Mendeley ��� A Last.fm for Research? Victor Henning, Bauhaus-University of Weimar, Germany / Mendeley Ltd., London, UK Jan Reichelt, University of Cologne, Germany / Mendeley Ltd., London, UK Introduction New developments in information technology influence the way researchers work, especially in boundary-spanning teams and interdisciplinary research fields. Currently, discussions of how to turn Web 2.0 applications into productive social research tools can be found everywhere (e.g. European Science Open Forum 2008, Science Blogging Conference 2008, Science in the 21st Century Conference, ���Science 2.0��� related discussions in Friendfeed, etc.), and the main questions are always ��� Which tools are being used? How do these tools improve collaboration between researchers? Does the academic world embrace social networks for research? Both multi-purpose social software, such as wikis, blogs, and social networks, and more specific services such as Twitter, Friendfeed, or CiteULike are currently being used and evaluated by a number researchers and academics. However, despite their apparent helpfulness for daily research, it is even more important to analyze the underlying principles and concepts of these services in order to evaluate their long-term impact and therefore usefulness for the academic community. Therefore, this paper aims to explore how the principles of a well-known Web 2.0 service, namely the world���s largest social music service ���Last.fm��� (www.last.fm), can be applied to research, which potential it could have in the world of research (e.g. an open and interdisciplinary database, usage-based reputation metrics, and collaborative filtering) and which challenges such a model would face in academia (e.g. scale, initial value proposition, and privacy). A real-world application of these principles, ���Mendeley��� (www.mendeley.com), will then be demoed at the IEEE e-Science conference. How Last.fm works Last.fm, which bills itself as a ���social music service���, has managed to create the largest ontological classification (and the largest open database) of music in the world, by aggregating the musical tastes of its 20 million users and then data-mining it for similar musical genres, artists, and songs. The users form a social network that is not based on pre-existing real- world relationships instead, Last.fm���s network emerges around data that describes its users��� listening behavior and musical preferences. Can academia perhaps learn something from the world���s largest social music network and can its principles be applied to research? Last.fm works like this: Its ���Audioscrobbler��� desktop software, after having been installed on a user���s PC, starts tracking the user���s music listening behavior. The listening data is sent to the Last.fm website, where a profile of the user���s musical tastes is created. Listening statistics for each song, album, artist, and genre are aggregated and made available online. In this way, Last.fm has created the world���s largest open music database, comprising over 80 million songs, accessible by everyone. The user-generated data also lays the foundation for personalization, collaborative filtering, and ontological classifications: - Users can view timelines and statistics about their own listening behavior, - view the most popular tracks for each of their favorite artists, and most popular artists for their favorite genre, - receive music recommendations based on the song library already existing on their PC, and - discover similar tracks/artists for every track/artist in the Last.fm database. The model of Last.fm applied to research Last.fm���s service is based on aggregating the users��� existing music libraries, relationships between artists writing songs in different genres, and the users��� music listening behavior. Similarly, a service for academic researchers could be based on aggregating scholars��� existing research paper libraries, relationships between researchers writing papers in different disciplines, and the scholars��� paper reading behavior. Along these lines, a ���Last.fm for research��� would be able to display statistics to each individual user about his personal library, to aggregate readership statistics about papers, authors, journals, and academic disciplines, and to recommend interesting articles and researchers to the user. We envision that such a tool consists of two parts: First, a desktop application which helps researchers manage their academic papers (in PDF format) and anonymously tracks their reading habits and literature usage. Second, a website where the users can discover aggregated statistics, top papers, trends and charts for each discipline, paper recommendations, and introductions to people with similar research interests. Adoption of such a service would have a number of advantages for academia at large, of which we will discuss three important ones. The creation of an open and interdisciplinary database: Similar to Last.fm���s efforts in the space of music, a tool which aggregates metadata, tags and article usage of a large number of researchers could lead to an open, interdisciplinary and ontological