Visualizing data visualization using scopus

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Academic research on data visualization has seen an explosive growth in the last 15 years. In this presentation I use Elsevier's Scopus to search for scholarly research on data visualization and to present visual summaries of the vast literature.

Technology

Visualizing Data Visualization
February 2017
Keiko Ono, Ph.D.
1

Two largest databases of academic literature.
In this presentation I use Elsevier’s Sciverse
Scopus.
2

Using “data visualization” (with quotation marks) as search term in title,
abstract or keywords produces over 20,000 results.
3

That’s a lot of information. To narrow the scope and find what
we’re really looking for, we could use additional search terms.
But sometimes we want to understand, make sense of the
really big body of literature (in this case n=20,262)
In Scopus there’s a default built-in “Analyze Results” function
that allows visual summary of the bibliometric data.
For each, there’s a frequency table too, so one can create own
graphics.
4

One can use “Analyze results” function to see how (1) the # of
citations have changed over the years…
Until 1987, the number
of citations per year was
single digit. It reached
133 in 1996 and 1,168 in
2007. In 2016, there were
2,217 scholarly
publications on data
visualization!
5

Or to see (2) which journals have published the greatest
number of articles on this topic…
6
No.1. on the list
is Lecture
Notes in
Computer
Science
(including
Subseries
Lecture Notes
in Artificial
Intelligence and
Lecture Notes
in
Bioinformatics)

Or to see (3) who the leading authors are on this topic.
7

And (4) where they are affiliated.
8
The leaders in data
visualization
research are U of
California Davis,
Georgia Institute of
Technology, U of
Utah…and so on.
University of Tokyo is
ranked #18.

(6) Scopus also visually presents the document type
breakdown
10

(7) And by subject area
11
Not clear how
these
percentages are
calculated…??

In summary, research on data visualization has
seen an explosive growth in the 21st century. The
leading fields of research are computer science,
engineering, and mathematics. The United States
is the leader in this area of research, followed by
China, Germany and UK.
Alternatively, using “infographics” as seach term
produces far fewer results (only about 200).
12

Presentation at the 16th International Conference on Scientometrics & Informetrics, Wuhan, China, October 19, 2017. A term map offers a visualization of a network of terms that co-occur in scientific publications. Term maps are usually created based on the titles and abstracts of publications. In this paper, we explore the use of full-text data for creating term maps. We create and compare a series of term maps based on the full text of publications in Journal of Informetrics. We use our results to discuss the advantages and disadvantages of different approaches for creating term maps.

Accuracy of citation data in Web of Science and Scopus

Nees Jan van Eck

Presentation at the 16th International Conference on Scientometrics & Informetrics, Wuhan, China, October 19, 2017. We present a large-scale analysis of the accuracy of citation data in the Web of Science and Scopus databases. The analysis is based on citations given in publications in Elsevier journals. We reveal significant data quality problems for both databases. Missing and incorrect references are important problems in Web of Science. Duplicate publications are a serious problem in Scopus.

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PR173 : Automatic Chemical Design Using a Data-Driven Continuous Representati...

Sunghoon Joo

In this paper we present a study where we visually analyzed science citation data to investigate the competitiveness of world countries in selected categories of science. The dataset that we worked on in our study includes the number of papers published and the number of citations made in the ESI (Essential Science Indicators) database in 2004. The dataset lists these values for practically every country in the world. In analyzing the data, we employ methods and software tools developed and used in the data mining and information visualization fields of the Computer Science. Some of the questions for which we look for answers in this study are the following: (a) Which countries are most competitive in the selected categories of science? (i.e. Engineering, Computer Science, Economics & Business) (b) What type of correlations exist between different categories of science? For example, do countries with many published papers in the field of Engineering science also have many papers published on Computer Science or Economics & Business? (c) Which countries produce the most influential papers? This analysis is needed since a country may have many papers published but these papers may be cited very rarely. (d) Can we gain useful and actionable insights by combining science citation data with socioeconomic and geographical data? http://research.sabanciuniv.edu.

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ertekg

Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/visual-mining-of-science-citation-data-for-benchmarking-scientific-and-technological-competitiveness-of-world-countries/ In this paper we present a study where we visually analyzed science citation data to investigate the competitiveness of world countries in selected categories of science. The dataset that we worked on in our study includes the number of papers published and the number of citations made in the ESI (Essential Science Indicators) database in 2004. The dataset lists these values for practically every country in the world. In analyzing the data, we employ methods and software tools developed and used in the data mining and information visualization fields of the Computer Science. Some of the questions for which we look for answers in this study are the following: (a) Which countries are most competitive in the selected categories of science? (i.e. Engineering, Computer Science, Economics & Business) (b) What type of correlations exist between different categories of science? For example, do countries with many published papers in the field of Engineering science also have many papers published on Computer Science or Economics & Business? (c) Which countries produce the most influential papers? This analysis is needed since a country may have many papers published but these papers may be cited very rarely. (d) Can we gain useful and actionable insights by combining science citation data with socioeconomic and geographical data?

