Enabling reusability of plant phenomic datasets with MIAPPE 1.1

2020/03/14 by Evangelia A. Papoutsoglou, Evangelia Papoutsoglou, Daniel Faria +42 · 172 citations
Biochemistry, Genetics and Molecular Biology · Environmental Science · #Biomedical Text Mining and Ontologies #CLARITY #Computer science #Data mining #Data science #Domain (mathematical analysis) #Ecology #Genomics and Phylogenetic Studies #Implementation #Interoperability #Key (lock) #Programming language #Reusability #Reuse #Software engineering #Species Distribution and Climate Change #World Wide Web

paper · pdf · doi:10.1111/nph.16544

published in New Phytologist 227(1), 260-273 (Wiley)

openalex publication_date 2020/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

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.

Citations

• The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments

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