About the Companion Book

This documentation and the book Research Data that Can Be Trusted describe the same open-source platform and are written by the same authors. The two are designed to complement each other: the documentation is self-contained and free — every page can be read, and every example can be run, using only the code and configuration in this repository together with openly available data (synthetic where the real data would require a data use agreement) — while the book offers an extended conceptual treatment of the ideas behind the platform, including data provenance, the regulatory context for health data, and a taxonomy of data transformations. The book is optional enrichment; it is never a prerequisite for anything in this documentation.

Bibliographic information

• Title: Research Data that Can Be Trusted

• Authors: Bouzinier et al.

• Series: SpringerBriefs in Computer Science

• Publisher: Springer, 2026

• Link: https://tidd.ly/4y1ClDH

Scan the QR code (or follow the link above) to get the book.

Chapter-to-documentation map

Each book chapter that describes the platform (Chapters 5–8 and the appendices) has a single canonical landing page in this documentation; the earlier chapters cover conceptual background (provenance, the regulatory context, a taxonomy of transformations) with no direct code counterpart:

Book chapter	Documentation page
Ch. 5, “Language Design”	Concepts: the Dorieh approach
Ch. 6, “Proof of Concept Implementation”	Concepts: the Dorieh approach
Ch. 7, “Sample Application: Building ML-Ready Datasets”	Climate tutorial
Ch. 8, “Dorieh Medicare Claims Data Pipeline”	Medicare case study and the Medicare pipeline tutorial
Appendix A (core YAML DSL syntax)	Data modeling reference
Appendix B (DSL extensions)	Data modeling extensions

Using this documentation without the book

If you do not have the book, the following reading order covers the same ground end to end:

1. Read Why a data platform for the motivation behind the platform, then Concepts: the Dorieh approach for the vocabulary and design ideas used throughout the documentation. (If you already know the motivation, start directly with Concepts.)

2. Work through the climate tutorial, a runnable Bronze–Silver–Gold pipeline built on open data.

3. Follow the Medicare tutorial, a guided path through the same patterns applied to a production-scale health data pipeline (runnable with synthetic data), with the Medicare case study as its reference.

4. Consult the DSL reference — Data modeling and Data modeling extensions — when you write your own data models.