Search (64 results, page 1 of 4)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Informetrie"
  1. Herb, U.; Beucke, D.: ¬Die Zukunft der Impact-Messung : Social Media, Nutzung und Zitate im World Wide Web (2013) 0.07 
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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.
  2. Stuart, D.: Web metrics for library and information professionals (2014) 0.06 
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    RSWK
    Bibliometrie / Semantic Web / Soziale Software
    Subject
    Bibliometrie / Semantic Web / Soziale Software
  3. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: Science mapping software tools : review, analysis, and cooperative study among tools (2011) 0.01 
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    Abstract
    Science mapping aims to build bibliometric maps that describe how specific disciplines, scientific domains, or research fields are conceptually, intellectually, and socially structured. Different techniques and software tools have been proposed to carry out science mapping analysis. The aim of this article is to review, analyze, and compare some of these software tools, taking into account aspects such as the bibliometric techniques available and the different kinds of analysis.
  4. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: SciMAT: A new science mapping analysis software tool (2012) 0.01 
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    Abstract
    This article presents a new open-source software tool, SciMAT, which performs science mapping analysis within a longitudinal framework. It provides different modules that help the analyst to carry out all the steps of the science mapping workflow. In addition, SciMAT presents three key features that are remarkable in respect to other science mapping software tools: (a) a powerful preprocessing module to clean the raw bibliographical data, (b) the use of bibliometric measures to study the impact of each studied element, and (c) a wizard to configure the analysis.
  5. Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.01 
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    Date
    18. 3.2014 19:13:22
  6. Thelwall, M.: Web indicators for research evaluation : a practical guide (2016) 0.01 
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    Abstract
    In recent years there has been an increasing demand for research evaluation within universities and other research-based organisations. In parallel, there has been an increasing recognition that traditional citation-based indicators are not able to reflect the societal impacts of research and are slow to appear. This has led to the creation of new indicators for different types of research impact as well as timelier indicators, mainly derived from the Web. These indicators have been called altmetrics, webometrics or just web metrics. This book describes and evaluates a range of web indicators for aspects of societal or scholarly impact, discusses the theory and practice of using and evaluating web indicators for research assessment and outlines practical strategies for obtaining many web indicators. In addition to describing impact indicators for traditional scholarly outputs, such as journal articles and monographs, it also covers indicators for videos, datasets, software and other non-standard scholarly outputs. The book describes strategies to analyse web indicators for individual publications as well as to compare the impacts of groups of publications. The practical part of the book includes descriptions of how to use the free software Webometric Analyst to gather and analyse web data. This book is written for information science undergraduate and Master?s students that are learning about alternative indicators or scientometrics as well as Ph.D. students and other researchers and practitioners using indicators to help assess research impact or to study scholarly communication.
  7. Scholarly metrics under the microscope : from citation analysis to academic auditing (2015) 0.01 
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    Date
    22. 1.2017 17:12:50
  8. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01 
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    Date
    22. 8.2014 17:05:18
  9. Ohly, P.: Dimensions of globality : a bibliometric analysis (2016) 0.01 
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    Date
    20. 1.2019 11:22:31
  10. Haley, M.R.: Ranking top economics and finance journals using Microsoft academic search versus Google scholar : How does the new publish or perish option compare? (2014) 0.01 
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    Abstract
    Recently, Harzing's Publish or Perish software was updated to include Microsoft Academic Search as a second citation database search option for computing various citation-based metrics. This article explores the new search option by scoring 50 top economics and finance journals and comparing them with the results obtained using the original Google Scholar-based search option. The new database delivers significantly smaller scores for all metrics, but the rank correlations across the two databases for the h-index, g-index, AWCR, and e-index are significantly correlated, especially when the time frame is restricted to more recent years. Comparisons are also made to the Article Influence score from eigenfactor.org and to the RePEc h-index, both of which adjust for journal-level self-citations.
  11. Crespo, J.A.; Herranz, N.; Li, Y.; Ruiz-Castillo, J.: ¬The effect on citation inequality of differences in citation practices at the web of science subject category level (2014) 0.01 
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    Abstract
    This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.
