FAIR Data

End-to-end FAIR data management – data capture to publishing

APPN data management is focussed on delivering the highest-quality research-grade results possible from every plant phenotyping study, following best-practice recommendations to ensure that all data are FAIR: Findable, Accessible, Interoperable and Reusable. Making data truly FAIR requires planning at all stages, from study design through manual and automated data collection and processing, through to packaging and publishing the results for future reuse. The goal is to ensure that every element in the published data is documented so that a human reader or a software program can reliably understand exactly what was measured and how the data were generated. 

Data scientists internationally have collaborated to develop a shared conceptual model that applies to any plant phenotyping study, the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard. MIAPPE identifies the components that should be described for any experiment so that future researchers can benefit from the data collected. It provides the basic tools to ensure that APPN and international partners can all structure their data in a consistent way. Datasets that are compliant with MIAPPE will clearly document all relevant components: the set of plants or plots under study (which crop species and variety), the environmental conditions that they all share (soil characteristics, temperature, humidity, lighting, etc.), specific treatments applied to a subset of these plants (e.g. differing levels of water or nutrients), what cameras and other devices were used to collect data, what measurements and images were collected, how these digital products were then processed (e.g. computer vision or machine learning algorithms) and the time series of plant trait estimates produced by the study. MIAPPE is sufficiently generic that it can be applied both to plants grown in the field and to those grown in highly-controlled environments where many variables are rigorously managed.