User observatory of the SoDaNet research infrastructure data catalogue

The SoDaNet research infrastructure is the unique national research infrastructure for social sciences in Greece. Its primary service involves access to the data catalog. The catalog hosts over 600 data projects and provides end users with a large volume of data enriched with metadata. The resources of the data projects are made available either as open access or through a connection (either directly or after a proper request). In the case of access via a connection, the user registers through a relevant form, allowing their profile to be identified. For other user cases, SoDaNet has activated the Google Analytics service. At the same time, the data catalog has an inherent mechanism for measuring usage (Metrics) of the data project resources. Specifically, it features an aggregate mechanism for providing usage statistics through the processing of tracking data, such as the total number of downloads and views of resources, the number of views per data project or per collection of data projects, etc. This paper presents the profiles of SoDaNet users and the usage of resources based on the above mechanisms. Additionally, it explores mechanisms for more effective user management, such as the creation of an observatory mechanism, which will consistently extract key performance indicators (KPIs) related to users and the use of the infrastructure. The retention of user profiles will always be done with their consent and will be safeguarded (both technically and operationally) through the set of rules established in SoDaNet's data protection policy. The main goal of the observatory mechanism is to record, measure, and monitor the usage and utilization of the available digital content by the research community, as well as by third-party systems through tracking, interoperability, and feedback mechanisms. For this purpose, modern tracking and logging mechanisms will be used, and user profile management forms will be redesigned. By managing and analyzing these data, the infrastructure will be able to create personalized navigation and display options based on each user's profile, and it could also lead to the development of a digital content recommendation system, utilizing machine learning algorithms from both the user's previous choices and their profile information.