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    Are web mentions accurate substitutes for inlinks for Spanish universities?

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    This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limitedurpose – Title and URL mentions have recently been proposed as web visibility indicators instead of inlink counts. The objective of this study is to determine the accuracy of these alternative web mention indicators in the Spanish academic system, taking into account their complexity (multi-domains) and diversity (different official languages). Design/methodology/approach – Inlinks, title and URL mentions from 76 Spanish universities were manually extracted from the main search engines (Google, Google Scholar, Yahoo!, Bing and Exalead). Several statistical methods, such as correlation, difference tests and regression models, were used. Findings – Web mentions, despite some limitations, can be used as substitutes for inlinks in the Spanish academic system, although these indicators are more likely to be influenced by the environment (language, web domain policy, etc.) than inlinks. Research limitations/implications – Title mentions provide unstable results caused by the multiple name variants which an institution can present (such as acronyms and other language versions). URL mentions are more stable, but they may present atypical points due to some shortcomings, the effect of which is that URL mentions do not have the same meaning as inlinks. Practical implications – Web mentions should be used with caution and after a cleaning-up process. Moreover, these counts do not necessarily signify connectivity, so their use in global web analysis should be limited. Originality/value – Web mentions have previously been used in some specific academic systems (US, UK and China), but this study analyses, in depth and for the first time, an entire non-English speaking European country (Spain), with complex academic web behaviour, which helps to better explain previous web mention results.Ortega, JL.; Orduña Malea, E.; Aguillo, IF. (2014). Are web mentions accurate substitutes for inlinks for Spanish universities?. Online Information Review. 38(1):59-77. doi:10.1108/OIR-10-2012-0189S5977381Adecannby, J. (2011), “Web link analysis of interrelationship between top ten African universities and world universities”, Annals of Library and Information Studies, Vol. 58 No. 2, pp. 128-138.Aguillo, I. (2009). Measuring the institution’s footprint in the web. Library Hi Tech, 27(4), 540-556. doi:10.1108/073788309Aguillo, I.F. , Ortega, J.L. and Fernández, M. 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Journal of the American Society for Information Science and Technology, 60(8), 1537-1549. doi:10.1002/asi.21085Kretschmer, H., & Aguillo, I. F. (2004). Visibility of collaboration on the Web. Scientometrics, 61(3), 405-426. doi:10.1023/b:scie.0000045118.68430.fdOrduña-Malea, E. (2012), “Fuentes de enlaces web para análisis cibermétricos (2012)”, Anuario Thinkepi, Vol. 6 No. 1, pp. 276-280.Orduña-Malea, E. (2013), “Espacio universitario español en la Web (2010): estudio descriptivo de instituciones y productos académicos a través del análisis de subdominios y subdirectorios”, Revista Española de Documentación Científica, Vol. 36 No. 3.Orduña-Malea, E., & Ontalba-Ruipérez, J.-A. (2012). Proposal for a multilevel university cybermetric analysis model. Scientometrics, 95(3), 863-884. doi:10.1007/s11192-012-0868-5Orduña-Malea, E., Serrano-Cobos, J., Ontalba-Ruipérez, J. A., & Lloret-Romero, N. (2010). Presencia y visibilidad web de las universidades públicas españolas. Revista española de Documentación Científica, 33(2), 246-278. doi:10.3989/redc.2010.2.740Ortega, J. L., & Aguillo, I. F. (2008). Visualization of the Nordic academic web: Link analysis using social network tools. Information Processing & Management, 44(4), 1624-1633. doi:10.1016/j.ipm.2007.09.010Ortega, J. L., & Aguillo, I. F. (2009). Análisis estructural de la web académica iberoamericana. Revista española de Documentación Científica, 32(3), 51-65. doi:10.3989/redc.2009.3.689Ortega, J. L., Aguillo, I., Cothey, V., & Scharnhorst, A. (2007). Maps of the academic web in the European Higher Education Area — an exploration of visual web indicators. Scientometrics, 74(2), 295-308. doi:10.1007/s11192-008-0218-9Qiu, J., Chen, J., & Wang, Z. (2004). An analysis of backlink counts and Web Impact Factorsfor Chinese university websites. Scientometrics, 60(3), 463-473. doi:10.1023/b:scie.0000034387.76981.83Seeber, M., Lepori, B., Lomi, A., Aguillo, I., & Barberio, V. (2012). 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    Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years

