leydesdorff , rafols (2009)a global map of science based on the isi subject categories

A Global Map of Science Based on the ISI SubjectCategories

Loet LeydesdorffAmsterdam School of Communications Research (ASCoR), University of Amsterdam, Kloveniersburgwal 48,1012 CX, Amsterdam, The Netherlands. E-mail: [email protected].

Ismael RafolsScience and Technology Policy Research (SPRU), University of Sussex, Freeman Centre, Falmer,Brighton, East Sussex BN1 9QE, United Kingdom. E-mail: [email protected].

The decomposition of scientific literature into disci-plinary and subdisciplinary structures is one of the coregoals of scientometrics. How can we achieve a gooddecomposition? The ISI subject categories classify jour-nals included in the Science Citation Index (SCI). Theaggregated journal-journal citation matrix contained inthe Journal Citation Reports can be aggregated on thebasis of these categories. This leads to an asymmetricalmatrix (citing versus cited) that is much more denselypopulated than the underlying matrix at the journal level.Exploratory factor analysis of the matrix of subject cate-gories suggests a 14-factor solution. This solution couldbe interpreted as the disciplinary structure of science.The nested maps of science (corresponding to 14 fac-tors, 172 categories, and 6,164 journals) are online athttp://www.leydesdorff.net/map06. Presumably, inaccu-racies in the attribution of journals to the ISI subjectcategories average out so that the factor analysis revealsthe main structures.The mapping of science could, there-fore, be comprehensive and reliable on a large scalealbeit imprecise in terms of the attribution of journals tothe ISI subject categories.

IntroductionThe decomposition of the Science Citation Index into

disciplinary and subdisciplinary structures has fascinatedscientometricians and information analysts ever since thebeginning of this index. Price (1965) conjectured thatthe database would contain the structure of science. He sug-gested that journals would be the appropriate units of analysis,and that aggregated citation relations among journals mightreveal disciplinary and finer-grained delineations such asthose among specialties.

Received October 21, 2008; revised August 25, 2008; accepted August 25,2008

2008 ASIS&T Published online 3 October 2008 in Wiley InterScience(www.interscience.wiley.com). DOI: 10.1002/asi.20967

Carpenter and Narin (1973) tried to cluster the Sci-ence Citation Index database in terms of aggregated journalcitation patterns using the methods available at the time.However, the size of the database2,200 journals in 1969(Garfield, 1972, p. 472) and 6,164 journals in 2006makesit difficult to use algorithms more sophisticated than single-linkage clustering. Single-linkage clustering is based onrecursive selection of the two most-similar subsets, and usingrelational database management one can operate on rankorders in lists without loading the relatively large matricesinto memory.1

Small and Sweeney (1985) added a variable threshold tosingle-linkage clustering in their effort to map the sciencesglobally using cocitation analysis at the document level.However, the choice of thresholds, similarity criteria, andclustering algorithms was somewhat arbitrary. Because ofthe focus on relations, the latent dimensions of the matrix (itseigenvectors) could not be revealed using single-linkage clus-tering (Leydesdorff, 1987). A structural approach requiresmultivariate analysis, for example, based on distinguishingorthogonal dimensions using factor analysis: Units of analy-sis may be positioned similarly in a multidimensional spacewithout necessarily maintaining strong relations among them(Burt, 1982; Leydesdorff, 2006).

The factor-analytical approach is limited even today toapproximately 3,000 variables using the latest version ofSPSS,2 while in the meantime the Science Citation Indexhas grown to more than 6,000 journals. Most researchershave therefore focused on chunks of the database or used

1Single-linkage clustering (or nearest-neighbor) sorts relations hierarchi-cally in terms of decreasing order of their strength. The single strongest linkis clustered in each round. However, this may lead to chaining, whereelements cluster that otherwise might be considered as rather distant fromeach other.

2For technical reasons, SPSS can address approximately two gigabytes ofinternal memory of a computer as workspace.

