@article{boerner2012design, abstract = {Global maps of science can be used as a reference system to chart career trajectories, the location of emerging research frontiers, or the expertise profiles of institutes or nations. This paper details data preparation, analysis, and layout performed when designing and subsequently updating the UCSD map of science and classification system. The original classification and map use 7.2 million papers and their references from Elsevier’s Scopus (about 15,000 source titles, 2001–2005) and Thomson Reuters’ Web of Science (WoS) Science, Social Science, Arts & Humanities Citation Indexes (about 9,000 source titles, 2001–2004)–about 16,000 unique source titles. The updated map and classification adds six years (2005–2010) of WoS data and three years (2006–2008) from Scopus to the existing category structure–increasing the number of source titles to about 25,000. To our knowledge, this is the first time that a widely used map of science was updated. A comparison of the original 5-year and the new 10-year maps and classification system show (i) an increase in the total number of journals that can be mapped by 9,409 journals (social sciences had a 80% increase, humanities a 119% increase, medical (32%) and natural science (74%)), (ii) a simplification of the map by assigning all but five highly interdisciplinary journals to exactly one discipline, (iii) a more even distribution of journals over the 554 subdisciplines and 13 disciplines when calculating the coefficient of variation, and (iv) a better reflection of journal clusters when compared with paper-level citation data. When evaluating the map with a listing of desirable features for maps of science, the updated map is shown to have higher mapping accuracy, easier understandability as fewer journals are multiply classified, and higher usability for the generation of data overlays, among others.}, author = {Börner, Katy and Klavans, Richard and Patek, Michael and Zoss, Angela M. and Biberstine, Joseph R. and Light, Robert P. and Larivière, Vincent and Boyack, Kevin W.}, doi = {10.1371/journal.pone.0039464}, interhash = {c27eeafd6c2d77f7022ce10236e3dd47}, intrahash = {494fdcbea8cd30a1a04a06aa1696fce6}, journal = {PLoS ONE}, month = jul, number = 7, pages = {e39464}, publisher = {Public Library of Science}, title = {Design and Update of a Classification System: The UCSD Map of Science}, url = {http://dx.doi.org/10.1371%2Fjournal.pone.0039464}, volume = 7, year = 2012 } @article{larowe2009scholarly, abstract = {The Scholarly Database aims to serve researchers and practitioners interested in the analysis, modelling, and visualization of large-scale data sets. A specific focus of this database is to support macro-evolutionary studies of science and to communicate findings via knowledge-domain visualizations. Currently, the database provides access to about 18 million publications, patents, and grants. About 90% of the publications are available in full text. Except for some datasets with restricted access conditions, the data can be retrieved in raw or pre-processed formats using either a web-based or a relational database client. This paper motivates the need for the database from the perspective of bibliometric/scientometric research. It explains the database design, setup, etc., and reports the temporal, geographical, and topic coverage of data sets currently served via the database. Planned work and the potential for this database to become a global testbed for information science research are discussed at the end of the paper.}, author = {La Rowe, Gavin and Ambre, Sumeet and Burgoon, John and Ke, Weimao and Börner, Katy}, doi = {10.1007/s11192-009-0414-2}, interhash = {1819f263b0ea1b99ec15d0c22b38207e}, intrahash = {c24611ec1f2efbdcf7f5b26d49af320e}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 2, pages = {219--234}, publisher = {Springer Netherlands}, title = {The Scholarly Database and its utility for scientometrics research}, url = {http://dx.doi.org/10.1007/s11192-009-0414-2}, volume = 79, year = 2009 } @article{sun2013social, abstract = {The birth and decline of disciplines are critical to science and society. How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.