Combining dissimilarity measures for quantifying changes in research fields

Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity me...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zheng, Lukun [verfasserIn] Jiang, Yuhang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Dissimilarity measures Measure of evolution Principal component analysis

Anmerkung:	© Akadémiai Kiadó, Budapest, Hungary 2022

Übergeordnetes Werk:	Enthalten in: Scientometrics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978, 127(2022), 7 vom: 18. Juni, Seite 3751-3765
Übergeordnetes Werk:	volume:127 ; year:2022 ; number:7 ; day:18 ; month:06 ; pages:3751-3765

Links:	Volltext

DOI / URN:	10.1007/s11192-022-04415-5

Katalog-ID:	SPR047730625

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520			\|a Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods.
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allfields	10.1007/s11192-022-04415-5 doi (DE-627)SPR047730625 (SPR)s11192-022-04415-5-e DE-627 ger DE-627 rakwb eng Zheng, Lukun verfasserin (orcid)0000-0001-8253-1267 aut Combining dissimilarity measures for quantifying changes in research fields 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods. Dissimilarity measures (dpeaa)DE-He213 Measure of evolution (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Jiang, Yuhang aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 7 vom: 18. Juni, Seite 3751-3765 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:7 day:18 month:06 pages:3751-3765 https://dx.doi.org/10.1007/s11192-022-04415-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 7 18 06 3751-3765
spelling	10.1007/s11192-022-04415-5 doi (DE-627)SPR047730625 (SPR)s11192-022-04415-5-e DE-627 ger DE-627 rakwb eng Zheng, Lukun verfasserin (orcid)0000-0001-8253-1267 aut Combining dissimilarity measures for quantifying changes in research fields 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods. Dissimilarity measures (dpeaa)DE-He213 Measure of evolution (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Jiang, Yuhang aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 7 vom: 18. Juni, Seite 3751-3765 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:7 day:18 month:06 pages:3751-3765 https://dx.doi.org/10.1007/s11192-022-04415-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 7 18 06 3751-3765
allfields_unstemmed	10.1007/s11192-022-04415-5 doi (DE-627)SPR047730625 (SPR)s11192-022-04415-5-e DE-627 ger DE-627 rakwb eng Zheng, Lukun verfasserin (orcid)0000-0001-8253-1267 aut Combining dissimilarity measures for quantifying changes in research fields 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods. Dissimilarity measures (dpeaa)DE-He213 Measure of evolution (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Jiang, Yuhang aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 7 vom: 18. Juni, Seite 3751-3765 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:7 day:18 month:06 pages:3751-3765 https://dx.doi.org/10.1007/s11192-022-04415-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 7 18 06 3751-3765
allfieldsGer	10.1007/s11192-022-04415-5 doi (DE-627)SPR047730625 (SPR)s11192-022-04415-5-e DE-627 ger DE-627 rakwb eng Zheng, Lukun verfasserin (orcid)0000-0001-8253-1267 aut Combining dissimilarity measures for quantifying changes in research fields 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods. Dissimilarity measures (dpeaa)DE-He213 Measure of evolution (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Jiang, Yuhang aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 7 vom: 18. Juni, Seite 3751-3765 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:7 day:18 month:06 pages:3751-3765 https://dx.doi.org/10.1007/s11192-022-04415-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 7 18 06 3751-3765
allfieldsSound	10.1007/s11192-022-04415-5 doi (DE-627)SPR047730625 (SPR)s11192-022-04415-5-e DE-627 ger DE-627 rakwb eng Zheng, Lukun verfasserin (orcid)0000-0001-8253-1267 aut Combining dissimilarity measures for quantifying changes in research fields 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods. Dissimilarity measures (dpeaa)DE-He213 Measure of evolution (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213 Jiang, Yuhang aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 7 vom: 18. Juni, Seite 3751-3765 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:7 day:18 month:06 pages:3751-3765 https://dx.doi.org/10.1007/s11192-022-04415-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 7 18 06 3751-3765
language	English
source	Enthalten in Scientometrics 127(2022), 7 vom: 18. Juni, Seite 3751-3765 volume:127 year:2022 number:7 day:18 month:06 pages:3751-3765
sourceStr	Enthalten in Scientometrics 127(2022), 7 vom: 18. Juni, Seite 3751-3765 volume:127 year:2022 number:7 day:18 month:06 pages:3751-3765
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Dissimilarity measures Measure of evolution Principal component analysis
isfreeaccess_bool	false
container_title	Scientometrics
authorswithroles_txt_mv	Zheng, Lukun @@aut@@ Jiang, Yuhang @@aut@@
publishDateDaySort_date	2022-06-18T00:00:00Z
hierarchy_top_id	320589099
id	SPR047730625
language_de	englisch
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author	Zheng, Lukun
spellingShingle	Zheng, Lukun misc Dissimilarity measures misc Measure of evolution misc Principal component analysis Combining dissimilarity measures for quantifying changes in research fields
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topic_title	Combining dissimilarity measures for quantifying changes in research fields Dissimilarity measures (dpeaa)DE-He213 Measure of evolution (dpeaa)DE-He213 Principal component analysis (dpeaa)DE-He213
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title	Combining dissimilarity measures for quantifying changes in research fields
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title_full	Combining dissimilarity measures for quantifying changes in research fields
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title_sort	combining dissimilarity measures for quantifying changes in research fields
title_auth	Combining dissimilarity measures for quantifying changes in research fields
abstract	Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods. © Akadémiai Kiadó, Budapest, Hungary 2022
abstractGer	Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods. © Akadémiai Kiadó, Budapest, Hungary 2022
abstract_unstemmed	Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods. © Akadémiai Kiadó, Budapest, Hungary 2022
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title_short	Combining dissimilarity measures for quantifying changes in research fields
url	https://dx.doi.org/10.1007/s11192-022-04415-5
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up_date	2024-07-03T14:36:53.853Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR047730625</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230508004429.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220731s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11192-022-04415-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR047730625</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11192-022-04415-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zheng, Lukun</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-8253-1267</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Combining dissimilarity measures for quantifying changes in research fields</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Akadémiai Kiadó, Budapest, Hungary 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The changes in research fields has been attracting much attention in recent years. One of the key issues here is to quantify the dissimilarity between two collections of scientific publications in literature. Many existing works on this topic based their study on one or two dissimilarity measures, despite the fact that there are numerous such dissimilarity measures. It is of fundamental importance to find appropriate dissimilarity measures among such a sizeable collection of choices. In this article, we develop a new measure of the evolution combining 12 keyword-based temporal dissimilarities of the research fields using the method of principal component analysis. To demonstrate the usage of this new measure, we chose four research fields: environmental science, information science and library science, medical informatics, and religion. A database consisting of 274,453 bibliographic records in these four chosen fields from 1991 to 2019 are built. The results show that all these four research fields share an overall decreasing trend in evolution from 1991 to 2019 and different fields exhibits different evolution patterns during different time periods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dissimilarity measures</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Measure of evolution</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Principal component analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiang, Yuhang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Scientometrics</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978</subfield><subfield code="g">127(2022), 7 vom: 18. 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