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seed journals for collection of a sample (Doreian & Farraro,1985; Leydesdorff, 1986, 2006; Tijssen, de Leeuw, & vanRaan, 1987). In contrast, Boyack, Klavans, and Brner (2005)used the VxInsight algorithm (Davidson, Hendrickson,Johnson, Meyers, & Wylie, 1998) in order to map thewhole journal structure as a representation of the structureof science.3 Moya-Anagn et al. (2004, 2007) used cocita-tion and PathFinder for mapping the whole of science onthe basis of the ISI subject categories.4 However, subjectdelineations in the maps are based necessarily on trade-offsbetween accuracy, readability, and simplicity since the jour-nal sets are overlapping (Bensman, 2001; Boyack, Brner, &Klavans, 2007). Klavans and Boyack (2007, p. 438) notedthat a journal may occupy a different position in a differentcontext: Many journals report on developments in multipledisciplines; journals can also function as a major source ofreferences in more than one specialty. The position of eachjournal in the multidimensional space of journal-citation vec-tors allows for a specific perspective (Leydesdorff, 2007; Zitt,Ramanana-Rahary, & Bassecoulard, 2005).

In addition to interjournal citations, the Science CitationIndex database has been mapped using cocitations (Small,1973; Small & Griffith, 1974; Small & Sweeney, 1985) orco-occurrences of title words (Callon, Courtial, Turner, &Bauin, 1983; Callon, Law, & Rip, 1986; Leydesdorff, 1989),at various levels of aggregation. However, using lower-levelunits (like documents) instead of journals means abandon-ing Prices grandiose vision to map the whole of scienceusing the structure present in the aggregated journal-journal(co)citation matrix (Price, 1965).

Since citation relations among journals are dense indiscipline-specific clusters and otherwise virtually nonexis-tent, the journal-journal citation matrix can be considerednearly decomposable.5 While a decomposable matrix is asquare matrix such that an identical rearrangement of rowsand columns leaves a set of square submatrices on the prin-cipal diagonal and zeros everywhere else, in the case of anearly decomposable matrix some zeros are replaced by rela-tively small nonzero numbers (Simon &Ando, 1961;Ando &Fisher, 1963). Near-decomposability is a general property ofcomplex and evolving systems (Simon, 1973, 2002). Thenext-order units represented by the square submatricesand representing in this case disciplines or specialtiesarereproduced in relatively stable sets (of journals), whichmay change over time. The sets of journals are functionalsubsystems that show a high density in terms of relationswithin the center (i.e., core journals), but are more open tochange in relations at the margins. The organization amongthe subsystems can also change. The decomposition intonearly decomposable matrices has no analytical solution.However, algorithms can provide heuristic decompositions

3See http://mapofscience.com for maps of science based on this algorithm.4See http://www.atlasofscience.net for maps of science based on this

algorithm.5In 2006, the database contained only 1,201,562 of the 37,994,896

(= 6,1642) possible relations. This corresponds to a density of 3.16%.

when there is no single unique correct answer (Newman,2006a, 2006b).

ISI Subject CategoriesHitherto, the organization into components and clusters

has been based mainly on the results of algorithms fromgraph or factor analysis operating on journal-journal citationmatrices. The designation is then based on an ex post factoappreciation of these results. However, the Institute of Scien-tific Information (ISI) has added a substantive classifier to thedatabase: the subject category or subject categories of eachjournal included. These categories are assigned by ISI staffon the basis of a number of criteria including the journalstitle and its citation patterns (Pudovkin & Garfield, 2002, atp. 1113; McVeigh, personal communication, March 9, 2006).

The subject categories of the ISI cannot be consideredas based on literary warrant like the classification of theLibrary of Congress (Chan, 1999). A classification schemebased on literary warrant is inductively developed in refer-ence to the holdings of a particular library, or to what is or hasbeen published (Leydesdorff & Bensman, 2006, p. 1473). Inother words, it is based on what the actual literature of thetime warrants. For example, each of the individual sched-ules in the classification of the Library of Congress (LC) wasinitially drafted by subject specialists, who consulted bib-liographies, treatises, comprehensive histories, and existingclassification schemes to determine the scope and content ofan individual class and its subclasses. The LC has a policyof continuous revision to take current literary warrant intoaccount, so that new areas are developed and obsolete ele-ments are removed or revised. The ISI categories, however,are changed in terms of respective coverage, but cannot berevised from the perspective of hindsight.

