Research Paper

Visualization of Disciplinary Profiles: Enhanced Science Overlay Maps

  • Stephen Carley 1 ,
  • Alan L. Porter 1, 2 ,
  • Ismael Rafols 3 ,
  • Loet Leydesdorff 4
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  • 1Search Technology Inc., Norcross, GA 30092, USA
  • 2Program in Science, Technology & Innovation Policy (STIP), School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30332, USA;
  • 3Ingenio (CSIC-UPV); Universitat Politècnica de València, València, Spain; and Science Policy Research Unit (SPRU), University of Sussex, Brighton, UK;
  • 4Amsterdam School of Communication Research (ASCoR), University of Amsterdam, P.O. Box 15793, 1001 NG Amsterdam, The Netherlands;

Online published: 2017-08-25

Copyright

Open Access

Abstract

Purpose: The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps.

Design/methodology/approach: We use the combined set of 2015 Journal Citation Reports for the Science Citation Index (n of journals = 8,778) and the Social Sciences Citation Index (n = 3,212) for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map (“wc15.exe”) is available at http://www.leydesdorff.net/wc15/index.htm.

Findings: Findings appear in the form of visuals throughout the manuscript. In Figures 1-9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies.

Research limitations: As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science.

Practical implications: Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper.

Originality/value: The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.

Cite this article

Stephen Carley , Alan L. Porter , Ismael Rafols , Loet Leydesdorff . Visualization of Disciplinary Profiles: Enhanced Science Overlay Maps[J]. Journal of Data and Information Science, 2017 , 2(3) : 68 -111 . DOI: 10.1515/jdis-2017-0015

1 Introduction

This paper advances science overlay mapping processes. The intent is to provide the research communities using scientometrics with an improved methodology to generate overlay maps (Rafols, Porter, & Leydesdorff, 2010). An overlay map is a global map of science over which a subset of publications is projected, thus allowing the visualization of disciplinary scope for the scientific production of a given organization, individuals, territory, etc. Such maps can help analysts and readers grasp the mix of disciplines engaging a given topic or the portfolio of research interests reflected in the publication (sub)set of an organization (see Wallace and Rafols (2015) for a discussion of research portfolios).
The paper briefly overviews the heritage of the use of Web of Science subject categories (WCs) and of science overlay mapping. It then presents enhanced methodology to generate the maps, followed by examples to illustrate novel application opportunities. The paper updates the visualization process and provides an advanced 2015 basemap.

1.1 Efforts to Classify Research

In order to understand the multidisciplinary profile of publication sets, disciplinary or sub-disciplinary categories can be assigned to the publications. These categories can then be used to represent the position of a publication set in the overall structure of science—i.e. to overlay a specific research activity onto the map of science (Rafols, Porter, & Leydesdorff, 2010).
One method to assign publications to a disciplinary category is to rely on the journal of the publication as an estimate of the scientific field. However, disciplines and fields of science develop above the level of individual journals. Scientometricians proposed the normalization of citations in terms of journal categories (ISI Subject Categories, now known as Web of Science Categories)—as proxies of scientific fields defined above the level of individual journals—in a series of publications during the 1980s (e.g. Schubert, Glänzel, & Braun, 1986; Schubert, Glänzel, & Braun, 1989; Vinkler, 1986).
Using these categories, Moed, de Bruin, & van Leeuwenet (1995) further developed the “crown indicator” at the Center for Science and Technology Studies (CWTS) in Leiden that was later improved as the “Mean Normalized Citation Score” (MNCS). This indicator remains based on the same subject categories, and it is currently the most widely used method to provide normalized comparisons across scientific areas.
The WCs tagged to the 11,000+ journals covered by the Science Citation Index (SCI) and the Social Sciences Citation Index (SSCI) are assigned by indexers on the basis of a number of criteria, including field experts’ judgment of relevance to a given field, the journal’s title, and its citation patterns (Bensman & Leydesdorff, 2009). As of 2015, there are 227 WCs covering SCI and SSCI. Pudovkin and Garfield (2002) described the methods used by the ISI (then provided by Thomson Reuters, and now Clarivate Analytics), and concluded that in many fields these categories are “sufficient;” but “in many areas of research these “classifications” are crude and do not permit the user to quickly learn which journals are most closely related” (p. 1113). Boyack, Börner, and Klavans (2007) estimated that the assignment of WCs is correct in approximately 50% of cases across the file. That said, the “correct” assignment based on detailed article content would usually be proximate.
On the basis of a comparison of this classification with algorithmically generated ones, Rafols and Leydesdorff (2009) (p. 1830) concluded that the WCs can be used for aggregate statistical purposes (i.e. above 100 or so publications, depending on the desired granularity); but are not well-suited for detailed analyses (e.g. to assess an individual’s research). The WCs sometimes cover similar sets of journals; for example, in the domain of biomedicine. In other cases, the categories added by an indexer cover areas that could be considered as separate sub-disciplines or subfields (Leydesdorff & Bornmann, 2016; van Eck et al., 2013). In the case of interdisciplinary publications, problems of imprecise or potentially erroneous classifications can be expected (Rafols & Meyer, 2010)(1)((1)In scientometric evaluations, journals are sometimes attributed percentages proportional to the categoriesunder which they are subsumed. These multiple categories have also been considered indicators of theinterdisciplinarity of journals (Bordons, Bravo, Barrigon, 2004; Katz Hicks, 1995; Morillo, Bordons, Gomez, 2001).). Klavans and Boyack (in press) recommended using classification schemes based on fine-grained publication-level clustering; but these classifications, which we would recommend where possible, are not publicly available yet—one exception being that provided by Waltman and van Eck (2012).
Notwithstanding these issues, WCs are a main basis for scientometric analyses. The use of these journal categories has become conventional among scientometricians (e.g. Rehn et al., 2014), including use to assess research portfolios. For example, InCites—a customized, Web-based research evaluation tool developed by Thomson Reuters—routinely provides normalizations of citation impact using WCs for the delineation of reference sets (e.g. Costas, van Leeuwen, & Bordons, 2010; Leydesdorff, Hammarfelt, & Salah, 2011). The Flemish ECOOM unit for evaluation in Leuven (SOOI) has developed a new classification system for journals (Glänzel & Schubert, 2003). Other authors have refined the journal lists within specific
WCs to enable a more precise evaluation of a given discipline (van Leeuwen & Calero Medina, 2012). Another journal classification system in terms of fields and subfields has been made available by Elsevier’s Scopus in the meantime, but Wang and Waltman (2016) found it to be more problematic than WCs, in particular due to the high rate of multiple category assignments of a journal(2)((2)The field/subfield classification of Scopus is available in the journal list from http://www.elsevier.com/online-tools/scopus/content-overview. WCs are available (under subscription) athttp://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html.).

1.2 Description of WCs as Fields of Science

WCs can also be considered “macro-journals” representing fields and subfields of science. Their sub-disciplinary level of detail fits well with a US National Academies recommendation for study of interdisciplinarity (2005). The current (2015 WoS data) matrix of 227 WCs citing one another can be decomposed using multi-variate (e.g. clustering) analysis. It can be analyzed as a network using, for example, community-finding algorithms. Initially (refer to Leydesdorff & Rafols, 2009; Rafols, Porter, & Leydesdorff, 2010), we used 2007 data to develop a global map of science. At that time, drawing a map using the approximately 10,000 journals in the database was technically not feasible due, among other things, to the cluttering of the labels on the screen. This problem was elegantly solved by VOSviewer (which became available in 2009), by allowing interactive zoom in/out functionality in the visualization (Klavans & Boyack, 2009; van Eck & Waltman, 2010)(3)((3)Available at http://www.vosviewer.com.).
Earlier maps were developed into an overlay-toolkit(4)((4)http://www.leydesdorff.net/overlaytoolkit) that enabled users to visualize portfolios as overlays using Pajek(5)((5)Pajek is a network analysis and visualization program freely available for non-commercial usage at http://mrvar.fdv.uni-lj.si/pajek.) (e.g. Leydesdorff, Carley, & Rafols, 2013; Rahman et al., 2015; Riopelle, Leydesdorff, & Li, 2014; Soós and Kampis, 2011). At that time, however, further integration between community-finding algorithms (Blondel et al., 2008), network analysis (e.g. Pajek (de Nooy, Mrvar, & Batgelj, 2011)), and visualization programs such as VOSviewer and Gephi were still emerging (Waltman, van Eck, & Noyons, 2010). VOSviewer, for example, was fully integrated into Pajek in July, 2012, following incorporation of the Blondel (“Louvain”) algorithm for community-finding in January of that year (Blondel et al., 2008). This algorithm offers appeal to provide an improved location of the WCs as nodes in a suitable visual rendition. The overlay process, then superimposes colored and sized nodes on top of that base to convey concentrations of activity. The enhanced science overlay mapping process provides an option to generate networking links among those nodes based on co-occurrence intensities. These can be rendered to augment the maps, with particular appeal to show network evolution over time for a given local domain of research activity.
We now make some choices differently from the ones we made some ten years ago. The wide use by a variety of stakeholders (including not only some researchers, but also scientometric students and practitioners) and requests for a current database, together with technical improvements in visualization during recent years, lead us to revise the overlay basemaps and toolkit based on the most recent version of the Journal Citation Reports (JCR), i.e. 2015.

2 Data and Methods

2.1 The Mapping

We use the combined set of the JCRs 2015 for the Science Citation Index (SCI) (n of journals = 8,778) and the Social Sciences Citation Index (SSCI) (n = 3,212) leading to a total number of 11,365 journals; 625 journals are covered by both databases (Table 1). A JCR for the Arts & Humanities Citation Index is not available, but, in any event, the behavior of those journals’ citation practices differs considerably from that of SCI and SSCI journals (Leydesdorff, Hammarfelt, & Salah, 2011). We also note that Web of Science has expanded its coverage of other research resources, especially conference proceedings and books. Those are not included in the maps presented here.
Table 1 Numbers of journals and Web of Science categories in SCI and SSCI.
Journals WCS
SCI 8,778 177
SSCI 3,212 57
Sum 11,990 234
Total 11,365 227
Overlap 625 6(6)
The set of WCs covering SCI and SSCI has expanded from 224 in 2010 to 227 in 2015. The three newly added WCs are: “Audiology & speech-language pathology,” “Green & sustainable science & technology,” and “Logic.” The former WC—“Biology, miscellaneous”—was no longer in use in 2010 and, therefore, not included in the analysis; it is also absent from the 2015 data and the current maps.
Using dedicated software, the matrix of 227 × 227 cells was generated on the basis of whole-number citation counting. As previously, we normalize this matrix using the cosine function. However, the default VOSviewer setting normalizes using Zitt, Bassecoulard, and Okubo’s so-called “probabilistic activity index” (PAI) (2000). PAI is equal to the ratio between observed and expected values in a contingency table based on a probability calculus (Equations (1) and (2)):
PAI = pij / (pi × pj) (1)
= nij× Σi Σj nij / Σi nij × Σjnij. (2)
In the context of VOSviewer, this measure is renamed as the “association strength” (van Eck & Waltman, 2009).
Unlike the cosine, which is symmetrical, PAI can be used to normalize asymmetrically the vertical and horizontal dimension of a matrix. However, this possible advantage is not exploited in VOSviewer because the matrix is first made symmetric using the sums of lower and upper triangle values (cellij + cellji) in a new matrix. The cosine-normalized matrix remains worth investigating, because one is able to show the difference between the citation as the current activity (citing) versus the cited structures as archival representations (Wouters, 1998).
Taking these issues into consideration, we first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. This map is a “descendant” of our previous maps; strong relationship can be seen in comparing Figure 1 (our 2015 basemap from VOSviewer) with A-1 in Appendix A (the 2010 basemap from Pajek). A routine for making overlays on the basis of the map (“wc15.exe”) is provided at http://www.leydesdorff.net/wc15/index.htm and described in Appendix B. If the file cosine.dbf is additionally downloaded from the website, the routine writes a value for the Rao-Stirling measure of diversity, which is a proxy of the disciplinary breadth of the publication subset (Stirling, 2007; Zhang, Rousseau, & Glänzel, 2016)(7),(8) to the screen, based on using (1 - cosine) as the distance measure (p. 986 of Jaffe (1986)).
Figure 1. Five-cluster basemap for 2015 (based on VOSviewer)(9).
As an additional resource, one can feed the citation matrix of 227 WCs (citing versus cited, but without prior normalization) into VOSviewer and develop a similar map (Appendix A, Figure A-2). Analogous routines as wc15.exe are provided by mtrx15.exe that produces a file “mtrx15.csv” as an input file for the mapping of a portfolio in VOSviewer using the non-normalized citation data (Figure A-2). In the case of a non-normalized matrix, a distance measure is not provided: the number of possible similarity criteria is large (see Klavans & Boyack (in press), and US National Academies (2005)) and the choice can be left to the user (using, for example, SPSS).
The routines also provide cluster and vector files for cos15.paj and matrix.paj made available on the website for Pajek, respectively (as previously). Pajek and Gephi contain a suite of tools for network analysis and visualization such as various decompositions, layouts, and visualization options. Using Pajek or Gephi, for example, one can also obtain the results of the Louvain algorithm (Blondel et al., 2008) for the decomposition in a format that can again be visualized in programs such as VOSviewer or Gephi.
Using VOSviewer, the user can change the number of clusters by changing the resolution parameter and running the clustering algorithm again. Using default values, both maps (i.e. cosine-normalized or not) show five clusters, but chi-square statistics reject the zero-hypothesis that the two classifications are similar (Cramer’s V = 0.707; p < 0.01). The corresponding five colors (blue, red, green, yellow, and pink) will also be used for the overlays, but the user can change this. Changing the granularity requires one to import the file with network data. More detailed instructions can be found in Appendix B and at http://www.leydesdorff.net/wc15/index.htm.

2.2 Measures of Disciplinary Diversity

As mentioned in the previous section, the cosine-similarity matrix for the WCs provides both the basis for locating the WCs as nodes in science maps (Figure 1), and the basis to calculate measures of diversity. Footnotes 7 and 8 remind the users of Stirling’s measure and how it can be calculated using the 227-by-227 WC cosine-similarity matrix (see Rafols, Porter, & Leydesdorff (2010) for details).
Porter and colleagues introduced measures of interdisciplinarity and multidisciplinarity called “Integration scores” and “Specialization scores,” extended by Carley and Porter to “Diffusion scores” as well (Carley & Porter, 2012; Porter et al., 2007; Porter & Rafols, 2009). For a given set of publication from WoS, Specialization scores indicate the disciplinary diversity of the set based on the distribution of their WCs. Integration scores reflect the diversity of those publications’ cited references—again, using the cited WCs. Downloading the “cited references” of a given WoS search set allows one to pursue this metric. Conversely, Diffusion scores reflect the diversity of the disciplines citing a given set of papers, based on the citing journals’ WCs. This requires a citation search and data downloading from WoS.
These scores are different instances of the Rao-Stirling diversity measures (Footnote 7) (Stirling, 2007). As introduced earlier in this section, one can obtain the Specialization score (Rao-Stirling diversity for the WCs represented in the WoS search set) along with a science overlay map if desired, directly from the script provided at http://www.leydesdorff.net/wc15.
Integration or Diffusion scores need more detailed computation. Scripts have been prepared to run in VantagePoint software(10).

2.3 Mapping Options

As what is introduced earlier in this paper, and enabled at the website (http://www.leydesdorff.net/wc15), one can perform a topical search at WoS and take the output as an “analyze.txt” file to enter directly at the site to generate the corresponding science overlay map in VOSviewer. And, as noted, one can vary the resulting overlay maps in several ways in VOSviewer to accentuate points of interest(11).
The website provides the option to generate either five-cluster science overlay maps or finer scaled (color-differentiated) 18-cluster overlay maps. Both cluster solutions were generated in VOSviewer, using its algorithm(12). Appendix map A-3 shows the 18-cluster basemap. Appendix map A-4 shows an overlay for the London School of Economics as an example.

