34 research outputs found
“A Process of Controlled Serendipity”: An Exploratory Study of Historians’ and Digital Historians’ Experiences of Serendipity in Digital Environments
We investigate historians\u27 experiences with serendipity in both physical and digital environments through an online survey. Through a combination of qualitative and quantitative data analyses, our preliminary findings show that many digital historians select a specific digital environment because of the expectation that it may elicit a serendipitous experience. Historians also create heuristic methods of using digital tools to integrate elements of serendipity into their research practice. Four features of digital environments were identified by participants as supporting serendipity: exploration, highlighted triggers, allowed for keyword searching and connected them to other people
Recommended from our members
Discovering the Unfindable: The Tension Between Findability and Discoverability in a Bookshop Designed for Serendipity
Serendipity is a key aspect of user experience, particularly in the context of information acquisition - where it is known as information encountering. Unexpectedly encountering interesting or useful information can spark new insights while surprising and delighting. However, digital environments have been designed primarily for goal-directed seeking over loosely-directed exploration, searching over discovering. In this paper we examine a novel physical environment - a bookshop designed primarily for serendipity - for cues as to how information encountering might be helped or hindered by digital design. Naturalistic observations and interviews revealed it was almost impossible for participants to find specific books or topics other than by accident. But all unexpectedly encoun-tered interesting books, highlighting a tension between findability and discoverability. While some of the bookshop’s design features enabled information en-countering, others inhibited it. However, encountering was resilient, as it occurred despite participants finding it hard to understand the purpose of even those features that did enable it. Findings suggest the need to consider how transparent or opaque the purpose of design features should be and to balance structure and lack of it when designing digital environments for findability and discoverability
Towards a data publishing framework for primary biodiversity data: challenges and potentials for the biodiversity informatics community
Background: Currently primary scientific data, especially that dealing with biodiversity, is neither
easily discoverable nor accessible. Amongst several impediments, one is a lack of professional
recognition of scientific data publishing efforts. A possible solution is establishment of a ‘Data
Publishing Framework’ which would encourage and recognise investments and efforts by
institutions and individuals towards management, and publishing of primary scientific data
potentially on a par with recognitions received for scholarly publications.
Discussion: This paper reviews the state-of-the-art of primary biodiversity data publishing, and
conceptualises a ‘Data Publishing Framework’ that would help incentivise efforts and investments by
institutions and individuals in facilitating free and open access to biodiversity data. It further
postulates the institutionalisation of a ‘Data Usage Index (DUI)’, that would attribute due recognition
to multiple players in the data collection/creation, management and publishing cycle.
Conclusion: We believe that institutionalisation of such a ‘Data Publishing Framework’ that
offers socio-cultural, legal, technical, economic and policy environment conducive for data
publishing will facilitate expedited discovery and mobilisation of an exponential increase in quantity
of ‘fit-for-use’ primary biodiversity data, much of which is currently invisible
Proposal for a multilevel university cybermetric analysis model
The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-012-0868-5Universities’ online seats have gradually become complex systems of dynamic information where all their institutions and services are linked and potentially accessible. These online seats now constitute a central node around which universities construct and document their main activities and services. This information can be quantitative measured by cybermetric techniques in order to design university web rankings, taking the university as a global reference unit. However, previous research into web subunits shows that it is possible to carry out systemic web analyses, which open up the possibility of carrying out studies which address university diversity, necessary for both describing the university in greater detail and for establishing comparable ranking units. To address this issue, a multilevel university cybermetric analysis model is proposed, based on parts (core and satellite), levels (institutional and external) and sublevels (contour and internal), providing a deeper analysis of institutions. Finally the model is integrated into another which is independent of the technique used, and applied by analysing Harvard University as an example of use.Orduña Malea, E.; Ontalba Ruipérez, JA. (2013). Proposal for a multilevel university cybermetric analysis model. Scientometrics. 95(3):863-884. doi:10.1007/s11192-012-0868-5S863884953Acosta Márquez, T., Igartua Perosanz, J.J. & Gómez Isla, J. (2009). Páginas web de las universidades españolas. Enred: revista digital de la Universidad de Salamanca, 5 [online; discontinued].Aguillo, I. F. (1998). Hacia un concepto documental de sede web. El Profesional de la Información, 7(1–2), 45–46.Aguillo, I. F. (2009). Measuring the institutions’ footprint in the web. Library Hi Tech, 27(4), 540–556.Aguillo, I. F., Granadino, B., Ortega, J. L., & Prieto, J. A. (2006). Scientific research activity and communication measured with cybermetrics indicators. Journal of the American Society for Information Science and Technology, 57(10), 1296–1302.