Visualizing the Structure of Science [electronic resource] /by Benjamín Vargas-Quesada, Félix de Moya-Anegón.
by Vargas-Quesada, Benjamín [author.]; Moya-Anegón, Félix de [author.]; SpringerLink (Online service).
Material type:
BookPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.Description: VII, 311 p. 81 illus. online resource.ISBN: 9783540697282.Subject(s): Computer science | Science -- Philosophy | Information systems | Social sciences -- Data processing | Computer Science | User Interfaces and Human Computer Interaction | Philosophy of Science | Computer Appl. in Social and Behavioral Sciences | Information Systems Applications (incl.Internet)DDC classification: 005.437 | 4.019 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| QA76.9.H85 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | QA76.9.U83 (Browse shelf) | Available |
Visualization -- Visualization of Scientific Information and Domain Analysis -- Methodological Aspects Previous to Scientography -- Material Used -- Methodology -- Results -- Discussion -- Epilogue.
Constructing a great map of the sciences has been a persistent dream since the Middle Ages. In modern times this need has become even more urgent because of the requirement to combine and link research in adjacent areas, often resulting in new disciplines such as bioinformatics and nanotechnologies. Computer visualization helps humans to perceive and understand large and complex structures, such as molecular structures or data dependencies. Vargas-Quesada and Moya-Anegón propose a methodology for visualizing large scientific domains. They create science maps, so-called "scientograms", based on the interactions between authors and their papers through citations and co-citations, using approaches such as domain analysis, social networks, cluster analysis and pathfinder networks. The resulting scientograms offer manifold possibilities. Domain analysts can discover the most significant connections between categories of a given domain, and they can also see how these categories are grouped into major thematic areas and how they are interrelated through a logical internal, while information scientists or researchers new to an area may appreciate a durable image of the essential structure of a domain.
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