}, author = {Sun, Xiaoling and Kaur, Jasleen and Milojevic, Stasa and Flammini, Alessandro and Menczer, Filippo}, doi = {10.1038/srep01069}, interhash = {5cd31392e997555d78596f962044f84b}, intrahash = {721dcd5644cca27fd50d8e6ffd667056}, journal = {Scientific Reports}, month = jan, publisher = {Macmillan Publishers Limited}, title = {Social Dynamics of Science}, url = {http://dx.doi.org/10.1038/srep01069}, volume = 3, year = 2013 } @article{aguillo2009measuring, abstract = {Purpose – The purpose of this paper is to provide an alternative, although complementary, system for the evaluation of the scholarly activities of academic organizations, scholars and researchers, based on web indicators, in order to speed up the change of paradigm in scholarly communication towards a new fully electronic twenty-first century model. Design/methodology/approach – In order to achieve these goals, a new set of web indicators has been introduced, obtained mainly from data gathered from search engines, the new mediators of scholarly communication. Findings – It was found that three large groups of indicators are feasible to obtain and relevant for evaluation purposes: activity (web publication); impact (visibility) and usage (visits and visitors). As a proof of concept, a Ranking Web of Universities has been built with Webometrics data. There are two relevant findings: ranking results are similar to those obtained by other bibliometric-based rankings; and there is a concerning digital divide between North American and European universities, which appear in lower positions when compared with their USA and Canada counterparts. Research limitations/implications – Cybermetrics is still an emerging discipline, so new developments should be expected when more empirical data become available. Practical implications – The proposed approach suggests the publication of truly electronic journals, rather than digital versions of printed articles. Additional materials, such as raw data and multimedia files, should be included along with other relevant information arising from more informal activities. These repositories should be Open Access, available as part of the public web, indexed by the main commercial search engines. It is expected that these actions could generate larger web-based audiences, reduce the costs of publication and access and allow third parties to take advantage of the knowledge generated, without sacrificing peer review, which should be extended (pre- and post-) and expanded (closed and open). Originality/value – A full taxonomy of web indicators is introduced for describing and evaluating research activities, academic organizations and individual scholars and scientists. Previous attempts for building such classification were incomplete and did not take into account feasibility and efficiency.}, address = {Bingley}, author = {Aguillo, Isidro}, doi = {10.1108/073788309}, interhash = {116e889174766cd359f7e79eb1a36302}, intrahash = {9734b1f272204883b30dedaa8069fdad}, issn = {0737-8831}, journal = {Library Hi Tech}, number = 4, pages = {540--556}, publisher = {Emerald Group Publishing Limited}, title = {Measuring the institution's footprint in the web}, url = {http://www.emeraldinsight.com/journals.htm?articleid=1812469&show=abstract}, volume = 27, year = 2009 } @article{ortega2009mapping, abstract = {A visual display of the most important universities in the world is the aim of this paper. It shows the topological characteristics and describes the web relationships among universities of different countries and continents. The first 1000 higher education institutions from the Ranking Web of World Universities were selected and their link relationships were obtained from Yahoo! Search. Network graphs and geographical maps were built from the search engine data. Social network analysis techniques were used to analyse and describe the structural properties of the whole of the network and its nodes. The results show that the world-class university network is constituted from national sub-networks that merge in a central core where the principal universities of each country pull their networks toward international link relationships. The United States dominates the world network, and within Europe the British and the German sub-networks stand out.}, author = {Ortega, Jose Luis and Aguillo, Isidro F.}, doi = {10.1016/j.ipm.2008.10.001}, interhash = {1c46addf1c5019aa75a11365a35da757}, intrahash = {f5960b8cb33d8b10b82abadd17b9a4e7}, issn = {0306-4573}, journal = {Information Processing & Management}, number = 2, pages = {272--279}, title = {Mapping world-class universities on the web}, url = {http://www.sciencedirect.com/science/article/pii/S0306457308001015}, volume = 45, year = 2009 } @article{ortega2008academic, abstract = {This paper shows maps of the web presence of the European Higher Education Area (EHEA) on the level of universities using hyperlinks and analyses the topology of the European academic network. Its purpose is to combine methods from Social Network Analysis (SNA) and cybermetric techniques in order to ask for tendencies of integration of the European universities visible in their web presence and the role of different universities in the process of the emergence of an European Research Area. We find as a main result that the European network is set up by the aggregation of well-defined national networks, whereby the German and British networks are dominant. The national networks are connected to each other through outstanding national universities in each country.