3 Case Examples

Our intent here is to present a range of maps to illustrate differences that the new science overlay mapping can convey. We hope that these promote thinking of additional uses of science overlay mapping, potentially augmented by enabling calculation of diversity measures (e.g. Specialization and Integration scores) with the same tool suite.
Figures 2 and 3 compare two multinational companies’ research publications in WoS for 2010-2015. Both show biomedical and physical science strengths. Unlike Unilever, Pfizer also has a pronounced portfolio in “economics” and “statistics and probability” as fields of science. These visualizations facilitate exploration of shared and complementary research interests, potentially of use in considering collaboration (as well as tracking competition) among organizations or nations.
Figure 2. Science overlay map for Pfizer.
Figure 3. Science overlay map for Unilever.
Figures 4, 5, and 6 present three contrasting university profiles. Patterns stand out quite boldly among the engineering-oriented Georgia Tech, the social science emphases of the London School of Economics, and the full spectrum University of Amsterdam research. In contrast to Figure 5, Figure A-4 (Appendix) presents the same data using an 18-cluster map that facilitates finer comparisons.
Figure 4. Science overlay map for Georgia Tech.
Figure 5. Science overlay map for the London School of Economics.
Figure 6. Science overlay map for the University of Amsterdam.
Usually one would want to focus more tightly—e.g. on a particular research unit or even on an individual researcher’s work (say to ascertain complementarity with another research group or emphases of a funding program). As one step in that direction, contrast the emphases seen in Figure 4 to its subset for one department of Georgia Tech, the School of Public Policy, shown in Figure 7.
Figure 7. Science overlay map for the School of Public Policy, Georgia Tech.
Conversely, one can observe even broader research profiles—Figure A-5 does so for a country, South Africa. Not surprisingly, one sees a very broad spectrum of research activity at this level. One could pursue via further analyses—e.g. to identify researchers active in a particular sub-domain as spotted on a map. We envision various uses for such technical intelligence, ranging from identification of others pursuing one’s area of interest to identifying complementary strengths for research center development, or such.
Figures 2 to 7 map the research outputs of a given organization. One can map other WoS search sets as well. For instance, in a study of the outputs and impacts of an NSF research program on Human & Social Dynamics (HSD), science overlay mapping was useful for those assessing the merits of that program to see the diversity of the publications generated by HSD support. However, it was even more interesting to see the spread of papers citing those publications across the disciplines. Those showed that this funding from the Social, Behavioral & Economic Sciences Directorate was actively cited beyond those social sciences by natural sciences and engineering (Garner et al., 2013).
Another appealing opportunity arises in mapping topical searches. Figure 8 illustrates for an emerging energy technology, dye-sensitized solar cells (DSSCs), dominated by materials science and related research. “Big Data” (using a first approach) (Figure 9) shows a strong concentration in Computer Science and related fields, but note the incredible breadth of publication as virtually all fields consider how Big Data and Analytics can enhance their R&D. Such research profiling could support funding agencies’ confirmation of interdisciplinary research programs.
Figure 8. Science overlay map for dye-sensitized solar cells.
Figure 9. Science overlay map for “Big Data.”

4 Discussion

This article bolsters science overlay mapping as a tool for researchers and analysts to help understand the disciplinary profiles of organizations, funding programs, topics, or other types of publication sets. Visualization of the disciplinary profile, operationalized at the sub-discipline level of 227 Web of Science Categories (WCs) can now offer an adjustable, “birds eye” view of the fields involved. By choosing the 18-cluster option (Figure A-3) or the five-cluster option (Figure 1), one can show the analysis at a narrow or broad disciplinary description.
We use a cosine-normalized basemap in this paper’s examples, but note the option of a non-normalized matrix that can default to VOSviewer’s internal normalization scheme for a different presentation (e.g. Figure A-2). We favor the cosine-normalization as 1) yielding more intuitive results, 2) consistent with our prior overlay maps (see Figure A-1), 3) and shown to be consistent with consensus science mapping (e.g. various renditions by Klavans & Boyack (in press), and others (Klavans, & Boyack, 2009), and 4) conducive to use as a diversity measure in calculating diversity indexes (Rao-Stirling). Comparing to Figure A-1 also shows the general continuity between the previous Pajek visualization to the current VOSviewer one. It also shows some differences, both in the visual rendition and in node localizations. We now favor VOSviewer for its ease of use and accessible richness of the visualization options.
As illustrated in the case examples, these science overlay maps can provide a quick and intuitive perspective on the disciplinary profiles of organizations. As explained in Rafols, Porter, and Leydesdorff (2010) (see also Leydesdorff & Bornmann, 2016; Rafols & Leydesdorff, 2009; Rafols & Meyer, 2010; van Eck et al. 2013), the main downside of this visualization tool is the lack of accuracy in the WCs—which nevertheless is the most widely used and easily available classification system. As shown in a previous study (Rafols, Porter, &Leydesdorff, 2010), the lack of accuracy of WCs is less problematic at a relatively high level of aggregation. Most errors in locating specific research are nearby in the mapping. For fine-grained descriptions, article-based clustering is preferred (Waltman & van Eck, 2012). However, that does not match the WC-based mapping for communication of which fields are engaged, to what degree.
We believe these new science overlay maps open opportunities for future research. For one, exploration of the differences between the global science maps over time (e.g. between 2010 and 2015 basemaps), shows promise to elucidate real shifts in global research emphases. For instance, is medical science becoming more closely related to biological sciences and less linked to chemistry? The basemaps appear to evolve slowly as shown by the fact that the underlying 2010 and 2015 citation matrices among WCs are very similar (QAP correlation r = 0.937; p < 0.001) in spite of considerable changes in WoS journal inclusion over that period. This justifies their use for overlays over a certain temporal range.
In stepping through the case analyses, we have pointed to a variety of appealing applications for the science overlay mapping. We believe the enhanced clustering of the WCs, improved visualization, and simplified processing will enable various scientometric applications. We do not repeat those here, but note a synergistic capability offered by the integrated data processing hereby enabled. Namely, analysts can now treat multiple aspects of cross-disciplinary engagement in tandem—science overlay mapping, social network analyses (e.g. by comparing connection strengths among WC nodes over time), and diversity (e.g. through calculation of Specialization, Integration, and/or Diffusion scores).

Acknowledgements

We thank Thomson-Reuters for making the data available.

Author Contributions

All authors on this manuscript made material and significant contributions to its production. L. Leydesdorff (loet@leydesdorff.net) and I. Rafols (i.rafols@ingenio.upv.es) made the most significant contributions to the development of the procedure used in this study (which is made available at L. Leydesdorff’s website). A.L. Porter (alan.porter@isye.gatech.edu) and S. Carley (stephen.carley@searchtech.com, corresponding author) applied these procedures and produced visuals for the same to numerous universities, companies, and technologies. S. Carley developed a script that runs the procedure advanced in this study from software called VantagePoint (see www.thevantagepoint.com). All authors invested significant time drafting and redrafting the text of this work, but A.L. Porter produced the lion’s share of the article’s main text.

Appendix A

Here is the 2010 science basemap showing four “meta-clusters” of disciplinarynodes. We also used a 19 “macro-cluster” version of the same nodes grouped intofiner sets. The “clustering” for 2010 was done using factor analysis in SPSS fromthe citing-to-cited WC matrix.
Figure A-1. Basemap 2010, using Pajek with a four-factor analysis decomposition.
Figure A-2: Basemap 2015, using VOSViewer with a five-cluster decomposition. Settings: Attraction 2, Repulsion 0(13).
Figure A-3. 2015 basemap with a 18-cluster decomposition.
Figure A-4. 18-cluster science overlay map for the London School of Economics (LSE).
Figure A-5. Science overlay map for South Africa (based on 72,937 records in a simple search on cu = South Africa for 2010-2015 in SCI+SSCI).

Appendix B: Creating Your Own Science Overlay Maps

The steps described below rely on access to the Web of Science and the files available on (and downloadable from) http://www.leydesdorff.net/wc15. The objective is to obtain the set of Web-of-Science Categories (WCs) of the journals of a given set of documents; provide this set to a network visualization software; and output the overlay information to a robust basemap of the science structure (see Rafols, Porter, & Leydesdorff (2010) for a description of the concept). We describe below the procedures for using Pajek and/or VOSviewer. However, Pajek files can also 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14. 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 analyzed 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. See Riopelle’s step by step tutorial15 for a detailed description of the process.
The file “analyze.txt”—make sure that the file has this name!—can be transformed by the program at http://www.leydesdorff.net/wc15/wc15.exe to a WC15.ve for upload as a vector into Pajek, and to the file vos.csv for use in VOSviewer. When using the wc15.exe script, one has to download the file (http://www.leydesdorff.net/wc15/cos_map.dbf) in the same folder.
Rao-Stirling diversity and Zhang, Rousseau, & Glänzel’s (2016) measure of true diversity are provided on screen if the file (http://www.leydesdorff.net/wc15/cosine.dbf) is made available (downloaded) in the same folder as the files analyze.txt (downloaded from WoS), cos_map.dbf, and the routine WC15.exe.

VOSviewer

The easiest way to generate a science map is to use the visualization program VOSviewer. Click on the “Open” tab in VOSviewer. The program WC15.exe generates the file vos.csv which can be opened in VOSviewer as a so-called “map-files.” (the extension “csv” stands for “comma-separated variables;” 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 different colouring.
For experienced users, the so-called network file, which provides information on the similarity between WoS Categories as the edges of the network, is available from http://www.leydesdorff.net/wc15/cos015n.txt. Loading this file into VOSViewer enables the user to run the program with different parameters (see also Leydesdorff & Rafols (2012)).

Pajek

One can download and install the freeware program Pajek for network analysis and visualizations. After opening this program, press F1 and read the basemap(16) (after downloading). 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 users to visualize 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. Clicking on nodes allows moving WCs to other positions. The image can be exported selecting Export>2D> in the menu of the Draw window.

Rao-Stirling Diversity

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, Rousseau, & Glänzel’s (2016) measure of true diversity (2DS = 1⁄(1 - Δ)) if the file(17) is downloaded to the same folder. The file cosine.dbf is needed because the value (1 - cos(ij)) is used as the cognitive distance between WCs i and j.
The materials (citation matrix, cosine matrix, and classification scheme) are available at http://www.leydesdorff.net/matrix15.xlsx.

Appendix C: Details on Data Searches for the Example Maps

Several searches use Web of Science’s “organization-enhanced” feature to capture recognized name variations.
Many of the searches were conducted inclusively—e.g. for all years, for all available Web of Knowledge databases. We examined results and then, unless otherwise noted, used VantagePoint to reduce the record set used in making the science overlay maps to SCI + SSCI, usually for 2010-2015.
For Pfizer, for instance, we compared the WC set for SCI+SSCI vs. that for all Web of Knowledge databases available at Georgia Tech. Notably, that includes Arts & Humanities Citation Index (A&HCI), Science and Social Science conference proceedings, and book citation index items as well. For Pfizer this added 2,757 records to the 11,525 located by searching just in SCI+SSCI. However, 2,443 are duplicates of SCI+SSCI records, so the net addition is 314 records. Those records are associated with 612 WC instances (recall that some journals are associated with multiple WCs). So this would alter the resulting maps. We compared for Pfizer and the differences are small.
Unless otherwise noted, the searches were conducted in August or September, 2016.

Web of Science Search for University of Amsterdam (AMS) Publications

You searched for: ORGANIZATION-ENHANCED: ((AMSTERDAM UNIV AND University of Amsterdam) OR (AMSTERDAM UNIV LIB AND University of Amsterdam) OR (BIJ UNIV AMSTERDAM AND University of Amsterdam)
OR (BIOCENTRUM UNIV AMSTERDAM AND University of Amsterdam) OR (CHEMIEWINKEL UNIV AMSTERDAM AND University of Amsterdam) OR (CTR UNIV AMSTERDAM AND University of Amsterdam) OR (GEMEENTE UNIV AMSTERDAM AND University of Amsterdam) OR (GEMEENTELIJKE UNIV AMSTERDAM AND University of Amsterdam) OR (INHOLLAND UNIV AMSTERDAM AND University of Amsterdam) OR (NETSPAR UNIV AMSTERDAM AND University of Amsterdam) OR (RES UNIV AMSTERDAM AND University of Amsterdam) OR (RIJKSUNIV AMSTERDAM AND University of Amsterdam) OR (U AMSTERDAM AND University of Amsterdam) OR (UNI AMSTERDAM AND University of Amsterdam) OR (UNIV AMSTERDAM AND University of Amsterdam) OR (UNIV AMSTERDAM 1 AND University of Amsterdam) OR (UNIV AMSTERDAM G1 106 AND University of Amsterdam) OR (UNIV AMSTERDAM IBED AND University of Amsterdam) OR (UNIV AMSTERDAM IBIS UVA AND University of Amsterdam) OR (UNIV AMSTERDAM LIB AND University of Amsterdam) OR (UNIV AMSTERDAM MED INFORMAT AND University of Amsterdam) OR (UNIV AMSTERDAM MED PHYS LAB AND University of Amsterdam) OR (UNIV AMSTERDAM MEDIA STUDIES AND University of Amsterdam) OR (UNIV AMSTERDAM NETSPAR AND University of Amsterdam) OR (UNIV AMSTERDAM NIKHEF AND University of Amsterdam) OR (UNIV AMSTERDAM NUTR DIETET AND University of Amsterdam) OR (UNIV AMSTERDAM POLIT SCI BIOL AND University of Amsterdam) OR (UNIV AMSTERDAM SANQUIN RES AND University of Amsterdam) OR (UNIV AMSTERDAM TAALWETENSCHAP AND University of Amsterdam) OR (UNIV AMSTERDAM UVA AND University of Amsterdam) OR (UNIV AMSTERDAM WILHELMINA GASTHUIS AND University of Amsterdam) OR (UNIV AMSTERDAM ZOOL LAB AND University of Amsterdam) OR (UVA UNIV AMSTERDAM AND University of Amsterdam) OR (UVA AMSTERDAM AND University of Amsterdam) OR (ZOOL UNIV AMSTERDAM AND University of Amsterdam)) AND YEAR PUBLISHED: (2010-2015)
Timespan: All years. Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC.

Web of Science Search for Georgia Tech Publications

ORGANIZATION-ENHANCED: ((Georgia Institute of Technology OR Georgia Institute of Technology OR (GEORGIA INST TECH AND Georgia Institute of Technology) OR (GEORGIA INST TECH LIB INFORMAT CTR AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL 325716 AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL CIVIL ERVIRONM ENGN AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL ELECT COMP ENGN AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL EMORY AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL EMORY UNIV AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL GEORGIA TECH AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL IND SYST ENGN AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL ITERATED SYST AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL LIB AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL LIB INFORMAT CTR AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL SAVANNAH AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL SPACE SCI TECHNOL AND Georgia Institute of Technology) OR (GEORGIA INST TECHNOL TECH AND Georgia Institute of Technology) OR (GEORGIA TECH AND Georgia Institute of Technology) OR (GEORGIA TECH ATHLET ASSOC AND Georgia Institute of Technology) OR (GEORGIA TECH ATHLET DEPT AND Georgia Institute of Technology) OR (GEORGIA TECH ATHLETIC ASSOC AND Georgia Institute of Technology) OR (GEORGIA TECH COLL COMP AND Georgia Institute of Technology) OR (GEORGIA TECH ECE AND Georgia Institute of Technology) OR (GEORGIA TECH ECON DEV INST AND Georgia Institute of Technology) OR (GEORGIA TECH ECON DEV TECHNOL VENTURES AND Georgia Institute of Technology) OR (GEORGIA TECH EES AND Georgia Institute of Technology) OR (GEORGIA TECH EMORY AND Georgia Institute of Technology) OR (GEORGIA TECH EMORY BIOMED ENGN AND Georgia Institute of Technology) OR (GEORGIA TECH EMORY BIOMED ENGN DEPT AND Georgia Institute of Technology) OR (GEORGIA TECH EMORY CTR ENGN LIVING TISSUED AND Georgia Institute of Technology) OR (GEORGIA TECH EMORY CTR ENGN LIVING TISSUES AND Georgia Institute of Technology) OR (GEORGIA TECH EMORY DEPT BIOMED ENGN AND Georgia Institute of Technology) OR (GEORGIA TECH EMORY UNIV AND Georgia Institute of Technology) OR (GEORGIA TECH FUS RES CTR AND Georgia Institute of Technology) OR (GEORGIA TECH IBB AND Georgia Institute of Technology) OR (GEORGIA TECH INTERACT MEDIA TECHNOL CTR AND Georgia Institute of Technology) OR (GEORGIA TECH LIB AND Georgia Institute of Technology) OR (GEORGIA TECH LIB INFORMAT CTR AND Georgia Institute of Technology) OR (GEORGIA TECH REG ENGN PROGRAM AND Georgia Institute of Technology) OR (GEORGIA TECH RES CORP AND Georgia Institute of Technology) OR (GEORGIA TECH RES INST AND Georgia Institute of Technology) OR (GEORGIA TECH SAVANNAH AND Georgia Institute of Technology) OR (GEORGIA TECHNOL RES INST AND Georgia Institute of Technology)) AND YEAR PUBLISHED: (2010-2015)
Timespan: All years. Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, CCR-EXPANDED, IC.

Web of Science Search for London School of Economics (LSE) Publications

You searched for: ORGANIZATION-ENHANCED: ((LON SCH ECON AND London School Economics & Political Science) OR (LOND SCH ECON AND London School Economics & Political Science) OR (LONDON ECON SCH AND London School Economics & Political Science) OR (LONDON SCH ECON AND London School Economics & Political Science) OR (LONDON SCH ECON A450 AND London School Economics & Political Science) OR (LONDON SCH ECON CONSULTANT AND London School Economics & Political Science) OR (LONDON SCH ECON CTR AND London School Economics & Political Science) OR (LONDON SCH ECON LSE AND London School Economics & Political Science) OR (LONDON SCH ECON MEDIA COMMUN AND London School Economics & Political Science) OR (LONDON SCH ECON PINPOINT ANAL LTD AND London School Economics & Political Science) OR (LONDON SCH ECON POLIT AND London School Economics & Political Science) OR (LONDON SCH ECON POLIT SCI AND London School Economics & Political Science) OR (LONDON SCH ECON POLIT SCI LSE AND London School Economics & Political Science) OR (LONDON SCH ECON POLIT SCI RES AND London School Economics & Political Science) OR (LONDON SCH ECON POLITICAL SCI AND London School Economics & Political Science) OR (LONDON SCH ECON SOCIAL POLIT SCI AND London School Economics & Political Science) OR (LONDON SCH ECON SOCIAL SCI AND London School Economics & Political Science) OR (LONDON SCH ECON SOCIOL AND London School Economics & Political Science) OR (LONDON SCH ECON UNITED KINGDOM AND London School Economics & Political Science) OR (LONDON SCH ECONOM AND London School Economics & Political Science) OR (LONDON SCH ECONOM POLIT SCI AND London School Economics & Political Science) OR (LONDON SCH ECONOMICS AND London School Economics & Political Science) OR (LONDON SCH POLIT ECON AND London School Economics & Political Science) OR (LONDONS SCH ECON AND London School Economics & Political Science) OR (LONS SCH ECON AND London School Economics & Political Science) OR (LONSON SCH ECON AND London School Economics & Political Science) OR (LSE AND London School Economics & Political Science) OR (LSE CTR STUDY HUMAN RIGHTS AND London School Economics & Political Science) OR (LSE DEPT INT RELAT AND London School Economics & Political Science) OR (LSE FINANCIAL MKT GRP AND London School Economics & Political Science) OR (LSE GENDER INST AND London School Economics & Political Science) OR (LSE GLOBAL GOVERNANCE AND London School Economics & Political Science) OR (LSE HLTH AND London School Economics & Political Science) OR (LSE HLTH SOCIAL CARE AND London School Economics & Political Science) OR (LSE PERSONAL SOCIAL SERV RES UNIT AND London School Economics & Political Science) OR (LSE SOCIAL POLICY AND London School Economics & Political Science) OR (LSE STICERD AND London School Economics & Political Science)) AND YEAR PUBLISHED: (2010-2015)
Timespan: All years. Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC.

