Search (79 results, page 1 of 4)

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Content

One of a series of three publications influenced by the travelling exhibit Places & Spaces: Mapping Science, curated by the Cyberinfrastructure for Network Science Center at Indiana University. - Additional materials can be found at http://http://scimaps.org/atlas2. Erweitert durch: Börner, Katy. Atlas of Science: Visualizing What We Know.

Date

22. 1.2017 16:54:03
22. 1.2017 17:10:56

LCSH

Science / Atlases
Science / Study and teaching / Graphic methods
Communication in science / Data processing

Subject

Science / Atlases
Science / Study and teaching / Graphic methods
Communication in science / Data processing

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Abstract

We propose an approach to visualizing the scientific world and its evolution by constructing minimum spanning trees (MSTs) and a two-dimensional map of scientific journals using the database of the Science Citation Index (SCI) during 1994-2001. The structures of constructed MSTs are consistent with the sorting of SCI categories. The map of science is constructed based on our MST results. Such a map shows the relation among various knowledge clusters and their citation properties. The temporal evolution of the scientific world can also be delineated in the map. In particular, this map clearly shows a linear structure of the scientific world, which contains three major domains including physical sciences, life sciences, and medical sciences. The interaction of various knowledge fields can be clearly seen from this scientific world map. This approach can be applied to various levels of knowledge domains.

Object

Science Citation Index
Map of Science

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.11, S.1461-1469

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Abstract

Aggregated journal-journal citation networks based on the Journal Citation Reports 2004 of the Science Citation Index (5,968 journals) and the Social Science Citation Index (1,712 journals) are made accessible from the perspective of any of these journals. A vector-space model Is used for normalization, and the results are brought online at http://www.leydesdorff.net/jcr04 as input files for the visualization program Pajek. The user is thus able to analyze the citation environment in terms of links and graphs. Furthermore, the local impact of a journal is defined as its share of the total citations in the specific journal's citation environments; the vertical size of the nodes is varied proportionally to this citation impact. The horizontal size of each node can be used to provide the same information after correction for within-journal (self-)citations. In the "citing" environment, the equivalents of this measure can be considered as a citation activity index which maps how the relevant journal environment is perceived by the collective of authors of a given journal. As a policy application, the mechanism of Interdisciplinary developments among the sciences is elaborated for the case of nanotechnology journals.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.1, S.25-38

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Abstract

Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.

Object

Science Citation Index

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1622-1634

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Date

1. 2.2016 18:25:22

Series

Lecture notes in computer science ; 9398

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Date

4. 2.2015 9:22:39

Footnote

Vgl.: http://publiscologne.fh-koeln.de/frontdoor/index/index/id/334/docId/334.

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Abstract

Boyack, Wylie, and Davidson developed VxInsight which transforms information from documents into a landscape representation which conveys information on the implicit structure of the data as context for queries and exploration. From a list of pre-computed similarities it creates on a plane an x,y location for each item, or can compute its own similarities based on direct and co-citation linkages. Three-dimensional overlays are then generated on the plane to show the extent of clustering at particular points. Metadata associated with clustered objects provides a label for each peak from common words. Clicking on an object will provide citation information and answer sets for queries run will be displayed as markers on the landscape. A time slider allows a view of terrain changes over time. In a test on the microsystems engineering literature a review article was used to provide seed terms to search Science Citation Index and retrieve 20,923 articles of which 13,433 were connected by citation to at least one other article in the set. The citation list was used to calculate similarity measures and x.y coordinates for each article. Four main categories made up the landscape with 90% of the articles directly related to one or more of the four. A second test used five databases: SCI, Cambridge Scientific Abstracts, Engineering Index, INSPEC, and Medline to extract 17,927 unique articles by Sandia, Los Alamos National Laboratory, and Lawrence Livermore National Laboratory, with text of abstracts and RetrievalWare 6.6 utilized to generate the similarity measures. The subsequent map revealed that despite some overlap the laboratories generally publish in different areas. A third test on 3000 physical science journals utilized 4.7 million articles from SCI where similarity was the un-normalized sum of cites between journals in both directions. Physics occupies a central position, with engineering, mathematics, computing, and materials science strongly linked. Chemistry is farther removed but strongly connected.

