41,444 research outputs found

    Electronic Publishing: Research Issues for Academic Librarians and Users

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    Classifying document types to enhance search and recommendations in digital libraries

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    In this paper, we address the problem of classifying documents available from the global network of (open access) repositories according to their type. We show that the metadata provided by repositories enabling us to distinguish research papers, thesis and slides are missing in over 60% of cases. While these metadata describing document types are useful in a variety of scenarios ranging from research analytics to improving search and recommender (SR) systems, this problem has not yet been sufficiently addressed in the context of the repositories infrastructure. We have developed a new approach for classifying document types using supervised machine learning based exclusively on text specific features. We achieve 0.96 F1-score using the random forest and Adaboost classifiers, which are the best performing models on our data. By analysing the SR system logs of the CORE [1] digital library aggregator, we show that users are an order of magnitude more likely to click on research papers and thesis than on slides. This suggests that using document types as a feature for ranking/filtering SR results in digital libraries has the potential to improve user experience.Comment: 12 pages, 21st International Conference on Theory and Practise of Digital Libraries (TPDL), 2017, Thessaloniki, Greec

    Access to information in digital libraries : users and digital divide

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    Recognising the importance of information and knowledge in all spheres of human life, the recently held World Summit on Information Society came up with a plan of action for building a global information society. The goal of the world information society initiatives is the same as that of digital library research and development - to make information and knowledge accessibleto everyone in the world. Digital libraries have progressed very rapidly over the past ten or soyears. This paper addresses the two most important aspects of the information society - information users and digital divide. Findings of some large-scale studies on human information behaviour on the web and digital libraries have been discussed. The major findings of a study on access to electronic resources by university students are the presented. Proposed that a one-stop window approach with a task-based information organisation and access system may be the way forward

