SciMAT: A New Science Mapping Analysis Software Tool

M.J. Cobo, A.G. Lopez-Herrera, E. Herrera-Viedma, and F. Herrera

Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, E-18071-Granada, Spain. E-mail: {mjcobo, lopez-herrera, viedma, herrera}

This article presents a new open-source software tool, SciMAT, which performs science mapping analysis within a longitudinal framework. It provides different modules that help the analyst to carry out all the steps of the science mapping workflow. In addition, SciMAT presents three key features that are remarkable in respect to other science mapping software tools: (a) a powerful preprocessing module to clean the raw bibliographical data, (b) the use of bibliometric measures to study the impact of each studied element, and (c) a wizard to configure the analysis.


Science mapping, or bibliometric mapping, is an important research topic in the field of bibliometrics (Morris & Van Der Veer Martens, 2008; van Eck & Waltman, 2010). It is a spatial representation of how disciplines, fields, specialties, and individual documents or authors are related to one another (Small, 1999). It is focused on monitoring a scientific field and delimiting research areas to determine its cognitive structure and its evolution (Noyons, Moed, & van Raan, 1999). In other words, science mapping aims at displaying the structural and dynamic aspects of scientific research (Borner, Chen, & Boyack, 2003; Morris & Van Der Veer Martens, 2008; Noyons, Moed, & Luwel, 1999).

It is common to find scientific papers and reports that contain a science mapping analysis to show and uncover the hidden key elements (e.g., documents, authors, institutions, topics, etc.) in a specific interest area (Bail6n-Moreno, Jurado-Alameda, & Ruiz-Banos, 2006; L6pez-Herrera, Cobo, Herrera-Viedma, & Herrera, 2010; L6pez-Herrera et al., 2009; Porter & Youtie, 2009; van Eck & Waltman, 2007). Some of these works were undertaken for academic purposes and others with competitive animus, such as those related to patent analysis in R&D business departments (Porter & Cunningham, 2004).

Received June 15, 2011; revised February 23, 2012; accepted February 28, 2012

© 2012 ASIS&T Published online in Wiley Online Library ( DOI: 10.1002/asi.22688

Currently, some studies use nonspecific science mapping software (e.g., Pajek, Gephi, or UCINET), and others use specific (and sometimes ad hoc) science mapping software tools (e.g., CoPalRed, Science of Science Tool, or VOS-viewer). A list of software tools widely used in the literature can be found in Borner et al. (2010) and Cobo, L6pez-Herrera, Herrera-Viedma, and Herrera (2011b).

In Cobo et al. (2011b), we presented an analysis of the features, advantages, and drawbacks of the different science mapping software tools available. As a result, we concluded that there was no single science mapping software tool powerful and flexible enough to incorporate all the key elements (data retrieval, preprocessing, network extraction, normalization, mapping, analysis,visualization, and interpretation) in any science mapping workflow (Cobo et al., 2011b). Therefore, researchers usually have to use more than one (and sometimes several) software tools to perform a deep science mapping analysis. For example, it is common practice to use an ad hoc software tool to clean the data in the preprocessing stage, then to apply another tool to build the science maps, and sometimes it is necessary to use a third-party software tool to visualize, navigate, and interact with the results.

Bibliometric measures and indicators can be employed to carry out a performance analysis of the generated maps (Cobo, L6pez-Herrera, Herrera-Viedma, & Herrera, 2011a). This kind of analysis allows us to quantify and measure the performance, quality, and impact of the generated maps and their components, as shown in Cobo, L6pez-Herrera, Herrera, and Herrera-Viedma (2012).

In this article, we present a new open-source1 science mapping software tool called SciMAT 2 (Science Mapping Analysis software Tool) which incorporates methods, algorithms, and measures for all the steps in the general science mapping workflow, from preprocessing to the visualization of the results. SciMAT allows the user to carry out studies based on several bibliometric networks (co-word, cocitation,


Data retrieval


Network extraction






ISIWoS Scopus PubMed Etc.

De-duplicating Data reduction Etc.


Coupling Direct linkage Etc.

Association Strength Equivalence Index Salton’s Cosine Etc.

Clustering PFNets Etc.

Network Geospatial Temporal Burst detection


FIG. 1. Workflow of science mapping.

author cocitation, journal cocitation, coauthor, bibliographic coupling, journal bibliographic coupling, and author bibliographic coupling). Different normalization and similarity measures can be used over the data (association strength, Equivalence Index, Inclusion Index, Jaccard Index, and Sal-ton’s cosine). Several clustering algorithms can be chosen to cut up the data. In the visualization module, three representations (strategic diagrams, cluster networks, and evolution areas) are jointly used, which allows the user to better understand the results. Furthermore, SciMAT presents three key features that other science mapping software tools either do not have or have only in limited form:

This article is organized as follows. The general workflow of a science mapping analysis is described, and some representative software tools developed for this kind of analysis are briefly shown. Next, we describe SciMAT, together with its main characteristics, functionalities, architecture, and technologies used. We then present some possible scenarios where SciMAT could be employed and conclude this section with a real-use case, showing the flow of steps needed to carry out the analysis with SciMAT, and some conclusions that an analyst could draw from interpretation of the results. The results of a formal validation of SciMAT are shown, and some concluding remarks are made.

Foundations of Science Mapping

In this section, different aspects of the science mapping analysis are described. First, the general workflow in a science mapping analysis is shown, describing each step. Second, a brief review of science mapping software tools is carried out.

The Workflow of Science Mapping

The general workflow in a science mapping analysis has different steps (Borner et al., 2003; Cobo et al., 2011b) (see Figure 1): data retrieval, data preprocessing, network extraction, network normalization, mapping, analysis, and visualization. At the end of this process, the analyst has to interpret and obtain conclusions from the results.

