Search (226 results, page 11 of 12)

  • × theme_ss:"Informetrie"
  1. Lercher, A.: Correlation over time for citations to mathematics articles (2013) 0.01 
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    Date
    22. 3.2013 19:23:35
  2. Schubert, T.; Michels, C.: Placing articles in the large publisher nations : is there a "free lunch" in terms of higher impact? (2013) 0.01 
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    Date
    22. 3.2013 19:45:49
  3. Liu, S.; Chen, C.: ¬The differences between latent topics in abstracts and citation contexts of citing papers (2013) 0.01 
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    22. 3.2013 19:50:00
  4. Costas, R.; Zahedi, Z.; Wouters, P.: ¬The thematic orientation of publications mentioned on social media : large-scale disciplinary comparison of social media metrics with citations (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
  5. Moed, H.F.; Halevi, G.: On full text download and citation distributions in scientific-scholarly journals (2016) 0.01 
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    Date
    22. 1.2016 14:11:17
  6. Kuan, C.-H.; Liu, J.S.: ¬A new approach for main path analysis : decay in knowledge diffusion (2016) 0.01 
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    Date
    22. 1.2016 14:23:00
  7. Thelwall, M.: Are Mendeley reader counts high enough for research evaluations when articles are published? (2017) 0.01 
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    Date
    20. 1.2015 18:30:22
  8. Castanha, R.C.G.; Wolfram, D.: ¬The domain of knowledge organization : a bibliometric analysis of prolific authors and their intellectual space (2018) 0.01 
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    Source
    Knowledge organization. 45(2018) no.1, S.13-22
  9. Ortega, J.L.: ¬The presence of academic journals on Twitter and its relationship with dissemination (tweets) and research impact (citations) (2017) 0.01 
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    Date
    20. 1.2015 18:30:22
  10. Song, M.; Kang, K.; An, J.Y.: Investigating drug-disease interactions in drug-symptom-disease triples via citation relations (2018) 0.01 
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    Date
    1.11.2018 18:19:22
  11. Thelwall, M.; Thelwall, S.: ¬A thematic analysis of highly retweeted early COVID-19 tweets : consensus, information, dissent and lockdown life (2020) 0.01 
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    Date
    20. 1.2015 18:30:22
  12. Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023) 0.01 
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    Date
    22. 1.2023 18:37:33
  13. Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023) 0.01 
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    Date
    21. 3.2023 19:22:02
  14. Asubiaro, T.V.; Onaolapo, S.: ¬A comparative study of the coverage of African journals in Web of Science, Scopus, and CrossRef (2023) 0.01 
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    Date
    22. 6.2023 14:09:06
  15. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.01 
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    Date
    22. 6.2023 18:07:12
  16. Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Why are coauthored academic articles more cited : higher quality or larger audience? (2023) 0.01 
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    Date
    22. 6.2023 18:11:50
  17. Umstätter, W.: Szientometrische Verfahren (2004) 0.00 
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  18. Glänzel, W.; Debackere, K.: Messen von Wissenschaftlicher Kommunikation und Forschungsleistung : Möglichkeiten und Beschränkungen bibliometrischer Methoden (2005) 0.00 
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  19. Stock, W.G.; Weber, S.: Facets of informetrics : Preface (2006) 0.00 
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    Abstract
    According to Jean M. Tague-Sutcliffe "informetrics" is "the study of the quantitative aspects of information in any form, not just records or bibliographies, and in any social group, not just scientists" (Tague-Sutcliffe, 1992, 1). Leo Egghe also defines "informetrics" in a very broad sense. "(W)e will use the term' informetrics' as the broad term comprising all-metrics studies related to information science, including bibliometrics (bibliographies, libraries,...), scientometrics (science policy, citation analysis, research evaluation,...), webometrics (metrics of the web, the Internet or other social networks such as citation or collaboration networks), ..." (Egghe, 2005b,1311). According to Concepcion S. Wilson "informetrics" is "the quantitative study of collections of moderatesized units of potentially informative text, directed to the scientific understanding of information processes at the social level" (Wilson, 1999, 211). We should add to Wilson's units of text also digital collections of images, videos, spoken documents and music. Dietmar Wolfram divides "informetrics" into two aspects, "system-based characteristics that arise from the documentary content of IR systems and how they are indexed, and usage-based characteristics that arise how users interact with system content and the system interfaces that provide access to the content" (Wolfram, 2003, 6). We would like to follow Tague-Sutcliffe, Egghe, Wilson and Wolfram (and others, for example Björneborn & Ingwersen, 2004) and call this broad research of empirical information science "informetrics". Informetrics includes therefore all quantitative studies in information science. If a scientist performs scientific investigations empirically, e.g. on information users' behavior, on scientific impact of academic journals, on the development of the patent application activity of a company, on links of Web pages, on the temporal distribution of blog postings discussing a given topic, on availability, recall and precision of retrieval systems, on usability of Web sites, and so on, he or she contributes to informetrics. We see three subject areas in information science in which such quantitative research takes place, - information users and information usage, - evaluation of information systems, - information itself, Following Wolfram's article, we divide his system-based characteristics into the "information itself "-category and the "information system"-category. Figure 1 is a simplistic graph of subjects and research areas of informetrics as an empirical information science.
  20. Wettlauf der Wissenschaft (2004) 0.00 
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    Source
    Online Mitteilungen. 2004, Nr.79, S.22-23 [=Mitteilungen VÖB 57(2004) H.2]

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