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Seeber M.,University of Lugano | Lepori B.,University of Lugano | Lomi A.,University of Lugano | Aguillo I.,Cybermetrics Laboratory | Barberio V.,University of Vienna
Journal of Informetrics | Year: 2012

We examine the extent to which the presence and number of web links between higher education institutions can be predicted from a set of structural factors like country, subject mix, physical distance, academic reputation, and size. We combine two datasets on a large sample of European higher education institutions (HEIs) containing information on inter-university web links, and organizational characteristics, respectively. Descriptive and inferential analyses provide strong support for our hypotheses: we identify factors predicting the connectivity between HEIs, and the number of web links existing between them. We conclude that, while the presence of a web link cannot be directly related to its underlying motivation and the type of relationship between HEIs, patterns of network ties between HEIs present interesting statistical properties which reveal new insights on the function and structure of the inter organizational networks in which HEIs are embedded. © 2012 Elsevier Ltd.


Ortega J.L.,VICYT CSIC | Aguillo I.F.,Cybermetrics Laboratory
Journal of the American Society for Information Science and Technology | Year: 2012

This paper introduces a keyword map of the labels used by the scientists registered in the Google Scholar Citations (GSC) database from December 2011. In all, 15,000 random queries were formulated to GSC to obtain a list of 26,682 registered users. From this list a network graph of 6,660 labels was built and classified according to the Scopus Subject Area classes. Results display a detailed label map of the most used (>15 times) tags. The structural analysis shows that the core of the network is occupied by computer science-related disciplines that account for the most used and shared labels. This core is surrounded by clusters of disciplines related or close to computing such as Information Sciences, Mathematics, or Bioinformatics. Classical areas such as Chemistry and Physics are marginalized in the graph. It is suggested that GSC would in the future be an accurate source to map Science because it is based on the labels that scientists themselves use to describe their own research activity. © 2012 ASIS&T.


Ortega J.L.,VICYT CSIC | Aguillo I.F.,Cybermetrics Laboratory
Journal of the Association for Information Science and Technology | Year: 2014

This article offers a comparative analysis of the personal profiling capabilities of the two most important free citation-based academic search engines, namely, Microsoft Academic Search (MAS) and Google Scholar Citations (GSC). Author profiles can be useful for evaluation purposes once the advantages and the shortcomings of these services are described and taken into consideration. In total, 771 personal profiles appearing in both the MAS and the GSC databases were analyzed. Results show that the GSC profiles include more documents and citations than those in MAS but with a strong bias toward the information and computing sciences, whereas the MAS profiles are disciplinarily better balanced. MAS shows technical problems such as a higher number of duplicated profiles and a lower updating rate than GSC. It is concluded that both services could be used for evaluation proposes only if they are applied along with other citation indices as a way to supplement that information. © 2014 ASIS&T.


Ortega J.L.,VICYT CSIC | Aguillo I.F.,Cybermetrics Laboratory
Journal of Informetrics | Year: 2013

The purpose of this paper is to analyse and describe the topological properties of the institutional and national collaboration network from the profiles extracted from Google Scholar Citations (GSC). 19,912 unique profiles with " co-authors" were obtained from a web crawl performed in March 2012. Several statistical and network analysis techniques were used to map and analyse these collaboration relationships at the country and institution level. Results show that The United States dominates the world scientific map and that every research institution is grouped by national, geographical and cultural criteria. A clustering phenomenon based on the self-similarity and fractal properties of scale-free networks is also observed. We conclude that GSC is a suitable tool for collaboration studies only at macro level between countries and institutions. © 2013 Elsevier Ltd.


Ortega J.L.,Cybermetrics Laboratory
Journal of Informetrics | Year: 2014

The main objective of this study is to analyze the relationship between research impact and the structural properties of co-author networks. A new bibliographic source, Microsoft Academic Search, is introduced to test its suitability for bibliometric analyses. Citation counts and 500 one-step ego networks were extracted from this engine. Results show that tiny and sparse networks - characterized by a high Betweenness centrality and a high Average path length - achieved more citations per document than dense and compact networks - described by a high Clustering coefficient and a high Average degree. According to disciplinary differences, Mathematics, Social Sciences and Economics & Business are the disciplines with more sparse and tiny networks; while Physics, Engineering and Geosciences are characterized by dense and crowded networks. This suggests that in sparse ego networks, the central author have more control on their collaborators being more selective in their recruitment and concluding that this behaviour has positive implications in the research impact. © 2014 Elsevier Ltd.