Nowadays, there are several online bibliographic (and also bibliometric) databases where the data can be retrieved, with the ISI Web of Science3 (ISIWoS), Scopus,4 Google Scholar,5 and the National Library of Medicine’s MEDLINE6 being the most important. Moreover, a science mapping analysis can be made using patents (e.g., by downloading the data from the U.S. Patent and Trademark Office7 or from the European Patent Office8) or funding data (e.g., from the National Science Foundation9).

Usually, the data retrieved from the bibliographic sources contain errors, so a preprocessing process must be applied first. In fact, the preprocessing step is one of the most important to obtain good results in science mapping analysis. Different preprocessing processes can be applied to the raw data, such as detecting duplicate and misspelled items, time slicing, data reduction, and network reduction (for more information, see Cobo et al., 2011b). The data reduction is carried out to select the most representative data for the analysis, so it is performed after the de-duplicating process.

Once the data has been preprocessed, a network is built using a unit of analysis, with journals, documents, cited references (full reference, author’s reference, or source reference can be used), authors (author’s affiliation also can be used), and descriptive terms or words (Borner et al., 2003) being the most common.

Several relations among the units of analysis can be established, such as co-occurrence, coupling, or direct linkage. A co-occurrence relation is established between two units (authors, terms, or references) when they appear together in a set of documents; that is, when they co-occur throughout the corpus. A coupling relation is established between two documents when they have a set of units (authors, terms, or references) in common. Furthermore, the coupling can be established using a higher level unit of aggregation, such as authors or journals. That is, a coupling between two authors or journals can be established by counting the units shared by their documents (using the author’s or journal’s oeuvres). Finally, a direct linkage establishes a relation between documents and references, particularly a citation relation.

These relations can be represented as a graph or network, where the units are the nodes, and the relations between them represent an edge between two nodes. In the co-occurrence relation, the nodes can be authors, terms, or references whereas in the coupling relation, the nodes are documents, and in the aggregated coupling relation, the nodes can be authors or journals. (Other units can be selected as aggregation data.)

In addition, different aspects of a research field can be analyzed depending on the units of analysis used and the kind of relation selected (Cobo et al., 2011b). For example, using the authors, a coauthor or coauthorship analysis can be performed to study the social structure of a scientific field (Ganzel, 2001; Peters & van Raan, 1991). Using terms or words, a co-word (Callon, Courtial, Turner, & Bauin, 1983) analysis can be performed to show the conceptual structure and the main concepts dealt with by a field. Cocitation (Small, 1973) and bibliographic coupling (Kessler, 1963) are used to analyze the intellectual structure of a scientific research field. A description of these techniques and networks can be found in Cobo et al. (2011b).

When the network of relationships between the selected units of analysis has been built, a normalization process is needed (van Eck & Waltman, 2009). Different measures have been used in the literature to normalize a bibliometric network: Salton’s cosine (Salton & McGill, 1983), Jaccard Index (Peters & van Raan, 1993), Equivalence Index (Callon, Courtial, & Laville, 1991), and association strength (Coulter, Monarch, & Konda, 1998; van Eck & Waltman, 2007).

Once the normalization process is finished, we can apply different techniques to build the science map. Dimensionality reduction techniques such as principal component analysis or multidimensional scaling, clustering algorithms (Chen et al., 2010; Chen & Redner, 2010; Coulter et al., 1998; Kandylas, Upham, & Ungar, 2010; Rosvall & Bergstrom, 2010; Small and Sweeney, 1985), and Pathfinder networks (PFNETs) (Quirin, Cordon, Santamaria, Vargas-Quesada, & Moya-Anegon, 2008; Schvaneveldt, Durso, & Dearholt, 1989) have been widely used (Borner et al., 2003).

Analysis methods for science mapping allow us to discover useful knowledge from data, networks, and maps (Cobo et al., 2011b). There are different analysis methods, such as network analysis (Carrington, Scott, & Wasserman, 2005; Cook & Holder, 2006; Skillicorn, 2007; Wasserman & Faust, 1994), temporal or longitudinal analysis (Garfield, 1994; Price & Gursey, 1975), geospatial analysis (Batty, 2003; Leydesdorff & Persson, 2010; Small & Garfield, 1985), performance analysis (Cobo et al., 2011a), and so on. Each kind of analysis allows us to discover different views and knowledge. In addition, these analyses can be applied over the maps or directly over the networks. For example, the network analysis can measure the centrality of a given node on the whole network, or the centrality of a cluster (if a cluster algorithm was applied to build the map) on the map. The results of the analysis methods can even be used to build the map. In this sense, the geospatial analysis can help to lay out the elements over a geographical map. Similarly, the network analysis can be used to lay out the map elements according to certain network measures.

As described in Cobo et al. (2011a), the performance analysis uses bibliometric measures and indicators (based on citations), such as the h-index (Alonso et al., 2009; Hirsch, 2005), g-index (Egghe, 2006), hg-index (Alonso et al., 2010), or q2-index (Cabrerizo et al., 2010) to quantify the importance, impact, and quality of the different elements of the maps (e.g., clusters), and also of the network. For this reason, a set of documents has to be added to each element of the whole network and map.

In a bibliometric network, each node (unit of analysis) could have an associated set of documents. With this set of documents, a performance analysis could be carried out. For example, we could calculate the amount of documents associated with a node, the citations achieved by those documents, the h-index, and so on.

As mentioned earlier, the whole network is usually split into subnetworks or clusters. To obtain performance indicators of each subnetwork, we need a list/set of documents associated to the whole subnetwork. So, we need to aggregate the set of documents of all nodes in the subnetwork into a single one. To do that, an aggregation function, called document mapper in this article, has to be defined.

Different document mapper functions can be defined:

[1], [2], [3], [4], [7]

[5], [6], [8], [9]

[2], [3]

[1], [2], [3], [4], [5], [6], [7],

[8], [9]


As a visual example, suppose the subnetwork shown in Figure 2. Each sphere represents a node, and its associated documents are placed inside. If we want to assign a set of document to the subnetwork, a document mapper function must be applied. The result of the five aforementioned document mappers are shown in Table 1.

