New Jupyter Notebooks released!
Several new Jupyter Notebooks released in our GitLab!
In time for the ONB Labs Jupyter Notebook Workshop on March 26, 2026, we have expanded our offerings to include several additional Jupyter Notebooks (JNBs) developed by Sonja Dorfbauer as part of our “Data Analysis in the ONB Labs” project.
With this collection of eight JNBs, we are providing a comprehensive, modular learning and analysis package that explores various aspects, ranging from the basics of notebook usage to complex data-driven research workflows. The notebooks were developed in parallel with the ONB Labs data sets and are designed to provide practical guidance on how to systematically retrieve, process, analyze, and visualize open data from the Austrian National Library.
Please note that the text part of the JNBs is in German.
Here are some details on the respective JNBs:
Introduction to Jupyter Notebooks
This Notebook provides an easy-to-understand introduction to the structure and functionality of JNBs and illustrates how code and documentation (as Markdown cells) interact. Through clear examples and short exercises, it provides the foundational knowledge needed to start your own projects independently.
Introduction to Data Analysis and Visualization
In this JNB, users learn how to load, filter, clean, and clearly visualize data using pandas, based on a clear example data set from the ANNO search. The Notebook demonstrates how even simple steps can lead to meaningful results, thereby laying a solid foundation for further analysis.
Introduction to Regular Expressions (RegEx)
Here, users gain clear, structured access to regular expressions—a powerful tool for identifying patterns in text and automatically extracting information. With plenty of examples, exercises, and a handy cheat sheet, the RegEx notebook makes the subject accessible even to beginners.
Two Web Archive notebooks—one for beginners and one for advanced users—show how to explore the Austrian Web Archive in a way that is both intuitive and research-oriented—from initial queries, seeds, crawls, and full-text searches to watchlists and visual analyses of complex web histories. The JNB for advanced users also presents more in-depth analyses such as heat maps, checksums, or complete hit lists, thereby opening up new perspectives on web archive data.
Catalogue Data: Collection, Analysis & Visualization
This notebook introduces readers to the world of library metadata and demonstrates how OAI-PMH and SRU data can be automatically retrieved, processed, and compared. Through visualizations of temporal or thematic patterns, it quickly becomes clear just how diverse and richly structured the library’s collections are—and how much potential they hold for research and analysis.
ANNO Data Collection & Visualization
This guide provides a step-by-step explanation of how to retrieve search results from the ANNO Search API in their entirety, organize them in a structured format, and then visualize them interactively. The resulting charts make it easy to grasp trends over time, thematic distributions, or regional focuses in historical newspapers, and encourage further exploration.
Ariadne: Analysis of Data on Historical Clubs and Organizations
Here, a complete data pipeline is being established—from scraping historical organizational data from the ONB’s women and gender-specific knowledge portal (Ariadne), through data cleaning and geocoding, to interactive maps that visualize spatial developments and network relationships. The visualizations provide an impressive window into historical women’s and association networks and open up new possibilities for spatiotemporal analyses.
Are you interested in the Jupyter Notebooks mentioned? You can find them on the ONB Labs GitLab.
Would you like to share your feedback on the JNBs with us? If so, please feel free to email us at labs@onb.ac.at at any time. Thank you very much!
The ONB Labs Team thanks CLARIAH-AT for financial support!