In order to enhance flexibility in the database, the Sci-ence Citation Index is organized with a CD-ROM versionfor each year separately (which is by definition fixed atthe date of delivery), and the SCI-Expanded version on theInternet, to which relevant data can be added from the per-spective of hindsight in order to optimize the database forinformation-retrieval purposes. The Journal Citation Reports,however, are provided as a separate service. The Web ver-sion of this database is kept in complete agreement withthe yearly CD-ROM. Thus, the subject categories themselvesare not systematically updated, although new categories canbe added and obsolete ones may no longer be used.

In addition to the subject categories, Thomson-ISI alsoassigns each journal in the Essential Science Indica-tors database (12,845 journals) to one of 22 so-called broadfields (at http://www.in-cites.com/journal-list/index.html)Journals are uniquely classified to a single broad field, whilethey can be classified under multiple subject categories inthe Science Citation Index. The Essential Science Indicatorsprovides statistics for government policy makers, universityor corporate research administrators, and so forth, while themain service of the Science Citation Index is informationretrieval for the research process.

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGYFebruary 2009 349DOI: 10.1002/asi

FIG. 1. Frequency of 27 ISI Subject Categories that occur more than hundred times (JCR 2006).

Unlike the 22 broad fields, the 172+ ISI subject cate-gories are not hierarchically organized, but interconnected,because more than one category is often attributed to a jour-nal. Furthermore, they are more finely grained and thereforethis organization contains more information. In an attempt toreconstruct these subject categories on the basis of the aggre-gated journal-journal citation matrix, Leydesdorff (2006,p. 612) concluded that one cannot develop a conclusive clas-sification on the basis of a statistical analysis of citationrelations, but the quality of a proposed classification can betested against the structure in the data. Glnzel and Schubert(2003), for example, proposed 12 instead of 22 broad fields,but these categories are again different from the scheme of 12or 16 categories proposed by Boyack et al. (2005) and Moya-Anagn et al. (2007), respectively. In this study, we focuson the Science Citation Index, while these other studies alsoincluded the Social Science Citation Index. Given our factor-analytical approach, the differences in citation behavior andprocesses of codification between the social sciences and thenatural sciences could reduce the methods effectiveness byintroducing another source of variance (Leydesdorff, 2004;Leydesdorff & Hellsten, 2005). Bensman (2008), for exam-ple, argued that the impact factors of journals are differentlydistributed between the two databases.

The number of category attributions in the Science CitationIndex is 9,848 for 6,164 journals in 2006 or, in other words,approximately 1.6 categories per journal. The coverage ofthe 172 categories ranges from 262 journals sorted underBiochemistry and Molecular Biology to 5 journals sortedunder a single category.6 The average number of journals percategory is 56.3 (see Figure 1).

6Three more categories, which are no longer actively indexed, subsumeone or two journals.

The ISI subject categories match poorly with classifica-tions derived from the database itself on the basis of ananalysis of the principal components of the network matricesgenerated by citations (Leydesdorff, 2006, p. 611f). Using adifferent methodology, Boyack et al. (2005) found that insomewhat more than 50% of the cases the ISI categoriescorresponded closely with the clusters based on interjournalcitation relations. These results accord with the expectationthat many journals can be assigned unambiguous affiliationsin one core set or another, but the remainder, which is also alarge group, is heterogeneous (Garfield, 1971, 1972).

The ISI subject categories can be considered as macrojour-nals. Because more than one category can be attributed to ajournal, one can expect that the matrix of the citation relationsamong categories is less empty than the aggregated journal-journal citation matrix. However, the multidimensional spacespanned by these 172+ subject categories offers a wealth ofoptions for generating representations. One should not expecta unique map of science, but a number of possible repre-sentations (Leydesdorff, 2007; Zitt et al., 2005). Each mapcontains a projection from a specific perspective. However,one can ask whether there is a robust structure in terms of thelatent dimensions of the underlying matrix.

