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Table 8.1 provides an overview of existing tools used in scientometrics research, see also (Fekete and Börner-chairs 2004). The tools are sorted by the date of their creation. Domain refers to the field in which they were originally developed such as social science (SocSci), scientometrics (Scientom), biology (Bio), geography (Geo), and computer science (CS). Coverage aims to capture the general functionality and types of algorithms available, e.g., Analysis and Visualization (A+V), see also description column.
Table 8.1 Network analysis and visualization tools commonly used in scientometrics research.

Tool

Year

Domain

Coverage

Description

UI

Open Source

Operating System

References

S&T Dynamics Toolbox

1985

Scientom.

Scientom.

Tools from Loet Leydesdorff for organization analysis, and visualization of scholarly data.

Command-line

No

Windows

(Leydesdorff 2008)

In Flow

1987

SocSci

A + V

Social network analysis software for organizations with support for what-if analysis.

Graphical

No

Windows

(Krebs 2008)

Pajek

1996

SocSci*

A + V

A network analysis and visualization program with many analysis algorithms, particularly for social network analysis.

Graphical

No

Windows

(Batagelj and Mrvar 1998)

UCINet

2000

SocSci*

A + V

Social network analysis software particularly useful for exploratory analysis.

Graphical

No

Windows

(Borgatti, Everett et al. 2002)

Boost Graph Library

2000

CS

Analysis and Manipulation

Extremely efficient and flexible C++ library for extremely large networks.

Library

Yes

All Major

(Siek, Lee et al. 2002)

Visone

2001

SocSci

A + V

Social network analysis tool for research and teaching, with a focus on innovative and advanced visual methods.

Graphical

No

All Major

(Brandes and Wagner 2008)

GeoVISTA

2002

Geo

GeoVis

GIS software that can be used to lay out networks on geospatial substrates.

Graphical

Yes

All Major

(Takatsuka and Gahegan 2002)

Cytoscape

2002

Bio*

Visualization

Network visualization and analysis tool focusing on biological networks, with particularly nice visualizations.

Graphical

Yes

All Major

(Cytoscape-Consortium 2008)

Tulip

2003

CS

Visualization

Graph visualization software for networks over
1,000, 000 elements.

Graphical

Yes

All Major

(Auber 2003)

iGraph

2003

CS

Analysis and Manipulation

A library for classic and cutting edge network analysis usable with many programming languages.

Library

Yes

All Major

(Csárdi and Nepusz 2006)

CiteSpace

2004

Scientom

A + V

A tool to analyze and visualize scientific literature, particularly co-citation structures.

Graphical

Yes

All Major

(Chen 2006)

HistCite

2004

Scientom

A + V

Analysis and visualization tool for data from the Web of Science.

Graphical

No

Windows

(Garfield 2008)

R

2004

Statistics

A + V

A statistical computing language with many libraries for sophisticated network analyses.

Command-line

Yes

All Major

(Ihaka and Gentleman 1996)

Prefuse

2005

Visualization

Visualization

A general visualization framework with many capabilities to support network visualization and analysis.

Library

Yes

All Major

(Heer, Card et al. 2005)

GUESS

2007

Networks

Visualization

A tool for visual graph exploration that integrates a scripting environment.

Graphical

Yes

All Major

(Adar 2007)

GraphViz

2004

Networks

Visualization

Flexible graph visualization software.

Graphical

Yes

All Major

(AT&T-Research-Group 2008)

NWB Tool

2006

Bio,
SocSci, Scientom

A + V

Network analysis & visualization tool conducive to new algorithms supportive of many data formats.

Graphical

Yes

All Major

(Huang 2007)

BibExcel

2006

Scientom

A + V

Transforms bibliographic data into forms usable in Excel, Pajek, NetDraw, and other programs.

Graphical

No

Windows

(Persson 2008)

Publish or Perish

2007

Scientom

Data Collection and Analysis

Harvests and analyzes data from Google Scholar, focusing on measures of research impact.

Web-based

No

Windows, Linux

(Harzing 2008)

TINA

2011

Scientom,
Networks

A + V

Tool for interactive assessment of projects portfolio and visualization of scientific landscapes

Graphical

Yes

All Major

(Cointet 2008)

Many of these tools are very specialized and capable. For instance, BibExcel and Publish or Perish are great tools for bibliometric data acquisition and analysis. HistCite and CiteSpace each support very specific insight needs – from studying the history of science to the identification of scientific research frontiers. The S&T Dynamics Toolbox provides many algorithms commonly used in scientometrics research and it provided bridges to more general tools. Pajek and UCINET are very versatile, powerful network analysis tools that are widely used in social network analysis. Cytoscape is excellent for working with biological data and visualizing networks.

The Network Workbench Tool has fewer analysis algorithms than Pajek and UCINET, and less flexible visualizations than Cytoscape. Network Workbench, however, makes it much easier for researchers and algorithm authors to integrate new and existing algorithms and tools that take in diverse data formats. The OSGi (http://www.osgi.org) component architecture and CIShell algorithm architecture (http://cishell.org) built on top of OSGi make this possible. Cytoscape is also adopting an architecture based on OSGi, though it will still have a specified internal data model and will not use CIShell in the core. Moving to OSGi will make it possible for the tools to share many algorithms, including adding Cytoscape's visualization capabilities to Network Workbench.

Several of the tools listed in the table above are also libraries. Unfortunately, it is often difficult to use multiple libraries, or sometimes any outside library, even in tools that allow the integration of outside code. Network Workbench, however, was built to integrate code from multiple libraries (including multiple versions of the same library). For instance, two different versions of Prefuse are currently in use, and many algorithms use JUNG (the Java Universal Network/Graph Framework). We feel that the ability to adopt new and cutting edge libraries from diverse sources will help create a vibrant ecology of algorithms.

Although it is hard to discern trends for tools which come from such diverse backgrounds, it is clear that over time the visualization capabilities of scientometrics tools have become more and more sophisticated. Scientometrics tools have also in many cases become more user friendly, reducing the difficulty of common scientometrics tasks as well as allowing scientometrics functionality to be exposed to non-experts. Network Workbench embodies both of these trends, providing an environment for algorithms from a variety of sources to seamlessly interact in a user-friendly interface, as well as providing significant visualization functionality through the integrated GUESS tool.

Many other tools are available outside the scope of network analysis that are still useful for studying the data of science. One such tool is the web-based Data Science Toolkit, a web-based collection of open-source data sets and tools which allows the user to query for geographical data, parse text, and run named entity recognition.

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