Search (113 results, page 1 of 6)

  • × theme_ss:"Informetrie"
  • × year_i:[2010 TO 2020}
  1. Ohly, P.: Dimensions of globality : a bibliometric analysis (2016) 0.05
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    Date
    20. 1.2019 11:22:31
  2. ¬Die deutsche Zeitschrift für Dokumentation, Informationswissenschaft und Informationspraxis von 1950 bis 2011 : eine vorläufige Bilanz in vier Abschnitten (2012) 0.05
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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).
  3. Albarrán, P.; Ruiz-Castillo, J.: References made and citations received by scientific articles (2011) 0.04
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    Abstract
    This article studies massive evidence about references made and citations received after a 5-year citation window by 3.7 million articles published in 1998 to 2002 in 22 scientific fields. We find that the distributions of references made and citations received share a number of basic features across sciences. Reference distributions are rather skewed to the right while citation distributions are even more highly skewed: The mean is about 20 percentage points to the right of the median, and articles with a remarkable or an outstanding number of citations represent about 9% of the total. Moreover, the existence of a power law representing the upper tail of citation distributions cannot be rejected in 17 fields whose articles represent 74.7% of the total. Contrary to the evidence in other contexts, the value of the scale parameter is above 3.5 in 13 of the 17 cases. Finally, power laws are typically small, but capture a considerable proportion of the total citations received.
  4. Huang, M.-H.; Huang, W.-T.; Chang, C.-C.; Chen, D. Z.; Lin, C.-P.: The greater scattering phenomenon beyond Bradford's law in patent citation (2014) 0.04
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    Date
    22. 8.2014 17:11:29
  5. Kronegger, L.; Mali, F.; Ferligoj, A.; Doreian, P.: Classifying scientific disciplines in Slovenia : a study of the evolution of collaboration structures (2015) 0.04
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    Date
    21. 1.2015 14:55:22
  6. Thelwall, M.; Sud, P.: Mendeley readership counts : an investigation of temporal and disciplinary differences (2016) 0.04
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    Date
    16.11.2016 11:07:22
  7. Haustein, S.; Sugimoto, C.; Larivière, V.: Social media in scholarly communication : Guest editorial (2015) 0.03
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    Abstract
    This year marks 350 years since the inaugural publications of both the Journal des Sçavans and the Philosophical Transactions, first published in 1665 and considered the birth of the peer-reviewed journal article. This form of scholarly communication has not only remained the dominant model for disseminating new knowledge (particularly for science and medicine), but has also increased substantially in volume. Derek de Solla Price - the "father of scientometrics" (Merton and Garfield, 1986, p. vii) - was the first to document the exponential increase in scientific journals and showed that "scientists have always felt themselves to be awash in a sea of the scientific literature" (Price, 1963, p. 15), as, for example, expressed at the 1948 Royal Society's Scientific Information Conference: Not for the first time in history, but more acutely than ever before, there was a fear that scientists would be overwhelmed, that they would be no longer able to control the vast amounts of potentially relevant material that were pouring forth from the world's presses, that science itself was under threat (Bawden and Robinson, 2008, p. 183).
    One of the solutions to help scientists filter the most relevant publications and, thus, to stay current on developments in their fields during the transition from "little science" to "big science", was the introduction of citation indexing as a Wellsian "World Brain" (Garfield, 1964) of scientific information: It is too much to expect a research worker to spend an inordinate amount of time searching for the bibliographic descendants of antecedent papers. It would not be excessive to demand that the thorough scholar check all papers that have cited or criticized such papers, if they could be located quickly. The citation index makes this check practicable (Garfield, 1955, p. 108). In retrospective, citation indexing can be perceived as a pre-social web version of crowdsourcing, as it is based on the concept that the community of citing authors outperforms indexers in highlighting cognitive links between papers, particularly on the level of specific ideas and concepts (Garfield, 1983). Over the last 50 years, citation analysis and more generally, bibliometric methods, have developed from information retrieval tools to research evaluation metrics, where they are presumed to make scientific funding more efficient and effective (Moed, 2006). However, the dominance of bibliometric indicators in research evaluation has also led to significant goal displacement (Merton, 1957) and the oversimplification of notions of "research productivity" and "scientific quality", creating adverse effects such as salami publishing, honorary authorships, citation cartels, and misuse of indicators (Binswanger, 2015; Cronin and Sugimoto, 2014; Frey and Osterloh, 2006; Haustein and Larivière, 2015; Weingart, 2005).
