Apologies for the delay. It took longer than I expected to have the file and a stable internet connection at the same time. You’ll find the notes on the SlideShare page.
A couple of weeks ago I blogged about an idea I had that involved combining subject data from SerialsSolutions with use data for our ejournals to get a broad picture of ejournal use by subject. It took a bit of tooling around with Access tables and queries, including making my first crosstab, but I’ve finally got the data put together in a useful way.
It’s not quite comprehensive, since it only covers ejournals for which SerialsSolutions has assigned a subject, which also have ISSNs, and are available through sources that provide COUNTER or similar use statistics. But, it’s better than nothing.
It’s a mashup of two of my favorite things — data visualization and social media. Of course I’m going to make one.
The interesting thing is that for some reason I come across as a gamer according to the algorithms. Unless you count solitaire, sudoku, and Words with Friends, I’m not really a gamer at all. The PS2, games, and accessories I bought from my sister last November that is are sitting in a corner unassembled are also a testament to how little I game.
Anyway, click on the image to get the full-sized view, and if you make your own, be sure to share the link in the comments.
[I took notes on paper because my netbook power cord was in my checked bag that SFO briefly lost on the way here. This is an edited transfer to electronic.]
presenter: Joseph Baisano
Dashboards pull information together and make it visible in one place. They need to be simple, built on existing data, but expandable.
Baisano is at SUNY Stonybrook, and they opted to go with Microsoft SharePoint 2010 to create their dashboards. The content can be made visible and editable through user permissions. Right now, their data connections include their catalog, proxy server, JCR, ERMS, and web statistics, and they are looking into using the API to pull license information from their ERMS.
In the future, they hope to use APIs from sources that provide them (Google Analytics, their ERMS, etc.) to create mashups and more on-the-fly graphs. They’re also looking at an open source alternative to SharePoint called Pentaho, which already has many of the plugins they want and comes in free and paid support flavors.
presenter: Cindi Trainor
[Trainor had significant technical difficulties with her Mac and the projector, which resulted in only 10 minutes of a slightly muddled presentation, but she had some great ideas for visualizations to share, so here’s as much as I captured of them.]
Graphs often tell us what we already know, so look at it from a different angle to learn something new. Gapminder plots data in three dimensions – comparing two components of each set over time using bubble graphs. Excel can do bubble graphs as well, but with some limitations.
In her example, Trainor showed reference transactions along the x-axis, the gate count along the y-axis, and the size of the circle represented the number of circulation transactions. Each bubble represented a campus library and each graph was for the year’s totals. By doing this, she was able to suss out some interesting trends and quirks to investigate that were hidden in the traditional line graphs.
My friend Brent suggested that measuring caffeine intake by the ounces of the beverages consumed isn’t a good calculation, and he’s right. So, I went back and used this chart to determine the milligrams of caffeine per ounce depending on the beverage consumed. Here’s how the totals break down:
I found it interesting that while I drank about 35 more ounces of diet soda than coffee, coffee was clearly the major source of my caffeine intake.
I gave the hours of sleep a multiplier of 100 so that it would be easier to compare them visually. There are definitely some points where the hours of sleep decrease and the milligrams of caffeine increase.