In other words, our research question is different fromBoyack et al.s (2005) effort to generate a new classificationusing a bottom-up strategy and from that of Moya-Anagnet al. (2007), who employed the ISI subject categories as unitsof measurement (at p. 2169), and used factor analysis of thecocitation matrix for the validation of their so-called factorscientograms. We wish to question the quality and valid-ity of using the ISI subject categories for mapping purposes.Can these subject classifications be used in further researchto demarcate the sciences and perhaps as field delineations,and if so, under what conditions? Like any classification,one can expect that these classifications can be used for

350 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGYFebruary 2009DOI: 10.1002/asi

mapping purposes, but what is the value of these units oforganization?

This question is urgent because, first, there is a need for thedelineation of fields in citation analysis given that publica-tion and citation practices differ among fields of science (e.g.,Martin & Irvine, 1983; Moed, Burger, Frankfort, & van Raan,1985; Leydesdorff, 2008). Secondly, various studies of inter-disciplinarity have been based on the assumption that journalscan be grouped using the ISI subject categories (e.g., Morillo,Bordons, & Gmez, 2001, 2003; Van Leeuwen & Tijssen,2000). Interdisciplinarity is often a policy objective, whilenew developments may take place at borders of or across dis-ciplines (Zitt, 2005). One of the potential uses of a map ofscience is to help us understand the cognitive base and therelative positions of emerging fields (Bordons, Morillo, &Gmez, 2004; Porter, Cohen, Roessner, & Perreault, 2007;Porter, Roessner, Cohen, & Perreault, 2006;Van Raan, 2000).

As noted above, Moya-Anegn et al. (2007, p. 2173)used factor analysis of the cocitation matrix of the 218 cat-egories of the Science Citation Index and Social ScienceCitation Index (2002) combined for the validation of theirvisualizations. These authors stated that a scree test hadled them to the choice of 16 factors. The screeplot basedon Table 1 of their paper, is provided here in Figure 2.

TABLE 1. Highest factor loadings on the last factor in a 13-, 14-, and15-factor solution.

Highest factor loadings Highest factor loadings Highest factor loadingson factor 13 in the case on factor 14 in the case on factor 15 in the caseof a 13-factor solution of a 14-factor solution of a 15-factor solution

0.779 0.786 0.5930.715 0.721 0.5390.672 0.698 0.4840.669 0.687 0.472

FIG. 2. Screeplot of eigenvalues provided in Table 1 of Moya-Anegn et al. (2007, p. 2173).

In our opinion, this screeplot does not support the inferencebecause the line flattens after eight factors at the most. (Ascan be seen from Table 1 of Moya-Anegn et al.s paper,the 16-factor solution instead corresponds to including per-centages of variance explained equal or larger than unity.)Furthermore, this analysis was based on the symmetricalcocitation matrix instead of the asymmetrical citation matrix(cf. Leydesdorff & Vaughan, 2006). However, the focusof these authors was not on the analysis, but the visuali-zation technique (e.g., PathFinder) for showing relations andclusters; the factor analysis was successfully used to rational-ize the visualizations ex post facto. We approach the problemfirst factor-analytically using the asymmetrical matrix ofaggregated citations among categories, and will subsequentlytry to map the sciences hierarchically top-down insofar as ourresults show that it is legitimate for us to do so.

MethodsThe data was harvested from the CD-ROM version of

the Journal Citation Reports of the Science Citation Index2006. As indicated above, 175 subject categories are used.Three categories (Psychology, biological, Psychology,experimental, and Transportation) are no longer used asclassifiers in the citing dimension, but four journals are stillindicated with these three categories in the cited dimension.Thus, we work with 172 citing and 175 cited categories.

