Search (250 results, page 12 of 13)

  • × theme_ss:"Informetrie"
  1. Ibáñez, A.; Armañanzas, R.; Bielza, C.; Larrañaga, P.: Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices (2016) 0.01 
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  2. Arora, S.K.; Li, Y.; Youtie, J.; Shapira, P.: Using the wayback machine to mine websites in the social sciences : a methodological resource (2016) 0.01 
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    Abstract
    Websites offer an unobtrusive data source for developing and analyzing information about various types of social science phenomena. In this paper, we provide a methodological resource for social scientists looking to expand their toolkit using unstructured web-based text, and in particular, with the Wayback Machine, to access historical website data. After providing a literature review of existing research that uses the Wayback Machine, we put forward a step-by-step description of how the analyst can design a research project using archived websites. We draw on the example of a project that analyzes indicators of innovation activities and strategies in 300 U.S. small- and medium-sized enterprises in green goods industries. We present six steps to access historical Wayback website data: (a) sampling, (b) organizing and defining the boundaries of the web crawl, (c) crawling, (d) website variable operationalization, (e) integration with other data sources, and (f) analysis. Although our examples draw on specific types of firms in green goods industries, the method can be generalized to other areas of research. In discussing the limitations and benefits of using the Wayback Machine, we note that both machine and human effort are essential to developing a high-quality data set from archived web information.
  3. Chen, R.H.-G.; Chen, C.-M.: Visualizing the world's scientific publications (2016) 0.01 
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  4. McKeown, K.; Daume III, H.; Chaturvedi, S.; Paparrizos, J.; Thadani, K.; Barrio, P.; Biran, O.; Bothe, S.; Collins, M.; Fleischmann, K.R.; Gravano, L.; Jha, R.; King, B.; McInerney, K.; Moon, T.; Neelakantan, A.; O'Seaghdha, D.; Radev, D.; Templeton, C.; Teufel, S.: Predicting the impact of scientific concepts using full-text features (2016) 0.01 
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  5. Bauer, J.; Leydesdorff, L.; Bornmann, L.: Highly cited papers in Library and Information Science (LIS) : authors, institutions, and network structures (2016) 0.01 
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    Abstract
    As a follow-up to the highly cited authors list published by Thomson Reuters in June 2014, we analyzed the top 1% most frequently cited papers published between 2002 and 2012 included in the Web of Science (WoS) subject category "Information Science & Library Science." In all, 798 authors contributed to 305 top 1% publications; these authors were employed at 275 institutions. The authors at Harvard University contributed the largest number of papers, when the addresses are whole-number counted. However, Leiden University leads the ranking if fractional counting is used. Twenty-three of the 798 authors were also listed as most highly cited authors by Thomson Reuters in June 2014 (http://highlycited.com/). Twelve of these 23 authors were involved in publishing 4 or more of the 305 papers under study. Analysis of coauthorship relations among the 798 highly cited scientists shows that coauthorships are based on common interests in a specific topic. Three topics were important between 2002 and 2012: (a) collection and exploitation of information in clinical practices; (b) use of the Internet in public communication and commerce; and (c) scientometrics.
  6. Ping, Q.; He, J.; Chen, C.: How many ways to use CiteSpace? : a study of user interactive events over 14 months (2017) 0.01 
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  7. Farys, R.; Wolbring, T.: Matched control groups for modeling events in citation data : an illustration of nobel prize effects in citation networks (2017) 0.01 
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    Abstract
    Bibliometric data are frequently used to study the effects of events, such as the honoring of a scholar with an award, and to investigate changes of citation impact over time. However, the number of yearly citations depends upon time for multiple reasons: a) general time trends in citation data, b) changing coverage of databases, c) individual citation life-cycles, and d) selection on citation impact. Hence, it is often ill-advised to simply compare the average number of citations before and after an event to estimate its causal effect. Using a recent publication in this journal on the potential citation chain reaction of a Nobel Prize, we demonstrate that a simple pre-post comparison can lead to biased and misleading results. We propose using matched control groups to improve causal inference and illustrate that the inclusion of a tailor-made synthetic control group in the statistical analysis helps to avoid methodological artifacts. Our results suggest that there is neither a Nobel Prize effect as regards citation impact of the Nobel laureate under investigation nor a related chain reaction in the citation network, as suggested in the original study. Finally, we explain that these methodological recommendations extend far beyond the study of Nobel Prize effects in citation data.
  8. Sieben Fragen zu Bibliometrie : Antworten von Ilka Agricola, Friedrich Götze, Martin Grötschel, Klaus Hulek, Peter Michor, Gabriele Nebe, Hans Jürgen Prömel, Jens Vygen, Gerhard Woeginger (2017) 0.01 
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    Abstract
