Abstract
Summary Enabling data reuse and knowledge discovery is increasingly critical in modern science, and requires an effort towards standardising data publication practices. This is particularly challenging in the plant phenotyping domain, due to its complexity and heterogeneity.We have produced the MIAPPE 1.1 release, which enhances the existing MIAPPE standard in coverage, to support perennial plants, in structure, through an explicit data model, and in clarity, through definitions and examples.We evaluated MIAPPE 1.1 by using it to express several heterogeneous phenotyping experiments in a range of different formats, to demonstrate its applicability and the interoperability between the various implementations. Furthermore, the extended coverage is demonstrated by the fact that one of the datasets could not have been described under MIAPPE 1.0.MIAPPE 1.1 marks a major step towards enabling plant phenotyping data reusability, thanks to its extended coverage, and especially the formalisation of its data model, which facilitates its implementation in different formats. Community feedback has been critical to this development, and will be a key part of ensuring adoption of the standard.
Highlights
The volume of data being generated in the life sciences demands good data management practices to enable reusability
We describe the following refinements: (i) the extension of MIAPPE to accommodate a wider range of use cases; (ii) the specification of a data model underlying the standard, to facilitate its interpretation and usage; (iii) the formalisation of MIAPPE in a computer-interpretable format to enable dataset validation and computational analysis; and (iv) the alignment of MIAPPE and Breeding API (BrAPI) to enable the exposure of MIAPPE-compliant datasets via BrAPI endpoints
The datasets span model, crop and perennial plants in a variety of experimental settings, as well as various MIAPPE 1.1 implementations. They demonstrate the ability of MIAPPE to handle diverse experimental designs, including automated glasshouses (IPK and VIB datasets), field networks for crops (GnpIS) and forest trees with multiple scales and repetitions
Summary
The volume of data being generated in the life sciences demands good data management practices to enable reusability. The development of automated high-throughput and high-resolution technologies has contributed to a scale-up in the number, complexity and size of plant phenotyping datasets. This has been amplified by the increasing number of long-term, highly multilocal phenotyping networks aiming to decipher the interaction between genotype and environment (Millet et al, 2019). The reuse and meta-analyses of phenotyping data are challenging due to the heterogeneity of this domain that encompasses many types of experimental sites (field, glasshouse, controlled environment), plants (crops, forest trees), collected data (images, physical measurements, chemical assays, molecular biology assays), and experimental designs (factors being tested, timing, field layouts, etc.). Plant phenotype hinges on the interaction between genotype and environment, and developmental stage and epigenome status (King et al, 2010), which raises the challenges of integrating genotypic and phenotypic data (Pommier et al, 2019b)
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