A friendly, powerful way to explore the world's soil nitrous oxide data — and pull out exactly what you need.
Farmers add nitrogen to grow the food we eat, but only about half of it actually ends up in the crop. Much of the rest leaks into the environment, and some escapes as nitrous oxide (N2O), a greenhouse gas roughly 270 times more potent than CO2.
Most national greenhouse-gas accounting still leans on simple Tier 1 emission-factor methods, which miss the strong spatial and temporal variability of N2O and the nonlinear jump in emissions under excess fertilization. Process-based Tier 3 models (DayCENT, DNDC, APSIM) and newer machine-learning approaches capture that complexity far better, but they're hungry for large, harmonized, covariate-rich datasets.
The bottleneck isn't a lack of measurements — it's that they're scattered across hundreds of publications, often trapped in figures, and recorded in incompatible units, naming conventions, and structures. The GRAND Explorer harmonizes that data into one clean, queryable database and gives you simple tools to explore it, inspect it, and export exactly the slice you need.
Narrow the whole database with simple dropdowns — instrument, crop, management, and which covariates are available.
Results plot instantly on an interactive world map. Click any site for its experiment and treatment details.
Pop up graphs of flux, temperature, moisture, and fertilizer events to size up a dataset at a glance.
Add datasets like an online cart. Your picks are remembered through refreshes, navigation, and new sessions.
A live dashboard shows geographic coverage, crop and soil diversity, and flux distribution — so you catch gaps early.
One click exports a clean, self-contained CSV plus a README — ready to drop straight into an analysis or ML pipeline.
The GRAND Explorer is open-source top to bottom. Raw datasets pass through Python pipelines that convert units and map inconsistent terminology (say, "conventional till" vs. "moldboard plow") onto a single harmonized schema. The cleaned data lives in a MySQL relational database that mirrors the natural hierarchy of field experiments: measurements belong to treatments, treatments to experiments, experiments to sites, keeping every flux value linked to its soil, climate, and management context.
A Django backend serves it through an ORM that builds fast, injection-safe queries and migrates the schema without downtime, while an AJAX-driven frontend keeps exploration smooth and interruption-free. We build in the open because shared infrastructure is what turns the ideal of open science into everyday practice.
This is a starting point, not the finish line. We're exploring gap-filling for sparse measurements, ready-made train/test splits for fairer model comparisons, and even open modeling competitions. Built by, with, and for the research community, because good infrastructure is what makes open science actually happen.
The GRAND Explorer stands on the work of the many researchers who collected this data. If you use it, please cite both the platform and the original data sources (a full reference list is generated automatically with every download).