The objective of this paper is to understand the relationship between the diffusion and mention of research papers in Twitter according to whether their authors are members or not of that micro-blogging service. To that end, 4166 articles from 76 Twitter users and 124 from non-Twitter users were analysed. Data on Twitter mentions were extracted from PlumX Analytics, information on each Twitter user was taken from the own platform and citations were collected from Scopus public API. Results show that papers from Twitter users are 33 % more tweeted than documents of non-Twitter users. From Twitter users, the increase of followers produces 30 % more tweets. No differences were found between the citation impact (i.e. number of citations) of papers authored by Twitter users and non-Twitter users. However, the number of followers indirectly influences the citation impact. The main conclusion is that the participation on Twitter affects the dissemination of research papers, and in consequence, it indirectly favours the likelihood that academic outputs being cited. © 2016 Akadémiai Kiadó, Budapest, Hungary


Ortega J.L.,R and D Analysis | Aguillo I.,Cybermetrics Laboratory
Journal of Informetrics | Year: 2010

The aim of this paper is to characterize the distribution of number of hits and spent time by web session. It also expects to find if there are significant differences between the length and the duration of a session with regard to the point of access-search engine, link or root. Web usage mining was used to analyse 17,174 web sessions that were identified from the webometrics.info web site. Results show that both distribution of length and duration follow an exponential decay. Significant differences between the different origins of the visits were also found, being the search engines' users those who spent most time and did more clicks in their sessions. We conclude that a good SEO policy would be justified, because search engines are the principal intermediaries to this web site. © 2010 Elsevier Ltd.


This study explores the connections between social and usage metrics (altmetrics) and bibliometric indicators at the author level. It studies to what extent these indicators, gained from academic sites, can provide a proxy for research impact. Close to 10,000 author profiles belonging to the Spanish National Research Council were extracted from the principal scholarly social sites: ResearchGate, Academia.edu and Mendeley and academic search engines: Microsoft Academic Search and Google Scholar Citations. Results describe little overlapping between sites because most of the researchers only manage one profile (72%). Correlations point out that there is scant relationship between altmetric and bibliometric indicators at author level. This is due to the almetric ones are site-dependent, while the bibliometric ones are more stable across web sites. It is concluded that altmetrics could reflect an alternative dimension of the research performance, close, perhaps, to science popularization and networking abilities, but far from citation impact. © 2014 Elsevier Ltd.


Ortega J.L.,R and D Analysis | Aguillo I.F.,Cybermetrics Laboratory
Scientometrics | Year: 2010

This paper aims to analyse the collaboration network of the 6th Framework Programme of the EU, specifically the "Life sciences, genomics and biotechnology for health" thematic area. A collaboration network of 2,132 participant organizations was built and several variables were added to improve the visualization such as type of organization and nationality. Several statistical tests and structural indicators were used to uncover the main characteristic of this collaboration network. Results show that the network is constituted by a dense core of government research organizations and universities which act as large hubs that attract new partners to the network, mainly companies and non-profit organizations. © 2010 Akadémiai Kiadó, Budapest, Hungary.


This paper intends to describe the population evolution of a scientific information web service during 2011–2012. Quarterly samples from December 2011 to December 2012 were extracted from Google Scholar Citations to analyse the number of members, distribution of their bibliometric indicators, positions, institutional and country affiliations and the labels to describe their scientific activity. Results show that most of the users are young researchers, with a starting scientific career and mainly from disciplines related to information sciences and technologies. Another important result is that this service is settled by waves emanating from specific institutions and countries. This work concludes that this academic social network presents some biases in the population distribution that does not make it representative of the real scientific population. © 2015, Akadémiai Kiadó, Budapest, Hungary.

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