Knowledge Discovery for GA2 based on GA1

Introduction

This repository is using the Linked Open Data representation of the Global Asssessment 1 to identify new knowledge / literature produced after GA1 (2018).

Methods

We use snowballing to identify the literature. The key-papers are the publications which were used for the Backbround Mesages (BMs) as identified by the LOD representation of GA1.

Building

The pipeline is run via the targets package:

targets::tar_make()                                 # run everything, including the report
targets::tar_make(names = "report_fact_checker")    # just build/render the report
targets::tar_visnetwork()                           # visualise the dependency graph

See CLAUDE.md for build system details (credentials, system dependencies) and the full pipeline architecture.

Reports

• IPBES_Fact_Checker.html — the main fact-checker report (built by report_fact_checker; render with targets::tar_make(names = "report_fact_checker"))
• TD_targets.html — the targets pipeline architecture design doc (rendered from TD_targets.qmd)