-
Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
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Leydesdorff, L.; Bornmann, L.; Wagner, C.S.: ¬The relative influences of government funding and international collaboration on citation impact (2019)
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- Abstract
- A recent publication in Nature reports that public R&D funding is only weakly correlated with the citation impact of a nation's articles as measured by the field-weighted citation index (FWCI; defined by Scopus). On the basis of the supplementary data, we up-scaled the design using Web of Science data for the decade 2003-2013 and OECD funding data for the corresponding decade assuming a 2-year delay (2001-2011). Using negative binomial regression analysis, we found very small coefficients, but the effects of international collaboration are positive and statistically significant, whereas the effects of government funding are negative, an order of magnitude smaller, and statistically nonsignificant (in two of three analyses). In other words, international collaboration improves the impact of research articles, whereas more government funding tends to have a small adverse effect when comparing OECD countries.
- Date
- 8. 1.2019 18:22:45
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Leydesdorff, L.; Bornmann, L.: How fractional counting of citations affects the impact factor : normalization in terms of differences in citation potentials among fields of science (2011)
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- Abstract
- The Impact Factors (IFs) of the Institute for Scientific Information suffer from a number of drawbacks, among them the statistics-Why should one use the mean and not the median?-and the incomparability among fields of science because of systematic differences in citation behavior among fields. Can these drawbacks be counteracted by fractionally counting citation weights instead of using whole numbers in the numerators? (a) Fractional citation counts are normalized in terms of the citing sources and thus would take into account differences in citation behavior among fields of science. (b) Differences in the resulting distributions can be tested statistically for their significance at different levels of aggregation. (c) Fractional counting can be generalized to any document set including journals or groups of journals, and thus the significance of differences among both small and large sets can be tested. A list of fractionally counted IFs for 2008 is available online at http:www.leydesdorff.net/weighted_if/weighted_if.xls The between-group variance among the 13 fields of science identified in the U.S. Science and Engineering Indicators is no longer statistically significant after this normalization. Although citation behavior differs largely between disciplines, the reflection of these differences in fractionally counted citation distributions can not be used as a reliable instrument for the classification.
- Date
- 22. 1.2011 12:51:07
-
Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014)
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- Date
- 18. 3.2014 19:13:22
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Leydesdorff, L.; Bornmann, L.; Mutz, R.; Opthof, T.: Turning the tables on citation analysis one more time : principles for comparing sets of documents (2011)
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- Abstract
- We submit newly developed citation impact indicators based not on arithmetic averages of citations but on percentile ranks. Citation distributions are-as a rule-highly skewed and should not be arithmetically averaged. With percentile ranks, the citation score of each paper is rated in terms of its percentile in the citation distribution. The percentile ranks approach allows for the formulation of a more abstract indicator scheme that can be used to organize and/or schematize different impact indicators according to three degrees of freedom: the selection of the reference sets, the evaluation criteria, and the choice of whether or not to define the publication sets as independent. Bibliometric data of seven principal investigators (PIs) of the Academic Medical Center of the University of Amsterdam are used as an exemplary dataset. We demonstrate that the proposed family indicators [R(6), R(100), R(6, k), R(100, k)] are an improvement on averages-based indicators because one can account for the shape of the distributions of citations over papers.
-
Besselaar, P. van den; Wagner, C,; Bornmann, L.: Correct assumptions? (2016)
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Leydesdorff, L.; Wagner, C,; Bornmann, L.: Replicability and the public/private divide (2016)
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-
Bornmann, L.; Bauer, J.; Haunschild, R.: Distribution of women and men among highly cited scientists (2015)
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-
Mutz, R.; Bornmann, L.; Daniel, H.-D.: Testing for the fairness and predictive validity of research funding decisions : a multilevel multiple imputation for missing data approach using ex-ante and ex-post peer evaluation data from the Austrian science fund (2015)
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- Abstract
- It is essential for research funding organizations to ensure both the validity and fairness of the grant approval procedure. The ex-ante peer evaluation (EXANTE) of N?=?8,496 grant applications submitted to the Austrian Science Fund from 1999 to 2009 was statistically analyzed. For 1,689 funded research projects an ex-post peer evaluation (EXPOST) was also available; for the rest of the grant applications a multilevel missing data imputation approach was used to consider verification bias for the first time in peer-review research. Without imputation, the predictive validity of EXANTE was low (r?=?.26) but underestimated due to verification bias, and with imputation it was r?=?.49. That is, the decision-making procedure is capable of selecting the best research proposals for funding. In the EXANTE there were several potential biases (e.g., gender). With respect to the EXPOST there was only one real bias (discipline-specific and year-specific differential prediction). The novelty of this contribution is, first, the combining of theoretical concepts of validity and fairness with a missing data imputation approach to correct for verification bias and, second, multilevel modeling to test peer review-based funding decisions for both validity and fairness in terms of potential and real biases.
