Information and Library Science

Permanent link for this communityhttps://hdl.handle.net/2022/15555

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Approaches to Understanding and Measuring Interdisciplinary Scientific Research (IDR): A Review of the Literature
(Elsevier, 2011-01) Wagner, Caroline S.; Roessner, J. David; Bobb, Kamau; Klein, Julie Thompson; Boyack, Kevin W.; Keyton, Joann; Rafols, Ismael; Börner, Katy
Interdisciplinary scientific research (IDR) challenges the study of science from a number of fronts, including one of creating output science and engineering (S&E) indicators. This literature review began with a narrow focus on quantitative measures of the output of IDR, but expanded the scope as it became clear that differing definitions, assessment tools, evaluation processes, and measures all shed light on aspects of IDR. Key among the broader aspects are (a) characterizing the concept of knowledge integration, and (b) recognizing that it can occur within a single mind or as the result of team dynamics. Output measures alone cannot adequately capture this process. Among the quantitative measures considered, bibliometrics (co-authorships, collaborations, references, citations and co-citations) are the most developed, but leave considerable gaps in understanding. Emerging measures in diversity, entropy, and network dynamics are promising, but require sophisticated interpretations and thus would not serve well as S&E indicators. Combinations of quantitative and qualitative assessments coming from evaluation studies appear to reveal S&E processes but carry burdens of expense, intrusion, and lack of reproducibility. This review is a first step toward providing a more holistic view of measuring IDR; several avenues for future research highlight the need for metrics to reflect the actual practice of IDR.
Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches
(Public Library of Science, 2011) Boyack, Kevin W.; Newman, David; Duhon, Russell J.; Klavans, Richard; Patek, Michael; Biberstine, Joseph R.; Schijvenaars, Bob; Skupin, André; Ma, Nianli; Börner, Katy
We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents.
Making Sense of Mankind’s Scholarly Knowledge and Expertise: Collecting, Interlinking, and Organizing What We Know and Different Approaches to Mapping (Network) Science
(Pion Publications, 2007-07) Börner, Katy
In this paper I discuss and compare different approaches to collecting, interlinking, organizing, and making sense of scholarly knowledge and expertise in a comprehensive and timely fashion. ‘Comprehensive’ refers to the need for collecting and interlinking multilingual, multidisciplinary records from multiple sources such as publications, patents, grants, and others to truly capture all relevant knowledge. By ‘timely’ I want to emphasize that there has to be a way to integrate the most recent—that is, today’s—publications with existing holdings of scholarly knowledge and expertise. I then discuss the advantages and limitations of using search engines to access, and text mining and data mining to help extract, meaning from mankind’s wisdom. Next I suggest the usage of semantic association networks as a viable and complementary alternative for interlinking and making sense of scholarly knowledge and expertise. The second part of the paper exemplifies and contrasts three approaches that can be used to delineate and make sense of scholarly knowledge. The first approach uses questionnaire data, the second uses citation data from a major digital library, and the third uses personal bibliography files. These approaches are exemplified by mapping the emerging research area of network science. A particular focus is the identification of major experts, papers, and research areas and geospatial locations in which network science research is conducted. The paper concludes with a summary and outlook.
Mapping the Structure and Evolution of Chemistry Research
(Springer Verlag, 2009) Boyack, Kevin W.; Börner, Katy; Klavans, Richard
How does our collective scholarly knowledge grow over time? What major areas of science exist and how are they interlinked? Which areas are major knowledge producers; which ones are consumers? Computational scientometrics – the application of bibliometric/scientometric methods to large-scale scholarly datasets – and the communication of results via maps of science might help us answer these questions. This paper represents the results of a prototype study that aims to map the structure and evolution of chemistry research over a 30 year time frame. Information from the combined Science (SCIE) and Social Science (SSCI) Citations Indexes from 2002 was used to generate a disciplinary map of 7,227 journals and 671 journal clusters. Clusters relevant to study the structure and evolution of chemistry were identified using JCR categories and were further clustered into 14 disciplines. The changing scientific composition of these 14 disciplines and their knowledge exchange via citation linkages was computed. Major changes on the dominance, influence, and role of Chemistry, Biology, Biochemistry, and Bioengineering over these 30 years are discussed. The paper concludes with suggestions for future work.
Scholarly Networks on Resilience, Vulnerability and Adaptation within the Human Dimensions of Global Environmental Change
(Elsevier, 2006-08) Janssen, Marco A.; Schoon, Michael L.; Ke, Weimao; Börner, Katy
This paper presents the results of a bibliometric analysis of the knowledge domains resilience, vulnerability and adaptation within the research activities on human dimensions of global environmental change. We analyzed how 2,286 publications over the last 30 years are related in terms of co-authorship relations, and citation relations. The number of publications in the three knowledge domains increased rapidly during the last decade. However, the resilience knowledge domain is only weakly connected with the other two domains in terms of co-authorships and citations. The resilience knowledge domain has a background in ecology and mathematics with a focus on theoretical models, while the vulnerability and adaptation knowledge domains have a background in geography, natural hazards research with a focus on case studies and climate change research. There is an increasing number of cross citations and papers classified in multiple knowledge domains. This seems to indicate on a merge of the different knowledge domains.
Taxonomy Visualization in Support of the Semi-Automatic Validation and Optimization of Organizational Schemas
(Elsevier, 2007-07) Börner, Katy; Hardy, Elisha; Herr, Bruce; Holloway, Todd; Paley, W. Bradford
Never before in history, mankind had access to and produced so much data, information, knowledge, and expertise as today. To organize, access, and manage these highly valuable assets effectively, we use taxonomies, classification hierarchies, ontologies, and controlled vocabularies among others. We create directory structures for our files. We use organizational hierarchies to structure our work environment. However, the design and continuous update of these organizational schemas that potentially have thousands of class nodes to organize millions of entities is challenging for any human being. The Taxonomy Visualization and Validation (TV) tool introduced in this paper supports the semi-automatic validation and optimization of organizational schemas such as file directories, classification hierarchies, taxonomies, or any other structure imposed on a data set as a means of organization, structuring, and naming. By showing the “goodness of fit” of a schema and the potentially millions of entities it organizes, the TV eases the identification and reclassification of misclassified information entities, the identification of classes that grew over-proportionally, the evaluation of the size and homogeneity of existing classes, the examination of the “well-formedness” of an organizational schema, etc. The TV is exemplarily applied to display the United States Patent and Trademark Office patent classification, which organizes more than three million patents into about 160,000 distinct patent classes. The paper concludes with a discussion and an outlook to future work.

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