Source

Journal of the American Society for Information Science and Technology. 53(2002) no.9, S.764-774

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Abstract

Generally, Computer Science (CS) classifications are inconsistent in taxonomy strategies. t is necessary to develop CS taxonomy research to combine its historical perspective, its current knowledge and its predicted future trends - including all breakthroughs in information and communication technology. In this paper we have analyzed the ACM Computing Classification System (CCS) by means of visualization maps. The important achievement of current work is an effective visualization of classified documents from the ACM Digital Library. From the technical point of view, the innovation lies in the parallel use of analysis units: (sub)classes and keywords as well as a spherical 3D information surface. We have compared both the thematic and semantic maps of classified documents and results presented in Table 1. Furthermore, the proposed new method is used for content-related evaluation of the original scheme. Summing up: we improved an original ACM classification in the Computer Science domain by means of visualization.

Date

22. 7.2010 19:36:46

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Abstract

Automated methods for the analysis, modeling, and visualization of large-scale scientometric data provide measures that enable the depiction of the state of world scientific development. We aimed to integrate minimum span clustering (MSC) and minimum spanning tree methods to cluster and visualize the global pattern of scientific publications (PSP) by analyzing aggregated Science Citation Index (SCI) data from 1994 to 2011. We hypothesized that PSP clustering is mainly affected by countries' geographic location, ethnicity, and level of economic development, as indicated in previous studies. Our results showed that the 100 countries with the highest rates of publications were decomposed into 12 PSP groups and that countries within a group tended to be geographically proximal, ethnically similar, or comparable in terms of economic status. Hubs and bridging nodes in each knowledge production group were identified. The performance of each group was evaluated across 16 knowledge domains based on their specialization, volume of publications, and relative impact. Awareness of the strengths and weaknesses of each group in various knowledge domains may have useful applications for examining scientific policies, adjusting the allocation of resources, and promoting international collaboration for future developments.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.10, S.2477-2488

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Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.
With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."

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Abstract

This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature - an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.

Date

22. 7.2006 16:11:05

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.359-377

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Content

From the contents:.- Basics of screen design.- Navigation and orientation.- Information.- Screen layout.Interaction.- Motivation.- Innovative prospects.- Appendix.Glossary.- Literature.- Index

Date

22. 3.2008 14:29:25

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Date

9.12.2018 16:22:25

Source

Journal of the Association for Information Science and Technology. 69(2018) no.12, S.1428-1445

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Abstract

Studies on the state and structure of digital knowledge concerning science generally relate to macro and meso scales. Supported by visualizations, these studies can deliver knowledge about emerging scientific fields or collaboration between countries, scientific centers, or groups of researchers. Analyses of individual activities or single scientific career paths are rarely presented and discussed. The authors decided to fill this gap and developed a web application for visualizing the scientific output of particular researchers. This free software based on bibliographic data from local databases, provides six layouts for analysis. Researchers can see the dynamic characteristics of their own writing activity, the time and place of publication, and the thematic scope of research problems. They can also identify cooperation networks, and consequently, study the dependencies and regularities in their own scientific activity. The current article presents the results of a study of the application's usability and functionality as well as attempts to define different user groups. A survey about the interface was sent to select researchers employed at Nicolaus Copernicus University. The results were used to answer the question as to whether such a specialized visualization tool can significantly augment the individual information space of the contemporary researcher.

Date

21.12.2018 17:22:13

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Abstract

Science maps are visual representations of the structure and dynamics of scholarly knowl­edge. They aim to show how fields, disciplines, journals, scientists, publications, and scientific terms relate to each other. Science mapping is the body of methods and techniques that have been developed for generating science maps. This entry is an introduction to science maps and science mapping. It focuses on the conceptual, theoretical, and methodological issues of science mapping, rather than on the mathematical formulation of science mapping techniques. After a brief history of science mapping, we describe the general procedure for building a science map, presenting the data sources and the methods to select, clean, and pre-process the data. Next, we examine in detail how the most common types of science maps, namely the citation-based and the term-based, are generated. Both are based on networks: the former on the network of publications connected by citations, the latter on the network of terms co-occurring in publications. We review the rationale behind these mapping approaches, as well as the techniques and methods to build the maps (from the extraction of the network to the visualization and enrichment of the map). We also present less-common types of science maps, including co-authorship networks, interlocking editorship networks, maps based on patents' data, and geographic maps of science. Moreover, we consider how time can be represented in science maps to investigate the dynamics of science. We also discuss some epistemological and sociological topics that can help in the interpretation, contextualization, and assessment of science maps. Then, we present some possible applications of science maps in science policy. In the conclusion, we point out why science mapping may be interesting for all the branches of meta-science, from knowl­edge organization to epistemology.

