Search (279 results, page 1 of 14)

  • × theme_ss:"Informetrie"
  1. Su, Y.; Han, L.-F.: ¬A new literature growth model : variable exponential growth law of literature (1998) 0.05
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    Date
    22. 5.1999 19:22:35
  2. Botting, N.; Dipper, L.; Hilari, K.: ¬The effect of social media promotion on academic article uptake (2017) 0.03
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    Abstract
    Important emerging measures of academic impact are article download and citation rates. Yet little is known about the influences on these and ways in which academics might manage this approach to dissemination. Three groups of papers by academics in a center for speech-language-science (available through a university repository) were compared. The first group of target papers were blogged, and the blogs were systematically tweeted. The second group of connected control papers were nonblogged papers that we carefully matched for author, topic, and year of publication. The third group were papers by different staff members on a variety of topics-Unrelated Control Papers. The results suggest an effect of social media on download rate, which was limited not just to Target Papers but also generalized to Connected Control Papers. Unrelated Control Papers showed no increase over the same amount of time (main effect of time, F(1,27)?=?55.6, p?<?.001); Significant Group×Time Interaction, F(2,27)?=?7.9, p?=?.002). The effect on citation rates was less clear but followed the same trend. The only predictor of the 2015 citation rate was downloads after blogging (r?=?0.450, p?=?.012). These preliminary results suggest that promotion of academic articles via social media may enhance download and citation rate and that this has implications for impact strategies.
  3. Hayer, L.: Lazarsfeld zitiert : eine bibliometrische Analyse (2008) 0.03
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    Abstract
    Um sich einer Antwort auf die Frage anzunähern, welche Bedeutung der Nachlass eines Wissenschaftlers wie jener Paul F. Lazarsfelds (mit zahlreichen noch unveröffentlichten Schriften) für die aktuelle Forschung haben könne, kann untersucht werden, wie häufig dieser Wissenschaftler zitiert wird. Wenn ein Autor zitiert wird, wird er auch genutzt. Wird er über einen langen Zeitraum oft genutzt, ist vermutlich auch die Auseinandersetzung mit seinem Nachlass von Nutzen. Außerdem kann aufgrund der Zitierungen festgestellt werden, was aus dem Lebenswerk eines Wissenschaftlers für die aktuelle Forschung relevant erscheint. Daraus können die vordringlichen Fragestellungen in der Bearbeitung des Nachlasses abgeleitet werden. Die Aufgabe für die folgende Untersuchung lautete daher: Wie oft wird Paul F. Lazarsfeld zitiert? Dabei interessierte auch: Wer zitiert wo? Die Untersuchung wurde mit Hilfe der Meta-Datenbank "ISI Web of Knowledge" durchgeführt. In dieser wurde im "Web of Science" mit dem Werkzeug "Cited Reference Search" nach dem zitierten Autor (Cited Author) "Lazarsfeld P*" gesucht. Diese Suche ergab 1535 Referenzen (References). Werden alle Referenzen gewählt, führt dies zu 4839 Ergebnissen (Results). Dabei wurden die Datenbanken SCI-Expanded, SSCI und A&HCI verwendet. Bei dieser Suche wurden die Publikationsjahre 1941-2008 analysiert. Vor 1956 wurden allerdings nur sehr wenige Zitate gefunden: 1946 fünf, ansonsten maximal drei, 1942-1944 und 1949 überhaupt keines. Zudem ist das Jahr 2008 noch lange nicht zu Ende. (Es gab jedoch schon vor Ende März 24 Zitate!)
