DashboardDU provides the members of the University of Trento with a collection of dashboards that can be used for self-evaluation and to make informed decisions in various settings: research performances, degree programs and courses, human resources and internationalization. In this pilot phase, access to DashboardDU is limited to department directors and the University core management, for testing purposes.
Digital University: Dashboard
Access to DashboardDU and its dashboards depends on the user’s role in the University. Some dashboards are only available to people with institutional or managerial positions and focus on data concerning the whole University (the “GOVERNANCE” profile) or a single academic department or centre (the “DIRECTOR” profile). Other dashboards are available to each member of the university community and are based on their own data (the “INDIVIDUAL” profile), for self-evaluation purposes. Other dashboards are available to all members of the university community (the “UNIVERSITY” profile), as they are based on publicly available data. People with more than one role may choose among different profiles. For example, department directors would select “INDIVIDUAL” when they wish to monitor their own publications, and “DIRECTOR” to explore data about the publications of the members of their department.
Navigating DashboardDU and its dashboards is quite intuitive. The header displays the user name and allows users to select the profile that they wish to use in the session (it can be changed at any time). It also allows users to logout at the end of the session.
A navigation menu is located on the left under the Digital University logo. The menu provides a list of available dashboards, organized in categories. Once a specific dashboard is selected from the menu, the menu is collapsed to leave more space to the dashboard. The menu can be made visible again by selecting the arrow icon on the left. The name of the dashboard currently displayed is shown next to the Digital University logo, at the left side of the header.
At the opposite side of the header, an information link () allows users to read methodological and practical information for the analytics shown in the dashboard. We strongly encourage users to read these methodological notes for a better understanding of the analytics, and about the data sources, quality and methods.
Finally, different widgets with statistics and graphs are presented within each dashboard. In many cases, by clicking on specific entries of a chart or table, users can filter data and obtain the same analytics for the selected sub-set of data. The top left corner under the header provides information on the selected filters and allows users to remove filters.
DashboardDU uses data extracted from several databases of the University of Trento and from available external Open Data sources. We first select statistical indicators by balancing priorities set by department directors and data availability. We then select data based on statistical indicators requirements. Users are invited to read information provided under the information link ( ) in each dashboard for more details about specific sources, data quality and methods.
Source databases were designed and developed in view of specific administrative and management processes, and the data they contain has been organized accordingly. DU came at a later stage and its purpose is to reuse data contained in those databases, as a valuable resource for informed decision-making. That is why data is not always appropriate for the functions of DashboardDU. Known limitations are described under the information link () available in each dashboard.
DashboardDU has been designed to monitor statistical indicators. Downloading data is out of the scope of DashboardDU.
Access is granted only to university staff playing a crucial role in internal monitoring and evaluation with the goal of improving overall quality, productivity and sustainability. Please consult the privacy statement for further details.
Please contact the DU helpdesk if – after reading the methodological information ( ) – you still think there is something wrong with the analytics.