Search (21 results, page 1 of 2)

  • × year_i:[2020 TO 2030}
  • × theme_ss:"Informetrie"
  1. Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023) 0.02 
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    Abstract
    During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.
    Date
    21. 3.2023 19:22:02
  2. 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.
    Object
    h-index
    Source
    Mitteilungen der Deutschen Mathematiker-Vereinigung. 2021, H.3, S.124-128
  3. 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
  4. Springer, M.: Erlahmt die Forschung? (2023) 0.01 
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    Source
    Spektrum der Wissenschaft. 2023, H.3, S.30
  5. Lorentzen, D.G.: Bridging polarised Twitter discussions : the interactions of the users in the middle (2021) 0.01 
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    Date
    20. 1.2015 18:30:22
  6. Milard, B.; Pitarch, Y.: Egocentric cocitation networks and scientific papers destinies (2023) 0.01 
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    Date
    21. 3.2023 19:22:14
  7. Dederke, J.; Hirschmann, B.; Johann, D.: ¬Der Data Citation Index von Clarivate : Eine wertvolle Ressource für die Forschung und für Bibliotheken? (2022) 0.01 
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    Source
    B.I.T. Online. 25(2022) H.1, S.21-
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023) 0.01 
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    Date
    22. 6.2023 18:15:06
  14. Positionspapier der DMV zur Verwendung bibliometrischer Daten (2020) 0.01 
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    Object
    h-index
    Source
    Mitteilungen der Deutschen Mathematiker-Vereinigung. 2019, H.3-4, S.112-117
  15. Reichmann, G.; Schlögl, C.: Möglichkeiten zur Steuerung der Ergebnisse einer Forschungsevaluation (2021) 0.01 
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    Source
    Information - Wissenschaft und Praxis. 72(2021) H.4, S.212-220
  16. Herb, U.; Geith, U.: Kriterien der qualitativen Bewertung wissenschaftlicher Publikationen : Befunde aus dem Projekt visOA (2020) 0.01 
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    Information - Wissenschaft und Praxis. 71(2020) H.2/3, S.77-85
  17. 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.
    Source
    Bibliotheksdienst. 56(2022) H.2, S.94-108
  18. Zhou, H.; Dong, K.; Xia, Y.: Knowledge inheritance in disciplines : quantifying the successive and distant reuse of references (2023) 0.01 
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    Abstract
    How the knowledge base of disciplines grows, renews, and decays informs their distinct characteristics and epistemology. Here we track the evolution of knowledge bases of 19 disciplines for over 45 years. We introduce the notation of knowledge inheritance as the overlap in the set of references between years. We discuss two modes of knowledge inheritance of disciplines-successive and distant. To quantify the status and propensity of knowledge inheritance for disciplines, we propose two indicators: one descriptively describes knowledge base evolution, and one estimates the propensity of knowledge inheritance. When observing the continuity in knowledge bases for disciplines, we show distinct patterns for STEM and SS&H disciplines: the former inherits knowledge bases more successively, yet the latter inherits significantly from distant knowledge bases. We further discover stagnation or revival in knowledge base evolution where older knowledge base ceases to decay after 10 years (e.g., Physics and Mathematics) and are increasingly reused (e.g., Philosophy). Regarding the propensity of inheriting prior knowledge bases, we observe unanimous rises in both successive and distant knowledge inheritance. We show that knowledge inheritance could reveal disciplinary characteristics regarding the trajectory of knowledge base evolution and interesting insights into the metabolism and maturity of scholarly communication.
  19. Zhou, H.; Guns, R.; Engels, T.C.E.: Are social sciences becoming more interdisciplinary? : evidence from publications 1960-2014 (2022) 0.00 
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  20. Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023) 0.00 
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    Abstract
    This study investigates scholars' citation behaviors from a fine-grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision-making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest-based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision-making, followed by citation authority and citation inertia. However, big-name scholars whose h-indexes rank among the top 1% exhibit a unique pattern of citation behaviors-their citation decision-making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.

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  • e 14
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