U.S. academic libraries: understanding their web presence and their relationship with economic indicators

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-013-1001-0The main goal of this research is to analyze the web structure and performance of units and services belonging to U.S. academic libraries in order to check their suitability for webometric studies. Our objectives include studying their possible correlation with economic data and assessing their use for complementary evaluation purposes. We conducted a survey of library homepages, institutional repositories, digital collections, and online catalogs (a total of 374 URLs) belonging to the 100 U.S. universities with the highest total expenditures in academic libraries according to data provided by the National Center for Education Statistics. Several data points were taken and analyzed, including web variables (page count, external links, and visits) and economic variables (total expenditures, expenditures on printed and electronic books, and physical visits). The results indicate that the variety of URL syntaxes is wide, diverse and complex, which produces a misrepresentation of academic libraries’ web resources and reduces the accuracy of web analysis. On the other hand, institutional and web data indicators are not highly correlated. Better results are obtained by correlating total library expenditures with URL mentions measured by Google (r = 0.546) and visits measured by Compete (r = 0.573), respectively. Because correlation values obtained are not highly significant, we estimate such correlations will increase if users can avoid linkage problems (due to the complexity of URLs) and gain direct access to log files (for more accurate data about visits).Orduña Malea, E.; Regazzi, JJ. (2014). U.S. academic libraries: understanding their web presence and their relationship with economic indicators. Scientometrics. 98(1):315-336. doi:10.1007/s11192-013-1001-0S315336981Adecannby, J. (2011). 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Search engine results over time—A case study on search engine stability”. Cybermetrics, 2/3. Retrieved February 18, 2013 from http://www.cindoc.csic.es/cybermetrics/articles/v2i1p1.html.Bar-Ilan, J. (2001). Data collection methods on the Web for informetric purposes: A review and analysis. Scientometrics, 50(1), 7–32.Bermejo, F. (2007). The internet audience: Constitution & measurement. New York: Peter Lang Pub Incorporated.Buigues-Garcia, M., & Gimenez-Chornet, V. (2012). Impact of Web 2.0 on national libraries. International Journal of Information Management, 32(1), 3–10.Chu, H., He, S., & Thelwall, M. (2002). Library and information science schools in Canada and USA: A Webometric perspective. Journal of education for Library and Information Science, 43(2), 110–125.Chua, Alton, Y. K., & Goh, D. H. (2010). A study of Web 2.0 applications in library websites. Library and Information Science Research, 32(3), 203–211.Gallego, I., García, I.-M., & Rodríguez, L. (2009). 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Social media metrics for new research evaluation

This chapter approaches, both from a theoretical and practical perspective, the most important principles and conceptual frameworks that can be considered in the application of social media metrics for scientific evaluation. We propose conceptually valid uses for social media metrics in research evaluation. The chapter discusses frameworks and uses of these metrics as well as principles and recommendations for the consideration and application of current (and potentially new) metrics in research evaluation.Comment: Forthcoming in Glanzel, W., Moed, H.F., Schmoch U., Thelwall, M. (2018). Springer Handbook of Science and Technology Indicators. Springe

Social media metrics for new research evaluation

Merit, Expertise and Measuremen

Inter-population comparisons of copper resistance and accumulation in the red seaweed, Gracilariopsis longissima.

Copper (Cu) resistance and accumulation of five populations of the red seaweed Gracilariopsis longissima collected from sites in south west England (Fal Estuary, Helford Estuary and Chesil Fleet) that differ in their degree of Cu contamination was assessed under controlled laboratory conditions, on two separate occasions (April and October). The effects of a range of Cu concentrations (0-250 μg l(-1)) on relative growth rates was the same for all populations with reductions observable at concentrations as low as 12 μg l(-1) and cessation of growth at 250 μg l(-1). There was no significant difference in the calculated EC(50) values for the April and October samples, with means of 31.1 and 25.8 μg l(-1), respectively. Over the range of concentrations used in this study, copper content increased linearly and the pattern of accumulation was the same for all populations at both time periods. From the linear regressions of the pooled data a concentration factor of 2.25 × 10(3) was calculated. These results imply that G. longissima has an innate tolerance to Cu and that populations have not evolved copper-tolerant ecotypes. In laboratory studies, accumulated Cu was released when transferred to 'clean' seawater with approximately 80% being lost after 8 days, with no significant difference between populations in their response. The results from a 30 days in situ transplantation experiment using two populations from the Fal Estuary provided further evidence for dynamic changes in Cu content in response to changes in Cu bioavailability. The findings in this study are discussed in the context of implications for seaweed biomonitoring