This article introduces a new method for evaluating national publication activities. This new indicator, thought leadership, captures whether the nation is a thought leader (building on the more recently cited literature for that field) or follower (building on the older cited literature for that field). Publication data for 2003 are used to illustrate which nations tend to build on the more recent discoveries in chemistry and clinical medicine. Finally, implications for national and laboratory policy are discussed.
Research on the effects of collaboration in scientific research has been increasing in recent years. A variety of studies have been done at the institution and country level, many with an eye toward policy implications. However, the question of how to identify the most fruitful targets for future collaboration in high-performing areas of science has not been addressed. This paper presents a method for identifying targets for future collaboration between two institutions. The utility of the method is shown in two different applications: identifying specific potential collaborations at the author level between two institutions, and generating an index that can be used for strategic planning purposes. Identification of these potential collaborations is based on finding authors that belong to the same small paper-level community, using a paper-level map of science from the combined 2003 SCIE/SSCI/Proceedings databases containing nearly 1 million papers organized into 117,435 communities.
Over the past several years, techniques have been developed for clustering very large segments of the technical literature using sources such as Thomson ISI's Science Citation Index. The primary objective of this work has been to develop indicators of potential impact at the paper level to enhance planning and evaluation of research. These indicators can also be aggregated at different levels to enable profiling of departments, institutions, agencies, etc. Results of this work are presented as maps of science and technology with various overlays corresponding to the indicators associated with a particular search or question.
This article describes recent improvements in mapping a highly representative set of the world-wide scientific literature. The process described in this article extends existing work in this area in three major ways. First, we argue that a separate structural analysis of current literature vs. reference literature is required for R&D planning. Second, visualization software is used to improve coverage of the literature while maintaining structural integrity. Third, quantitative techniques for measuring the structural integrity of a map are introduced. Maps with high structural integrity, covering far more of the available literature, are presented.
This article examines how well one can predict the importance of a current paper (a paper that is recently published in the literature). We look at three factors--journal importance, reference importance and author reputation. Citation-based measures of importance are used for all variables. We find that journal importance is the best predictor (explaining 22.3% out of a potential 29.1% of the variance in the data), and that this correlation value varies significantly by discipline. Journal importance is a better predictor of citation in Computer Science than in any other discipline. While the finding supports the present policy of using journal impact statistics as a surrogate for the importance of current papers, it calls into question the present policy of equally weighting current documents in text-based analyses. We suggest that future researchers take into account the expected importance of a document when attempting to describe the cognitive structure of a field.
This paper presents a new map representing the structure of all of science, based on journal articles, including both the natural and social sciences. Similar to cartographic maps of our world, the map of science provides a bird's eye view of today's scientific landscape. It can be used to visually identify major areas of science, their size, similarity, and interconnectedness. In order to be useful, the map needs to be accurate on a local and on a global scale. While our recent work has focused on the former aspect, this paper summarizes results on how to achieve structural accuracy. Eight alternative measures of journal similarity were applied to a data set of 7,121 journals covering over 1 million documents in the combined Science Citation and Social Science Citation Indexes. For each journal similarity measure we generated two-dimensional spatial layouts using the force-directed graph layout tool, VxOrd. Next, mutual information values were calculated for each graph at different clustering levels to give a measure of structural accuracy for each map. The best co-citation and inter-citation maps according to local and structural accuracy were selected and are presented and characterized. These two maps are compared to establish robustness. The inter-citation map is then used to examine linkages between disciplines. Biochemistry appears as the most interdisciplinary discipline in science.
