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1994 18: 98Eval RevRobert J.W. Tijssen and Anthony F.J. Van Raan

Co-Occurrence Analysis of the R&D LiteratureMapping Changes in Science and Technology: Bibliometric

  

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98

MAPPING CHANGES IN

SCIENCE AND TECHNOLOGY

Bibliometric Co-Occurrence

Analysis of the R&D Literature

ROBERT J. W. TIJSSENANTHONY F. J. VAN RAAN

University of Leiden, the Netherlands

This article presents basic principles and examples of spatial representations derived from theanalysis of co-occurrence frequency data pertaining to bibliographic information elements, suchas key words and citations, in research publications and patents. These bibliometric mapsprovide a means for communicating information on relational features of the science andtechnology (S&T) system—either for analytical or representational purposes. Characteristicsof the main types ofbibliometric maps are outlined, and their potential for practical applicationsin S&T policy and research and development management are discussed. An emphasis is placedon more recent developments, in particular bibliometric maps produced by the Centre forScience and Technology Studies (CWTS) for depicting temporal changes in the S&T system.Three empirical examples ofsuch maps are presented with a focus on their application for impactassessment in both scientific as well as technological fields: (1) the emergence of new researchtopics in worldwide research on manufacturing technology, (2) changes in patterns of (inter)na-tional collaboration within Dutch research on coal and coal products, and (3) the role ofinstruments in materials science.

cience and technology (S&T) constitute complex, heterogeneousknowledge systems that consist of many different fields of activityand that are characterized by a multitude of interrelated aspects. Systematicinvestigation of the network of social and cognitive interrelations withinscience and technology, and in particular their interface, in a crucial elementin the study of knowledge structures underlying research and development(R&D) developments. Both internal developments (e.g., scientific discover-ies, new instruments) as well as external factors (e.g., government programs

EVALUATION REVIEW, Vol. 18 No. 1, February 1994 98-115 5@ 1994 Sage Publications, Inc.

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for stimulating international collaboration) continually exert impact on theS&T system affecting its nature and structure.

Nowadays, there is an enormous and ever-increasing amount of readilyavailable data on the S&T system embedded in research publications andpatents (R&D literature). It is a challenge to develop methods for extractingrelevant information (i.e., noticeable features and patterns) from this im-mense reservoir of encoded knowledge on R&D activities. Bibliometricsoffers a range of quantitative methods for analyzing the contents of the R&Dliterature. Bibliometric methods are mostly used to generate scalar indicatorsfor monitoring the state of the S&T system based on frequency counts (i.e.,occurrences) of bibliographic elements. For instance, the number of U.S.patents granted in Japan, or the number of citations received by scientificarticles (their citation &dquo;impact&dquo;). Another class of bibliometric methods canbe defined as relational indicators. These indicators provide quantitative dataon relational structures in the S&T system based on co-occurrences of

bibliographic elements (e.g., coauthorships). Pictorial representations(bibliometric maps) of the relational structures underlying the data mayprovide overviews revealing relevant subject-related (cognitive) links as wellas social features (e.g., geopolitical).

Scalar bibliometric indicators are increasingly exploited for science pol-icy purposes-both as a descriptive and a diagnostic tool. Bibliometric mapshave also become an established analytical tool in scientometric studies (cf.Tijssen 1992a), but their appreciation with respect to applications in practicalissues of S&T policy and R&D management is still more a matter of

&dquo;developer-push&dquo; than &dquo;user-pull&dquo; (cf. Van Raan 1992).

CAPTURING RELATIONAL ASPECTS:BIBLIOMETRIC CO-OCCURRENCE DATA

We illustrate the phenomenon of bibliometric co-occurrence with twobasic examples. Assume we have a French research publication. The subjectclassification codes assigned to this publication by a database indexer indi-cate that this publication belongs to the field of solid-state physics. Hencethe specific pair of information elements country (France) and field (solid-state physics) co-occur. If the same pair co-occurs more and more often, thelink between France and solid-state physics becomes stronger and stronger.Co-occurrence of similar information elements is also possible: publicationsin solid-state physics may have addresses from two countries, say France andGermany. The more publications from France and Germany co-occur, the

100

stronger the collaborative link between France and Germany becomes in thatparticular research field. To take the first example one step further, imaginethat we collect the number of publications for a set of twenty countries inthirty different fields. The result is an array (a data table) with twenty rowsfor the countries, and thirty columns for the fields. Each specific cell in thiscountry-to-field co-occurrence array contains the observed number of pub-lications for a specific country in a specific field. As for the second example,counting publications that belong to two (or more) countries enables theconstruction of a country-to-country array. Here each cell in the array containsthe number of publications a pair of countries have in common. The observedco-occurrence frequencies provide the raw data. The actual analysis is oftendone on derivative quantitative measures, for example, on a size-independentmeasure of the countries’ publication profiles across those thirty fields.

