The science of science: From the perspective of complex systems

Zeng, An; Shen, Zhesi; Zhou, Jingming; Wu, Jinshan; Fan, Ying; Wang, Yougui; Stanley, H. Eugene

doi:10.1016/j.physrep.2017.10.001

Cited by 341 publications

(198 citation statements)

References 411 publications

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“…In previous studies, the success of the scores to measure novelty has been investigated by citation analyses (see the overview of Zeng et al, 2017). The results of Uzzi et al (2013) including "17.9 million papers across all scientific fields suggest […] that the highest-impact science draws on primarily highly conventional combinations of prior work, with an intrusion of combinations unlikely to have been joined together before.…”

Section: Discussionmentioning

confidence: 99%

Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data

Bornmann

Tekles

Zhang

et al. 2019

Journal of Informetrics

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Lee, Walsh, and Wang (2015)based on Uzzi, Mukherjee, Stringer, andJones (2013)andWang, Veugelers, andStephan (2017) proposed scores based on cited references (cited journals) data which can be used to measure the novelty of papers (named as novelty scores U and W in this study). Although previous research has used novelty scores in various empirical analyses, no study has been published up to nowto the best of our knowledgewhich quantitatively tested the convergent validity of novelty scores: do these scores measure what they propose to measure? Using novelty assessments by faculty members (FMs) atF1000Prime for comparison, we tested the convergent validity of the two novelty scores (U and W). FMs' assessments not only refer to the quality of biomedical papers, but also to their characteristics (by assigning certain tags to the papers): for example, are the presented findings or formulated hypotheses novel (tags "new findings" and "hypothesis")? We used these and other tags to investigate the convergent validity of both novelty scores. Our study reveals different results for the novelty scores: the results for novelty score U are mostly in agreement with previously formulated expectations. We found, for instance, that for a standard deviation (one unit) increase in novelty score U, the expected number of assignments of the "new finding" tag increase by 7.47%. The results for novelty score W, however, do not reflect convergent validity with the FMs' assessments: only the results for some tags are in agreement with the expectations. Thus, we proposebased on our resultsthe use of novelty score U for measuring novelty quantitatively, but question the use of novelty score W.

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Section: Discussionmentioning

confidence: 99%

Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data

Bornmann

Tekles

Zhang

et al. 2019

Journal of Informetrics

View full text Add to dashboard Cite

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“…The rapidly growing body of scientific information reflects past and current states of the art and represents an invaluable socio-economic resource guiding future research activity, policies and Pfrieger, Workforce Analysis 3 investments (Mukherjee et al, 2017;Fischhoff and Scheufele, 2019). Moreover, scientific publications are explored by the "Science of Science" aiming to understand the inner workings of science from global points of view (Clauset et al, 2017;Zeng et al, 2017;Fortunato et al, 2018;Fischhoff and Scheufele, 2019;Hardwicke et al, 2020). The utility of this information relies on bibliographic databases, on refined methods to search and analyse content (Lu, 2011;McLevey and McIlroy-Young, 2017) and on efficient science communication (Fischhoff and Scheufele, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The utility of this information relies on bibliographic databases, on refined methods to search and analyse content (Lu, 2011;McLevey and McIlroy-Young, 2017) and on efficient science communication (Fischhoff and Scheufele, 2019). For global analyses complex algorithms process large data sets (Muller et al, 2004;Zeng et al, 2017;Miotto et al, 2018;Kastrin and Hristovski, 2019), whereas a typical user queries a bibliographic database on a specific topic using relevant keywords. From the resulting list of publications, scientific content can be extracted, but the workforce driving the field, its size, dynamics and key contributors remain largely inaccessible.…”

Section: Introductionmentioning

confidence: 99%

TeamTree analysis: a new approach to evaluate scientific production

Pfrieger

2020

Preprint

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Advances in biomedicine and other fields of science and technology depend on research teams and their peer-reviewed publications. The scientific literature represents an invaluable socioeconomic resource guiding future research. Typically, this growing body of information is explored by queries in bibliographic databases concerning topics of interest and by subsequent scrutiny of matching publications. This approach informs readily about content, but leaves the workforce driving the field largely unexplored. The hurdle can be overcome by a transparent team-centered analysis that visualizes the teams working in a field of interest and that delineates their genealogic and collaborative relations. Context-specific, but citation-independent metrics gauge team impact and reveal key contributors valuing publication output, mentorship and collaboration. The new insight into the structure, dynamics and performance of the workforce driving research in distinct disciplines complements ongoing efforts to mine the scientific literature, foster collaboration, evaluate research and guide future policies and investments.

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“…Graph theory and networks are widely used to quantify the structures of social, biological, neural, and many other complex systems [1][2][3][4][5][6]. These graphs are composed of nodes (points with labels i = 1, 2, .…”

Section: Introductionmentioning

confidence: 99%

Statistical analyses of cyclic and starlike hierarchical dominances in directed graphs

Szabó

Borsos

Leitner³

2019

Phys. Rev. E

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The mathematical framework of matrix decomposition implies the possibility to perform statistical analyses of directed graphs focused on the distributions of the independent cyclic and starlike hierarchical components. In this approach the weighted directed graphs with n nodes are built up as a linear combination of starlike graphs with n outgoing edges and a suitable set of three-edge cyclic subgraphs. The applicability of this approach is illustrated by quantifying several general features: e.g., ratio of the cyclic and hierarchical components and asymmetry in the hierarchical components. The applicability of these methods is illustrated by considering the averages over random directed graphs and comparing these values with those characterizing simple directed graphs of tournaments.

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The science of science: From the perspective of complex systems

Cited by 341 publications

References 411 publications

Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data

Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data

TeamTree analysis: a new approach to evaluate scientific production

Statistical analyses of cyclic and starlike hierarchical dominances in directed graphs

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