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    [EN] Research collaboration is necessary, rewarding, and beneficial. Cohesion between team members is related to their collective efficiency. To assess collaboration processes and their eventual outcomes, agencies need innovative methods-and social network approaches are emerging as a useful analytical tool. We identified the research output and citation data of a network of 61 research groups formally engaged in publishing rare disease research between 2000 and 2013. We drew the collaboration networks for each year and computed the global and local measures throughout the period. Although global network measures remained steady over the whole period, the local and subgroup metrics revealed a growing cohesion between the teams. Transitivity and density showed little or no variation throughout the period. In contrast the following points indicated an evolution towards greater network cohesion: the emergence of a giant component (which grew from just 30 % to reach 85 % of groups); the decreasing number of communities (following a tripling in the average number of members); the growing number of fully connected subgroups; and increasing average strength. Moreover, assortativity measures reveal that, after an initial period where subject affinity and a common geographical location played some role in favouring the connection between groups, the collaboration was driven in the final stages by other factors and complementarities. The Spanish research network on rare diseases has evolved towards a growing cohesion-as revealed by local and subgroup metrics following social network analysis.The Spanish Ministry of Economics and Competitiveness partially supported this research (Grant Number ECO2014-59381-R).Benito Amat, C.; Perruchas, F. (2016). Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years. Scientometrics. 108(1):41-56. https://doi.org/10.1007/s11192-016-1952-zS41561081Aymé, S., & Schmidtke, J. (2007). Networking for rare diseases: A necessity for Europe. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 50(12), 1477–1483. doi: 10.1007/s00103-007-0381-9 .Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3–4), 590–614. doi: 10.1016/S0378-4371(02)00736-7 .Bettencourt, L. M. A., Kaiser, D. I., & Kaur, J. (2009). Scientific discovery and topological transitions in collaboration networks. Journal of Informetrics, 3(3), 210–221. doi: 10.1016/j.joi.2009.03.001 .Bian, J., Xie, M., Topaloglu, U., Hudson, T., Eswaran, H., & Hogan, W. (2014). Social network analysis of biomedical research collaboration networks in a CTSA institution. Journal of Biomedical Informatics, 52, 130–140. doi: 10.1016/j.jbi.2014.01.015 .Bordons, M., Aparicio, J., González-Albo, B., & Díaz-Faes, A. A. (2015). The relationship between the research performance of scientists and their position in co-authorship networks in three fields. Journal of Informetrics, 9(1), 135–144. doi: 10.1016/j.joi.2014.12.001 .Börner, K., Dall’Asta, L., Ke, W., & Vespignani, A. (2005). Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams. Complexity, 10(4), 57–67. doi: 10.1002/cplx.20078 .Casey-Campbell, M., & Martens, M. L. (2009). Sticking it all together: A critical assessment of the group cohesion–performance literature. International Journal of Management Reviews, 11(2), 223–246. doi: 10.1111/j.1468-2370.2008.00239.x .Chiocchio, F., & Essiembre, H. (2009). Cohesion and performance: A meta-analytic review of disparities between project teams, Production teams, and service teams. Small group research, 40(4), 382–420. doi: 10.1177/1046496409335103 .Cho, A. (2011). Particle physicists’ new extreme teams. Science, 333(6049), 1564–1567. doi: 10.1126/science.333.6049.1564 .Cooke, N. J., & Hilton, M. L. (2015). Enhancing the effectiveness of team science. Washington, D.C.: National Academies Press. Recuperado a partir de http://www.nap.edu/catalog/19007/enhancing-the-effectiveness-of-team-science .Cugmas, M., Ferligoj, A., & Kronegger, L. (2015). The stability of co-authorship structures. Scientometrics, 106(1), 163–186. doi: 10.1007/s11192-015-1790-4 .Estrada, E. (2011). The structure of complex networks: Theory and applications. Oxford: University Press.Gallivan, M., & Ahuja, M. (2015). Co-authorship, homophily, and scholarly influence in information systems research. Journal of the Association for Information Systems, 16(12), 980.Ghosh, J., Kshitij, A., & Kadyan, S. (2014). Functional information characteristics of large-scale research collaboration: Network measures and implications. Scientometrics, 102(2), 1207–1239. doi: 10.1007/s11192-014-1475-4 .Heymann, S. (2014). Gephi. In R. Alhajj & J. Rokne (Eds.), Encyclopedia of social network analysis and mining (pp. 612–625). New York: Springer.Himmelstein, D. S., & Powell, K. (2016). Analysis for “the history of publishing delays” blog post v1.0. Zenodo,. doi: 10.5281/zenodo.45516 .Hunt, J. D., Whipple, E. C., & McGowan, J. J. (2012). Use of social network analysis tools to validate a resources infrastructure for interinstitutional translational research: A case study. Journal of the Medical Library Association, 100(1), 48–54. doi: 10.3163/1536-5050.100.1.009 .Kolaczyk, E. D., & Csardi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York: Springer.Kumar, S. (2015). Efficacy of a giant component in co-authorship networks: Evidence from a Southeast Asian dataset in economics. Aslib Journal of Information Management, 68(1), 19–32. doi: 10.1108/AJIM-12-2014-0172 .Larivière, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323–1332. doi: 10.1002/asi.23266 .Laudel, G. (2002). What do we measure by co-authorships? Research Evaluation, 11(1), 3–15. doi: 10.3152/147154402781776961 .Liu, X., Bollen, J., Nelson, M. L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information Processing and Management, 41(6), 1462–1480. doi: 10.1016/j.ipm.2005.03.012 .Liu, P., & Xia, H. (2015). Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics, 103(1), 101–134. doi: 10.1007/s11192-014-1525-y .Ministerio de Sanidad y Consumo. Resolución de 30 de marzo de. (2006) del Instituto de Salud Carlos III, por la que se convocan ayudas destinadas a financiar estructuras estables de investigación cooperativa, en el área de biomedicina y ciencias de la salud, en el marco de la iniciativa Ingenio 2010, programa Consolider, acciones CIBER, 83 Boletín Oficial del Estado (pp. 13770–13777).Newman, M. E. J. (2001a). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64(1), 016132. doi: 10.1103/PhysRevE.64.016132 .Newman, M. E. J. (2001b). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(1), 016131. doi: 10.1103/PhysRevE.64.016131 .Newman, M. E. J. (2003a). Mixing patterns in networks. Physical Review E, 67(2), 026126. doi: 10.1103/PhysRevE.67.026126 .Newman, M. E. J. (2003b). The structure and function of complex networks. SIAM Review, 45, 167–256.OECD. (2010). Measuring innovation: A new perspective. Paris: OCDE Publishing.Ramasco, J., & Morris, S. (2006). Social inertia in collaboration networks. Physical Review E, 73(1), 016122. doi: 10.1103/PhysRevE.73.016122 .Sonnenwald, D. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology, 41(1), 643–681. doi: 10.1002/aris.2007.1440410121 .Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039. doi: 10.1126/science.1136099

