12.3 Taking Libraries' Cultural Content to the User – Approaches and Experiences from the EEXCESS Project
Creators: name: Timo Borst, Maren Lehmann
Publisher: name: Zenodo
Year: 2016
Subjects: EEXCESS project, libraries, cultural, knowledge, users

description: Since their beginnings, libraries and related cultural institutions were confident in the fact that users had to visit them in order to search, find and access their content. With the emergence and massive use of the World Wide Web and associated tools and technologies, this situation has drastically changed: if those institutions still want their content to be found and used, they must adapt themselves to those environments in which users expect digital content to be available. In the presentation, we show some of the approaches, use cases and technical implementations we developed in the EU project EEXCESS (http://eexcess.eu) to support this new situation. The general approach is to ‘inject’ digital content (both metadata and object files) into users’ daily environments like browsers, authoring environments like content management systems or Google Docs, or e-learning environments. Content is not just provided, but <em>recommended</em> by means of an organisational and technical framework of distributed partner recommenders and user profiles. Once a content partner has connected to this framework by establishing an Application Program Interface (API) and constantly responding to the EEXCESS queries, the results will be listed and merged with the results of the other partners. Depending on the software component installed either on a user’s local machine or on an application server, the list of recommendations is displayed in different ways: from a classical, text-oriented list, to a visualisation of metadata records. We outline some user stories which we conceive as representative for our target groups (students and researchers). As an example of <em>content consumption</em>, we take the reading of a Wikipedia page in a local browser. During the reading process, literature matching the topic of the article – or a section of it – is automatically recommended. As an example of <em>content production</em>, we consider the creation and publishing of a blog post, which is supported by automatically recommending and citing related articles. In addition to these application scenarios, we provide some results from the iterative user tests and feedback we already received. As a general outcome, the overall acceptance of EEXCESS content and tools is high, but heavily depend on the relevance of recommendations and usability of software components. We conclude the presentation by depicting some of the methods of achieving dissemination and sustainability of the project results. One approach is the maintenance of software components developed in EEXCESS in the context of user resp. developer platforms like the Chrome Web Store or the WordPress Plugin repository. Although the alignment with policy guidelines – and, in particular, with later releases of software environments like browsers or WordPress applications – may imply some additional efforts, this way of disseminating both project results and libraries’ content looks promising to us. Another activity is the support for setting up a new partner recommender in terms of a ‘partner wizard’, so that future partners can easily link into the EEXCESS recommender infrastructure. All software modules developed in the project are Open Source and can be found on GitHub. <strong>Timo Borst </strong>received his Master of Computer Science from the Technical University, Berlin, and his PhD degree in Political Sciences, with a focus on empirical linguistic research, from the University of Marburg, Germany. His main topic is infrastructures for Open Science, with a particular focus on Repositories and Semantic Web. He works in the German National Library for Economics (ZBW) and is Head of the Department for Information Systems and Publishing Technologies. In addition, he is an occasional lecturer at Hamburg University of Applied Sciences (HAW).