Library
Everything the book produced, in one place
The chapter hubs point into this layer. If you would rather browse by kind of material than by chapter, this is the index.
Data and studies
CSS Empirical Studies Database
122 published computational studies, each annotated for the research-design stages it illustrates. Searchable, filterable by stage and discipline, downloadable as CSV or BibTeX.
Worked notebooks and data
Three notebooks with committed output (text representation, topic modeling, network analysis), plus the sample network data the Chapter 8 notebook runs on.
Templates and starting points
Project skeleton
The Rules / Memory / Knowledge directory structure, as a zip or as individual files.
Supervised learning demo
A runnable classifier script needing only scikit-learn. The dependency-light way into the Chapter 6 and 8 material.
Guides that stay current
Tool lists go out of date faster than print can follow, so the versions here are maintained and each carries a last-reviewed date.
Data management
Repositories, retrieval, cleaning, versioning.
Automated coding
Dictionaries, models, classifiers, coding agents.
Computational analysis
Networks, simulation, prediction, embeddings.
Communication
Reporting, dashboards, visualization.
Primers
Short, gentle walkthroughs of the three things the exercises assume. Each starts from no background and ends where the book’s exercises begin.
- Working with text data: JSON, tokenization, parquet
- Supervised learning, gently: training and evaluating a classifier
- Prompting and its failure modes: prompts as instruments
Going further
- Resource list: readings, courses, and materials beyond this book