The matrix, accordingly, contains two structures: a citedand a citing one. Saltons cosine was used for normalizationin both the cited and citing directions (Ahlgren, Jarneving, &Rousseau, 2003; Salton & McGill, 1983). The cosine is equalto the Pearson correlation, but without normalization to thearithmetic mean (Jones & Furnas, 1987). Pajek is used forthe visualizations (Batagelj & Mrvar, 2007) and SPSS (v15)for the factor analysis. The threshold for the visualizations is


pragmatically set at cosine 0.2.7 Visualizations are basedon the algorithm of Kamada and Kawai (1989). The size ofthe nodes is proportional to the number of citations in a givencategory (or in Figure 4, below, the logarithm of this value).The thickness and grey-shade of the links is proportionalto the cosine values in five equal steps of 0.2. The threshold ofcosine 0.2 will consistently be used for the visualizationsat the various levels.8

Using a factor model, the crucial question is the numberof factors to be extracted. Unless one has a priori reasons fortesting an assumption, this number has to be determined onempirical grounds. Unlike principal-component analysis, therotated component matrix is not an analytical rewrite ofthe original data. While both principal-component analysisand rotated component analysis allow for data reduction, thecriterion for the optimization in the case of factor analysisis no longer to explain as much variance as possible in thedata, but to find common factors in the set that explainthe covariance between the variables. Factor analysis is nec-essarily based on an assumption about the number of factorsthat span the multidimensional space (Leydesdorff, 2006).SPSS includes by default all factors with an eigenvalue largerthan unity. However, the resulting screeplot of the eigenval-ues can be used for an initial assessment of the number ofmeaningful factors. This assumption has to be tested againstthe data (Kim, 1975; Kim & Mueller, 1978).

Results

Unlike the aggregated journal-journal citation matrix, thematrix of 172 (citing) times 175 (cited) categories is notsparse: 11,577 of the (172 175=) 30,100 cells have a zerovalue. This corresponds to 38.46% of the number of cells.9Since the categories are unevenly distributed, one cannot seta threshold value across the matrix without normalization.The factor analysis, however, begins with a normalizationusing the Pearson correlation coefficient. As noted, the visu-alizations are based on cosine values (Egghe & Leydesdorff,2008).

Let us focus on the structure in the citing dimensionbecause this structure is actively maintained by the indexingservice and is therefore current. The screeplot of the eigen-values suggests further exploration of a 14-factor solutionbecause the continuity in the curve is interrupted at the valueof 14 in Figure 3.

7A threshold is needed for the visualization because the cosine-basednetworks are often almost complete. Using the cosine, a threshold cannot beset on analytical grounds.

8The effects of a threshold at cosine 0.2 on the density of the matricesunderlying the figures in this study, are as follows:

cosine 0.0 cosine 0.2Figure 4 0.994 (N = 172) 0.175 (N = 171)Figure 5 0.929 (N = 14) 0.357 (N = 14)

9UCINet computes a density for this matrix after binarization of0.7538 0.4308.

FIG. 3. Scree plot of the factor analysis (citing).

Table 1 shows the four highest loadings on the last factorin the case of extracting 13, 14, or 15 factors in the citingdimension, respectively. This confirms that the quality ofthe factors declines considerably after extracting 14 factors.The 14-factor solution explains 51.8% of the variance of thematrix in the citing projection (and 47.9% of the variance inthe cited projection).

The factor loadings for the 172 categories on the 14 fac-tors in the citing dimension are provided in the Appendix.They can be interpreted in terms of disciplines, such asphysics, chemistry, clinical medicine, neurosciences, engi-neering, and ecology. (These designations are ours.) Thefactors in the cited dimension can be designated using pre-cisely the same disciplinary classifications, but their rankorder (that is, the percentage of variance explained by eachfactor) is different (Table 2). Out of the 172 categories, 154(89%) fall in the same factor in both the citing and cited pro-jections. The 18 categories that are classified differently inthe citing and cited projections are listed in Table 3.

TABLE 2. Fourteen disciplines distinguished on the basis of ISI subjectcategories in 2006 ( > .95; p < .01).