Web of Science Search for Pfizer Publications

You searched for: ORGANIZATION-ENHANCED: (((PHARMACOKINET PHARMACODYNAM DRUG METAB PFIZER I AND Pfizer) OR (PRICING REIMBURSEMENT AUTHOR DEPT PFIZER AND Pfizer) OR (ABT MIKROBIOL H MACK ZOONOSIS PFIZER AND Pfizer) OR (BIOTHERAPEUT PHARMACEUT SCI PFIZER INC AND Pfizer) OR (INFLAMMAT IMMUNOL MED CHEM PFIZER AND Pfizer) OR (ACROSTUDY MED OUTCOMES PFIZER ENDOCRINE CARE AND Pfizer) OR (H MACK NACHF GMBH CO PFIZER AND Pfizer) OR (PARKE DAVIS R D DEPT PFIZER GRP AND Pfizer) OR (PHARMACIA ITALIA SPA PFIZER GRP AND Pfizer) OR (DRUG SAFETY EVALUAT PFIZER AND Pfizer) OR (AGOURON PHARMACEUT PFIZER AND Pfizer) OR (KIMS MED OUTCOMES PFIZER ENDOCRINE CARE AND Pfizer) OR (BIOPHARMACEUT QA PFIZER AND Pfizer) OR (GLOBAL EPIDEMIOL PFIZER AND Pfizer) OR (GLOBAL EPIDEMIOL PFIZER INC AND Pfizer) OR (TRUSTEES BOWLING PFIZER SETTLEMENT FUNDS AND Pfizer) OR (GESCHAFTSFUHRER PFIZER PHARMA GMBH AND Pfizer) OR (VET MED RES DEV PFIZER INC AND Pfizer) OR (DISCOVERY BIOL PFIZER LTD AND Pfizer) OR (GLOBAL RES DEV PFIZER AND Pfizer) OR (KLIN FORSCH FA PFIZER AND Pfizer) OR (PHARMACEUT R D PFIZER GLOBAL R D AND Pfizer) OR (CAPSUGEL DDIV PFIZER AND Pfizer) OR (DISCOVERY RES PFIZER AND Pfizer) OR (RES PHARMACOL PFIZER INC AND Pfizer) OR (ST LOUIS LABS PFIZER INC AND Pfizer) OR (US MED DETROL PFIZER INC AND Pfizer) OR (US PHARMACEUT PFIZER INC AND Pfizer) OR (ANAND SISTLA PFIZER INC AND Pfizer) OR (DEVOPS INDIA PFIZER GLOBAL R D AND Pfizer) OR (OUTCOMES RES PFIZER CANADA AND Pfizer) OR (PARKE DAVIS PFIZER AND Pfizer) OR (GLOBAL R D PFIZER AND Pfizer) OR (UNIDAD MED PFIZER AND Pfizer) OR (UNIDAD MED PFIZER ESPANA AND Pfizer) OR (UNIDAD MED PFIZER IDI AND Pfizer) OR (INFLAMMAT PFIZER GLOBAL RES DEV AND Pfizer) OR (PHARMA AB PFIZER CORP AND Pfizer) OR (PHARMACIA PFIZER AND Pfizer) OR (CHEM R D PFIZER INC AND Pfizer) OR (CLIN DEV PFIZER INC AND Pfizer) OR (CNS CLIN PFIZER GLOBAL RES DEV AND Pfizer) OR (CTR RECH PFIZER AND Pfizer) OR (DEPT I D PFIZER AND Pfizer) OR (MED DEPT PFIZER AND Pfizer) OR (MED DEPT PFIZER BELGIUM AND Pfizer) OR (MED DEPT PFIZER SPAIN AND Pfizer) OR (MED UNIT PFIZER SPAIN AND Pfizer) OR (AGOURON PFIZER AND Pfizer) OR (AGOURON PFIZER GLOBAL GRD AND Pfizer) OR (AGOURON PFIZER GLOBAL RES DEV AND Pfizer) OR (AGOURON PFIZER GLOVAL R D AND Pfizer) OR (EMPRESA PFIZER ESPANA AND Pfizer) OR (MED DIV PFIZER CANADA AND Pfizer) OR (PAIN RU PFIZER GLOBAL RES DEV AND Pfizer) OR (FORMER PFIZER WORLDWIDE DEV AND Pfizer) OR (GROTON PFIZER GLOBAL RES DEV AND Pfizer) OR (A DIV PFIZER GLOBAL RES DEV AND Pfizer) OR (AVIAX PFIZER ANIM HLTH AND Pfizer) OR (RINAT PFIZER INC AND Pfizer) OR (WWMOR PFIZER AND Pfizer) OR (LABS PFIZER AND Pfizer) OR (LABS PFIZER ESPANA AND Pfizer) OR (LABS PFIZER LTDA AND Pfizer) OR (LABS PFIZER SA AND Pfizer) OR (PGRD PFIZER INC AND Pfizer) OR (VMRD PFIZER AUSTRALIA AND Pfizer) OR (CNS PFIZER INC AND Pfizer) OR (DIV PFIZER AND Pfizer) OR (DIV PFIZER GLOBAL RES DEV AND Pfizer) OR (DIV PFIZER INC AND Pfizer) OR (GRP PFIZER AND Pfizer) OR (GRP PFIZER INC AND Pfizer) OR (LAB PFIZER AND Pfizer) OR (LAB PFIZER LTDA AND Pfizer) OR (LAB PFIZER SAUDE ANIM AND Pfizer) OR (PDM PFIZER AND Pfizer) OR (R D PFIZER LTD AND Pfizer) OR (FA PFIZER TIERGESUNDHEIT AND Pfizer) OR (MA PFIZER CENT RES AND Pfizer) OR (MS PFIZER LTD AND Pfizer) OR (UK PFIZER HLTH SOLUT AND Pfizer) OR (A PFIZER CO AND Pfizer) OR (PFIZER AND Pfizer) OR (PFIZER AB AND Pfizer) OR (PFIZER AG AND Pfizer) OR (PFIZER AG SUISSE AND Pfizer) OR (PFIZER ALACLARI AS AND Pfizer) OR (PFIZER AMBOISE AND Pfizer) OR (PFIZER ANAL RES DEV AND Pfizer) OR (PFIZER ANALYT R D AND Pfizer) OR (PFIZER ANALYT RES CTR UGENT AND Pfizer) OR (PFIZER ANALYT RES DEV AND Pfizer) OR (PFIZER ANIM GENET AND Pfizer) OR (PFIZER ANIM HLTH AND Pfizer) OR (PFIZER ANIM HLTH AUSTRALIA AND Pfizer) OR (PFIZER ANIM HLTH BV AND Pfizer) OR (PFIZER ANIM HLTH EUROPE AND Pfizer) OR (PFIZER ANIM HLTH GRP AND Pfizer) OR (PFIZER ANIM HLTH INC AND Pfizer) OR (PFIZER ANIM HLTH KOREA AND Pfizer) OR (PFIZER ANIM HLTH LTD AND Pfizer) OR (PFIZER ANIM HLTH METAB SAFETY AND Pfizer) OR (PFIZER ANIM HLTH VET MED AND Pfizer) OR (PFIZER ANIM HLTH VET MED RES DEV AND Pfizer) OR (PFIZER ANIM HLTH VET MED RES DEV BIOL AND Pfizer) OR (PFIZER ANIM HLTH VET MED RES DEV METAB SAFETY AND Pfizer) OR (PFIZER ANIM HLTH VMRD AND Pfizer) OR (PFIZER ANN ARBOR LABS AND Pfizer) OR (PFIZER APS AND Pfizer) OR (PFIZER ARGENTINA AND Pfizer) OR (PFIZER ARGENTINA SRL AND Pfizer) OR (PFIZER AS AND Pfizer) OR (PFIZER AUSTRALIA AND Pfizer) OR (PFIZER AUSTRALIA PTY LTD AND Pfizer) OR (PFIZER BAYERN AND Pfizer) OR (PFIZER BELGIUM AND Pfizer) OR (PFIZER BIOL AND Pfizer) OR (PFIZER BIOPHARMACEUT AND Pfizer) OR (PFIZER BIOPROC RES DEV AND Pfizer) OR (PFIZER BIOSTAT AND Pfizer) OR (PFIZER BIOTHERAPEUT AND Pfizer) OR (PFIZER BIOTHERAPEUT BIOINNOVAT TECHNOL CTR AND Pfizer) OR (PFIZER BIOTHERAPEUT PHARMACEUT SCI AND Pfizer) OR (PFIZER BIOTHERAPEUT PHARMACEUT SCI R D AND Pfizer) OR (PFIZER BIOTHERAPEUT R D AND Pfizer) OR (PFIZER BIOTHERAPEUT R D PHARMACEUT SCI AND Pfizer) OR (PFIZER BIOTHERAPEUT RES AND Pfizer) OR (PFIZER BIOTHERAPEUT RES DEV AND Pfizer) OR (PFIZER BV AND Pfizer) OR (PFIZER CAMBRIDGE LABS AND Pfizer) OR (PFIZER CAMBRIDGE RES CTR AND Pfizer) OR (PFIZER CANADA AND Pfizer) OR (PFIZER CANADA INC AND Pfizer) OR (PFIZER CANADA LTD AND Pfizer) OR (PFIZER CANADA MED DIV AND Pfizer) OR (PFIZER CANMADA INC AND Pfizer) OR (PFIZER CARDIOVASC RES AND Pfizer) OR (PFIZER CENT AND Pfizer) OR (PFIZER CENT CHEM AND Pfizer) OR (PFIZER CENT RECH AND Pfizer) OR (PFIZER CENT RES AND Pfizer) OR (PFIZER CENT RES ANIM HLTH AND Pfizer) OR (PFIZER CENT RES DEV AND Pfizer) OR (PFIZER CENT RES DIV AND Pfizer) OR (PFIZER CENT RES GROTON CT AND Pfizer) OR (PFIZER CENT RES INC AND Pfizer) OR (PFIZER CENT RES LABS AND Pfizer) OR (PFIZER CENT RES LTD AND Pfizer) OR (PFIZER CENT RES UNIT AND Pfizer) OR (PFIZER CENT RS AND Pfizer) OR (PFIZER CENTR RES AND Pfizer) OR (PFIZER CHEM EUROPE AFRICA AND Pfizer) OR (PFIZER CHEM RES DEV AND Pfizer) OR (PFIZER CHINA RES DEV CTR AND Pfizer) OR (PFIZER CLIN DEV AND Pfizer) OR (PFIZER CLIN DEV MED AFFAIRS AND Pfizer) OR (PFIZER CLIN EDUC AND Pfizer) OR (PFIZER CLIN EDUC DEPT AND Pfizer) OR (PFIZER CLIN R D AND Pfizer) OR (PFIZER CLIN RES AND Pfizer) OR (PFIZER CLIN RES GRP AND Pfizer) OR (PFIZER CLIN RES MANAGEMENT AND Pfizer) OR (PFIZER CLIN RES UNIT AND Pfizer) OR (PFIZER CLIN RES UNIT ERASME AND Pfizer) OR (PFIZER CLIN STAT AND Pfizer) OR (PFIZER CLINSCI CNS AND Pfizer) OR (PFIZER CO AND Pfizer) OR (PFIZER COLL AND Pfizer) OR (PFIZER COLLEGEVILLE AND Pfizer) OR (PFIZER CONSULTANT AND Pfizer) OR (PFIZER CONSUMER HEALTHCARE AND Pfizer) OR (PFIZER CONSUMER HEALTHCARE R D AND Pfizer) OR (PFIZER CONSUMER HEALTHCARE RES DEV AND Pfizer) OR (PFIZER CONSUMER HLTH AND Pfizer) OR (PFIZER CONSUMER HLTH CARE AND Pfizer) OR (PFIZER CONSUMER HLTHCARE AND Pfizer) OR (PFIZER CORP AND Pfizer) OR (PFIZER CORP PHARMACOMETR AND Pfizer) OR (PFIZER CROATIA AND Pfizer) OR (PFIZER CTR RECH AND Pfizer) OR (PFIZER CTR RES AND Pfizer) OR (PFIZER DENMARK AND Pfizer) OR (PFIZER DENMARK APS AND Pfizer) OR (PFIZER DEPT PHARMACOKINET DISTRIBUT METAB AND Pfizer) OR (PFIZER DEUTSCHLAND GMBH AND Pfizer) OR (PFIZER DISCOVERY BIOL AND Pfizer) OR (PFIZER DISCOVERY CHEM AND Pfizer) OR (PFIZER DISCOVERY RES AND Pfizer) OR (PFIZER DISCOVERY TECHNOL CTR AND Pfizer) OR (PFIZER DOO RADNICKA ZAGREB AND Pfizer) OR (PFIZER DRUG SAFETY EVALUAT AND Pfizer) OR (PFIZER DRUG SAFETY RES DEV AND Pfizer) OR (PFIZER DSRD AND Pfizer) OR (PFIZER DTC AND Pfizer) OR (PFIZER ENDOCRINE CARE AND Pfizer) OR (PFIZER ENDOCRINE CARE EUROPE AND Pfizer) OR (PFIZER EPIDEMIOL AND Pfizer) OR (PFIZER ESPANA AND Pfizer) OR (PFIZER ESPANA ALCOBENDAS AND Pfizer) OR (PFIZER ESPANA SA AND Pfizer) OR (“PFIZER EUCAN OR” AND Pfizer) OR (PFIZER EUROPE AND Pfizer) OR (PFIZER EUROPEAN BRAND TEAM AND Pfizer) OR (PFIZER FINLAND AND Pfizer) OR (PFIZER FLOBAL RES DEV AND Pfizer) OR (PFIZER FRANCE AND Pfizer) OR (PFIZER FRESNES AND Pfizer) OR (PFIZER FRESNES LABS AND Pfizer) OR (PFIZER GERMANY AND Pfizer) OR (PFIZER GLOABL RES DEV AND Pfizer) OR (PFIZER GLOB RES DEV AND Pfizer) OR (PFIZER GLOBA R D AND Pfizer) OR (PFIZER GLOBA RES DEV AND Pfizer) OR (PFIZER GLOBABL RES DEV AND Pfizer) OR (PFIZER GLOBAL AND Pfizer) OR (PFIZER GLOBAL ANIM HLTH AND Pfizer) OR (PFIZER GLOBAL BIOL AND Pfizer) OR (PFIZER GLOBAL BIOL PHARMACEUT SCI AND Pfizer) OR (PFIZER GLOBAL BIOTHERAPEUT TECHNOL AND Pfizer) OR (PFIZER GLOBAL CENT RES AND Pfizer) OR (PFIZER GLOBAL CLIN TECHNOL AND Pfizer) OR (PFIZER GLOBAL DEV AND Pfizer) OR (PFIZER GLOBAL DISCOVERY RES AND Pfizer) OR (PFIZER GLOBAL DRUG SAFETY RES DEV AND Pfizer) OR (PFIZER GLOBAL EPIDEMIOL AND Pfizer) OR (PFIZER GLOBAL EPIDEMIOL SAFETY RISK MANAGEMENT AND Pfizer) OR (PFIZER GLOBAL HLTH ECON AND Pfizer) OR (PFIZER GLOBAL MED AND Pfizer) OR (PFIZER GLOBAL MED DEV SCI AND Pfizer) OR (PFIZER GLOBAL MED INC AND Pfizer) OR (PFIZER GLOBAL MFG AND Pfizer) OR (PFIZER GLOBAL OUTCOMES RES AND Pfizer) OR (PFIZER GLOBAL PHARMACEUT AND Pfizer) OR (PFIZER GLOBAL PHARMACEUT INC AND Pfizer) OR (PFIZER GLOBAL PHARMACEUT OPERAT AND Pfizer) OR (PFIZER GLOBAL PHARMACEUT PFIZER INC AND Pfizer) OR (PFIZER GLOBAL PHARMACEUT SCI AND Pfizer) OR (PFIZER GLOBAL PHARMACUET LABS AND Pfizer) OR (PFIZER GLOBAL R D AND Pfizer) OR (PFIZER GLOBAL R D DRUG SAFETY AND Pfizer) OR (PFIZER GLOBAL R D FRESNES LABS AND Pfizer) OR (PFIZER GLOBAL R D GROTON LABS AND Pfizer) OR (PFIZER GLOBAL R D INC AND Pfizer) OR (PFIZER GLOBAL R D LA JOLLA AND Pfizer) OR (PFIZER GLOBAL R D LABS AND Pfizer) OR (PFIZER GLOBAL R D NAGOYA LABS AND Pfizer) OR (PFIZER GLOBAL R D RES CTR AND Pfizer) OR (PFIZER GLOBAL R D SANDWICH AND Pfizer) OR (PFIZER GLOBAL R HOLLAND LABS AND Pfizer) OR (PFIZER GLOBAL R7D AND Pfizer) OR (PFIZER GLOBAL RED DEV AND Pfizer) OR (PFIZER GLOBAL RES AND Pfizer) OR (PFIZER GLOBAL RES 7 DEV AND Pfizer) OR (PFIZER GLOBAL RES CTR AND Pfizer) OR (PFIZER GLOBAL RES DEV AND Pfizer) OR (PFIZER GLOBAL RES DEV AGOURON PHARMACEUT AND Pfizer) OR (PFIZER GLOBAL RES DEV ALAMEDA LABS AND Pfizer) OR (PFIZER GLOBAL RES DEV ANALAYT R D AND Pfizer) OR (PFIZER GLOBAL RES DEV ANN ARBOR AND Pfizer) OR (PFIZER GLOBAL RES DEV ANN ARBOR LABS AND Pfizer) OR (PFIZER GLOBAL RES DEV CHEM AND Pfizer) OR (PFIZER GLOBAL RES DEV CLIN INFECT DIS AND Pfizer) OR (PFIZER GLOBAL RES DEV CLIN SCI AND Pfizer) OR (PFIZER GLOBAL RES DEV CN8000 AND Pfizer) OR (PFIZER GLOBAL RES DEV DEPT COMPARAT MED AND Pfizer) OR (PFIZER GLOBAL RES DEV DISCOVERY BIOL AND Pfizer) OR (PFIZER GLOBAL RES DEV EASTERN POINT RD GROTON AND Pfizer) OR (PFIZER GLOBAL RES DEV GLOBAL BIOL AND Pfizer) OR (PFIZER GLOBAL RES DEV GLOBLA CLIN TECHNOL AND Pfizer) OR (PFIZER GLOBAL RES DEV GROTON AND Pfizer) OR (PFIZER GLOBAL RES DEV GROTON LABS AND Pfizer) OR (PFIZER GLOBAL RES DEV GRP AND Pfizer) OR (PFIZER GLOBAL RES DEV HEADQUARTERS AND Pfizer) OR (PFIZER GLOBAL RES DEV INC AND Pfizer) OR (PFIZER GLOBAL RES DEV INST AND Pfizer) OR (PFIZER GLOBAL RES DEV IPC 432 AND Pfizer) OR (PFIZER GLOBAL RES DEV LA JOLLA AND Pfizer) OR (PFIZER GLOBAL RES DEV LA JOLLA AGOURON PHARMACE AND Pfizer) OR (PFIZER GLOBAL RES DEV LA JOLLA LABS AND Pfizer) OR (PFIZER GLOBAL RES DEV LABS AND Pfizer) OR (PFIZER GLOBAL RES DEV LAJOLLA AND Pfizer) OR (PFIZER GLOBAL RES DEV LTD AND Pfizer) OR (PFIZER GLOBAL RES DEV PGRD AND Pfizer) OR (PFIZER GLOBAL RES DEV PHARMACEUT SCI AND Pfizer) OR (PFIZER GLOBAL RES DEV PHARMACOKINET DYNAM MET AND Pfizer) OR (PFIZER GLOBAL RES DEV SAFETY SCI AND Pfizer) OR (PFIZER GLOBAL RES DEV SANDWICH AND Pfizer) OR (PFIZER GLOBAL RES DEV SANDWICH LABS AND Pfizer) OR (PFIZER GLOBAL RES DEV SEXUAL HLTH AND Pfizer) OR (PFIZER GLOBAL RES DEV ST LOUIS LABS