GEMS Exchange Features
PUT TO PRACTICAL USE
GEMS Exchange is designed to solve real-world agri-food challenges that are increasingly amenable to data-intensive solutions.
Data scientists require dynamic access to a complex set of interoperable data streams that concord in both time and space, across diverse agroecologies and production systems. GEMS Exchange delivers. Read More
In agriculture, a single data stream is rarely sufficient. Often a few critical data sets form the building blocks necessary to solve a critical problem. As the collection of critical interopreable data sets in GEMS Exchange expands, end users can more easily mix and match, adding in their own data to solve the problem at hand.
SPATIAL AND PROGRAMMATIC INTEROPERABILITY
All spatially- and temporally-explicit APIs provided by GEMS operate on a hierarchical, discrete gridding system designed by GEMS scientists and NASA collaborators.
Agriculture is intrinsically spatial, whether you are dealing with plot, field or landscape level data. The GEMS Grid enables easily scaling of data from 1 m to 36 km resolution. Working with multiple data streams at different resolutions? No problem. Read More
Further, we provide data on API endpoints that are structured consistently, with documentation and user tutorials in programming languages (R, Python) that are common among data scientists.
A PORTFOLIO API
GEMS provides data streams driving innovations from molecules to markets.
To solve problems in agriculture one requires data that spans across G x E x M x S. GEMS provides APIs to support: (1) better breeding decisions, (2) more sustainable agricultural practices linking agricultural outcomes to environmental consequences (and vice versa), and (3) decisions de-risking agricultural production from climate and pest pressures. Read More
It is not uncommon for a single scientific inquiry or innovative solution to require 3, 4 or more API streams to be used together. GEMS Scientists use the same APIs that our collaborators and clients use. Linking farm practices to improvements in lake water quality required 6 APIs. Similarly, predicting crop yield and multiple quality traits draws on 4 APIs.