Collections as Data

These notebooks contain tutorials and examples designed to facilitate engagement with library collections data available in Colenda, while learning about Python as a tool for data manipulation.

Visit project

Colenda Digital Repository at Penn Libraries is a digital repository for digitized and born-digital material. It provides direct access and long-term stewardship for these important resources. Much of Colenda’s content consists of materials owned and digitized by the Penn Libraries, including significant collections that have been donated.

These notebooks use data from the Arnold and Deanne Kaplan Collection of Early American Judaica to explore how to work with data from Colenda.

• Explore Colenda Records (kaplan_explore_records): In this notebook we’ll have a preliminary look at data harvested from Colenda. What kind of data and files can we access in Colenda? We’ll introduce how to calculate basic shape/stats of the data, split and concatenate columns, and access images in the collection.

• Explore Colenda Items Over Time (kaplan_explore_time): In this notebook we’ll explore the temporal dimensions of data harvested from Colenda. When were items created, collected, or used? To do that we’ll extract the nested temporal data, see what’s there, and create a few charts.

• Explore Colenda Items by Place (kaplan_explore_places): In this notebook we’ll explore the spatial dimensions of data harvested from Colenda. What places are associated with these items? To do that we’ll extract the spatial data, see what’s there, and create a few maps.

Resources

• Exploring Library Collections in Colenda: This blog discusses the history and context of the Jupyter notebooks.