Downloaded 37 times
Uploaded byGauhati University
information visualisation and its application in scientometrics
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.
More Related Content
Similar to information visualisation and its application in scientometrics
information visualisation and its application in scientometrics
- 1. Information Visualisation andits Application in Scientometrics 10th Student Seminar DRTC Date : 18-12-2014 Presenter : Manash Kumar
- 2. Content ● Introduction ● Visualisation ● Information Visualisation ● Types andLevels 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 likea 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 andseeing. ● 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...) ● Visualizationis 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 famousNapoleon's march to Moscow
- 12. Visualisation of famousNapoleon's march to Moscow (continued...)
- 13. Visualisation of famousNapoleon'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 ofAnalysis ● Statistical Analysis / Profiling
- 16. Different Types ofAnalysis (continued...) ● Temporal Analysis (When)
- 17. Different Types ofAnalysis (continued...) ● Geospatial Analysis (Where)
- 18. Different Types ofAnalysis (continued...) ● Topical Analysis (What)
- 19. Different Types ofAnalysis (continued...) ● Network Analysis (With Whom)
- 20. Different Levels ofAnalysis ● Micro Individual Level ● Meso/Local Group Level ● Macro/Global Population Level
- 21. Types of Visualisation (ReferenceSystem) 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 (Representationof 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 VisualisationTools 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 VisualisationTools 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 InformationVisualisation in Scientometric
- 26. Scientometrics ● Scientometrics isthe 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 ScientometricsData ● 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 forScientometrics 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 ScienceTool (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 dataformat 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.
- 33.
- 34.
- 35.
- 36.
- 37.
- 38. Another Example of ScientometricsInfoVis Using Sci2
- 39. Another Example ofScientometrics InfoVis Using Sci2 (continued...)
- 40. Another Example ofScientometrics InfoVis Using Sci2(continued...)
- 41. Conclusion ● Information Visualisationcan 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.