In 2016, the Scholars@Cornell project was initiated aiming to advance the visibility and accessibility of Cornell scholarship and to preserve them for future generations. However, in data life cycle, data preservation and providing access to the recorded data is not the final stage. Data stored in a database is merely a record and can be of use only if human experience and insight is applied to it, data analysis is performed and data is transformed into a knowledge. The faculty and publication data is capable of revealing much more about patterns and dynamics of scholarship and the institution. Such data can support universities in their systems for managing faculty information, scholar's websites, faculty reporting and strategic decisions in general. We explore the scholarship data from the lens of a scholar, academic unit and an institutions. Unlike systems that provide web pages of researcher profiles using lists and directory-style metaphors, our work explores the power of graph analytics and infographics for navigating a rich semantic graph of scholarly data. We believe that the scholarship data, accessible in RDF format through VIVO webpages, is not easy to reuse, specifically by the software developers who have limited knowledge of semantic technologies and the VIVO data model. In Scholars@Cornell, the scholarship data is open for reuse in different ways. The data can be accessed via Data Distribution API in RDF or in JSON format. The infographics built using D3 javascript libraries can be embedded on different institutional websites. Additionally, new web applications can be developed that use scholarship knowledge graph, showcasing research areas and expertise. In this presentation, I will present an overview of the project, lessons learnt and will emphasis on data reuse and data analysis. I will discuss about our journey, how we moved from counting list items to connected graph, from data list views to data analysis and from data in peace to data in use.