Clearly, there is a large variety of bibliographic information elements.Examples are author or inventor names; institute or company affiliations;references (citations); key words and subject classification codes that referto ideas, concepts, and instruments. These items may, in principle, be ob-tained from various entities ranging from the microlevel (e.g., individualresearchers, publications, patents) via the mesolevel (e.g., groups ofreseachers, universities, companies, scientific journals), to the macrolevel(scientific/technological fields, countries). In principle, any combination ofinformation elements can be analyzed: item-item, entity-entity, or item-entity, thus encompassing hierarchical (vertical) as well as horizontal rela-tional structures. Moreover, time series of co-occurrence data allow ananalysis of the dynamics of the S&T system. Findings may comprise, forinstance, emerging research topics, identification of processes of synthesisand fragmentation in scientific fields, the contribution of specific researchgroups to these developments, or the role of instruments in &dquo;big science.&dquo;

MAPPING BIBLIOMETRIC CO-OCCURRENCE DATA

Data-analytical methods based on mathematical concepts and algorithmsdeveloped within the field of applied statistics can convert the informationin a co-occurrence array into a spatial configuration (a map). The location ofeach information element is determined by its relationships with the otherelements on the map. As for our first example, if a country (France) is veryactive in a specific field (solid-state physics), this country and field arepositioned close together on the map. As previously outlined, bibliometricmaps can also be built from any combinations of items and entities. These

101

graphical representations provide important advantages as compared totextual and tabular analyses and representations: they offer a panoramicoverview of the structure underlying large, complex masses of data, whichtakes less time to assimilate, to remember, and to reproduce. Moreover, thegraph provides a structure in which R&D activities can be placed in context,for example, by labeling points and linkages in the spatial configuration withthe &dquo;actors&dquo; concerned (e.g., individual researchers, research units, or firms).

The process of constructing science and technology maps requires deci-sions regarding analysis parameters (e.g., the choice of key words, or thesimilarity index) that involve a certain degree of subjectivity. Still, bibliomet-ric maps provide an objectified point of view, in the sense that the basicbibliographic data are relatively independent of subjective expert opinions.This does not mean that bibliomeric maps should replace those opinions. Onthe contrary, a fruitful application of these maps requires a process ofinteraction between map producers and users. Confrontation between design-ers of bibliometric maps and user groups have generated mixed results thatindicate that bibliometric maps are not automatically transparent tools forapplication (e.g., Healey, Rothman, and Hoch 1986). This is to some degreeinevitable because bibliometric maps are, by definition, artificial &dquo;snapshots&dquo;of a system in perpetual motion where researchers and engineers are activelyconcerned with creating new facts and tangible R&D products and linking itto the extant knowledge structure (Rip 1988).

Intuitively, one can expect that bibliometric maps will only partiallycoincide with a subject expert’s knowledge structure of a field. This discrep-ancy may result in one of the main dilemmas in the practical use of bibliomet-ric maps: if the expert’s mental map (Tijssen 1992c) and the bibliometric maplook alike, it is often argued that the latter adds nothing to the existingknowledge. If, on the other hand, these structures differ considerably, thevalidity of the map may be questioned, which may undermine the credibilityof maps. However, the capacity of maps for generating new information-whether or not in congruence with expert opinions-may also be seen aslegitimation of their value. Moreover, understanding the differences betweenan expert opinion and the bibliometric map, and the reasons for thesedifferences, may yield valuable evaluation insights.