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In the last decade, several Scientific Knowledge Graphs (SKG) were released, representing scientific knowledge in a structured, interlinked, and semantically rich manner. But, what kind of information they describe? How they have been built? What can we do with them? In this lecture, I will first provide an overview of well-known SKGs, like Microsoft Academic Graph, Dimensions, and others. Then, I will present the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21M publications and 8M patents according to i) the research topics drawn from the Computer Science Ontology, ii) the type of the author's affiliations (e.g, academia, industry), and iii) 66 industrial sectors (e.g., automotive, financial, energy, electronics) from the Industrial Sectors Ontology (INDUSO). Finally, I will showcase a number of tools and approaches using such SKGs, supporting researchers, companies, and policymakers in making sense of research dynamics.

British Library

clarivate

Sci 2011 big_data(30_may13)2nd revised _ loetHan Woo PARK

Decomposing Social and Semantic Networks in Emerging “Big Data” Research

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The International Journal of Business Management and Technology

Big data is prevalent in our daily life. Not surprisingly, big data becomes a hot topic discussedby commercial worlds, media, magazines, general publics and elsewhere. From academic point of view, isit a research area of potential worth being explored? Or it is just another hype? Are there only computer orIS related scholars suitable for big data research due to its nature? Or scholars from other research areas are alsosuitable for this subject? This study aims to answer these questions through the use of informetricsapproach and data source form the SSCI Journal database, leveraging informetric‟s robust natures ofquantitative power of analyze information in any form onto the data source of representativeness. This research shows that big data research is at its growth phase with an exponential growth patternsince 2012 and with great potential for years to come. And perhaps surprisingly, computer or IS relateddisciplinesare not on the top 5 research areas fromthis research results. In fact, the top five research disciplinesare more diversified then expected: business economics (#1), Government Law (#2), InformationScience/ Library Science (#3), Social Science (#4) and Computer Science (#5). Scholars from the USuniversities are the most productive in this subject while Asian countries, including Taiwan, are alsovisible. Besides, this study also identifies that big data publications from SSCI journal database during2005-2015 do fit Lotka‟s law. This study contributes tounderstand the current big data research trends and also show the ways toresearchers who are interested to conduct future research in big data regardless of their research backgrounds.

Data behind figures in AAS journals

Chris Biemesderfer

Substantial amounts of digital data are produced in the scientific enterprise, and much of it is carefully analyzed and processed. Often resulting from a good deal of intellectual effort, many of these highly-processed products are published in the scholarly literature. Many of these data - or more precisely, representations of these data - are committed to the scholarly record in the forms of figures and tables that appear within articles: the AAS journals publish more than 30,000 figures and nearly 10,000 tables each year. For more than a decade, the AAS journals have accepted machine-readable tables that provide the data behind (some of) the tables, and recently the journals have started to encourage the submission of the data behind figures. (See the related poster by Greg Schwarz.) During this time, the journals have been refining techniques for acquiring and managing the digital data that underlie figures and tables. In 2012 the AAS was awarded a grant by the US NSF so that the journals can extend the methods for providing access to these data objects, through a deeper collaboration with the VO and with organizations like DataCite, and by spearheading discussions about the formats and metadata that will best facilitate long-term data management and access. An important component of these activities is educating scientists about the importance and benefits of making such data sets available.

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Researchworkshop

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Philipp Zumstein

Talk at CNI 2015 Spring Membership Meeting in Seattle on April 14th, 2015, see http://www.cni.org/events/membership-meetings/upcoming-meeting/spring-2015/ Abstract: The goal of the InFoLiS project is to connect research data and publications. Links between data and literature are created automatically by means of text mining and made available as Linked Open Data (LOD) for seamless integration into different retrieval systems. This enables scientists to directly access information about corresponding research data in a literature information system, and, vice versa, it is possible to directly find different interpretations and analyses in the literature of the same research data. In our talk, we will describe our methods for generating the links and give insight into the Linked Data infrastructure including the services we are currently building. Most importantly, we will detail how our solutions can be used by other institutions and invite all interested participants to discuss with us their ideas and thoughts on the requirements for these services to ensure broad interoperability with existing systems and infrastructures. InFoLiS is a joint project by the GESIS – Leibniz Institute for the Social Sciences, Cologne, Mannheim University Library, and Mannheim University supported by a grant from the DFG – German Research Foundation.

How to conduct a bibliometric analysis?

Anandhan22

Tutors India offers assistance in quantitative studies and data analyses and explains the steps to conduct a bibliometric analysis. https://www.tutorsindia.com/blog/how-to-conduct-bibliometric-analyses/ Contact Address: UK 10 Park Place, Manchester M4 4EY +44-1143520021 INDIA 10, Kutty Street, Nungambakkam, Chennai – 600034 +91-4448137070 Mail ID- info@tutorsindia.com Social medial address ► Website: https://www.tutorsindia.com/ ► Facebook: https://www.facebook.com/TutorsIndiaGlobalAcademia/ ► Youtube:https://www.youtube.com/@tutorsindia9273 ► Instagram: https://www.instagram.com/tutors_india/ ► Twitter: https://twitter.com/tutorsindia/ ► Linkedin: https://www.linkedin.com/company/tutors-india/ ► Pinterest: https://in.pinterest.com/TutorsIndia/

PDF-How to Conduct Bibliometric Analyses.pdf

Tutors India

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