GLAM Jupyter resources
Here are some more Jupyter notebooks and resources for exploring GLAM data.
- ACMI public API – Interactive examples
- Collections As Data Notebooks, Penn Libraries
- Data exploration, Library of Congress Labs
- Data Foundry, National Library of Scotland
- Datasets and Digital objects, Deutsche Nationalbibliotek
- GLAM Jupyter Notebooks, Biblioteca Virtual Miguel de Cervantes Labs
- Jupyter Notebooks using the British Library’s Digital Collections & Data, British Library
- V&A Collections API Guide and Data Explorations, Victoria and Albert Museum
- Swiss GLAM platforms e-rara, e-manuscripta and e-periodica, University Library Bern
Teaching and learning¶
- Collections as data, Jeffrey C. Oliver – downloading and analyzing scanned OCR text from a collection of southwestern US borderlands newspapers
- Computational Archival Science Educational System, Advanced Information Collaboratory
- Constellate, ITHAKA
- Getting started with ODate, Shawn Graham
- Humanities Data Analysis: Case Studies with Python, Folgert Karsdorp, Mike Kestemont, and Allen Riddell
- Introduction to Cultural Analytics & Python, Melanie Walsh
- Introduction to Jupyter Notebooks, Quinn Dombrowski, Tassie Gniady, and David Kloster, Programming Historian
Contributing to this list¶
- Click on the pencil icon to open the page for editing on GitHub.
- Follow these instructions to enter, preview, and save your suggested changes.
When you click on the pencil icon, GitHub automatically creates a copy of the repository in your own account. Once you've finished making your changes it'll prompt you to create a pull request, to feed the changes back to the main GLAM Workbench repository. Each pull request is reviewed before the changes go live.
See Add links to related resources for more information.