    Wissenschaftliche Institutionen und einzelne WissenschaftlerInnen werden zunehmend nach bibliometrischen Daten beurteilt. Jeder Mathematiker hat Vorbehalte gegenüber dieser Praxis. Über diese beiden Dinge waren sich alle TeilnemerInnen und Wortmeldungen des Panels zur Bibliometrie auf der DMV-Jahrestagung in Salzburg einig. Für einzelne WissenschaftlerInnen hat die IMU bereits 2014 (tinyurl.com/ycd7wvh7) die Vorbehalte gegenüber rein bibliometrischen Beurteilungen klar formuliert. Universitätsleitungen, Politik und andere bis hin zur öffentlichen Diskussion verlassen sich aber teilweise aif diese Art der Bewertung. Konkret kann das so aussehen: Zur Beurteilung von Departements zählt eine Universität Publikationen und gewichtet sie nach ihrer Güte. Die Güte entnimmt man einer Einteilung von Fachzeitschriften etwa in A, B und C. Liegt eine solche Einteilung nicht vor, sind alle Journale des Faches mit 1 gewichtet, d.h. 'junk'. Die Diskrepanz zwischen unserer Einschätzung und der allgemeinen Wirkmächtigkeit von Biblimetrie wirft für die DMV als Fachgegsellschaft die Frage auf, wie sie Einfluss nehmen kann und soll. Die 'Mitteilungen' versuchen an dieser Stelle zunächst, ein möglichst breites Meinungsbild zu sammeln. Wir haben sieben Fragen an eine Reihe von Kolleginnen und Kollegen gestellt, die am Panel teilgenmmen haben oder aus anderen Gründen mit dem Thema besfasst sind.
  9. Ahlgren, P.; Colliander, C.; Sjögårde, P.: Exploring the relation between referencing practices and citation impact : a large-scale study based on Web of Science data (2018) 0.01 
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  10. Min, C.; Ding, Y.; Li, J.; Bu, Y.; Pei, L.; Sun, J.: Innovation or imitation : the diffusion of citations (2018) 0.01 
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  11. Leydesdorff, L.; Bornmann, L.; Mingers, J.: Statistical significance and effect sizes of differences among research universities at the level of nations and worldwide based on the Leiden rankings (2019) 0.01 
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    Abstract
    The Leiden Rankings can be used for grouping research universities by considering universities which are not statistically significantly different as homogeneous sets. The groups and intergroup relations can be analyzed and visualized using tools from network analysis. Using the so-called "excellence indicator" PPtop-10%-the proportion of the top-10% most-highly-cited papers assigned to a university-we pursue a classification using (a) overlapping stability intervals, (b) statistical-significance tests, and (c) effect sizes of differences among 902 universities in 54 countries; we focus on the UK, Germany, Brazil, and the USA as national examples. Although the groupings remain largely the same using different statistical significance levels or overlapping stability intervals, these classifications are uncorrelated with those based on effect sizes. Effect sizes for the differences between universities are small (w < .2). The more detailed analysis of universities at the country level suggests that distinctions beyond three or perhaps four groups of universities (high, middle, low) may not be meaningful. Given similar institutional incentives, isomorphism within each eco-system of universities should not be underestimated. Our results suggest that networks based on overlapping stability intervals can provide a first impression of the relevant groupings among universities. However, the clusters are not well-defined divisions between groups of universities.
  12. He, J.; Ping, Q.; Lou, W.; Chen, C.: PaperPoles : facilitating adaptive visual exploration of scientific publications by citation links (2019) 0.01 
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  13. Zhang, Y.; Zhang, C.: Enhancing keyphrase extraction from microblogs using human reading time (2021) 0.01 
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  14. Cabanac, G.; Labbé, C.: Prevalence of nonsensical algorithmically generated papers in the scientific literature (2021) 0.01 
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  15. Ikae, C.; Savoy, J.: Gender identification on Twitter (2022) 0.01 
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  16. Zhu, Y.; Quan, L.; Chen, P.-Y.; Kim, M.C.; Che, C.: Predicting coauthorship using bibliographic network embedding (2023) 0.01 
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  17. Wettlauf der Wissenschaft (2004) 0.01 
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    Source
    Online Mitteilungen. 2004, Nr.79, S.22-23 [=Mitteilungen VÖB 57(2004) H.2]
  18. Ahlgren, P.; Jarneving, B.; Rousseau, R.: Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient (2003) 0.01 
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    Date
    9. 7.2006 10:22:35
  19. Freitas, J.L.; Gabriel Jr., R.F.; Bufrem, L.S.: Theoretical approximations between Brazilian and Spanish authors' production in the field of knowledge organization in the production of journals on information science in Brazil (2012) 0.01 
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    Content
    Beitrag einer Section "Selected Papers from the 1ST Brazilian Conference on Knowledge Organization And Representation, Faculdade de Ciência da Informação, Campus Universitário Darcy Ribeiro Brasília, DF Brasil, October 20-22, 2011" Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_39_2012_3_g.pdf.
  20. Costas, R.; Perianes-Rodríguez, A.; Ruiz-Castillo, J.: On the quest for currencies of science : field "exchange rates" for citations and Mendeley readership (2017) 0.01 
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    Date
    20. 1.2015 18:30:22

Authors

Years

Languages

  • e 225
  • d 22
  • ? 1
  • m 1
  • ro 1
  • More… Less…

Types

  • a 244
  • m 4
  • el 3
  • s 2
  • r 1
  • More… Less…

Themes

Subjects

Classifications