}, affiliation = {CINDOC-CSIC Cybermetrics Lab Joaquín Costa, 22 28002 Madrid Spain}, author = {Ortega, Jose and Aguillo, Isidro and Cothey, Viv and Scharnhorst, Andrea}, doi = {10.1007/s11192-008-0218-9}, interhash = {9c9dcd171a50875d82f75f5a12d3c41f}, intrahash = {22102ef820bb00f432c842edf6e98a1f}, issn = {0138-9130}, journal = {Scientometrics}, keyword = {Computer Science}, number = 2, pages = {295--308}, publisher = {Akadémiai Kiadó}, title = {Maps of the academic web in the European Higher Education Area — an exploration of visual web indicators}, url = {http://dx.doi.org/10.1007/s11192-008-0218-9}, volume = 74, year = 2008 } @article{pham2011development, abstract = {In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.}, address = {Wien}, affiliation = {Information Systems and Database Technology, RWTH Aachen University, Aachen, Ahornstr. 55, 52056 Aachen, Germany}, author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias}, doi = {10.1007/s13278-011-0024-x}, interhash = {193312234ed176aa8be9f35d4d1c4e72}, intrahash = {8ae08cacda75da80bfa5604cfce48449}, issn = {1869-5450}, journal = {Social Network Analysis and Mining}, keyword = {Computer Science}, number = 4, pages = {321--340}, publisher = {Springer}, title = {Development of computer science disciplines: a social network analysis approach}, url = {http://dx.doi.org/10.1007/s13278-011-0024-x}, volume = 1, year = 2011 } @article{bollen2009clickstream, abstract = {Background Intricate maps of science have been created from citation data to visualize the structure of scientific activity. However, most scientific publications are now accessed online. Scholarly web portals record detailed log data at a scale that exceeds the number of all existing citations combined. Such log data is recorded immediately upon publication and keeps track of the sequences of user requests (clickstreams) that are issued by a variety of users across many different domains. Given these advantages of log datasets over citation data, we investigate whether they can produce high-resolution, more current maps of science. Methodology Over the course of 2007 and 2008, we collected nearly 1 billion user interactions recorded by the scholarly web portals of some of the most significant publishers, aggregators and institutional consortia. The resulting reference data set covers a significant part of world-wide use of scholarly web portals in 2006, and provides a balanced coverage of the humanities, social sciences, and natural sciences. A journal clickstream model, i.e. a first-order Markov chain, was extracted from the sequences of user interactions in the logs. The clickstream model was validated by comparing it to the Getty Research Institute's Architecture and Art Thesaurus. The resulting model was visualized as a journal network that outlines the relationships between various scientific domains and clarifies the connection of the social sciences and humanities to the natural sciences. Conclusions Maps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the underrepresentation of the social sciences and humanities that is commonly found in citation data.}, author = {Bollen, Johan and van de Sompel, Herbert and Hagberg, Aric and Bettencourt, Luis and Chute, Ryan and Rodriguez, Marko A. and Balakireva, Lyudmila}, doi = {10.1371/journal.pone.0004803}, interhash = {3a371a1ed31d14204770315b52023b96}, intrahash = {e61bd0c26cc1c08cff22a8301d03044f}, journal = {PLoS ONE}, month = mar, number = 3, pages = {e4803}, publisher = {Public Library of Science}, title = {Clickstream Data Yields High-Resolution Maps of Science}, url = {http://dx.doi.org/10.1371/journal.pone.0004803}, volume = 4, year = 2009 } @article{newman2001structure, abstract = {The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE biomedical research, the Los Alamos e-Print Archive physics, and NCSTRL computer science. I show that these collaboration networks form ” small worlds,” in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.}, author = {Newman, M. E. J.}, doi = {10.1073/pnas.98.2.404}, eprint = {http://www.pnas.org/content/98/2/404.full.pdf+html}, interhash = {8c5edd915b304ae09fc08e0a51dfd5e9}, intrahash = {a4d3149c7198762a99102935da4d1bdb}, journal = {Proceedings of the National Academy of Sciences}, number = 2, pages = {404--409}, title = {The structure of scientific collaboration networks}, url = {http://www.pnas.org/content/98/2/404.abstract}, volume = 98, year = 2001 }