ArticleThe New phytologist2020
Enabling reusability of plant phenomic datasets with MIAPPE 1.1.
Article in The New phytologist, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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Who cites it
87 citing papers in PubMed.
- Fragment-level FAIRness: annotating scientific data and its provenance using data fragment selectors.Journal of integrative bioinformatics · 2026Article
- Chemical Ontogeny in Plants: Developmental Dynamics of Specialized Metabolism and Implications for Ecology, Quality Control, and Bioprospecting.Plants (Basel, Switzerland) · 2026Review
- Advances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.Journal of experimental botany · 2026Review
- Fruit and Vegetable By-Products as Postharvest Tissue Fractions: A Raw-Material Framework for Plant-Based Food Development.Plants (Basel, Switzerland) · 2026Review
- Genomic forecasting for climate-resilient fruit trees.The New phytologist · 2026Review
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Long-term preservation strategy for legume root nodule phenotypes coupled with a comprehensive evaluation method.BMC plant biology · 2026Article
- Integrating plant phenotypic and genotypic data in the AGENT project: a BrAPI service implementation.Bioinformatics (Oxford, England) · 2026Article
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- CartograPlant: bridging genomic, phenotypic, and environmental data to advance plant resilience and eco-evolutionary insight.Genetics · 2026Article
- Wheat historical phenotypic data from European genebanks as an important resource for research and breeding.Scientific data · 2026Article
- FIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping.Scientific data · 2026Article
- Article
- Beyond the clipboard: data collection with GridScore NEXT.BMC bioinformatics · 2026Article
- Harnessing the untapped genetic diversity of local landraces: omics technologies as a gateway to horticultural breeding.Frontiers in plant science · 2026Review
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Post-transcriptional regulation of light-stress responses and predictive modeling in vegetableFrontiers in plant science · 2026Review
- FAIRness at all costs? Practical limitations in applying the FAIR principles to plant genetic resources data.Frontiers in plant science · 2026Article
- Genome-Wide, High-Density Genotyping Approaches for Plant Germplasm Characterisation (Methods and Applications).International journal of molecular sciences · 2025Review
27 more citing papers are in PubMed but not listed here.
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Authors and funding
37 authors.
Funding
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.
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