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      Referencing behaviours across disciplines: publication types and common metadata for defining bibliographic references

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          Abstract

          In this work, we investigate existing citation practices by analysing a huge set of articles published in journals to measure which metadata are used across the various scholarly disciplines, independently from the particular citation style adopted, for defining bibliographic reference. We selected the most cited journals in each of the 27 subject areas listed in the SCImago Journal Rank in the 2015–2017 triennium according to the SCImago total cites ranking. Each journal in the sample was represented by five articles (in PDF format) published in the most recent issue published in October 2019, for a total of 729 articles. We extracted all 34,140 bibliographic references in the bibliographic references lists of these articles. Finally, we detected the types of cited works in each discipline and the structure of bibliographic references and in-text reference pointers for each type of cited work. By analysing the data gathered, we observed that the bibliographic references in our sample referenced 36 different types of cited works. Such a considerable variety of publications revealed the existence of particular citing behaviours in scientific articles that varied from subject area to subject area.

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          Science of science

          BACKGROUND The increasing availability of digital data on scholarly inputs and outputs—from research funding, productivity, and collaboration to paper citations and scientist mobility—offers unprecedented opportunities to explore the structure and evolution of science. The science of science (SciSci) offers a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales: It provides insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science. In the past decade, SciSci has benefited from an influx of natural, computational, and social scientists who together have developed big data–based capabilities for empirical analysis and generative modeling that capture the unfolding of science, its institutions, and its workforce. The value proposition of SciSci is that with a deeper understanding of the factors that drive successful science, we can more effectively address environmental, societal, and technological problems. ADVANCES Science can be described as a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas. This representation has unveiled patterns characterizing the emergence of new scientific fields through the study of collaboration networks and the path of impactful discoveries through the study of citation networks. Microscopic models have traced the dynamics of citation accumulation, allowing us to predict the future impact of individual papers. SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon. For example, measurements indicate that scholars are risk-averse, preferring to study topics related to their current expertise, which constrains the potential of future discoveries. Those willing to break this pattern engage in riskier careers but become more likely to make major breakthroughs. Overall, the highest-impact science is grounded in conventional combinations of prior work but features unusual combinations. Last, as the locus of research is shifting into teams, SciSci is increasingly focused on the impact of team research, finding that small teams tend to disrupt science and technology with new ideas drawing on older and less prevalent ones. In contrast, large teams tend to develop recent, popular ideas, obtaining high, but often short-lived, impact. OUTLOOK SciSci offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas because it facilitates the identification of fundamental mechanisms responsible for scientific discovery. These interdisciplinary data-driven efforts complement contributions from related fields such as sciento-metrics and the economics and sociology of science. Although SciSci seeks long-standing universal laws and mechanisms that apply across various fields of science, a fundamental challenge going forward is accounting for undeniable differences in culture, habits, and preferences between different fields and countries. This variation makes some cross-domain insights difficult to appreciate and associated science policies difficult to implement. The differences among the questions, data, and skills specific to each discipline suggest that further insights can be gained from domain-specific SciSci studies, which model and identify opportunities adapted to the needs of individual research fields. The complexity of science. Science can be seen as an expanding and evolving network of ideas, scholars and papers. SciSci searches for universal and domain-specific laws underlying the structure and dynamics of science.
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            Automatic classification of citation function

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              FaBiO and CiTO: Ontologies for describing bibliographic resources and citations

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                International Journal on Digital Libraries
                Int J Digit Libr
                Springer Science and Business Media LLC
                1432-5012
                1432-1300
                September 2024
                March 27 2023
                September 2024
                : 25
                : 3
                : 443-455
                Article
                10.1007/s00799-023-00351-8
                5e5544f6-71cc-4bfb-9df2-6c876b33b03c
                © 2024

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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