Library
Browse the book’s supporting materials by task
Browse by task below, or use Chapter materials to find resources for an assignment.
Data and studies
CSS Empirical Studies Database
122 published computational studies, with annotations organized by research-design stage. Searchable, filterable by stage and discipline, downloadable as CSV or BibTeX.
Worked notebooks and data
Follow text representation, topic modeling, and network analysis through notebooks with saved outputs. Sample network data are included; the text notebooks need your own corpus.
Templates and starting points
Project skeleton
The Rules / Memory / Knowledge directory structure, as a zip or as individual files.
Supervised learning demo
A small classifier example using NumPy, pandas, and scikit-learn. Start here to learn how training and evaluation work before using a larger model.
Guides that stay current
These guides are updated as software changes; 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
These walkthroughs introduce the concepts and code used in the exercises. Begin with Getting started if you need a Python environment, then choose the primer that supports your next task.
- 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