    Auditing scholarly journals published in Malaysia and assessing their visibility

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    The problem with the identification of Malaysian scholarly journals lies in the lack of a current and complete listing of journals published in Malaysia. As a result, librarians are deprived of a tool that can be used for journal selection and identification of gaps in their serials collection. This study describes the audit carried out on scholarly journals, with the objectives (a) to trace and characterized scholarly journal titles published in Malaysia, and (b) to determine their visibility in international and national indexing databases. A total of 464 titles were traced and their yearly trends, publisher and publishing characteristics, bibliometrics and indexation in national, international and subject-based indexes were described

    Chemistry

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    published or submitted for publicatio

    The Open Research Web: A Preview of the Optimal and the Inevitable

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    The multiple online research impact metrics we are developing will allow the rich new database , the Research Web, to be navigated, analyzed, mined and evaluated in powerful new ways that were not even conceivable in the paper era – nor even in the online era, until the database and the tools became openly accessible for online use by all: by researchers, research institutions, research funders, teachers, students, and even by the general public that funds the research and for whose benefit it is being conducted: Which research is being used most? By whom? Which research is growing most quickly? In what direction? under whose influence? Which research is showing immediate short-term usefulness, which shows delayed, longer term usefulness, and which has sustained long-lasting impact? Which research and researchers are the most authoritative? Whose research is most using this authoritative research, and whose research is the authoritative research using? Which are the best pointers (“hubs”) to the authoritative research? Is there any way to predict what research will have later citation impact (based on its earlier download impact), so junior researchers can be given resources before their work has had a chance to make itself felt through citations? Can research trends and directions be predicted from the online database? Can text content be used to find and compare related research, for influence, overlap, direction? Can a layman, unfamiliar with the specialized content of a field, be guided to the most relevant and important work? These are just a sample of the new online-age questions that the Open Research Web will begin to answer

    Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

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    Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

    Channels of published research communication used by Malaysian authors in computer science and information technology

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    Analyse 389 records retrieved from Inspec (1990-1999), Compendex (1987-1999) and IEL (IEE/IEEE Electronic library)(1987-1999). The records comprised 159 journal articles, 229 conference papers and 1 monograph chapter. The subject coverage was computer science and information technology. The yearly output of Malaysian publications indicated a gentle upward trend. The highest contributions was 87 published in 1997. The channels used to publish differ slightly from the norm for scientists. Conference papers were preferred to journal articles. The spread of conference papers used to publish indicate three zonal distributions; the nucleus, moderate and low productivity in the ratio of 19 : 41 : 88, leading to a clustering index of 2.15. This shows that Malaysian conference contributions were concentrated in a few proceedings. No clear core journals can be identified for the journal articles and contributions were distributed in a wide variety of journal titles. Malaysian Journal of Computer Science published the highest number of journal articles. More than 83 of the articles were published in journals from the UK, USA, the Netherlands and Malaysia
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