Following the science mapping workflow, the visualization techniques are used to represent a science map and the result of the different analyses. The visualization technique employed is very important to allow a good understanding and better interpretation of the output. The network results from the mapping step can be represented with different visualization tools such as, for example, heliocentric maps (Moya-Anegon et al., 2005), geometrical models (Skupin, 2009), thematic networks (Bailon-Moreno et al., 2006; Cobo et al., 2011a), or maps where the proximity between items represents their similarity (Davidson, Hendrickson, Johnson, Meyers, & Wylie, 1998; Fabrikant, Montello, & Mark, 2010; Polanco, Francois, & Lamirel, 2001; van Eck & Waltman, 2010). The clusters detected in a network can be categorized using a strategic diagram (Callon et al., 1991; Cobo et al., 2011a). To show the evolution of detected clusters in successive time periods (temporal or longitudinal analysis), different visualization techniques have been used: cluster string (Small, 2006; Small & Upham, 2009; Upham & Small, 2010), rolling clustering (Kandylas et al., 2010), alluvial diagrams (Rosvall & Bergstrom, 2010), ThemeRiver visualization (Havre, Hetzler, Whitney, & Nowell, 2002), and thematic areas (Cobo et al., 2011a). Furthermore, visualization can be improved using the results of a performance analysis, which allows us to add a third dimension to the visualized elements. For example, the strategic diagram can show spheres whose volume is proportional to the citations achieved by each cluster.

Note that although the visualization and mapping steps are different, they are also interdependent. The visualization technique used will vary depending on the method selected to build the map. For example, the strategic diagram only visualizes maps built with a clustering algorithm.

Finally, when the science mapping analysis is finished, the analysts have to interpret the results and maps using their experience and knowledge. In the interpretation step, the analyst aims to discover and extract useful knowledge that could be used to make decisions.

Tools for Science Mapping Analysis

Science mapping analysis can be carried out with different software tools. Some general software tools not specifically designed for science mapping analysis can be employed for this task (Borner et al., 2010), such as Pajek (Batagelj & Mrvar, 1998), Gephi (Bastian, Heymann, & Jacomy, 2009), UCINET (Borgatti, Everett, & Freeman, 2002), or Cytoscape (Shannon et al., 2003). However, there are a variety of software tools specifically developed to perform a science mapping analysis.

In Cobo et al. (2011b), we describe and compare nine representative science mapping software tools: Bibexcel (Persson, Danell, & Wiborg Schneider, 2009), CiteSpace II (Chen, 2004, 2006), CoPalRed (Bailon-Moreno et al., 2006; Bailon-Moreno, Jurado-Alameda, Ruiz-Banos, & Courtial, 2005), IN-SPIRE (Wise, 1999), Loet Leydes-dorff’s software, Network Workbench Tool (Borner et al., 2010; Herr, Huang, Penumarthy, & Borner, 2007), Science of Science Tool (Sci2Team, 2009), VantagePoint (Porter & Cunningham, 2004), and VOSviewer (van Eck & Waltman, 2010).

These tools have different characteristics and implement different methods and algorithms. Consequently, we can make the following points:

We therefore think it would be desirable to develop a science mapping software tool that satisfies the following requirements: (a) it should incorporate modules to carry out all the steps of the science mapping workflow, (b) it should present a powerful de-duplicating module, (c) it should be able to build a large variety of bibliometric networks, (d) it should be designed with good visualization techniques, and (e) it should enrich the output with bibliometric measures. We have taken into account all these requirements in the development of SciMAT.


SciMAT is a new, open-source science mapping software tool that implements the aforementioned software requirements. It can be freely downloaded, modified, and redistributed according to the terms of the GPLv3 license. The executable file, user guide, and source code can be downloaded through its Web site ( scimat).

SciMAT is based on the science mapping analysis approach presented in Cobo et al. (2011a), which allows us to carry out science mapping studies under a longitudinal framework (Garfield, 1994; Price & Gursey, 1975). Although this approach was originally developed to carry out a conceptual science mapping analysis, we have extended it in SciMAT to perform any kind of science mapping analysis (including intellectual and social). This science mapping analysis approach establishes the following steps (Cobo et al., 2011a):

The main characteristics of SciMAT are:

In the following subsections, we describe the SciMAT software tool. First, the structure of its knowledge base is analyzed in detail. Second, the architecture and the different algorithms and methods provided by SciMAT to perform a science mapping analysis are described. The technologies used in the development of the tool then are summarized.

SciMAT generates a knowledge base from a set of scientific documents where the relations of the different entities related to each document (authors, keywords, journal, references, etc.) are stored. This structure helps the analyst to edit and preprocess the knowledge base to improve the quality of the data and, consequently, obtain better results in the science mapping analysis.

The knowledge base is composed of 16 entities: Affiliation, Author, Author Group, Author-Reference, AuthorReference Group, Document, Journal, Publish Date, Period, Reference, Reference Group, Reference-Source, ReferenceSource Group, Subject-Category, Word, and Word Group.

The principal entity is the Document, which represents a scientific document (usually articles, letters, reviews, or proceedings papers). It contains information such as the title, abstract, doi, citations, and so on. The Document has a variety of information associated with it, such as the authors, affiliations, keywords, cited references, the journal (or conference), and the publication year. Each one is considered an entity in the knowledge base.

The Author is the entity that represents the person who has been involved in the development of a Document. An Author can be associated with a set of Documents, and similarly a Document can have a set of Authors. Furthermore, an Author has an associated position in his or her Documents.

The Affiliation represents the author’s affiliations. Given that the authors may work in different places (universities, institutes, etc.) during their research, an Author has a set of associated Affiliations.

