A faculty research and training program for undergraduates in the sciences. (65/635)

Faculty enthusiasm, with actual hands-on involvement, is a critical factor in establishing student research interest and excitement in a university or college science environment. Such faculty involvement is infectious to students and therefore key to restoring United States leadership in science and technology in the next decades. Most scientists acknowledge that they were initially attracted into scientific careers through one or two notable teachers who served as role models. However, with the introduction of so-called "big science" and its distraction of university faculty away from meaningful, direct student contacts, and with associated withdrawal of funding from "little science" in the college teacher's laboratory, research languishes in nearly all undergraduate teaching institutions. The inspiring college science teacher seems essentially gone, tired or burnt out, unable to keep pace with the rigorous demands of an active research lab while simultaneously meeting the exhausting load of 15-18 (or more) contact teaching hours per week. With all of the associated lecture preparations, student counseling, and Dean's committee assignments, the teacher has little or no scholarly "think time" or opportunity to inspire even the bright students. Without the teacher's honest and evident involvement and deep commitment, the student fails to experience the essential impact of a convincing role model. It is therefore necessary to restore the college science teacher's opportunity and aspirations to be personally involved in research. This can only be accomplished by providing time, facilities, incentives, and encouragement to do what originally attracted the teacher into a career in science and teaching in the first place.(ABSTRACT TRUNCATED AT 250 WORDS)  (+info)

Complex medical case histories as portals to medical practice and integrative, scientific thought. (66/635)

Complex clinicopathological conferences from the New England Journal of Medicine are used to introduce first-year medical and graduate students to scientific reasoning at the level of the whole organism and to help them mobilize and integrate the knowledge obtained in their previous studies. The approach involves outlining the etiology of the case history. This becomes a framework for thought allowing students to easily cope with the profusion of data. The method is cost effective: a single professor can interact with a large class, yet engage students on a one-to-one basis. It is a powerful adjunct to, but does not replace, lecture or small group activities such as problem-based learning. An annotated case history involving diabetes mellitus is provided.  (+info)

Crossmaps: visualization of overlapping relationships in collections of journal papers. (67/635)

A crossmapping technique is introduced for visualizing multiple and overlapping relations among entity types in collections of journal articles. Groups of entities from two entity types are crossplotted to show correspondence of relations. For example, author collaboration groups are plotted on the x axis against groups of papers (research fronts) on the y axis. At the intersection of each pair of author group/research front pairs a circular symbol is plotted whose size is proportional to the number of times that authors in the group appear as authors in papers in the research front. Entity groups are found by agglomerative hierarchical clustering using conventional similarity measures. Crossmaps comprise a simple technique that is particularly suited to showing overlap in relations among entity groups. Particularly useful crossmaps are: research fronts against base reference clusters, research fronts against author collaboration groups, and research fronts against term co-occurrence clusters. When exploring the knowledge domain of a collection of journal papers, it is useful to have several crossmaps of different entity pairs, complemented by research front timelines and base reference cluster timelines.  (+info)

Mapping subsets of scholarly information. (68/635)

We illustrate the use of machine learning techniques to analyze, structure, maintain, and evolve a large online corpus of academic literature. An emerging field of research can be identified as part of an existing corpus, permitting the implementation of a more coherent community structure for its practitioners.  (+info)

Finding scientific topics. (69/635)

A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.  (+info)

Scientifically based safety criteria for ultrasonography. (70/635)

One of the major contributions of E. L. Carstensen to medical ultrasonography lies in the research studies by him and his colleagues on biophysical mechanisms for biological effects. These studies have done much to provide a basis for predicting conditions under which ultrasound will affect living systems. The scientific information that now exists makes possible an improved approach to decisions on safety for diagnostic ultrasonography. In particular, estimates of the maximum temperature produced in a sonographic examination provide an index that can be used to ensure safety from thermal hazards.  (+info)

The simultaneous evolution of author and paper networks. (71/635)

There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, grants) and computers and algorithms capable of handling this enormous stream of data. This article reviews major work on models that aim to capture and recreate the structure and dynamics of scientific evolution. We then introduce a general process model that simultaneously grows coauthor and paper citation networks. The statistical and dynamic properties of the networks generated by this model are validated against a 20-year data set of articles published in PNAS. Systematic deviations from a power law distribution of citations to papers are well fit by a model that incorporates a partitioning of authors and papers into topics, a bias for authors to cite recent papers, and a tendency for authors to cite papers cited by papers that they have read. In this TARL model (for topics, aging, and recursive linking), the number of topics is linearly related to the clustering coefficient of the simulated paper citation network.  (+info)

Mapping topics and topic bursts in PNAS. (72/635)

Scientific research is highly dynamic. New areas of science continually evolve; others gain or lose importance, merge, or split. Due to the steady increase in the number of scientific publications, it is hard to keep an overview of the structure and dynamic development of one's own field of science, much less all scientific domains. However, knowledge of "hot" topics, emergent research frontiers, or change of focus in certain areas is a critical component of resource allocation decisions in research laboratories, governmental institutions, and corporations. This paper demonstrates the utilization of Kleinberg's burst detection algorithm, co-word occurrence analysis, and graph layout techniques to generate maps that support the identification of major research topics and trends. The approach was applied to analyze and map the complete set of papers published in PNAS in the years 1982-2001. Six domain experts examined and commented on the resulting maps in an attempt to reconstruct the evolution of major research areas covered by PNAS.  (+info)