Search (28 results, page 1 of 2)

  • × theme_ss:"Informetrie"
  • × year_i:[2020 TO 2030}
  1. Wiggers, G.; Verberne, S.; Loon, W. van; Zwenne, G.-J.: Bibliometric-enhanced legal information retrieval : combining usage and citations as flavors of impact relevance (2023) 0.05 
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  2. Dederke, J.; Hirschmann, B.; Johann, D.: ¬Der Data Citation Index von Clarivate : Eine wertvolle Ressource für die Forschung und für Bibliotheken? (2022) 0.04 
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    Abstract
    Der Data Citation Index (DCI) stellt eine durchsuchbare Sammlung bibliografischer Metadaten zu Forschungsdaten in Datensätzen und Datenstudien ausgewählter Repositorien dar. Der DCI deckt alle wissenschaftlichen Disziplinen ab.
  3. 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) 0.02 
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    Date
    22. 1.2023 18:37:33
  4. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.02 
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    Date
    22. 6.2023 18:07:12
  5. 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) 0.02 
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    Date
    22. 6.2023 18:11:50
  6. Liu, M.; Bu, Y.; Chen, C.; Xu, J.; Li, D.; Leng, Y.; Freeman, R.B.; Meyer, E.T.; Yoon, W.; Sung, M.; Jeong, M.; Lee, J.; Kang, J.; Min, C.; Zhai, Y.; Song, M.; Ding, Y.: Pandemics are catalysts of scientific novelty : evidence from COVID-19 (2022) 0.01 
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  7. Fang, Z.; Dudek, J.; Costas, R.: Facing the volatility of tweets in altmetric research (2022) 0.01 
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  8. Liu, X.; Bu, Y.; Li, M.; Li, J.: Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration (2024) 0.01 
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  9. Springer, M.: Erlahmt die Forschung? (2023) 0.01 
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    Abstract
    In der Flut der Fachartikel gehen wegweisende Resultate unter.
    Source
    Spektrum der Wissenschaft. 2023, H.3, S.30
  10. Jiang, X.; Zhu, X.; Chen, J.: Main path analysis on cyclic citation networks (2020) 0.01 
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  11. Ikae, C.; Savoy, J.: Gender identification on Twitter (2022) 0.01 
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  12. Chen, L.; Ding, J.; Larivière, V.: Measuring the citation context of national self-references : how a web journal club is used (2022) 0.01 
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  13. Kulczycki, E.; Huang, Y.; Zuccala, A.A.; Engels, T.C.E.; Ferrara, A.; Guns, R.; Pölönen, J.; Sivertsen, G.; Taskin, Z.; Zhang, L.: Uses of the Journal Impact Factor in national journal rankings in China and Europe (2022) 0.01 
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  14. Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Do altmetric scores reflect article quality? : evidence from the UK Research Excellence Framework 2021 (2023) 0.01 
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  15. Thelwall, M.; Kousha, K.; Stuart, E.; Makita, M.; Abdoli, M.; Wilson, P.; Levitt, J.: In which fields are citations indicators of research quality? (2023) 0.01 
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  16. Krattenthaler, C.: Was der h-Index wirklich aussagt (2021) 0.01 
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    Abstract
    Diese Note legt dar, dass der sogenannte h-Index (Hirschs bibliometrischer Index) im Wesentlichen dieselbe Information wiedergibt wie die Gesamtanzahl von Zitationen von Publikationen einer Autorin oder eines Autors, also ein nutzloser bibliometrischer Index ist. Dies basiert auf einem faszinierenden Satz der Wahrscheinlichkeitstheorie, der hier ebenfalls erläutert wird.
    Content
    Vgl.: DOI: 10.1515/dmvm-2021-0050. Auch abgedruckt u.d.T.: 'Der h-Index - "ein nutzloser bibliometrischer Index"' in Open Password Nr. 1007 vom 06.12.2021 unter: https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzM3NCwiZDI3MzMzOTEwMzUzIiwwLDAsMzQ4LDFd.
