Overview
A modular, fillable scaffold for book-length technical works: a data-driven manuscript (parts/chapters/labs/questions), a tested computational backbone, deterministic figures and Mermaid diagrams, and a content scaffold/validation engine.
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Associated artifacts
GitHub release: v0.1.2 (https://github.com/docxology/template_textbook/releases/tag/v0.1.2)
DOI: https://doi.org/10.5281/zenodo.20533125
Zenodo: https://zenodo.org/records/20533125
PDF SHA-256: 7b67cb2d9118f0a72001395f0a94c529313804d7e5cdf7f6ca2dc9f7ab57d168
Overview source: Curated paper metadata.
Methods and contributions
Read the source for the full argument, qualifications, and evidence.
Findings and contributions
- The book is explicitly a scaffold rather than a finished work: every structural element is present and author-specific passages are marked stubs.
- It provides twelve chapter shells across four parts, each with a matching lab and question bank.
- Claims building the book reproduces byte-identical figures and numbers, since nothing in the prose is computed by hand.
- The worked case study's linear fit gives slope 1.375 and R2 = 0.999, while warning that three averaged points cannot support prediction or extrapolation.
Methods
- Single config.yaml source of truth for book structure — Parts, chapters, front matter, appendices, labs and question banks are declared in config.yaml, from which TOC, numbering and integrity tests read.
- Tested Python backbone (textbook.models) and deterministic figures — Worked equations are implemented as tested Python functions that chapter prose calls, and figures are generated deterministically from code.
- Pandoc + pandoc-crossref rendering pipeline — The manuscript is assembled from Markdown in declared order after analysis scripts produce figures, then rendered to PDF via Pandoc with pandoc-crossref.
- Stub-marker counting audit and manuscript-integrity tests — A quality audit counts STUB/TODO/TKTK markers and pytest checks the per-chapter content contract, unique labels, citations and glossary anchors.
- Two filled worked-reference chapters (logistic growth; dose-response) — First Principles derives the logistic growth law; Case Studies applies a linear dose-response fit to a small dataset of replicate measurements across control, low and high conditions.
Summary sources: Paper metadata and evidence · Extracted source text.
Document downloads
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- Friedman_2026_Template_7b67cb2d.pdf PDF · 0.90 MiB
- Friedman_2026_Template_c6c97f24.pdf PDF · 1.07 MiB · extracted-text source
- template_textbook_combined.pdf PDF · 1.07 MiB
These files may be different versions or companion documents. The archive does not identify a latest edition.
Citation
Citation metadata follows the unified bibliography.
Catalog details and resources
Related works
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