Footnote

Beitrag in einem Special issue on 'Science and knowledge organization' mit längeren Überblicken zu wichtigen Begriffen der Wissensorgansiation.

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Abstract

Acquiring knowledge in any field involves information retrieval, i.e. searching the available documents to identify answers to the queries concerning the selected objects. Knowing the keywords which are names of the objects will enable situating the user's query in the information space organized as a thesaurus or faceted classification. Objectives: Identification the areas in the information space which correspond to gaps in the user's personal knowledge or in the domain knowledge might become useful in theory or practice. The aim of this paper is to present a realistic information-space model of a self-authored full-text document on information culture, indexed by the author of this article. Methodology: Having established the relations between the terms, particular modules (sets of terms connected by relations used in facet classification) are situated on a plain, similarly to a communication map. Conclusions drawn from the "journey" on the map, which is a visualization of the knowledge contained in the analysed document, are the crucial part of this paper. Results: The direct result of the research is the created model of information space visualization of a given document (book, article, website). The proposed procedure can practically be used as a new form of representation in order to map the contents of academic books and articles, beside the traditional index form, especially as an e-book auxiliary tool. In teaching, visualization of the information space of a document can be used to help students understand the issues of: classification, categorization and representation of new knowledge emerging in human mind.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Abstract

Science mapping aims to build bibliometric maps that describe how specific disciplines, scientific domains, or research fields are conceptually, intellectually, and socially structured. Different techniques and software tools have been proposed to carry out science mapping analysis. The aim of this article is to review, analyze, and compare some of these software tools, taking into account aspects such as the bibliometric techniques available and the different kinds of analysis.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.7, S.1382-1402

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Content

As the team describe in a paper posted (http://arxiv.org/abs/1605.04951) on arXiv, they found that figures did indeed matter-but not all in the same way. An average paper in PubMed Central has about one diagram for every three pages and gets 1.67 citations. Papers with more diagrams per page and, to a lesser extent, plots per page tended to be more influential (on average, a paper accrued two more citations for every extra diagram per page, and one more for every extra plot per page). By contrast, including photographs and equations seemed to decrease the chances of a paper being cited by others. That agrees with a study from 2012, whose authors counted (by hand) the number of mathematical expressions in over 600 biology papers and found that each additional equation per page reduced the number of citations a paper received by 22%. This does not mean that researchers should rush to include more diagrams in their next paper. Dr Howe has not shown what is behind the effect, which may merely be one of correlation, rather than causation. It could, for example, be that papers with lots of diagrams tend to be those that illustrate new concepts, and thus start a whole new field of inquiry. Such papers will certainly be cited a lot. On the other hand, the presence of equations really might reduce citations. Biologists (as are most of those who write and read the papers in PubMed Central) are notoriously mathsaverse. If that is the case, looking in a physics archive would probably produce a different result.
Dr Howe and his colleagues do, however, believe that the study of diagrams can result in new insights. A figure showing new metabolic pathways in a cell, for example, may summarise hundreds of experiments. Since illustrations can convey important scientific concepts in this way, they think that browsing through related figures from different papers may help researchers come up with new theories. As Dr Howe puts it, "the unit of scientific currency is closer to the figure than to the paper." With this thought in mind, the team have created a website (viziometrics.org (http://viziometrics.org/) ) where the millions of images sorted by their program can be searched using key words. Their next plan is to extract the information from particular types of scientific figure, to create comprehensive "super" figures: a giant network of all the known chemical processes in a cell for example, or the best-available tree of life. At just one such superfigure per paper, though, the citation records of articles containing such all-embracing diagrams may very well undermine the correlation that prompted their creation in the first place. Call it the ultimate marriage of chart and science.

Footnote

Vgl.: http://www.economist.com/news/science-and-technology/21700620-surprisingly-simple-test-check-research-papers-errors-come-again.

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Source

Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff

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Content

Vgl. unter: http://eprints.rclis.org/28725/. Der Artikel basiert auf meiner mit dem VFI-Förderungspreis 2014 prämierten Masterarbeit "Informationsvisualisierung und Retrieval im Fokus der Informationspraxis". Diese ist auf dem Publikationsserver des Instituts für Informationswissenschaft der Fachhochschule Köln unter "http://publiscologne.fh-koeln.de/frontdoor/index/index/id/334/docId/334" verfügbar.