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      The Scientific Competitiveness of Nations

      1 , * , 1 , 2 , 3 , 4
      PLoS ONE
      Public Library of Science

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          We use citation data of scientific articles produced by individual nations in different scientific domains to determine the structure and efficiency of national research systems. We characterize the scientific fitness of each nation—that is, the competitiveness of its research system—and the complexity of each scientific domain by means of a non-linear iterative algorithm able to assess quantitatively the advantage of scientific diversification. We find that technological leading nations, beyond having the largest production of scientific papers and the largest number of citations, do not specialize in a few scientific domains. Rather, they diversify as much as possible their research system. On the other side, less developed nations are competitive only in scientific domains where also many other nations are present. Diversification thus represents the key element that correlates with scientific and technological competitiveness. A remarkable implication of this structure of the scientific competition is that the scientific domains playing the role of “markers” of national scientific competitiveness are those not necessarily of high technological requirements, but rather addressing the most “sophisticated” needs of the society.

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          The scientific impact of nations.

          David King (2004)
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            Big Science vs. Little Science: How Scientific Impact Scales with Funding

            Agencies that fund scientific research must choose: is it more effective to give large grants to a few elite researchers, or small grants to many researchers? Large grants would be more effective only if scientific impact increases as an accelerating function of grant size. Here, we examine the scientific impact of individual university-based researchers in three disciplines funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). We considered four indices of scientific impact: numbers of articles published, numbers of citations to those articles, the most cited article, and the number of highly cited articles, each measured over a four-year period. We related these to the amount of NSERC funding received. Impact is positively, but only weakly, related to funding. Researchers who received additional funds from a second federal granting council, the Canadian Institutes for Health Research, were not more productive than those who received only NSERC funding. Impact was generally a decelerating function of funding. Impact per dollar was therefore lower for large grant-holders. This is inconsistent with the hypothesis that larger grants lead to larger discoveries. Further, the impact of researchers who received increases in funding did not predictably increase. We conclude that scientific impact (as reflected by publications) is only weakly limited by funding. We suggest that funding strategies that target diversity, rather than “excellence”, are likely to prove to be more productive.
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              Measuring Co-Authorship and Networking-Adjusted Scientific Impact

              Appraisal of the scientific impact of researchers, teams and institutions with productivity and citation metrics has major repercussions. Funding and promotion of individuals and survival of teams and institutions depend on publications and citations. In this competitive environment, the number of authors per paper is increasing and apparently some co-authors don't satisfy authorship criteria. Listing of individual contributions is still sporadic and also open to manipulation. Metrics are needed to measure the networking intensity for a single scientist or group of scientists accounting for patterns of co-authorship. Here, I define I1 for a single scientist as the number of authors who appear in at least I1 papers of the specific scientist. For a group of scientists or institution, In is defined as the number of authors who appear in at least In papers that bear the affiliation of the group or institution. I1 depends on the number of papers authored Np . The power exponent R of the relationship between I1 and Np categorizes scientists as solitary (R>2.5), nuclear (R = 2.25–2.5), networked (R = 2–2.25), extensively networked (R = 1.75–2) or collaborators (R<1.75). R may be used to adjust for co-authorship networking the citation impact of a scientist. In similarly provides a simple measure of the effective networking size to adjust the citation impact of groups or institutions. Empirical data are provided for single scientists and institutions for the proposed metrics. Cautious adoption of adjustments for co-authorship and networking in scientific appraisals may offer incentives for more accountable co-authorship behaviour in published articles.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                10 December 2014
                : 9
                : 12
                : e113470
                [1 ]Istituto dei Sistemi Complessi (ISC-CNR) UoS Università “Sapienza”, Rome, Italy
                [2 ]IMT Institute for Advanced Studies, Piazza San Ponziano 6, Lucca, Italy
                [3 ]Centro Studi e Ricerche Enrico Fermi, Compendio del Viminale, Rome, Italy
                [4 ]Istituto dei Sistemi Complessi (ISC-CNR), Rome, Italy
                University of Warwick, United Kingdom
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GC AG FSL. Performed the experiments: GC. Analyzed the data: GC AG FSL. Contributed reagents/materials/analysis tools: GC FSL. Wrote the paper: GC AG FSL.

                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                : 25 July 2014
                : 23 October 2014
                Page count
                Pages: 11
                This work was supported by the European project FET-Open GROWTHCOM (grant num. 611272) and the Italian PNR project CRISIS-Lab. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Computer and Information Sciences
                Network Analysis
                Physical Sciences
                Applied Mathematics
                Research and Analysis Methods
                Research Assessment
                Citation Analysis
                Research Quality Assessment
                Science Policy
                Research Funding
                Science Policy and Economics
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. Citation data is available from the Scimago database ( www.scimagojr.comm). Data on Higher Education Expenditure on Research & Development (HERD) is available from the Organization for Economic Cooperation and Development ( www.oecd.org/).



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