A method for generating overlay maps on the basis of aggregated journal-journal citation relations in 2015
A 2016 update to:
· Leydesdorff, L., Carley, S., & Rafols, I. (2013). “Global Maps of Science based on the new Web-of-Science Categories Scientometrics,” 94(2), 589-593; doi:10.1007/s11192-012-0784-8
· Ismael Rafols, Alan L. Porter, and Loet Leydesdorff, “Science overlay maps: a new tool for research policy and library management,” Journal of the American Society for Information Science & Technology 61(9) (2010) 1871-1887; <pdf-version>;
We follow the method introduced in “Science overlay maps: a new tool for research policy and library management” (see also: Leydesdorff & Rafols, 2009; Rafols & Meyer, 2010) to create overlay maps on the basis of a global map of science based on aggregated journal citation data in 2015. The steps described below rely on access to the Web of Science and the files available on (and downloadable from) this page. The objective is to obtain the set of Web-of-Science Categories (WCs) for a given set of articles; provide this to network software; and output overlay information to add to a suitable basemap. We describe here below the procedures for using Pajek and/or VOSviewer. However, Pajek files can be read by Gephi, UCInet, and most other network analysis and visualization software.
First, the analyst has to conduct one’s own search in the Web of Science of Thomson Reuters (www.webofknowledge.com). Users should note that this initial step is crucial and should be done carefully: author names, for example, can be retrieved with different initials; addresses are sometimes inaccurate, and only some types of document, may be of interest (e.g., only so-called citable items: articles, proceedings papers, and reviews). Once the analyst has chosen a set of documents from searches at Web of Science, one can click the tab, Analyze results at the right top of the results page. At a new webpage, the selected document set can then be analysed along various criteria (top left hand tab). The Web of Science Category choice produces a list with the number of documents in each Category. The resulting list can be downloaded into a file with the default name analyze.txt.
This file “analyze.txt”—make sure that the file has this name!—can be transformed by the mini-programme WC15.exe to WC15.vec for upload as a vector into Pajek, and to the file vos.csv for use in VOSviewer. One has to download the files cos_map.db_ and cosine.db_ in the same folder.
Files with the extension .db_ should be renamed into .dbf after downloading.
Rao-Stirling diversity, Zhang et al.’s (2016) measure of true diversity 2D3, DIV (Leydesdorff et al., 2019) and DIV* (Rousseau, 2019) are provided on screen if the file cosine.dbf is available (downloaded) in the same folder as the files analyze.txt (downloaded from WoS), cos_map.dbf, and the routine WC15.exe. Entering this routine, the user is first prompted for a label of the specific run. The diversity values are stored in the file div_wc15.dbf. This file is created from scratch if it is not available. A new record is always added with the stored diversity values.
The easiest way to generate a science map is to use the visualisation programme VOSviewer. Click on the ‘Open’ tab in VOSviewer. The program WC15.exe generates the file vos5.txt which can be opened in VOSviewer as a so-called “map-files.” (The file can be edited both in excel and using a text editor.) One is advised to consult the VOSviewer manual (in the left pane of the program after installation) for further options such as a different colouring. Five clusters are distinguished using default values of VOSviewer.
The file vos18.txt is different from vos5.txt only in distinguishing 18 clusters instead of five. The files vos5a.txt and vos18a.txt use a different layout based on setting the parameters of VOSviewer as follows: repulsion = 0, resolution = 4.5, and the minimum cluster size is six. For experienced users, the so-called network file is available from here. Loading this file into VOSViewer enables the user to run the program with different parameters (see also: Leydesdorff & Rafols, 2012).
One can download and install the freeware programme Pajek for network analysis and visualizations. After opening this programme, press F1 and read the basemap map15.paj (after downloading it to disk). Then, go to the main menu File>Vector>Read to upload the above prepared file “WC15.vec.” Selecting from the menu Draw>Draw-Partition-Vector (alternatively, pressing Ctrl-Q), the overlay map is generated.
At this stage, the size of nodes will often need adjustment, which can be done by selecting Options>Size of Vertices in the new draw window. Crtl-L and Ctrl-D allow visualise and delete, respectively, the labels. The cluster file wc15.cls is also generated and allows for the Options>Mark vertices using>Mark cluster only in the drawing screen of Pajek. Clickling on nodes allows to move WCs to other positions.
The image can be exported selecting Export>2D> in the menu of the Draw window. (See also here for improving the picture.)
WC15.exe also generates the file wc15.dbf. This file contains the distribution of WCs and can thus be used as input to the computation of the Rao-Stirling diversity Δ (= Σij pi pj dij) and Zhang et al.’s (2016) measure of true diversity 2DS [= 1 / (1 – Δ) ] if the file cosine.dbf is downloaded to the same folder. The file is needed because the value [1 – cos(ij)] is used as the distance between WCs i and j.
In 2019, DIV (Leydesdorff et al., 2019) and DIV* (Rousseau, 2019) were added to this routine. Using WC15.exe, the user is first prompted for a label of the specific run. The diversity values are additionally stored in the file “div_wc15.dbf”. This file is created from scratch if it is not available. A new record is always added with the stored diversity values.
For the installation for previous years (2007, 2008, and 2009) one can click here; 2010 from here. The materials (citation matrix, cosine matrix, and classification scheme) for 2015 are available from here.
Leydesdorff, L. & Rafols, I. (2009). A Global Map of Science Based on the ISI Subject Categories. Journal of the American Society for Information Science & Technology, 60(2), 348-362.
Leydesdorff, L. & Rafols, I. (2012). Interactive Overlays: A New Method for Generating Global Journal Maps from Web-of-Science Data, Journal of Informetrics, 6(3), 318-332.
Leydesdorff, L., Wagner, C. S., & Bornmann, L. (2019). Interdisciplinarity as Diversity in Citation Patterns among Journals: Rao-Stirling Diversity, Relative Variety, and the Gini coefficient. Journal of Informetrics, 13(1), 255-264.
Rafols, I. & Meyer, M. (2010). Diversity and Network Coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics, 82(2), 263-287.
Ismael Rafols, Alan L. Porter, and Loet Leydesdorff, “Science overlay maps: a new tool for research policy and library management,” Journal of the American Society for Information Science & Technology 61(9) (2010) 1871-1887.
Waltman, L., van Eck, N. J., & Noyons, E. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629-635.
Zhang, L., Rousseau, R., & Glänzel, W. (2016). Diversity of references as an indicator for interdisciplinarity of journals: Taking similarity between subject fields into account. Journal of the Association for Information Science and Technology, 67(5), 1257-1265. doi: 10.1002/asi.23487
August 12, 2016; March 18, 2019