Okay, so DAAF helps researchers use AI more rigorously. But what does that actually mean in practice? What does that actually look like?
Great question! Let's take a deep dive together.
This page is a transparent walk-through of a real end-to-end analysis with DAAF: from a single natural-language prompt to a fully reproducible data analytic pipeline complete with a consolidated and cleaned analytic dataset, several thoughtfully-constructed data visualizations, supplementary regression analyses, and an in-depth data analysis report pulling it all together. Because DAAF is designed from the ground-up to trace and log everything it does on your behalf, every artifact you'll see here is pulled from the actual files generated by an actual run with DAAF -- no cherry-picking or hiding.
To start, you can inspect the initial data analysis report (right-hand panel on desktop, click the "View Analytic Report" button on the bottom of your screen on mobile) that DAAF produces by default in this "Full Pipeline Mode": the full end-to-end analytic workflow. The goal of this document is to walk the human researcher through the key findings of DAAF's analysis, after which you can proceed to making revisions, extensions, or translating it into publication-level products for various venues like journals and policymaker briefs.
As you scroll, you'll see exactly how DAAF takes that initial prompt and methodically steps through an extremely deliberate research pipeline. For each step of that workflow, you can read exactly in-depth explanations for what workflow step is and display what it actually looks like in conversation with DAAF via chat logs. If you want to read more detail about any step, you can expand each one to see (a) exactly what each specialized assistant is doing in that step of the workflow, (b) exactly what reference files each assistant reads and references to guide its work, and (c) exactly what each assistant produces in terms of analytic code, data interpretations, or research artifacts for downstream use. Every single artifact can be viewed in the right-hand file viewer panel, as well as in the full GitHub sample project folder.
Altogether, DAAF allows researchers to massively kickstart an analytic project like this one -- bringing together 8 different datasets from two different data providers to answer a high-level research question with in-depth data visualizations, regression analyses, and interpretation -- in all of ~30 minutes of raw human time. And from there, the researcher can use DAAF to conduct arbitrary additional analyses, data visualizations, policymaker briefs, interactive dashboards, press releases, academic paper drafting, and more -- all just another prompt or two away. Nothing DAAF produces should be treated uncritically and absolutely needs to be reviewed by the human expert, but it nonetheless represents an enormous value-add for rapidly accelerating research in alignment with our core scientific principles.
Importantly, Full Pipeline Mode is just one of the many ways researchers can use DAAF to extend, enhance, and support various research workflows and tasks. Learn more about DAAF more generally at the GitHub repos and tutorial videos linked below, or begin the walkthrough below to see how complex AI-empowered research workflows actually look in practice.