This repository, from the Global Initiative Against Transnational Organized Crime (GITOC) Digital Tool team, is a working application aimed at scoping and selecting data collection fields for online Illegal Wildlife Trade (IWT) monitoring programs. It's a niche but genuinely consequential corner of civic tech: helping researchers and NGOs decide what data points to capture when tracking wildlife trafficking across online marketplaces, social platforms, and dark-web forums.

Written in R, it appears to be a Shiny-style application (a reasonable inference given GITOC's published tooling pattern) that lets domain experts iterate on a shared data model rather than negotiating field definitions over endless spreadsheets. That sounds dull until you realise that inconsistent schemas are the single biggest reason multi-org monitoring efforts produce data that can't be aggregated. A tool that forces alignment up front saves months of post-hoc cleanup.

What makes it interesting:

• Real-world impact — IWT is a multi-billion-dollar criminal economy; better monitoring tooling directly supports enforcement and policy.
• R outside academia — it's refreshing to see R used for an applied, ops-facing tool rather than a one-off analysis notebook.
• Open methodology — putting field-selection logic in a public repo invites scrutiny and reuse by adjacent monitoring efforts (human trafficking, counterfeit goods, illicit timber).

Who'd benefit:

• Conservation technologists building OSINT pipelines for wildlife crime.
• NGOs designing harmonised data collection across partners — the field taxonomy alone is a useful reference.
• R developers curious about Shiny-based collaborative data-modelling apps.
• Policy researchers needing a defensible, transparent basis for what they chose to measure (and what they didn't).