Search (1373 results, page 1 of 69)

  • × theme_ss:"Informetrie"
  1. Herb, U.; Beucke, D.: ¬Die Zukunft der Impact-Messung : Social Media, Nutzung und Zitate im World Wide Web (2013) 0.11 
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    Content
    Vgl. unter: https://www.leibniz-science20.de%2Fforschung%2Fprojekte%2Faltmetrics-in-verschiedenen-wissenschaftsdisziplinen%2F&ei=2jTgVaaXGcK4Udj1qdgB&usg=AFQjCNFOPdONj4RKBDf9YDJOLuz3lkGYlg&sig2=5YI3KWIGxBmk5_kv0P_8iQ.
    Type
    a
  2. Hjoerland, B.: Does informetrics need a theory? : a rejoinder to professor anthony van raan (2017) 0.07 
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.12, S.2846
    Type
    a
  3. Krauskopf, E.: Call for caution in the use of bibliometric data (2017) 0.07 
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.8, S.2029-2032
    Type
    a
  4. Noyons, E.C.M.; Raan, A.F.J. van: Monitoring scientific developments from a dynamic perspective : self-organized structuring to map neural network research (1998) 0.06 
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    Abstract
    With the help of bibliometric mapping techniques, we have developed a methodology of 'self-organized' structuring of scientific fields. This methodology is applied to the field of neural network research
    Source
    Journal of the American Society for Information Science. 49(1998) no.1, S.68-81
    Type
    a
  5. Vezzani, A.; Coad, A.; Gkotsis, P.: Concerns about the consequences of patenting on scientometric research (2017) 0.06 
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.9, S.2293-2295
    Type
    a
  6. Haley, M.R.: On the normalization and distributional adjustment of journal ranking metrics : a simple parametric approach (2017) 0.05 
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    Abstract
    This paper presents a simple parametric statistical approach to comparing different citation-based journal ranking metrics within a single academic field. The mechanism can also be used to compare the same metric across different academic fields. The mechanism operates by selecting an optimal normalization factor and an optimal distributional adjustment for the rank-score curve, both of which are instrumental in making sound intermetric and interfield journal comparisons.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.6, S.1590-1593
    Type
    a
  7. Smolinsky, L.J.: Discrete power law with exponential cutoff and Lotka's law (2017) 0.05 
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    Abstract
    One of the first bibliometric laws appeared in Alfred J. Lotka's 1926 examination of author productivity in chemistry and physics. The result was a productivity distribution described by a power law. In this paper, Lotka's original data on author productivity in chemistry are reconsidered. We define a discrete power law with exponential cutoff, test Lotka's data, and compare the fit to the discrete power law.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.7, S.1792-1795
    Type
    a
  8. Nichols, D.M.; Twidale, M.B.: Metrics for openness (2017) 0.05 
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    Abstract
    The characterization of scholarly communication is dominated by citation-based measures. In this paper we propose several metrics to describe different facets of open access and open research. We discuss measures to represent the public availability of articles along with their archival location, licenses, access costs, and supporting information. Calculations illustrating these new metrics are presented using the authors' publications. We argue that explicit measurement of openness is necessary for a holistic description of research outputs.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.4, S.1048-1060
    Type
    a
  9. Bornmann, L.; Haunschild, R.: Relative Citation Ratio (RCR) : an empirical attempt to study a new field-normalized bibliometric indicator (2017) 0.04 
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    Abstract
    Hutchins, Yuan, Anderson, and Santangelo (2015) proposed the Relative Citation Ratio (RCR) as a new field-normalized impact indicator. This study investigates the RCR by correlating it on the level of single publications with established field-normalized indicators and assessments of the publications by peers. We find that the RCR correlates highly with established field-normalized indicators, but the correlation between RCR and peer assessments is only low to medium.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.4, S.1064-1067
    Type
    a
  10. Bensman, S.J.; Smolinsky, L.J.: Lotka's inverse square law of scientific productivity : its methods and statistics (2017) 0.04 
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    Abstract
    This brief communication analyzes the statistics and methods Lotka used to derive his inverse square law of scientific productivity from the standpoint of modern theory. It finds that he violated the norms of this theory by extremely truncating his data on the right. It also proves that Lotka himself played an important role in establishing the commonly used method of identifying power-law behavior by the R2 fit to a regression line on a log-log plot that modern theory considers unreliable by basing the derivation of his law on this very method.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.7, S.1786-1791
    Type
    a
  11. Tang, L.; Hu, G.; Liu, W.: Funding acknowledgment analysis : queries and caveats (2017) 0.04 
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.3, S.790-794
    Type
    a
  12. Ronda-Pupo, G.A.; Katz, J.S.: ¬The scaling relationship between citation-based performance and coauthorship patterns in natural sciences (2017) 0.04 
