White paper: Landscape of data gathering and analysis at the University Libraries, University of New Mexico
Tamara M. McMahon
Abstract
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Tamara M. McMahon
Abstract
Open-access reader
Currently, the primary factor behind gathering usage data at the University Libraries (UL) at the University of New Mexico (UNM) is the necessity of its contribution to the annual Association of Research Libraries (ARL) report. Other reasons for statistical gathering, such information needed for better-informed budgetary decisions, exist but are not addressed in a consistent manner by UL. Each year, many people in the libraries spend hours compiling statistics for the ARL report. While this results in a comprehensive collection of data, it is not always accurate or efficient. Data gathering methods are not consistently recorded, resulting in varying methods for data collection each year. Additionally, data are often not reviewed for trends analysis. The current data gathering methods do not provide the UL with a good understanding of user needs and behaviors as they relate to the libraries. This paper proposes several options for addressing these issues.
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Currently, the primary factor behind gathering usage data at the University Libraries (UL) at the University of New Mexico (UNM) is the necessity of its contribution to the annual Association of Research Libraries (ARL) report. Other reasons for statistical gathering, such information needed for better-informed budgetary decisions, exist but are not addressed in a consistent manner by UL. Each year, many people in the libraries spend hours compiling statistics for the ARL report. While this results in a comprehensive collection of data, it is not always accurate or efficient. Data gathering methods are not consistently recorded, resulting in varying methods for data collection each year. Additionally, data are often not reviewed for trends analysis. The current data gathering methods do not provide the UL with a good understanding of user needs and behaviors as they relate to the libraries. This paper proposes several options for addressing these issues.
Key concepts: White (mutation), Data collection, Geography, Library science, Computer science, Sociology, Social science, Gene