A Reverse Engineering Approach to the Suppression of Citation Biases Reveals Universal Properties of Citation Distributions (Articolo in rivista)

Type
Label
  • A Reverse Engineering Approach to the Suppression of Citation Biases Reveals Universal Properties of Citation Distributions (Articolo in rivista) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1371/journal.pone.0033833 (literal)
Alternative label
  • Filippo Radicchi (1,2,3); Claudio Castellano (4,5) (2012)
    A Reverse Engineering Approach to the Suppression of Citation Biases Reveals Universal Properties of Citation Distributions
    in PloS one
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Filippo Radicchi (1,2,3); Claudio Castellano (4,5) (literal)
Pagina inizio
  • e33833 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0033833 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 7 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 3 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1 Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Catalunya, Spain 2 Howard Hughes Medical Institute (HHMI), Northwestern University, Evanston, Illinois, United States of America 3 Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America 4 Istituto dei Sistemi Complessi (ISC-CNR), Italy 5 Dipartimento di Fisica, \"Sapienza\" Universit√† di Roma, Roma, Italy (literal)
Titolo
  • A Reverse Engineering Approach to the Suppression of Citation Biases Reveals Universal Properties of Citation Distributions (literal)
Abstract
  • The large amount of information contained in bibliographic databases has recently boosted the use of citations, and other indicators based on citation numbers, as tools for the quantitative assessment of scientific research. Citations counts are often interpreted as proxies for the scientific influence of papers, journals, scholars, and institutions. However, a rigorous and scientifically grounded methodology for a correct use of citation counts is still missing. In particular, cross-disciplinary comparisons in terms of raw citation counts systematically favors scientific disciplines with higher citation and publication rates. Here we perform an exhaustive study of the citation patterns of millions of papers, and derive a simple transformation of citation counts able to suppress the disproportionate citation counts among scientific domains. We find that the transformation is well described by a power-law function, and that the parameter values of the transformation are typical features of each scientific discipline. Universal properties of citation patterns descend therefore from the fact that citation distributions for papers in a specific field are all part of the same family of univariate distributions. (literal)
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