    Furthermore, the rise of the web, and subsequently, the social web, has challenged the quasi-monopolistic status of the journal as the main form of scholarly communication and citation indices as the primary assessment mechanisms. Scientific communication is becoming more open, transparent, and diverse: publications are increasingly open access; manuscripts, presentations, code, and data are shared online; research ideas and results are discussed and criticized openly on blogs; and new peer review experiments, with open post publication assessment by anonymous or non-anonymous referees, are underway. The diversification of scholarly production and assessment, paired with the increasing speed of the communication process, leads to an increased information overload (Bawden and Robinson, 2008), demanding new filters. The concept of altmetrics, short for alternative (to citation) metrics, was created out of an attempt to provide a filter (Priem et al., 2010) and to steer against the oversimplification of the measurement of scientific success solely on the basis of number of journal articles published and citations received, by considering a wider range of research outputs and metrics (Piwowar, 2013). Although the term altmetrics was introduced in a tweet in 2010 (Priem, 2010), the idea of capturing traces - "polymorphous mentioning" (Cronin et al., 1998, p. 1320) - of scholars and their documents on the web to measure "impact" of science in a broader manner than citations was introduced years before, largely in the context of webometrics (Almind and Ingwersen, 1997; Thelwall et al., 2005):
    There will soon be a critical mass of web-based digital objects and usage statistics on which to model scholars' communication behaviors - publishing, posting, blogging, scanning, reading, downloading, glossing, linking, citing, recommending, acknowledging - and with which to track their scholarly influence and impact, broadly conceived and broadly felt (Cronin, 2005, p. 196). A decade after Cronin's prediction and five years after the coining of altmetrics, the time seems ripe to reflect upon the role of social media in scholarly communication. This Special Issue does so by providing an overview of current research on the indicators and metrics grouped under the umbrella term of altmetrics, on their relationships with traditional indicators of scientific activity, and on the uses that are made of the various social media platforms - on which these indicators are based - by scientists of various disciplines.
    Date
    20. 1.2015 18:30:22
  8. Thelwall, M.; Sud, P.; Wilkinson, D.: Link and co-inlink network diagrams with URL citations or title mentions (2012) 0.03
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    Date
    6. 4.2012 18:16:22
  9. Heneberg, P.: Supposedly uncited articles of Nobel laureates and Fields medalists can be prevalently attributed to the errors of omission and commission (2013) 0.03
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    Date
    22. 3.2013 19:21:46
  10. Costas, R.; Zahedi, Z.; Wouters, P.: ¬The thematic orientation of publications mentioned on social media : large-scale disciplinary comparison of social media metrics with citations (2015) 0.03
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    Date
    20. 1.2015 18:30:22
  11. Mutschke, P.; Mayr, P.: Science models for search : a study on combining scholarly information retrieval and scientometrics (2015) 0.02
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  12. Vinkler, P.: Quantity and impact through a single indicator (2013) 0.02
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  13. Mayr, P.; Scharnhorst, A.: Scientometrics and information retrieval - weak-links revitalized (2015) 0.02
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  14. Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.02
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    Date
    18. 3.2014 19:13:22
  15. Vinkler, P.: Application of the distribution of citations among publications in scientometric evaluations (2011) 0.02
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    Abstract
    The ?-indicator (or ?v-indicator) of a set of journal papers is equal to a hundredth of the total number of citations obtained by the elite set of publications. The number of publications in the elite set P(?) is calculated as the square root of total papers. For greater sets the following equation is used: P(?v) = (10 log P) - 10, where P is the total number of publications. For sets comprising a single or several extreme frequently cited paper, the ?-index may be distorted. Therefore, a new indicator based on the distribution of citations is suggested. Accordingly, the publications are classified into citation categories, of which lower limits are given as 0, and (2n + 1), whereas the upper limits as 2n (n = 0, 2, 3, etc.). The citations distribution score (CDS) index is defined as the sum of weighted numbers of publications in the individual categories. The CDS-index increases logarithmically with the increasing number of citations. The citation distribution rate indicator is introduced by relating the actual CDS-index to the possible maximum. Several size-dependent and size-independent indicators were calculated. It has been concluded that relevant, already accepted scientometric indicators may validate novel indices through resulting in similar conclusions ("converging validation of indicators").
  16. Trevorrow, P.: ¬The use of H-index for the assessment of journals' performance will lead to shifts in editorial policies : a response (2012) 0.02
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  17. Walter, P.: Wie (un)zuverlässig ist die Forschung? (2017) 0.02
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  18. Vezzani, A.; Coad, A.; Gkotsis, P.: Concerns about the consequences of patenting on scientometric research (2017) 0.02
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  19. Botting, N.; Dipper, L.; Hilari, K.: ¬The effect of social media promotion on academic article uptake (2017) 0.01
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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.
  20. Ye, F.Y.: ¬A theoretical approach to the unification of informetric models by wave-heat equations (2011) 0.01
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    Abstract
    A simple distribution function f(x, t)=p(x+q)**-ße**alpha*t obeys wave and heat equations, that constructs a theoretical approach to the unification of informetric models, with which we can unify all informetric laws. While its space-type distributions deduce naturally Lotka-type laws in size approaches and Zipf-type laws in rank approaches, its time-type distributions introduce the mechanism of Price-type and Brookes-type laws.

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