Usually, the scientific documents have a set of keywords associated with them, commonly provided by the authors (author’s words). Moreover, depending on the bibliometric database used to retrieve the data, the documents may contain descriptive words provided by the database (source’s words). For example, ISIWoS adds a set of keywords called ISI Keywords PLUS to each document. In addition, sometimes the analyst needs to add more words to those documents which contain few descriptive terms (words). These words can be selected from the title, abstract, or body of the document, or they can be added manually. In our context, this set of words will be called added words. In this sense, the entity Word represents a descriptive term of a document. A set of Words can appear in different Documents, and each Document can have a set of Words. Each Word can have different roles in the Documents in which it appears. In this way, a Document can have words provided by the authors (author’s words role), provided by the database (source’s words role), or added in the preprocessing step (added words role).

The entity Reference represents the intellectual base of a scientific document. Similarl to the entity Word, a Document has a set of References associated with it, and each Reference can be presented in different Documents. The References can often be divided into small pieces of information.

Eugene Garfield

Garfield, E.

Eugene Garfield

Garfield, E.

Depending on the database used to retrieve the data, these pieces may be different, but some information appears more often, such as author, journal, and year. For this reason, there are two entities related to the Reference: the AuthorReference and the Source-Reference.

Other entities associated with a Document are Journal and Publish Date. Logically, a Document can have only one Journal (or conference) and one Publish Date associated with it whereas both entities can have one set of Documents associated. Moreover, the Journal and Publish Date entities have an associated Subject Category which represents a global category, often given by the bibliometric database, that classifies the journal into the main knowledge categories. The Journal can be associated with many Subject Categories, and this relation can change over the years. That is, it is possible for a journal to have a different category associated to it each year.

The entity Period represents a set of (not necessarily disjointed) years. Usually, a set of Periods is defined to perform a longitudinal science mapping analysis (Garfield, 1994).

Note that five of the aforementioned entities can be used as a unit of analysis in the science mapping analysis carried out by SciMAT: Author, Word, Reference, AuthorReference, and Source-Reference. These entities should be carefully preprocessed, paying special attention to the misspelling and de-duplicating process. Usually, the de-duplicating process joins the similar items, so only one of them remains. For example, suppose that two items, Garfield, E. and Eugene Garfield, are stored in the knowledge base. Both items represent the same author and therefore should be joined (joining its association with the other entities). But, when two items are joined, only one of them is kept in the knowledge base (obviously, this item contains the association of the second item), and it is impossible to know the initial items joined. For this reason, our knowledge base provides the concept of group for each unit of analysis. A group is a set of items that represents the same entity. Thus the knowledge base contains five kinds of groups: Author Group, Word Group, Reference Group, Author-Reference Group, and Source-Reference Group. A group can be marked as stop group, in which case it will not be taken into account in the science mapping analysis.

An example of groups and how they help in the de-duplicating process is shown in Figure 3. On the left, we can see the items before being processed, and on the right we can see the group items. The shadow ellipse represents the

File of Groups < Export




Module to manage KB

Analysis wizard



FIG. 4. Architecture of SciMAT.

group (in this case, an Author Group), and the remaining ellipses represent the entities (Authors) associated with this group.

Architecture, Modules, Functionalities, and Algorithms

In this subsection, we describe the architecture of SciMAT, showing the tool’s inner workings and how its modules interact. We also describe the different functionalities and algorithms available in SciMAT.

Internally, SciMAT is composed of several independent modules that interact with each other to carry out a science mapping analysis. Some modules are involved in the management of the graphical user interface (GUI) as well as the interaction with the user. Other modules are not visible to the user, these being the core of SciMAT. Figure 4 illustrates the architecture of SciMAT. The shaded boxes represent the modules responsible for the management of the GUI.

The main core of SciMAT is made up of:

The model is the bridge between the GUI and the knowledge base stored in the database. To perform its function, the model uses two pattern designs: Data Access Object (DAO) and Data Transfer Object (DTO). The DAOs are responsible for communication with the database, performing the editing and selecting operations. The DTOs represent the different entities (discussed earlier) of the knowledge base, and they are used to transmit the information from the database to the different methods that make use of it.

Another important element of the core of SciMAT is its API, which contains all the necessary methods to carry out a science mapping analysis with several configurations. Thanks to the object-oriented programming techniques used in the development of SciMAT, the API can be easily extended and improved to add new methods, algorithms, and measures. Specifically, the API provides methods to import data from different formats to the knowledge base, various methods to filter the data which will be used in the analysis (data and network reduction), several techniques to build the bibliometric network, the most common measures to normalize the network, different clustering algorithms to construct the map, several analysis techniques, and various kinds of visualizations. That is, the SciMAT API contains the necessary methods, techniques, and measures to develop a specific science mapping analysis.

The SciMAT API also can be used by an advanced user to develop ad hoc tools or specific scripts that allow him or her to configure and carry out a science mapping analysis. In this way, the advanced user can develop his or her own algorithms or a new loader to read his or her own data and import it into the knowledge base of SciMAT.

Taking into account the GUI, there are three important modules: (a) a module dedicated to the management of the knowledge base and its entities, (b) a module (wizard) responsible for configuring the science mapping analysis, and (c) a module to visualize the generated results and maps. These modules allow the analyst to carry out the different steps of the science mapping workflow.

Module to manage the knowledge base. This module contains the necessary methods and algorithms responsible for the management of the knowledge base. As mentioned earlier, it communicates with the database (knowledge base) through the model, which responds to the request returning a DTO object. Moreover, the different actions performed by the user in the knowledge base are carried out using the model through its DAO object.

Regarding its functionalities, the module to manage the knowledge base is responsible for building the knowledge base, importing the raw data from different bibliographical

Module to manage the knowledge base

FIG. 5. SciMAT workflow.

sources, and cleaning and fixing the possible errors in the entities. It can be considered as a first stage in the preprocessing step.

This module incorporates loaders to read bibliographical information exported from bibliometric sources, such as ISIWoS and Scopus (RIS format). Moreover, it is possible to import data from a specific CSV format. SciMAT uses these loaders to import the bibliographical data to a new or an existing knowledge base.