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

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    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). Web link analysis of interrelationship between top ten African universities and world universities. Annals of library and information studies, 58(2), 128–138.Aguillo, I. F. (2009). Measuring the institutions’ footprint in the web. Library Hi Tech, 27(4), 540–556.Aguillo, I. F., Ortega, J. L., & Fernández, M. (2008). Webometric Ranking of World Universities: Introduction, methodology, and future developments. Higher education in Europe, 33(2/3), 234–244.Aguillo, I. F., Ortega, J. L., Fernandez, M., & Utrilla, A. M. (2010). Indicators for a webometric ranking of open Access repositories. Scientometrics, 82(3), 477–486.Arakaki, M., & Willet, P. (2009). Webometric analysis of departments of librarianship and information science: A follow-up study. Journal of information science, 35(2), 143–152.Arlitsch, K., & O’Brian, P. S. (2012). Invisible institutional repositories: Addresing the low indexing ratios of IR in Google Scholar. Library Hi Tech, 30(1), 60–81.Bar-Ilan, J. (1999). 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). Universities’ websites: Disclosure practices and the revelation of financial information. The International Journal of Digital Accounting Research, 9(15), 153–192.Gomes, B. & Smith, B. T. (2003). Detecting query-specific duplicate documents. [Patent]. Retrieved February 18, 2013 from http://www.patents.com/Detecting-query-specific-duplicate-documents/US6615209/en-US .Harinarayana, N. S., & Raju, N. V. (2010). Web 2.0 features in university library web sites. Electronic Library, 28(1), 69–88.Lewandowski, D., Wahlig, H., & Meyer-Bautor, G. (2006). The freshness of web search engine databases. Journal of Information Science, 32(2), 131–148.Mahmood, K., & Richardson, J. V, Jr. (2012). Adoption of Web 2.0 in US academic libraries: A survey of ARL library websites. Program, 45(4), 365–375.Orduña-Malea, E., & Ontalba-Ruipérez, J-A. (2012). Selective linking from social platforms to university websites: A case study of the Spanish academic system. Scientometrics. (in press).Ortega, J. L., & Aguillo, I. F. (2009). Mapping World-class universities on the Web. Information Processing and Management, 45(2), 272–279.Ortega, José L. & Aguillo, Isidro F. (2009b). North America Academic Web Space: Multicultural Canada vs. The United States Homogeneity. In: ASIST & ISSI pre-conference symposium on informetrics and scientometrics.Phan, T., Hardesty, L., Sheckells, C., & George, A. (2009). Documentation for the academic libraries survey (ALS) public-use data file: Fiscal year 2008. Washington DC: National Center for Education Statistics. Institute of Education Sciences U.S. Department of Education.Qiu, J., Cheng, J., & Wang, Z. (2004). An analysis of backlinks counts and web impact factors for Chinese university websites. Scientometrics, 60(3), 463–473.Regazzi, J. J. (2012a). Constrained?—An analysis of U.S. Academic Libraries and shifts in spending, staffing and utilization, 1998–2008. College and Research Libraries, 73(5), 449–468.Regazzi, J. J. (2012b). Comparing Academic Library Spending with Public Libraries, Public K-12 Schools, Higher Education Public Institutions, and Public Hospitals Between 1998–2008. Journal of Academic Librarianship, 38(4), 205–216.Rousseau, R. (1999). Daily time series of common single word searches in AltaVista and NorthernLight. Cybermetrics, 2/3. Retrieved February 18, 2013 from http://www.cindoc.csic.es/cybermetrics/articles/v2i1p2.html .Sato, S., & Itsumura, H. (2011). How do people use open access papers in non-academic activities? A link analysis of papers deposited in institutional repositories. Library, Information and Media Studies, 9(1), 51–64.Scholze, F. (2007). Measuring research impact in an open access environment. Liber Quarterly: The Journal of European Research Libraries, 17(1–4), 220–232.Smith, A. G. (2011). Wikipedia and institutional repositories: An academic symbiosis? In: Proceedings of the ISSI 2011 conference. Durban, South Africa, 4–7 July 2011. Retrieved February 18, 2013 from http://www.vuw.ac.nz/staff/alastair_smith/publns/SmithAG2011_ISSI_paper.pdf .Smith, A.G. (2012). Webometric evaluation of institutional repositories. In: Proceedings of the 8th international conference on webometrics informetrics and scientometrics & 13th collnet meeting. Seoul (Korea), 722–729.Smith, A., & Thelwall, M. (2002). Web impact factors for Australasian Universities. Scientometrics, 54(3), 363–380.Tang, R., & Thelwall, M. (2008). A hyperlink analysis of US public and academic libraries’ web sites. Library Quarterly, 78(4), 419–435.Thelwall, M. (2008). Extracting accurate and complete results from search engines: Case study Windows Live. Journal of the American Society for Information Science and Technology, 59(1), 38–50.Thelwall, M. (2009). Introduction to webometrics: Quantitative web research for the social sciences. San Rafael: Morgan & Claypool.Thelwall, M., & Sud, P. (2011). A comparison of methods for collecting web citation data for academic organisations. Journal of the American Society for Information Science and Technology, 62(8), 1488–1497.Thelwall, M., Sud, P., & Wilkinson, D. (2012). Link and co-inlink network diagrams with URL citations or title mentions. Journal of the American Society for Information Science and Technology, 63(10), 1960–1972.Thelwall, M., & Zuccala, A. (2008). A University-centred European Union link analysis. Scientometrics, 75(3), 407–442.Uyar, A. (2009a). Google stemming mechanisms. Journal of Information Science, 35(5), 499–514.Uyar, A. (2009b). Investigation of the accuracy of search engine hit counts. Journal of Information Science, 35(4), 469–480.Zuccala, A., Thelwall, M., Oppenheim, C., & Dhiensa, R. (2007). Web intelligence analyses of digital libraries: A case study of the National Electronic Library for Health (NeLH). Journal of Documentation, 63(4), 558–589

    The onus on us? Stage one in developing an i-Trust model for our users.

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    This article describes a Joint Information Systems Committee (JISC)-funded project, conducted by a cross-disciplinary team, examining trust in information resources in the web environment employing a literature review and online Delphi study with follow-up community consultation. The project aimed to try to explain how users assess or assert trust in their use of resources in the web environment; to examine how perceptions of trust influence the behavior of information users; and to consider whether ways of asserting trust in information resources could assist the development of information literacy. A trust model was developed from the analysis of the literature and discussed in the consultation. Elements comprising the i-Trust model include external factors, internal factors and user's cognitive state. This article gives a brief overview of the JISC funded project which has now produced the i-Trust model (Pickard et. al. 2010) and focuses on issues of particular relevance for information providers and practitioners

    Users' trust in information resources in the Web environment: a status report

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    This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

    Information Outlook, December 2006

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    Volume 10, Issue 12https://scholarworks.sjsu.edu/sla_io_2006/1011/thumbnail.jp
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