Visualization of scientific co-authorship in Spanish universities: from regionalization to internationalization

Purpose – To visualize the inter-university and international collaboration networks generated by Spanish universities based on the co-authorship of scientific articles. Design/methodology/approach - Formulation based on a bibliometric analysis of Spanish university production from 2000 to 2004 as contained in Web of Science databases, applying social network visualization techniques. The co-authorship data used were extracted with the total counting method from a database containing 100,710 papers. Findings – Spanish inter-university collaboration patterns appear to be influenced by both geographic proximity and administrative and political affiliation. Inter-regional co-authorship encompasses regional sub-networks whose spatial scope conforms rather closely to Spanish geopolitical divisions. Papers involving international collaboration are written primarily with European Union and North and Latin American researchers. Greater visibility is attained with international co-authorship than any other type of collaboration studied. Research limitations/implications - Impact was measured in terms of journals rather than each individual article. The co-authorship data were taken from the Web of Knowledge and were not compared to data from other databases. Practical implications - The data obtained may provide guidance for public policy makers seeking to enhance and intensify the internationalization of scientific production in Spanish universities. Originality – The Spanish university system is in the midst of profound structural change. This is the first article to describe Spanish university collaboration networks using social network visualization techniques, covering an area not previously addressed.Publicad

The development of a social network analysis software tool

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2009Includes bibliographical references (leaves: 33-34)Text in English; Abstract: Turkish and Englishix, 38 leavesNowadays the amount of spam is increasing in an uncontrollable way. This situation decreases the email trust of the Internet users and reduces the usability of the emails to the critical level. On the other hand, this makes a lot of money loss for the hosting companies. There are many anti-spam tools that are developed against spammers. Some of these tools are really succesful. However; since the spammers improve their techniques, spams gain immune to these tools. In this thesis, the problem of detecting spams in in-coming and out-going emails is adressed.. To achieve this goal, an email social network is constructed by using the traffic of emails between users.While constructing this network, only information that can be gained from the email structure is used. The results on the real data set show that the techniques applied have been effective and also point to new directions of research in this area

Impact of Funding on Scientific Output and Collaboration

This dissertation reports the results of a comprehensive quantitative analysis of the inter-relations among research funding, scientific output, and collaboration. The research employed various methods and methodologies (i.e. data and text mining, statistical analysis, social network analysis, bibliometrics, survey data analysis, and visualization techniques) to investigate the impact of influencing factors on researchers’ performance, their amount of funding, and collaboration patterns. Moreover, a machine learning framework was suggested and validated for scientific evaluation of the researchers based on their productivity and level of funding. The Natural Sciences and Engineering Research Council of Canada (NSERC) was selected as the source of funding in this research since it is the main federal funding organization in Canada and almost all the Canadian researchers in natural sciences and engineering receive at least a basic research grant from NSERC. The required data on the scientific publications (e.g. co-authors, their affiliations, year of publication) was collected from Elsevier’s Scopus. SCImago was selected for collecting the impact factor information of the journals in which the articles were published in as well as the annual citation counts of publications. The data was gathered and integrated for the time span of 1996 to 2010. The most significant contributions are: 1) the unique data extraction and gathering procedure that enhanced the accuracy of the target data, 2) the comprehensive triangulation technique which was employed in this research that included various methodologies and used new variables for assessing the inter-relations, 3) the proposed machine learning framework for classifying researchers and predicting their productivity and level of funding