The lab is also a replicable model. The data democracy depends on won’t be defended by one lab in Boulder — so we publish our operating design, openly, for any university that wants to stand up a public-interest data clinic.

The guide is organized around three pillars:

• Structure — how the lab is staffed and governed: a pipeline from undergraduate researchers through graduate researchers to visiting professionals, modeled on the teaching hospital and legal-aid clinic traditions of supervised, real-stakes practice.
• Services — how the help desk intakes, triages, and staffs public-interest requests, and how we protect sensitive work.
• Curriculum — the competencies students build (data liberation, documentation, reproducible analysis, responsible AI) and how coursework maps onto real engagements.

We’re informed by peer efforts — data-science clinics at the Alan Turing Institute and Northeastern’s NULab, and the science-journalism fellowship tradition — and we share back so the network grows rather than each lab reinventing it.

Materials are shared under permissive, open licenses — CC BY for the documents, MIT for the code — so you can adapt a syllabus, an intake form, or a service playbook to your institution without asking permission. This site itself is part of the guide: it’s open source on GitHub, built to be forked.

Read about what we build, explore the data liberation toolkit, or get in touch if you’re starting something similar.