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      The preeminence of ethnic diversity in scientific collaboration

      research-article
      1 , , 1 , 2 , , 1 , 3 ,
      Nature Communications
      Nature Publishing Group UK

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          Abstract

          Inspired by the social and economic benefits of diversity, we analyze over 9 million papers and 6 million scientists to study the relationship between research impact and five classes of diversity: ethnicity, discipline, gender, affiliation, and academic age. Using randomized baseline models, we establish the presence of homophily in ethnicity, gender and affiliation. We then study the effect of diversity on scientific impact, as reflected in citations. Remarkably, of the classes considered, ethnic diversity had the strongest correlation with scientific impact. To further isolate the effects of ethnic diversity, we used randomized baseline models and again found a clear link between diversity and impact. To further support these findings, we use coarsened exact matching to compare the scientific impact of ethnically diverse papers and scientists with closely-matched control groups. Here, we find that ethnic diversity resulted in an impact gain of 10.63% for papers, and 47.67% for scientists.

          Abstract

          Diversity is believed to raise effectiveness and performance but it contains many aspects. Here the authors studied the relationship between research impact and five classes of diversity and found that ethnic diversity had the strongest correlation with scientific impact.

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          Quantifying the evolution of individual scientific impact.

          Despite the frequent use of numerous quantitative indicators to gauge the professional impact of a scientist, little is known about how scientific impact emerges and evolves in time. Here, we quantify the changes in impact and productivity throughout a career in science, finding that impact, as measured by influential publications, is distributed randomly within a scientist's sequence of publications. This random-impact rule allows us to formulate a stochastic model that uncouples the effects of productivity, individual ability, and luck and unveils the existence of universal patterns governing the emergence of scientific success. The model assigns a unique individual parameter Q to each scientist, which is stable during a career, and it accurately predicts the evolution of a scientist's impact, from the h-index to cumulative citations, and independent recognitions, such as prizes.
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            Does Diversity Pay?: Race, Gender, and the Business Case for Diversity

            C Herring (2009)
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              Quantifying Long-Term Scientific Impact

              The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.
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                Author and article information

                Contributors
                bedoor@deeplearn.net
                talal.rahwan@nyu.edu
                wlwoon@deeplearn.net
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                4 December 2018
                4 December 2018
                2018
                : 9
                : 5163
                Affiliations
                [1 ]ISNI 0000 0004 1762 9729, GRID grid.440568.b, Department of Computer Science, Masdar Institute, , Khalifa University of Science and Technology, ; Abu Dhabi, P.O. Box 54224, UAE
                [2 ]Computer Science, New York University, Abu Dhabi, P.O. Box 129188 UAE
                [3 ]ISNI 0000 0004 0399 5285, GRID grid.497099.a, Expedia Inc., ; 333 108th AVE NE, Bellevue, WA 98004 USA
                Author information
                http://orcid.org/0000-0003-1775-5162
                http://orcid.org/0000-0002-6155-1741
                Article
                7634
                10.1038/s41467-018-07634-8
                6279741
                30514841
                bbb9b1e6-1b3d-4250-95ea-01c7ceeb6673
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 May 2018
                : 12 November 2018
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