This document summarizes a presentation about using information visualization in scientometrics. It discusses how visualization can be used to analyze and understand scientometrics data at different levels, from individual to global. It provides examples of tools like Sci2 that can perform network analysis and visualization of scientometrics datasets. Sci2 allows input of various data formats and visualization of temporal, geospatial, topical and network patterns in scientometric data.

1. Information Visualisation and its
Application in Scientometrics
10th
Student Seminar
DRTC
Date : 18-12-2014
Presenter : Manash Kumar

2. Content
●
Introduction
●
Visualisation
●
Information Visualisation
●
Types and Levels of Analysis of InfoVi
● Component of Information Visualisation
● Some Information Visualisation tool
● Purpose of Scientometrics
● Some InfoVis tool for Scientometrics
● Sci2
● Use of Information Visualisation in Scientometrics
● Conclusion

3. It looks like a swirl. There are smaller swirls at the edges. It
has different shades of red at the outside, and is mostly
green at the inside. The smaller swirls have purple highlights.
The green has also different shades. Each small swirl is
composed of even smaller ones. The swirls go clockwise.
Inside the object, there are also red highlights. Those have
different shades of red also. The green shades vary in a fan,
while the purple ones are more uni-color. The green shades
get darker towards the outside of the fan......

5. Visualisation
● Understanding and seeing.
● Defination of visualisation :
– The use of computer-supported, visual
representation of data to amplify cognition
● The purpose of visualisation is insight not the
picture.

6. Visualisation (continued...)
● Visualization is a generic concept in which two
specific domains are distinguished
– Scientific Visualisation = Visualistion applied to
physical data
● Human body, the earth, molecules or other.
– Information Visualisation = Visualisation applied to
abstract data
● For example computer file systems, databases,
documents, stocks etc.

7. Information Visualisation
● Information + Visualisation

10. Why Information Visualisation
● Visualisation
– Enhance cognition
– Answer questions
– Generate hypotheses
– Make decisions
– See data in context
– Expand memory
– Support computational analysis
– Find patterns
– Tell a story

11. Visualisation of famous Napoleon's march to Moscow

12. Visualisation of famous Napoleon's march to Moscow
(continued...)

13. Visualisation of famous Napoleon's march to Moscow
(continued...)

14. Information Visualisation Goals
● Analyse data to support reasoning
● Record information
● Confirm hypotheses
● Communicate ideas to others

15. Different Types of Analysis
● Statistical Analysis / Profiling

16. Different Types of Analysis
(continued...)
● Temporal Analysis (When)

17. Different Types of Analysis
(continued...)
● Geospatial Analysis (Where)

18. Different Types of Analysis
(continued...)
● Topical Analysis (What)

19. Different Types of Analysis
(continued...)
● Network Analysis (With Whom)

20. Different Levels of Analysis
● Micro
Individual Level
● Meso/Local
Group Level
● Macro/Global
Population Level

21. Types of Visualisation
(Reference System)
1.Charts: No reference system—e.g., Wordle.com, pie charts
2. Tables: Categorical axes that can be selected, reordered; cells can be color coded and
might contain proportional symbols. Special kind of graph.
3. Graphs: Quantitative or qualitative (categorical) axes. Timelines, bar graphs, scatter
plots.
4. Geospatial maps: Use latitude and longitude reference system. World or city maps.
5. Network graphs: Node position might depends on node attributes or node similarity. Tree
graphs: hierarchies, taxonomies, genealogies. Networks: social networks, migration flows.

22. Graphic Variable Types
(Representation of Data Attributes)
●
Position
– X, Y and possibly Z Quantitative
●
Form
– Size Quantitative
– Shape Qualitative
●
Color
– Value (Brightness) Quantitative
– Hue Qualitative
– Saturation Quantitative
●
Texture
– Pattern, Rotation, Density Gradient Quantitative
●
Optics
– Crispness, Transparancy, Shading Qualitative

23. Some Information Visualisation Tools
Tool
Interface Open Source
(yes/no)
Operating
System
Distingushing
features
D3js JavaScript library Yes Web browsers, js
engines
Static and
dinamic
presentation
DataScene GUI No Linux, Windows 2D & 3D
graphics,
animated graphs,
data analysis,.
EJS GUI Yes Linux, Mac OS X,
Windows
Ready-to-publish
Java applets
GeoGebra GUI Yes Linux, Mac OS X,
Windows
Useful for
rendering
Geometry,
Graphs,
Statistical
Diagrams
GNU Octave GUI, command
line, C, C++,
Fortran
Yes FreeBSD, Linux,
Mac OS X,
Windows, Solaris
MATLAB
compatible,
extensive user
contributed
toolboxes