    Date
    22. 6.2008 12:54:12
  4. Aksnes, D.W.; Rorstad, K.; Piro, F.; Sivertsen, G.: Are female researchers less cited? : a large-scale study of Norwegian scientists (2011) 0.03
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  5. Jonkers, K.; Moya Anegon, F. de; Aguillo, I.F.: Measuring the usage of e-research infrastructure as an indicator of research activity (2012) 0.03
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  6. Yoshikane, F.; Kageura, K.; Tsuji, K.: ¬A method for the comparative analysis of concentration of author productivity, giving consideration to the effect of sample size dependency of statistical measures (2003) 0.03
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  7. Wan, X.; Liu, F.: Are all literature citations equally important? : automatic citation strength estimation and its applications (2014) 0.03
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    Date
    22. 8.2014 17:12:35
  8. Kronegger, L.; Mali, F.; Ferligoj, A.; Doreian, P.: Classifying scientific disciplines in Slovenia : a study of the evolution of collaboration structures (2015) 0.03
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    Date
    21. 1.2015 14:55:22
  9. Didegah, F.; Thelwall, M.: Co-saved, co-tweeted, and co-cited networks (2018) 0.03
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    Date
    28. 7.2018 10:00:22
  10. Stvilia, B.; Hinnant, C.C.; Schindler, K.; Worrall, A.; Burnett, G.; Burnett, K.; Kazmer, M.M.; Marty, P.F.: Composition of scientific teams and publication productivity at a national science lab (2011) 0.02
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    Date
    22. 1.2011 13:19:42
  11. Ajiferuke, I.; Lu, K.; Wolfram, D.: ¬A comparison of citer and citation-based measure outcomes for multiple disciplines (2010) 0.02
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    Date
    28. 9.2010 12:54:22
  12. ¬Die deutsche Zeitschrift für Dokumentation, Informationswissenschaft und Informationspraxis von 1950 bis 2011 : eine vorläufige Bilanz in vier Abschnitten (2012) 0.02
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    Date
    22. 7.2012 19:35:26
    Footnote
    Besteht aus 4 Teilen: Teil 1: Eden, D., A. Arndt, A. Hoffer, T. Raschke u. P. Schön: Die Nachrichten für Dokumentation in den Jahren 1950 bis 1962 (S.159-163). Teil 2: Brose, M., E. durst, D. Nitzsche, D. Veckenstedt u. R. Wein: Statistische Untersuchung der Fachzeitschrift "Nachrichten für Dokumentation" (NfD) 1963-1975 (S.164-170). Teil 3: Bösel, J., G. Ebert, P. Garz,, M. Iwanow u. B. Russ: Methoden und Ergebnisse einer statistischen Auswertung der Fachzeitschrift "Nachrichten für Dokumentation" (NfD) 1976 bis 1988 (S.171-174). Teil 4: Engelage, H., S. Jansen, R. Mertins, K. Redel u. S. Ring: Statistische Untersuchung der Fachzeitschrift "Nachrichten für Dokumentation" (NfD) / "Information. Wissenschaft & Praxis" (IWP) 1989-2011 (S.164-170).
  13. Onodera, N.; Iwasawa, M.; Midorikawa, N.; Yoshikane, F.; Amano, K.; Ootani, Y.; Kodama, T.; Kiyama, Y.; Tsunoda, H.; Yamazaki, S.: ¬A method for eliminating articles by homonymous authors from the large number of articles retrieved by author search (2011) 0.02
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  14. Didegah, F.; Bowman, T.D.; Holmberg, K.: On the differences between citations and altmetrics : an investigation of factors driving altmetrics versus citations for finnish articles (2018) 0.02
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  15. 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
  16. Narin, F.; Moll, J.K.: Bibliometrics (1977) 0.02
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  17. Kousha, K.; Thelwall, M.: How is science cited on the Web? : a classification of google unique Web citations (2007) 0.02
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    Abstract
    Although the analysis of citations in the scholarly literature is now an established and relatively well understood part of information science, not enough is known about citations that can be found on the Web. In particular, are there new Web types, and if so, are these trivial or potentially useful for studying or evaluating research communication? We sought evidence based upon a sample of 1,577 Web citations of the URLs or titles of research articles in 64 open-access journals from biology, physics, chemistry, and computing. Only 25% represented intellectual impact, from references of Web documents (23%) and other informal scholarly sources (2%). Many of the Web/URL citations were created for general or subject-specific navigation (45%) or for self-publicity (22%). Additional analyses revealed significant disciplinary differences in the types of Google unique Web/URL citations as well as some characteristics of scientific open-access publishing on the Web. We conclude that the Web provides access to a new and different type of citation information, one that may therefore enable us to measure different aspects of research, and the research process in particular; but to obtain good information, the different types should be separated.
  18. Shibata, N.; Kajikawa, Y.; Takeda, Y.; Matsushima, K.: Comparative study on methods of detecting research fronts using different types of citation (2009) 0.02
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    Date
    22. 3.2009 17:52:50
  19. Liu, D.-R.; Shih, M.-J.: Hybrid-patent classification based on patent-network analysis (2011) 0.02
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    Abstract
    Effective patent management is essential for organizations to maintain their competitive advantage. The classification of patents is a critical part of patent management and industrial analysis. This study proposes a hybrid-patent-classification approach that combines a novel patent-network-based classification method with three conventional classification methods to analyze query patents and predict their classes. The novel patent network contains various types of nodes that represent different features extracted from patent documents. The nodes are connected based on the relationship metrics derived from the patent metadata. The proposed classification method predicts a query patent's class by analyzing all reachable nodes in the patent network and calculating their relevance to the query patent. It then classifies the query patent with a modified k-nearest neighbor classifier. To further improve the approach, we combine it with content-based, citation-based, and metadata-based classification methods to develop a hybrid-classification approach. We evaluate the performance of the hybrid approach on a test dataset of patent documents obtained from the U.S. Patent and Trademark Office, and compare its performance with that of the three conventional methods. The results demonstrate that the proposed patent-network-based approach yields more accurate class predictions than the patent network-based approach.
    Date
    22. 1.2011 13:04:21
  20. Song, M.; Kang, K.; An, J.Y.: Investigating drug-disease interactions in drug-symptom-disease triples via citation relations (2018) 0.02
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    Date
    1.11.2018 18:19:22

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