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, 1 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.

Measurements of the impact and history of research literature provide a useful complement to scientific digital library collections. Bibliometric indicators have been extensively studied, mostly in the context of journals. However, journal-based metrics poorly capture topical distinctions in fast-moving fields, and are increasingly problematic with the rise of open-access publishing. Recent developments in latent topic models have produced promising results for automatic sub-field discovery. The fine-grained, faceted topics produced by such models provide a clearer view of the topical divisions of a body of research literature and the interactions between those divisions. We demonstrate the usefulness of topic models in measuring impact by applying a new phrase-based topic discovery model to a collection of 300,000 Computer Science publications, collected by the Rexa automatic citation indexing system.

The aggregated citation relations among journals included in the Science Citation Index provide us with a huge matrix which can be analyzed in various ways. Using principal component analysis or factor analysis, the factor scores can be used as indicators of the position of the cited journals in the citing dimensions of the database. Unrotated factor scores are exact, and the extraction of principal components can be made stepwise since the principal components are independent. Rotation may be needed for the designation, but in the rotated solution a model is assumed. This assumption can be legitimated on pragmatic or theoretical grounds. Since the resulting outcomes remain sensitive to the assumptions in the model, an unambiguous classification is no longer possible in this case. However, the factor-analytic solutions allow us to test classifications against the structures contained in the database. This will be demonstrated for the delineation of a set of biochemistry journals.

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.

This article describes recent improvements in mapping the world-wide scientific literature. Existing research is extended in three ways. First, a method for generating maps directly from the data on the relationships between hundreds of thousands of documents is presented. Second, quantitative techniques for evaluating these large maps of science are introduced. Third, these techniques are applied to data in order to evaluate eight different maps. The analyses suggest that accuracy can be increased by using a modified cosine measure of relatedness. Disciplinary bias can be significantly reduced and accuracy can be further increased by using much lower threshold levels. In short, much larger samples of papers can and should be used to generate more accurate maps of science.

This paper presents an approach for identifying similar documents that can be used to assist scientists in finding related work. The approach called Citation Proximity Analysis (CPA) is a further development of co-citation analysis, but in addition, considers the proximity of citations to each other within an article‟s full-text. The underlying idea is that the closer citations are to each other, the more likely it is that they are related. In comparison to existing approaches, such as bibliographic coupling, co-citation analysis or keyword based approaches the advantages of CPA are a higher precision and the possibility to identify related sections within documents. Moreover, CPA allows a more precise automatic document classification. CPA is used as the primary approach to analyse the similarity and to classify the 1.2 million publications contained in the research paper recommender system Scienstein.org.

This paper discusses the history of domain maps in the context of similar developments in the field of cartography. Drawing from its underlying definitions, this paper identifies the purposes of domain maps and discusses how they might specifically be applied in the field of education and as front-ends to digital libraries. Keywords--- domain mapping, history, theory, cartography, digital libraries, education, future 1.

To date, research output has been evaluated based on the journal in which it is published. Each scientific journal is classified into a major field despite the fact that journals are progressively covering a wider array of disciplines and paradigms (sub-topics) that are not properly reflected in their field classification. The nature of this “square peg in a round hole ” system leaves critical interdisciplinary research overlooked and unaccounted for – allowing only a simplistic and reductionist view of current institutional research initiatives. SciVal Spotlight was developed to overcome this knowledge gap by providing a broader and deeper view of research performance. The purpose of this document is to describe how SciVal Spotlight creates paradigms (also known as clusters) to reveal interdisciplinary research trends within institutions.

This paper presents an approach for identifying similar documents that can be used to assist scientists in finding related work. The approach called Citation Proximity Analysis (CPA) is a further development of co-citation analysis, but in addition, considers the proximity of citations to each other within an article‟s full-text. The underlying idea is that the closer citations are to each other, the more likely it is that they are related. In comparison to existing approaches, such as bibliographic coupling, co-citation analysis or keyword based approaches the advantages of CPA are a higher precision and the possibility to identify related sections within documents. Moreover, CPA allows a more precise automatic document classification. CPA is used as the primary approach to analyse the similarity and to classify the 1.2 million publications contained in the research paper recommender system Scienstein.org.