Search (26 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. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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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    d
  12. Springer, M.: Erlahmt die Forschung? (2023) 0.01 
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  13. Liu, X.; Bu, Y.; Li, M.; Li, J.: Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration (2024) 0.00 
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    Abstract
    Collaboration across disciplines is a critical form of scientific collaboration to solve complex problems and make innovative contributions. This study focuses on the association between multidisciplinary collaboration measured by coauthorship in publications and the disruption of publications measured by the Disruption (D) index. We used authors' affiliations as a proxy of the disciplines to which they belong and categorized an article into multidisciplinary collaboration or monodisciplinary collaboration. The D index quantifies the extent to which a study disrupts its predecessors. We selected 13 journals that publish articles in six disciplines from the Microsoft Academic Graph (MAG) database and then constructed regression models with fixed effects and estimated the relationship between the variables. The findings show that articles with monodisciplinary collaboration are more disruptive than those with multidisciplinary collaboration. Furthermore, we uncovered the mechanism of how monodisciplinary collaboration disrupts science more than multidisciplinary collaboration by exploring the references of the sampled publications.
  14. Reichmann, G.; Schlögl, C.: Möglichkeiten zur Steuerung der Ergebnisse einer Forschungsevaluation (2021) 0.00 
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  15. Herb, U.; Geith, U.: Kriterien der qualitativen Bewertung wissenschaftlicher Publikationen : Befunde aus dem Projekt visOA (2020) 0.00 
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  16. Krattenthaler, C.: Was der h-Index wirklich aussagt (2021) 0.00 
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  17. Dorsch, I.; Haustein, S.: Bibliometrie (2023) 0.00 
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  18. Positionspapier der DMV zur Verwendung bibliometrischer Daten (2020) 0.00 
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  19. Thelwall, M.; Foster, D.: Male or female gender-polarized YouTube videos are less viewed (2021) 0.00 
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  20. Lu, C.; Zhang, Y.; Ahn, Y.-Y.; Ding, Y.; Zhang, C.; Ma, D.: Co-contributorship network and division of labor in individual scientific collaborations (2020) 0.00 
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  • e 18
  • d 8

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