Each entity can be edited, and its attributes and associations can be modified. Furthermore, by means of Groups, the de-duplicating step can be performed. The user can join the items that represent the same entity, under the same group. In addition, this module incorporates methods to help the analyst in the de-duplicating process, such as finding similar items by plural or by Levenshtein distance, or importing the groups and their associated items from a file (in XML format). Finally, the time-slicing step is performed using the Period entity.

Wizard to configure the science mapping analysis. This wizard is one of the most important modules of the GUI. It is responsible for generating a particular configuration for carrying out the science mapping analysis so that the user can select the methods and algorithms that will be used to perform each step. Once the user has specified the desired configuration, it is sent to the module responsible for carrying out the analysis, which, using the SciMAT API, will perform the science mapping analysis. Finally, the results are stored in a file and are then sent to the visualization module.

Although the wizard has been implemented according to the steps of the science mapping workflow (see Figure 1), some steps are performed in a different order. For example, the de-duplicating and time-slicing preprocessing has to be done earlier, using the knowledge base manager. To summarize, the SciMAT workflow is implemented as in Figure 5.

As shown, the workflow is divided into four main stages: (a) to build the data set, (b) to create and normalize the network, (c) to apply a cluster algorithm to get the map, and (d) to perform a set of analyses. These stages and their respective steps are described below:

(a) Select the way in which the network will be built: The network can be built using different methods such as co-occurrence, coupling, and aggregated coupling. Depending on the unit of analysis selected and the kind of relation chosen, different bibliometric networks can be built. For example, if the network is built using words as the unit of analysis and co-occurrence as the relation, a co-word bibliometric network will be built. Likewise, if references are selected as the unit of analysis and coupling as the relation, a bibliographic coupling network will be built. In Table 2, a summary of the kinds of networks available depending on the configuration of unit and relation is shown. As can be seen, SciMAT is able to build the most common bibliometric networks used in the literature, as well as other advanced and less common bibliometric networks (marked as an asterisk in Table 2). For example, selecting words as the unit of analysis and coupling as the relation, a conceptual coupling network could be built; moreover, it could be aggregated by authors or journals. Although this kind of bibliometric network is not frequently required, SciMAT enables its construction.

, _X ej

the network. S can be defined as d = 100-=—, with n

i andj items belonging to the cluster and n the number of items in the theme. These measures are useful to categorize the detected clusters of a given period in a strategic diagram (Cobo et al., 2011a).

SciMAT incorporates five different document mappers for co-occurrence networks: (a) core mapper (Cobo et al., 2011a), (b) secondary mapper (Cobo et al., 2011a), (c) k-core mapper, (d) union mapper, and (e) intersection mapper.

For coupling networks, SciMAT has two kinds of document mappers depending on the kind of coupling used. That is, if a basic coupling has been selected (each item of the cluster will be a document), the basic coupling document mapper is the only one available, which adds the items of the cluster as documents. If an aggregated coupling is selected, the aggregated coupling document mapper can be selected, which adds the documents associated with its items to each cluster (author’s or journal’s oeuvres). Note that each node of the cluster also has a set of associated documents. These documents correspond to the set of documents associated with the item (node) in the corresponding dataset.

Once the sets of documents have been associated to each cluster, a set of performance bibliometric measures can be added to each set. SciMAT adds by default the number of documents as the performance measure. Moreover, the citations of a set of documents are used to assess the quality and impact of the clusters. In this sense, basic measures such as the sum, minimum, maximum, and average citations or complex measures such as the h-index (Alonso et al., 2009; Hirsch, 2005), g-index (Egghe, 2006), hg-index (Alonso et al., 2010), or q2-index (Cabrerizo et al., 2010) can be used, even simultaneously.

Visualization module. This module is responsible for showing the results obtained by the system (using the SciMAT API) and helping the user to analyze and interpret them. It allows the user to navigate and interact with the results, focusing on those aspects that he or she wants to analyze in detail.

Different visualization techniques are available, such as the strategic diagram, cluster network, evolution map, and overlapping map. The strategic diagram (Figure 6a) shows the detected clusters of each period in a two-dimensional space, and categorizes them according to their Callon’s density and centrality measures. Each cluster in the strategic diagram can be enriched by the bibliometric measures selected in the wizard. The associated network for each cluster is shown as well (for a graph of the relationship between its items, see Figure 6b).

The results of the temporal or longitudinal analysis are shown using an evolution map and an overlapping-items graph (see Figure 7). As an example, in Figure 7a, we can observe two different evolution areas delimited by differently shaded shadows. One is composed of Cluster A1 and Cluster A2, and the other is composed of clusters Cluster B1, Cluster B2, and Cluster C2. Cluster D1 is discontinued, and Cluster D2 is considered to be a new cluster. The solid lines (Lines 1 and 2) mean that the linked cluster shares the



Highly developed and isolated cluster

Motor clusters


Basic and

Emerging or

declining clusters transversal clusters

FIG. 6. The strategic diagram and cluster network. (a) The strategic diagram. (b) An example of a cluster network.

main item (usually the most significant one). A dotted line (Line 3) means that the themes share elements that are not the main item. The thickness of the edges is proportional to the Inclusion Index, and the volume of the spheres is proportional to the number of published documents associated with each cluster. Following this example, in Figure 7b, the overlapping-items graph across the two consecutive periods is shown. The circles represent the periods and their number of associated items (unit of analysis). The horizontal arrow represents the number of items shared by both periods, the Stability Index between them is shown in parentheses. The upper incoming arrow represents the number of new items in Period 2, and the upper outgoing arrow represents the items that are presented in Period 1, but not in Period 2.

The visualization module can build a report in HTML or LATEX format using the API. The images (strategic diagrams, overlapping-items map, etc.) are exported in PNG and SVG formats so the user can easily edit them. Furthermore, the cluster networks and evolution maps are exported in Pajek format.


SciMAT has been programmed in Java. This allows the tool to run on any platform such as Windows, MacOS, Linux, and so on.