Citing factors Cited factors

Biomedical sciences 1 1Materials sciences 2 2Computer sciences 3 4Clinical medicine 4 5Neurosciences 5 3Ecology 6 7Chemistry 7 9Geosciences 8 6Engineering 9 8Infectious diseases 10 10Environmental sciences 11 12Agriculture 12 11Physics 13 13General medicine; health 14 14


TABLE 3. Eighteen ISI subject categories that are classified differently in the citing and cited dimensions.ISI subject category Citing factor Cited factor

Urology & nephrology Biomedical sciences Clinical medicinePharmacology & pharmacy Biomedical sciences NeurosciencesPhysiology Biomedical sciences NeurosciencesMedicine, legal Biomedical sciences ChemistryToxicology Biomedical sciences Environmental sciencesBiotechnology & applied microbiology Biomedical sciences AgricultureNutrition & dietetics Biomedical sciences AgricultureMathematical & computational biology Biomedical sciences General medicine; healthEnergy & fuels Materials sciences EngineeringComputer science, Interdisciplinary Applications Computer sciences EngineeringMathematics Engineering Computer sciencesEngineering, industrial Computer sciences PhysicsChemistry, physical Chemistry Materials sciencesMaterials science, biomaterials Chemistry Materials sciencesChemistry, applied Chemistry AgricultureMaterials science, composites Engineering Materials sciencesMycology Infectious diseases AgricultureMedicine, general & internal Medicine, general Clinical medicine

The strong overlap between the results of the factor anal-ysis in the cited and the citing dimension (Table 2) suggeststhat the matrix is nearly decomposable in terms of centraltendencies. Table 3 indicates cases where the scholars pub-lishing in one category cite on average differently from howthey are cited. For example, Mathematics exhibits a nega-tive factor loading on the engineering dimension in its citingpattern, while Mathematics, applied is loading primarilyon this dimension. In the cited dimension, however, the twocategories are both classified as engineering. This relativeinterdisciplinarity of mathematics accords with the find-ings by the SCImago Group (Moya-Anegn et al., 2007,p. 2172). Using a different technique (see above), they couldalso not find a separate factor for mathematics.10

In other cases, it is more difficult to provide an interpre-tation of the differences. Why would Biotech and AppliedChemistry be assigned to Agriculture in the cited dimensionand to Biomedical Sciences in the citing dimension? Is thisan indication of the interdisciplinary relation between thesetwo contexts of application for biotechnology?

Figure 4 shows the map of 171 ISI subject categories thatrelate above the threshold of cosine 0.2.11 (Only the cat-egory Agricultural Economics and Politics is no longerrelated at this threshold level.) The nodes represent the cate-gories and are colored in terms of the 14 factors. (The picture

10In a response to the critique of the International Mathematical Unionon the use of citation analysis (Adler, Ewing, & Taylor 2008), Bensman(June 27, 2008, at http://listserv.utk.edu/cgi-bin/wa?A2=ind0806&L=sigmetrics&D=1&O=D&P=16332) noted that the range of impact factorsamong mathematics journals was extraordinarily low and tight and the topjournals on the impact factor had no review articles. He added, This issuggestive of an extremely random citation pattern with no developmentof consensual paradigms. Therefore, math acts like a humanities in terms ofits literature use, and citation analysis is probably not applicable to thisdiscipline.

11A colored version of this map can be retrieved at http://www.leydesdorff.net/map06/Figure4.

in the cited dimension is very similar.) In this chart, the nodesizes were set proportional to the logarithm of the number ofcitations (in the respective subject category) in order to keepthe visualization readable.

Whereas the traditional disciplines are represented byclear factors (e.g., Physics or Chemistry), specific fields ofapplication in mathematics or engineering do not fall inthe disciplinary classification, but in the factor representingtheir topic. For example, Mathematical Physics is classi-fied as Physics. However, Chemical Engineering loads onthe Chemistry factor more than on the one representingengineering.

