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, configuration and synthetic data in this repository — 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

QR code linking to the book Research Data that Can Be Trusted

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.