Collaboration Towards a More Inclusive Society: The Case of South African ICT4D Researchers

In this study, research collaboration in the context of South African Information and Communication for Development (ICT4D) researchers was investigated using a mixed methods approach. South Africa, a country with stark development challenges and on the other hand a well-established ICT infrastructure, provides an appropriate context for ICT4D research. Firstly, a quantitative analysis of South African research collaboration between 2003 and 2016 was conducted to determine the existing research collaboration patterns of South African ICT4D researchers. This is based on the publications in three top ICT4D journals namely the Electronic Journal of Information Systems in Developing Countries (EJISDC), Information Technologies & International Development (ITID), and Information Technology for Development (ITD). The results show that most co-authored papers were intra-institutional collaborations, with limited inter-institutional collaboration between South African authors or between South African and other African authors. Secondly, interviews were conducted with South African researchers who emerged as inter- and intra-institutional collaborators to gain insight into the technology, drivers and barriers affecting South African research collaboration. We report our findings and discuss the implications for employing research collaboration as a mechanism for addressing inequality and supporting inclusion.School of Computin

Factors affecting inter-regional academic scientific collaboration within Europe: the role of economic distance

This paper offers some insights into scientific collaboration (SC) at the regional level by drawing upon two lines of inquiry. The first involves examining the spatial patterns of university SC across the EU-15 (all countries belonging to the European Union between 1995 and 2004). The second consists of extending the current empirical analysis on regional SC collaboration by including the economic distance between regions in the model along with other variables suggested by the extant literature. The methodology relies on co-publications as a proxy for academic collaboration, and in order to test the relevance of economic distance for the intensity of collaboration between regions, we put forward a gravity equation. The descriptive results show that there are significant differences in the production of academic scientific papers between less-favoured regions and core regions. However, the intensity of collaboration is similar in both types of regions. Our econometric findings suggest that differences in scientific resources (as measured by R&D expenditure) between regions are relevant in explaining academic scientific collaborations, while distance in the level of development (as measured by per capita GDP) does not appear to play any significant role. Nevertheless, other variables in the analysis, including geographical distance, specialization and cultural factors, do yield significant estimated coefficients, and this is consistent with the previous literature on regional SC

An empirical investigation of the influence of collaboration in Finance on article impact

We investigate the impact of collaborative research in academic Finance literature to find out whether and to what extent collaboration leads to higher impact articles (6,667 articles across 2001-2007 extracted from the Web of Science). Using the top 5 % as ranked by the 4-year citation counts following publication, we also follow related secondary research questions such as the relationships between article impact and author impact; collaboration and average author impact of an article; and, the nature of geographic collaboration. Key findings indicate: collaboration does lead to articles of higher impact but there is no significant marginal value for collaboration beyond three authors; high impact articles are not monopolized by high impact authors; collaboration and the average author impact of high-impact articles are positively associated, where collaborative articles have a higher mean author impact in comparison to single-author articles; and collaboration among the authors of high impact articles is mostly cross-institutional

Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years

[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. 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Does distance hinder the collaboration between Australian universities in the humanities, arts and social sciences?

Australia is a vast country with an average distance of 1911 km between its eight state capital cities. The quantitative impact of this distance on collaboration practices between Australian universities and between different types of Australian universities has not been examined previously and hence our knowledge about the spatial distribution effects, if any, on collaboration practices and opportunities is very limited. The aim of the study reported here was therefore to analyse the effect of distance on the collaboration activities of humanities, arts and social science scholars in Australia, using co-authorship as a proxy for collaboration. In order to do this, gravity models were developed to determine the distance effects on external collaboration between universities in relation to geographic region and institutional alliance of 25 Australian universities. Although distance was found to have a weak impact on external collaboration, the strength of the research publishing record within a university (internal collaboration) was found to be an important factor in determining external collaboration activity levels. This finding would suggest that increasing internal collaboration within universities could be an effective strategy to encourage external collaboration between universities. This strategy becomes even more effective for universities that are further away from each other. Establishing a hierarchical structure of different types of universities within a region can optimise the location advantage in the region to encourage knowledge exchange within that region. The stronger network could also attract more collaboration between networks

Proximity Dimensions and Scientific Collaboration among Academic Institutions in Europe: The Closer, the Better?

The main objective of this paper is to examine the effect of various proximity dimensions (geographical, cognitive, institutional, organizational, social and economic) on academic scientific collaborations (SC). The data to capture SC consists of a set of co-authored articles published between 2006 and 2010 by universities located in EU-15, indexed by the Science Citation Index (SCI Expanded) of the ISI Web of Science database. We link this data to institution-level information provided by the EUMIDA dataset. Our final sample consists of 240,495 co-authored articles from 690 European universities that featured in both datasets. Additionally, we also retrieved data on regional R&D funding from Eurostat. Based on the gravital equation, we estimate several econometrics models using aggregated data from all disciplines as well as separated data for Chemistry & Chemical Engineering, Life Sciences and Physics & Astronomy. Our results provide evidence on the substantial role of geographical, cognitive, institutional, social and economic distance in shaping scientific collaboration, while the effect of organizational proximity seems to be weaker. Some differences on the relevance of these factors arise at discipline level