This SAND report is the final report on Sandia's Grand Challenge LDRD Project 27328, 'A Revolution in Lighting -- Building the Science and Technology Base for Ultra-Efficient Solid-state Lighting.' This project, which for brevity we refer to as the SSL GCLDRD, is considered one of Sandia's most successful GCLDRDs. As a result, this report reviews not only technical highlights, but also the genesis of the idea for Solid-state Lighting (SSL), the initiation of the SSL GCLDRD, and the goals, scope, success metrics, and evolution of the SSL GCLDRD over the course of its life. One way in which the SSL GCLDRD was different from other GCLDRDs was that it coincided with a larger effort by the SSL community - primarily industrial companies investing in SSL, but also universities, trade organizations, and other Department of Energy (DOE) national laboratories - to support a national initiative in SSL R&D. Sandia was a major player in publicizing the tremendous energy savings potential of SSL, and in helping to develop, unify and support community consensus for such an initiative. Hence, our activities in this area, discussed in Chapter 6, were substantial: white papers; SSL technology workshops and roadmaps; support for the Optoelectronics Industry Development Association (OIDA), DOE and Senator Bingaman's office; extensive public relations and media activities; and a worldwide SSL community website. Many science and technology advances and breakthroughs were also enabled under this GCLDRD, resulting in: 55 publications; 124 presentations; 10 book chapters and reports; 5 U.S. patent applications including 1 already issued; and 14 patent disclosures not yet applied for. Twenty-six invited talks were given, at prestigious venues such as the American Physical Society Meeting, the Materials Research Society Meeting, the AVS International Symposium, and the Electrochemical Society Meeting. This report contains a summary of these science and technology advances and breakthroughs, with Chapters 1-5 devoted to the five technical task areas: 1 Fundamental Materials Physics; 2 111-Nitride Growth Chemistry and Substrate Physics; 3 111-Nitride MOCVD Reactor Design and In-Situ Monitoring; 4 Advanced Light-Emitting Devices; and 5 Phosphors and Encapsulants. Chapter 7 (Appendix A) contains a listing of publications, presentations, and patents. Finally, the SSL GCLDRD resulted in numerous actual and pending follow-on programs for Sandia, including multiple grants from DOE and the Defense Advanced Research Projects Agency (DARPA), and Cooperative Research and Development Agreements (CRADAs) with SSL companies. Many of these follow-on programs arose out of contacts developed through our External Advisory Committee (EAC). In h s and other ways, the EAC played a very important role. Chapter 8 (Appendix B) contains the full (unedited) text of the EAC reviews that were held periodically during the course of the project.
High throughput instruments and analysis techniques are required in order to make good use of the genomic sequences that have recently become available for many species, including humans. These instruments and methods must work with tens of thousands of genes simultaneously, and must be able to identify the small subsets of those genes that are implicated in the observed phenotypes, or, for instance, in responses to therapies. Microarrays represent one such high throughput method, which continue to find increasingly broad application. This project has improved microarray technology in several important areas. First, we developed the hyperspectral scanner, which has discovered and diagnosed numerous flaws in techniques broadly employed by microarray researchers. Second, we used a series of statistically designed experiments to identify and correct errors in our microarray data to dramatically improve the accuracy, precision, and repeatability of the microarray gene expression data. Third, our research developed new informatics techniques to identify genes with significantly different expression levels. Finally, natural language processing techniques were applied to improve our ability to make use of online literature annotating the important genes. In combination, this research has improved the reliability and precision of laboratory methods and instruments, while also enabling substantially faster analysis and discovery.