Period 2

Period 1

Cluster A1

luster A2

Cluster C2

Cluster D1

Cluster B1


Cluster B2

Cluster D2

FIG. 7. Examples of evolution. (a) Evolution areas. (b) Stability between periods. [Color figure can be viewed in the online issue, which is available at]

Taking into account the programming technique used, SciMAT has been developed under the object-oriented methodology. Furthermore, different design patterns have been used, such as the pattern Observable, Observer, Command, Edit, Singleton, Factory, Data Access Object, Data Transfer Object, and so on. With the use of these patterns, and the abstract techniques employed, SciMAT can be extended easily to incorporate new methods and algorithms.

Furthermore, SQLite (Kreibich, 2010) has been used as database engine to store the knowledge base. SQLite is a public-domain software package that provides a relational database management system. It has some good properties—serverless, zero configuration, cross-platform, self-contained, small runtime footprint, transactional, highly reliable (Kreibich, 2010)—and has minimum system requirements. Thanks to these capabilities, the SciMAT knowledge base can be opened with any database browser that reads SQLite files.

Finally, SciMAT uses different technologies such as SVG, HTML, and XML to export the result obtained in the performed analysis.

Scenarios and Potential-Use Case Using SciMAT

As mentioned earlier, science mapping analysis is a useful technique to discover the social, intellectual, and conceptual aspects of research fields, specialties, or individual documents or authors. Furthermore, the combined use of bibliometric indicators helps to quantify their impact and quality.

There are several academic scenarios in which SciMAT could be applied:

In these scenarios, SciMAT could help the analyst to obtain tentative results. However, for a better, more precise analysis, the analyst has to perform a careful preprocessing process over the units of analysis. Moreover, an adequate selection of the parameters used in the analysis must be done. In any case, the analyst should check if SciMAT’s features are suitable to his or her problem.

To show how SciMAT could help in a real scenario, we next illustrate the strength of SciMAT through one of these possible scenarios of use. Specifically, we focus on a researcher who would like to know the research issues raised in a particular field or area to understand and deepen his or her knowledge of the field and identify the hot topics.

Analyzing a Field: A Practical Example

Suppose that a researcher is interested in a new research field. Moreover, he or she would like to know the themes on which the research community is working hard, those that are highly cited, and those that seem to be disappearing. With SciMAT, that researcher could discover the themes closet to his or her current research and those most appropriate for the investment of his or her effort. For example, suppose that the researcher wants to analyze the fuzzy sets theory (FST) field (Zadeh, 1965, 2008).

We could analyze FST using the papers published in the two most important and prestigious journals in the field, according to their impact factor: Fuzzy Sets and Systems (FSS) and IEEE Transactions on Fuzzy Systems (IEEE-TFS). As FSS was founded in 1978, we could consider the publications in both journals for the years 1978 to 2009, but slicing the data into five consecutive periods (1978-1989, 1990-1994, 1995-1999, 2000-2004, and 2005-2009) to uncover the conceptual evolution of the FST field. In the following subsections, we show how the analysis of the field is performed using SciMAT and some interpretations that could be drawn from that analysis.

Performing the analysis with SciMAT. Initially, the researcher should retrieve the raw data from bibliographic sources. In this case, the researcher retrieved the necessary data from the ISIWoS (for years 1980-2009), Scopus (for year 1993 of the journal IEEE-TFS), and Science Direct10 (for years 1978 and 1979 of the journal FSS), because no source covered all the years. A total of 6,823 documents are retrieved.

Once the raw bibliographic data have been downloaded from the bibliographic sources, the first step in SciMAT would be to build a knowledge base and load the retrieved data using the importation capabilities of the knowledge base management module.

The second step carried out by the researcher would be editing the knowledge base, to fix possible errors (in titles, authors, references, etc.) and improve the quality of the data. To do this, SciMAT incorporates a manager for each entity (Document, Author, Reference, Word, Journal, etc.) so that the researcher can easily edit the information associated with each entity and its relations with other entities.

Note that all the managers have the same structure: on the left side, a list of entities is shown, and on the right side, the fields of the selected entity and its relations with other entities are shown.

In Figure 8, the Document’s manager is shown. In the list of documents (left side), one of the most cited articles in the knowledge base is selected. On the right side, its associated information (title, abstract, publication data, citations, etc.) and associations are shown.

As mentioned earlier, five entities can be employed as the unit of analysis using the concept of group: Author Group, Word Group, Reference Group, Author-Reference Group, and Source-Reference Group. These have special managers to perform the de-duplicating process. These managers have a common structure: The left side shows a list of defined groups, and the right side shows the entities associated with the selected group (header-table) and the entities without an associated group (foot-table).

Because of this, the researcher would be interested in carrying out a conceptual evolution analysis, with keywords used as the unit of analysis. Thus, he or she should perform a de-duplicating step over the words. To do this, he or she should define the Word Groups, joining those words that represent the same concept. This could be done using the Word Group’s manual set capability; that is, the special manager to perform the de-duplicating process.

Figure 9 displays the manager to perform the manual set of the Word Groups. For example, it can be seen

FIG. 8. Document’s manager. [Color figure can be viewed in the online issue, which is available at]

that a particular word group with the name GROUPDECISION-MAKING has been defined (left side). It also can be observed that this word group collects four different word names or variants (top right side) for the concept: GROUP-CONSENSUS-OPINION, GROUP-DECISION-ANALYSIS, GROUP-DECISION-MAKING, and GROUP-DECISION-MAKING-(GDM). The lower right side allows the user to add more variants of the concept GROUPDECISION-MAKING.

After cleaning the knowledge base, the third step should be to define the time slices in which the study is going to be performed; that is, to establish the groups of years that will be used later in the longitudinal analysis. The periods are defined using the Period manager. In particular, we consider five consecutive periods: 1978-1989, 1990-1994, 1995-1999, 2000-2004, and 2005-2009.