MAPPING METHODS

There is a large variety of mapping methods. The choice for a particularmapping method is governed not only by analytical considerations but is also

102

related to a specific (data-)theoretical view of science. For instance, one mayassume that all information elements (e.g., U.S. patents) are more or lessequally important, and that all relations between these elements (e.g., accord-ing to subject classification codes) should, by definition, be treated equiva-lently in the analysis. One may then choose a mapping method that yieldsthe best possible display of all relationships between all elements. Anotherapproach is a more interactive one: elements, and relationships betweenthem, are selected in advance or during the analysis process, based on specifictheoretical notions or empirical findings (e.g., Callon et al. 1983). Interactivemapping procedures can do more justice to specific notions regarding theS&T system. For example, the distinction between trivial and key linkagesbetween research topics. Another important consideration is the mathemati-cal formalism that underlies the representation of the relationships on themap. One may prefer Euclidean distances as a metric, or a clustering ofelements based on other metrics~r neither, in which case the map generallyreverts to a diagram.

Multidimensional scaling (MDS) is one of the most commonly usedbibliometric mapping methods yielding a map in the true sense, that is, basedon a geometric distance metric where the position of an element reflects itsrelatedness with the other elements (Tijssen 1992a). Each element is assigneda spatial coordinate for each dimension. The configuration of elements is thenmapped in a two- or three-dimensional space according to those coordinates.

Like MDS, most other techniques used in bibliometric mapping belongto the class of multivariate data-analytical (MDA) techniques (cf. Tijssen andDe Leeuw 1988). Over the past decades, scientometric studies have applieda wide range of other MDA techniques, most notably: cluster-analysistechniques (e.g., single-linkage clustering, Small and Griffith 1974), andnetwork-analysis techniques (e.g., blockmodeling, Breiger 1976; minimalspanning trees, Small 1986).

Maps derived with MDS, though, tend to provide better recognizable andinterpretable views of relational data-particularly compared to results derivedfrom other MDA techniques such as factor analysis. MDS maps also allow fora more easy and systematic superimposing of additional (relational) data on thegeometrical structure to augment the interpretability of the maps. These mapstherefore provide a suitable spatial framework for placing R&D activities in

context. For example, by labeling elements on the map with the R&D actors

involved (e.g., university departments or firms) or other key information(e.g., the research grant or research program). The applicability of bibliomet-ric maps can be further enhanced by using MDS for the basic configurationin combination with network or cluster analysis for the fine-grained structure.Examples of such hybrid maps are presented further in the article.

103

BIBLIOMETRIC CO-OCCURRENCE METHODS

Below are four principal methodologies for analyzing bibliometric co-occurrence data:

COCITATION ANALYSIS

The archetype of bibliometric mapping, cocitation analysis, has becomea well-established descriptive tool, which is particularly due to the fact thatit was developed within the Institute for Scientific Information (ISI) forexploitation of its Science Citation Index database (Small 1973). A recentkey example of cocitation analysis is Braam, Moed, and Van Raan (1991a,1991b) focusing on the relatedness of different cocitation clusters throughkey word similarity analysis.

Cocitation analysis is based on the principle that when an article X1 citestwo earlier articles A and B, these latter articles are cocited. The strength ofsuch a cocitation link is determined by the number of citing articles (XI, X2,X3, etc.) each with the pair (A, B) in their lists of cited articles (references).Article B can also form a cocitation of a specific field in one particularpublication year. The next analytical step generates a structure of interlinkedcocited pairs creating sets of (co)cited articles, that is, clustering of cocitedarticles yielding aggregates of the size of scientific fields or disciplines.Finally, the network of cocitation linkages within and between clusters isvisualized by applying MDS. Small (1993) deals with the dynamic charac-teristics of cocitation clusters. Franklin and Johnston (1988) present anoverview of practical issues concerning the use of cocitation analysis forR&D management.

Cocitation clusters represent research-front specialties, in terms of relatedscientific work (i.e., based on the same publications, as far as reflected bythe cited literature). However, these clusters may reflect cognitive as well associal networks. Moreover, drawbacks of cocitation analysis are its time laginherent to citation data in general; noncited publications are excluded fromthe analysis; the cluster algorithm may involve somewhat arbitrary setting ofthreshold values, yielding less informative results in case of nonoptimalthresholds; and inadequate coverage of technology-related research.

So far, cocitation analysis has been applied to scientific fields. For

technology fields, the same technique could be applied to patents. Here wehave to analyze to which extent two patents are cited together in other patents.Although much pioneering work on citations in patents has been done byNarin and colleagues (see Narin and Olivastro 1988), there is as yet no

104

systematic exploration of technology fields by cocitaiton analysis. One of thechief problems is that the number of citations in patents is usually consider-ably lower than the number of citations in scientific publications.

