Experimental design-driven FAIRification of data matrices: example of a principled approach

We outline a principled approach to data FAIRification rooted in the notions of study design. This is an example of retrospective data FAIRification, using as a metabolomics dataset associated to a published in a journal article. 

SUMMARY

• Our first data source: article by Raymond et al. Nat Genet. 50:772-777 (2018) https://doi.org/10.1038/s41588-018-0110-3; this is targeted metabolite profiling study of strain-related chemical signatures of the rose fragrance; the biological materials was selected to allow a comparison between parts of the plant, and across cultivars in the same tissue type.

  • Our starting point: their human-understandable data in the supplementary table https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip, containing the mean concentrations of 61 metabolites measured in three different parts of the rose flower, in six distinct genotypes.

• Our second data source: article by Magnard et al. Science.Jul 3;349(6243):81-3 (2015) https://doi.org/10.1126/science.aab0696; this is early work of the same group author of the first data source.

• Our approach: we performed a retrospective curation and re-annotation of the data matrices, disambiguating of the experimental design, using community, open interoperability standards from FAIRsharing (https://fairsharing.org); we focused on the clarity of the statistical results to ensure reusability and reproducibility of the analytical workshop by humans and machines. The FAIRification steps for the first data source are documented in the sections below; the same steps were applied to the second data source to assess inter-experiment agreement, as both studies used the same varieties of rose and plant parts. 

• Our results: semantically-anchored data matrices served as Linked Data, deposited in public archives (Zenodo and MetaboLights), and consumable by software agents for queries like “Retrieve study predictor variables and their levels” and “What is sample size used to compute the means?” to support study results review and assessment.

• It is associated to the following project: https://github.com/proccaserra/rose2018ng-notebook with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.