Figure 5 shows the citation relations among the 14 groups.(The depiction in the cited dimension is again very sim-ilar to this one in the citing dimension.) While Figure 4gave greater detail about the relations among subdisciplinesand specialties, the factor-analytical categories allow us todepict these ISI subject categories in Figure 4 with differ-ent colors in terms of the disciplinary affiliations provided inFigure 5. Both levels are interactively related with hyperlinksat http://www.leydesdorff.net/map06/index.htm.

The largest factor is designated as Biomedical Sciences.It includes at the disaggregated level

1. The core biological sciences, such as Biochemistry andMolecular Biology, Developmental Biology, Genetics,and Cell Biology;

2. The methodologies crucial for the biological sciences,such as Microscopy and Biochemical Research Methods;

3. Disciplines that fall into medicine but are highly interre-lated with the biological sciences, such as Oncology andPathology.

Among the latter, eight subject categories (e.g., Physiol-ogy, Toxicology, or Nutrition Sciences) have a citing patternin the factor of the Biomedical Sciences (that is, they drawon basic biological knowledge), but they show a cited pattern


FIG. 4. 171 ISI subject categories in the citing dimension; cosine 0.2. Node sizes set proportional to the logarithm of the number of citations given byeach category. A colored version of this map can be retrieved at http://www.leydesdorff.net/map06/Figure4.

FIG. 5. Fourteen disciplines in the citing dimension; cosine 0.2. (The colors correspond with those used in Figure 4.) A colored version of this map canbe retrieved at http://www.leydesdorff.net/map06.


in factors more related to specific applications, such as Neu-rosciences, Environmental Sciences, or Infectious Diseases(see Table 3 above).

Four factors are closely associated with the BiomedicalSciences: Clinical Medicine, Neurosciences, Infectious Dis-eases, and General Medicine and Health.At the opposite poleof the medicine-related factors, we find factors based on thehard sciences: the factors of Physics, Engineering, MaterialsSciences, and Computer Sciences, are among them. Chem-istry plays a brokerage role between Physics and MaterialSciences, on the one side, and core Biomedical Sciences suchas Biophysics and Biochemistry, on the other.

The relative positions of the subject categories withinFigure 4 inform us prima facie about their disciplinary orinterdisciplinary character. However, one should be cautiousin drawing conclusions from a visual inspection of maps. Amap remains a two-dimensional projection of a space (in thiscase, a 14-dimensional one), and one therefore needs a largenumber of projections from different angles before one canformulate hypotheses on this basis. On the basis of a numberof these projectionsthat is, variants of Figure 4we feelcomfortable in suggesting that the connection between themedical pole and the hard-science pole is achieved byway of three main routes:

1. A direct link between the Computer Sciences and some ofthe medical specialties such as Psychology, Neuroimag-ing, and Medical Informatics;

2. Through Chemistry, which appears to play a brokeragerole between Physics and Material Sciences, on the oneside, and the core Biomedical Sciences such as Biophysicsand Biochemistry, on the other;

3. Through a path that links Engineering and Material Sci-ences with Geosciences and Environmental Sciences, andalso connects these two latter factors with Ecology andAgriculture. The latter are related to Infectious Diseasesand the large set of journals in the Biomedical Sciences.This path can be considered as a small cluster with a focuson environmental issues.

Our results are consistent with previously reported maps(Boyack et al. 2005; Boyack & Klavans, 2007; Moya-Anagnet al., 2007), but we chose to exclude the social sciences.We would expect differences and similarities when map-ping the social sciences (using the Social Science CitationIndex) because of the different order of magnitude of cita-tions in the journal-journal citation network, differences incitation behavior and codification processes (Leydesdorff &Hellsten, 2005), and the potentially different functionsof citations as relations among texts in these sciences(Bensman, 2008).