JOURNAL-TO-JOURNAL CITATION ANALYSIS

Citations linking individual articles can be aggregated to the level oflearned journals. These journals are the major carriers of scientific knowl-edge and can be taken as institutionalized representations of specialties,subfields, and fields of science. The network of journal citations reflectsmacrolevel structures of scientific activities, and changes in these structuresmay indicate the dynamics of scientific fields. Price (1965) recognized thepotential of such an interjournal citation network to create bibliometric mapsof science. There is a plethora of examples of this type of bibliometricmap-most of which involve MDS. For instance, Leydesdorff and Van deSchaar (1987) present a time series of journal-based citation maps of the fieldof wind energy and solar energy. Rice, Borgman, and Reeves (1988) depictthe asymmetry of citation interrelationships by mapping the cited and citingmode of journals in combination with a network analysis of the journal-to journal citation flows. The method’s major drawbacks concern the incom-plete coverage of the journal literature by ISI’s citation indexes, especiallyfor the applied sciences, social sciences, and humanities.

CO-WORD ANALYSIS

This type of analysis involves co-occurrences of key words, given byauthors or indexers, or words extracted from publication titles or from theirfull text. These word co-occurrences reflect the network of conceptualrelations from the viewpoint of scientists and engineers active in the field.The co-word frequency array is used to construct a co-word map that

represents the intellectual content of a field (i.e., cognitive themes and theirinterrelations) by means of cluster analysis and network analysis (Callonet al. 1983). A recent development is the coanalysis without prior selectionand indexing of words where the word co-occurrences are generated directlyfrom the text itself (Kostoff 1993).

Co-word analysis is completely independent of citation practices, thusrendering it applicable to fields where citation data are not available, or wherecitation practices are such that citations do not sufficiently cover researchactivities. This is particularly important for application-oriented research, thesocial sciences, and the humanities. Moreover, it is applicable to patents that

105

cover any specific field of technology. A further advantage of co-wordanalysis is that words are the foremost carrier of scientific and technologicalconcepts; their use is unavoidable and they cover an unlimited intellectualdomain. Words often have a context-dependent meaning. Co-word analysisdeals with this semantic problem by placing words in relation to one another.Co-word maps can therefore be regarded as a (albeit, primitive) semanticrepresentation of knowledge structures. Moreover, co-word analysis has amuch better retrieval rate than cocitation analysis; only a small part of thepublications on which cocitation analysis is based are represented by thecocitation map, whereas co-word analysis represents the majority of itssource publications (see Braam et al. 1991a, 1991b).

More recent applications of co-word analysis are, for example, Courtialand Law (1989) who present a co-word map of artificial intelligence accom-panied by a summary description of the structure that is referred to as astrategic graph. A time series of MDS-based co-word maps of neural-networks research is presented by Van Raan and Tijssen (1993) and iscompared to expert narratives in review articles. Peters and Van Raan (1992a,1992b) present a comprehensive overview of co-word mapping techniquesapplied to the field of chemical engineering. Co-word maps of specifictechnological fields, technology as a whole, as well as science-technologyinterface maps have been produced by Engelsman and Van Raan (1993).

COCLASSIFICATION ANALYSIS

This type of analysis operates on the co-occurrence of terms (or codes)that are used to classify publications for ease of access in bibliographicdatabases. These indexer-given information items are derived from a thesau-rus and may represent scientific (or technological) topics, specialties, orfields. Compared to key words, subject classification terms have a well-defined and consistent meaning over the entire knowledge domain, whichmakes them particularly attractive for studying and depicting the maincognitive structure across large scientific and technological areas. The mainpractical restrictions are imposed by the fixed classification scheme. More-over, classification codes are assigned primarily for information retrievalpurposes and do not necessarily reflect intellectual concepts. Key examplesare, for example, Van Raan and Peters (1989) who use the co-occurrence ofclassification codes to construct MDS maps depicting the dynamics in thestructure of chemical engineering. Tijssen (1992b) uses an MDS mapping ofcoclassification structures together with network-analysis methods for iden-tifying temporal changes in the cognitive links between fields of energy

106

research. Engelsman and Van Raan (1993) present a coclassification mapdepicting the structure of relations between all technological fields, accord-ing to the international patent classification scheme, and compare its config-uration to a map of technology derived by means of co-word analysis.

