Visualizing the marrow of science

Anegón, Félix de Moya; Vargas-Quesada, Benjamín; Chinchilla‐Rodríguez, Zaida; Corera-Álvarez, Elena; Muñoz-Fernández, Francisco José; Herrero-Solana, Víctor

doi:10.1002/asi.20683

Cited by 97 publications

(82 citation statements)

References 29 publications

(33 reference statements)

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“…Among many other achievements, science mapping become the keystone of visualizing and analyzing computer graphics (Chen et al, 2001), using ISI categories to represent science (Moya-Anegón et al, 2004), mapping the backbone of science (Boyack et al, 2005), evaluating large maps of disciplines (Klavans and Boyack, 2006), visualizing the citation impact of scientific journals (Leydesdorff, 2007a), mapping interdisciplinarity (Leydesdorff, 2007b), viewing the marrow of science (Moya-Anegón et al, 2007b), creating dynamic animations of journal maps (Leydesdorff and Schank, 2008), mapping the structure and evolution of chemistry research , proposing a consensus map of science , creating a journal map using Scopus data , mapping the geography of science (Leydesdorff and Persson, 2010), clustering over two million biomedical publications , creating more accurate document-level maps of research fields , detecting and visualizing the evolution of the fuzzy sets theory field (Cobo et al, 2011b), proposing a new global science map (Leydesdorff et al, 2013a,b;Boyack and Klavans, 2014), analyzing the investigation in integrative and complementary medicine (Moral-Muñoz et al, 2014), analyzing intelligent transportation systems , showing the evolution of bases knowledge systems , showing the scientific evolution of social work , outlining animal science research (Rodriguez-Ledesma et al, 2015), studying the conceptual evolution of marketing research (Murgado-Armenteros et al, 2015) identifying and depicting the intellectual structure and research fronts in nanoscience and nanotechnology in the world (Muñoz-Écija et al, 2017), and exploring the scientific evolution of e-Government (Alcaide-Muñoz et al, in press), among other brave new initiatives.…”

Section: Related Workmentioning

confidence: 99%

Identification and Visualization of the Intellectual Structure in Graphene Research

Vargas-Quesada

Chinchilla‐Rodríguez

Rodríguez

2017

Front. Res. Metr. Anal.

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Since the discovery of the promising properties of graphene, research in the field has attracted numerous grants and sponsors, leading to an exponential rise in the number of papers and applications. This article presents a global map of graphene research and its intellectual structure, drawn using the terms of more than 50,000 documents extracted from Scopus database, years 1998-2015. The unit of analysis consisted of descriptors (including Author Keywords and Indexed Keywords), with the co-occurrence of descriptor as the unit of measure, using fractional counting. The main research lines identified are: Fundamental Research, Functionalization and Biomedical Applications, Technology and Devices, Materials Science, Energy Storage, Optics and Chemical Properties and Sensors. Using overlay maps, we depict the graphene research efforts of the United States, the European Union (Europe-28), and China, and project their evolution through longitudinal maps to facilitate comparison. The United States was initially at the head of world output in graphene research, but was surpassed by China in 2011 and by Europe in 2014, as a result of their respective scientific policies and financial support. The output of China has since been so intense that it can be said to mark graphene research trends. We believe this information may be valuable for the core community involved in this scientific field, as it offers a large-scale analysis showing how research has changed over time. It is therefore also helpful for policy makers and research planners. The resulting maps are a useful and attractive tool for the graphene research community, as they reveal the main lines of exploration at a glance. The methodology described here could be re-created in any other field of science to uncover and display its intellectual structure and evolution over time.

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Section: Related Workmentioning

confidence: 99%

Identification and Visualization of the Intellectual Structure in Graphene Research

Vargas-Quesada

Chinchilla‐Rodríguez

Rodríguez

2017

Front. Res. Metr. Anal.

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“…Consistent with this expectation, Newman [29] showed that subdiscipline-specific collaboration graphs have distinctive community structures, and in particular that collaborations in the biomedical sciences exhibited far less clustering -and hence greater apparent randomness -than collaborations in subdisciplines of physics or the information sciences. Distinctive community structures and disciplinary and subdisciplinary clustering have been confirmed by subsequent studies of coauthorship in discipline-specific data sets [31] and of citation patterns in multi-disciplinary data sets [22,28,40]. Such community structure is also observed in metabolic, electronic, and other non-social networks (e.g.…”

Section: Introductionmentioning

confidence: 57%

Some Effects of the Human Genome Project on the Erdős Collaboration Graph

Fields¹

2014

jhummath

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The Human Genome Project introduced large-scale collaborations involving dozens to hundreds of scientists into biology. It also created a pressing need to solve discrete mathematics problems involving tens of thousands of elements. In this paper, we use minimal path lengths in the Erdős Collaboration Graph between prominent individual researchers as a measure of the distance between disciplines, and we show that the Human Genome Project brought laboratory biology as a whole closer to mathematics. We also define a novel graph reduction method and a metric that emphasizes the robustness of collaborative connections between researchers; these can facilitate the analysis of both within-and betweencommunity connectivity in collaboration graphs.

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“…A scientogram or map of science based on SJR journals and their subsequent subject assignment derived from applying VOS community detection clustering. Despite various differences in the data set, methodology and techniques used in our proposal, there is considerable resemblance between this scientogram and those of Moya-Anegón et al (2007) or Leydesdorff et al (2015a;2015b). Indeed, the scientogram shape, like a croissant, was evoked by Leydesdorff et al (2013), who asserted that "this croissant-like structure also accords with Klavans and Boyack's (2009) conclusion that a consensus has increasingly emerged regarding the shape of journal maps based on aggregated citations".…”

Section: Discussionmentioning

confidence: 92%

Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering

Gómez-Núñez¹,

Vargas-Quesada²,

Chinchilla‐Rodríguez

et al. 2016

AJIM

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PurposeThe objective was to visualize the structure of SCImago Journal & Country Rank (SJR) coverage of the extensive citation network of Scopus journals, examining this bibliometric portal through an alternative approach, applying clustering and visualization techniques to a combination of citation-based links. MethodologyThree SJR journal-journal networks containing direct citation, co-citation and bibliographic coupling links are built. The three networks were then combined into a new one by summing up their values, which were later normalized through geo-normalization measure. Finally, the VOS clustering algorithm was executed and the journal clusters obtained were labeled using original SJR category tags and significant words from journal titles. FindingsThe resultant scientogram displays the SJR structure through a set of communities equivalent to SJR categories that represent the subject contents of the journals they cover. A higher level of aggregation by areas provides a broad view of the SJR structure, facilitating its analysis and visualization at the same time. ValueThis is the first study using Persson's combination of most popular citation-based links (direct citation, co-citation and bibliographic coupling) in order to develop a scientogram based on Scopus journals from SJR. The integration of the three measures along with performance of the VOS community detection algorithm gave a balanced set of clusters. The resulting scientogram is useful for assessing and validating previous classifications as well as for information retrieval and domain analysis.

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Visualizing the marrow of science

Cited by 97 publications

References 29 publications

Identification and Visualization of the Intellectual Structure in Graphene Research

Identification and Visualization of the Intellectual Structure in Graphene Research

Some Effects of the Human Genome Project on the Erdős Collaboration Graph

Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering

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