In Scholars@Cornell, we provide aggregate views of scholarship data where dynamic visualizations become the entry points into a rich graph of knowledge that can be explored interactively to answer questions such as who are the experts in what areas? Which departments collaborate with each other? What are patterns of interdisciplinary research, and more . We will discuss the new theme and the D3 visualizations that allowed us to move from List Views to Viz Views and leverages the power of state of the art dynamic web languages. We integrate visualizations at different level. Research interest of a faculty member are presented at the department level using Person to Subject Area Network Map visualization. The presented research interest are the subject area classifications of the publication venues where a faculty members have published their articles. We map these subject areas using Science-Metrix and Web of Science Journal classification. The person-to-subject-area map is helpful for the identification of i) list of research interests of a faculty member and ii) list of potential collaborators. The map demonstrates the overlap of research interests among different faculty members. This information can be helpful to identify future coauthors and potential collaborators. To demonstrate the Domain Expertise of a faculty member, we use the keywords from their authored articles and present them in the form a Keyword Cloud. These keywords are either asserted by the authors (i.e. keywords mentioned in the keyword section of an article), tagged by the publishers (e.g. MeSH terms tagged by PubMed) or inferred in our post-processing module. The size of each keyword (in the cloud) is directly proportional to the number of articles in which the keyword is been mentioned. The tooltips on each keyword displays the list of relevant articles. Interdepartmental and cross-unit co-authorships are presented at the College level using Co-Authorship Wheels. We present Global Collaborations at the homepage where academic organizations are mapped to their GRID ids wherever possible. We will discuss our process for selection, design, and development of an initial set of visualizations as well as our approach to the underlying technical architecture. What data is necessary for the generation of these visualization, and how it is modelled. By engaging an initial set of pilot partners, we are evaluating the use of these data-driven visualizations by multiple stakeholders, including faculty, students, librarians, administrators, and the public.