Jointly, the co-occurrence methodologies can be seen as constituting ananalytical framework that can be applied to any set of bibliographic itemsand entities on any level of aggregation. In addition to the above examples,we mention maps of (inter)national coauthorship networks, or coaffiliationnetworks (see below). Moreover, several sets of co-occurrence data can beanalyzed jointly. We refer to Braam, Moed, and Van Raan (1989) for acombination of cocitation and co-word data. Another example is given byTijssen et al. (1990) who present maps of journal-journal interrelationshipscombining citation co-occurrence data and word occurrence frequencies.

EXAMPLES OF IMPACTASSESSMENT INVOLVING BIBLIOMETRIC MAPS

A CO-WORD MAP OF EMERGING RESEARCH

TOPICS IN MANUFACTURING TECHNOLOGY

A recent study by Tijssen and Van der Velde (1992), commissioned by theDutch Ministry of Economics Affairs, was aimed at identifying emergingtopics in manufacturing technology through a quantitative analysis of theworldwide research literature. The bibliometrics study was based on keywords extracted from bibliographic data on publications covering engineer-ing (bibliographic database: COMPENDEX) and physics and electronics(INSPEC). Both indexer-given (controlled) words as well as (uncontrolled)words from the text itself were analyzed. This first part of the study compriseda trend analysis of two groups of key words: (1) established key wordsshowing a noticeable increase in occurrence, and (2) relatively new keywords (i.e., those emerging in the period 1989-1992). The second partconsists of co-word maps depicting the cognitive links between the selectedwords.

Figure 1 presents an MDS map depicting co-occurrences involving oneof those new key words: fuzzy logic, a mathematical formalism that enablesthe incorporation of the concept of uncertainty in decision theory. This keyword was found in 42 out of a total of 29,019 research publications over theperiod 1991-1992. Fuzzy logic is linked to a variety of key words, rangingfrom established and frequently used terms such as manufacturing process-

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ing to new words such as configuration management and planning: artificialintelligence. In the map we first notice relatively strong cognitive links withneural net and with research on knowledge-based systems (more than 25%of those 42 publications also carry the word knowledge-based system). Thespatial configuration as a whole shows that fuzzy logic is starting to have animpact on various aspects of manufacturing technology research, particularlyon neural networks and knowledge-based systems for controlling and plan-ning production systems.

COAFFILIATION MAPS OF DUTCHRESEARCH ON COAL AND COAL PRODUCTS

Van Leeuwen and Tij ssen ( 1991 ) report on a large-scale bibliometric studyof international energy research including a time series analysis of domesticand international collaborative links involving Dutch organizations (univer-sities, companies) active in research on coal and coal products in the secondhalf of the 1980s-a field in a state of flux marked by a decrease ingovernment funding and a reorientation toward research on combustion andgasification. The bibliometric data were extracted from the internationaldatabase Energy Research and Technology. Cooperation is defined as theco-occurrence of different affiliations on research publications.

Figure 2a shows an MDS map, composed of a linkage network and acluster structure, which depicts the state of affairs based on the 1984-1986publications. Figure 2b depicts the network for the period 1987-1989. Thetransition is clearly visible in the structure of the network and the organiza-tions involved. Noticeable features are (1) declining international collabora-tion, less participation of companies (e.g., Shell), (3) a decrease in the numberof Dutch universities (e.g., University of Amsterdam), (4) less collaborationof universities with industry, and (5) an increasing emphasis on environmen-tal aspects (waste management), while European Community (EC) programscontinue to play a significant role through the EC joint research centers.

CO-WORD MAPS OF MATERIALS SCIENCE

For the time period 1990-1991 we collected, with the help of INSPECdatabase, keywords for all materials science publications (total number ofpublications is 39,044). With a key word frequency analysis, we identifiedthe 100 most frequent words, assuming that these most frequent wordsrepresent in first and good approximation the (mainstream of the) field understudy. We determined for each key word the number of times (i.e., the number

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of publications) it is mentioned together with any of the other 100 key words.In this way, we composed a 100 100 word co-occurrence array. The spatialconfiguration resulting from the co-word array (with help of MDS) gives usa geometrical pattern of word relations.

As discussed earlier, distance on the map implies an important cognitivemeaning: the closer words are positioned together, the more they indicatestrongly related topics. An additional cluster technique emphasizes thestrongest local relations: word clusters indicated by encircling a group ofstrongly linked words. In this way, a modulelike structure of the materialsscience &dquo;landscape&dquo; is created, as shown in Figure 3. The maps werediscussed with experts in the field and compared to the 1990/91 map withmaps of 1984/84 and 1988/89 (see Van Raan and Van der Velde 1991).

