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Abstract

Multiple perspectives on the nonlinear processes of medical innovations can be distinguished and combined using the Medical Subject Headings (MeSH) of the MEDLINE database. Focusing on three main branches-"diseases," "drugs and chemicals," and "techniques and equipment"-we use base maps and overlay techniques to investigate the translations and interactions and thus to gain a bibliometric perspective on the dynamics of medical innovations. To this end, we first analyze the MEDLINE database, the MeSH index tree, and the various options for a static mapping from different perspectives and at different levels of aggregation. Following a specific innovation (RNA interference) over time, the notion of a trajectory which leaves a signature in the database is elaborated. Can the detailed index terms describing the dynamics of research be used to predict the diffusion dynamics of research results? Possibilities are specified for further integration between the MEDLINE database on one hand, and the Science Citation Index and Scopus (containing citation information) on the other.

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Abstract

We present a novel routine, namely medlineR, based on the R language, that allows the user to match data from Medline/PubMed with records indexed in the ISI Web of Science (WoS) database. The matching allows exploiting the rich and controlled vocabulary of medical subject headings (MeSH) of Medline/PubMed with additional fields of WoS. The integration provides data (e.g., citation data, list of cited reference, list of the addresses of authors' host organizations, WoS subject categories) to perform a variety of scientometric analyses. This brief communication describes medlineR, the method on which it relies, and the steps the user should follow to perform the matching across the two databases. To demonstrate the differences from Leydesdorff and Opthof (Journal of the American Society for Information Science and Technology, 64(5), 1076-1080), we conclude this artcle by testing the routine on the MeSH category "Burgada syndrome."

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Abstract

This paper examines the use of scientometric overlay mapping as a tool of "strategic intelligence" to aid the governing of emerging technologies. We develop an integrative synthesis of different overlay mapping techniques and associated perspectives on technological emergence across geographical, social, and cognitive spaces. To do so, we longitudinally analyze (with publication and patent data) three case studies of emerging technologies in the medical domain. These are RNA interference (RNAi), human papillomavirus (HPV) testing technologies for cervical cancer, and thiopurine methyltransferase (TPMT) genetic testing. Given the flexibility (i.e., adaptability to different sources of data) and granularity (i.e., applicability across multiple levels of data aggregation) of overlay mapping techniques, we argue that these techniques can favor the integration and comparison of results from different contexts and cases, thus potentially functioning as a platform for "distributed" strategic intelligence for analysts and decision makers.