How the GRAND database is built — unifying science from around the globe.
The GRAND database brings together temporally-resolved N2O flux measurements and their covariates from several major public sources — GRACEnet, Ag Data Commons, the NANORP N2O Network, and the Global N2O Database — plus roughly 45 additional peer-reviewed publications. Every record carries its original source citation, delivered with each download.
Getting that data into one consistent, queryable form takes several careful steps:
Where data was published as tables, we ingest it directly. Where it lived only inside a figure, we digitize it point-by-point with WebPlotDigitizer, then add covariates and metadata by hand from the manuscript text.
Values read from figures carry the small reading uncertainty inherent to digitizing a plot.
Source datasets rarely agree on units, names, or structure. Python pipelines convert every measurement to common units and map inconsistent terminology onto a single vocabulary — crops as Scientific (Common) names, instruments as Static Chamber / Automatic Chamber / Eddy Covariance, and so on.
Replicate plots within a treatment are averaged to a single daily value, so each record represents a treatment's mean flux on a given date (with the replicate spread retained alongside it), rather than one individual chamber reading.
Soil texture, pH, bulk density, organic carbon, and site climate (MAP/MAT) are taken from the source study wherever reported. Where a study didn't report a value, we fall back to gridded global products — SoilGrids for soil, NASA POWER for climate — and flag every such value as modeled rather than measured, so its origin is always clear.
A few characteristics of the data worth keeping in mind as you use it:
Full column definitions and complete per-record citations ship in the README bundled with every download. For the complete methodology, see Ackett et al. (2026).