24. Some Information Visualisation Tools
Tool Interface Open source
(yes/no)
Operating
System
Distingushing
features
Gnuplot Commandd line,
Python, Ruby,
Smalltalk, third-
party GUIs
Yes Amiga, Atari ST,
BeOS, Linux,
Mac, MS-DOS,
OS/2, OS-9/68k,
Ultrix, Windows,
VMS
Built in scripting
language
MATLAB GUI No Linux, Mac OS X,
Windows
Matrix system
Sci2 GUI Yes Linux, Windos,
Mac OS X
Mapping of
science
Xgraph GUI, Command
line
No Any (web basedd
application)
Online
spreadsheet

25. Scientometrics
Application of Information Visualisation in
Scientometric

26. Scientometrics
● Scientometrics is the study of measuring and
analysing science, technology and innovation.
● Scientometrics involves studies in
– History of science
– Growth of science and scientific institutions
– Behaviour of science and scientists.
– Science policy and decision making

27. Purpose of Scientometrics
● Citation mapping .
● Visualization of the bibliographic coupling among authors .
● Identifying patterns and trends in scientific literature.
● Bibliographic analysis .

28. Information Visualisation of
Scientometrics Data
● Visualisation of scientometrics data helps us to
answer questions such as
– What are the major research areas, experts,
institutions, regions, nations, grants, publications?
– Which areas are most insular?
– What are the main connections for each area?
– What is the relative speed of areas?
– Which areas are the most dynamic/static?
– What new research areas are evolving?
– How does funding influence the number and quality of
publications?

29. Tools Used for Scientometrics
Information Visualisation
Tool Purpose Type
Authormap It is used for citation mapping and visualization Web tool
Bibcouple It is used for visualization of the bibliographic
coupling among authors
Software application
Citespace It is used for visualizing patterns and trends in
scientific literature
Map
HitCite Bibliographic analysis and visualization
software
Software
Science of Science Used for mapping and visualising all types of
scientometrics data
Software

30. Science of Science Tool (Sci2)
● Open source.
● Is a modular toolset specifically designed for
the study of science.
● It supports the
– temporal, geospatial, topical, and network analysis.
– visualization of datasets at the micro (individual),
meso (local), and macro (global) levels.

31. Sci2
● Input data format supported :
– Bibtex (.bib)
– TreeML (.xml)
– CSV (*.csv)
– Edgelist (.edge)
– Endnote Export Format (.enw)
– GraphML (.xml or .graphml)
– ISI (*.isi)
– NSF csv format (.nsf)
– NSF format (.nsf)
– Pajek (*.net)
– Scopus format (*.scopus)
– XGMML (.xml)
– NWB (.nwb)
– Pajek Matrix (.mat)

32. Visualisation Creation by Sci2

33. Work Flow

34. Work Flow (continued...)

35. Work Flow (continued...)

36. Work Flow (continued...)

37. Work Flow (continued...)

38. Another Example of
Scientometrics InfoVis Using Sci2

39. Another Example of Scientometrics
InfoVis Using Sci2 (continued...)

40. Another Example of Scientometrics
InfoVis Using Sci2(continued...)

41. Conclusion
● Information Visualisation can be used in many
different fields.
● There are a number of recently developed
visualization techniques.
● Different technique is used according to user
need and data types.
● Wrong visualistion representation can mislead
interpretation.

42. References
● Garfield,Eugene. (2007), From The Science of Science to
Scientometrics : Visualizing the History of Science with HistCite
Software.
● Elmqvist, Niklas and Tsigas, Philippas. CiteWiz: A Tool for the
Visualization of Scientific Citation Networks.
●
Sci2
Tool. Retrieved on 12th
Dec, 2014 from
https://sci2.cns.iu.edu/user/documentation.php
●
Information Visualization MOOC. Retrieved on 2nd
Dec, 2014
from http://ivmooc2014.appspot.com/course
● Few, Stephen. (2008), What Ordinary People Need Most from
Information Visualization Today.

43. ?