We present the results of a comprehensive multi-pass analysis of visualization paper keywords supplied by authors for their papers published in the IEEE Visualization conference series (now called IEEE VIS) between 1990–2015. From this analysis we derived a set of visualization topics that we discuss in the context of the current taxonomy that is used to categorize papers and assign reviewers in the IEEE VIS reviewing process. We point out missing and overemphasized topics in the current taxonomy and start a discussion on the importance of establishing common visualization terminology. Our analysis of research topics in visualization can, thus, serve as a starting point to (a) help create a common vocabulary to improve communication among different visualization sub-groups, (b) facilitate the process of understanding differences and commonalities of the various research sub-fields in visualization, (c) provide an understanding of emerging new research trends, (d) facilitate the crucial step of finding the right reviewers for research submissions, and (e) it can eventually lead to a comprehensive taxonomy of visualization research. One additional tangible outcome of our work is an online query tool ( http: keyvis.org ) that allows visualization researchers to easily browse the 3952 keywords used for IEEE VIS papers since 1990 to find related work or make informed keyword choices.
 The goal of this paper is to show how co-word analysis techniques can be used to study interactions between academic and technological research. It is based upon a systematic content analysis of publications in the polymer science field over a period of 15 years. The results concern a.) the evolution of research in different subject areas and the patterns of their interaction; b.) a description of subject area “life cycles”; c.) an analysis of ”research trajectories” given factors of stability and change in a research network; d.) the need to use both science push and technology pull theories to explain the interaction dynamics of a research field. The co-word techniques developed in this paper should help to build a bridge between research in scientometrics and work underway to better understand the economics of innovation. In this paper the adequacy of the co-word method for mapping the structure of scientific inquiry is explored. Co-word analysis of both the keywords and the titles of a set of papers in acidification research' is undertaken and the results are found to be comparable, though the keyword-derived results provide greater detail. This strongly suggests that keyword indexing doest not, as has sometimes been claimed, distort coword findings. It also points to differences between titles (which often emphasize the supposed originality of an article) and keywords (which tend to show the relationship between the paper and other publications). The paper also explores important differences between the methodological assumptions that underlie the Paris Keele co-word clustering algorithms and the factor analysis method for creating clusters. The use of topic models to analyze domain-specific texts often requires manual validation of the latent topics to ensure that they are meaningful. We introduce a framework to support such a large-scale assessment of topical relevance. We measure the correspondence between a set of latent topics and a set of reference concepts to quantify four types of topical misalignment: junk, fused, missing, and repeated topics. Our analysis compares 10,000 topic model variants to 200 expert-provided domain concepts, and demonstrates how our framework can inform choices of model parameters, inference algorithms, and intrinsic measures of topical quality. This paper describes recent developments in the co-word method and illustrates, for the case of acid rain research, the way in which the method can be used to detect (a) the themes of research to be found in a given area of science, (b) the relationships between those themes, (c) the extent to which they are central to the area in question and (d) the degree to which they are internally structured. It is also suggested that the method may be used to draw comparative research profiles for different countries. Though the data used are only preliminiary, it is argued that the method has now been developed to the point where its results are both quite robust and easily assimilable. It is, accordingly, now an appropriate tool for policy analysis. Preface Historical Overview A Short History The Emergence of Institutions Metrics: Approaches, Methods, Techniques Measurement: Concepts and Issues Selecting a Metric Inputs to Science and Technology Outputs from Science and Technology: Categories and Metrics Economic and Financial Metrics Bibliometric Measures: Publications and Citations Co-Word Analysis and Mapping of Science and Technology The Metric of Patents The Metric of Peer Review The Metric of Process Outcomes Performance of Science and Technology Applications: The Value--In Practice--of Science and Technology Metrics and Evaluation of Academic Science and Technology Metrics and Evaluation of Industrial Science and Technology Science, Technology, and Strategy Metrics and Evaluation of Public-Sector Science and Technology Methods and Evaluation of National Innovation Systems Values, Ethics, and Implications Selected Bibliography Index 