    Source
    Mitteilungen der Deutschen Mathematiker-Vereinigung. 2021, H.3, S.124-128
  17. Manley, S.: Letters to the editor and the race for publication metrics (2022) 0.01 
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    Date
    6. 4.2022 19:22:26
  18. Positionspapier der DMV zur Verwendung bibliometrischer Daten (2020) 0.01 
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    Abstract
    Bibliometrische Daten werden heute zunehmend in der Evaluation von Forschungsergebnissen benutzt. Diese Anwendungen reichen von der (indirekten) Verwendung bei der Peer-Evaluation von Drittmittelanträgen über die Beurteilung von Bewerbungen in Berufungskommissionen oder Anträgen für Forschungszulagen bis hin zur systematischen Erhebung von forschungsorientierten Kennzahlen von Institutionen. Mit diesem Dokument will die DMV ihren Mitgliedern eine Diskussionsgrundlage zur Verwendung bibliometrischer Daten im Zusammenhang mit der Evaluation von Personen und Institutionen im Fachgebiet Mathematik zur Verfügung stellen, insbesondere auch im Vergleich zu anderen Fächern. Am Ende des Texts befindet sich ein Glossar, in dem die wichtigsten Begriffe kurz erläutert werden.
    Source
    Mitteilungen der Deutschen Mathematiker-Vereinigung. 2019, H.3-4, S.112-117
  19. Tausch, A.: Zitierungen sind nicht alles : Classroom Citation, Libcitation und die Zukunft bibliometrischer und szientometrischer Leistungsvergleiche (2022) 0.01 
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    Abstract
    Der Beitrag soll zeigen, welche fortgeschrittenen bibliometrischen und szientometrischen Daten für ein bewährtes Sample von 104 österreichischen Politikwissenschaftler*innen und 51 transnationalen Verlagsunternehmen enge statistische Beziehungen zwischen Indikatoren der Präsenz von Wissenschaftler*innen und transnationalen Verlagsunternehmen in den akademischen Lehrveranstaltungen der Welt (Classroom Citation, gemessen mit Open Syllabus) und anderen, herkömmlicheren bibliometrischen und szientometrischen Indikatoren (Libcitation gemessen mit dem OCLC Worldcat, sowie der H-Index der Zitierung in den vom System Scopus erfassten Fachzeitschriften der Welt bzw. dem Book Citation Index) bestehen. Die statistischen Berechnungen zeigen, basierend auf den Faktorenanalysen, die engen statistischen Beziehungen zwischen diesen Dimensionen. Diese Ergebnisse sind insbesondere in den Tabellen 5 und 9 dieser Arbeit (Komponentenkorrelationen) ableitbar.
    Die multivariaten Rankings, die auf den mit den Eigenwerten gewichteten Factorscores beruhen, zeigen in der Tabelle 6, welche 15 österreichischen Politikwissenschaftler*innen sich auf den Weltmärkten der Wissenschaften am besten bewährten. Außerdem zeigen diese Methoden in Tabelle 10 auch, dass die im Beitrag analysierten führenden akademischen Verlage der Welt insbesondere für Wissenschaftler*innen der jüngeren Generation gute Weggefährten sein werden, um in Zeiten institutioneller und finanzieller Unsicherheit die Ergebnisse der Wissenschaft verlässlich um den Globus zu verbreiten und die akademischen Karrieren zu befördern.
  20. Reichmann, G.; Schlögl, C.: Möglichkeiten zur Steuerung der Ergebnisse einer Forschungsevaluation (2021) 0.00 
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    Abstract
    Ein Leistungsvergleich zwischen den (ehemaligen) Instituten für Informationswissenschaft der Universitäten Düsseldorf und Graz auf Basis der Forschungsleistung für einen Zeitraum von zehn Jahren zeigt, wie sehr die Ergebnisse einer Forschungsevaluation gesteuert werden können. Durch die Wahl "geeigneter" Indikatoren gelingt es - je nach Wunsch - entweder das eine oder das andere Institut an die erste Stelle zu bringen. Hält man sich dagegen an das wissenschaftliche Gebot der Unparteilichkeit, führt dies im hier gezeigten Anwendungsbeispiel zu gemischten Ergebnissen.

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