The Bigger Picture: Creating a Statistics Dashboard That Ties Collection Building to Research
Speaker: Shannon Tharp, University of Wyoming
How can they tie the collection building efforts with the university’s research output? Need to articulate value to the stakeholders and advocate for budget increases.
She used Tableau to develop the dashboard and visualizations. Started with a broad overview of collections and then have expanded from there. The visualizations include a narrative and an intuitive interface to access more information.
The dashboard also includes qualitative interviews of faculty and research staff. They are tentatively calling this “faculty talk” and plan to have it up soon, with rotating interviews displaying. They are thinking about including graduate and undergraduate student interviews as well.
(e)Book Snapshot: Print and eBook Use in an Academic Library Consortium
Speaker: Joanna Voss, OhioLINK
What can we do to continue to meet the needs of students and faculty through the print to electronic book transition? Are there any patterns or trends in their use that will help? Anecdotally we hear about users preferring print to electronic. How do we find data to support this and to help them?
They cleaned up the data using Excel and OpenRefine, and then used Tableau for the analysis and visualization. OpenRefine is good for really messy data.
A Brief History of PaperStats
Speaker: Whitney Bates-Gomez, Memorial Sloan Kettering Cancer Center
Web-based tool for generating cost-per-use reports. It’s currently in beta and only working with JR1 reports. It works most of the time for COUNTER and SUSHI reports, but not always. The costs function requires you to upload the costs in a CSV format, and they were able to get that data from their subscription agent.
But, too bad for you, it’s going away at the end of the spring, but there might be a revised version out there some day. It’s through PubGet and Copyright Clearance Center decided to not renew their support.
Library data is meaningless in and of itself – you need to interpret it to give it meaning. Piotr Adamczyk did much of the work for the presentation, but was not able to attend today due to a schedule conflict.
They created the visual dashboard for many reasons, including a desire to expose the large quantities of data they have collected and stored, but in a way that is interesting and explanatory. It’s also a handy PR tool for promoting the library to benefactors, and to administrators who are often not aware of the details of where and how the library is being effective and the trends in the library. Finally, the data can be targeted to the general public in ways that catch their attention.
The dashboard should also address assessment goals within the library. Data visualization allows us to identify and act upon anomalies. Some visualizations are complex, and you should be sensitive to how you present it.
The ILS is a great source of circulation/collections data. Other statistics can come from the data collected by various library departments, often in spreadsheet format. Google Analytics can capture search terms in catalog searches as well as site traffic data. Download/search statistics from eresources vendors can be massaged and turned into data visualizations.
The free tools they used included IMA Dashboard (local software, Drupal Profile) and IBM Many Eyes and Google Charts (cloud software). The IMA Dashboard takes snapshots of data and publishes it. It’s more of a PR tool.
Many Eyes is a hosted collection of data sets with visualization options. One thing I like was that they used Google Analytics to gather the search terms used on the website and presented that as a word cloud. You could probably do the same with the titles of the pages in a page hit report.
Google Chart Tools are visualizations created by Google and others, and uses Google Spreadsheets to store and retrieve the data. The motion charts are great for showing data moving over time.
Lessons learned… Get administrative support. Identify your target audience(s). Identify the stories you want to tell. Be prepared for spending a lot of time manipulating the data (make sure it’s worth the time). Use a shared repository for the data documents. Pull from data your colleagues are already harvesting. Try, try, and try again.