Academic libraries generate a wide range of data that reflects how users interact with resources, services, and spaces. The Vanderbilt University Libraries support research, teaching, and learning through a variety of resources and services, including collections, research guides, research assistance, and library spaces. These activities generate data that provides insight into how the campus community engages with the library.
Understanding what data exists, where it comes from, and how it is collected is an essential first step in working with library analytics. Before building dashboards or drawing insights, it is important to understand the structure and context of the data.
Library Data Landscape
Library data is collected across multiple systems, each capturing a different aspect of library activity. These datasets can be broadly grouped into categories based on how users interact with library resources and services:
• Digital Usage– Online resource access
• Physical Resource– Interaction with books and equipment
• Engagement– Interactions between users and library staff
• Space Utilization– Use of physical library spaces
The following are key datasets and sources that capture different aspects of library activity:
EZProxy – EZproxy data represents access to subscription-based digital resources such as databases, e-journals, and e-books. It provides information about when and how resources are accessed.
This data is processed into structured datasets and integrated into MetriDoc, where it supports reporting and analysis of digital resource usage.
JSTOR – JSTOR data represents the use of digital collections such as scholarly journals, books, images, and primary source materials. It provides information about how these resources are used, including interactions with and downloads of digital content.
Alma – Alma is the library’s management system that supports collections and circulation activities. It manages both physical and electronic resources and includes information on activities such as checkouts, returns, renewals, and holds.
Physical Items – Physical items data represents information about library-owned materials such as books, laptops, chargers, and other equipment. This data is part of the broader Alma system.
Aeon – Aeon data represents requests and access to special collections and archival materials.
VU Library Statistics – VU Library Statistics data represents engagement activities between library staff and patrons, including consultations, instruction sessions, workshops, programs/events, and outreach activities.
Gate Count – Library entry data capturing building usage and visitor counts. Each of these datasets is generated through different collection methods, including system-generated logs, user transactions, and manual data entry.
How Library Data is Collected Across Systems
The datasets described above are collected through a combination of automated systems and structured workflows. Understanding how data is collected provides important context for how it should be interpreted and used.
System-generated data is produced through the use of digital platforms and resources. It provides information about resource usage, reflecting general patterns of access and use.
Transaction-based data is generated through activities within operational systems. It reflects information about the use of library resources and services, such as borrowing, returning materials, and requests for resources.
Form-based data collection is used for capturing engagement and service-related activities. Library staff enter information about consultations, instruction sessions, and outreach efforts into structured forms, ensuring consistency and standardization.
Different data collection methods result in varying levels of detail and structure. System-generated data typically captures high-volume activity with limited context, while manually entered data provides richer descriptive information but may vary in consistency. Understanding these differences is important when working with and interpreting library data. Once collected, data from these systems is transferred into a centralized data warehouse such as MetriDoc, where it can be stored, organized, and prepared for reporting and analysis.
Conclusion
Library data is generated through a combination of digital systems, operational workflows, and system activity. From automated access logs to manually recorded engagement activities, each dataset reflects a different aspect of how the academic community uses library resources and services. By understanding what data exists and how it is collected, institutions can build a strong foundation for analysis, visualization, and informed decision-making. The library Assessment team uses this data to explore questions such as:
• How are digital resources used across VU and VUMC?
• When do library spaces experience peak usage?
• How many items are circulated in a fiscal year, and how does this vary by library unit?
• What percentage of instruction sessions incorporate AI-related topics, and how has this changed over time?
• How does student use of library resources vary across academic terms?
• How many consultations are provided by library staff, and how has this changed over time?
• Which library programs and events see the highest levels of participation?
If you need specific metrics, visualizations, or insights related to library data, you can submit a INFORM request. A member of the LTAS (Library Technology and Assessment Services) team will assist you in reviewing your request and providing accurate and consistent reporting.