When the knowledge base is cleaned and the groups and periods are defined, the fourth step should be to configure all the necessary parameters that the analysis needs, using the wizard to perform the science mapping analysis. The analyst could use the groups’ statistics (Figure 10) to estimate the correct parameters.

As shown earlier, the wizard allows the researcher to select the unit of analysis11 (see Figure 11a), the similarity measure used to normalize the network, the clustering algorithm, the document mappers, the bibliometric measures (see Figure 11b), and the remaining key aspects needed to configure the science mapping analysis.

Specifically, we could fix the following configuration: Word as the unit of analysis (author, source,12 and added keywords), co-occurrence as the way to build the network, Equivalence Index as the similarity measure to normalize the network, and the Simple Centers Algorithm as the clustering algorithm.

The bibliometric measures chosen could be the sum of the citations and the h-index, and these measures could be calculated for the documents mapped to each cluster by using the core and secondary document mappers.

At the end of all the steps in the wizard, the map would be built using the selected configuration. Then, the results would be saved to a file, and the visualization module loaded. The visualization module has two views: Longitudinal and Period.

The Period view (see Figure 12) shows detailed information for each period, its strategic diagram, and for each cluster, the bibliometric measures, the network, and their associated nodes. As an example, Figure 12 shows information about the period 2005 to 2009. Furthermore, it shows the cluster H-INFINITY-CONTROL, with its performance measures and cluster network.

Finally, in the Longitudinal view the overlapping map and evolution map are shown. This view helps us to detect the evolution of the clusters throughout the different periods, and study the transient and new items of each period and the items shared by two consecutive periods. In Figure 13, an example of the longitudinal view is shown.

FIG. 9. Manual set Words Groups. [Color figure can be viewed in the online issue, which is available at]

FIG. 10. Word groups’ statistics. [Color figure can be viewed in the online issue, which is available at]



FIG. 11. The wizard to configure the analysis. (a) Choosing the unit of analysis. (b) Selecting the quality measures. [Color figure can be viewed in the online issue, which is available at]

As an example, we can observe the evolution of the cluster FUZZY-CONTROL and detect the themes that have been present in the majority of periods, such as T-NORM.

Interpretation of the results. Once the analysis has been performed, we have to interpret the results and obtain conclusions about the analyzed research field. In this case, Figure 12 shows the strategic diagram for the period 2005 to 2009. We can observe that there are two important motor themes (H-INFINITY-CONTROL and GROUP-DECISION-MAKING), and many basic and transversal themes which are the base of the remaining ones. Taking into account quantitative measures such as the number of documents associated with each theme (cluster), we can discover

» SciMAT LI - AnayUs view

FIG. 12. Period view. [Color figure can be viewed in the online issue, which is available at]


ID                  Name


slsubpenod 1978-1989


I Subpenod 1990-1994


2 Subpenod 1395-1999




4 Subpenod 2305-2009































9 36





























secondar /Documents



second or (Documents



FIG. 13. Longitudinal view. [Color figure can be viewed in the online issue, which is available at]

where the fuzzy community has been employing a great effort (e.g., H-INFINITY-CONTROL, FUZZY-CONTROL, T-NORM, etc.). Similarly, taking into account the qualitative measure, we could identify the themes with a greater impact; that is, the themes that have been highly cited.

In Figure 13, we can observe the conceptual evolution of the FST field. In this sense, it is easy to identify the themes that have been treated in all the periods (FUZZY-CONTROL, FUZZY-TOPOLOGY, FUZZY-RELATION), those that have disappeared (e.g., FUZZY-SUBGROUP), or those that have emerged in the last periods (e.g., GROUP-DECISION-MAKING).

Note that the analysis could continue by examining other structural aspects such as the main coauthorship networks in the area, to thereby raise possible collaborations with these researchers, or the main references of the FST field; that is, those most used and those that are commonly co-cited. The former could be performed selecting the authors as the unit of analysis in the wizard. The latter could be performed selecting the references as the unit of analysis. In both cases, the co-occurrence network should be selected in the wizard.

Validating SciMAT

To check the practical utility and usability of SciMAT, a user validation test has been performed. Version 1.0 of SciMAT was provided to a variety of potential end users, including senior researchers, PhD students, heads of research groups, and technical staff of the Research and Policy Research Office of the University of Granada. Thus, 15 people have used SciMAT, and they have given us valuable suggestions and comments after using SciMAT with their own data set, including five different (science and social) research topics (computer science, information science, psychology, marketing, and chemistry).

For systematic and objective data acquisition, we used an adapted version of the Questionnaire for User Interface Satisfaction (Chin, Diehl, & Norman, 1988), which is a widely used and extended user questionnaire for evaluating software (Shneiderman, Plaisant, Cohen, & Jacobs, 2009; Vilar, 2010). The questionnaire used for testing SciMAT is shown in the Appendix. Five dimensions are considered: (a) Overall Reaction to the Software, (b) Screen, (c) Terminology and System Information, (d) Learning, and (e) System Capabilities. Several aspects are queried for each dimension, with 10-point scales as the response method. The user questionnaire is completed with two “free comment” boxes to collate both negative and positive aspects of SciMAT.

The majority of suggestions and comments offered were oriented toward improving the interface and/or interactivity of SciMAT, specifically the navigation flow and the information displayed on the interface. The users suggested adding more useful information in several modules, such as the number of documents associated with each item (words, authors, journals, etc., and their related groups). Another important suggestion was to incorporate in SciMAT a module to visualize (with illustrative graphs) statistical information about each unit of analysis, with the main aim of using this statistical information for a better configuration of the various parameters of the analysis.

With respect to the functionality of SciMAT, the majority of the users were very satisfied with the different options, techniques, algorithms, and units of analysis present in SciMAT, although one user asked us to incorporate a more flexible module to load data.

Several users required a more complete report in both HTML and LATEX formats, including more statistical information, adding more information for each detected cluster or evolution area. As a result of these suggestions, the HTML and LATEX reports have been improved, building a detailed subsection for each cluster and showing the specific configuration of the performed analysis. Furthermore, a new advanced report (in both HTML and LATEX formats) has been added. The advanced report completes the information with the documents (showing the full reference, including citations) associated with each period and cluster.