AND Pfizer) OR (PFIZER GLOBAL RES DEV UK AND Pfizer) OR (PFIZER GLOBAL RES DEV UK LTD AND Pfizer) OR (PFIZER GLOBAL RES DEVELOP AND Pfizer) OR (PFIZER GLOBAL RES DEVELOPMENT AND Pfizer) OR (PFIZER GLOBAL RES DEVT SEXUAL HLTH AND Pfizer) OR (PFIZER GLOBAL RES DISCOVERY AND Pfizer) OR (PFIZER GLOBAL RES DIV AND Pfizer) OR (PFIZER GLOBAL RES DRV AND Pfizer) OR (PFIZER GLOBAL RES INC AND Pfizer) OR (PFIZER GLOBAL RES LTD AND Pfizer) OR (PFIZER GLOBAL RND AND Pfizer) OR (PFIZER GLOBAL RS DEV AND Pfizer) OR (PFIZER GLOBAL RSCH DEV AND Pfizer) OR (PFIZER GLOBAL RSCH DEV NUCL MOL MED PHARMA AND Pfizer) OR (PFIZER GLOBAL RSRCH DEV AND Pfizer) OR (PFIZER GLOBAL SUPPLY CHAIN AND Pfizer) OR (PFIZER GLOBAOL R D AND Pfizer) OR (PFIZER GLOBARL RES DEV AND Pfizer) OR (PFIZER GLOBE RES DEV AND Pfizer) OR (PFIZER GLOCAL RES DEV AND Pfizer) OR (PFIZER GLOCAL RES DEV PROGRAM AND Pfizer) OR (PFIZER GLOV AND Pfizer) OR (PFIZER GLOVAL RES DEV AND Pfizer) OR (PFIZER GLOVBAL RES DEV AND Pfizer) OR (PFIZER GMBH AND Pfizer) OR (PFIZER GMBH ANIM HLTH AND Pfizer) OR (PFIZER GMBH DEUTSCHLAND AND Pfizer) OR (PFIZER GMBH TIERGESUNDHEIT AND Pfizer) OR (PFIZER GMBH TIERGESUNDHEIT KARLSRUHE AND Pfizer) OR (PFIZER GOBAL RES DEV AND Pfizer) OR (PFIZER GRD AND Pfizer) OR (PFIZER GRD FRESNES AND Pfizer) OR (PFIZER GRD GROTON LABS AND Pfizer) OR (PFIZER GRD SANDWICH LABS AND Pfizer) OR (PFIZER GRONINGEN AND Pfizer) OR (PFIZER GRONINGEN BV AND Pfizer) OR (PFIZER GROTON AND Pfizer) OR (PFIZER GROTON LABS AND Pfizer) OR (PFIZER GRP AND Pfizer) OR (PFIZER GRP INC AND Pfizer) OR (PFIZER GRP PHARMACEUT AND Pfizer) OR (PFIZER HELLAS AND Pfizer) OR (PFIZER HELLAS AE AND Pfizer) OR (PFIZER HLTH AND Pfizer) OR (PFIZER HLTH AB AND Pfizer) OR (PFIZER HLTH OUTCOMES RES AND Pfizer) OR (PFIZER HLTH SOLUT AND Pfizer) OR (PFIZER HLTH SOLUT INC AND Pfizer) OR (PFIZER HOSP AND Pfizer) OR (PFIZER HOSP PROD LTD AND Pfizer) OR (PFIZER HUNGARIA AND Pfizer) OR (PFIZER HUNGARIA KFT AND Pfizer) OR (PFIZER HUNGARY LTD AND Pfizer) OR (PFIZER ILACLARI AP AND Pfizer) OR (PFIZER ILACLARI AS AND Pfizer) OR (PFIZER ILACLARI LTD AND Pfizer) OR (PFIZER INC AND Pfizer) OR (PFIZER INC CHESTERFIELD AND Pfizer) OR (PFIZER INC CO AND Pfizer) OR (PFIZER INC GLOBAL RES DEV AND Pfizer) OR (PFIZER INC MED AND Pfizer) OR (PFIZER INC PGD GROTON AND Pfizer) OR (PFIZER INC PGRD AND Pfizer) OR (PFIZER INC SANDWICH AND Pfizer) OR (PFIZER IND AND Pfizer) OR (PFIZER INDIA AND Pfizer) OR (PFIZER INFLAMMAT IMMUNOL AND Pfizer) OR (PFIZER INFLAMMAT RES AND Pfizer) OR (PFIZER INS AND Pfizer) OR (PFIZER INST PHARMACEUT MAT SCI AND Pfizer) OR (PFIZER INT AND Pfizer) OR (PFIZER INT OPERAT AND Pfizer) OR (PFIZER INVESTMENT CO LTD AND Pfizer) OR (PFIZER ITALAIA SRL AND Pfizer) OR (PFIZER ITALI SRL AND Pfizer) OR (PFIZER ITALIA AND Pfizer) OR (PFIZER ITALIA SPA AND Pfizer) OR (PFIZER ITALIA SRL AND Pfizer) OR (PFIZER ITALIANA AND Pfizer) OR (PFIZER ITALIANA SPA AND Pfizer) OR (PFIZER ITALIE SRF AND Pfizer) OR (PFIZER ITALY AND Pfizer) OR (PFIZER JAPAN AND Pfizer) OR (PFIZER JAPAN INC AND Pfizer) OR (PFIZER JAPAN LTD AND Pfizer) OR (PFIZER KEIO RES LABS AND Pfizer) OR (PFIZER KEIO RSCH LAB AND Pfizer) OR (PFIZER KFT AND Pfizer) OR (PFIZER KOREA AND Pfizer) OR (PFIZER LA JOLLA AND Pfizer) OR (PFIZER LA JOLLA GLOBAL RES DEV AND Pfizer) OR (PFIZER LA JOLLA INC AND Pfizer) OR (PFIZER LA JOLLA LAB AND Pfizer) OR (PFIZER LA JOLLA LABS AND Pfizer) OR (PFIZER LA JOLLA LABS CB5 AND Pfizer) OR (PFIZER LAB MOL GENET AND Pfizer) OR (PFIZER LABS AND Pfizer) OR (PFIZER LABS BRAZIL AND Pfizer) OR (PFIZER LABS LTD AND Pfizer) OR (PFIZER LABS PTY LTD AND Pfizer) OR (PFIZER LEBANON AND Pfizer) OR (PFIZER LEGACY WYETH RES AND Pfizer) OR (PFIZER LTD AND Pfizer) OR (PFIZER LTD CENT RES AND Pfizer) OR (PFIZER LTD IPC 160 AND Pfizer) OR (PFIZER LTD IPC 330 AND Pfizer) OR (PFIZER LTD IPC 432 AND Pfizer) OR (PFIZER LTD IPC 5 2 54 AND Pfizer) OR (PFIZER LTD OUTCOMES RES AND Pfizer) OR (PFIZER LTD UK AND Pfizer) OR (PFIZER LTDA AND Pfizer) OR (PFIZER LUXEMBOURG SARL AND Pfizer) OR (PFIZER MACK MICROBIOL LABS R D AND Pfizer) OR (PFIZER MACK RES DEV AND Pfizer) OR (PFIZER MACK RES LAB AND Pfizer) OR (PFIZER MADRID AND Pfizer) OR (PFIZER MALAYSIA SDN BHD AND Pfizer) OR (PFIZER MED AND Pfizer) OR (PFIZER MED CANADA AND Pfizer) OR (PFIZER MED DEPT AND Pfizer) OR (PFIZER MED DIV AND Pfizer) OR (PFIZER MED HUMANITIES INITIAT AND Pfizer) OR (PFIZER MED OUTCOMES AND Pfizer) OR (PFIZER MED TECHNOL GRP AND Pfizer) OR (PFIZER MED UNIT AND Pfizer) OR (PFIZER MERCK AND Pfizer) OR (PFIZER MEXICO AND Pfizer) OR (PFIZER MFG BELGIUM NV AND Pfizer) OR (PFIZER MOL BIOL RES FACIL AND Pfizer) OR (PFIZER MOL CELLULAR TOXICOL LAB AND Pfizer) OR (PFIZER MOL PHARMACOL AND Pfizer) OR (PFIZER NAGOYA AND Pfizer) OR (PFIZER NAGOYA LABS AND Pfizer) OR (PFIZER NEUROSCI AND Pfizer) OR (PFIZER NEUROSCI PRINCETON AND Pfizer) OR (PFIZER NEUROSCI RES UNIT AND Pfizer) OR (PFIZER NEW YORK AND Pfizer) OR (PFIZER NORDIC BENELUX REG AND Pfizer) OR (PFIZER NORWAY AND Pfizer) OR (PFIZER NUTR AND Pfizer) OR (PFIZER NV AND Pfizer) OR (PFIZER NV SA AND Pfizer) OR (PFIZER NV SA WPO AND Pfizer) OR (PFIZER NY INC AND Pfizer) OR (PFIZER ONCOL AND Pfizer) OR (PFIZER ONCOL EUROPE AND Pfizer) OR (PFIZER ONCOL MED AFFAIRS AND Pfizer) OR (PFIZER ONCOL OPHTHALMOL ENDOCRINE AND Pfizer) OR (PFIZER OPHTHALM AND Pfizer) OR (PFIZER OPHTHALMOL AND Pfizer) OR (PFIZER OUTCOMES RES AND Pfizer) OR (PFIZER OUTCOMES RES ALCOBENDAS AND Pfizer) OR (PFIZER OUTCOMES RES EUROPE AND Pfizer) OR (PFIZER OUTCOMES RES EVIDENCE BASED MED AND Pfizer) OR (PFIZER OUTCOMES RSCH AND Pfizer) OR (PFIZER OY AND Pfizer) OR (PFIZER PARIS AND Pfizer) OR (PFIZER PARKE DAVIS AND Pfizer) OR (PFIZER PARKE DAVIS PHARMACEUT AND Pfizer) OR (PFIZER PFIZER SALUD ANIM AND Pfizer) OR (PFIZER PGP AND Pfizer) OR (PFIZER PGRD AND Pfizer) OR (PFIZER PGRD AMBOISE AND Pfizer) OR (PFIZER PGRD DISCOVERY BIOL AND Pfizer) OR (PFIZER PGRD GROTON LABS AND Pfizer) OR (PFIZER PHARM GMBH AND Pfizer) OR (PFIZER PHARMA AND Pfizer) OR (PFIZER PHARMA GERMANY AND Pfizer) OR (PFIZER PHARMA GMBH AND Pfizer) OR (PFIZER PHARMA THERAPEUT AND Pfizer) OR (PFIZER PHARMA THERAPEUT RES GRP AND Pfizer) OR (PFIZER PHARMACEUT AND Pfizer) OR (PFIZER PHARMACEUT CANADA AND Pfizer) OR (PFIZER PHARMACEUT CLIN EDUC AND Pfizer) OR (PFIZER PHARMACEUT CO AND Pfizer) OR (PFIZER PHARMACEUT CO LTD AND Pfizer) OR (PFIZER PHARMACEUT CORP AND Pfizer) OR (PFIZER PHARMACEUT DEV AND Pfizer) OR (PFIZER PHARMACEUT FACIL AND Pfizer) OR (PFIZER PHARMACEUT GRP AND Pfizer) OR (PFIZER PHARMACEUT GRP PARIS AND Pfizer) OR (PFIZER PHARMACEUT GRP UK AND Pfizer) OR (PFIZER PHARMACEUT INC AND Pfizer) OR (PFIZER PHARMACEUT INDIA PVT LTD AND Pfizer) OR (PFIZER PHARMACEUT KOREA LTD AND Pfizer) OR (PFIZER PHARMACEUT LTD AND Pfizer) OR (PFIZER PHARMACEUT LTD CO AND Pfizer) OR (PFIZER PHARMACEUT OUTCOMES RES AND Pfizer) OR (PFIZER PHARMACEUT PROD GRP AND Pfizer) OR (PFIZER PHARMACEUT PVT LTD AND Pfizer) OR (PFIZER PHARMACEUT RES AND Pfizer) OR (PFIZER PHARMACEUT SCI AND Pfizer) OR (PFIZER PHARMACEUT TRIALS TAC AND Pfizer) OR (PFIZER PHARMACOKINET DYNAM METAB AND Pfizer) OR (PFIZER PHARMACOMETRICS GLOBAL CLIN PHARMACOL AND Pfizer) OR (PFIZER PHARMATHERAPEUT AND Pfizer) OR (PFIZER PHARMATHERAPEUT DIV AND Pfizer) OR (PFIZER PHARMATHERAPEUT R D AND Pfizer) OR (PFIZER PHARMATHERAPEUT RES DEV AND Pfizer) OR (PFIZER PHARMATX RES DEV AND Pfizer) OR (PFIZER PHC CORP AND Pfizer) OR (PFIZER PLC AND Pfizer) OR (PFIZER POLSKA AND Pfizer) OR (PFIZER POLSKA SP ZOO AND Pfizer) OR (PFIZER POLSKA SP ZOO MED AFFAIRS AND Pfizer) OR (PFIZER POLSKA SPZ OO MED AFFAIRS AND Pfizer) OR (PFIZER POULTRY HLTH DIV AND Pfizer) OR (PFIZER PRECLIN STAT AND Pfizer) OR (PFIZER PRIMARY CARE BUSINESS UNIT AND Pfizer) OR (PFIZER PRIVATE LTD AND Pfizer) OR (PFIZER PROD INFORMAT AND Pfizer) OR (PFIZER PROD PLC AND Pfizer) OR (PFIZER PTE LTD AND Pfizer) OR (PFIZER PTY LTD AND Pfizer) OR (PFIZER R D AND Pfizer) OR (PFIZER R D GLOBAL BIOL AND Pfizer) OR (PFIZER R D LABS AND Pfizer) OR (PFIZER REGENERAT MED AND Pfizer) OR (PFIZER RES AND Pfizer) OR (PFIZER RES CLIN AND Pfizer) OR (PFIZER RES CORP AND Pfizer) OR (PFIZER RES CTR AND Pfizer) OR (PFIZER RES DEV AND Pfizer) OR (PFIZER RES FORMULAT AND Pfizer) OR (PFIZER RES GLOBAL DIV AND Pfizer) OR (PFIZER RES LABS AND Pfizer) OR (PFIZER RES TECHNOL AND Pfizer) OR (PFIZER RES TECHNOL CTR AND Pfizer) OR (PFIZER RES TECHNOL RES CTR AND Pfizer) OR (PFIZER RES TECHNOPL CTR AND Pfizer) OR (PFIZER RINAT LABS AND Pfizer) OR (PFIZER RTC AND Pfizer) OR (PFIZER RTC CAMBRIDGE AND Pfizer) OR (PFIZER S A AND Pfizer) OR (PFIZER SA AND Pfizer) OR (PFIZER SA ALCOBENDAS AND Pfizer) OR (PFIZER SA MADRID AND Pfizer) OR (PFIZER SA VALENCIA AND Pfizer) OR (PFIZER SAFETY SCI RES DEV AND Pfizer) OR (PFIZER SALUD ANIM AND Pfizer) OR (PFIZER SANDWICH AND Pfizer) OR (PFIZER SANDWICH LABS AND Pfizer) OR (PFIZER SANTE ANIM AND Pfizer) OR (PFIZER SAUDI ARABIA AND Pfizer) OR (PFIZER SCHWEIZ AG AND Pfizer) OR (PFIZER SOLLENTUNA AND Pfizer) OR (PFIZER SPA AND Pfizer) OR (PFIZER SPAIN AND Pfizer) OR (PFIZER SPAIN ALCOBENDAS AND Pfizer) OR (PFIZER SPAIN MED DEPT AND Pfizer) OR (PFIZER SPAIN SA AND Pfizer) OR (PFIZER SPECIALTIES LTD AND Pfizer) OR (PFIZER SPECIALTY CARE AND Pfizer) OR (PFIZER SPECIALTY CARE BUSINESS UNIT AND Pfizer) OR (PFIZER SPOL SRO AND Pfizer) OR (PFIZER SRL AND Pfizer) OR (PFIZER ST LOUIS AND Pfizer) OR (PFIZER ST LOUIS LABS AND Pfizer) OR (PFIZER ST LOUIS RES LABS AND Pfizer) OR (PFIZER STRUCT COMPUTAT BIOL AND Pfizer) OR (PFIZER SWEDEB AND Pfizer) OR (PFIZER SWEDEN AND Pfizer) OR (PFIZER TAIWAN PFIZER GLOBAL PHARMACEUT AND Pfizer) OR (PFIZER TIEGESUNDHEIT KARLSRUHE AND Pfizer) OR (PFIZER TIERGESUNDHEIT AND Pfizer) OR (PFIZER TIERGESUNDHEIT GMBH BERLIN AND Pfizer) OR (PFIZER TIERGESUNDHEIT KARLSRUHE AND Pfizer) OR (PFIZER TRANSLAT IMMUNOL AND Pfizer) OR (PFIZER UK AND Pfizer) OR (PFIZER UK GRP LTD AND Pfizer) OR (PFIZER UK LTD AND Pfizer) OR (PFIZER UNIV GRANADA AND Pfizer) OR (PFIZER UNIV GRANADA ANDALUCIAN GOVERMENT CTR GENO AND Pfizer) OR (PFIZER UNIV GRANADA JUNTA ANDALUCIA AND Pfizer) OR (PFIZER UNIV GRANADA JUNTA DE ANDALUCIA AND Pfizer) OR (PFIZER US OUTCOMES RES AND Pfizer) OR (PFIZER US OUTCOMES RES GRP AND Pfizer) OR (PFIZER US PHARMACEUT AND Pfizer) OR (PFIZER VACCINE CLIN RES AND Pfizer) OR (PFIZER VACCINE OTTAWA AND Pfizer) OR (PFIZER VACCINE RES AND Pfizer) OR (PFIZER VACCINES RES AND Pfizer) OR (PFIZER VENEZUELA AND Pfizer) OR (PFIZER VET MED AND Pfizer) OR (PFIZER VET MED CLIN DEV BIOL AND Pfizer) OR (PFIZER VET MED RES DEV AND Pfizer) OR (PFIZER WARNER LAMBERT AND Pfizer) OR (PFIZER WORLD WIDE MED AND Pfizer) OR (PFIZER WORLD WIDE OUTCOMES RES AND Pfizer) OR (PFIZER WORLD WIDE RES DEV AND Pfizer) OR (PFIZER WORLDWIDE AND Pfizer) OR (PFIZER WORLDWIDE CLIN DEV AND Pfizer) OR (PFIZER WORLDWIDE DEV AND Pfizer) OR (PFIZER WORLDWIDE DEV OPERAT AND Pfizer) OR (PFIZER WORLDWIDE DEV SAFETY RISK MANAGEMENT AND Pfizer) OR (PFIZER WORLDWIDE MED CHEM AND Pfizer) OR (PFIZER WORLDWIDE MED OUTCOMES RES AND Pfizer) OR (PFIZER WORLDWIDE PHARMACEUT OPERAT AND Pfizer) OR (PFIZER WORLDWIDE PHARMACEUT OPERAT WALTON OAKS AND Pfizer) OR (PFIZER WORLDWIDE R D AND Pfizer) OR (PFIZER WORLDWIDE RES AND Pfizer) OR (PFIZER WORLDWIDE RES DEV AND Pfizer) OR (PFIZER WORLDWIDE RES DEV SANDWICH LABS AND Pfizer) OR (PFIZER WORLDWIDE SAFETY SCI AND Pfizer) OR (PFIZER WORLDWIDE TECHNOL AND Pfizer) OR (PFIZER WPO BELGIQUE AND Pfizer) OR (PFIZER WPO BELGIUM AND Pfizer) OR (PFIZER WW SAFETY SCI AND Pfizer) OR (PFIZERGLOBAL R D AND Pfizer) OR (PFIZERGLOBAL RES DEV AND Pfizer) OR (PFIZERONCOL AND Pfizer) OR (PFIZERS EUROPEAN R D AND Pfizer))) AND YEAR PUBLISHED: (2010-2015)
Timespan: All years. Indexes: SCI-EXPANDED, SSCI.