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    Abstract
    The aim of this paper is to extend our knowledge about the power-law relationship between citation-based performance and coauthorship patterns in papers in the natural sciences. We analyzed 829,924 articles that received 16,490,346 citations. The number of articles published through coauthorship accounts for 89%. The citation-based performance and coauthorship patterns exhibit a power-law correlation with a scaling exponent of 1.20?±?0.07. Citations to a subfield's research articles tended to increase 2.1.20 or 2.30 times each time it doubled the number of coauthored papers. The scaling exponent for the power-law relationship for single-authored papers was 0.85?±?0.11. The citations to a subfield's single-authored research articles increased 2.0.85 or 1.89 times each time the research area doubled the number of single-authored papers. The Matthew Effect is stronger for coauthored papers than for single-authored. In fact, with a scaling exponent <1.0 the impact of single-authored papers exhibits a cumulative disadvantage or inverse Matthew Effect.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.5, S.1257-1265
    Type
    a
  13. Thelwall, M.: Interpreting social science link analysis research : a theoretical framework (2006) 0.04 
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    Abstract
    Link analysis in various forms is now an established technique in many different subjects, reflecting the perceived importance of links and of the Web. A critical but very difficult issue is how to interpret the results of social science link analyses. lt is argued that the dynamic nature of the Web, its lack of quality control, and the online proliferation of copying and imitation mean that methodologies operating within a highly positivist, quantitative framework are ineffective. Conversely, the sheer variety of the Web makes application of qualitative methodologies and pure reason very problematic to large-scale studies. Methodology triangulation is consequently advocated, in combination with a warning that the Web is incapable of giving definitive answers to large-scale link analysis research questions concerning social factors underlying link creation. Finally, it is claimed that although theoretical frameworks are appropriate for guiding research, a Theory of Link Analysis is not possible.
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.1, S.60-68
    Type
    a
  14. Gooch, D.; Vasalou, A.; Benton, L.: Impact in interdisciplinary and cross-sector research : opportunities and challenges (2017) 0.04 
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    Abstract
    Impact is embedded in today's research culture, with increasing importance being placed on the value of research to society. In interdisciplinary and cross-sector projects, team members may hold distinct views on the types of impact they want to create. Set in the context of an interdisciplinary, cross-sector project comprised of partners from academia, industry, and the nonprofit sector, our paper unpacks how these diverse project members understand impact. Our analysis shows that interdisciplinary projects offer a unique opportunity to create impact on a number of different levels. Moreover, it demonstrates that a lack of accountable design and collaboration practices can potentially hinder pathways to impact. Finally, we find that the interdisciplinary perspectives that such projects introduce encourage a rich gamut of sustainable outcomes that go beyond commercialization. Our findings support researchers working in these complex contexts to appreciate the opportunities and challenges involved in interdisciplinary cross-sector research contexts while imparting them with strategies for overcoming these challenges.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.2, S.378-391
    Type
    a
  15. Kurtz, M.J.; Henneken, E.A.: Measuring metrics : a 40-year longitudinal cross-validation of citations, downloads, and peer review in astrophysics (2017) 0.04 
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    Abstract
    Citation measures, and newer altmetric measures such as downloads are now commonly used to inform personnel decisions. How well do or can these measures measure or predict the past, current, or future scholarly performance of an individual? Using data from the Smithsonian/NASA Astrophysics Data System we analyze the publication, citation, download, and distinction histories of a cohort of 922 individuals who received a U.S. PhD in astronomy in the period 1972-1976. By examining the same and different measures at the same and different times for the same individuals we are able to show the capabilities and limitations of each measure. Because the distributions are lognormal, measurement uncertainties are multiplicative; we show that in order to state with 95% confidence that one person's citations and downloads are significantly higher than another person's, the log difference in the ratio of counts must be at least 0.3dex, which corresponds to a multiplicative factor of 2.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.3, S.695-708
    Type
    a
  16. Shi, D.; Rousseau, R.; Yang, L.; Li, J.: ¬A journal's impact factor is influenced by changes in publication delays of citing journals (2017) 0.04 
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    Abstract
