Teaching & OER
The NYC Open Data Lab is built on a teaching model that integrates reproducible research, open data, and public-facing outputs into a single, connected workflow.
Rather than treating assignments as isolated tasks, this approach is designed so that student work is continuously developed, refined, and ultimately published as part of a broader ecosystem.
A Reproducible Learning Model
Courses within the Lab use R and Quarto to guide students through end-to-end reproducible workflows.
Students move from:
- asking meaningful questions about New York City
- to analyzing real-world data
- to producing fully reproducible research
- to publishing their work publicly
This model ensures that learning is not only technical, but also applied, transparent, and impactful.
Open Educational Resources
The Lab supports this approach through the development of open educational resources (OER), including textbooks designed around reproducible research workflows.
These resources provide structure, guidance, and accessibility—allowing both students and external learners to engage with the same tools and methods.
Core Textbooks
Tools
The Lab also develops tools that directly support its teaching model, such as the reproresearchR package, which provides functions and workflows designed to reinforce reproducible research practices in the classroom.
From Coursework to Public Work
A key goal of this model is to move beyond “one-and-done” assignments.
Student work is designed to: - persist beyond the semester
- contribute to public-facing platforms
- serve as portfolio-ready artifacts
This approach connects directly to the Lab’s broader ecosystem, including NYC Open Data Stories and the Student Gallery.
Relevance Drives Rigor
When students work with real data about real systems, engagement increases—and so does the quality of their analysis.
By grounding technical skills in meaningful context, the Lab creates an environment where rigor emerges naturally from relevance.