Authorship identity has long been an Achilles’ heel in bibliometric analyses at the individual level. This problem appears
in studies of scientists’ productivity, inventor mobility and scientific collaboration. Using the concepts of cognitive maps
from psychology and approximate structural equivalence from network analysis, we develop a novel algorithm for name disambiguation
based on knowledge homogeneity scores. We test it on two cases, and the results show that this approach outperforms other
common authorship identification methods with the ASE method providing a relatively simple algorithm that yields higher levels
of accuracy with reasonable time demands.
Authors:Peter A. Schulz and Edmilson J. T. Manganote
fingerprints on research patterns on the macro-level reveal cultural identities and characteristic differences among different scientific fields and countries, as well as the evolution in time of country research profile, taking into account field strengths and
Authors:Jian Wang, Kaspars Berzins, Diana Hicks, Julia Melkers, Fang Xiao, and Diogo Pinheiro
directly from features of papers and authors. Some studies found that coauthor information was very effective (Torvik et al. 2005 ; Kang et al. 2009 ). Tang and Walsh ( 2010 ) viewed referencing as a fingerprint of author's cognitive knowledge base and
Authors:Stanislaw Kosecki, Robbin Shoemaker, and Charlotte Kirk Baer
interrelationships among fields based on research output, a map of USDA science was assembled, which provides a unique “fingerprint” of USDA's research.
Areas of USDA science strengths are identified in the science map ( Fig. 2 ) derived from co
(retrieved from the ISI databases), and weighting them according to their role in the profile of A . Thereby a so-called overlay map, or a customized version of the global map is created, as a kind of “fingerprint” characterizing the position, or (from a
On the basis of the measured time-dependent distribution of references in recent scientific publications, we formulate a novel model on the ageing of recent scientific literature. The framework of this model is given by a basic set of mathematical expressions that allows us to understand and describe large-scale growth and ageing processes in science over a long period of time. In addition, a further and striking consequence results in a self- consistent way from our model. After the Scientific Revolution in 16th century Europe, the 'Scientific Evolution' begins, and the driving processes growth and ageing unavoidably lead - just as in our biological evolution - to a fractal differentiation of science. A fractal structure means a system build up with sub-systems characterised by a power-law size distribution. Such a distribution implies that there is no preference of size or scale. Often this phenomenon is regarded as a fingerprint of self-organisation. These findings are in agreement with earlier empirical findings concerning the clustering of scientific literature. Our observations reinforce the idea of science as a complex, largely self-organising 'cognitive eco-system'. They also refute Kuhn's paradigm model of scientific development.