The specialty of collagen research is tracked over a ten year period, 1970–1979, using the methodology of co-citation cluster strings. Independently obtained annual clusters are linked together over time by the percentage of highly cited documents countinuing from year to year. All inter-year links are clustered by single-linkage to form the strings, one of which corresponds to the collagen specialty. Maps of the individual year clusters within the string reveal an alternating pattern of expansion/innovation followed by contraction/consolidation. At the same time the subject focus of research gradually shifts. The institutional affiliation and funding sources for highly cited documents show a trend from early dominance by a few institutions and sources to a multiplicity and collaboration of centers and sources later on, due in part to the migration of researchers from an initially dominant institution.
The techniques of co-citation clustering and citation context analysis are combined to concretely define the shared knowledge within a research specialty. The cluster for a large and fast moving biomedical specialty, recombinant-DNA, is presented in terms of the highly cited documents comprising it and their co-citation links. By examining citation contexts in the papers citing the highly cited documents, it is possible to label each of the documents in the cluster with its specific cognitive meaning for the citing authors. Co-citation contexts are used to reveal the relationships among the concepts symbolized by the highly cited documents, providing a cognitive equivalent of the co-citation links. This may open a new way to the investigation of the logic of conceptual change at the specialty level.
Previous attempts to map science using the co-citation clustering methodology are reviewed, and their shortcomings analyzed. Two enhancements of the methodology presented in Part I of the paper-fractional citation counting and variable level clustering—are briefly described and a third enhancement, the iterative clustering of clusters, is introduced. When combined, these three techniques improve our ability to generate comprehensive and representative mappings of science across the multidisciplinaryScience Citation Index (SCI) data base. Results of a four step analysis of the 1979SCI are presented, and the resulting map at the fourth iteration is described in detail. The map shows a tightly integrated network of approximate disciplinary regions, unique in that for the first time links between mathematics and biomedical science have brought about a closure of the previously linear arrangement of disciplines. Disciplinary balance between biomedical and physical science has improved, and the appearance of less cited subject areas, such as mathematics and applied science, makes this map the most comprehensive one yet produced by the co-citation methodology. Remaining problems and goals for future work are discussed.