Complex research interfaces need acquisition, normalization, source triage, review discipline, and reproducible publication.
Evidence systems / Research infrastructure / 2026
Evidence systems lab: source-aware data pipelines and AI-assisted review
The site connects genocide, heritage, conflict, displacement, disaster-risk, COVID, route optimization, complexity, and Linear A materials into visible instruments.
Public-data ingestion, coordinate review, layer typing, anomaly checks, source ranking, and AI-assisted extraction support.
A working evidence stack that connects datasets, maps, review consoles, and publication pages.
Visible-source reconstruction
What can be shown precisely from the public material.
Local project files, atlas datasets, update scripts, generated JSON assets, and public interface pages.
Download, parse, and normalize public datasets into stable local assets
Separate confirmed records, context material, coordinate review, and source-only references
Use AI as extraction and anomaly-review support, not as a substitute for evidence
Displayed as a research systems room: sources, layers, review, and interfaces
Coordinates
sourceslayersreviewinterfaces
Source