to humidity. In this work, we investigated the possibility to synthesize metal-oxide materials with reduced sensitivity to water vapors. Dry synthesis of SnO2 nanoparticles was implemented in the gas phase by spark discharge, which allowed us to produce powder with specific surface area of about 40 m2/g after additional annealing at 610 Β°C. The drop of sensor resistance does not exceed 20%, when air humidity increases from 40 to 100%, whereas the response to 100 ppm of hydrogen is of a factor of 8 with very short response time of about 1 s