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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.
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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
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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
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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
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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
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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.
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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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360 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND
TECHNOLOGYFebruary 2009DOI: 10.1002/asi
-
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JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND
TECHNOLOGYFebruary 2009 361DOI: 10.1002/asi
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362 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND
TECHNOLOGYFebruary 2009DOI: 10.1002/asi