Web of Science Search for Unilever Publications

You searched for: ORGANIZATION-ENHANCED: (((ASLAN BEY MEVKII VAKIFLAR KOYU ALGIDA UNILEVER SA AND Unilever) OR (ASTERIADOU UNILEVER R D AND Unilever) OR (COLWORTH DISCOVER UNILEVER R D AND Unilever) OR (DEUT UNILEVER GMBH AND Unilever) OR (DEUTSCH UNILEVER GMBH AND Unilever) OR (DEUTSCHE UNILEVER GMBH AND Unilever) OR (DEUTSCHEN UNILEVER GMBH AND Unilever) OR (EAC UNILEVER RES LAB AND Unilever) OR (FACILITAT UNIT UNILEVER RES VLAARDINGEN AND Unilever) OR (FAT TECHNOL UNILEVER R D VLAARDINGEN AND Unilever) OR (FOOD RES CTR UNILEVER AND Unilever) OR (FOODS RES CTR UNILEVER R D AND Unilever) OR (GREEN MR UNILEVER R D AND Unilever) OR (HINAUSTAN UNILEVER RES CTR AND Unilever) OR (HINDUSTAN UNILEVER LTD AND Unilever) OR (HINDUSTAN UNILEVER RES CTR AND Unilever) OR (LAB RECH UNILEVER AND Unilever) OR (LAUNDRY DISCOVERY UNILEVER RES DEV AND Unilever) OR (LEVER ASOCIADOS SACIF UNILEVER AND Unilever) OR (LEVER BROS UNILEVER LTD AND Unilever) OR (MARGARINE UNION GMBH UNILEVER AND Unilever) OR (PORT SUNLIGHT LAB UNILEVER RES AND Unilever) OR (PORT SUNLIGHT UNILEVER RES LAB AND Unilever) OR (PROC SUPPLY CHAIN DESIGN UNILEVER FOOD HLTH R AND Unilever) OR (RES DEV UNILEVER AND Unilever) OR (SAFETY ENVIRONM ASSURANCE CTR UNILEVER AND Unilever) OR (SEAC UNILEVER AND Unilever) OR (SEAC UNILEVER COLWORTH LAB AND Unilever) OR (SEAC UNILEVER SHARNBROOK AND Unilever) OR (TUSCC UNILEVER RES AND Unilever) OR (UNILEVER AND Unilever) OR (UNILEVER AEROSOLS CTR EXPERTISE AND Unilever) OR (UNILEVER AG AND Unilever) OR (UNILEVER ARCH AND Unilever) OR (UNILEVER ASIA PVT LTD AND Unilever) OR (UNILEVER AUSTRALASIA AND Unilever) OR (UNILEVER AUSTRALIA AND Unilever) OR (UNILEVER AUSTRALIA LTD AND Unilever) OR (UNILEVER AUSTRALIA PROPRIETARY LTD AND Unilever) OR (UNILEVER AUSTRALIA PTY LTD AND Unilever) OR (UNILEVER BEDFORD AND Unilever) OR (UNILEVER BENELUX AND Unilever) OR (UNILEVER BENELUX BV AND Unilever) OR (UNILEVER BEST FOOD NA AND Unilever) OR (UNILEVER BEST FOODS AND Unilever) OR (UNILEVER BESTFOOD EUROPE AND Unilever) OR (UNILEVER BESTFOOD GERMANY AND Unilever) OR (UNILEVER BESTFOODS AND Unilever) OR (UNILEVER BESTFOODS AUSTRIA AND Unilever) OR (UNILEVER BESTFOODS BRAZIL AND Unilever) OR (UNILEVER BESTFOODS DEUTSCH AND Unilever) OR (UNILEVER BESTFOODS DEUTSCHLAND AND Unilever) OR (UNILEVER BESTFOODS EUROPE AND Unilever) OR (UNILEVER BESTFOODS FRANCE AND Unilever) OR (UNILEVER BESTFOODS FRANCE AMORA MAILLE AND Unilever) OR (UNILEVER BESTFOODS FRANCE AMORA MAILLE DIJON AND Unilever) OR (UNILEVER BESTFOODS GERMANY AND Unilever) OR (UNILEVER BESTFOODS N AMER AND Unilever) OR (UNILEVER BESTFOODS NA AND Unilever) OR (UNILEVER BESTFOODS NETHERLANDS AND Unilever) OR (UNILEVER BESTFOODS NL AND Unilever) OR (UNILEVER BESTFOODS R D AND Unilever) OR (UNILEVER BESTFOODS R D COLWORTH LAB AND Unilever) OR (UNILEVER BESTFOODS RES DEV AND Unilever) OR (UNILEVER BESTFOODS UK AND Unilever) OR (UNILEVER BRASIL LTDA AND Unilever) OR (UNILEVER CATEGORY TECHNOL CTR AND Unilever) OR (UNILEVER CENT RES AND Unilever) OR (UNILEVER CENT RES LABS AND Unilever) OR (UNILEVER CENT RESOURCES AND Unilever) OR (UNILEVER CENT RESOURCES LTD AND Unilever) OR (UNILEVER CLIN AND Unilever) OR (UNILEVER CO AND Unilever) OR (UNILEVER COE ICE FOODS AND Unilever) OR (UNILEVER COLWORTH AND Unilever) OR (UNILEVER COLWORTH HOUSE AND Unilever) OR (UNILEVER COLWORTH LAB AND Unilever) OR (UNILEVER COLWORTH PK AND Unilever) OR (UNILEVER COLWORTH R D AND Unilever) OR (UNILEVER COLWORTH RES LAB AND Unilever) OR (UNILEVER COLWORTH RES LABS AND Unilever) OR (UNILEVER COLWORTH SCI PK AND Unilever) OR (UNILEVER COLWORTH SCI PK SAFETY ENVIRONM ASSURA AND Unilever) OR (UNILEVER COLWORTH UK AND Unilever) OR (UNILEVER COMP SERV LTD AND Unilever) OR (UNILEVER CONSUMER MKT INSIGHTS AND Unilever) OR (UNILEVER CORP AND Unilever) OR (UNILEVER CORP RES AND Unilever) OR (UNILEVER CORP RES BIOSCI AND Unilever) OR (UNILEVER CORP RES COLWORTH AND Unilever) OR (UNILEVER CORP RES CORP RES AND Unilever) OR (UNILEVER CORP RES CTR AND Unilever) OR (UNILEVER CORP RSCH AND Unilever) OR (UNILEVER CORPORATE RES AND Unilever) OR (UNILEVER CTR RES AND Unilever) OR (UNILEVER DENT RES AND Unilever) OR (UNILEVER DEUTSCHLAND AND Unilever) OR (UNILEVER DEUTSCHLAND GMBH AND Unilever) OR (UNILEVER DISCOVER AND Unilever) OR (UNILEVER DISCOVER COLWORTH AND Unilever) OR (UNILEVER DISCOVER PORT SUNLIGHT AND Unilever) OR (UNILEVER DISCOVER R D AND Unilever) OR (UNILEVER DISCOVER RES DEV AND Unilever) OR (UNILEVER DISCOVER SHANGHAI AND Unilever) OR (UNILEVER DISCOVER VLAARDINGEN AND Unilever) OR (UNILEVER DISCOVERY AND Unilever) OR (UNILEVER EMERY NV AND Unilever) OR (UNILEVER ENGN AND Unilever) OR (UNILEVER ENGN ENVIRONM RES LAB AND Unilever) OR (UNILEVER ENGN LONDON AND Unilever) OR (UNILEVER ENGN SERV AND Unilever) OR (UNILEVER ENV SAFETY LAB AND Unilever) OR (UNILEVER ENVIRONM SAFETY LAB AND Unilever) OR (UNILEVER ENVIRONM SCI LAB AND Unilever) OR (UNILEVER ESL AND Unilever) OR (UNILEVER EUROPE AND Unilever) OR (UNILEVER FOOD HLTH INST AND Unilever) OR (UNILEVER FOOD HLTH RES AND Unilever) OR (UNILEVER FOOD HLTH RES INST AND Unilever) OR (UNILEVER FOOD HLTH RES INST UFHRI AND Unilever) OR (UNILEVER FOOD HLTH RES INST VLAARDINGEN AND Unilever) OR (UNILEVER FOOD RES CTR AND Unilever) OR (UNILEVER FOOD RES INST AND Unilever) OR (UNILEVER FOODS AND Unilever) OR (UNILEVER FOODS AMER R D AND Unilever) OR (UNILEVER FOODS ESPANA SA AND Unilever) OR (UNILEVER FOODS HLTH RES INST AND Unilever) OR (UNILEVER FOODS NA AND Unilever) OR (UNILEVER FOODS R D AND Unilever) OR (UNILEVER FOODS RES AND Unilever) OR (UNILEVER FOODS RES CTR AND Unilever) OR (UNILEVER FORSCH AND Unilever) OR (UNILEVER FORSCH GESELL AND Unilever) OR (UNILEVER FORSCH GESELL GMBH AND Unilever) OR (UNILEVER FORSCH GESELL MBH AND Unilever) OR (UNILEVER FORSCH GESELL MBN AND Unilever) OR (UNILEVER FORSCH GESELLSCH AND Unilever) OR (UNILEVER FORSCH GESELLSCH MBH AND Unilever) OR (UNILEVER FORSCH GESELLSCHAFT MBH AND Unilever) OR (UNILEVER FORSCH GESSEL MBH AND Unilever) OR (UNILEVER FORSCH GMBH AND Unilever) OR (UNILEVER FORSCH LAB AND Unilever) OR (UNILEVER FORSCH SGESELL MBH AND Unilever) OR (UNILEVER FORSCHUNGSGESELL GMBH AND Unilever) OR (UNILEVER FORSCHUNGSGESELL MBH AND Unilever) OR (UNILEVER FORSH GESELL GMBH AND Unilever) OR (UNILEVER FORSH GESELL MBH AND Unilever) OR (UNILEVER FRANCE AND Unilever) OR (UNILEVER FS GLOBAL HR AND Unilever) OR (UNILEVER GLOBAL RES CTR AND Unilever) OR (UNILEVER GLOBAL SKIN INNOVAT CTR AND Unilever) OR (UNILEVER GLOBAL SKIN TECHNOL CTR AND Unilever) OR (UNILEVER GLOBAL SOCIAL MISSION AND Unilever) OR (UNILEVER GREECE AND Unilever) OR (UNILEVER HLTH FOOD RES INST AND Unilever) OR (UNILEVER HLTH INST AND Unilever) OR (UNILEVER HLTH INST VLAARDINGEN AND Unilever) OR (UNILEVER HLTH RES CTR AND Unilever) OR (UNILEVER HOME PERSONAL CARE AND Unilever) OR (UNILEVER HOME PERSONAL CARE N AMER AND Unilever) OR (UNILEVER HOME PERSONAL CARE NA AND Unilever) OR (UNILEVER HOME PERSONAL CARE R D PORT SUNLIGHT AND Unilever) OR (UNILEVER HOME PERSONAL CARE R PORT SUNLIGHT AND Unilever) OR (UNILEVER HOME PERSONAL CARE USA AND Unilever) OR (UNILEVER HOUSE AND Unilever) OR (UNILEVER HPC AND Unilever) OR (UNILEVER HPC NA AND Unilever) OR (UNILEVER HPC R D AND Unilever) OR (UNILEVER HPC R D PORT SUNLIGHT AND Unilever) OR (UNILEVER HPC RES US AND Unilever) OR (UNILEVER HPC SPAIN AND Unilever) OR (UNILEVER HPC USA AND Unilever) OR (UNILEVER INST AND Unilever) OR (UNILEVER INT MANAGEMENT CONSULTANTS LTD AND Unilever) OR (UNILEVER ISRAEL AND Unilever) OR (UNILEVER ITALIA AND Unilever) OR (UNILEVER ITALY HOLDINGS SRL AND Unilever) OR (UNILEVER JAPAN KK AND Unilever) OR (UNILEVER KENYA AND Unilever) OR (UNILEVER KK AND Unilever) OR (UNILEVER KNORR FACTORY AND Unilever) OR (UNILEVER KOREA AND Unilever) OR (UNILEVER LAB AND Unilever) OR (UNILEVER LAB COLWORTH AND Unilever) OR (UNILEVER LAB RECH AND Unilever) OR (UNILEVER LIFE SCI AND Unilever) OR (UNILEVER LTD AND Unilever) OR (UNILEVER LTD UNILEVER RES AND Unilever) OR (UNILEVER MEASUREMENT SCI AND Unilever) OR (UNILEVER MED SERV AND Unilever) OR (UNILEVER MERSEYSIDE LTD AND Unilever) OR (UNILEVER N AMER AND Unilever) OR (UNILEVER N V VITALITY PROGRAMME FOODS AND Unilever) OR (UNILEVER NEDERLAND NV AND Unilever) OR (UNILEVER NUTR CTR AND Unilever) OR (UNILEVER NV AND Unilever) OR (UNILEVER NV PLC AND Unilever) OR (UNILEVER NV ROTTERDAM AND Unilever) OR (UNILEVER OCCUPAT HLTH AND Unilever) OR (UNILEVER ORAL CARE AND Unilever) OR (UNILEVER ORAL CARE RES AND Unilever) OR (UNILEVER ORAL CARE UK AND Unilever) OR (UNILEVER PERFUME COMPETENCE CTR AND Unilever) OR (UNILEVER PLANTAT AND Unilever) OR (UNILEVER PLC AND Unilever) OR (UNILEVER PORT SUNLIGHT AND Unilever) OR (UNILEVER PORT SUNLIGHT LAB AND Unilever) OR (UNILEVER PORT SUNLIGHT LABS AND Unilever) OR (UNILEVER PORT SUNLIGHT RES AND Unilever) OR (UNILEVER PORT SUNLIGHT RES LAB AND Unilever) OR (UNILEVER R COLWORTH AND Unilever) OR (UNILEVER R D AND Unilever) OR (UNILEVER R D BANGALORE AND Unilever) OR (UNILEVER R D BEBINGTON AND Unilever) OR (UNILEVER R D CHINA AND Unilever) OR (UNILEVER R D COLOWORTH AND Unilever) OR (UNILEVER R D COLWORTH AND Unilever) OR (UNILEVER R D COLWORTH HOUSE AND Unilever) OR (UNILEVER R D COLWORTH LAB AND Unilever) OR (UNILEVER R D COLWORTH PK AND Unilever) OR (UNILEVER R D COLWORTH SAFETY ENVIRONM ASSURANCE AND Unilever) OR (UNILEVER R D COLWORTH SCI PK AND Unilever) OR (UNILEVER R D COLWORTH SCI PK SHARNBROOK AND Unilever) OR (UNILEVER R D CTR AND Unilever) OR (UNILEVER R D DISCOVER AND Unilever) OR (UNILEVER R D DISCOVER VLAARDINGEN AND Unilever) OR (UNILEVER R D FOODS AND Unilever) OR (UNILEVER R D HEILBRONN AND Unilever) OR (UNILEVER R D HPC AND Unilever) OR (UNILEVER R D INDIA AND Unilever) OR (UNILEVER R D LAB AND Unilever) OR (UNILEVER R D LAB PORT SUNLIGHT AND Unilever) OR (UNILEVER R D PORT SUNLIGHT AND Unilever) OR (UNILEVER R D PORT SUNLIGHT BIRMINGHAM AND Unilever) OR (UNILEVER R D PORT SUNLIGHT LAB AND Unilever) OR (UNILEVER R D SHANGHAI AND Unilever) OR (UNILEVER R D STRUCT MAT PROC SCI AND Unilever) OR (UNILEVER R D SUNLIGHT AND Unilever) OR (UNILEVER R D TRUMBULL AND Unilever) OR (UNILEVER R D US AND Unilever) OR (UNILEVER R D US EDGEWATER LAB AND Unilever) OR (UNILEVER R D VLAARDINGEN AND Unilever) OR (UNILEVER R D VLAARDINGEN ADV MEASUREMENT IMAGIN AND Unilever) OR (UNILEVER R D VLAARDINGEN BV AND Unilever) OR (UNILEVER R D VLAARDINGEN COE STRUCTURED EMULS AND Unilever) OR (UNILEVER R D VLAARDINGENI AND Unilever) OR (UNILEVER RANDD PORT SUNLIGHT AND Unilever) OR (UNILEVER RD COLWORTH AND Unilever) OR (UNILEVER RE AND Unilever) OR (UNILEVER RECH DEV AND Unilever) OR (UNILEVER RES AND Unilever) OR (UNILEVER RES BANGALORE LAB AND Unilever) OR (UNILEVER RES CHINA AND Unilever) OR (UNILEVER