Conclusions and DiscussionWhy do the ISI subject categories that were found to be

a poor match for journal-citation patterns in other research(Boyack et al., 2005; Leydesdorff, 2006) perform relativelywell when aggregated in order to provide comprehensive

maps of science in both the cited and citing dimensions?The explanation is statistical: Boyack et al. (2005) notedthat the ISI subject categories match in approximately 50%of the cases and mismatch consequently in the remaining50%. The error, however, is not systematic so that the 50%matching cases prevail in the aggregate. Factor analysisenables us to distinguish the pattern as a signal from thenoise. Thus, a clear factor structure can be discerned at thisintermediate level.

From the top-down perspective of the factor structure,the noise at the bottom level can be considered as merevariation, which is distributed stochastically. Factor analy-sis enables us to reduce the complexity in the data. As wenoted above, the resulting maps match well with the previ-ously published mappings of the team of Boyack, Brner, andKlavans and the ones of the SCImago Group (see http://www.atlasofscience.net and http://mapofscience.com, respec-tively). The matrix of aggregated intercategory citationsused in this study is available at http://www.leydesdorff.net/map06/data.xls for users to draw their own maps or maketheir own extractions and inferences.

The maps are also available as a nested structure athttp://www.leydesdorff.net/map06/index.htm. One can clickon each of the 14 categories visualized in Figure 5, opena map of the corresponding discipline in terms of subjectcategories, and then relate to the journal sets subsumedunder the respective category. Top-down one is thus guidedto the individual journal maps as available at http://www.leydesdorff.net/jcr06/citing/index.htm. Like the other maps,our maps have the disadvantage of being static representa-tions of science, based on a single year of data. In futureresearch, we hope to expand this line of research withdynamic analysis of journal maps (Leydesdorff & Schank,2008). Systematic comparisons between maps based on theScience Citation Index and Social Science Citation Indexremain also part of our research agenda.

AcknowledgmentWe are grateful for suggestions and remarks of a number

of anonymous referees.

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117

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(Con

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App

endi

x.(C

ontin

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Citin

gfa

ctor

ISIS

ubjec

tCat

egor

y

Biomedical

sciences

Materials

sciences

Computer

sciences

Clinical

sciences

Neurosciences

Ecology

Chemistry

Geosciences

Engineering

Infectious

Diseases

Environmental

sciences

Agriculture

Physics

Gen.medicine;

health

Agr

icul

ture

Hor

ticul

ture

0.11

90.

101

0.80

2A

gron

omy

0.17

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791

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130

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ces

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30.

223

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od

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nce

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chno

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0.14

60

.151

0.17

70

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0.13

60.

571

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scie

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0.22

10.

159

0.26

30.

452

Inte

grat

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&co

mpl

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tary

med

icin

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199

0.13

80.

321

0.16

20.

135

0.32

20.

258

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169

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220

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ater

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109

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ics

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iplin

ary

0.20

70.

114

0.86

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mat

ical

0.27

40.

781

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ics,

nucl

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687

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ics,

fluid

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456

0.59

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337

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00.

484

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ics,

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r,&

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ical

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400

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lear

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107

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3

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eral

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care

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nces

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183

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40.

786

heal

thM

edic

alet

hics

0.13

90.

721

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ic,e

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tal,

&o

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patio

nal

0.11

30.

227

0.17

80.

698

heal

thM

edic

ine,

gene

ral&

inte

rnal

0.14

40.

498

0.12

90.

203

0.68

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edic

alin

form

atic

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0.22

60.

100

0.1

570.

564

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70.

150

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ion,

scie

ntifi

cdi

scip

lines

0.16

80.

411

Stat

istic

s&pr

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ility

0.11

60

.184

0.21

20.

115

0.15

00.

195

0.1

780.

241

Note

.Ex

tract

ion

met

hod:

Prin

cipa

lco

mpo

nent

anal

ysis.

Rot

atio

nm

etho

d:Va

rimax

with

Kai

serN

orm

aliz

atio

n.R

otat

ion

conv

erge

din

11ite

ratio

ns.


leydesdorff , rafols (2009)a global map of science based on the isi subject categories

Documents

mapping of science

journal citation reports

aggregated citation

global map of science

journal level

nested maps of science

exploratory factor analysis

subdisciplinary structures