Science maps

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.

Over the last few years, microblogging has gained prominence as a form of personal broadcasting media where information and opinion are mixed together without an established order, usually tightly linked with current reality. Location awareness and promptness provide researchers using the Internet with the opportunity to create “psychological landscapes"—that is, to detect differences and changes in voiced (twittered) emotions, cognitions, and behaviors. In our article, we present iScience Maps, a free Web service for researchers, available from http://maps.iscience.deusto.es/ and http://tweet miner.eu/. Technologically, the service is based on Twitter's streaming and search application programming interfaces (APIs), accessed through several PHP libraries, and a Java- Script frontend. This service allows researchers to assess via Twitter the effect of specific events in different places as they are happening and to make comparisons between cities, regions, or countries regarding psychological states and their evolution in the course of an event. In a step-by-step example, it is shown how to replicate a study on affective and personality characteristics inferred from first names (Mehrabian & Piercy, Personality and Social Psychology Bulletin, 19, 755–758 1993) by mining Twitter data with iScience Maps. Results from the original study are replicated in both world regions we tested (the western U.S.and the U.K.); we also discover base rate of names to be a confound that needs to be controlled for in future research.

Scientific output analysis in Poland takes place in many ways: by use both centraland local databases. The article discusses the contents and bibliometric functionsof the most important bibliographic databases, i.e. “People of science”, PolishScientific Bibliography and employeers local registration system Expertus. Theauthors evaluate these tools from the perspective of the ability to make compa-risons of the effectivity of individual researchers as well as to stimulate the deve-lopment  of  scientific  careers.  As  alternative  solution  relative  to  the  analyticalspectrum of all external tools, the authors present own application that allows vi-sualization of scientific achievements. According authors’ observation, the Scien-tific Visualiser can enrich the individual information space of the contemporaryscientist. Dedicated application certainly facilitates the evaluation of the publica-tion activity, increases an awareness of updating of the bibliographic data, helpsin discovering the relationship between the research fields, inspires to broadenintelectual horizons and cooperation networks. From another side, it can also bea tool supporting administration activities, such as: employees evaluation, promo-tion proceedings, accreditation, experts selection, distribution of funds.

Analysis of information retrieved from microblogging services such as Twitter can provide valuable insight into public sentiment in a geographic region. This insight can be enriched by visualising information in its geographic context. Two underlying approaches for sentiment analysis are dictionary-based and machine learning. The former is popular for public sentiment analysis, and the latter has found limited use for aggregating public sentiment from Twitter data. The research presented in this paper aims to extend the machine learning approach for aggregating public sentiment. To this end, a framework for analysing and visualising public sentiment from a Twitter corpus is developed. A dictionary-based approach and a machine learning approach are implemented within the framework and compared using one UK case study, namely the royal birth of 2013. The case study validates the feasibility of the framework for analysis and rapid visualisation. One observation is that there is good correlation between the results produced by the popular dictionary-based approach and the machine learning approach when large volumes of tweets are analysed. However, for rapid analysis to be possible faster methods need to be developed using big data techniques and parallel methods.