RES CO AND Unilever) OR (UNILEVER RES COLWORTH AND Unilever) OR (UNILEVER RES COLWORTH HOUSE AND Unilever) OR (UNILEVER RES COLWORTH LAB AND Unilever) OR (UNILEVER RES COLWORTH LJA AND Unilever) OR (UNILEVER RES COLWORTH WELWYN AND Unilever) OR (UNILEVER RES COLWORTH WELWYN LAB AND Unilever) OR (UNILEVER RES CORP AND Unilever) OR (UNILEVER RES CTR AND Unilever) OR (UNILEVER RES DEPT AND Unilever) OR (UNILEVER RES DEV AND Unilever) OR (UNILEVER RES DEV ADV MEASUREMENT DATA MODELIN AND Unilever) OR (UNILEVER RES DEV ADV MEASUREMENT IMAGING AND Unilever) OR (UNILEVER RES DEV BANGALORE AND Unilever) OR (UNILEVER RES DEV CHINA AND Unilever) OR (UNILEVER RES DEV COLWORTH AND Unilever) OR (UNILEVER RES DEV COLWORTH HOUSE AND Unilever) OR (UNILEVER RES DEV COLWORTH LAB AND Unilever) OR (UNILEVER RES DEV CTR AND Unilever) OR (UNILEVER RES DEV CTR SHANGHAI AND Unilever) OR (UNILEVER RES DEV CTR VLAARDINGEN AND Unilever) OR (UNILEVER RES DEV EDGEWATER AND Unilever) OR (UNILEVER RES DEV FOODS AND Unilever) OR (UNILEVER RES DEV LAB AND Unilever) OR (UNILEVER RES DEV PORT SUNLIGHT AND Unilever) OR (UNILEVER RES DEV PORT SUNLIGHT LAB AND Unilever) OR (UNILEVER RES DEV PORT SUNLIGHT LABS AND Unilever) OR (UNILEVER RES DEV SHANGHAI AND Unilever) OR (UNILEVER RES DEV SPECT AND Unilever) OR (UNILEVER RES DEV TRUMBULL AND Unilever) OR (UNILEVER RES DEV US AND Unilever) OR (UNILEVER RES DEV VLAARDINGEN AND Unilever) OR (UNILEVER RES DEV VLAARDINGEN BV AND Unilever) OR (UNILEVER RES DEV VLAARDINGEN COLWORTH AND Unilever) OR (UNILEVER RES DIUVEN AND Unilever) OR (UNILEVER RES DIV AND Unilever) OR (UNILEVER RES DUIVEN AND Unilever) OR (UNILEVER RES DUIVEN LAB AND Unilever) OR (UNILEVER RES EDGEWATER LAB AND Unilever) OR (UNILEVER RES EDITORIAL AND Unilever) OR (UNILEVER RES EE DEV VLAARDINGEN AND Unilever) OR (UNILEVER RES ENGN AND Unilever) OR (UNILEVER RES ENGN LABS AND Unilever) OR (UNILEVER RES ENVIRONM SAFETY LAB AND Unilever) OR (UNILEVER RES ENVIRONM SAFETY LABS AND Unilever) OR (UNILEVER RES GB AND Unilever) OR (UNILEVER RES INC AND Unilever) OR (UNILEVER RES INDIA AND Unilever) OR (UNILEVER RES INDIA HINDUSTAN LEVER RES CTR AND Unilever) OR (UNILEVER RES INDIA HINDUSTAN UNILEVER RES CTR AND Unilever) OR (UNILEVER RES INST AND Unilever) OR (UNILEVER RES ISLEWORTH LAB AND Unilever) OR (UNILEVER RES LAB AND Unilever) OR (UNILEVER RES LAB COLWARTH WELWYN AND Unilever) OR (UNILEVER RES LAB COLWORTH AND Unilever) OR (UNILEVER RES LAB COLWORTH WELWYN AND Unilever) OR (UNILEVER RES LAB PORT SUNLIGHT AND Unilever) OR (UNILEVER RES LAB VLAARDINGEN AND Unilever) OR (UNILEVER RES LAB VLAARDINGEN DUIVEN AND Unilever) OR (UNILEVER RES LAB YLAARDINGEN AND Unilever) OR (UNILEVER RES LABORATORIUM VLAARDINGEN AND Unilever) OR (UNILEVER RES LABS AND Unilever) OR (UNILEVER RES LABS COLWORTH AND Unilever) OR (UNILEVER RES LABS PORT SUNLIGHT AND Unilever) OR (UNILEVER RES LABS VLAARDINGEN AND Unilever) OR (UNILEVER RES LB AND Unilever) OR (UNILEVER RES LFEGTC AND Unilever) OR (UNILEVER RES LTD AND Unilever) OR (UNILEVER RES NETHERLANDS AND Unilever) OR (UNILEVER RES PLC AND Unilever) OR (UNILEVER RES PORT AND Unilever) OR (UNILEVER RES PORT SUNLIGHT AND Unilever) OR (UNILEVER RES PORT SUNLIGHT LAB AND Unilever) OR (UNILEVER RES PORT SUNLIGHT LAB BEBINGTON AND Unilever) OR (UNILEVER RES PORT SUNLIGHT LABS AND Unilever) OR (UNILEVER RES PORTSUNLIGHT LABS AND Unilever) OR (UNILEVER RES POST SUNLIGHT LAB AND Unilever) OR (UNILEVER RES PT SUNLIGHT LAB AND Unilever) OR (UNILEVER RES R D AND Unilever) OR (UNILEVER RES SHANGHAI AND Unilever) OR (UNILEVER RES SHARNBROOK AND Unilever) OR (UNILEVER RES SIOC AND Unilever) OR (UNILEVER RES UNITED STATES AND Unilever) OR (UNILEVER RES US AND Unilever) OR (UNILEVER RES US EDGEWATER LAB AND Unilever) OR (UNILEVER RES US INC AND Unilever) OR (UNILEVER RES US LAB AND Unilever) OR (UNILEVER RES USA AND Unilever) OR (UNILEVER RES VIAARDINGEN AND Unilever) OR (UNILEVER RES VLAAERDINGEN AND Unilever) OR (UNILEVER RES VLAARDINGEN AND Unilever) OR (UNILEVER RES VLAARDINGEN DUIVEN AND Unilever) OR (UNILEVER RES VLAARDINGEN LAB AND Unilever) OR (UNILEVER RESEARCH AND Unilever) OR (UNILEVER RESEARCH LAB AND Unilever) OR (UNILEVER RESS LAB AND Unilever) OR (UNILEVER ROTTERDAM AND Unilever) OR (UNILEVER RSCH AND Unilever) OR (UNILEVER RSCH DEV AND Unilever) OR (UNILEVER RT AND Unilever) OR (UNILEVER S AFRICA AND Unilever) OR (UNILEVER SAFETY ASSURANCE CTR AND Unilever) OR (UNILEVER SAFETY ENVIRONM ASSURANCE CTR AND Unilever) OR (UNILEVER SAFETY ENVIRONM ASSURANCE CTR SEAC AND Unilever) OR (UNILEVER SAFETY ENVIRONM SAFETY LAB AND Unilever) OR (UNILEVER SCI PK AND Unilever) OR (UNILEVER SEAC AND Unilever) OR (UNILEVER SHARNBROOK AND Unilever) OR (UNILEVER SKIN GLOBAL INNOVAT CTR AND Unilever) OR (UNILEVER SKIN R D AND Unilever) OR (UNILEVER SPAIN AND Unilever) OR (UNILEVER STAT GRP AND Unilever) OR (UNILEVER STAT SENSORY SCI GRP AND Unilever) OR (UNILEVER SUSTAINABLE AGR TEAM AND Unilever) OR (UNILEVER TEA KENYA LTD AND Unilever) OR (UNILEVER THAI HOLDINGS LTD AND Unilever) OR (UNILEVER THAI TRADING LTD AND Unilever) OR (UNILEVER TRUMBULL RES AND Unilever) OR (UNILEVER UHFRI AND Unilever) OR (UNILEVER UK AND Unilever) OR (UNILEVER UK CENT RESOURCES LTD AND Unilever) OR (UNILEVER UK HOLDINGS LTD AND Unilever) OR (UNILEVER UK LTD AND Unilever) OR (UNILEVER UNITED STATES INC AND Unilever) OR (UNILEVER VLAARDINGEN AND Unilever) OR (UNILEVERLFEDC AND Unilever) OR (UNIMILLS GMBH UNILEVER OIL MILLING DIV AND Unilever) OR (VANDENBERGH JURGENS BV UNILEVER AND Unilever))) AND YEAR PUBLISHED: (2010-2015)
Timespan: All years. Indexes: SCI-EXPANDED, SSCI.

Web of Science Search for Dye-Sensitized Solar Cell (DSSC) Publications

DSSCs - search in WoS for 2010-2014:
TS=((Dye* or Pigment*) and (Sensiti*) and (Solar* or Photovoltaic*) and (Cell* or Batter*))
This yielded 9,748 records of various document types (primarily articles) as performed on October 13, 2015.
Table C1 shows the updated results of Big Data search in WoS dated January 27, 2016, and we extracted 11,139 records for 2010-2015 (including various document types, led by proceedings papers (5,661) and articles (4,042)).
Table C1 Search results of Big Data.
No. Search strategy Big Data search terms (search conducted on 1/27/2016 by Alan) Hits - 2006-2015
1 Core lexical query TS = (“Big Data” or Bigdata or “Map Reduce” or MapReduce or Hadoop or Hbase or Nosql or Newsql) 8,602
2 Expanded lexical query TS = ((Big Near/1 Data or Huge Near/1 Data) or “Massive Data” or “Data Lake” or “Massive Information” or “Huge Information” or “Big Information” or “Large-scale Data” or Petabyte or Exabyte or Zettabyte or “Semi-Structured Data” or “Semistructured Data” or “Unstructured Data”) 11,798
TS = (“Cloud Comput*” or “Data Min*” or “Analytic*” or “Privacy” or “Data Manag*” or “Social Media*” or “Machine Learning” or “Social Network*” or “Security” or “Twitter*” or “Predict*” or “Stream*” or “Architect*” or “Distributed Comput*” or “Business Intelligence” or “GPU” or “Innovat*” or “GIS” or “Real-Time” or “Sensor Network*” or “Smart Grid*” or “Complex Network*” or “Genomics” or “Parallel Comput*” or “Support Vector Machine” or “SVM” or “Distributed” or “Scalab*” or “Time Serie*” or “Data Science” or “Informatics*” or “OLAP”) 3,113,113
(part A AND part B = 7,696)
3 #1 OR (#2 AND #3); 2006-2016 SCI = 4,673; SSCI = 1,026, of which 541 are not also in SCI - download 541; AHCI (not in SSCI) = 45 down; CPCI-S & CPCI-SSH = 6,267 (of which 6,093 not in SCI-SSCI - download) - hit 5,000 limit, so split - download 6,093; BCI-S & BCI-SSH = 376 - download all (ignore possible overlaps)
ESCI - search #1 = 0; so leave that dB out; ** save the separate downloaded into VP files in case we want to analyze sometime - note trend behavior for 2015 differs greatly from SCI/SSCI (UP) to CPCI’s (DOWN). I think due largely to incomplete indexing at this date in WoS. Also saved the combo - 11,728 total - removed dups to get 11,684 (saved with the component files on the flash memory).
(1) In scientometric evaluations, journals are sometimes attributed percentages proportional to the categories under which they are subsumed. These multiple categories have also been considered indicators of the interdisciplinarity of journals (Bordons, Bravo, & Barrigon, 2004; Katz & Hicks, 1995; Morillo, Bordons, & Gomez, 2001).
(2) The field/subfield classification of Scopus is available in the journal list from http://www.elsevier.com/online-tools/scopus/content-overview. WCs are available (under subscription) at http://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html.
(3) Available at http://www.vosviewer.com.
(5) Pajek is a network analysis and visualization program freely available for non-commercial usage at http://mrvar.fdv.uni-lj.si/pajek.
(6) The journal Language and Cognitive Processes is additionally assigned with “OY,” one of the categories of the Arts & Humanities Citation Index.
(7) Rao-Stirling diversity is a measure that takes into account both the variety, balance, and the disparity of categories in a distribution. In the case of publication or patent portfolios the categories can be respectively, WCs or IPC classes. The indicator is defined as Equation (3) (Rao, 1982; Stirling, 2007):
Δ = Σij pi pj dij, (3)
where dij is a disparity measure between two categories i and j and pi is the proportion of elements assigned to each category i. As the disparity measure, we use (1 - cosine).
(8) Zhang, Rousseau, and Glänzel (2016) and Garner at al. (2013) argue that 2DS provides a true diversity measure that outperforms Rao-Stirling diversity (Δ) because 2DS = 2.0 is twice as diverse as 2DS = 1.0. In Equation (4), these authors formulate:
2DS = 1⁄(1 - Δ), (4)
where Δ is the Rao-Stirling diversity. This improved measure varies from 1 to ∞ when Δ varies from 0 to 1. The transformation is monotonic and the value of 2DS follows directly from that of the Rao-Stirling diversity using Equation (3).
(10) Scripts available at http://www.vpinstitute.com/.
(11) Another way to compute the maps is to use VantagePoint (http://www.thevantagepoint.com) to process a search set downloaded from WoS. If one mainly wants a science overlay map of the full search set as is, it is easier to output the “analyze.txt” file from WoS for entry into http://www.leydesdorff.net/wc15. However, if you have cause to process the search set data further, VantagePoint provides helpful tools to facilitate data cleaning (e.g. to remove inappropriate items from the search set) or to analyze sub-data sets (e.g. to compare what selected organizations have published on, say, nanotechnology).
(12) Our previous clustering solutions were generated using factor analyses in SPSS, resulting in 4 “meta-disciplines” (see Appendix Figure A-1) and 19 “macro-disciplines” for 2010 base data.

