Teaching Hub

Roll Data! Teaching with Datasets

by Vincent F. Scalfani, University Libraries

You may be aware of the rich information resources that UA Libraries provides to the campus community. For example, UA Libraries provides access to over 3 million print volumes, 1.5 million electronic books, and 200,000 electronic journals. These resources are easily discoverable from our Scout database and our E-Journals webpage. Another lesser-known resource that UA Libraries provides access to is datasets [1].

Incorporating datasets into the classroom can greatly enrich the experience of students, particularly as datasets can provide practice for data analysis classwork, or even allow students to re-examine existing datasets, potentially uncovering unknown information. However, datasets are not as easy to find compared to the more traditional forms of information including books and journals. Over the past several years, UA Libraries has added numerous resources that provide access to datasets across the disciplines.

A great place to begin your search for datasets is on our A-Z Database List. You can limit the display list by type of databases “Datasets.” Within this list, you will find interesting datasets such as the Knovel material and substance property datasets, the SimplyAnalytics business/marketing datasets, or the Sage Research Methods social science datasets. In many cases, the datasets can be downloaded in a variety of formats such as comma-separated value text files or Microsoft Excel files.

There is also a multitude of datasets available through publicly accessible data repositories. These are a bit trickier to find, but a good first stop is the Registry of Research Data Repositories (Re3data) and DataCite. At Re3data and DataCite, you can search for particular datasets and even browse by discipline.

As one example of bringing datasets into the classroom, I use the Knovel material and substance property datasets to teach students how to visualize and analyze data in Origin. Origin is a scientific graphing and analysis application available from the Office of Information Technology. We discuss strategies for importing the data, cleaning the data, and preparing the data for visualization and analysis—all skills that students can transfer to other data analysis projects. Knovel makes it easy for me to select an appropriate dataset for the instruction session.

Perhaps there is a way that datasets can enhance your teaching? I would encourage you to contact your Library Liaison to discuss your dataset needs. Library Liaisons are experts in their assigned subject areas and can help you locate specific datasets. We look forward to learning more about your data needs.

Roll Data!

[1] A dataset herein is defined as a collection of machine-readable data facts. One could easily argue that a dataset is simply data, but I will leave the semantics out of this blog post.