Visualization of scientific frontiers is a relatively new field, yet it has a long history and many predecessors. The application of science to science itself has been undertaken for decades with notable early contributions by Derek Price, Thomas Kuhn, Diana Crane, Eugene Garfield, and many others. What is new is the field of information visualization and application of its techniques to help us understand the process of science in the making. In his new book, Chaomei Chen takes us on a journey through this history, touching on predecessors, and then leading us firmly into the new world of Mapping Scientific Frontiers. Building on the foundation of his earlier book, Information Visualization and Virtual Environments, Chen's new offering is much less a tutorial in how to do information visualization, and much more a conceptual exploration of why and how the visualization of science can change the way we do science, amplified by real examples. Chen's stated intents for the book are: (1) to focus on principles of visual thinking that enable the identification of scientific frontiers; (2) to introduce a way to systematize the identification of scientific frontiers (or paradigms) through visualization techniques; and (3) to stimulate interdisciplinary research between information visualization and information science researchers. On all these counts, he succeeds. Chen's book can be broken into two parts which focus on the first two purposes stated above. The first, consisting of the initial four chapters, covers history and predecessors. Kuhn's theory of normal science punctuated by periods of revolution, now commonly known as paradigm shifts, motivates the work. Relevant predecessors outside the traditional field of information science such as cartography (both terrestrial and celestial), mapping the mind, and principles of visual association and communication, are given ample coverage. Chen also describes enabling techniques known to information scientists, such as multi-dimensional scaling, advanced dimensional reduction, social network analysis, Pathfinder network scaling, and landscape visualizations. No algorithms are given here; rather, these techniques are described from the point of view of enabling 'visual thinking'. The Generalized Similarity Analysis (GSA) technique used by Chen in his recent published papers is also introduced here. Information and computer science professionals would be wise not to skip through these early chapters. Although principles of gestalt psychology, cartography, thematic maps, and association techniques may be outside their technology comfort zone, or interest, these predecessors lay a groundwork for the 'visual thinking' that is required to create effective visualizations. Indeed, the great challenge in information visualization is to transform the abstract and intangible into something visible, concrete, and meaningful to the user. The second part of the book, covering the final three chapters, extends the mapping metaphor into the realm of scientific discovery through the structuring of literatures in a way that enables us to see scientific frontiers or paradigms. Case studies are used extensively to show the logical progression that has been made in recent years to get us to this point. Homage is paid to giants of the last 20 years including Michel Callon for co-word mapping, Henry Small for document co-citation analysis and specialty narratives (charting a path linking the different sciences), and Kate McCain for author co-citation analysis, whose work has led to the current state-of-the-art. The last two chapters finally answer the question - 'What does a scientific paradigm look like?' The visual answer given is specific to the GSA technique used by Chen, but does satisfy the intent of the book - to introduce a way to visually identify scientific frontiers. A variety of case studies, mostly from Chen's previously published work - supermassive black holes, cross-domain applications of Pathfinder networks, mass extinction debates, impact of Don Swanson's work, and mad cow disease and vCJD in humans - succeed in explaining how visualization can be used to show the development of, competition between, and eventual acceptance (or replacement) of scientific paradigms. Although not addressed specifically, Chen's work nonetheless makes the persuasive argument that visual maps alone are not sufficient to explain 'the making of science' to a non-expert in a particular field. Rather, expert knowledge is still required to interpret these maps and to explain the paradigms. This combination of visual maps and expert knowledge, used jointly to good effect in the book, becomes a potent means for explaining progress in science to the expert and non-expert alike. Work to extend the GSA technique to explore latent domain knowledge (important work that falls below the citation thresholds typically used in GSA) is also explored here.
VxInsight provides a visual mechanism for browsing, exploring and retrieving information from a database. The graphical display conveys information about the relationship between objects in several ways and on multiple scales. In this way, individual objects are always observed within a larger context. For example, consider a database consisting of a set of scientific papers. Imagine that the papers have been organized in a two dimensional geometry so that related papers are located close to each other. Now construct a landscape where the altitude reflects the local density of papers. Papers on physics will form a mountain range, and a different range will stand over the biological papers. In between will be research reports from biophysics and other bridging disciplines. Now, imagine exploring these mountains. If we zoom in closer, the physics mountains will resolve into a set of sub-disciplines. Eventually, by zooming in far enough, the individual papers become visible. By pointing and clicking you can learn more about papers of interest or retrieve their full text. Although physical proximity conveys a great deal of information about the relationship between documents, you can also see which papers reference which others, by drawing lines between the citing and cited papers. For even more information, you can choose to highlight papers by a particular researcher or a particular institution, or show the accumulation of papers through time, watching some disciplines explode and other stagnate. VxInsight is a general purpose tool, which enables this kind of interaction with wide variety of relational data: documents, patents, web pages, and financial transactions are just a few examples. The tool allows users to interactively browse, explore and retrieve information from the database in an intuitive way.