The authors have declared that no competing interests exist.

[1]
Bensman,S.J., & Leydesdorff,L.(2009). Definition and identification of journals as bibliographic and subject entities: Librarianship vs. ISI Journal Citation Reports (JCR) methods and their effect on citation measures. Journal of the American Society for Information Science and Technology, 60(6), 1097-1117.This paper explores the ISI Journal Citation Reports (JCR) bibliographic and subject structures through Library of Congress (LC) and American research libraries cataloging and classification methodology. The 2006 Science Citation Index JCR Behavioral Sciences subject category journals are used as an example. From the library perspective, the main fault of the JCR bibliographic structure is that the JCR mistakenly identifies journal title segments as journal bibliographic entities, seriously affecting journal rankings by total cites and the impact factor. In respect to JCR subject structure, the title segment, which constitutes the JCR bibliographic basis, is posited as the best bibliographic entity for the citation measurement of journal subject relationships. Through factor analysis and other methods, the JCR subject categorization of journals is tested against their LC subject headings and classification. The finding is that JCR and library journal subject analyses corroborate, clarify, and correct each other.

DOI

[2]
Blondel V.D., Guillaume J.L., Lambiotte R., & Lefebvre E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 8, 10008.

[3]
Boyack K., Börner K., & Klavans R. (2007). Mapping the structure and evolution of chemistry research. In D. Torres-Salinas, & H. Moed (Eds.), Proceedings of the 11th International Conference of the International Society for Scientometrics and Informetrics (pp. 112-123). Madrid, Spain: Consejo Superior de Investigaciones Cientificas.react-text: 552 Measuring the relatedness between bibliometric units (journals, documents, authors, or words) is a central task in bibliometric analysis. Relatedness measures are used for many different tasks, among them the generating of maps, or visual pictures, showing the relationship between all items from these data. Despite the importance of these tasks, there has been little written on how to... /react-text react-text: 553 /react-text [Show full abstract]

[4]
Bordons M., Bravo C., & Barrigon S. (2004). Time-tracking of the research profile of a drug using bibliometric tools. Journal of the American Society for Information Science and Technology, 55(5), 445-461.Abstract This study explores the usefulness of bibliometric analyses to detect trends in the research profile of a therapeutic drug, for which Aspirin was selected. A total of 22,144 documents dealing with Aspirin and published in journals covered by MEDLINE during the years 1965鈥2001 are studied. The research profile of Aspirin over the 37-year period is analyzed through Aspirin subheadings and MeSH indexing terms. Half of the documents had Aspirin as a major indexing term, being the main aspects studied therapeutic uses (28% of the documents), pharmacodynamics (26%), adverse effects (18%), and administration and dosage (10%). A frequency data table crossing indexing terms 脳 years is examined by correspondence analysis to obtain time trends, which are shown graphically in a map. Four time periods with a different distribution of indexing terms are identified through cluster analysis. The indexing term profile of every period is obtained by comparison of the distribution of indexing terms of each cluster with that of the whole period by means of the Chi-2 test. The research profile of the drug tends to change faster with time. The most relevant finding is the expanding therapeutic profile of Aspirin over the period. The main advantages and limitations of the methodology are pointed out.

DOI

[5]
Carley,S., & Porter,A.L. (2012). A forward diversity index. Scientometrics, 90(2), 407-427.Abstract<br/>We introduce an indicator to measure the diffusion of scientific research. Consistent with Stirling’s 3-factor diversity model, the diffusion score captures not only variety and balance, but also disparity among citing article cohorts. We apply it to benchmark article samples from six 1995 Web of Science subject categories (SCs) to trace trends in knowledge diffusion over time since publication. Findings indicate that, for most SCs, diffusion scores steadily increase with time. Mathematics is an outlier. We employ a typology of citation trends among benchmark SCs and correlate this with diffusion scores. We also find that self-cites do not, in most cases, significantly influence diffusion scores.<br/>

DOI

[6]
Costas R., van Leeuwen T.N., & Bordons M. (2010). A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact. Journal of the American Society for Information Science and Technology, 61(8), 1564-1581.Abstract The authors set forth a general methodology for conducting bibliometric analyses at the micro level. It combines several indicators grouped into three factors or dimensions, which characterize different aspects of scientific performance. Different profiles or “classes” of scientists are described according to their research performance in each dimension. A series of results based on the findings from the application of this methodology to the study of Spanish National Research Council scientists in Spain in three thematic areas are presented. Special emphasis is made on the identification and description of top scientists from structural and bibliometric perspectives. The effects of age on the productivity and impact of the different classes of scientists are analyzed. The classificatory approach proposed herein may prove a useful tool in support of research assessment at the individual level and for exploring potential determinants of research success.

DOI

[7]
de Nooy,W., Mrvar, A., & Batgelj, V. (2011). Exploratory social network analysis with Pajek (2nd Edition). New York, NY: Cambridge University Press.

[8]
Garner J., Porter A.L., Borrego M., Tran E., & Teutonico R. (2013). Facilitating social and natural science cross-disciplinarity: Assessing the human and social dynamics program. Research Evaluation, 22(2), 134-144.Research that integrates the social and natural sciences is vital to address many societal challenges, yet is difficult to arrange, conduct, and disseminate. This article analyses the cross-disciplinary character of the research supported by a unique US National Science Foundation program on Human and Social Dynamics (HSD). It presents evidence that research publications deriving from this support chiefly pertain to the Social and Behavioral Sciences, but extend widely into the Bio and Medical Sciences, Environmental Sciences, and Physical Sciences and Engineering. Integration scores, based on the diversity of references cited, indicate that the HSD-derived publications are notably more interdisciplinary than those of comparable programs. Diffusion scores, together with science overlay maps, show that uptake of the HSD publications extends into the natural, as well as social, sciences. Research networking analyses, together with a new composite mapping approach, point toward successful catalysis of a new research community. The measures and maps of cross-disciplinary research activity that are advanced here may prove useful in other research assessments.

DOI

[9]
Glänzel,W., & Schubert,A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357-367.A two-level hierarchic system of fields and subfields of the sciences, social sciences and arts and humanities is proposed. The system was specifically designed for scientometric (evaluation) purposes with the ultimate goal of classifying every single document into a well-defined category. This goal was achieved using a three-step iterative process. The basic concepts and some preliminary results are presented.

DOI

[10]
Jaffe,A.B. (1986). Technological opportunity and spillovers of R&D: Evidence from firm’s patents, profits, and market value. American Economic Review, 76(5), 984-1001.

[11]
Katz,J.S., & Hicks,D.M. (1995). The classification of interdisciplinary journals: A new approach. In Proceedings of the fifth biennial conference of the international society for scientometrics and infometrics (pp. 245-255). Medford, NJ: Learned Information, Inc.A new approach for classifying Science Citation Index journals for the purposes of bibliometric analysis has been devised. It was developed in response to a need to be able to identify changing publication activity in interdisciplinary journals and the unavailability of an updated CHI journal classification. The new scheme which is based on the ISI 154 sub-field classification was examined by UK policy makers and users and tested on a unified 1981-91 (now update to 1994) UK SCI data set. This inter-disciplinary journal scheme allowed us to tracked publication trends in traditional disciplines (natural, life and engineering & materials sciences), as well as publication activity in inter-field (inter-field natural, inter-field life and inter-field engineering & materials) and inter-disciplinary (life-natural, life-engineering & mateirals and natural-engineering&materials) journals while maintaining the capable for detailed analysis at the 154 sub-field level. Preliminary results suggest that this new journal classification scheme maybe useful for developing indirect indicators of the change in interdisciplinary scientific research publications. 1

[12]
Klavans,R., & Boyack,K. (2009). Towards a consensus map of science. Journal of the American Society for Information Science and Technology, 60(3), 455-476.A consensus map of science is generated from an analysis of 20 existing maps of science. These 20 maps occur in three basic forms: hierarchical, centric, and noncentric (or circular). The consensus map, generated from consensus edges that occur in at least half of the input maps, emerges in a circular form. The ordering of areas is as follows: mathematics is (arbitrarily) placed at the top of the circle, and is followed clockwise by physics, physical chemistry, engineering, chemistry, earth sciences, biology, biochemistry, infectious diseases, medicine, health services, brain research, psychology, humanities, social sciences, and computer science. The link between computer science and mathematics completes the circle. If the lowest weighted edges are pruned from this consensus circular map, a hierarchical map stretching from mathematics to social sciences results. The circular map of science is found to have a high level of correspondence with the 20 existing maps, and has a variety of advantages over hierarchical and centric forms. A one-dimensional Riemannian version of the consensus map is also proposed.

DOI

[13]
Klavans R.,& Boyack, K.W. (in press). Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge? Journal of the American Society for Information Science and Technology. Retrieved on June 30, 2017, from .

[14]
Leydesdorff,L., & Bornmann,L. (2016). The operationalization of “fields” as WoS subject categories (WCs) in evaluative bibliometrics: The cases of “Library and Information Science” and “Science & Technology Studies”. Journal of the Association for Information Science and Technology, 67(3), 707-714.Abstract: Normalization of citation scores using reference sets based on Web-of-Science Subject Categories (WCs) has become an established ("best") practice in evaluative bibliometrics. For example, the Times Higher Education World University Rankings are, among other things, based on this operationalization. However, WCs were developed decades ago for the purpose of information retrieval and evolved incrementally with the database; the classification is machine-based and partially manually corrected. Using the WC "information science & library science" and the WCs attributed to journals in the field of "science and technology studies," we show that WCs do not provide sufficient analytical clarity to carry bibliometric normalization in evaluation practices because of "indexer effects." Can the compliance with "best practices" be replaced with an ambition to develop "best possible practices"? New research questions can then be envisaged.

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[15]
Leydesdorff,L., & Rafols,I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348-362.

[16]
Leydesdorff,L. (2008). On the normalization and visualization of author co-citation data: Salton’s cosine versus the Jaccard index. Journal of the American Society for Information Science and Technology, 59(1), 77-85.Abstract Top of page Abstract Introduction The Jaccard Index Results Conclusions Acknowledgment References The debate about which similarity measure one should use for the normalization in the case of Author Co-citation Analysis (ACA) is further complicated when one distinguishes between the symmetrical co-citation—or, more generally, co-occurrence—matrix and the underlying asymmetrical citation—occurrence—matrix. In the Web environment, the approach of retrieving original citation data is often not feasible. In that case, one should use the Jaccard index, but preferentially after adding the number of total citations (i.e., occurrences) on the main diagonal. Unlike Salton's cosine and the Pearson correlation, the Jaccard index abstracts from the shape of the distributions and focuses only on the intersection and the sum of the two sets. Since the correlations in the co-occurrence matrix may be spurious, this property of the Jaccard index can be considered as an advantage in this case.

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[17]
Leydesdorff L., Carley S., & Rafols I. (2013). Global maps of science based on the new Web-of-Science categories. Scientometrics, 94(2), 589-593.In August 2011, Thomson Reuters launched version 5 of the Science and Social Science Citation Index in the Web of Science (WoS). Among other things, the 222 ISI Subject Categories (SCs) for these two databases in version 4 of WoS were renamed and extended to 225 WoS Categories (WCs). A new set of 151 Subject Areas was added, but at a higher level of aggregation. Perhaps confusingly, these Subject Areas are now abbreviated “SC” in the download, whereas “WC” is used for WoS Categories. Since we previously used the ISI SCs as the baseline for a global map in Pajek (Pajek is freely available at http://vlado.fmf.uni-lj.si/pub/networks/pajek/ ) (Rafols et al., Journal of the American Society for Information Science and Technology 61:1871–1887, 2010) and brought this facility online (at http://www.leydesdorff.net/overlaytoolkit ), we recalibrated this map for the new WC categories using the Journal Citation Reports 2010. In the new installation, the base maps can also be made using VOSviewer (VOSviewer is freely available at http://www.VOSviewer.com/ ) (Van Eck and Waltman, Scientometrics 84:523–538, 2010).

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[18]
Leydesdorff L., Hammarfelt B., & Salah A.A.A. (2011). The structure of the Arts & Humanities Citation Index: A mapping on the basis of aggregated citations among 1,157 journals. Journal of the American Society for Information Science and Technology, 62(12), 2414-2426.Using the Arts & Humanities Citation Index (A&HCI) 2008, we apply mapping techniques previously developed for mapping journal structures in the Science and Social Sciences Citation Indices. Citation relations among the 110,718 records were aggregated at the level of 1,157 journals specific to the A&HCI, and the journal structures are questioned on whether a cognitive structure can be reconstructed and visualized. Both cosine-normalization (bottom up) and factor analysis (top down) suggest a division into approximately 12 subsets. The relations among these subsets are explored using various visualization techniques. However, we were not able to retrieve this structure using the Institute for Scientific Information Subject Categories, including the 25 categories that are specific to the A&HCI. We discuss options for validation such as against the categories of the Humanities Indicators of the American Academy of Arts and Sciences, the panel structure of the European Reference Index for the Humanities, and compare our results with the curriculum organization of the Humanities Section of the College of Letters and Sciences of the University of California at Los Angeles as an example of institutional organization.

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[19]
Moed H.F., de Bruin R.E., & van Leeuwen T.N. (1995). New bibliometric tools for the assessment of national research performance: Database description, overview of indicators and first applications. Scientometrics, 33(3), 381-422.

[20]
Morillo F., Bordons M., & Gomez I. (2001). An approach to interdisciplinarity through bibliometric indicators. Scientometrics, 51(1), 203-222.lt;a name="Abs1"></a>Interdisciplinarity has become of increasing interest in science in the past few years. Thispaper is a case study in the area of Chemistry, in which a series of different bibliometric indicatorsfor measuring interdisciplinarity are presented. The following indicators are analysed: a) ISI multiclassificationof journals in categories, b) patterns of citations and references outside category andc) multi-assignation of documents in Chemical Abstracts sections. Convergence between thedifferent indicators is studied. Depending on the size of the unit analysed (area, category orjournal) the most appropriate indicators are determined.

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[21]
Porter,A.L., & Rafols,I. (2009. Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719-745. Retrieved on July 10, 2017, from .

[22]
Porter A.L., Cohen A.S., Roessner J.D., & Perreault M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72(1), 117-147.<a name="Abs1"></a>We offer two metrics that together help gauge how interdisciplinary a body of research is. Both draw upon Web of Knowledge Subject Categories (SCs) as key units of analysis. We have assembled two substantial Web of Knowledge samples from which to determine how closely individual SCs relate to each other. &#8220;Integration&#8221; measures the extent to which a research article cites diverse SCs. &#8220;Specialization&#8221; considers the spread of SCs in which the body of research (e.g., the work of a given author in a specified time period) is published. Pilot results for a sample of researchers show a surprising degree of interdisciplinarity.

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[23]
Pudovkin,A.I., & Garfield,E. (2002). Algorithmic procedure for finding semantically related journals. Journal of the American Society for Information Science and Technology, 53(13), 1113-1119.Using citations, papers and references as parameters a relatedness factor (RF) is computed for a series of journals. Sorting these journals by the RF produces a list of journals most closely related to a specified starting journal. The method appears to select a set of journals that are semantically most similar to the target journal. The algorithmic procedure is illustrated for the journal Genetics. Inter-journal citation data needed to calculate the RF were obtained from the 1996 ISI Journal Citation Reports on CD-ROM漏. Out of the thousands of candidate journals in JCR漏, thirty have been selected. Some of them are different from the journals in the JCR category for genetics and heredity. The new procedure is unique in that it takes varying journal sizes into account.

