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  • × author_ss:"Bornmann, L."
  1. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.02
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    Date
    22. 8.2014 17:05:18
  2. Leydesdorff, L.; Bornmann, L.; Wagner, C.S.: ¬The relative influences of government funding and international collaboration on citation impact (2019) 0.02
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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
  3. Leydesdorff, L.; Bornmann, L.; Mutz, R.; Opthof, T.: Turning the tables on citation analysis one more time : principles for comparing sets of documents (2011) 0.01
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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.
  4. Bornmann, L.; Bauer, J.; Haunschild, R.: Distribution of women and men among highly cited scientists (2015) 0.01
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  5. 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) 0.01
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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.
  6. Bornmann, L.; Mutz, R.; Daniel, H.D.: Do we need the h index and its variants in addition to standard bibliometric measures? (2009) 0.01
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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.
  7. 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) 0.01
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    Editor
    Mutz, R.
  8. Bornmann, L.; Haunschild, R.: Relative Citation Ratio (RCR) : an empirical attempt to study a new field-normalized bibliometric indicator (2017) 0.01
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  9. 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
  10. 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) 0.01
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  11. Bornmann, L.; Haunschild, R.: Overlay maps based on Mendeley data : the use of altmetrics for readership networks (2016) 0.01
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  12. Bornmann, L.; Mutz, R.; Daniel, H.-D.: Are there better indices for evaluation purposes than the h index? : a comparison of nine different variants of the h index using data from biomedicine (2008) 0.00
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  13. Bornmann, L.; Mutz, R.; Daniel, H.-D.: Multilevel-statistical reformulation of citation-based university rankings : the Leiden ranking 2011/2012 (2013) 0.00
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  14. Bornmann, L.; Mutz, R.: Growth rates of modern science : a bibliometric analysis based on the number of publications and cited references (2015) 0.00
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  15. Bornmann, L.; Haunschild, R.: ¬An empirical look at the nature index (2017) 0.00
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  16. 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) 0.00
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    Date
    22. 3.2013 19:44:17
  17. 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) 0.00
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    Date
    22. 1.2011 12:51:07