Features enhancements and text analytics on institutional repositories

Abstract

As part of National Institute of Education (NIE) Library’s LIBRIS 21 strategic master plans to transform the Library, NIE Library seized the opportunity to engage users and value-add to academics’ research ecosystem through the implementation of an Institutional Repository in 2009. With the paradigm shift in research dissemination through institutional repository and the ease of copyright restrictions imposed by publishers, along with the innovative content recruitment efforts by NIE Librarians, the institutional repository become highly popular and significant content growth was observed since the institutional repository was first launched. Within a short span of four years, the NIE institutional repository has gathered more than 15,000 digital objects. Being encouraged by the participation rate, NIE Library seized the opportunity to further enhance users’ experience by upgrading NIE Institutional Repository platform (DSpace) to the latest version in September 2014. This paper will share NIE Library’s experience in the upgrade, present selected advanced features of the new version and show case how they add value to our users. In addition, with the accessibility of text analytics technologies and the fact that institutional repository have huge amount of valuable textual information, NIE has come up with several text analytics prototypes that could further enhance user experience. This paper will detail the features provided by text analytics and how they are developed using text analytics technologies. Specifically, text analytics applications such as subject terms predictions, trend analysis, articles recommendations and contextual search terms suggestions are explored in this paper.