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.Ayan, N., Li, W.-S., & Kolak, O. (2002). Automatic extraction of logical domains in a web site. Data & Knowledge Engineering, 43(2), 179–205.Barjak, F., Li, X., & Thelwall, M. (2007). Which factors explain the Web impact of scientists’ personal homepages? Journal of the American Society for Information Science and Technology, 58(2), 200–211.Berners-Lee, T., & Fischetti, M. (2000). Tejiendo la Red. Madrid: Siglo XXI.Björneborn, L., & Ingwersen, P. (2004). Toward a basic framework for webometrics. Journal of the American Society for Information Science and Technology, 55(14), 1216–1227.Buenadicha, M., Chamorro, A., Miranda, F. J., & González, O. R. (2001). A new web assessment index: Spanish Universities Analysis. Internet Research, 11(3), 226–234.Castells, M. (2001). La galaxia Internet. Barcelona: Plaza y Janés.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.Crowston, K., & Williams, M. (2000). Reproduced and Emergent Genres of Communication on the World Wide Web. The Information Society: an International Journal, 16(3), 201–215.Goldfarb, A. (2006). The (teaching) role of universities in the diffusion of the Internet. International Journal of Industrial Organization, 24(2), 203–225.Ingwersen, P. (1998). The calculation of web impact factors. Journal of Documentation, 54(2), 236–243.Katz, R. N. (2008a). The tower and the cloud: Higher education in the age of cloud computing. USA: Educause.Katz, R. N. (2008b). The gathering cloud: is this the end of the middle. In R. N. Katz (Ed.), The tower and the cloud: Higher education in the age of cloud computing (p. 2008). USA: Educause.Li, X. (2005). National and international university departmental Web site interlinking: a webometric analysis. [Unpublished doctoral dissertation]. Wolverhampton, UK: University of Wolverhampton.Li, X., Thelwall, M., Musgrove, P., & Wilkinson, D. (2003). The relationship between the links/Web Impact Factors of computer science departments in UK and their RAE (Research Assessment Exercise) ranking in 2001. Scientometrics, 57(2), 239–255.Middleton, I., McConnell, M., & Davidson, G. (1999). Presenting a model for the structure and content of a University World Wide Web site. Journal of Information Science, 25(3), 217–219.Orduña-Malea, E. (2012). Propuesta de un modelo de análisis redinformétrico multinivel para el estudio sistémico de las universidades españolas (2010). Valencia: Polytechnic University of Valencia.Ortega, J. L., & Aguillo, Isidro. F. (2007). La web académica española en el contexto del Espacio Europeo de Educación Superior: estudio exploratorio. El profesional de la información, 16(5), 417–425.Pareja, V. M., Ortega, J. L., Prieto, J. A., Arroyo, N., & Aguillo, I. F. (2005). Desarrollo y aplicación del concepto de sede web como unidad documental de análisis en Cibermetría. Jornadas Españolas de Documentación, 9, 325–340.Saorín, T. (2012). Arquitectura de la dispersión: gestionar los riesgos cíclicos de fragmentación de las webs corporativas. Anuario ThinkEPI, 6, 281–287.Tang, R., & Thelwall, M. (2003). U.S. academic departmental Web-site interlinking: disciplinary differences. Library & Information Science Research, 25(4), 437–458.Tang, R., & Thelwall, M. (2004). Patterns of national and international web inlinks to US academic departments: an analysis of disciplinary variations. Scientometrics, 60(3), 475–485.Thelwall, M. (2002a). A research and institutional size based model for national university Web site interlinking. Journal of Documentation, 58(6), 683–694.Thelwall, M. (2002b). Conceptualizing documentation on the Web: an evaluation of different heuristic-based models for counting links between university web sites. Journal of the American Society for Information Science and Technology, 53(12), 995–1005.Thelwall, M. (2003). Web use and peer interconnectivity metrics for academic Web sites. Journal of Information Science, 29(1), 11–20.Thelwall, M. (2009). Introduction to Webometrics: quantitative web research for the social sciences. San Rafael: Morgan & Claypool.Thelwall, M., & Harries, G. (2004a). Can personal Web pages that link to universities yield information about the wider dissemination of research? Journal of Information Science, 30(3), 243–256.Thelwall, M., & Harries, G. (2004b). Do better scholars’ Web publications have significantly higher online impact? Journal of American Society for Information Science and Technology, 55(2), 149–159.Thelwall, M., Vaughan, L., & Björneborn, L. (2005). Webometrics. Annual Review of Information Science and Technology, 39, 81–135.Thomas, O., & Willet, P. (2000). Webometric analysis of Departments of librarianship and information science. Journal of Information Science, 26(6), 421–428.Tíscar, L. (2009). El papel de la universidad en la construcción de su identidad digital. Revista de universidad y sociedad del conocimiento, 6(1), 15–21.Van Vught, F. A. (2009). Diversity and differentiation in higher education. In F. Van Vught (Ed.), Mapping the higher education landscape: toward a European classification of higher education (pp. 1–16). The Netherlands: Springer.Yolku, O. (2001). Use of news articles and announcements on official websites of universities. Turkish Online Journal of Educational Technology, 10(2), 287–296
“That Looks Like Me or Something i Can Do”: Affordances and Constraints in the Online Identity Work of US LGBTQ+ Millennials
This article examines how search engines and social networking sites enable and constrain the identity-related information practices of lesbian, gay, bisexual, transgender,and queer (LGBTQ+) millennials in the United States
Webometrics benefitting from web mining? An investigation of methods and applications of two research fields
Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms