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      Scientific X-ray: Scanning and quantifying the idea evolution of scientific publications

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          Abstract

          The rapid development of modern science nowadays makes it rather challenging to pick out valuable ideas from massive scientific literature. Existing widely-adopted citation-based metrics are not adequate for measuring how well the idea presented by a single publication is developed and whether it is worth following. Here, inspired by traditional X-ray imaging, which returns internal structure imaging of real objects along with corresponding structure analysis, we propose Scientific X-ray, a framework that quantifies the development degree and development potential for any scientific idea through an assembly of ‘X-ray’ scanning, visualization and parsing operated on the citation network associated with a target publication. We pick all 71,431 scientific articles of citation counts over 1,000 as high-impact target publications among totally 204,664,199 publications that cover 16 disciplines spanning from 1800 to 2021. Our proposed Scientific X-ray reproduces how an idea evolves from the very original target publication all the way to the up to date status via an extracted ‘idea tree’ that attempts to preserve the most representative idea flow structure underneath each citation network. Interestingly, we observe that while the citation counts of publications may increase unlimitedly, the maximum valid idea inheritance of those target publications, i.e., the valid depth of the idea tree, cannot exceed a limit of six hops, and the idea evolution structure of any arbitrary publication unexceptionally falls into six fixed patterns. Combined with a development potential index that we further design based on the extracted idea tree, Scientific X-ray can vividly tell how further a given idea presented by a given publication can still go from any well-established starting point. Scientific X-ray successfully identifies 40 out of 49 topics of Nobel prize as high-potential topics by their prize-winning papers in an average of nine years before the prizes are released. Various trials on articles of diverse topics also confirm the power of Scientific X-ray in digging out influential/promising ideas. Scientific X-ray is user-friendly to researchers with any level of expertise, thus providing important basis for grasping research trends, helping scientific policy-making and even promoting social development.

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          Most cited references54

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          Collective dynamics of 'small-world' networks.

          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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            Searching for intellectual turning points: progressive knowledge domain visualization.

            C. Chen (2004)
            This article introduces a previously undescribed method progressively visualizing the evolution of a knowledge domain's cocitation network. The method first derives a sequence of cocitation networks from a series of equal-length time interval slices. These time-registered networks are merged and visualized in a panoramic view in such a way that intellectually significant articles can be identified based on their visually salient features. The method is applied to a cocitation study of the superstring field in theoretical physics. The study focuses on the search of articles that triggered two superstring revolutions. Visually salient nodes in the panoramic view are identified, and the nature of their intellectual contributions is validated by leading scientists in the field. The analysis has demonstrated that a search for intellectual turning points can be narrowed down to visually salient nodes in the visualized network. The method provides a promising way to simplify otherwise cognitively demanding tasks to a search for landmarks, pivots, and hubs.
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                Author and article information

                Contributors
                Role: Formal analysisRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Supervision
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: InvestigationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2022
                28 September 2022
                : 17
                : 9
                : e0275192
                Affiliations
                [1 ] Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
                [2 ] Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
                [3 ] State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
                University of Bologna, ITALY
                Author notes

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

                Author information
                https://orcid.org/0000-0001-8089-8348
                https://orcid.org/0000-0002-0357-8356
                https://orcid.org/0000-0002-6120-5806
                Article
                PONE-D-22-10140
                10.1371/journal.pone.0275192
                9518912
                36170296
                f2b05221-45f1-456f-8d54-75fb7166c282
                © 2022 Li et al

                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.

                History
                : 6 April 2022
                : 12 September 2022
                Page count
                Figures: 4, Tables: 3, Pages: 21
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 42050105,62020106005,62061146002,6196026002,61822206,61832013,61829201
                Award Recipient :
                This work is supported by National Natural Science Foundation of China (No. 42050105, 62020106005, 62061146002, 61960206002, 61822206, 61832013, 61829201).
                Categories
                Research Article
                Research and Analysis Methods
                Research Assessment
                Citation Analysis
                Physical Sciences
                Physics
                Thermodynamics
                Entropy
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Trees
                Computer and Information Sciences
                Neural Networks
                Biology and Life Sciences
                Neuroscience
                Neural Networks
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Bone Imaging
                X-Ray Radiography
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Bone Imaging
                X-Ray Radiography
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Bone Imaging
                X-Ray Radiography
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                X-Ray Radiography
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                X-Ray Radiography
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                X-Ray Radiography
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Deep Learning
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Change
                Custom metadata
                All data and codes related to the submssion is available in https://github.com/liqilcn/scientific_x-ray.

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