-
Herb, U.; Beucke, D.: ¬Die Zukunft der Impact-Messung : Social Media, Nutzung und Zitate im World Wide Web (2013)
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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.
-
¬Die deutsche Zeitschrift für Dokumentation, Informationswissenschaft und Informationspraxis von 1950 bis 2011 : eine vorläufige Bilanz in vier Abschnitten (2012)
0.03
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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).
-
Burrell, Q.L.: Predicting future citation behavior (2003)
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- Abstract
- In this article we further develop the theory for a stochastic model for the citation process in the presence of obsolescence to predict the future citation pattern of individual papers in a collection. More precisely, we investigate the conditional distribution-and its mean- of the number of citations to a paper after time t, given the number of citations it has received up to time t. In an important parametric case it is shown that the expected number of future citations is a linear function of the current number, this being interpretable as an example of a success-breeds-success phenomenon.
- Date
- 29. 3.2003 19:22:48
-
Li, T.-C.: Reference sources in periodicals : research note (1995)
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- Abstract
- Presents a list of 53 periodicals in 22 subject fields which regularly provide bibliographies of theses, research in progress and patents in their particular subject field. The fields of business, economics, history and literature have most periodical listings of dissertations and theses. Also lists 63 periodicals in 25 sub-disciplines which provide rankings or ratings. Rankings and ratings information predominates in the fields of business, sports and games, finance and banking, and library and information science
-
Thelwall, M.; Ruschenburg, T.: Grundlagen und Forschungsfelder der Webometrie (2006)
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- Date
- 4.12.2006 12:12:22
-
Hood, W.W.; Wilson, C.S.: Overlap in bibliographic databases (2003)
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- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.12, S.1091-1103
-
Möller, T.: Woher stammt das Wissen über die Halbwertzeiten des Wissens? (2017)
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- Source
- Theorie, Semantik und Organisation von Wissen: Proceedings der 13. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation (ISKO) und dem 13. Internationalen Symposium der Informationswissenschaft der Higher Education Association for Information Science (HI) Potsdam (19.-20.03.2013): 'Theory, Information and Organization of Knowledge' / Proceedings der 14. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation (ISKO) und Natural Language & Information Systems (NLDB) Passau (16.06.2015): 'Lexical Resources for Knowledge Organization' / Proceedings des Workshops der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation (ISKO) auf der SEMANTICS Leipzig (1.09.2014): 'Knowledge Organization and Semantic Web' / Proceedings des Workshops der Polnischen und Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation (ISKO) Cottbus (29.-30.09.2011): 'Economics of Knowledge Production and Organization'. Hrsg. von W. Babik, H.P. Ohly u. K. Weber
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Althouse, B.M.; West, J.D.; Bergstrom, C.T.; Bergstrom, T.: Differences in impact factor across fields and over time (2009)
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- Date
- 23. 2.2009 18:22:28
-
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)
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- Date
- 22. 8.2014 17:11:29
-
Ntuli, H.; Inglesi-Lotz, R.; Chang, T.; Pouris, A.: Does research output cause economic growth or vice versa? : evidence from 34 OECD countries (2015)
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- Date
- 8. 7.2015 22:00:42
-
Niemi, T.; Hirvonen, L.; Järvelin, K.: Multidimensional data model and query language for informetrics (2003)
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-
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)
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-
Schubert, T.; Michels, C.: Placing articles in the large publisher nations : is there a "free lunch" in terms of higher impact? (2013)
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- Date
- 22. 3.2013 19:45:49
-
Ortega, J.L.: ¬The presence of academic journals on Twitter and its relationship with dissemination (tweets) and research impact (citations) (2017)
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- Abstract
- Purpose The purpose of this paper is to analyze the relationship between dissemination of research papers on Twitter and its influence on research impact. Design/methodology/approach Four types of journal Twitter accounts (journal, owner, publisher and no Twitter account) were defined to observe differences in the number of tweets and citations. In total, 4,176 articles from 350 journals were extracted from Plum Analytics. This altmetric provider tracks the number of tweets and citations for each paper. Student's t-test for two-paired samples was used to detect significant differences between each group of journals. Regression analysis was performed to detect which variables may influence the getting of tweets and citations. Findings The results show that journals with their own Twitter account obtain more tweets (46 percent) and citations (34 percent) than journals without a Twitter account. Followers is the variable that attracts more tweets (ß=0.47) and citations (ß=0.28) but the effect is small and the fit is not good for tweets (R2=0.46) and insignificant for citations (R2=0.18). Originality/value This is the first study that tests the performance of research journals on Twitter according to their handles, observing how the dissemination of content in this microblogging network influences the citation of their papers.
- Date
- 20. 1.2015 18:30:22
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Egghe, L.: Untangling Herdan's law and Heaps' law : mathematical and informetric arguments (2007)
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- Abstract
- Herdan's law in linguistics and Heaps' law in information retrieval are different formulations of the same phenomenon. Stated briefly and in linguistic terms they state that vocabularies' sizes are concave increasing power laws of texts' sizes. This study investigates these laws from a purely mathematical and informetric point of view. A general informetric argument shows that the problem of proving these laws is, in fact, ill-posed. Using the more general terminology of sources and items, the author shows by presenting exact formulas from Lotkaian informetrics that the total number T of sources is not only a function of the total number A of items, but is also a function of several parameters (e.g., the parameters occurring in Lotka's law). Consequently, it is shown that a fixed T(or A) value can lead to different possible A (respectively, T) values. Limiting the T(A)-variability to increasing samples (e.g., in a text as done in linguistics) the author then shows, in a purely mathematical way, that for large sample sizes T~ A**phi, where phi is a constant, phi < 1 but close to 1, hence roughly, Heaps' or Herdan's law can be proved without using any linguistic or informetric argument. The author also shows that for smaller samples, a is not a constant but essentially decreases as confirmed by practical examples. Finally, an exact informetric argument on random sampling in the items shows that, in most cases, T= T(A) is a concavely increasing function, in accordance with practical examples.
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Kretschmer, H.; Kretschmer, T.: Well-ordered collaboration structures of co-author pairs in journals (2006)
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- Source
- Vom Wandel der Wissensorganisation im Informationszeitalter: Festschrift für Walther Umstätter zum 65. Geburtstag, hrsg. von P. Hauke u. K. Umlauf
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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)
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Ye, F.Y.: ¬A theoretical approach to the unification of informetric models by wave-heat equations (2011)
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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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Burrell, Q.L.: "Type/Token-Taken" informetrics : Some comments and further examples (2003)
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- Abstract
- Egghe has propounded the notion of Type/Token-Taken (T/TT) informetrics. In this note we show how his ideas relate to ones that are already well known in informetrics and resolve some of the specific problems posed.
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Egghe, L.; Ravichandra Rao, I.K.: ¬The influence of the broadness of a query of a topic on its h-index : models and examples of the h-index of n-grams (2008)
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- Abstract
- The article studies the influence of the query formulation of a topic on its h-index. In order to generate pure random sets of documents, we used N-grams (N variable) to measure this influence: strings of zeros, truncated at the end. The used databases are WoS and Scopus. The formula h=T**1/alpha, proved in Egghe and Rousseau (2006) where T is the number of retrieved documents and is Lotka's exponent, is confirmed being a concavely increasing function of T. We also give a formula for the relation between h and N the length of the N-gram: h=D10**(-N/alpha) where D is a constant, a convexly decreasing function, which is found in our experiments. Nonlinear regression on h=T**1/alpha gives an estimation of , which can then be used to estimate the h-index of the entire database (Web of Science [WoS] and Scopus): h=S**1/alpha, , where S is the total number of documents in the database.
