Abstract
BackgroundPlant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combining existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible provided that the collected observations are equipped with an adequate set of metadata. So far there have been no common standards governing phenotypic data description, which hampered data exchange and reuse.ResultsIn this paper we propose the guidelines for proper handling of the information about plant phenotyping experiments, in terms of both the recommended content of the description and its formatting. We provide a document called “Minimum Information About a Plant Phenotyping Experiment”, which specifies what information about each experiment should be given, and a Phenotyping Configuration for the ISA-Tab format, which allows to practically organise this information within a dataset. We provide examples of ISA-Tab-formatted phenotypic data, and a general description of a few systems where the recommendations have been implemented.ConclusionsAcceptance of the rules described in this paper by the plant phenotyping community will help to achieve findable, accessible, interoperable and reusable data.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-016-0144-4) contains supplementary material, which is available to authorized users.
Highlights
Plant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life
Minimum Information About a Plant Phenotyping Experiment (MIAPPE) The Minimum Information About a Plant Phenotyping Experiment is a list of attributes that we recommend for the description of phenotypic observations
To create the recommendations contained in MIAPPE, we took into account previously created Minimum Information documents for various branches of biological research: MIxS for sequences, MIAME/Plant for transcriptomics, MIAPE for proteomics, and CIMR for metabolomics, and have re-used their attribute definition where appropriate
Summary
Plant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combin‐ ing existing datasets. Their correct interpretation, and replicability, comparability and interoperability, is possible provided that the collected observations are equipped with an adequate set of metadata. [2, 3]), leading to ambiguity and inconsistency of phenotypic data description Both correct integration and interpretation of phenotyping experiments is hampered. The lack of common standards of plant phenotyping experiments’ description, both in terms of its content and the format, hampers the correct usage and re-usage of phenotypic data
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