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[24]
Rafols,I., & Leydesdorff,L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology, 60(9), 1823-1835.Abstract: The aggregated journal-journal citation matrix -based on the Journal Citation Reports (JCR) of the Science Citation Index- can be decomposed by indexers and/or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two content-based classifications of journals: the ISI Subject Categories and the field/subfield classification of Glaenzel & Schubert (2003). The content-based schemes allow for the attribution of more than a single category to a journal, whereas the algorithms maximize the ratio of within-category citations over between-category citations in the aggregated category-category citation matrix. By adding categories, indexers generate between-category citations, which may enrich the database, for example, in the case of inter-disciplinary developments. The consequent indexer effects are significant in sparse areas of the matrix more than in denser ones. Algorithmic decompositions, on the other hand, are more heavily skewed towards a relatively small number of categories, while this is deliberately counter-acted upon in the case of content-based classifications. Because of the indexer effects, science policy studies and the sociology of science should be careful when using content-based classifications, which are made for bibliographic disclosure, and not for the purpose of analyzing latent structures in scientific communications. Despite the large differences among them, the four classification schemes enable us to generate surprisingly similar maps of science at the global level. Erroneous classifications are cancelled as noise at the aggregate level, but may disturb the evaluation locally.

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[25]
Rafols,I., & Meyer,M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82(2), 263-287.lt;a name="Abs1"></a>The multidimensional character and inherent conflict with categorisation of interdisciplinarity makes its mapping and evaluation a challenging task. We propose a conceptual framework that aims to capture interdisciplinarity in the wider sense of knowledge integration, by exploring the concepts of diversity and coherence. Disciplinary diversity indicators are developed to describe the heterogeneity of a bibliometric set viewed from predefined categories, i.e. using a top-down approach that locates the set on the global map of science. Network coherence indicators are constructed to measure the intensity of similarity relations within a bibliometric set, i.e. using a bottom-up approach, which reveals the structural consistency of the publications network. We carry out case studies on individual articles in bionanoscience to illustrate how these two perspectives identify different aspects of interdisciplinarity: disciplinary diversity indicates the large-scale breadth of the knowledge base of a publication; network coherence reflects the novelty of its knowledge integration. We suggest that the combination of these two approaches may be useful for comparative studies of emergent scientific and technological fields, where new and controversial categorisations are accompanied by equally contested claims of novelty and interdisciplinarity.

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[26]
Rafols I., Porter A., & Leydesdorff L. (2010). Science overlay maps: A new tool for research policy and library management. Journal of the American Society for Information Science and Technology, 61(9), 1871-1887.Abstract: We present a novel approach to visually locate bodies of research within the sciences, both at each moment of time and dynamically. This article describes how this approach fits with other efforts to locally and globally map scientific outputs. We then show how these science overlay maps help benchmark, explore collaborations, and track temporal changes, using examples of universities, corporations, funding agencies, and research topics. We address conditions of application, with their advantages, downsides and limitations. Overlay maps especially help investigate the increasing number of scientific developments and organisations that do not fit within traditional disciplinary categories. We make these tools accessible to help researchers explore the ongoing socio-cognitive transformation of science and technology systems.

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[27]
Rahman A.J., Guns R., Rousseau R., & Engels T.C. (2015). Is the expertise of evaluation panels congruent with the research interests of the research groups: A quantitative approach based on barycenters. Journal of Informetrics, 9(4), 704-721.Discipline-specific research evaluation exercises are typically carried out by panels of peers, known as expert panels. To the best of our knowledge, no methods are available to measure overlap in expertise between an expert panel and the units under evaluation. This paper explores bibliometric approaches to determine this overlap, using two research evaluations of the departments of Chemistry (2009) and Physics (2010) of the University of Antwerp as a test case. We explore the usefulness of overlay mapping on a global map of science (with Web of Science subject categories) to gauge overlap of expertise and introduce a set of methods to determine an entity's barycenter according to its publication output. Barycenters can be calculated starting from a similarity matrix of subject categories (N dimensions) or from a visualization thereof (2 dimensions). We compare the results of the N -dimensional method with those of two 2-dimensional ones (Kamada鈥揔awai maps and VOS maps) and find that they yield very similar results. The distance between barycenters is used as an indicator of expertise overlap. The results reveal that there is some discrepancy between the panel's and the groups鈥 publications in both the Chemistry and the Physics departments. The panels were not as diverse as the groups that were assessed. The match between the Chemistry panel and the Department was better than that between the Physics panel and the Department.

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[28]
Rao,C.R. (1982), Diversity and dissimilarity coefficients: A unified approach. Theoretical Population Biology, 21(1), 24-43.Three general methods for obtaining measures of diversity within a population and dissimilarity between populations are discussed. One is based on an intrinsic notion of dissimilarity between individuals and others make use of the concepts of entropy and discrimination. The use of a diversity measure in apportionment of diversity between and within populations is discussed.

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[29]
Rehn C., Gornitzki C., Larsson A., & Wadskog D. (2014). Bibliometric Handbook for Karolinska Institutet. Stockholm: Karolinska Institute.

[30]
Riopelle K., Leydesdorff L., & Li J. (2014. How to create an overlay map of science using the Web of Science. Retrieved on July 10, 2017, from .

[31]
Schubert A., Glänzel W., & Braun T. (1986). Relative indicators of publication output and citation impact of European physics research, 1978-1980. Czechoslovak Journal of Physics, 36(1), 126-129.Not Available

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[32]
Schubert A., Glänzel W., & Braun T. (1989). Scientometric datafiles—A comprehensive set of indicators on 2649 journals and 96 countries in all major science fields and subfields 1981-1985. Scientometrics, 16(1-6), 3-478.

[33]
Soós,S., & Kampis,G. (2011). Towards a typology of research performance diversity: The case of top Hungarian players. Scientometrics, 87(2), 357-371.Measuring the intellectual diversity encoded in publication records as a proxy to the degree of interdisciplinarity has recently received considerable attention in the science mapping community. The present paper draws upon the use of the Stirling index as a diversity measure applied to a network model (customized science map) of research profiles, proposed by several authors. A modified version of the index is used and compared with the previous versions on a sample data set in order to rank top Hungarian research organizations (HROs) according to their research performance diversity. Results, unexpected in several respects, show that the modified index is a candidate for measuring the degree of polarization of a research profile. The study also points towards a possible typology of publication portfolios that instantiate different types of diversity.

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[34]
Stirling,A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4(15), 707-719.This paper addresses the scope for more integrated general analysis of diversity in science, technology and society. It proposes a framework recognizing three necessary but individually insufficient properties of diversity. Based on 10 quality criteria, it suggests a general quantitative non-parametric diversity heuristic. This allows the systematic exploration of diversity under different perspectives, including divergent conceptions of relevant attributes and contrasting weightings on different diversity properties. It is shown how this heuristic may be used to explore different possible trade-offs between diversity and other aspects of interest, including portfolio interactions. The resulting approach offers a way to be more systematic and transparent in the treatment of scientific and technological diversity in a range of fields, including conservation management, research governance, energy policy and sustainable innovation.

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[35]
US National Academies, Committee on Science, Engineering & Public Policy. (2005). Facilitating interdisciplinary research. Washington, DC: National Research Council.

[36]
van Eck,N.J., & Waltman,L. (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635-1651.Abstract In scientometric research, the use of cooccurrence data is very common. In many cases, a similarity measure is employed to normalize the data. However, there is no consensus among researchers on which similarity measure is most appropriate for normalization purposes. In this article, we theoretically analyze the properties of similarity measures for cooccurrence data, focusing in particular on four well-known measures: the association strength, the cosine, the inclusion index, and the Jaccard index. We also study the behavior of these measures empirically. Our analysis reveals that there exist two fundamentally different types of similarity measures, namely, set-theoretic measures and probabilistic measures. The association strength is a probabilistic measure, while the cosine, the inclusion index, and the Jaccard index are set-theoretic measures. Both our theoretical and our empirical results indicate that cooccurrence data can best be normalized using a probabilistic measure. This provides strong support for the use of the association strength in scientometric research.

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[37]
van Eck,N.J., & Waltman,L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.We present VOSviewer, a freely available computer program that we have developed for constructing and viewing bibliometric maps. Unlike most computer programs that are used for bibliometric mapping, VOSviewer pays special attention to the graphical representation of bibliometric maps. The functionality of VOSviewer is especially useful for displaying large bibliometric maps in an easy-to-interpret way. The paper consists of three parts. In the first part, an overview of VOSviewer&#8217;s functionality for displaying bibliometric maps is provided. In the second part, the technical implementation of specific parts of the program is discussed. Finally, in the third part, VOSviewer&#8217;s ability to handle large maps is demonstrated by using the program to construct and display a co-citation map of 5,000 major scientific journals.

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[38]
van Eck N., Waltman L., van Raan A., Klautz R., & Peul W. (2013). Citation analysis may severely underestimate the impact of clinical research as compared to basic research. PLoS ONE, 8(4), e62395-e62395.Abstract: Background: Citation analysis has become an important tool for research performance assessment in the medical sciences. However, different areas of medical research may have considerably different citation practices, even within the same medical field. Because of this, it is unclear to what extent citation-based bibliometric indicators allow for valid comparisons between research units active in different areas of medical research. Methodology: A visualization methodology is introduced that reveals differences in citation practices between medical research areas. The methodology extracts terms from the titles and abstracts of a large collection of publications and uses these terms to visualize the structure of a medical field and to indicate how research areas within this field differ from each other in their average citation impact. Results: Visualizations are provided for 32 medical fields, defined based on journal subject categories in the Web of Science database. The analysis focuses on three fields. In each of these fields, there turn out to be large differences in citation practices between research areas. Low-impact research areas tend to focus on clinical intervention research, while high-impact research areas are often more oriented on basic and diagnostic research. Conclusions: Popular bibliometric indicators, such as the h-index and the impact factor, do not correct for differences in citation practices between medical fields. These indicators therefore cannot be used to make accurate between-field comparisons. More sophisticated bibliometric indicators do correct for field differences but still fail to take into account within-field heterogeneity in citation practices. As a consequence, the citation impact of clinical intervention research may be substantially underestimated in comparison with basic and diagnostic research.

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[39]
van Leeuwen,T.N.& Calero Medina,C. (2012). Redefining the field of economics: Improving field normalization for the application of bibliometric techniques in the field of economics. Research Evaluation, 21(1), 61-70.Field normalization, and its effect of bibliometric indicators, is a widely discussed topic among bibliometricians. It is not the necessity of field normalization around which the debate evolves, but how to field normalize bibliometric indicators. In this article we present the results of a study in which publication data of a large disciplinary database in economics (EconLit) is combined with the multidisciplinary citation indexes produced by Thomson Reuters. The main purpose of the study is to investigate whether it would be possible to combine the classification scheme of the economics database with the advantages of the citation indexes (both multiple addresses and citation data), in order to improve the possible applicability of the citation indexes in research performance studies in the field of economics and its periphery. The authors show the starting points of both databases, the outcome of the matching and combining of both sets of publications, and the effects of EconLit field classification in terms of differences in impact levels. The study clearly shows that research performance exercises conducted in the field of economics would benefit from the labeling of publications in the citation indexes with a more detailed classification scheme as found in EconLit. Copyright The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.

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[40]
Vinkler,P. (1986). Evaluation of some methods for the relative assessment of scientific publications. Scientometrics, 10(3-4), 157-177.Some bibliometric methods for the assessment of the publication activity of research units are discussed on the basis of impact factors and citations of papers. “Average subfield impact factor” of periodicals representing subfields in chemistry is suggested. This indicator characterizes the average citedness of a paper in a given subfield. Comparing the total sum of impact factors of corresponding periodicals divided by the number of papers published by a research team to the average subfield impact factor a “publication strategy” indicator can be derived. A new bibliometric indicator, “relative subfield impact”, is introduced which compares the number of citations received by papers of a research unit to the average subfield impact factor.

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[41]
Wallace,M.L., & Rafols,I. (2015). Research portfolio analysis in science policy: Moving from financial returns to societal benefits. Minerva, 53(2), 89-115.Funding agencies and large public scientific institutions are increasingly using the term “research portfolio” as a means of characterising their research. While portfolios have long been used as a heuristic for managing corporate R&D (i.e., R&D aimed at gaining tangible economic benefits), they remain ill-defined in a science policy context where research is aimed at achieving societal outcomes. In this article we analyze the discursive uses of the term “research portfolio” and propose some general considerations for their application in science policy. We explore the use of the term in private R&D and related scholarly literature in existing science policy practices, and seek insight in relevant literature in science policy scholarship. While the financial analogy can in some instances be instructive, a simple transposition from the world of finance or of corporate R&D to public research is problematic. However, we do identify potentially fruitful uses of portfolio analysis in science policy. In particular, our review suggests that the concept of research portfolio can indeed be a useful analytical instrument for tackling complex societal challenges. Specifically, the strands of scholarship identified suggest that the use of research portfolio should: i) recognize the diversity of research lines relevant for a given societal challenge, given the uncertainty and ambiguity of research outcomes; ii) examine the relationships between research options of a portfolio and the expected societal outcomes; and iii) adopt a systemic perspective to research portfolios – i.e., examine a portfolio as a functional whole, rather than as the sum of the its parts. We argue that with these considerations, portfolio-driven approaches may foster social inclusion in science policy decisions, help deliberation between “alternative” portfolios to tackle complex societal challenges, as well as promote cost-effectiveness and transparency.

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[42]
Waltman,L., & van Eck,N.J. (2012). A new methodology for constructing a publication—level classification system of science. Journal of the American Society for Information Science and Technology, 63(12), 2378-2392.No abstract is available for this item.

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[43]
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.In the analysis of bibliometric networks, researchers often use mapping and clustering techniques in a combined fashion. Typically, however, mapping and clustering techniques that are used together rely on very different ideas and assumptions. We propose a unified approach to mapping and clustering of bibliometric networks. We show that the VOS mapping technique and a weighted and parameterized variant of modularity-based clustering can both be derived from the same underlying principle. We illustrate our proposed approach by producing a combined mapping and clustering of the most frequently cited publications that appeared in the field of information science in the period 1999鈥2008.

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[44]
Wang,Q., & Waltman,L. (2016). Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus. Journal of Informetrics, 10(2), 347-364.Journal classification systems play an important role in bibliometric analyses. The two most important bibliographic databases, Web of Science and Scopus, each provide a journal classification system. However, no study has systematically investigated the accuracy of these classification systems. To examine and compare the accuracy of journal classification systems, we define two criteria on the basis of direct citation relations between journals and categories. We use Criterion I to select journals that have weak connections with their assigned categories, and we use Criterion II to identify journals that are not assigned to categories with which they have strong connections. If a journal satisfies either of the two criteria, we conclude that its assignment to categories may be questionable. Accordingly, we identify all journals with questionable classifications in Web of Science and Scopus. Furthermore, we perform a more in-depth analysis for the field of Library and Information Science to assess whether our proposed criteria are appropriate and whether they yield meaningful results. It turns out that according to our citation-based criteria Web of Science performs significantly better than Scopus in terms of the accuracy of its journal classification system.

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[45]
Wouters,P. (1998). The signs of science. Scientometrics, 41(1-2), 225-241.

[46]
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 American Society for Information Science and Technology, 67(5), 1257-1265.The objective of this article is to further the study of journal interdisciplinarity, or, more generally, knowledge integration at the level of individual articles. Interdisciplinarity is operationalized by the diversity of subject fields assigned to cited items in the article's reference list. Subject fields and subfields were obtained from the Leuven-Budapest (ECOOM) subject-classification scheme, while disciplinary diversity was measured taking variety, balance, and disparity into account. As diversity measure we use a Hill-type true diversity in the sense of Jost and Leinster-Cobbold. The analysis is conducted in 3 steps. In the first part, the properties of this measure are discussed, and, on the basis of these properties it is shown that the measure has the potential to serve as an indicator of interdisciplinarity. In the second part the applicability of this indicator is shown using selected journals from several research fields ranging from mathematics to social sciences. Finally, the often-heard argument, namely, that interdisciplinary research exhibits larger visibility and impact, is studied on the basis of these selected journals. Yet, as only 7 journals, representing a total of 15,757 articles, are studied, albeit chosen to cover a large range of interdisciplinarity, further research is still needed.

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[47]
Zitt M., Bassecoulard E., & Okubo Y. (2000). Shadows of the past in international cooperation: Collaboration profiles of the top five producers of science. Scientometrics, 47(3), 627-657.lt;a name="Abs1"></a>This article aims at a characterization of the cooperation behavior among five large scientific countries (France, Germany, Japan, United Kingdom and United States of America) from 1986 to 1996. It looks at the cooperation profiles of these countries using classical measures such as the Probabilistic Affinity. The results show the major influence which historical, cultural and linguistic proximities may have on patterns of cooperation, with few changes over the period of time studied.A lack of specific affinities among the three largest European countries is revealed, and this contrasts with the strong linkage demonstrated between United States and Japan. The ensuing discussion raises some questions as to the process of Europeanization in science. The intensity of bilateral cooperation linkages is then studied with regard to field specialization by country, and this analysis yields no general patterns at the scale studied. Specific bilateral behaviors are also analyzed.

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