EXPERT DATA? BIBLIOMETRIC MAPS? OR BOTH?

Expert consultation is traditionally preferred to bibliometric methods-however more costly, time-consuming, and labor-intensive the former maybe. The last decade has, however, seen a steady technical and conceptualdevelopment of bibliometric indicators-both scalar and relational-towardtheir present state of sophistication. They can now be used as a cost-effectiveinformation tool for obtaining a valid and meaningful representation of dataretrieved from the R&D literature.

The appeal of bibliometric maps is their ability for scanning and summa-rizing large volumes of data, their relative immediacy, and wide applicabilityto the study of scientific outputs, whether in basic or strategic research. Thiscan be of particular importance in cognitive domains where blind spots ofindividual experts or panel committees may influence their views-forexample, in the case of broad and heterogeneous scientific fields, or theinterface of science and technology.

Obviously, bibliometric data capture only a part of the R&D process and

its outcome. Hence, in practical applications, the crucial point is often not somuch how a bibliometric map should be used, but rather how to supplementit in a meaningful way with other information-of a quantitative nature (e.g.,other bibliometric indicators, R&D personnel statistics, economic statistics,etc.), or qualitative views (peer review). However, bibliometric maps cannotalways produce configurations that are easily linked to those other accounts.Interaction between S&T analysts and users will therefore constitute anessential element in construction as well as the application of those maps(e.g., Tijssen 1992a). Securing a process of feedback may not only yield new

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insights in why differences occur between expert views and bibliometric databut may also stimulate users and independent subject experts to suggestadditional analyses or more fine-tuned analyses. The ability to incorporatecomments and criticisms may not only constitute an essential step toward abetter understanding and use of maps; it might prove to be one of the majoroutcomes of applying maps. Such a collaborative effort may significantlyenhance the utility of bibliometric maps as an external means for visualizingand analyzing the intricate fabric of science and technology. This type ofinformation may help R&D managers and S&T policymakers to obtain abetter view, and improve their understanding, of the state and the develop-ments of the S&T system. This in turn may contribute to more transparentand systematic evaluation processes of R&D activities.

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&mdash;. 1991a Mapping of science by combining co-citation and word analysis. I: Structuralaspects. Journal of the American Society for Information Science 42:233-51.

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Breiger, R. L. 1976. Career attributes and network structure: A blockmodel study of biomedicalresearch work. American Sociological Review 41:117-35.

Callon, M., J.-P. Courtial, W. A. Turner, and S. Bauin. 1983. From translations to problematicnetworks: An introduction to co-word analysis. Social Science Information 22:191-235.

Courtial, J.-P., and J. Law. 1989. A co-word study of artificial intelligence. Social Studies ofScience 19:301-11.

Engelsman, E. C., and A.F.J. Van Raan. 1993. A patent-based cartography of technology.Research Policy.

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Robert J. W Ttjssen is a senior researcher and a member of staff at the Centre for Science andTechnology Studies. He graduated In 1984 in mathematical psychology at the University ofLeiden where he also received his Ph.D. degree in quantitative studies of science in 1992. He isactive as the coordinator of the Netherlands Science and Technology Observatory and is amember of the Experts Group on Science and Technology Indicators of the OECD. His mainresearch interests include scientometric evaluation of research performance, science andtechnology indicators, mapping of science and technology through application of appliedstatistical methods, and cognitive studies of perceptions of scientific experts. He has contributedto a number of leamedJournals, including Scientometrics, Research Policy, Journal of Infor-mation Science, and the Journal of the American Society for Information Science.

Anthony F. J. Van Raan is Professor of Science Studies at the University of Leiden. Since 1985he has been Director of the Centre for Science and Technology Studies (CWTS). He graduatedIn 1969 at the University of Utrecht and in 1973 received his Ph.D. degree in Physics. He hasbeen a visiting scientist in several universities and research institutes In the United States, UK,and France. His previous work was in experimental atomic and molecular physics and in sciencepolicy and research management. He is editor of the Handbook of Quantitative Studies ofScience and Technology (Elsevier, 1988), and of the journal Research Evaluation. He is amember of the Experts Group on Science and Technology Indicators of the Organization forEconomic Cooperation and Development (OECD). His main research topics include researchperformance assessments by bibliometric methods, mapping of science and technology and thescience and technology interface, and science as a self-organizing ecosystem.