    In this article we describe another problem with journal impact factors by showing that one journal's impact factor is dependent on other journals' publication delays. The proposed theoretical model predicts a monotonically decreasing function of the impact factor as a function of publication delay, on condition that the citation curve of the journal is monotone increasing during the publication window used in the calculation of the journal impact factor; otherwise, this function has a reversed U shape. Our findings based on simulations are verified by examining three journals in the information sciences: the Journal of Informetrics, Scientometrics, and the Journal of the Association for Information Science and Technology.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.3, S.780-789
    Type
    a
  17. Frandsen, T.F.; Nicolaisen, J.: Citation behavior : a large-scale test of the persuasion by name-dropping hypothesis (2017) 0.04 
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    Abstract
    Citation frequencies are commonly interpreted as measures of quality or impact. Yet, the true nature of citations and their proper interpretation have been the center of a long, but still unresolved discussion in Bibliometrics. A comparison of 67,578 pairs of studies on the same healthcare topic, with the same publication age (1-15 years) reveals that when one of the studies is being selected for citation, it has on average received about three times as many citations as the other study. However, the average citation-gap between selected or deselected studies narrows slightly over time, which fits poorly with the name-dropping interpretation and better with the quality and impact-interpretation. The results demonstrate that authors in the field of Healthcare tend to cite highly cited documents when they have a choice. This is more likely caused by differences related to quality than differences related to status of the publications cited.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.5, S.1278-1284
    Type
    a
  18. Teplitskiy, M.; Lu, G.; Duede, E.: Amplifying the impact of open access : Wikipedia and the diffusion of science (2017) 0.04 
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    Abstract
    With the rise of Wikipedia as a first-stop source for scientific information, it is important to understand whether Wikipedia draws upon the research that scientists value most. Here we identify the 250 most heavily used journals in each of 26 research fields (4,721 journals, 19.4M articles) indexed by the Scopus database, and test whether topic, academic status, and accessibility make articles from these journals more or less likely to be referenced on Wikipedia. We find that a journal's academic status (impact factor) and accessibility (open access policy) both strongly increase the probability of it being referenced on Wikipedia. Controlling for field and impact factor, the odds that an open access journal is referenced on the English Wikipedia are 47% higher compared to paywall journals. These findings provide evidence is that a major consequence of open access policies is to significantly amplify the diffusion of science, through an intermediary like Wikipedia, to a broad audience.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.9, S.2116-2127
    Type
    a
  19. Shema, H.; Bar-Ilan, J.; Thelwall, M.: Do blog citations correlate with a higher number of future citations? : Research blogs as a potential source for alternative metrics (2014) 0.03 
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    Abstract
    Journal-based citations are an important source of data for impact indices. However, the impact of journal articles extends beyond formal scholarly discourse. Measuring online scholarly impact calls for new indices, complementary to the older ones. This article examines a possible alternative metric source, blog posts aggregated at ResearchBlogging.org, which discuss peer-reviewed articles and provide full bibliographic references. Articles reviewed in these blogs therefore receive "blog citations." We hypothesized that articles receiving blog citations close to their publication time receive more journal citations later than the articles in the same journal published in the same year that did not receive such blog citations. Statistically significant evidence for articles published in 2009 and 2010 support this hypothesis for seven of 12 journals (58%) in 2009 and 13 of 19 journals (68%) in 2010. We suggest, based on these results, that blog citations can be used as an alternative metric source.
    Type
    a
  20. An, J.; Kim, N.; Kan, M.-Y.; Kumar Chandrasekaran, M.; Song, M.: Exploring characteristics of highly cited authors according to citation location and content (2017) 0.03 
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    Abstract
    Big Science and cross-disciplinary collaborations have reshaped the intellectual structure of research areas. A number of works have tried to uncover this hidden intellectual structure by analyzing citation contexts. However, none of them analyzed by document logical structures such as sections. The two major goals of this study are to find characteristics of authors who are highly cited section-wise and to identify the differences in section-wise author networks. This study uses 29,158 of research articles culled from the ACL Anthology, which hosts articles on computational linguistics and natural language processing. We find that the distribution of citations across sections is skewed and that a different set of highly cited authors share distinct academic characteristics, according to their citation locations. Furthermore, the author networks based on citation context similarity reveal that the intellectual structure of a domain differs across different sections.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.8, S.1975-1988
    Type
    a

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