As to the facility of use, several users reported that it is difficult to learn to use SciMAT in comparison with other science mapping software (e.g., CoPalRed or VOSviewer), although they also informed us after prolonged use that they were comfortable with the tool and the results provided.

Finally, all the suggestions given by the users’ test were incorporated into Version 1.1 of SciMAT.

Concluding Remarks

We have presented here a new open-source software tool called SciMAT, to perform longitudinal science mapping, SciMAT has been developed on the basis of the science mapping approach proposed in Cobo et al. (2011a).

SciMAT presents three key features that other science mapping software tools do not have (or have in limited form): (a) a powerful preprocessing module, (b) the use of bibliometric measures, and (c) a wizard to configure the analysis. Different preprocessing processes can be applied, such as detecting duplicate and misspelled items, time slicing, data reduction, and network preprocessing. Bibliometric measures (mainly based on citations) such as the h-index (Alonso et al., 2009; Hirsch, 2005), g-index (Egghe, 2006), hg-index (Alonso et al., 2010), or q2-index (Cabre-rizo et al., 2010) are used by SciMAT to give information about the interest in and impact on the specialized research community of each detected cluster. Finally, the analysis is configured using a powerful wizard which allows the analyst to choose the algorithms, methods, and measures to be used in the analysis.

The main characteristics, methods, and algorithms present in SciMAT are:

In Table 3, a comparative summary of the characteristics of SciMAT versus other science mapping software tools is shown. We can see that SciMAT is one of the most complete

TABLE 3. Summary of SciMAT’s characteristics versus other science mapping software tools.

Software tool






Data and networks reduction


Salton’s cosine, Jaccard Index, or the Vladutz and Cook measures



Time slicing and data and networks reduction


Salton’s cosine, Dice or Jaccard strength

Burst detection, geospatial, network, temporal


De-duplication, time slicing, data reduction


Equivalence Index

Network, temporal


Loet Leydesdorff’s Software

Data reduction




Conditional probability

Salton’s cosine

Bust detection, network, temporal

Network Workbench Tool

De-duplication, time slicing, and data and networks reduction


User defined

Burst detection, network, temporal

Science of Science Tool

De-duplication, time slicing, and data and networks reduction


User defined

Burst detection, geospatial, network, temporal



De-duplication, time slicing, and data reduction


Pearson’s r, Salton’s cosine, or the max proportional

Association strength

Burst detection, geospatial, network, temporal



De-duplication, time slicing, and data reduction


Association strength, Equivalence Index, Inclusion Index, Jaccard Index, and Salton’s cosine

Network, temporal, performance

ABCA = author bibliographic coupling; DBCA = document bibliographic coupling; JBCA = journal bibliographic coupling; ACAA = author coauthor; CCAA = country coauthor; ICAA = institution coauthor; ACA = author cocitation; DCA = document cocitation; JCA = journal cocitation; CWA = co-word; DL = direct linkage.

tools taking into account all the previously enumerated characteristics.

Although SciMAT is a complete and powerful science mapping tool, other software tools have impressive and notable characteristics (Cobo et al., 2011b). For example, regarding the preprocessing methods, VantagePoint (Porter & Cunningham, 2004) is able to import data from many kinds of formats. The Network Workbench Tool (Borner et al., 2010; Herr et al., 2007) and Science of Science Tool (Sci2Team, 2009) have good network-reduction processes. CiteSpace (Chen, 2004, 2006), Science of Science Tool, and VantagePoint have several analysis methods (geospatial, burst detection, etc.). Furthermore, SciMAT is able to calculate only two network measures (Callon’s centrality and density) whereas The Network Workbench Tool and Science of Science Tool are able to add more network measures. Taking into account visualization capabilities, VOSviewer (van Eck & Waltman, 2010) has a powerful GUI that allows us to easily examine the generated maps. Similarly, Network Workbench Tool and Science of Science Tool allow us to configure the visual output with different scripts.

The main differences of SciMAT with respect to other science mapping tools are: (a) the capability to choose the methods, algorithms, and measures used to perform the analysis through the configuration wizard; (b) the use of impact measures to quantify the results; (c) the ability to perform all the steps of the science mapping workflow (Figures 1 and 5); (d) the integration of the whole process in a longitudinal framework; and (e) the methodological foundation, due to SciMAT being based on an extension of the science mapping approach presented in Cobo et al. (2011a).

Finally, SciMAT has been tested and improved according to the suggestions and comments of a wide variety of potential users, including senior researchers, PhD students, heads of research groups, and technical staff of the Research and Policy Research Office of the University of Granada.


We thank all who helped to improve SciMAT by giving us very valuable suggestions and comments. This work has been developed with the support of Project TIN2010-17876 and the Andalusian Excellence Projects TIC5299 and TIC05991.


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Questionnaire Used for Validating SciMAT














SciMAT itself




Starting up




Perceived utility




Obtaining Results

inadequate power

adequate power


Setting the analysis




Analysis capabilities
















Reading characters on the screen




Highlighting simplifies task

not at all

very much


Organization of information


very clear


Sequence of screens


very clear














Use of terms throughout SciMAT




Terminology related to task




Position of messages on screen




Prompts for input




Computer informs about its progress




Error messages
















Learning to operate SciMAT




Exploring new features by trial and error




Remembering names and use of commands




Performing tasks is straightforward




Help messages on the screen




Supplemental reference materials
















SciMAT speed

too slow

fast enough


SciMAT reliability




SciMAT tends to be




Correcting your mistakes




Designed for all levels of users



Negative Comments:

Positive Comments:

FIG. A1. Questionnaire used for validating SciMAT. It is an adapted version of the Questionnaire for User Interface Satisfaction (Chin et al., 1988).


DOI: 10.1002/asi



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