  12. Yan, E.: Finding knowledge paths among scientific disciplines (2014) 0.01 
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    Date
    26.10.2014 20:22:22
  13. Zhu, Q.; Kong, X.; Hong, S.; Li, J.; He, Z.: Global ontology research progress : a bibliometric analysis (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
    17. 9.2018 18:22:23
  14. Campanario, J.M.: Large increases and decreases in journal impact factors in only one year : the effect of journal self-citations (2011) 0.01 
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    Date
    22. 1.2011 12:53:00
  15. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.01 
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    Date
    22. 1.2011 13:02:21
  16. Schlögl, C.: Internationale Sichtbarkeit der europäischen und insbesondere der deutschsprachigen Informationswissenschaft (2013) 0.01 
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    Date
    22. 3.2013 14:04:09
  17. Vieira, E.S.; Cabral, J.A.S.; Gomes, J.A.N.F.: Definition of a model based on bibliometric indicators for assessing applicants to academic positions (2014) 0.01 
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    Date
    18. 3.2014 18:22:21
  18. Mayernik, M.S.; Hart, D.L.; Maull, K.E.; Weber, N.M.: Assessing and tracing the outcomes and impact of research infrastructures (2017) 0.00 
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    Abstract
    Recent policy shifts on the part of funding agencies and journal publishers are causing changes in the acknowledgment and citation behaviors of scholars. A growing emphasis on open science and reproducibility is changing how authors cite and acknowledge "research infrastructures"-entities that are used as inputs to or as underlying foundations for scholarly research, including data sets, software packages, computational models, observational platforms, and computing facilities. At the same time, stakeholder interest in quantitative understanding of impact is spurring increased collection and analysis of metrics related to use of research infrastructures. This article reviews work spanning several decades on tracing and assessing the outcomes and impacts from these kinds of research infrastructures. We discuss how research infrastructures are identified and referenced by scholars in the research literature and how those references are being collected and analyzed for the purposes of evaluating impact. Synthesizing common features of a wide range of studies, we identify notable challenges that impede the analysis of impact metrics for research infrastructures and outline key open research questions that can guide future research and applications related to such metrics.
  19. Zhang, Q.; Xue, H.; Tang, H.: Knowledge domain and emerging trends in vulnerability assessment in the context of climate change : a bibliometric analysis (1991-2017) (2018) 0.00 
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    Abstract
    Analysis of the knowledge structure and the evolution of research topics in specific areas has been the focus of information science (IS). Such analysis helps to enrich interested researchers' understanding of the functions, activities and evolving constraints of the knowledge domain. This study aims to investigate the knowledge domain associated with the vulnerability assessment in the context of climate change (VACC) research in this fast-growing field between January 1991 and December 2017. A bibliometric approach, along with CiteSpace software, was used to identify and visualize thematic patterns, landmark articles and emerging trends. The data used for the bibliometric analysis include 6,584 original research articles and reviews published between 1991 and 2017. The results indicate that the number of documents pertaining to VACC presented a general growth trend over the last twenty-seven years. Climatic Change was the most productive journal. Among countries, the USA, England and Australia predominated, and the University of Chinese Academy of Sciences and U.S. Geological Survey were the two institutions with the largest amount of VACC research. Existing studies in the field of VACC research have focused primarily on environmental sciences. Importantly, emerging trends in VACC research have shifted away from vulnerability assessments of natural ecosystems based on model simulation methods in the context of climate change toward indicator-based assessments of social ecosystem vulnerability, adaptive capacity and resilience under multidimensional stressors and shocks, which are likely to define the new frontier in the field of VACC research.
  20. Park, H.; You, S.; Wolfram, D.: Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields (2018) 0.00 
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    Abstract
    Data citation, where products of research such as data sets, software, and tissue cultures are shared and acknowledged, is becoming more common in the era of Open Science. Currently, the practice of formal data citation-where data references are included alongside bibliographic references in the reference section of a publication-is uncommon. We examine the prevalence of data citation, documenting data sharing and reuse, in a sample of full text articles from the biological/biomedical sciences, the fields with the most public data sets available documented by the Data Citation Index (DCI). We develop a method that combines automated text extraction with human assessment for revealing candidate occurrences of data sharing and reuse by using terms that are most likely to indicate their occurrence. The analysis reveals that informal data citation in the main text of articles is far more common than formal data citations in the references of articles. As a result, data sharers do not receive documented credit for their data contributions in a similar way as authors do for their research articles because informal data citations are not recorded in sources such as the DCI. Ongoing challenges for the study of data citation are also outlined.

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