A Workflow for Processing Global Datasets: Application to Intercropping

Field experiments are a key source of data and knowledge in agricultural research. An emerging practice is to compile the measurements and results of these experiments (rather than the results of publications, as in meta-analysis) into global datasets. Our aim in the present study was to provide several methodological paths related to the design and analysis of global datasets. We considered 35 field experiments as the use case for designing a global dataset and illustrated how tidying and disseminating the data is a first step towards open science practices. We developed a method to identify complete factorial designs within global datasets using tools from graph theory. Another practical contribution based on smoothing splines was applied to leverage data with greatly different sampling times. We discuss the position of global datasets in the continuum between data and knowledge, compared to other approaches such as meta-analysis. We argue that global datasets are a suitable tool for performing analyses within an informed big data framework. We advocate using global datasets more widely in agricultural research.