academic library collection data visualization

Finch, J. f., & Flenner, A. (2016). Using Data Visualization to Examine an Academic Library Collection. College & Research Libraries77(6), 765-778.

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Visualizations of library data have been used to: • reveal relationships among subject areas for users. • illuminate circulation patterns. • suggest titles for weeding. • analyze citations and map scholarly communications

Each unit of data analyzed can be described as topical, asking “what.”6 • What is the number of courses offered in each major and minor? • What is expended in each subject area? • What is the size of the physical collection in each subject area? • What is student enrollment in each area? • What is the circulation in specific areas for one year?

libraries, if they are to survive, must rethink their collecting and service strategies in radical and possibly scary ways and to do so sooner rather than later. Anderson predicts that, in the next ten years, the “idea of collection” will be overhauled in favor of “dynamic access to a virtually unlimited flow of information products.”  My note: in essence, the fight between Mark Vargas and the Acquisition/Cataloguing people

The library collection of today is changing, affected by many factors, such as demanddriven acquisitions, access, streaming media, interdisciplinary coursework, ordering enthusiasm, new areas of study, political pressures, vendor changes, and the individual faculty member following a focused line of research.

subject librarians may see opportunities in looking more closely at the relatively unexplored “intersection of circulation, interlibrary loan, and holdings.”

Using Visualizations to Address Library Problems

the difference between graphical representations of environments and knowledge visualization, which generates graphical representations of meaningful relationships among retrieved files or objects.

Exhaustive lists of data visualization tools include: • the DIRT Directory (http://dirtdirectory.org/categories/visualization) • Kathy Schrock’s educating through infographics (www.schrockguide.net/ infographics-as-an-assessment.html) • Dataviz list of online tools (www.improving-visualisation.org/case-studies/id=5)

Visualization tools explored for this study include Plotly, Microsoft Excel, Python programming language, and D3.js, a javascript library for creating documents based on data. Tableau Public©

Eugene O’Loughlin, National College of Ireland, is very helpful in composing the charts and is found here: https://youtu.be/4FyImh2G7N0.

p. 771 By looking at the data (my note – by visualizing the data), more questions are revealed,  The visualizations provide greater comprehension than the two-dimensional “flatland” of the spreadsheets, in which valuable questions and insights are lost in the columns and rows of data.

By looking at data visualized in different combinations, library collection development teams can clearly compare important considerations in collection management: expenditures and purchases, circulation, student enrollment, and course hours. Library staff and administrators can make funding decisions or begin dialog based on data free from political pressure or from the influence of the squeakiest wheel in a department.

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more on data visualization for the academic library in this IMS blog
http://blog.stcloudstate.edu/ims?s=data+visualization

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