-
Bornmann, L.; Mutz, R.; Daniel, H.D.: Do we need the h index and its variants in addition to standard bibliometric measures? (2009)
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- Abstract
- In this study, we investigate whether there is a need for the h index and its variants in addition to standard bibliometric measures (SBMs). Results from our recent study (L. Bornmann, R. Mutz, & H.-D. Daniel, 2008) have indicated that there are two types of indices: One type of indices (e.g., h index) describes the most productive core of a scientist's output and informs about the number of papers in the core. The other type of indices (e.g., a index) depicts the impact of the papers in the core. In evaluative bibliometric studies, the two dimensions quantity and quality of output are usually assessed using the SBMs number of publications (for the quantity dimension) and total citation counts (for the impact dimension). We additionally included the SBMs into the factor analysis. The results of the newly calculated analysis indicate that there is a high intercorrelation between number of publications and the indices that load substantially on the factor Quantity of the Productive Core as well as between total citation counts and the indices that load substantially on the factor Impact of the Productive Core. The high-loading indices and SBMs within one performance dimension could be called redundant in empirical application, as high intercorrelations between different indicators are a sign for measuring something similar (or the same). Based on our findings, we propose the use of any pair of indicators (one relating to the number of papers in a researcher's productive core and one relating to the impact of these core papers) as a meaningful approach for comparing scientists.
-
Bornmann, L.: How well does a university perform in comparison with its peers? : The use of odds, and odds ratios, for the comparison of institutional citation impact using the Leiden Rankings (2015)
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- Editor
- Mutz, R.
-
Bornmann, L.; Haunschild, R.: Relative Citation Ratio (RCR) : an empirical attempt to study a new field-normalized bibliometric indicator (2017)
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Bornmann, L.: How to analyze percentile citation impact data meaningfully in bibliometrics : the statistical analysis of distributions, percentile rank classes, and top-cited papers (2013)
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- Date
- 22. 3.2013 19:44:17
-
Leydesdorff, L.; Radicchi, F.; Bornmann, L.; Castellano, C.; Nooy, W. de: Field-normalized impact factors (IFs) : a comparison of rescaling and fractionally counted IFs (2013)
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Bornmann, L.; Moya Anegón, F. de; Mutz, R.: Do universities or research institutions with a specific subject profile have an advantage or a disadvantage in institutional rankings? (2013)
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-
Bornmann, L.; Haunschild, R.: Overlay maps based on Mendeley data : the use of altmetrics for readership networks (2016)
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Bornmann, L.; Daniel, H.-D.: Universality of citation distributions : a validation of Radicchi et al.'s relative indicator cf = c/c0 at the micro level using data from chemistry (2009)
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Bornmann, L.; Marx, W.: ¬The Anna Karenina principle : a way of thinking about success in science (2012)
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- Abstract
- The first sentence of Leo Tolstoy's (1875-1877/2001) novel Anna Karenina is: "Happy families are all alike; every unhappy family is unhappy in its own way." Here, Tolstoy means that for a family to be happy, several key aspects must be given (e.g., good health of all family members, acceptable financial security, and mutual affection). If there is a deficiency in any one or more of these key aspects, the family will be unhappy. In this article, we introduce the Anna Karenina principle as a way of thinking about success in science in three central areas in (modern) science: (a) peer review of research grant proposals and manuscripts (money and journal space as scarce resources), (b) citation of publications (reception as a scarce resource), and (c) new scientific discoveries (recognition as a scarce resource). If resources are scarce at the highly competitive research front (journal space, funds, reception, and recognition), there can be success only when several key prerequisites for the allocation of the resources are fulfilled. If any one of these prerequisites is not fulfilled, the grant proposal, manuscript submission, the published paper, or the discovery will not be successful.
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Bornmann, L.; Wagner, C.; Leydesdorff, L.: BRICS countries and scientific excellence : a bibliometric analysis of most frequently cited papers (2015)
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Bauer, J.; Leydesdorff, L.; Bornmann, L.: Highly cited papers in Library and Information Science (LIS) : authors, institutions, and network structures (2016)
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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.
